The Mixtape with Scott: Recent Episodes
scott cunningham
The Mixtape with Scott is a podcast in which Scott Cunningham, an economist and professor, interviews people in areas he finds interesting. Those areas are, respectively, causal inference, economists in the tech sector, economics and public policy commentary (including drug policy) and the Nobel Laureate Gary Becker's former students. He tries to travel back in time with his guests to listen and hear their stories before then talking with them about topics they care about now.
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After 16 episodes of working with Claude Code to study the effect of abortion clinic closures on marriage rates using a natural experiment, so to speak, in Texas called "House Bill 2" and continuous difference-in-differences with a treatment of "change in distance", Caitlin and I start our descent. Caitlin shares her own thoughts about returning a poisson estimator, and we continue to wrestle with the implications of differences in econometric estimators and interpretation, as well as what we are willing to live with. Plus a lot of discussion about the implications of AI for our lives as researchers manages to creep in its pretty little head!
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Caitlin Myers and myself have been studying the effect of House Bill 2 in Texas, which closed half the state's abortion clinics, on marriage rates using "continuous difference-in-differences" estimation for 14 episodes straight.
But behind every good diff-in-diff estimate lurks a parallel trends assumption, and so we asked Hannah to look into one confounder -- oil! fracking! energy! Perhaps things related to technologies that enhanced the extraction of oil from the ground, like fracking technology, could have alone shifted marriage rates by bringing men into communities but only for work, temporary residence, not permanence.
We talk about a literature where men in transit can alone reshape the marriage markets, which if those events happened disproportionately in our treatment counties, but also our control counties, then parallel trends mechanically might be violated. Because remember -- parallel trends is a) about things differentially impacting the treatment and control units on outcome trends and b) therefore can impact the treatment group or the control group. Hannah comes back with research she did for us on it.
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This week is a hoot! Caitlin and I finally get some estimates of the different target parameters using continuous treatment diff-in-diff! We discuss in detail how the estimator works and then go through our analysis. Watch to the end to see our reaction when we finally see some output in a beautiful deck!
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Caitlin and I are back after a one week hiatus as we each ran around traveling in our respective parts of the world. Probably for the best, as it allowed two weeks of twoway fixed effects decompositions to marinate. But now it’s time — can we finally see what a continuous treatment difference-in-differences estimator actually is for goodness sake? And the answer is sort of!
In this episode, me and Caitlin wrap up a walk through of what parameters we are identifying with our abortion-marriage paper. I was really puzzled to be honest in the last episode as to what a “dose” even meant in our context. As you may recall, we are studying the effect of House Bill 2 which caused half of Texas’s abortion clinics to close, and in turn made the distance to the nearest abortion clinic to rise. But that led us to wonder:
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Are we studying the effect of distance to the nearest clinic after House Bill 2, or
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Are we studying the effect of the change in distance to the nearest clinic after House Bill 2?
So, have fun as you listen to us talk through it out and finally realize at the end that it would appear our dose must be one of those and cannot be the other due to the nature of the design and diff-in-diff itself. Hint: no anticipation places some rails on us. See if you can figure out why.
But then we also dive into the continuous treatment diff-in-diff estimator. You’ll learn about splines! You’ll learn about kernels! You’ll learn about polynomials! You’ll learn about b-splines and wavelets and a bunch of other things that draw curvy lines! And you’ll learn about the one situation when you have the permission to interpret that line as a causal effect too!
Thanks again for all your support! We hope you enjoy this episode!
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In one sense, causal inference has two approaches. You can run a regression and then backwards engineer what it means. Think of Imbens and Angrist's 1994 classic Econometrica on the local average treatment effect (LATE) where they show that the Wald estimator (binary treatment, binary instrument) is the average effect for the complier subpopulation.
But the other way that causal inference often runs is you start with the parameter of interest, not the regression, and then build the regressions to identify them under minimal but acceptable assumptions. In this episode of the Odd Couple, we switch from estimation to description of the causal parameters introduced in Callaway, Goodman-Bacon and Sant'Anna (2026, AER). These are the well known ATT parameter, but not the ACRT, which is the slope of the dose response curve. We also puzzle over whether our treatment is, in fact, distance measured in levels or is it distance measured as changes. Which is probably one of the values of starting with parameters: it forces you to figure out what your question is!
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Welcome to the 11th episode of The Mixtape with Scott, season 5, “The Odd Couple” featuring Caitlin Myers! This week we continue the riveting material from last week where we walked through a decomposition of the twoway fixed effects estimator when it’s 2 period, diff-in-diff with a continuous treatment! Yes, you heard me right — be still my beating heart.
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Me and Caitlin continue to go through this deck that Claude made for us explaining the new Callaway, Goodman-Bacon and Sant’Anna paper, forthcoming at AER, about continuous treatment diff-in-diff. Mainly, though, we are just working our way painstakingly slow through this Frisch-Waugh-Lovell decomposition of the OLS regression to better understand just what OLS is doing.
I thought this episode was pretty interesting though your mileage may vary. I mean, if you don’t find two economists trying to help each other understand an econometrics paper, then probably the floor on this episode could be a little low. But that said, I did enjoy it. We both really seemed to help one another better understand the decomposition formula, plus we got to see it with our own eyes. And Claude made some really intuitive graphics that helped both of us.
So check it out! As always thanks for tuning in!
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Today will astonish and amaze because in this one, you will watch me explain the decomposition of the OLS twoway fixed effects estimator for the continuous treatment difference-in-differences! A first for podcast history I would be willing to bet! Thanks again for turning in! (Caitlin said she thought this turned out well, but your mileage may vary).
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This week's episode of "The Odd Couple" is just Caitlin and Hannah as I had to go to Georgetown to talk about Claude Code at a faculty retreat. But before we get going with a description, Hannah mentioned at the start during the ice breaker about the opening theme song to the podcast, and for those that don't recognize the lyrics, that's Mac Miller's "Small Worlds" sung by my two nephews.
So what is this episode about? One of the themes I have been emphasizing in my talks on AI Agents and my substack is that AI Agents have caused a separation between the historic bundling of the production of research and the verification of the results. Since AI Agents are now able to produce so many aspects of the research project autonomously -- that is without much direction from the human researcher -- one of the new tasks of the researcher is to verify them.
If you remember from a few weeks ago, Claude Code had nearly instantly worked up the county-level marriage data into a county panel of marriage rates and marriage counts by year. We brought Hannah Sayre, a recent college graduate and current economic consultant, into the project to help us work through the latter task of "human verification". Had Claude done it correctly? How do we verify that it is correct? And if it is not correct, why was it not correct, and how generalizable is that inaccuracy? Hannah was our eyes and ears, our boots on the ground, as she independently investigated the same question, the same task we gave Claude, to on the back end up help us determine whether Claude had indeed found the same irregularities in the original marriage dataset, and if so, what autonomous decisions had he made. And so in this episode, Hannah walks us through it, and she and Caitlin discuss both those findings, as well as begin the work of conceptualizing the process of verification in a world of AI Agents. While not definitive, this is a chance for others to hear more specifically about this. I at least anticipate that all of us will have to wrestle with verification going forward in ways we were not expecting, and maybe even are not prepared for, at least not universally, and definitely not necessarily if in fact AI Agents shrink the size of the project team members due to automation, and how best to respond to that smaller scale, and therefore, fewer people available to do the actual verification itself.
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Thanks again for tuning in! We hope you are having as much fun with this as we are!
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This week, Caitlin and Scott and Claude debate what a regression is, and then run some poisson regressions of county level marriages in Texas onto travel distance to the nearest county with an abortion provider! If that doesn’t get your heart beating fast, you should go to the doctor because you may be dead! This is a fun episode and hope you like it!
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As I’ve said on here, and others have said repeatedly, the bottleneck in research is probably less so production as it is verification now that researchers have access to AI Agents like Claude Code, Gemini and Codex. But how to verify, and what to verify, is largely something all researchers will have to bumble through themselves. We did it the old fashioned way — we asked Hannah to check Claude’s work by independently going through the marriage records we found herself. In today’s episode, Hannah comes back to tell us what she found.
And after today, we are now one episode closer to actually running some regressions! Famous last words.
Thanks again for your support of the Odd Couple podcast, as well as my substack.
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In this episode of the Mixtape with Scott, and season 5's "The Odd Couple", featuring Caitlin Myers and myself (Scott Cunningham), we work with Claude Code to make beautiful figures of our identification strategy -- the change in travel distance to the nearest abortion clinic caused by House Bill 2 closing half of Texas's abortion clinics. We are slowly beginning to prepare for our diff-in-diff strategy using continuous treatment, but not there yet.
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Me and Caitlin Myers are back with our trusted robot command line interface secret agent with a license to kill, Claude Code! This week we continue our live research project studying the closure of abortion clinics across Texas under House Bill 2 and its effect on county marriage certificates, or the flow of new marriages. In the previous weeks, recall Claude Code helped find, pull, store locally marriage certificates — with people’s names and selected demographics for goodness sake! — and then build a panel dataset. But Claude also helped us try to understand what was going on with these date when some irregularities were spotted. And to satisfy by seemingly endless itch, Claude also made us “beautiful decks” according to my rhetoric of decks philosophy at my MixtapeTools repository that contains skills I regularly use. And the deck had beautiful pictures in it.
This week we extend that exercise and make maps of Texas with more data as we continue pressing ahead to determine the relationship, potentially causal, of increased travel distance on the flow of people into marriage. Thanks again for tuning in. Tell your friends, family, your old second grade teacher, Ms. Lacy, your barista, the kids next door who sometimes play their music too loud about this amazing podcast with Caitlin Myers at Middlebury College, and me, Scott Cunningham, at Baylor University.
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If the concept of a podcast where two economists use Claude Code to do research together sounds absurd, well, you would not be wrong. But that has not stopped Caitlin Myers and I from doing it. This podcast is about the two of us using Claude Code to do a research project together on abortion clinic closures and the effect it had on marriage using a Texas natural experiment called House Bill 2, and county level marriage data we collected with Claude in an earlier episode. Some of you had asked to see the “beautiful deck” that Claude made for us last week and so here it is!
And here is the YouTube video if you’re wanting to watch us and meet Hannah.
The age of AI has shifted things somewhat for researchers where we have to bring in verification of what we do sooner and often. Figuring out how, when and where to do that is something me and Caitlin, as well as most listeners, are trying to figure out too. Caitlin had the idea of embodying our own verification methods with a real live human being — a former student of hers, Hannah Sayre, a recent graduate of Middlebury College. In this episode, we meet Hannah, talk with her and hear about her own story and journey as a young person aspiring to a PhD in economics, and how what her job will be on this project to confirm what we are doing with Claude Code.
Plus a little easter egg if you skip ahead is in the video because Caitlin is going to tell us about her new job!
Thanks again for your listener and viewer support! This podcast, just like the substack, is a labor of love. So sit back and enjoy!
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This podcast is part of my long running podcast called “The Mixtape with Scott”, which had historically been an oral history of economics through in-depth interviews with living economists. After around 130 interviews over four seasons, I’m taking a break to talk about Claude Code with my good friend and coauthor, Caitlin Myers! What we do on the podcast is we are doing a research project together, from start to finish, on abortion and marriage. Specifically, we are studying the effect that of a natural experiment called House Bill 2 that required abortion facilities’ clinicians and physicians to have admitting privileges at hospitals. This led to half the state’s clinics to close causing an increase in travel distance to the nearest abortion facility to rise. Several papers have been written about the effect this had, including one by us, but in this podcast we tackle a question that had not been studied yet — the effect it had on new marriages and new divorces.
But where did we get the data for this? Claude Code found it for us. While I knew of the data, we put Claude Code on the task of finding it — which it did. Claude Code found the data for us on its own, downloaded it for us, stored it in our local directory for us, and then did a benchmark analysis for us of that data against other published data sources on Texas marriages. And then Claude Code made a beautiful deck of slides walking us through what it found and what it all meant for us in our project! For the deck alone, I encourage you to follow along.
What a world we are living in!
Hopefully you find it interesting to see how the sausage gets made — how research projects start, how Caitlin thinks about doing research at all, how slow and meticulous she is about it, and how much fun research can be, as well as how we bring Claude Code into the research process itself. Thanks again for all your support! This has turned out to be a fun.
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In this week's episode of the Mixtape with Scott season 5, "The Odd Couple" Scott Cunningham (Professor of Economics at Baylor) and Catilin Myers (Professor of Economics at Middlebury College) set out to use Claude Code to get the data for their project studying travel distance to the nearest abortion clinic's effect on marriages in Texas after House Bill 2 shut down half the state's clinics. As they do, they talk about their project, the trappings of having a third party robot as a colleague and RA on this project done on the air, and articulate aloud the prompts as they do them!
Thanks again for all your support! This substack and the podcast are labors of love. Please consider becoming a subscriber at only $5/month!
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The Odd Couple
The Mixtape with Scott is back. Season 5. Season 5 of the Mixtape with Scott is going to be different, and fun, and different, and creative! It’ll be called The Odd Couple. And it’ll be called “The Mixtape with Scott (Featuring Caitlin Myers)”. It’ll have different naming conventions until Caitlin pick one we like! Let me tell you all about it.
I started the podcast around four years ago as a way of creating an oral history of economics while also tracing out the history of the credibility revolution through Orley Ashenfelter, his students, and the Industrial Relations Section at Princeton. I tacked on a bunch of other things too along the way like “the students of Gary Becker” and “economist in the tech industry”, as well as any number of eddies I wanted to swim in along the way.
And after 130 interviews, I more or less felt like I had tapped my creativity out. I largely came to understand the evolution of causal inference a particular way, which I wrote up across several substacks, as well as added throughout my new book, Causal Inference: the Remix (proofs came to me today in fact). It was very rewarding. Maybe one day I’ll write up the interviews as a book (even Claude Code cannot yet do that), but for now, I’m just ready to move on, as 130 interviews is a lot.
But move on to what? Well, that’s what I want to tell you about now. Today’s episode is the first episode in a season I’m calling “The Odd Couple” featuring the brilliant economist, Caitlin Myers. And the concept is simple:
Caitlin Myers and me will start a research project together which is only performed on the podcast. And we will use Claude Code to do this project on the air. While doing it, we will talk and laugh and share our thoughts about what we are doing. Think of Bob Ross talking while he paints trees. Only instead of trees, it’s estimated dosage parameters of abortion clinic closures’ effect on marriage using continuous diff-in-diff. And instead of a brush, we are using Claude Code who is using R, python and Stata. But other than those trivial details, it is exactly like Bob Ross, or maybe the View.
The Odd Couple featuring Caitlin Myers, Scott Cunningham and Claude Code
Caitlin Myers is the John G. McCullough Professor of Economics at Middlebury College in beautiful Vermont. And she is, at the time of this writing, arguably one of the leading economists working on reproductive policy in the United States, maybe the world. She’s been published a lot on the topic for a very long time, including this article in the Journal of Political Economy, our JHR on abortion clinic closures, and numerous others. You can find it all at her slick website.
She’s also been a contributor to the public good by creating public data repositories. She built this dashboard. She knows where every clinic opened and closed and when, going back decades. She’s meticulously described each and every relevant law regulating abortion access. If you’ve read a paper in the last ten years about abortion services, there’s a good chance a design by Caitlin, or data she helped curate and distribute, was somehow connected to it. Her influence in this space has been massive.
But in addition to being great, she’s also funny, thoughtful, and thinks really well on her feet. Which is one of the reasons I thought it would be great to have her as my research partner and conversation partner on the podcast. Because I think if this concept is going to work, a lot of planets have to align, and I had been thinking for a very long time that if there was such a square peg to fit a square hole, it would be her.
I would say that Caitlin and I are right at that sweet spot of professional acquaintances bordering on friends. That’s the type of person who you make a point to find when you are at a conference and get a drink with even if you aren’t at that moment writing a paper together. It’s that person who you shared a little about your private life with when you were on a car ride together to the airport. It’s that person who you text memes of Beyonce giving out high fives for no good reason. It’s that person you want to send a note to in class saying “Will you be my friend? Circle yes or no”.
No one does this on the air
So the idea of this podcast is that she and I are going to extend an old study of ours with Jason Lindo and Andrea Schlosser published in the Journal of Human Resources called “How Far Is Too Far?” It studied what happened when Texas passed HB2 in 2013 and nearly half the state’s abortion clinics closed overnight. We used the sudden, geographically uneven changes in driving distance to the nearest clinic to estimate the causal effect of access on abortion rates. The punchline was that distance matters, the effects are non-linear, and congestion at the surviving clinics matters too.
But what we want to do is extend the research design in a couple of ways. First, we want to study the effect that the abortion clinic closures had on marriage. While Caitlin has studied the effect of abortion access on marriages, no one has look at the clinic closures on marriage using, more specifically, the “travel distance design” as I call it. Secondly, we are going to be learning how to estimate treatment effect parameters, as well as what those estimands even mean, using the new conditionally accepted (at the AER — woo hoo fellas!) continuous diff-in-diff estimator by Callaway, Goodman-Bacon and Sant’Anna estimator. This estimator already has over a thousand cites and it’s only just now conditionally accepted — it’s not even really really accepted. It’s like the AER is saying it likes you, but does it really really like you? Not until it’s accepted you does the AER really really like you. Right now it’s a conditional accept which is more like a situationship. Anyway, I’m rooting that these two get hitched, and so we’re going to be using their estimator with this travel distance design to estimate a bunch of estimands that we’re going to learn about together. So that’s fun.
The AI angle
And then third, and maybe the goofiest of all — Claude Code. We are going to do all of this using Claude Code. The hope being that we can wrap our hands around just how to use this thing to do good, and not evil. And I think this is the funnest (most fun?) part because Caitlin is probably the more pessimistic towards AI, whereas I am the most optimistic, which on average means we are aloof to AI. And Claude is probably going to sometimes agree with me, sometimes with Caitlin, and sometimes just want to say we all have a great point. Anyhow, we are going to be doing this project together using Claude Code so that listeners and viewers can better see how we use Claude Code for practical empirical research, and how we go about trying to get it to not jump the electric fence, or if it does, not cause mayhem.
But as I said, Caitlin and I have very different priors on this. I’m the AI optimist and she’s the AI skeptic. While we have both been using Claude Code for months, and we’ve both seen what it can do, and we both agree we’re in the early innings of something that fundamentally changes how research gets done, I think we both have fundamental opinions and concerns that sometimes overlap with each other and other times don’t.
But she is, I think like me, curious to a fault. She wouldn’t be doing this if she weren’t — but she thinks AI is, in her words, an existential threat to humanity. And she is not being dramatic. She means it. And that’s not an uncommon worry among people, nor is it an uncommon position to take that people simultaneously are angry or upset about AI and want to better understand Claude Code’s utility for practical empirical research. That’s just the times that we are in that both of those can be true at the same time for the same person. She’s the person at the table asking the hard questions about what happens when these tools get good enough that the verification problem becomes the only problem.
So you have one person who thinks this is going to be incredible and one person who thinks it might end civilization, and we’re both using the same tool to do the same project. That tension is real, it’s productive, and it’s part of what you’ll hear.
And here’s the thing about podcasting with Claude Code running in the background: there’s a lot of time while it’s working. It’s reading files, writing scripts, compiling things, running pipelines. And during that time, Caitlin and I are talking. About AI, about science, about what we’re seeing in real time on the screen, about the project, about whether what just happened was impressive or terrifying or both, or just about life, about the meaning of being a researcher, about our worries and hopes and where, and so on. And we are joking around and bantering.
It’s like The View if The View had two economists staring at a terminal.
What to expect
Episodes will drop as we work through the project. Some will be data work — the kind of session where we’re elbow-deep in county FIPS codes and file format inconsistencies. Some will be methodological — working through the continuous diff-in-diff framework, figuring out what the identifying assumptions actually require. Some will be the conversations that happen in between — about AI, about the future of empirical research, about what it means to do science in public.
I don’t know how many episodes this will be. I don’t know what we’ll find. I don’t know if the marriage result will be a null or something real or something we can’t interpret. As they say in therapy, it’s about the journey not the destination! This podcast is about the journey, which is to say it’s about the joy researchers get from doing research, not necessarily from completing it. And it’s a podcast of two people talking while they do it.
The Mixtape with Scott is back. Season 5. The Odd Couple. Featuring Caitlin Myers. We're making the sausage, and you're invited to watch
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Welcome back to The Mixtape with Scott. I’m currently in the process of putting together a new slate of interviews, and while it’s not quite ready yet, I didn’t want to leave you hanging. So in the meantime, I’m re-sharing some conversations from earlier seasons — episodes that I think are worth revisiting or perhaps discovering for the first time.
Today’s rerun is from Season Two, and it’s one of my favorite interviews from that time: my conversation with Ariel Pakes, the Thomas Professor of Economics at Harvard University.
This was such a fun and rich interview. People either know Dr. Pakes very well or only by the letter “P”. He’s a towering figure in industrial organization and structural econometrics, with landmark contributions both theoretical and applied. Among many things, he’s the “P” in the Berry-Levinsohn-Pakes model — BLP — which remains one of the most influential tools for estimating demand in differentiated product markets. That paper — Automobile Prices in Market Equilibrium — published in Econometrica in 1995, has had a ripple effect not just in economics, but well beyond it.
But this interview wasn’t just about methods and models. Dr. Pakes and I talked about basketball, about growing up in a radical socialist youth group, about his early love of philosophy, and his own path through Harvard as a young man trying to straddle economics and philosophy before finding his place. He spoke softly, with depth and reflection, and he offered a glimpse into how he works — by getting himself in way over his head and then slowly, patiently, working his way out. It’s a way of thinking that hasn’t just shaped his own work but has helped shape the rest of ours too.
I hope you enjoy this one as much as I did.
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Greetings from Cambridge! I’m still mid-move and not fully settled—classes kick off next week and I’m wrapping the last bits of admin—so I’m sharing one more rerun before we close out Season 4.
Today’s guest is a Cambridge neighbor just down the Charles at MIT: Dr. Amy Finkelstein, John Bates Clark Award–winning economist.
If you’re new to her work: Amy is a leading health economist at MIT and coauthor of We’ve Got You Covered (with Liran Einav), a timely book from a couple of years ago. In it, they argue for universal basic coverage that guarantees financial protection from major medical costs, while leaving room for supplemental private insurance—simple, fair, and focused on what insurance is actually for.
In our conversation we cover the Oregon Medicaid Experiment and the ideas that shaped it, plus the arc of her career. I loved this one. Hope you enjoy the rerun.
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Greetings everyone. I’m still in moving mode, packing up life in Texas and getting ready for a year in Boston. I hit the road on Friday of this week for a three day road trip and am still behind on everything. That means the podcast is still on reruns for now, but I should have a new episode for you next time. This week’s rerun is one I really liked, though—my conversation from two years ago with Steven Berry.
Steven is the Sterling Professor of Economics at Yale and the inaugural Faculty Director of the Tobin Center. His work in industrial organization has shaped how economists think about markets in equilibrium, and his research spans industries from autos to airlines to media. He’s also a winner of the Frisch Medal, a member of the National Academy of Sciences, and one of the field’s most respected voices.
We talked about his path into economics—from the Midwest, to Wisconsin, to a career that’s helped define modern empirical IO. Naturally, we dug into the BLP model, the landmark framework he developed with James Levinsohn and Ariel Pakes that changed how we estimate demand in differentiated product markets. It’s one of those ideas that’s both deeply technical and hugely practical in policy and business.
If you missed it the first time, I think you’ll enjoy hearing Steven reflect on his career, his collaborators, and where the field is headed. Here’s my rerun conversation with Steven Berry.
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I’m still going through some older reruns for the summer due to my travel schedule. This one is an interview with Rocío Titiunik, a quantitative methods political scientist and professor in the department of politics at Princeton University, as well as a researcher that has been at the frontier of work on regression discontinuity designs.
Her name is synonymous with cutting-edge work on regression discontinuity design, developed in close collaboration with scholars like Sebastián Calonico, Matías Cattaneo, and Max Farrell. Together, they’ve shaped the modern landscape of causal inference, not only through groundbreaking theory but also through widely used software tools in R, Stata, and Python. In addition to her contributions to quantitative methodology, Rocío’s applied research — from electoral behavior to democratic institutions — has become a major voice in political science. She also holds a formidable editorial footprint: associate editor for Science Advances, Political Analysis, and the American Journal of Political Science, and APSR. It’s no exaggeration to say she helps steer the field as much as she contributes to it.
In this older interview, Rocío shared how her journey into economics began not with data, but with theory, literature, and the big questions that led her to the discipline. Her path into Berkeley’s PhD program in agricultural and resource economics was anything but linear, and even once there, she wasn’t sure how all the parts of herself — the scholar, the immigrant, the thinker — would fit together. During our conversation, she opened up about moments of uncertainty, of feeling lost in the sheer vastness of academic economics. Her honesty was disarming. It reminded me that no matter how decorated someone’s résumé may be, we’re all just trying to find our way — and sometimes, the most important breakthroughs happen when we admit we haven’t arrived yet.
Thanks again for tuning in! I hope you like listening to this older podcast interview.
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Greetings from San Sebastián Spain where I am on holiday with my daughter for another couple of weeks. I have still not done any new podcasts as I realized only after I left that I did not pack my microphone. And, I didn’t want to buy a new one, and I wasn’t really 100% positive if using my Apple AirPods would work well. All of that is to say — excuses.
So, this week we are going back down memory lane to an interview I did 1-2 years ago with one of my favorite young up and coming econometricians, Tymon Słoczyńsi from Brandeis University. Tymon is the author of a wonderful 2022 article on OLS models with, I’ll call it, “additive and separable” covariates under unconfoundedness. Autocorrect wanted that to be “addictive” instead of “additive”, which would’ve been a really clever Freudian slip.
Tymon’s interview was one of my favorites. I know I say that about every interview, but they all feel like that, but let’s just this one really really feels that way. And I think you’ll feel the same way.
One of the things I love about Tymon’s articles is how excellent the writing is. His paragraphs oftentimes feel like the kind of paragraphs that you can tell he wrote, and rewrote, and rewrote, and rewrote like a hundred times. It amazes me that English is not his first language and he writes this well. I don’t even mean this clear — I mean it’s beautiful writing. Here’s a paragraph I think is outstanding, for instance:
“To aid intuition for this surprising result, recall that an important motivation for using the model in equation (1) and OLS is that the linear projection of y on d and X provides the best linear predictor of y given d and X (Angrist & Pischke, 2009). However, if our goal is to conduct causal inference, then this is not, in fact, a good reason to use this method. Ordinary least squares is “best” in predicting actual outcomes, but causal inference is about predicting missing outcomes, defined as ym = y(1) × (1− d ) + y(0) × d. In other words, the OLS weights are optimal for predicting “what is.” Instead, we are interested in predicting “what would be” if treatment were assigned differently.”
A lot of his sentences are sentences that are so precise, so insightful, that I wish I could have written it. It’s superb, he’s superb, and if you haven’t listened to this, I hope you do, and if you already have listened to it, then I hope you listen to it again.
Thanks again for all your support. Wish me luck as I wrap up my summer in Europe, start making my plans to move to Boston, teach new students, meet new colleagues, and make new friends. And get some new clothes to replace the ones the gentleman who stole my luggage on the train in Switzerland is now in possession of.
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Welcome to the Mixtape with Scott — an interview-based podcast where I, Scott Cunningham, talk to living economists about their personal lives. I continue my travels in Europe without a good microphone, which has caused me to delay my newest interviews a little bit longer. Therefore this week’s episode is an oldie but a goodie — Jon Roth, a young econometrician at Brown University. Jon has had many high profile publications to his name already in a short period of time, many of which center around difference-in-differences. Several have focused on the event study (e.g., here, here and here) , whereas others have focused on the logarithm both within diff-in-diff but also outside of it. I think it is fair to say that Jon’s econometric contributions have been unusually practical to applied researchers while also scientifically robust and accurate. I remember enjoying this conversation with Jon a great deal, and if you haven’t listened to it, it’s a great time to do so now, and if you have listened to it, it’s a great time to listen to it again! Thank you again for all your support!
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This week’s episode of the Mixtape with Scott is a rerun of an earlier interview I did with Muhammad Akbarpour, an economic theorist at Stanford University. Muhammad tells his life story of growing up in Tehan, Iran and his long and windy road into economics and Stanford University, where he both went to grad school and is now an assistant professor. If you haven’t had a chance to listen to it or watch it, I highly recommend it again. Mohammad is one of my favorite young economists, particularly theorists, working today and I find talking to him to be really inspiring. This was one of my favorite, top 5 even, interviews I’ve had on the show so far too.
Thank you again for your support.
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Welcome back to The Mixtape with Scott, a podcast about the lives and stories of living economists. This show often unfolds in themed mini-series, and lately I’ve been exploring one that I’ve been curious about for a while: the economists who navigated and participated in the heterodox tradition in economics.
Today’s guest is Amitava Krishna Dutt, a development economist, now emeritus at the University of Notre Dame. His work sits at the intersection of structuralist macroeconomics, post-Keynesian theory, and development, with deep engagement in political economy. He’s long been committed to questions of global inequality, the dynamics of capitalist growth, and the limitations of orthodoxy in addressing the needs of the Global South.
So thank you for tuning in. I hope this is as interesting to you as it was to me.
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Welcome to this week’s episode of The Mixtape with Scott. Today’s podcast guest is our 127th guest on the show—Vitor Possebom, Assistant Professor in the Department of Economics at the Fundação Getulio Vargas. Vitor’s research sits at the intersection of two areas — econometrics and causal inference, and policy evaluation in Latin America, particularly Brazil.
His contributions revolve around refining and extending tools for estimating causal effects in observational data, especially under common data imperfections like selection bias, measurement error, and treatment effect heterogeneity.
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Sample selection and marginal treatment effects (e.g., “Identifying Marginal Treatment Effects in the Presence of Sample Selection” (Journal of Econometrics), “Crime and Mismeasured Punishment” (Review of Economics and Statistics))
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Misclassification and measurement error (e.g., “Potato Potahto in the FAO-GAEZ Productivity Measures?”)
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Inference and sensitivity in synthetic control methods (e.g., “Cherry Picking with Synthetic Controls”, “Synthetic Control Method: Inference, Sensitivity Analysis and Confidence Sets”)
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Probability of causation in non-experimental settings (e.g., “Probability of Causation with Sample Selection”)
I invited Vitor onto the podcast because of his creative contributions to causal inference, as he fits into a larger informal series I’ve been for the last several years on causal inference in general. In today’s conversation, we talk about Vitor’s path from Brazil to Yale University and then back. Vitor, thank you so much for joining us.
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Welcome to The Mixtape with Scott, a podcast dedicated to exploring the personal stories of living economists. I'm your host, Scott Cunningham, Professor of Economics at Baylor University.
Today, I'm delighted to introduce Jessica Brown, Assistant Professor of Economics at the Darla Moore School of Business at the University of South Carolina. Jessica is also a Research Fellow at IZA and a Faculty Affiliate at the Wilson-Sheehan Lab for Economic Opportunities.
I invited Jessica onto the podcast because of her deep connections to the credibility revolution, causal inference, and the esteemed tradition of labor economics nurtured at Princeton University’s Industrial Relations Section, where she completed her PhD in 2019.
Jessica is also joining us as part of a special series I've been hosting, loosely titled "The Students Of..." Within this series, she specifically contributes to our "Students of Alan Krueger" mini-series. Alan Krueger, a pioneering economist whose work profoundly shaped labor economics, tragically passed away in 2019. Jessica was one of Alan's last doctoral students, and his death came shortly before her dissertation defense.
In our conversation today, we'll explore Jessica's journey as an economist, her experiences studying under Alan Krueger, and the influence he had on her professional and personal development.
Jessica, thank you so much for joining us.
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Welcome to this week's episode of The Mixed Tape with Scott. I'm your host, Scott Cunningham. This podcast is devoted to the personal stories of living economists, diving into their lives, careers, and the fascinating paths they've walked.
This week's guest is Michael Anderson, an economist from the University of California Berkeley's Department of Agricultural Resource Economics. Michael earned his PhD at MIT in 2006 under the mentorship of Josh Angrist, making him part of a broader narrative I've been exploring—the Princeton Industrial Relations Section and the influential lineage of scholars who shaped the modern credibility revolution in economics.
In our conversation, we touch on Michael's rich and varied research. We discuss his insights into the returns to college athletic success, delve into his foundational work on the Perry Preschool program and the challenge of multiple inference, and explore the real-world impacts outlined in his American Economic Review paper on subway strikes and slowdowns. As always, though, this episode is much more than just research highlights—it's about Michael's journey through economics, his stories, and the experiences that have defined his path. I hope you enjoy the show!
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I’m thrilled to announce that our next guest on The Mixtape with Scott is Professor Philip Oreopoulos—one of the most impactful economists working today in education and labor. A PhD student advisee of David Card, Phil is part of the distinguished lineage that helped shape the credibility revolution in applied microeconomics.
Now a Professor of Economics and Public Policy at the University of Toronto, Phil has spent his career studying how education policies and interventions affect outcomes for students and workers. His work blends rigorous causal inference with real-world relevance to uncover how both the very large interventions we employ to help society, as well as the seemingly surgically narrow ones, shape the lives of workers and students.
He’s also a Research Associate at the National Bureau of Economic Research and a Research Fellow at the Canadian Institute for Advanced Research. His CV is full of important papers, but it’s the heart behind the work that really stands out—his curiosity about the world and his desire to make a difference.
In this episode, we go beyond the papers. We talk about his journey, what it was like working with David Card, and how he found his calling. It’s a thoughtful, warm conversation with a scholar who represents the very best of what economics can be.
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I'm excited to announce the newest episode to the podcast features a brilliant mind in econometrics and applied microeconomics: Dr. Liyang "Sophie" Sun from University College London. While Liyang has technically been a guest before, our previous conversation had been narrowly focused on econometric techniques. This time, we're shifting gears to align with the core purpose of the podcast—exploring the personal stories and journeys of living economists.
Many of you know Liyang by reputation or have cited her groundbreaking work. Her 2021 paper with Sarah Abraham in the Journal of Econometrics on difference-in-differences estimated using two-way fixed effects with leads and lags was recognized as one of the recipients of the Aigner award for 2022 —a remarkable achievement. That paper in particular helped clarify exactly what we were—and weren't—measuring in difference-in-differences event studies. Beyond diagnosing issues in existing approaches, they introduced a new and more accurate estimator, known formally as the interaction-weighted estimator, but which most of us now fondly call simply “SA” (Sun and Abraham). I love that paper; it has taught me a great deal.
Her research portfolio extends well beyond this, spanning instrumental variables, synthetic control methods, and other innovative approaches that have reshaped how we think about causal inference in economics.
In this episode, we'll dive into Liyang’s personal journey through growing up in China, coming to the United States as a high school student, and then through college, grad school and a career as a professional economist and econometrician. She generously shares the experiences, people and discoveries that have shaped her career and research directions. It was a genuine pleasure to hear more of her story, and I believe you'll find it both enlightening and inspiring.
Thank you again for all your support!
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Welcome to the Mixtape with Scott! This week’s guest is Nathan Nunn, professor in the Vancouver School of Economics at University of British Columbia. Nathan is a development economist and economic historian whose work on the development of the African continent has been viewed as pioneering, seminal even. Two of his major works focused on the African slave trade and its impact on trust (here in this AER) and the continent’s longterm development (here). The body of work is so massive that I can only point you to his webpage and vita. He’s currently an editor at Quarterly Journal of Economics, a member of NBER and a research fellow at BREAD. And here is his google scholar page. And for giggles, here are the people at NotebookLM explaining his vita!
Here’s that NotebookLM link for people looking on YouTube or podcast platforms like Apple Music or Spotify.
url: https://notebooklm.google.com/notebook/ac825f4e-3e35-4359-b154-bc82ef808a79/audio
Thanks again everyone and I hope you enjoy this great interview!
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Welcome welcome one and all! This is the newest episode of the The Mixtape with Scott where we talk to living economists, ask them what they wanted to be when they were little, learn what and how they did become, are becoming, what they became as an adult, and this week too, the road less traveled.
This week’s guest is named Jérémy L’Hour. I first learned about Jeremy because of a JASA on synthetic control he wrote with Alberto Abadie a few years ago entitled “A Penalized Synthetic Control Estimator for Disaggregated Data”. I then learned that Jérémy had studied with Xavier D’Haultfoueuille, the econometrician and coauthor to the famed difference-in-differences estimator in the AER that helped launch a thousand ships on difference-in-differences with differential timing. I reached out to see if we might talk as Jérémy has a story that I have not had a chance to hear about.
Jérémy is the author of Machine Learning for Econometrics with Christophe Galliac which is forthcoming at Oxford University Press. And of course he is the author of the JASA on synthetic control with Abadie. But interestingly, he is not an academic. Rather, he works for a hedge fund called Capital Fund Management. Which was another reason I wanted to talk to him.
The last many years, we’ve seen more and more talented economists go into industry rather than academia, but mostly I interview economists in tech. I haven’t interviewed anyone who is at a hedge fund before, and I thought that that might be an interesting guest. There’s always a lot of uncertainty in the job market, but maybe now more than ever, and hearing about more options in the private sector would be useful to people all over the world.
So thank you again everyone for supporting the substack and the podcast. I appreciate it immensely as it helps me to do what I love which is listening to people’s stories. I hope you enjoy this interview as much as I did.
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Welcome to episode 15 of season 4’s The Mixtape with Scott! I am of, you guessed it, Scott. And this is my podcast which is a podcast where I interview economists and ask them about their personal story. If you were dying to know the games that economists played when they were kids, or what books they read in high school, then man are you ever in luck because that’s literally what we talk about on here!
This week’s guest is Dmitry Arkhangelsky, an associate professor at CEMFI in Madrid, Spain. Dmitry is known to many people because of his 2021 American Economic Review article with an Avengers like team of econometricians and statisticians — Susan Athey, Guido Imbens, David Hirshberg and Stefan Wager — entitled “Synthetic Difference-in-Differences”. Synth diff-in-diff is a well known contribution to the pantheon of new causal panel methods and is quite versatile and flexible. Dmitry is currently on leave from CEMFI and had just arrived to Harvard for a research sabbatical when we did this interview.
Dmitry is an econometrician and machine learning, and as he’s connected to this new diff-in-diff and synth literature that has been exploding and evolving over the last few years, his work on those topics are well known. But I think as he’s not on social media, he’s not someone people may know as much about. So I hope you that this is an interesting interview for those of you wanting to learn about his life growing up in the bustling city of Moscow, Russia. It’s a bit of a rags to riches story in some way as unlike many Russian economists who are dialed into the best schools as a young person, where they are exposed to intensive training in mathematics early on, Dmitry’s journey was different, and I don’t want to spoil it. But I think it’s one that many of us may identify with.
Thank you again for all your support of the podcast. It’s a labor of love to get to have a chance to just pause, look at another person, and listen. I continue to believe that it’s in the moments when we can look at a person that we know ourselves. And so I enjoy doing it and appreciate your support and hope it is the same for you on some level. And thank you to Dmitry for being generous with his time to share a little about his life. Consider becoming a paying subscriber where you get full access to all kinds of weird posts ranging from econometrics, practical opinions about work, discussion of my classes, and taking care of my ailing dad, as well as a fairly regular reflection on the economic implications of new technologies.
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This week, I’m thrilled to have Diane Whitmore Schanzenbach as my guest. Diane is the Margaret Walker Alexander Professor in the School of Education and Social Policy at Northwestern University and a leading voice in the economics of poverty, education, and public policy. Her research focuses on how major programs like SNAP, Medicaid, and early childhood education impact children’s long-term outcomes. Diane has published in top-tier journals, testified before Congress, and served in key leadership roles, including as director of the Hamilton Project at Brookings and as director of Northwestern’s Institute for Policy Research.
Diane is also part of my ongoing series exploring economists with connections to Princeton’s Industrial Relations Section. As a former student of the late Alan Krueger, Diane brings a unique perspective to the show, and it was a privilege to hear about her journey—from her work at the Council of Economic Advisers to her impactful research and academic career.
Thank you, Diane, for joining me, and thank you for listening! I hope you enjoy the conversation.
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Welcome to this weeks episode of the Mixtape with Scott! This is a podcast about the personal stories of living economists and an oral history of the last 50 years, give or take. And today’s guest is part of a larger series about the students of the key founders of the credibility revolution. Today’s guest was Alan Krueger’s student at Princeton and her name is Marie Connolly, a labor economist and professor at Université du Québec à Montréal.
Marie Connolly earned her Ph.D. in Economics from Princeton University in 2007, where she worked under the mentorship of Alan B. Krueger. I first corresponded with Marie right after she published an article estimating intertemporal labor supply elasticities in Journal of Labor Economics in 2008. I was working on a similar paper as hers, in that I was using quasi-experimental changes in weather to estimate labor supply in sex work, but hers was interesting because she framed the project in relation to macroeconomic models that required much larger elasticities than what she and others found using quasi-experimental methods. Connolly’s work was emblematic of the “credibility revolution” in economics in that sense and not just through academic lineage at Princeton, Krueger and the Industrial Relations Section.
Throughout her career, Connolly has explored two fascinating domains: the economics of music and the intersection of family dynamics and labor markets. Her work on “Rockonomics,” often coauthored with Krueger, investigates the economics of popular music, delving into topics like concert pricing and the secondary ticket market. Equally compelling is her focus on family-related issues, such as child penalties and intergenerational income mobility. Her recent research on child penalties in Canada and the cognitive and non-cognitive effects of class size has echoes of her former advisor’s own work on class size. Connolly’s dual focus on music and family economics demonstrates her versatility and intellectual curiosity, making her a unique voice in labor economics.
Thank you again for your support of the podcast! I hope you find this interview as interesting as I did.
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Welcome to the last podcast interview of 2024! This is the fourth season, 10th episode, which I guess puts us between 110-120 interviews so far. This week’s interview with an economist, learning more about their personal story, is Ted Joyce.
Ted is a Professor of Economics at Baruch College and the Graduate Center, City University of New York (CUNY), and a Research Associate with the National Bureau of Economic Research’s Health Economics program. He’s renowned for his contributions to demography and reproductive health policy and his work has appeared in top journals such as the Journal of Political Economy, New England Journal of Medicine, and Review of Economics and Statistics.
Ted has been a role model for me ever since I graduated in 2007, graciously corresponding with me, meeting with me at conferences, and talking to me about research and navigating the ropes. He was Mike Grossman’s student at CUNY, who I interviewed before and who is himself a very prominent health economist who was also one of Gary Becker’s first students. As my advisor, David Mustard, was also a Becker student, that makes me and Ted cousins. So it was nice having a family reunion for this interview.
Happy new year everyone. May you all be at ease, be at peace, be safe and be happy. 2025 here we come!
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Welcome to this week’s episode of the Mixtape with Scott! Episode 9, season 4. And I just did the math, and we are at 113 episodes so far since I started. What a fun journey it’s been too. So many interesting people, so many interesting stories, so much fun to connect with them and be, for just one hour, getting to hear them all.
For those new to the podcast, this is a podcast about the personal stories of living economists where I listen to them share parts of the arc of their journey. Primarily as their life moving towards being an economist and having been one. It moves between the personal and professional in whatever way feels right at the moment. And this week’s guest is Francine Blau. Dr. Blau is the Frances Perkins Professor of Industrial and Labor Relations and Professor of Economics at Cornell University, and she’s had a long and prolific career studying two overlapping topics — labor economics and the gender wage gap. She is, if I can say it, the labor economist’s labor economist. Deep labor economics, relevant, empirical, pioneering. I can only imagine what it must have been like to be in the room with her at SOLE meetings and seminars from the very start.
In the interview, we learn a lot about her life. We discussed what it was like at Harvard in the early 1970s, why she chose Harvard over MIT, her father’s difficult story as a teacher in the NYC during a difficult time in US political history involving the unions, certain university’s bans on allowing women into their PhD programs (e.g., Princeton), and the importance that Richard Freeman had on her committee in what she ultimately ended up writing a dissertation on, which I’ll explain in a moment. I promised her an hour, so some of the things I’d wanted to ask, like how she saw the credibility revolution emerge around her, I never got to get to. But I loved what we did get to cover, and wish I had had another hour with her.
If I can geek out for just a moment, this is a bit of a longer opener as I normally write, but Francine Blau was truly a pioneer and I’ll just mention one thing — her dissertation. I kind of knew that she was a pioneer because I knew about her full body of work, which is frankly gigantic, which was why I wanted to interview her in the first place, but to be honest, I really didn’t know the start and that context at all. I think it’s fair to say that she was one of the very first economists to be focused on the gender wage gap. I think maybe Claudia Goldin, which I’ll mention in a second, would be an exception in that perhaps it’s a tie between them. There had been obviously work on the economics of discrimination; that had been Gary Becker’s dissertation topic at the University of Chicago in 1955. And Dr. Blau suggested that both Claudia Goldin and Yoram Ben-Porath had also worked on that, but in terms of timing, I think that Dr. Blau predates Ben-Porath but not necessarily Dr. Goldin. Dr. Goldin’s first publication on the gender differences is a 1977 article in the Journal of Economic History entitled “Female Labor Force Participation: The Origin of Black and White Differences, 1870 to 1880” and I don’t think anything Dr. Ben-Porath wrote when Dr. Blau had graduated in 1975 from Harvard. Probably of those two, it would be Goldin’s JEH that would be the closest to something as in-depth and which had comparable calendar date timing as to what and when Dr. Blau published her dissertation (as a book in 1977), but very different in that it was contemporary, not historical, and it concerned women in the modern work place, and specifically within the firm itself.
Dr. Blau’s dissertation was unlike the current style of dissertations which is the “three essay” model. It was a book length dissertation which she published in 1977 entitled Equal Pay in the Office. It was a 1975 dissertation that far predates the work that would come much later on the personnel economics literature we associate with Ed Lazear and Sherwin Rosen. Her dissertation explores many topics that would’ve perfectly fit into that material, but predates it by maybe 10 years arguably, and focuses intently on gender wage disparities between male and female office workers in the United States. She in that dissertation, written partly under the guidance of the labor economist Richard Freeman, examined the extent of wage differentials in the office place, explores the factors contributing to these disparities, and evaluates the effectiveness of equal pay legislation in addressing gender-based wage inequality. I found a copy of it, which I think may be out of print, and am ordering it now, but from what I have been able to gather, it was way ahead of its time, and I mean that.
Dr. Blau is a role model for many people, myself included. The steady march of her career, the consistency, the work ethic, the creativity — it’s the hallmark of a great economist and great scholar. I asked her how she managed to do it and it was interesting what she told me — she attributed a desire to not let down her coauthors as part of how she’s managed to maintain that steady body of work for the last 50 years. That’s a lesson I’m going to try to remember going forward.
This is again a great interview to share. Share with friends, family, students and colleagues, mentors, people outside economics, people inside economics. I was very inspired by the interview and hope you are too.
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Welcome to the latest episode of The Mixtape with Scott! This week’s guest on the podcast is Jann Spiess. Many of you probably know Jann from his work with Kirill Borusyak and Xavier Jaravel on diff-in-diff. Others may know him for his work on machine learning. Now you get to know him for a third reason which is contained on this podcast!
Jann is an assistant professor at Stanford. He’s one of a younger cohort of talented econometricians who have been making practically helpful contributions to the toolkit in causal inference and machine learning, including work on synthetic control with Guido Imbens and much more. This was a great interview and I learned a lot about Jann I didn’t know about. And I hope you enjoy it it too!
Thanks again for all your support! Share this video or podcast with whoever you think would like it!
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Welcome back to The Mixtape with Scott, the podcast where we explore the personal stories behind the professional lives of economists. I’m your host, Scott Cunningham, coming to you from Baylor University in Waco, Texas. Each week, we dive into the journeys, insights, and lives of economists whose work shapes how we understand the world.
This week’s guest is Elizabeth Cascio. Elizabeth studies education, public policy, and the well-being of children. Her research often looks at big policy changes in 20th-century America, like the spread of publicly funded early education and major civil rights, education, and immigration laws. Recently, she’s focused on childcare and early education, trying to understand how policy design, economic conditions, and political voice shape educational attainment and economic mobility.
Elizabeth’s work has been published in leading economics journals, including The Quarterly Journal of Economics, American Economic Journal: Economic Policy, and The Journal of Public Economics. She’s also written policy pieces for The Hamilton Project. She’s a professor at Dartmouth College and holds research affiliations with the National Bureau of Economic Research and the Institute for the Study of Labor. She’s served on editorial boards and is currently an editor at The Journal of Labor Economics.
This episode is also part of a series I’ve been doing called “The Students of…,” where I talk to students of economists in areas I’m particularly interested in. One of those areas is “The Students of David Card.” Elizabeth earned her Ph.D. at Berkeley, where David Card and Ken Chay—both key figures in the development of causal inference within labor economics—were significant influences on her work. Once you hear about her research, their impact becomes clear.
Elizabeth’s work touches on economic history, but she’s primarily a labor economist and public policy researcher. She uses history as a tool to understand policy and its impacts on children and families. Her work connects the past to the present in ways that make big questions about education and mobility clearer.
So, let’s jump in. Please join me in welcoming Elizabeth Cascio to The Mixtape with Scott. Elizabeth, thanks for being here.
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Welcome to the latest episode of the Mixtape with Scott. This week my guest is Tim Bartik from the Upjohn Institute. Let me briefly share some things about Tim. Many of you may know Tim from the shift-share instrument which oftentimes is referred to as Bartik instruments. That’s what I refer to it in a section of my book, for instance. It has been more carefully studied by econometricians over the last few years, such as Borusyak, Hull and Jaravel who have studied it from the shock side, and Goldsmith-Pinkham, Sorkin and Swift from the share side.
Tim has spent a career studying public policy as a a labor economist who focuses a lot on economic development and regional labor markets. This interview was a candid one where Tim generously shared many aspects of his professional journey, as well as his personal philosophical perspectives on work and public policy. I think many of you will find it interesting and even inspiring, particularly those of you whose first love is policy and labor. Thank you again for your support! I hope you find this an interesting and inspiring interview with a great economist.
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Greetings everyone! The leaves on the tree are turning orange as we inch our way towards Halloween and some of us in some unbearably hot portions of the world get to finally see how the good half live and have a whisper of pleasant weather even if it will only be here for a second or two.
This week’s guest on the Mixtape with Scott is Miikka Rokkanen. Miikka is in Consumer Behavior Analytics at Amazon and is part of two of my larger series. First, he is part of my stories about the PhD economists who have gone into the tech industry. But Miikka also fits in another long running story about the “children and grandchildren of the credibility revolution”. That is, Miikka went to MIT for his PhD and was one of Joshua Angrist’s advisees. Miikka can share his full story but he is an economist who started out in academia (Columbia University) and then early on moved into industry at Amazon and having lived both lives can share what that has been like for him. It was great getting to know him better and hearing what his life journey has been like and I hope you enjoy this interview.
For those new to the podcast, though, this is not really a podcast about economics but rather is a podcast about economists. It’s a podcast devoted to sharing economists’ stories about how they got from the point of being a little kid, going through grade school, high school, college and their PhDs into the careers they’re in now. And in learning and hearing these stories and sharing them, it’s hoped that over time it can form a bit of an oral history of the profession. I select people based on larger series I’m interested in — like “Economics who go into tech” or “the students of …” — so it is not exhaustive, and won’t be, but I hope nonetheless that these stories can help, encourage and expand your imagination. Thank you again for all your support.
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Welcome to this week’s episode of the Mixtape with Scott! It’s a pleasure to introduce this week’s guest from Stanford University, Maya Rossin-Slater. Maya is a health economist who specializes in areas related to families in particular. Early work of hers focused on public policies aimed at labor markets as an avenue for helping families, notably paid leave. Her work has been unique for focusing on all parts of the family — mothers, fathers, as well as children. Her more recent work has moved into distressful events that affect all people in the family, and which most recently has moved into focusing on school shootings’ effects on the survivors. When you list all of Maya’s work, you can see patterns — spillovers within and across families, within schools, distressful events impact on the family, assistance in the labor market and its effect on families, and various topics in health. It’s a robust research agenda that seems to have over time shown real patterns of interest spanning all topics relevant to our understanding of the family as an important part of society, and policies that can help, including policies that encourage work.
But it is also fun to have Maya on the show because as longtime listeners know, I have an abiding interest in the “children and grandchildren of the revolution” — meaning those economists who can trace their lineage back to the Princeton Industrial Relations Section. And Maya is also a grandchild of the revolution. Her advisor was Janet Currie whose advisors were Orley Ashenfelter and David Card. So it is interesting to see the propagation of that department moving through the profession. May is only a few links removed from it, but you can see all the finger prints of the Section on her work — meticulous, observant, seeking credible answers to important policy questions regarding workers and their families using credible sources of variation that resemble an experimental design framework.
So thank you for all tuning in! And thank you for all your wonderful support.
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Welcome to the Mixtape with Scott! Sometimes the shortest distance between point A and point B is a straight line, but other times the shortest distance is a winding path. This week’s guest, Mohammad Akbarpour from Stanford University, is perhaps an example of the latter. Mohammad is a micro theorist at Stanford who specializes in networks, mechanism and design and two sided matching. Mohammad is an emerging young theorist at Stanford, student of such luminaries as Matt Jackson and Al Roth, whose background in engineering, mathematics and computer science has given him a fresh approach to topics that I associate with Stanford’s theory people as a whole — policy oriented, applied work, mechanism design, networks and matching. He got into economics “the long way” — growing up in Iran, majoring in engineering, and then moving into Stanford’s operations research PhD program. In this interview, he generously shares a snippet of the arc of his life, and it’s a remarkable story, and one I really enjoyed hearing. I think you will too.
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Greetings! Today’s guest on the Mixtape needs no introduction, but I guess I will anyway. N. Greg Mankiw is a household name to many of us in economics. Either you are a macroeconomist, and his work in new Keynesian economics was something that you had come to know extremely well, or you are literally every other economist, and his principles of economics textbooks you know backwards and forwards because it was either the book you studied as a sophomore in college, or probably even more common, it was the book you used to learn how to teach economics. This interview was a lot of fun, and it kind of fits in a way with something that I keep gravitating towards which is to talk to people in economics who have written textbooks — people like Bill Greene, Mas Col-ell, Jeff Wooldridge, Angrist and Pischke. Thanks again for tuning in.
And I know I said I was going to move to doing these every other week, but man does it seem like it’s been a long time since I’ve done one, so I’m not sure but I will have to decide if I can handle doing them only every other week. We’ll see.
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Welcome the Mixtape with Scott! This is a podcast with a simple objective: listen to the personal stories of living economists who are the primary guests I have on the show. The secondary goal is to follow a thread of people around topics I care about and allow a patchwork story of the profession to form based on, from and through those personal narratives. This is the 105th episode of the podcast, and the first episode of season four. Wow! Time flies.
Today’s guest is name known to most — Dr. Janet Currie. Dr Currie attended Princeton for her PhD, graduating in 1988, spent a large chunk of her career at UCLA, before coming back to Princeton where she is now the Henry Putnam Professor in both the economics dept and the policy school. She’s had an illustrious and impactful career, which is still going, managing a deep portfolio of scientific contributions that I struggle to synthesize it easily. But broadly speaking, her work has focused a lot children, health, mental health, substance abuse and public policy. The work has so many connections over time but also across studies that it was surprising to be honest as we spoke how so much of her work went together, even when it seemed like it wasn’t obvious that it would — even her early work on collective bargaining and teachers unions leads to children, both through schools but also the household bargaining models of the early 80s. Her work on the mental health of children leads naturally into her later work on opiates when you consider the links connect through supply side treatment of attention deficit disorder and supply side prescriptions of opiates. All I could see as we spoke was this giant knowledge graph, like a spider web, connecting papers and topics to one another even when the topics themselves would shift. It was a real joy to have a chance to hear this career in her own words.
One of the themes of the podcast has been the credibility revolution, which is a paradigm regarding empirical work that emerged in the 1970s at Princeton University. It is largely associated with the Industrial Relations Section, Orley Ashenfelter, and his many students and the students of his students. And Janet was an Orley student, as well as the student of one of Orley’s students, the 2021 Nobel Laureate David Card. Having her on here, and the openness with which she shared her story with me, allowed me to learn more about the program at the time she was there, for which I am grateful on top of being grateful for hearing her story.
Thank you for your support and I hope this interview is one you enjoy. It’s 90 minutes but it’s a high mean low variance 90 minutes in my opinion!
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Welcome to this week’s episode of the Mixtape with Scott, a podcast devoted to listening to the personal stories of living economists and creating an oral history of the profession. This episode is partly inspired by my visit to San Sebastián, Spain, with my daughter right now and partly inspired by a 2003 article co-authored with Alberto Abadie studying the effect of terrorism on economic growth that introduced the synthetic control estimator. My guest is Javier Gardeazabal, a professor at the University of the Basque Country.
Javier Gardeazabal is a professor at the University of the Basque Country whose body of work has covered topics in macroeconomics, time series econometrics, labor economics, cultural economics, and political economy. He did his PhD at the University of Pennsylvania in May 1991, an experience that he will share about in the interview. He is from the Basque Country and returned to the Basque Country after graduation where he has been ever since. It is therefore inspiring to me that his home became the topic of a paper that he is perhaps most widely known for — a seminal contribution to both causal inference and measuring the economic costs of terrorism, coauthored with Alberto Abadie, in the 2003 American Economic Review paper, “The Economic Cost of Conflict: A Case Study of the Basque Country.” This groundbreaking study made a major contribution to causal inference by introducing the synthetic control estimator, but also assessing the economic impact of terrorism on economic growth in the Basque Country. It was a major contribution to the field possessing all the elements of great articles in economics — an important question answered extraordinarily well with clarity and rigor.
This influential paper not only cast a massive shadow over the evolution of causal inference and econometrics; it also accelerated Javier’s own research to include not only macroeconomics, but also the economics of terrorism and conflict. His career is evidence of an economist who followed his curiosity and intellectual interests to include understanding the economic costs of terrorism, introducing methods for measuring the aggregate cost of conflict, and the impact of political violence on economic well-being, but also exchange rate dynamics, time series econometrics, cultural policies, optimal test scoring methods, gender wage discrimination and more. Javier’s versatility is evident in his ability to adapt to and excel in a variety of economic topics and methodologies, continually evolving to address new and relevant economic issues.
Thank you again everyone for supporting the podcast and the substack. I hope that this interview speaks to you wherever you are, whenever you are.
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Welcome to this week’s episode of “The Mixtape with Scott”! My podcast tries to capture the personal stories of living economists and create an oral history of the profession from the narratives. And this week, I’m thrilled to welcome Dr. Avinash K. Dixit, a distinguished economist whose life’s work has influenced many fields within economics. But let me start by telling you a little about his background.
Dr. Dixit is the John J. F. Sherrerd ’52 University Professor of Economics Emeritus at Princeton University. He also serves as a Distinguished Adjunct Professor of Economics at Lingnan University in Hong Kong and is a Senior Research Fellow at Nuffield College, Oxford. For his many contributions to science, he has been awarded numerous accolades, including election to the American Academy of Arts and Sciences, the National Academy of Sciences, and the American Philosophical Society. He was also honored with India’s Padma Vibhushan in 2016, recognizing his outstanding contributions to literature and education.
As he will share, he was born in Mumbai, India and attended St. Xavier’s College where he earned a degree in Mathematics and Physics. Afterwards, he earned another degree (also in mathematics) from Cambridge before going to MIT to get his PhD where he was supervised by the late Robert Solow. After graduation, he went to Berkeley, Oxford, Warwick and then Princeton where he’s been since 1981. Both the sheer number of contributions he has made to many fields, but also their influence, is incredible. I put in the title for this episode simply “Microeconomics” after his name, but that was a difficult decision as his work spans microeconomic theory, game theory, international trade, industrial organization, and public economics, just to name a few. I could’ve written any one of those and it would’ve still been inadequate. His recent work continues to address pressing global issues, such as optimal policies for green power generation and the dynamics of social, political, and economic institutions. He is an example of someone who follows his heart and his mind, even taking risks throughout his career to leave entire fields of inquiry in search of more questions.
In addition to his long list of scientific manuscripts, there have also been many influential books, both textbooks but also more ones aimed at a broader population of readers. Things like “Theory of International Trade” (with Victor Norman), “Investment Under Uncertainty” (with Robert Pindyck), “The Art of Strategy” (with Barry Nalebuff), and “Games of Strategy” (with Susan Skeath and David Reiley).
So I’ll stop there and turn it over to the show’s host — myself — and my guest, Dr. Dixit. Thank you for tuning in to this episode of “The Mixtape with Scott.” If you enjoy our conversation, please share the podcast and help us continue to bring you stories from the world of economics.
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Welcome to this week’s episode of "The Mixtape with Scott”! This podcast is dedicated to capturing the personal stories of living economists and creating an oral history of the profession through these narratives. This week, I’m excited to welcome David Autor, an esteemed labor economist from MIT, where he serves as the Daniel (1972) and Gail Rubinfeld Professor, as well as the Margaret MacVicar Faculty Fellow. He was also last year's VP of the AEA, is on the Foreign Affairs board of the US State Department, and is a Digital Fellow at Stanford Digital Economy Lab. The number of accolades is too numerous to list, though, so I will just say that David's pioneering work in labor economics, particularly on the impact of trade, technological change, and the computerization of work, has significantly shaped and re-shaped our understanding of these critical areas.
David Autor is perhaps best known for his influential research on the economic impacts of globalization and technological advancements. His groundbreaking study with David Dorn and Gordon Hanson on the effects of Chinese trade on U.S. labor markets highlighted the deep and often painful economic adjustments faced by local labor markets exposed to import competition. Additionally, his work on the computerization of labor, including studies on skill-biased technological change, has provided crucial insights into how technological advancements reshape the labor market and wage structures.
One of the things you’ll learn in the interview, just as a teaser, is that David was mentored by Lawrence Katz and Alan Krueger, and that mentorship had a lasting effect. Not only did it changed his own human capital and trajectory, it seems also that it changed David’s own attitudes about mentorship. And although we couldn't delve into artificial intelligence in our conversation, Autor’s extensive research on the computerization of labor probably positions him as one of a handful of working economists at the moment whose voice will be kay in understanding the future intersections of AI and labor economics, and probably more than that. So with that I’ll stop, but thanks again to everyone for all your support. If you like the podcast, please share it!
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Welcome to another exciting episode of the Mixtape with Scott! Today, I get to have on the show someone who has become something of a friend the last few years, an expert in health economics and social policy, Adriana Lleras-Muney at UCLA, a Professor of Economics at UCLA.
Dr. Lleras-Muney's journey in economics is super impressive and even involves traveling through all the alleyways of causal inference. After earning her Ph.D. from Columbia University where she wrote a job market paper on compulsory schooling, at a time where it had just become accepted wisdom that the Angrist and Krueger 1991 article needed a fresh take. She then went to Princeton, the birth place of causal inference in labor, before making her way to UCLA where Guido Imbens had just gotten to, and who is also now one of her coauthors in a new article at the Quarterly Journal of Economics. So when I think about her story, it’s hard for me not to hear the echoes, I guess, of the history of causal inference too.
Her academic accolades are too many to name, but I’ll name a few. She's an associate editor for the Journal of Health Economics and serves on the board of editors for both the American Economic Review and Demography. She’s also been a permanent member of the Social Sciences and Population Studies Study Section at the National Institute of Health and an elected member of the American Economic Association Executive committee. In 2017, her contributions to the field were recognized with the prestigious Presidential Early Career Awards for Scientists and Engineers (PECASE).
But what really sets Adriana apart is her groundbreaking research. She's been at the forefront of exploring the relationships between socioeconomic status and health, with a particular focus on education, income, and policy. Her recent work has taken a fascinating turn, examining the long-term impact of government policies on children. She's been digging into programs like the Mother's Pension program and the Civilian Conservation Corps from the first half of the 20th century, uncovering insights that are still relevant today. Her work has appeared in all the major journals in economics such as the American Economic Review, Econometrica, The Review of Economic Studies, and the Quarterly Journal of Economics.
So, all that said, I hope you find this interview as interesting as I did. The video will be posted most likely later to YouTube; my Scottish hotel has surprisingly very slow internet and I’m still downloading the video, and so will likely be uploading it too all night. But thank you again for all your support.
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We have officially passed 100 episodes with today’s guest, and it’s wonderful to get to do it with my good friend, Manisha Shah. Manisha is the Chancelor’s Professor of Public Policy at University of California Berkeley. Manisha is an applied microeconomist who has historically specialized in topics related to health, education, gender and labor, with a particular focus on low and middle income countries. She has research appointments at NBER, BREAD, J-PAL, IZA and is also an editor at Journal of Health Economics as well as an associate editor at Review of Economics and Statistics. And if I can for just a moment tell you a little about that work, please bear with me.
First the main area of her work that I am familiar with is the part that overlaps with my own historical research agenda in sex markets. That is because Manisha is arguably the leading expert on the economics of sex markets and has been for many years. She has published on just that topic alone in many high impactful studies like the effect of both legalizing sex work (Review of Economic Studieswith me) and the effect of criminalizing it (Quarterly Journal of Economics with Lisa Cameron and Jennifer Seager), the identification of compensating wage differentials for unprotected sex (Journal of Political Economy with Paul Gentler and Stefano Bertozzi) as well as a Journal of Human Resources with Raj Arunachalam on a related topic, and more.
But that is just her work on sex markets. There are also her many papers related to children development, like her Journal of Political Economy examining investments in human capital and child labor supply, her work on left-handedness and child development in Demography, another paper of hers looking at parents’ investments in children by their underlying ability, her AEJ: Applied looking at the impact of children’s development on their mother’s own labor supply, her work on sanitation and child development, and it goes on and on. There is also her work looking at people’s own risk preferences and how it relates to natural disasters they have experienced.
One last thing and I’ll quit listing. But one of the things I admire about Manisha’s research is the shoe leather involved. Her usually involves primary data collection, running randomized field experiments, working directly with stakeholders, in places like Uganda, Mexico, India, Tanzania and more.
It’s such a nice treat, then, to get to interview her for the 100th episode, not just because I get to share her personal story to those who only know her by reputation, but also because I count her as one of my closest friends inside and outside the profession. We worked together on a study about the legalization of sex work in Rhode Island that took around ten years from start to completion to publication. It was during a difficult time for me personally and working on that project with her meant a lot to me everyday, but more than that, working with her meant a lot to me everyday. She says in the interview that me and her similar in that we are both intense and very into our projects, and that’s true. But I guess I never really noticed that about her — all I have ever seen with Manisha is someone who is unbelievably kind, unbelievably fun and funny, unbelievably down to earth, non-judgmental, approachable, disarming, insightful, and hard working. All I can is that she has never once made me feel anything other than better about myself. Being around her, being friends with her, I mean, always leaves me feeling better than I think I would feel without her, and for that I am beyond grateful for her presence in the world. Forget the profession — in the world. So with that let me introduce you to her.
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My producer is on vacation this week, and so I am unable to post my latest episode, so I thought I’d post an oldie but a goodie — my season 2 opening interview with Jeff Wooldridge, a much beloved econometrician and economist at Michigan State. So enjoy! Apologies and I’ll see you all next week with a new guest! Thanks again for all your support. Ciao!
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Greetings listeners! It is a pleasure to introduce this week’s guest on the podcast, Ashesh Rambachan, an assistant professor of economics at MIT. I wanted to talk to Ashesh for two main reasons. First, because I wanted to, and second, because I was aware of some of his recent work in econometrics. His recent article on evaluating the fragility of parallel trends in difference-in-differences just came out in the Review of Economic Studies. I’m also intrigued by his work with Sendhil Mullainathan on machine learning, algorithmic fairness as well as generative AI. Having a specialist in both causal inference, artificial intelligence and machine learning is rare, so I thought sitting down with him to learn more about his story would be a lot of fun, not just for me, but for others too. With that said, here you go! I hope you enjoy the interview! Thank you again for all your support!
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This week’s guest on the Mixtape with Scott is esteemed labor economist, Henry Farber, the Hughes-Rogers Professor of Economics at Princeton University.
Dr. Farber’s accolades are numerous: a Fellow of the Econometric Society, the Society of Labor Economists, and the Labor and Employment Relations Association, past President of the Society of Labor Economists, and recipient of the 2018 Jacob Mincer Award for Lifetime Contributions to the Field of Labor Economics. You can find more information about his background here in this short biography.
But ironically, it was for a different reason that I wanted to reach out to him. I was interested in reaching out to Dr. Farber because of his traditional approach to labor economics, such as his seminal work on labor unions and the behavior of New York City taxi drivers (to name just two). His research provides a distinct perspective on labor economics, one that doesn't fall squarely into the natural experiment momentum of his contemporaries at Princeton, despite being part of the Industrial Relations Section there. I hope you enjoy this interview as much as I did! Thank you again for all your support!
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This week's episode of "The Mixtape with Scott" features a conversation with Sarah Miller, a health economist at the University of Michigan. Sarah has made significant contributions to the field of economics, particularly in understanding gender dynamics and reproductive health. Her research has been influential in shaping public policy, and her groundbreaking study on the effect of Medicaid on mortality, conducted with Laura Wherry and Norman Johnson and published in the Quarterly Journal of Economics, stands out as a seminal work. In this episode, we delve into her academic journey, the personal experiences that have shaped her interests, and the impactful research that drives her career.
Beyond her impressive scholarly achievements, we explore the passion and curiosity that fuel her work, as well as her vision for future research. Sarah shares reflections on her personal life, offering a glimpse into the challenges and triumphs that have defined her path. Join us as we uncover the story of a dedicated scholar whose work not only advances economic theory but also has tangible impacts on public health and gender equity. This episode was a thought-provoking exploration of Sarah Miller's remarkable career and the innovative research that continues to inspire her.
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This week's episode of "The Mixtape with Scott" features an insightful conversation with E. Glen Weyl, a distinguished economist whose career has spanned academia and industry. Glen earned his PhD from Princeton, spent three years at the Harvard Society of Fellows, and served as an assistant professor at the University of Chicago, where he made significant contributions to micro theory applications to industrial organization. However, Glen’s journey took a transformative turn when he left academia to join Microsoft, where he currently leads the Plural Technology Collaboratory, focusing on technological solutions for societal cooperation.
Many listeners might recognize Glen from his influential book "Radical Markets," co-authored with Eric Posner. This work introduced the innovative voting mechanism known as quadratic voting, reflecting Glen's deepening interest in democratic processes and governance. His latest book, "Plurality: The Future of Collaborative Technology and Democracy,” (Amazon link) co-authored with Taiwan's Digital Minister Audrey Tang, serves as a manifesto for harnessing digital technology to foster social unity and diversity. The book presents bold ideas, from digitally empowered communication to transforming global trade, aiming to enrich relationships and ensure inclusivity.
In addition to his writing, Glen has also ventured into film as an executive producer of the documentary "Good Enough Ancestor," which highlights Audrey Tang's work in digital democracy. That trailer can be found here; Glen was executive producer on it.
Throughout our interview, Glen shares his experiences and insights from his varied projects, illustrating his renaissance man persona. From his academic roots to his pioneering efforts at Microsoft and beyond, Glen’s story is a testament to his innovative spirit and dedication to leveraging technology for societal good. This episode promises to be an engaging exploration of his remarkable career and visionary ideas.
So thank you for once again for tuning into the podcast! I hope you enjoy this interview as much as I did. Don’t forget to subscribe, follow, all that and tell people about it!
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This week on the podcast, Matthew Jackson from Stanford University is the guest and it was such a delight for me to talk to him and get to know his story a little better. I’d met him before, but only briefly, but I’d read a lot of his work because I once developed and taught a class on networks for our masters of economics students. His textbook on the economic and social networks is excellent but he also has a general interest book on networks if you’re wanting something more accessible.
As the podcast is technically both listening to the stories of living economists and an oral history project, maybe it is worth noting this (though I think it’s obvious to most listeners) that Matt is a micro theorist whose work has empirical content. Not all micro theory does and not all empirical work is necessarily theoretically driven, which is why I make that technical distinction. Networks are also, I think, so clearly an important part of human existence. We make friends, we catch diseases, we learn about opportunities (and maybe as importantly, don’t learn about opportunities) because of networks. And so in a very real sense, even the classical definition of economics proposed by Lionel Robbins, that economics is the study of the allocation of scarce resources by people with unlimited desires, can alone justify the study of networks if networks, as opposed to merely markets and market prices, are actually an important part of that resource allocation process itself. It’s so interesting — as someone nearly 50 to consider all the ways economics evolved over the last 50 years and continues to evolve while still remaining at its core connected to core questions like “how do humans manage to survive on this planet given they have so little time and so little resources?”
Anyway, one last thing. At the end of the podcast, I ask Matt about his new work on artificial intelligence. The paper is at PNAS and is currently unlocked. It’s entitled “A Turing Test of Whether AI Chatbots are Behaviorally Similar to Humans” and it’s by Matt, Qiaozhu Mei, Yutong Xie, and Walter Yuan. They had ChatGPT-4 play a variety of classic games, like dictator games, prisoner’s dilemma, and so on. And they mapped the way the chatbot played to the way humans have planed these games in the lab. The one thing that I found really interesting in what they found was that ChatGPT-4 is altruistic. “It” appears to play the game altruistically in the sense that it attempts to maximize a weighted average of both its payouts and its opponent’s payouts. What then should we expect if we in the long run end up with a network of chatbots? Hard to say what the general equilibrium will be as game theoretic equilibria are often surprising and not immediately intuitive and usually depend on institutions and incentives, but still it’s quite fascinating to me. I hope you liked this interview!
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Welcome to this week’s episode of the Mixtape with Scott where I get to interview Bruce Sacerdote, the Richard S. Braddock 1963 Professor in Economics at Dartmouth. Bruce is a prolific labor economist whose work spans the range of crime, education and peer effects. Some of his papers have been some of my favorite, even. His early work on crime with Ed Glaeser used to really interest me. But it was his work on peer effects that I found really fascinating. This old paper in the QJE about how friendships form I must have read almost 20 years and it still sticks in my head.
I think Bruce, though, was one of the first people that I ever encountered after graduating that was very clearly part of this credibility revolution. His papers, if it used instruments, typically would use lotteries as instruments. Or if he was studying peer effects, it was lotteries. Well, not surprisingly, Bruce was there at Harvard as a PhD student in the first class that Imbens co-taught with Don Rubin on causal inference. His classmates in that class were Rajeev Dehejia and Sadek Wahba, authors of classic applied papers on the propensity score. In fact, Bruce’s own project for that class was also published — a paper estimating the causal effect of winning lottery prizes on labor market outcomes (published in the 2001 AER). So this was fun, and I hope you enjoy it too. Apologies I ramble for so long at the start. Not sure what got into me.
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This week’s guest on the Mixtape with Scott is someone I’ve admired for a very long time, even before I entered graduate school in 2002. Peter J. Boettke is the Distinguished University Professor of Economics and Philosophy, the Director of the F. A. Hayek Program for Advanced Study in Philosophy, Politics and Economics and the BB&T Professor for the Study of Capitalism at the Mercatus Center at George Mason University. It’s hard to summarize just how important Peter has been to the story of Austrian economics, but in my mind, he’s been one of the most influential people in that long tradition, both for his scholarly work on political economy, public choice and institutions, his leadership at George Mason, where the Austrian tradition has continued to thrive, and as a mentor to young people.
I can only speak to myself, but I have looked up to Peter for a very long time as it was always very clear that he was a humble and serious scholar who also gave an incredible amount of time and mentorship to his students. All of those are to me examples of what I find to characterize some of the best of the profession’s larger story, and so it was a real pleasure for him to sit down with me to talk about his career. I found it so interesting to hear his story in his own words, the economists he looked up to as a young person, his genuine love of economics, as a field, and how much he holds up his students and colleagues. Thank you, as always, for taking the to tune in. I hope you enjoy this time with Peter as much as I did.
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This week’s guest on the Mixtape with Scott is Jesse Rothstein, the Carmel P. Friesen Chair in Public Policy at UC-Berkeley and the Faculty Director of the California Policy Lab. Jesse has a long list of things to which he’s made meaningful contributions, ranging from labor economics, to discrimination, to education, to causal inference and more. He’s also one of the “students of David Card” guests that I wanted to have on the podcast, as Card was his adviser way back in the day. For those curious about the paper we are talking about towards the end (“augmented synthetic control”), it’s one of my favorites in the synthetic control literature. The link to it is here. Good luck everyone this week and thanks for tuning is as always!
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Welcome to the Mixtape with Scott! We are getting closer to the hundredth episode! This is our 91st interview if I include Adam Smith (played by ChatGPT-4), which I absolutely will be counting. And the guest is someone I have admired for a long time — Martin Gaynor, or “Marty”. Marty is the J. Barone University Professor of Economics and Public Policy at Carnegie Mellon both in the economics department and their policy school, Heinz College. But he is also special adviser to Jonathan Kanter, assistant attorney general for the Antitrust Division at the federal Department of Justice, and it is not the first time that Marty has served in government as a public servant. He is also a former Director of the Bureau of Economics at the U.S. Federal Trade Commission. You can read some about his new position in the Department of Justice here.
Marty works on the supply side of health, you might say, as opposed to the demand side. He studies markets and concentration, hospitals, firm competition, pricing — not just our health behaviors, but also the supply of healthcare through a mixture of market and non-market processes. If you go through his vita, you can see he’s racked up a lot of awards and publications over the years.
There are many things you can say about Marty, and after this interview, two came to mind — resilient and kind. It was actually almost not the case that he would become as successful as an economist as he became, as he will share in this interview. He struggled initially to get a tenure track job, and even left academia briefly as a result. He is remarkably upbeat and realistic about the good fortune that he has had, though. And as you will see in this interview, it is very clear that he is a genuinely kind and warm hearted person.
Marty also is a survivor in a more literal sense. He was nearly murdered in the antisemitic terrorist attack at the Tree of Life synagogue in Pittsburgh. That is his story to tell in this interview, not mine, but I will leave it at that.
All of our stories matter. No matter who is listening or reading this, their personal story matters, and I hope that this interview is interesting and that you enjoy getting to know Marty a bit better. Thank you for all your support!
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Welcome to the 12th episode of the third season of the Mixtape with Scott, a podcast devoted to listening to the stories of living economists. This week's guest is Daniel Chen, an economist at the Toulouse School of Economics. I had a chance to meet Daniel when he came to Baylor and presented to use a tour de force of his body of scholarship, and I was mesmerized by it. Except for one other person, I had not met someone with that level of productive scholarly energy before. I was really stunned by how much work he had crammed into a career, spreading so many topics, and yet all held together under this umbrella of "political economy".
I knew of many of Daniel's works by reputation and one in particular we discuss which is about a law and economics program that trained federal judges, but I hadn't met him before, and I did not put two and two together that he had gone to MIT and had on his committee Bannerjee, Duflo, Kremer and Angrist -- four key Nobel laureates in the history of causal inference and the natural experiment movement that really captured the profession. So I asked him if we could talk and I could hear his story and he agreed.
Daniel will share it in this talk as we go through the kind of kid he was, and probably frankly still is, a deeply curious, very meticulous, thoughtful, and creative person. We talked about his childhood, majoring in applied math at Harvard, being very drawn to theory and yet people, making economics a surprising and unexpected opportunity for him, and eventually becoming what he told me was a "data rat" who collected datasets.
He also fits with this other part of the professional story that I’ve been wanting to share with people which are these economists that also go to law school and JDs. He after finishing MIT decided to get a JD at Harvard law school, and his explanation for it is kind of interesting because it all feels somehow unplanned and yet clearly he is, in my opinion anyway, driven by his own goals. I loved meeting him, loved talking to him, loved listening to his story, and I hope you do too! Thank you for tuning in as always!
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Welcome to the Mixtape with Scott! To set up this week’s guest, let me just share real quick a personal anecdote. When I graduated college, I got a job as a qualitative research analyst doing focus groups and in-depth interviews. I had majored in literature, so this was my first exposure to anything related to the social sciences. I loved the freedom the job gave me to collect my own data and develop my own theories about why people did the things they did.
In the evenings I would read articles and books in sociology and anthropology as I felt more grounding in the social sciences could help me in doing a better job. One night I read Gary Becker’s Nobel Prize speech, “The Economic Way of Looking at Life”, at the University of Chicago’s John M. Olin working paper series. I was hooked. By the time I finished his speech, I knew I wanted to be an economist. But then I read other things too, like a quantitative paper by John Lott and David Mustard’s quantitative study on concealed carry laws and crime, and was equally mesmerized. And in that working paper series, I kept coming across references to someone named Ronald Coase and I then went elsewhere to learn about him and his prolific work.
David Mustard was a Gary Becker student, and his paper on concealed carry had left an impression on me. He was an assistant professor at the University of Georgia so I applied there and one other school that used his county level crime data for studies on crime. I got into both and went with my ex-wife to visit the school and the faculty. In preparing for the trip, I read a paper by a professor at the University of Georgia named Peter Klein. The paper was entitled “New Institutional Economics” and it drew extensively on that Nobel Prize winning economist I had been learning about, Ronald Coase, another Nobel Laureate named Doug North at Washington University, and Oliver Williamson, a professor at Berkeley. The article was fascinating. It was about a field called “New Institutional Economics”, which I’d never heard of, and Klein explained it well. It was about the endogenous evolution of “institutions” to support and facilitate the organization of human interactions at a high level, most often to support commerce and trade though not just that. The ideas were deep and fascinating. I remember reading that article with a pen and highlighter, going over it and over it, hanging on every word. Not only was the topic fascinating, the author writing it was an excellent writer. There was not a wasted word in it. So when I met with the faculty, including Peter, I was sold on Georgia. But unfortunately, Peter was leaving Georgia for Mizzou and so I just barely missed being in the department with him.
So that is a long winded bit of background into telling you that today’s guest is someone I’ve known now for over 20 years — Peter Klein, the W. W. Caruth Endowed Chair at Baylor University in the Entrepreneurship department. Peter is now a professor as well as the department chair at Baylor in our Entrepreneurship department. And so it is my pleasure to introduce you to him. Peter did a PhD at Berkeley and studied under Oliver Williamson, who I mentioned earlier. Williamson would go on to win the Nobel Prize for extending Coase’s theory of the firm and helping develop a more robust theory based on transaction cost economics. Peter’s work on the firm extends a lot of this work on transaction cost economics continues in that line focusing on the organization of the firm. He is the author of countless articles as well as a new book entitled Why Managers Matter: The Perils of the Bossless Company (with Nicolai Foss). It has been a real joy having him here since I missed him the first time around.
As long time listeners know, though, I typically am doing a “mini-series” within the podcast, though, and Peter fits into one of those mini-series. Those mini-series are “the econometricians”, “causal inference and natural experiment methodology”, “Becker’s students”, “economists going to tech”, and then “public policy”. But another one I’m slowly picking at has to do with the wings of the profession that fall outside of the exclusively neoclassical tradition, one of which is Austrian economics. And Peter comes from that tradition, though he has mixed it with mainstream economics and made it into something of his own. So, with that being said, let me now turn you over to the podcast! Thanks again for tuning in!
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This week’s guest on the Mixtape with Scott is famed labor economist, Richard Blundell, the David Ricardo Professor of Political Economy at the University of College at London.
Dr. Blundell’s accolades are extensive: a Fellow of the Econometric Association, Fell of the American Academy of Arts and Science, former President of SOLE, of the Royal economic Society, recipient of the 2000 Frisch Prize, the 2020 Jacob Mincer Prize in Labor Economics, and on and on. You can find more information about his background here at this short biography.
But ironically, it was for a different reason that I wanted to reach out to him. I was interested in reaching out to Dr. Blundell because of some research I had been doing on the history of difference-in-differences and throughout the 1990s, I kept coming back to him. He had several things he wrote in the 1990s that left me with the distinct impression that he was attempting to educate others about the bridging of causal inference and natural experiment methodologies, so I was just curious to learn more about him. I hope you enjoy this interview as much as I did! Thank you again for all your support!
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Welcome to the Mixtape with Scott! Due to a technical difficulty with my producer’s computer, this week’s interview was not ready in time. So we are going to do another repeat from season one. This is with Petra Todd, a labor economist, econometrician and author of a new book on causal inference entitled, Impact Evaluation in International Development with Paul Glewwe. She was also elected to the Academy of Arts and Sciences last 2023. And she is Jim Heckman’s former student and coauthor, which fits with my slowly building deck of interviews on “Heckman’s students” (along with John Cawley and Chris Taber). But I also just loved this interview and so it’s also nice just to repost it. Plus, it’s probably nice I think to give people some breathing room given the pace at which these come out. Next week, though, I should be back on track with new episodes. Thanks again for tuning in!
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I’m still recovering from my travels over spring break, so I decided to repost an old interview I did in August 2022. This was my 27th podcast interview at the time and part of my “Economists in Tech” series, which has died down somewhat. The guest was Kyle Kretschman whose title at Spotify reads “Head of Economics”. This was a popular interview when it first came out, and I thought for newer listeners, they might like to listen to it again. Kyle came to Spotify after spending around 6-7 years at Amazon first. He graduated from the University of Texas at Austin with a PhD in economics in 2011. PhD economists going into tech in the early teens was really just at the beginning — the flow and the stock was much smaller than it is now. So it was really interesting to listen to Kyle’s story about that move away from academia into tech when it was not quite as common a story as it is now. And I think the story really resonated with a lot of people, in general, when it first came out so I thought I’d share it again. Here’s a Q&A that UT Austin did with him in December 2022 if you want to read more of his story there too. Thanks again for tuning in!
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This week’s guest on the Mixtape is Pierre Chiappori, a micro theorist at Columbia University. While Pierre is not technically a student of Gary Becker’s, there are many people who counted Becker as a colleague that probably at times did consider them also Becker’s student, and I suspect Pierre is one such person. I learned of Dr. Chiappori in graduate school while studying economics of the family. His collective models of the household always seemed a little bit outside of what I was studying, which was typically the Nash bargaining models of marriage, but I was also very interested too. It’s a run of papers he did in the 1990s, overlapping with when he was at Chicago with Becker, that sort of was the catalyst to ask him on the show. When I learned that he grew up in Monaco under the shadow of Princess Grace Kelly, and that he like me also loved Rear Window, I knew it was going to be an interesting talk. I hope you all enjoy it. Remember, the story of economics has been tributaries, many eddies, and listening all of them is in my opinion a way to show consideration to those people where consideration is nothing more than allowing their story to become real to us. I continue to believe that it is in the act of listening to stories that we are transformed and learn our own way. So I encourage you to listen closely to the story of Dr. Pierre Chiappori. Oh and this is our 87th interview. 13 more and we hit 100!
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Welcome to this week’s episode of the Mixtape with Scott. One of the new themes I’m hoping to pursue is the students of the 2021 winners of the Nobel Prize. And today’s interview is with Marianne Bitler, professor of economics at University of California Davis. Dr. Bitler was in the first cohort of Josh Angrist’s PhD advisees at MIT. She graduated in 1998 from MIT where Angrist was one of her advisors before going into a career in government. She took the long way to get into academia, moving through UC Irvine and landing at UC Davis. Her career has been marked by an interest in means tested poverty programs as well as reproductive health, but it’s also been marked by early interest in heterogenous treatment effects from a methodological perspective, making her contributions some of the earlier work that I think highlights some of the challenges we face when focusing exclusively on means. It was a pleasure talking to Marianne and I hope all of you find this as interesting to listen to as I did. Thanks again for tuning in!
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Welcome to season three of the Mixtape with Scott — a podcast devoted to listening to the stories of living economists and creating an oral history of the last 50 years of the profession. This week’s interview is with Wilbert van der Klaauw, economic research advisor in the Household and Public Policy Research Division and the director of the Center for Microeconomic Data with the New York Fed. Wilbert has an interesting story for many reasons. He fits with my longstanding interest in causal inference for his early work on regression discontinuity design, both alone and with Hahn and Todd in their 2001 Econometrica. But I also wanted to hear his story because of his decision to leave academia as a full professor at UNC Chapel Hill to work at the Federal Reserve. (Which again brings to mind that part of the story of the profession is the Federal Reserve itself but that’s for another day). So it was a real interesting experience to get to talk with Wilbert and hear more about his life coming from the Netherlands to study at Erasmus, where he met a young Guido Imbens — a detail I didn’t know about either — and studied econometrics as his undergraduate major (a major I also didn’t know existed apart from economics). So I hope you enjoy this interview!
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Welcome to the Mixtape with Scott! A podcast devoted to the personal stories of living economists and relaying an oral history of the profession (or at least a selected oral history of a selected part of the profession). Still working on an easy to say phrase that combines those two ideas of the micro and the macro. Anyway, today is part of the longer series on econometricians, and I am pleased to have on the show Bruce Hansen, the Mary Claire Aschenbrener Phipps Distinguished Chair and the Trygve Haavelmo Professor of Economics at the University of Wisconsin. Bruce has been a prolific and highly impactful econometrician for decades now, as well as the author of two new books. The first is an econometrics textbook that all of you should check out, especially if you’re teaching econometrics and especially if you’re taking econometrics, and especially if you’re wanting to learn more econometrics. So I guess that’s to say, especially if you are interested in what I do on this substack. The second book is a book on probability and statistics and I would say that book also is for those three groups of people, but also add to it people who teach probability and statistics and want to have a stronger background in this subjects. For years, Bruce provided both books for free on his website, and it was a real inspiration for me to do the same with my book. Bruce and I discussed a lot about his career, including the large changes that happened in econometrics starting in the 1990s and early 2000s, which he suggested was monumental in a lot of ways. I won’t spoil it. So thank you for tuning in and I hope you enjoy today’s interview as much as I did!
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This week’s guest on the Mixtape with Scott is Christopher Taber. Chris is a professor of economics at the University of Wisconsin where he is department chair, the James Heckman professor of economics and the Walker Family chair. Chris is a labor economist and econometrician who has made numerous contributions to both areas such as the returns to education, difference-in-differences with small numbers of interventions, techniques for evaluating claims of selection on observables and more. In addition to fitting into my long running interest in econometrics and labor economics, though, I wanted to talk with Chris because this year I’m wanting to interview more “the students of [BLANK].” And Chris was Jim Heckman’s student as a grad student at the University of Chicago and this year in addition to interviewing the students of Orley, Card, Angrist and Imbens, I am also want to interview the students of Jim Heckman as I continue to flesh out the causal inference revolution that began in labor economics in the 1970s, 1980s and 1990s at Princeton, Harvard, MIT, Chicago and Berkeley. Thanks for tuning in! I hope you enjoy this chance to listen to Chris’s story as much as I did.
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Welcome to another episode of the Mixtape with Scott! This week I have a guest who some of you know, and some of you don’t know (I suppose making them no different than anyone else) — Andrew Baker. Andrew is now an assistant professor in the law school at the University of California Berkeley. He specializes in topics at the intersection of law, policy and finance. And one of his papers, “How Much Should We Trust Staggered Difference-In-Differences Estimates?”, published in the Journal of Financial Economics, was the winner of the Jensen Prize for the best paper published in Corporate Finance. He is for many people permanently part of the last five year’s or so “credibility crisis in difference-in-differences” for both this paper, as well as other things he’s written and done. So I thought it would be great to have him on the show as part of the larger material on causal inference in economics.
But Andrew is not an economist. He has a joint JD/PHD from Stanford, but the PhD is in Business Administration with a special focus on accounting. I nonetheless included him in this series as part of the “story of economics” because like Carlos Celinni last week’s guest, Andrew started out in economics as an undergraduate at Georgetown, then went to work for an economic consulting firm, then did a predoc with John Donohue III, professor of law at Stanford and PhD economist from Yale. But then he instead went into Stanford’s law school before migrating into their doctoral program in business administration. And I thought this kind of story — the story of people staying in, but also of people exiting — is really a part of the larger economics story too. It’s still somewhat challenging to find these stories, so I’m going to keep trying, but I wanted to if I could by circling people who I knew it applied to.
But, as with others, the point of Andrew being a guest on the show is simply because I think he does have an interesting story, and I wanted to hear it. I hope others of you hear it too. Thanks again for tuning into the podcast. Please like share etc!
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Philosophy of the Podcast
Welcome to the Mixtape with Scott, a podcast devoted to hearing the stories of living economists and a non-randomly selected oral history of the economics profession of the last 50 years. Before I introduce this week’s guest, I wanted to start off with a quote from a book I’m reading that explains the philosophy of the podcast.
“For the large m majority of people, hearing others’ stories enables them to see their own experiences in a new, truthful light. They realize — usually instantaneously — that a story another has told is their own story, only with different details. This realization seems to sneak past their defenses. There is something almost irresistible about another person’s facing and honoring the truth, without fanfare of any kind, but with courage and clarity and assurance. The other participants feel invited, even emboldened, to stand unflinching before the truth themselves. By opening ourselves even a little to the remarkable spectacle of other people reconsidering their lives, we begin to reconsider our own.” — Terry Warner, Bonds That Make Us Free
The purpose of the podcast is not to tell the story of living economists. The purpose of the podcast is to hear the stories of living economists as they themselves tell it. It is to make an effort to without judgment just pay attention to the life lived of another person and not make them some non-playable character in the video game of our life. To immature people, others are not real, and the purpose of the podcast is, if for no one else, to listen to people so that they become real, and in that process of listening, for me to be changed.
They may sound heavy or it may sound even a little silly. After all, isn’t this first and foremost a conversation between two economists? But economists are people first, and the thing I just said is for people. And let’s be frank — aren’t man of us feeling, at least some of the time, alone in our work? And isn’t, at least some of the time, the case that our work is all consuming? I think there are people in my family who still don’t understand what my job is as a professor at a university, let alone what my actual research is about. There are colleagues like that too. Many of us are in departments where we may be the only ones in our field, and many of us are studying topics where our networks are thin. And so loneliness is very common. It is common for professors, it is common for students, it is common for people in industry, it is common for people non-profits and it is common for people in government. It is common for people in between jobs. And while the purpose of the podcast is not to alleviate loneliness, as that most likely is only something a person can do for themselves, the purpose is to share in the stories of other people on the hypothesis that that is a gift we give those whose stories we listen to, but it’s also maybe moreso the gift we give the deepest part of ourselves.
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Carlos Cinelli, PhD Statistics, University of Washington’s Statistics Department
So, with that said, let me introduce this week’s guest. Carlos Cinelli may seem like a guest who does not quite fit, but his is the story of the economics profession in a couple of ways. First, he is someone who left economics. Carlos was an undergraduate major in economics who then did a masters in economics and after doing so left economics (and econometrics) to become a statistician. The leaving of economics is not the road less traveled. By talking to Carlos, and hearing his story, the hope is that the survivor bias of the podcast guests might be weakened if only a tad bit.
But Carlos also fits into one of the broader themes of the podcast which is causal inference. Carlos studied at UCLA under two notable figures in the history of econometrics and causal inference: Ed Leamer in the economics department and Judea Pearl in the computer science department. And Carlos is now an assistant professor at University of Washington in the statistics department whose work consistently moved into domains of relevance in economics, such as his work in the linear of econometric theory and practice by Chris Taber, Emily Oster and others. That work is important and concerns sensitivity analysis with omitted variable bias. And he has also written an excellent paper with Judea Pearl and Andrew Forney detailing precisely the kinds of covariates we should be contemplating when trying to address the claims of unconfoundedness.
So without further ado, I will turn it over to Carlos. Thank you again for your support of the podcast. Please like, share and follow!
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The Mixtape with Scott is a weekly podcast devoted to building out a selected part of the collective story of the last 50 years of the economics profession by listening to the personal stories of living economists. And this week's guest is a the John G. McCullough Professor of Economics at Middlebury College in Vermont, Caitlin Myers who I am fortunate to count as both a coauthor and friend, as well as an professional admirer. Caitlin is a graduate of the University of Texas's economics department and is one of those young economists who hit the ground running and has only gotten faster. A senior economist told me recently she is the abortion researcher at this moment in time having made major contributions to both the scientific record and the policy discussion regarding abortion policy, its causes and its consequences. She is as far as economists go meticulous, thoughtful, passionate, principled and creative, and while she is not directly a student of Gary Becker, or Claudia Goldin for that matter, she is very clearly part of their influence on labor economics and on Caitlin in turn. Thank you again for tuning in to the podcast. If you like it, please share, follow and all that.
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Welcome to season 3 of The Mixtape with Scott! A podcast about the personal stories of economists and the collective story of economics of the last 50 years. We are kicking off season 3 with a bang: an interview with the distinguished labor economist, Richard Freeman, from Harvard University. Dr. Freeman holds is the Herbert Ascherman Professor of Economics at Harvard University and serves as the Co-Director of the Labor and Worklife Program at Harvard Law School. As you’ll learn, his educational journey started with a B.A. from Dartmouth in 1964 and went into a Ph.D. in Economics from Harvard University which he competed in 1969.
Freeman's work has been pivotal in reshaping perspectives on labor economics and industrial relations. His book "What Do Unions Do?" co-authored with James Medoff in 1984, challenged prevailing economic views by suggesting that unionism could enhance social efficiency. This groundbreaking work has been supported by subsequent studies, highlighting the positive impact of unions on productivity in various fields. Freeman has also made significant contributions to understanding the internationalization of science, the dynamics of the scientific workforce, and the implications of an overeducated American labor market.
This was a super fun and at times funny interview, and I hope you like listening to it as much as I had being in it. Thanks again for tuning in! Don’t forget to like, share, follow, etc.!
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And with that, season 2 of the Mixtape with Scott is complete! What a journey! Our final guest this year is an econometrician named Christine Pinto. Christine is an econometrician at INSPER Institute of Education and Research in São Paolo Brazil. And I know of Christine because of her work on synthetic control making her fit with my larger interest in causal inference. But ironically, Christine also was briefly a Guido Imbens student at Berkeley before he left, which makes her also part of the story of how causal inference spread through labor markets and not merely textbooks. It was a delight getting to talk to Christine and I hope you find this interview as enjoyable as I did. Thank you again for all your support these last two years. I have thoroughly enjoyed this journey throughout the world, hearing the stories of living economists, and helping broadcast them for whoever else out there that needs and wants to hear them. I hope all of you can leave behind the things that are no longer needed from 2023 and take only with you those things into 2024 that are essential. Best of luck to all you of you. Peace.
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Welcome to the Mixtape with Scott! This week is a blast. I’m talking this week Dr. Marianne Wanamaker, professor of economics at the University of Tennessee Knoxville and the new dean at the Howard H. Baker Jr. Center for Public Policy. Marianne has had a spectacular run since graduating from Northwestern in 2009: NBER, IZA, a stint in the White House (former chief domestic economist at Council of Economic Advisors and senior labor economist), a ton of other stuff. She’s an economic historian by training, a specialist in American economic history specifically and demography, and won the 2019 Kenneth J. Arrow award (with Marcella Alsan) for a paper published in the Quarterly Journal of Economics on the lasting impacts of the Tuskegee Syphilis Experiment on trust in the healthcare system among African-Americans. She is a brilliant and creative young economist, an excellent instructor, and a mix of entrepreneur and civil servant. I had a great time in this interview getting to know her better, and hope you are inspired to hear her story like I was. And as always, thank you for your support of the show!
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Welcome to this week’s episode of the Mixtape, I’m Scott Cunningham, the host. We are in the final stretch! Season two is almost over. When it’s all said and done, there’ll be 45 episodes in season two, and 34 from season which is [does math on a piece paper, scratches it out, starts over, then announces] 79 episodes. Man, what a fun this has been.
Today’s interview is with Dr. Jinyong Hahn, the chair of the economics department at University of California Los Angeles and a prominent econometrical. I knew of Dr. Hahn mainly from his 2001 paper in Econometrica with Petra Todd and Wilbert Van der Klauuw on identification and estimation in regression discontinuity designs though he’s been extremely prolific just that one. I learned a lot of new things, and you’ll hear my surprise as a bunch of things click in place.
I just wanted to say again thank you for all your support. I hope you have a great week as we head into the holidays.
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Welcome to this week’s episode of the Mixtape with Scott! I’m the host - Scott Cunningham. As some of you have probably seen, I’ve been studying a paper on OLS entitled “Interpreting OLS Estimands When Treatment Effects Are Heterogeneous: Smaller Groups Get Larger Weights” by Tymon Słocyński at Brandeis University. It’s been an interesting paper because of what it taught me about a model I thought was done teaching me. Well this week I am interviewing Tymon, who is a young econometrician who does really interesting work.
Tymon is an assistant professor at Brandeis and econometrician and I think one of my favorite young ones to boot. He’s a very deep, thorough econometrician, working on projects in a family of projects stemming from early applied work he did on the Oaxaca-Blinder decomposition, including this R&R of his at Restud on IV and LATE. I’ve learned so much from him and I hope you enjoy this! Don’t forget to like, share and maybe even review the podcast!
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Welcome to the Mixtape with Scott! I’m the host, Scott Cunningham. This week I decided to do a rerun from season one to give people a little time to catch their breath as I know at one interviewee a week can be like drinking from a firehose. This is an interview I did with Guido Imbens, the co-recipient of the 2021 Nobel Prize in Economics. I am hard pressed to say I have a favorite interview, as I have loved all of them, but I have a deep love and appreciation for Guido and thought if I was to give everyone a break and suggest a rerun, this interview with Guido would probably be one. I wanted to do this also because yesterday I reread Guido’s biographical piece he submitted. LinkedIn’s Nobel Prize account had said it was a “newer” biography, so I read it eagerly, but I think maybe it was the same one. Nevertheless, it reads so well and I recommend you read it too. As longtime listeners know, I am deeply affectionate about the connections between Princeton’s Industrial Relations Section in the 70s and 80s, Harvard’s stats department from the 1970s to 1990s (or at least a few people there), and Guido there in the economics Dept in the early to mid 1990s linking them with Josh Angrist. Maybe all stories are wonderful, and all I am doing by saying how much I love this particular story is revealing my biases. That’s fine. But I do love it. I think maybe some of you having been on this long journey of around 75 interviews over two years will also enjoy this old one again, as it has aged very well. Thanks again everyone for supporting the podcast these last two years. I hope you enjoy this rerun with Guido Imbens!
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Welcome to this week’s episode of the Mixtape with Scott! This week we have an outstanding guest named Avi Goldfarb of the University of Toronto. Avi is a PhD economist who graduated from Northwestern in the early 2000s specializing in the economics of the internet. He is now at the University of Toronto where he is a professor in the marketing department as well as chief data scientist with a very interesting lab called the Creative Destruction lab that among other things specializes in the economics of artificial intelligence. He is the author of two very popular and probably both best selling books aimed at a general audience on the economics of artificial intelligence: Power and Prediction and Prediction Machines (both with Joshua Gans and Ajay Agrawal). Given the popularity of AI, as well as the recent turn of events with AI giant, OpenAI, I think there couldn’t be a better time to to have him on the show. I loved this interview and accidentally went over, but Avi graciously hung in there with me. I hope you love it too. Don’t forget to like, share and comment! Happy Thanksgiving to all!
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This week on the Mixtape with Scott, I have a very special guest. Adam Smith, the so-called founder of economics, and author of two best selling books, The Theory of Moral Sentiments published in 1759 and An Inquiry into the Nature and Causes of the Wealth of Nations (buy it now for $2800 here at eBay!) published in 1776.
I know what you’re thinking. “But Scott, that would make Adam Smith very old, even probably dead, wouldn’t it?” And you’re right on both counts! Adam Smith was a moral philosopher born in 1723 in Scotland so it literally makes him 300 years old, and yes, very dead. But I decided to push through that anyway and a few months ago asked ChatGPT-4 to essentially pretend to be Adam Smith for my podcast without any awareness or surprise. This podcast is somewhere between a seance and a play. It is the ghost in the machine — literally. I did a one hour interview with ChatGPT-4 who played the part of Adam Smith using the same style of interviewing I do with all the economists on the show — personal stories. This was all done in the ChatGPT-4 browser, and it was then recorded using Amazon AWS Polly “text to voice” using a British male’s voice named “Arthur”.
This is part of a class assignment I have been doing this semester at Baylor University in my History of Economic Thought class. I got the idea to do this earlier this summer when I saw that the economist, Tyler Cowen, had interviewed Jonathan Swift using ChatGPT-4. So I decided to build into my classes an assignment where the students had to do it too. My students had to interview four 18th to early 20th century economists, with the final project being a recorded interview much like I did, and to show them it could be done, I interviewed Adam Smith. And boy was it fun. It was fun because of how novel it was, but it was also fun because of how thought provoking it was for me to learn about Smith’s first book Theory of Moral Sentiments, and listening to ChatGPT-4 speculate about the book’s connections to other ideas. I was mesmerized by the entire experience and really didn’t know what to make of it. After all, language models hallucinate; I already knew this. But then it dawned on me — this entire interview is a hallucination. What does it mean for a large language model to “be” Adam Smith when in fact Adam Smith never said any of these words? It means for ChatGPT-4 to hallucinate. Question is, though: is this a good hallucination or is it a bad one, and how to we judge that and should we even care? I wonder if hallucinating is a feature, not a bug, of ChatGPT-4.
Is this any good? Is it something useful? I think so. Students seemed to have gotten a lot out of it. It requires the suspension of disbelief but then so does watching fantasy, or ready science fiction. Your mileage may vary on how much you enjoy it, and maybe the things we discuss aren’t so profound but I didn’t know a lot about him before doing this. So it was just nice to listen and learn more about the man, though a Smith scholar will need to tell me what’s accurate and what isn’t (as I said, technically it’s inaccurate from start to finish by definition).
My PhD student, Jared Black, is in my history of economic thought class and has enjoyed being able to interrogate these old economists and their ideas. He decided to create his own GPT chatbot using OpenAI’s builder environment and said I could share it.
https://chat.openai.com/g/g-GJeexE26G-ask-an-economist
Ask to talk to Bentham or Nassau or Senior or Say or Marx. Just remember to be polite. A recent RCT found that if you’re nice to ChatGPT-4, it tends to perform tasks better. I swear I saw that study, but now I can’t find it, but it seems true so I’m going to cite it.
Thanks again for tolerating me on this podcast. Even though this may seem gimmicky, in a way it is fully consistent with the shows premise. The show is about the personal stories of economists and the hope that by simply listening to economists’ stories, we can better understand our own story. The hope, too, is that in the long run, we hear a story of the profession itself. After all, we use stories to navigate our lives, and though stories like models are in some sense “wrong”, sometimes they are useful. This story is wrong, too, but maybe it’ll be useful. Peace!
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Welcome to the Mixtape with Scott episode 38 of season 2! By my calculations, there have been 72 total episodes in the Mixtape with Scott podcast — 34 episodes in season 1, 38 this year. What better way to celebrate episode 72 (38) than with Dr. Andreu Mas-Colell from Pompeu Fabra University in Barcelona, Spain!
If you’re an economist, then you know Dr. Mas-Colell if for no other reason than that his book with Greene and Whinston taught you microeconomics in grad school. If you’re looking for a replacement copy of the textbook to put on your shelf, click here. I wanted to interview Dr. Mas-Colell for a lot of reasons. First, because his book probably unites us all because we all had to take the micro preliminary exam, we all had to use that book for our classes, and many of us depended on it for our livelihoods so we could pass those classes. So in a way, that’s not Mas-Colell’s book — that’s our book too. So I thought that given the podcast is about both the personal stories of economists but also an effort to tell “our story” as economists of the last 50 years, just like I interviewed Bill Greene, the author of a popular textbook in econometrics a few months ago, I wanted to also interview Dr. Mas-Colell. But Dr. Mas-Colell is also an important figure in the history of microeconomic theory and I also wanted those of you whose heroes are theorists to hear his journey, as I know oftentimes the podcast is nearly exclusively conversations with empiricists of various stripes with some exceptions.
Dr. Mas-Colell is also, I think, an inspiration to someone who has lived a life defined by his own personal integrity. I think many economists, young and old, but also non-economists too, will be inspired to hear his story of being someone who cared deeply about democracy in the shadow of a dictatorship and the willingness to continue to incur real personal costs for the sake of the body politic. Any day now, we will hear the conclusion to a case dating back to June 2021 when Spain's Court of Auditors found that he was among those responsible for government expenditure on the unconstitutional 2017 Catalan independence referendum. Spain’s courts announced its intention to fine Dr. Mas-Colell millions of euros. This led to a public outcry from the international community of economists two summers ago. The final verdict will be announced, Dr. Mas-Colell says in this interview, probably in the middle of this month.
It’s another long interview, and I want to give you a little warning ahead of time. I am not a micro theorist, which you probably guessed. I was surprised, nonetheless, by how little I remembered about the names of economists and departments and how the full history of micro theory fit together. As such, I know I left a ton of opportunities unrealized on the table. But I knew going into it that I was really not going to be able to have more than superficial understandings of the sociological history of micro theory as it had been too long. Anyway, I just wanted you to know that I left some money on the ground I’m sure. But it was a huge life lived, and I really just was wanting to hear as much as I could in what amounted to still a two hour interview. So I hope you enjoy and find this podcast interview inspiring and interesting and helpful as you continue to try and navigate your own life, and your own place in the story of economics.
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Welcome to this week’s episode of the Mixtape with Scott! Recently, the University of Chicago Press published a book entitled The Economic Approach: Unpublished Writings of Gary S. Becker. It was written obviously by Gary Becker who died almost 10 years ago at the age of 83 after an extremely long and fruitful career as an economist. Dr. Becker had many students — some like me were students from afar, but some, like our guest today, were his actual students. And today’s guest is Casey Mulligan, one of the editors of that aforementioned book, and a professor of economics at the University of Chicago. This was a fun interview to do. Casey walked us through his time at Harvard as an undergrad to his unusually rapid progression through Chicago’s economics PhD program where he stayed on and is now a professor. We discussed his own career but we also spent just a lot of time discussing what it was like with Becker, as well as his own later time at the Council of Economic Advisers. I hope you enjoy it!
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This week’s guest on The Mixtape with Scott is the Xiaokai Yang Chair of Business and Economics at Monash University, Sascha Becker. Sascha is an economist who is hard to pin down into just one field. He’s probably most widely known, across the most general set of economists, as a contemporary economic historian. One of his specializations within economic history has been religion, most notably the Protestantism in Europe and its relationship to long term literacy (particularly among women) and human capital more generally. But even within history, he writes on topics that go far beyond traditional economic questions — like, for instance, his work reexamining Max Weber’s the Protestant work ethic hypothesis, for instance, or his more recent work examining the relationship between the church and national socialism. Sascha studies a topic in religion that overlaps with my own religious tradition (Protestantism and the Reformation churches more specifically), and so it’s drawn me into enjoying a lot of and benefiting from his extensive ongoing research on the topics.
But I also have been interested in Sascha because of his role in the spread of causal inference throughout economics, particularly within Europe. As Sascha will share in the podcast, his advisor in graduate school, Andrea Ichino, came to Sascha’s program after graduating from MIT. When Andrea taught, then, a microeconometrics course in the late 1990s, he did not use a book — he used his lecture notes belonging to the class he’d taken in his own PhD program taught by Josh Angrist. Sascha implied, as I have long suspected, that the passing on of causal inference was coming, not through econometrics textbooks, but through the placements of students that could be directly tied back to original proponents, which is why (or rather I have conjectured) the spread of causal inference within economics spread through applied microeconomics fields, like labor, public and development, early on, as opposed to econometricians. Early on, Sascha wrote a package in Stata with Andrea on implementing matching with the propensity score that has almost 4000 citations to this day. And Sascha himself probably did dozens of workshops all across Europe in the early 00’s teaching matching to students and faculty who otherwise didn’t have the red phone direct access to Angrist. As he said, matching was huge back then (no doubt made even moreso by Dehejia and Wahba’s publications), but while he was teaching matching, it now seems more likely he was teaching all over Europe what we consider to be causal inference methodologies based on the Rubin causal model, including matching, IV and the LATE theorem.
I continue to remain fascinated by the spread of causal inference in its earliest days throughout economics, and the role that the applied fields, like labor economics, played. But this podcast touches on many topics, including that but much more than that too, and I hope you enjoy listening to it half as much as I enjoyed talking to and learning more about Sascha’s own life and journey. Thanks again for tuning in. If you like the podcast, consider sharing it with others, or leaving a rating on Spotify and Apple.
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Welcome to the Mixtape with Scott! This week's episode has a guest that some of you have come to know and appreciate, and some of you hopefully will after this episode — Andrew Goodman-Bacon (“Bacon”). In addition to having a great nickname, he also has a great job, a great personality and several great papers, one of which after only two years since publication has won an award at the Journal of Econometrics, and already has over 4,000 cites. I really wish I knew how to pull things from google scholar and I could see what other papers in the history of econometrics have had such a meteoric rise in terms of impact and influence. It’s been unusually impactful, though, let’s just say.
I have a hunch Bacon wasn’t given the “Most Likely to Actually Use Math After High School” award in high school. But as it turns out, he has, and has become a really great applied economist who works on topics both in econometrics, but also public policy and economic history. The trifecta. But a few years ago, he left academia to go work for the Federal Reserve in Minneapolis at the Opportunity & Inclusive Growth Institute. This interview was a real delight; it even involves Dungeons & Dragons and throwing knives.
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Bacon has done a lot for helping a lot of us better understand difference-in-differences — a technique that many of us thought (much to our chagrin) we probably understood better than we did (I know I didn’t). But now some of us better understand it and while Bacon isn’t the only one who helped advance that knowledge, he was one of them.
So I hope you enjoy this interview, and if you’re interested in learning more about difference-in-differences, don’t forget to check out Brant Callaway’s workshop on Mixtape Sessions tonight! You can sign up here! Don’t forget also to share the workshop to everybody you’ve ever met in your entire life, as well as post to your online dating profile!
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Welcome to another episode of the Mixtape with Scott podcast! This week is a special one for the economics community as we celebrate Claudia Goldin's well-deserved Nobel Prize win for her pioneering work on women in the labor market. It's serendipitous, then, that today's guest is Melanie Guldi, associate professor of economics at University of Central Florida, who has spent over 15 years since graduating in 2006 from the University of California — Davis doctoral program in economics carving out a unique path in related terrain focused on the economics of fertility. Melanie’s 2008 job market paper and subsequent publication in Demography examined in greater detail a question that Goldin had earlier suggested — did early access to oral contraception and abortion cause birth rates to decline? Melanie found some evidence it did, at least for some groups.
But, while Melanie's work has some thematic intersections with that of Dr. Goldin, Melanie has become an authority in her own right on the complex landscape of health economics and demography. Her expertise touches on a wide range of critical issues, from maternal labor supply to the impact of intensive care on infant survival, and she has developed novel hypotheses that have further enriched our understanding of these topics. So, without further ado, let's dive into this rich tapestry of research and insights with someone who has dedicated a decade and a half to becoming an expert in the field. Melanie, welcome to the show.
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Welcome to this week’s episode of the Mixtape with Scott. I guess I could say “And I am your host Scott Cunningham” but after over 50 of these, I guess you already know I’m the host. When I first became interested in economics, it was through an old working paper series called the John M. Olin working paper series, which was then affiliated with the University of Chicago’s law school. That was where I found Becker’s Nobel Prize speech which caused an immediate 180 on my career and led me into economics myself. But the thing that really stood out to me was that economics was about more than just banking and money; apparently it was also connected to law, and more specifically, there was a field called “law and economics” even. As I read more of the working papers at the working paper series, I became more and more interested with everything there, including law and economics. Which my way of creating a segue into this week’s guest.
I first met Jonah Gelbach, the Herman Selvin professor of law at UC Berkeley, at a conference in Paris on crime. He probably doesn’t even remember it. All I really remember of Jonah in that conference was two things. First, he pulled me aside and said a lot of nice things about my paper and told me thought it would publish really well. So that was encouraging and I made a note to myself, “Note to self, this guy said it would publish well. Hold him to it.” Second, I remember him passionately responding to his discussant that the paper he’d written had something called “a surface”. And I made a second note to myself, “Note to self, learn what a surface is.” It was something very clearly related to original, very technical, econometrics, and I made a third note to myself after that. “Note to self, this this guy’s name is Jonah Gelbach, and he apparently is a very good econometrician who also works on applied matters.”
Jonah’s an economist and a lawyer. He’s written several very influential articles in both econometrics but also applied economics. He did a PhD at MIT under Josh Angrist, if I remember correctly, during that heady time when causal inference was blossoming in Cambridge in the 1990s (he graduated in 1998). He then took a job at Maryland where he was eventually tenured, then to Arizona where he stayed until 2010. He then took the road less traveled: he quit a tenured job as an economics professor, went to Yale and got a JD in 2013, then went to Penn and is now at Berkeley where he writes in all the areas that he apparently loves — law, economics and econometrics.
I asked Jonah to be on the show for a few reasons. First, I made a note to myself to remember this guy for a reason. He’s very talented and very approachable, kind and thoughtful and funny. But two, as I said, he took the road less traveled. Quitting a tenured job in academia, giving up the golden handcuffs as they say, to go to law school to start over — it’s not the most common way to get a JD, arguably. And I guess I just wanted to learn that story a little better as I didn’t know it. But third, law and economics — now circling back where I started — is part of the story of economics of the last 50 years, and the podcast is ultimately about two things: the personal stories of economists building out the collective story of economics. We are a diverse tribe. Law and economics is part of that tribe’s story. And economists sitting inside law schools has also become part of that tribe’s story. And so I asked Jonah, and he graciously accepted, to be on the podcast so here he is! Thanks again for tuning in!
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Welcome to this week’s episode of the Mixtape with Scott! I’m your host - Scott Cunningham, a professor at Baylor in their economics department. This week's guest is with none other than Amy Finkelstein, the John Bates Clark award and MacArthur Genius grant winner, and professor of economics at MIT. This was a fun interview — super generous, giving guest who shared a lot of her life, how she grew up in New York and then through her own windy road found her way to economics. She has a new book out “We’ve Got You Covered: Rebooting American Health Care” (with Liran Einav).
I loved this interview. We talked about the Oregon Medicaid Experiment, which I talk about in my book in the instrumental variables chapter when discussing lotteries. She shares where that idea came from, and it was super exciting to hear about that. I hope you like the interview as much as me.
Remember if you like the interview, consider supporting it by subscribing, liking, sharing or even becoming a paying subscriber. Enjoy!
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Welcome to the Mixtape with Scott podcast! That feels strange typing since by now, I shouldn’t have to write it that way, but you never know — maybe someone is coming today for the first time and will be coming upon the second part of a two part interview with Nick Cox, a geographer at Durham in the UK, and longtime contributor to public and club goods around Stata, the software used by many economists at least. This is as I said Part 2 in a two part interview. The point of interviewing Nick as I said last time is that this podcast is an oral history of the economics profession of the last 50 years told through the personal stories of economists, and others that I think otherwise fit into that story. And the software we use to do our work, particularly when it allowed many of us to do empirical work for most of our careers, is a big part of that story. And Stata is a big part of that story, and Nick is a big part of Stata’s story, so I did a two part interview with him.
Apologies that I have been slow updating the substack. I’ve had what feels like a lot more grading and administrative work than usual. I’ve also got a tad bit of some personal things coming to a head right now, and I’m hoping that will end soon. Tomorrow I will post some information about a few upcoming workshops at Mixtape Sessions, but while I have your attention, one of them is on shift-share IV with Peter Hull which starts next week September 25 and 27 in the evening from 6-9PM. Details here at this link. Should be great. You’ll learn be essentially brought to the frontier of this material and there’s going to be a lot of coding so that should be fun. So check it out!
In the meantime, thanks for supporting the podcast, which as you’ve probably figured out by now is my labor of love, my passion project. I have found that personal grief in my own life has been made more meaningful for some reason through this style of documentary biography of others in our profession. I hope you find it useful and interesting too. If you like this podcast, please consider supporting it. But share it with someone who you think will find it useful too. As I tell myself and others — all of us belong. All of our stories matter. Yours and mine too, as well as the people I talk to. So have a great day.
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Apologies I double posted a podcast this morning. I will finish the Nick Cox interview next week.
Welcome to this week’s episode of the Mixtape with Scott — where we listen to the personal stories of economists and hope that what bubbles up in the long run is a curated collective story of the economics profession of the last 50 years. This week’s interview guest is part of my “Becker’s Students” series which highlights the students of the late economist, Gary Becker, a legendary giant of microeconomics from both Columbia University as well as the University of Chicago, and who I also personally have admired so much that when I first read his Nobel Prize, I decided I also wanted to be an economist.
This week’s interview is with someone I’ve come to count as a friend as well as being a long-time admirer — John Cawley, professor of economics at Cornell University. John has been a force of nature within health economics for several generations contribution to major topics in health like obesity and risky behaviors, as well as labor economics. Friendly and supportive to everyone, to a fault even, it was such a nice opportunity to get to talk to him in this interview. We discussed many things in this interview that I think it is probably just better left for John to share. But I am excited to get to share it with you now.
Thank you again for supporting the podcast. I hope if you like it you will share it with others and enjoy the rest of your week too!
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This week’s episode of the Mixtape with Scott is with a professor at Durham in England in the geography department, Dr. Nick Cox. Many economists will only know of Nick because of his presence on the Stata listserv where he was one of its most prolific contributors and moderators. As economics as a field gradually shifted from theory to empirical work, at least as a share of the total papers written and total people employed, people like Nick and others became more relevant people in our lives as empiricists. We would go to the Stata listserv with questions, and more times than not, it would be Nick answering them.
I wanted to interview Nick because as I told him, the purpose of the podcast is to tell the story of the last 50 years of the economics profession by listening to the personal stories of real people. Mostly, that has been economists, but sometimes not. And Nick is one of those sometimes not. He’s a geographer at Durham who, like Bill Greene the econometrician I interviewed a week ago, first began to see his love and aptitude for statistics mature along with a desire to help his colleagues with their own programming problems. That particular kind of worker for whom the latent understanding of statistics and econometrics also selects on skills with computing has and will likely remain a powerful complement, and for Nick it was indeed.
We go through his early life, growing up in England, and moving into geography and statistics in college, as well as over two separate interviews travel into his early time finding and becoming more a part of the Stata community. I tell Nick that I saw in him things I wanted for myself — someone who in his own way was part of community development within academia in the odd spaces of work, and had hoped we could talk to discuss more of what that journey was for him. And he graciously agreed. Apologies that my opener is longer than normal; I didn’t have a script so rambled (always a mistake). Thanks again for tuning in; like, share, follow, and consider maybe even becoming a subscriber!
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Good morning! Welcome to another episode of the Mixtape with Scott! This week is a lot of fun. I got to interview none other than William Greene, Professor Emeritus at NYU and author of 8 editions of a great textbook on Econometrics, as well as a software developer from an econometrics software called LimDep. What a fun trip through the past — through growing up in Long Island, his family moving to Ohio when a recession cost his dad his job, and moving into grad school where Bill began to realize his skills in computing and econometrics were complements. It was a fascinating story about early computing and applied econometrics software, and his career as an econometrician. I hope you enjoy it as much as I did. Please share! Like! Put it on your phone! Give it to your kids for Christmas!
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Welcome back to the Mixtape with Scott. I took a little break to let listeners breathe a little, but I’m back with my regular weekly uploads of new interviews with economists as part of my ongoing project (if that’s the right word) to listen to and relay the personal stories of economists and then let those stories then mount up and tell a collective story of the profession. Not “the story”, as there is no such thing as “the story”. Just a curated, selected story. But my hope is that some of you hearing it will have more lights along the path. And today I have the pleasure of introducing you to Ariel Pakes, the Thomas Professor of Economics at Harvard University.
This was a fun interview. People either know Dr. Pakes very very well, or they only know him by the letter “P”. He is a structural econometrician and theoretical economist in the industrial organization tradition who has made major contributions to econometrics and theory of the firm as well as applied practices in both. He is also the coauthor with Berry and Levihnson on an extremely popular method for estimating demand called “Berry-Levihnson-Pakes model” or BLP for short. When I asked the chief economist at Spotify, Kyle Kretschman, just as the interview was concluding, what paper in economics continues to have had the most lasting impression on him even all these years after grad school, he just smiled and said “BLP”. And that is hardly a minority position. The 1995 Econometrica by those three economists, “Automobile Prices in Market Equilibrium” has had a major effect within economics and outside.
Dr. Pakes has made seminal contributions to areas of lasting relevance to our understanding not just of econometrics but also advanced economies like the United States as he has brought great attention to issues around technological innovation, patents and market competition. And we talked about some of this, particularly towards the end, but we talked about it in a way that I think you’ll find interesting because we talked into it, we talked through and towards it.
You see one of the things that I’ve been trying to do in these interviews is to get over the “selection on the dependent variable” inherent in our understanding of the published impactful work of others, and the lasting careers others have had. As we look at these people, and we study their works, and we use them even psychologically and existentially in complex barely discernible ways to navigate our own lives, I think we don’t always notice that we are usually engaged in a form of extreme selection bias called “conditioning on the outcome variable”. That is, we are only observing these people after the work was done. The failures, the false starts, the experimentations, the experiences they had on that road, how they met their collaborators, how they worked together, how they didn’t work so well together, all the trillions of decisions — it isn’t that those things are somehow free of endogeneities either. All of life is endogenous, after all. But if we are wanting to better understand how to get from point A to point B in our own lives, I don’t think it’s all that helpful to just look at people at point C and try to therefore guess where it is we need to reach next. If nothing else, it’s at least also helpful to just listen to their story, watch them as they describe the movement as they remember it. And so I loved this interview with Dr. Pakes. Hearing about his love of NBA basketball, the outdoors with his family, and growing up in a radical socialist youth group. His soft spoken, thoughtful discussions of his life as he shared his love for philosophy as a young man and how his first year at Harvard he was straddling economics and philosophy was fascinating, and just learning that his own approach to live — getting himself way over his head on a problem in economics and having to dig a way out — managed to not just get him out, but maybe get the rest of us out too. It was wonderful and this was a really nice interview. I hope you love it as much as me.
Thanks again for tuning in. Good luck to all of you as the new semester starts up. My mom got Wordle on the first try this morning. After having played every single morning since day one, this is the first time she actually got it right on the first try. I have decided that not only is this a good omen for the fall, and not only is it good luck for me, but I have decided to share this good luck with all of you. So consider this as my effort to share the blessing with you all no matter where you are. Have a great new semester learning about our awesome, sometimes hard and tragic but always beautiful world full of amazing people.
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This week of the Mixtape with Scott, I have the pleasure of introducing you to Gábor Békés, an associate professor at Central European University in Austria and author of an exciting new textbook in data science and causal inference entitled Data Analysis for Business, Economics and Policy (Cambridge Press 2021).
I wanted to talk to Gábor for many reasons — one because I am interested in talking with people whose roles in the scientific production function is to create platforms of knowledge sharing. These include editors of journals, department chairs, organizers of conferences, and authors of textbooks. I interviewed Jeff Wooldridge at the start of this year, I’m interviewing Bill Greene later this year, and I’m interviewing Gábor today.
And on that point, I also wanted to talk to him about how his book, written with the late Gábor Kézdi who passed away around the time of the book’s publication, came about, what it was about, and who he sees his ideal audience to be. The book is a nearly perfect, flawless piece of writing. Not just in its pedagogy and what feels like an effortless precision, but also in what they cover and how they cover it. Someone who has never really worked with data before could take this book and move from the most fundamental issues around exploring data, from data collection and ensuring data quality, to data visualization, to learning canonical regression models, then moving into more advanced and contemporary areas like machine learning based predictive analytics and causal inference.
The care and precision of the book is reflected in its aesthetic too. It’s simply one of the most beautiful books to the touch I’ve seen — the purple and green colors, its width, the glossiness of the pages, and the rich opportunities to learn R coding — are just a wonderful delight. I highly recommend everyone own a copy, and consider assigning it this fall for your statistics courses. It’s the perfect companion, if not the actual textbook.
But I also just wanted to learn Gábor’s story, and I got to learn at least some of it. I learned about him growing up in Hungary during the throes communism in his formative years and how witnessing first hand a regime transition shaped his desire to learn economics which ultimately led him into study economic geography and international economics.
Thank you again for tuning in. I hope you enjoy this week’s interview as much as I did!
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Update before introducing the podcast episode.
Things have been light on the substack and I apologize for that. I spent two weeks traveling, seeing the country with my two high school aged daughters on a road trip from Waco, to Big Bend, to Marfa Texas, to the Grand Canyon. There at the Grand Canyon, I realized I was old and tired and it was hot and the girls gave it their best shot but sadly, they weren’t being blown away by the scenery, so we decided to then pull the plug a day early and go to Las Vegas. The Vegas trip was always meant to just be a way for me to find an airport for me to fly my youngest back to Waco in time for her camp, but then it turned into three days and two nights on the Strip where I learned you can pay $100 for a hamburger, two cheese steaks, a coke and two water bottles. At which point I told the girls we are going to try a new trick called “intermittent fasting” where none of us eats for the next 48 hours. Psyche! They ate but I did wish I could’ve lugged the Coleman stove and dehydrated beans up from the car at a few points as I had no idea Vegas was that expensive. We did Cirque de Soleil (we saw O which was beautiful).
To be honest, the girls hadn’t really ever been out of Texas. I mean they had been on vacations but we usually vacation in Texas — the Hill Country is our special spot, on the rivers. But my heart is for the open road — ever since I read On the Road by Jack Kerouac when I was 16, everything changed for me. I developed a habit of stream of consciousness writing, which I perfected as the years went by on social media, but which made writing the carefully disciplined academic articles much more difficult. And I fell in love with America, and seeing America from behind a steering wheel. And getting to share that with my girls meant the world to me. They’ve beautiful young ladies, 11th and 12th grade, and they wore outfits and we did a lot of pictures in front of the choreographed water fountains, we saw the new massive LED dome, and then the next morning, my daughter flew back, and me and my oldest daughter drove straight to San Francisco, which had always been the destiny. San Francisco. My favorite city in the United States, and one of my favorite in the world. I’d found us a really nice airbnb in the Mission District. My daughter really had no idea what was waiting for her, but I knew that if I could just get us there, and if we could just walk the streets together, moving between neighborhoods and commercial areas, that it would affect her. And it did. She was like me deeply moved by the beauty of the city. Another book that had a major impact on my development was Jane Jacobs’ The Death and Life of Great American Cities, and all I really wanted was my daughter to feel a great American city with her feet and eyes, moving down the streets, navigating the BART. I gave her the job of getting us everywhere we needed to go so that she could master the public transportation system; I sensed that that was all that she needed to really feel her self esteem lift. Navigating public transportation involves going to depots, putting money on a card, going through turn styles, watching the people, getting on the train, reading maps, sit in a seat as a train moves through the dark — all experiences someone who has never had has conjured up feelings that really are new. And we don’t have that in Waco. I doubt my daughter have ever taken a bus in Waco, as Texas is an automobile centered place.
So she did, and it was empowering just like I thought it would be. She said all she wanted to do was go to thrift stores, so we did. We went to Haight and for an entire day, all we did was go from thrift and vintage stores, one after another. I got some great Sam Smith Adidas for only $10. Couldn’t believe they fit. And I got a bunch of other things that looked great until I brought them home and then they didn’t, but she found the most beautiful sweater and pants. And I saw my daughter as this young woman and just thought how fortunate I am to be here with her. And so we had four days and three nights there. We even did touristy things — took a ferry to the Golden Gate Bridge and to Alcatraz where I didn’t wear a hat and so my bald head turned red as a beet.
But then we drove back. I really did not have a plan for the return trip at all. I had a plan to get there, but not so much to get back. So I decided — okay, where have I never been? And I’d never really been to the northern part of Nevada, or Utah, or even much of Colorado. So that’s what we did. We drove Northern Nevada, through Reno, and then to Utah. I don’t know what I was expecting Utah to be like. Growing up in Mississippi and Tennessee, our lives were either in those states or on vacation to Florida. The middle part of the country, anywhere where there were mountains— I’d never seen. I’d seen the Smoky mountains in college, because I went to UT-Knoxville, but I’d never seen the mountains of the rest of the country. And so as a kid, I just thought Utah, Idaho and Iowa were probably all basically the same state. I thought maybe they were all just the scenery from Hoosiers, or possibly they looked like what I imagined Notre Dame looked like from watching Rudy. I had absolutely no idea that the country up there was like that as I’d never gone. So when I drove through Salt Lake City with her, and I saw the mountains towering over the city, it was just stunning. And it remained stunning — we drove for days, through Utah, down through Colorado, through this thick never-ending national park. I was listening to The Name of the Rose by Umberto Eco, and we were just talking and looking at things that we’d never seen before. I saw things in that national park that I didn’t even know could look that way. I didn’t know we’d cut roads through mountains that way, or that there were roads overlooking valleys like that — endless roads through endless mountains through endless valleys, all covered with endless trees, forests laid out like a blanket over the mountain.
They’re getting older. Next year, my oldest will graduate. Then the year after, my youngest will graduate. I’ve begun transitioning my life into a level of building into the next phase of my career. Writing, the workshops, more ambitious research projects that seem to take forever, consulting. I’ve gotten off social media, because it is such a negative place for me mentally. I suspect that things that happened on social media both last year but even really in the years leading up to it, left some scars. I noticed as much when recently I had something like a “flashback” when a new paper about online harassment of economists came out. I carry inside my body this feeling like the top layer of my skin is somehow slightly pulled away from the bone. It’s hard to explain. I’m going to see someone about it hopefully in the next couple of weeks. Decided I wanted to kick the tires, see what’s going on. Maybe try to get a better handle on why I am having a little trouble moving on, or why some things really get to me that to others looking in don’t make a lot of sense. I wish I was on the other side of my story where I’m a wise old man who says many wise things and floats along the ground, his feet never touching, having evolved. But I’m instead a man with his feet firmly on the ground, sometimes feeling planted into the ground, stuck. I make progress, and think it’s all done, all in the past, but then an event happens, and I wake up and think where am I? What happened? Am I really back at square one again?
So, that’s why things have been quiet on here. I had some things I was going to write, papers I was hoping to read closely and write up, but I just have been slow to really feel confident I understand why the papers had been written. There is a guy at Brandeis name Tymon Sloczynski, a young econometrician, whose work I have grown to admire immensely. It’s full of insights, and it’s probably things he’s written that forced me to start looking more closely at covariates in regressions. He’s got an Restat where he shows that the regression model with additive covariates ends up putting weights on groups that are more or less the opposite of what you’d think it would do — weighting up the smallest groups in the data, and weighting down the largest ones, causing the OLS estimates of aggregate causal parameters in certain specifications to not be as easily interpretable. I just was struck by how much I don’t understand about OLS, and so I was hoping to do a series through several of his papers because I really think Tymon is an amazing writer and thinker.
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Podcast
So, with that all out of the way, I’d like to introduce you to this week’s podcast guest, Jonathan Meer, a professor at Texas A&M in the economics department. Waco Texas is they say “centrally located”. They say that because you can get to Dallas in 100 minutes, Austin in 90 minutes and College Station in 90 minutes. And if you’re willing to go a little further, Houston isn’t much further. It’s not pretty driving the way driving through Northern Nevada or Utah is, unfortunately, but waiting for you when you get there is Jonathan Meer, one of the funniest and thought provoking friends in my community of economist-friends. So there’s always consumer surplus associated with making the drive.
I reached out to Jonathan because Jonathan isn’t really one of the “applied microeconomists” crowd that popped up at some point over the last couple of decades as much as he is a traditional microeconomist. The applied microeconomists were people like me who sort of took Becker as their inspiration to look at all of human behavior through an economic lens on the one hand, and then took Angrist and the credibility revolution to be the tools through which they’d do it. Meer was different. Meer was what I sometimes call on here “real economist” because he studied classic topics in labor and public finance, as well as ventured out on his own into areas around charitable giving. He moved between quasi-experimental and experimental work, but his overall grasp of economic theory was deep. He is considered one of the best instructors at all at A&M from what I can gather (I think the long name he has on his title is associated with his teaching skills). He teaches one of those massive micro economics classes with more students enrolled than populated some of the towns I drove through in Colorado. And he, from what I can gather, when he holds court, they all are on the edge of their seat, loving the lessons.
Meer knew what he wanted to be from a young age — an economist. He wasn’t one of these Johnny-come-lately types, like me, who learned about economics super late. He learned about economics early, way earlier in fact than anyone I spoke to so far. He went to Princeton then Stanford. If you get to know him, he’s basically the classic “work hard play hard” guy. He loves people, and is constantly having big parties at his house in College Station where he invites people from diverse walks of life to come and watch him make these ridiculous cocktails and tell jokes. He reminds me of characters from Walker Percy novels. I have grown to appreciate our friendship as the years have passed. And I was grateful to have this chance to talk to him and hear more of his story. I hope you find it interesting too.
Thanks as always for tuning in. Thanks for supporting this effort to run a podcast of personal stories aggregating into an oral history of the profession. All stories matter. Each of our stories matter. Your story matters. It is important that we believe that and not cheapen the experience of others by forcing their realities into our two dimensional caricatures of who they are or what this life is about. I do these podcasts to remember that people are good and valuable, not as a means to an end, but as an end in itself. And I do them as part of my own effort to remain whole and sane and get better and just not let go of the thread of yarn that links me back to thing I hold dear — economics. So sit back and enjoy this interview with Jonathan!
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This week’s interview is with a professor at Brigham Young University named Joseph Price, or Joe. Joe is a professor who graduated from Cornell around the same time that I graduated from the University of Georgia (i.e., 2007 cohort). He’s a labor economist, Fellow at the Wheatley Institution, NBER research associate, Director of BYU’s Linking Lab, co-editor at Economics of Education Review and the author of something like 45 peer reviewed articles in top economics journals like the Quarterly Journal of Economics, Journal of Labor Economics, Journal of Human Resources, Management Science, as well as countless interdisciplinary journals on numerous topics in sports, the family and more.
I will just list two studies that I have for many years found very interesting. One of them with Justin Wolfers made a major splash both in economics on the topic of discrimination as well as the broader public, including the National Basketball Association (NYT link here). who has written on a variety of topics like discrimination in the NBA. “Racial Discrimination Among NBA Referees” with Justin Wolfers appeared in one of the 2010 issues of the Quarterly Journal of Economics and claimed to find evidence of racial discrimination, mostly likely caused by unconscious bias than animus, among NBA referees in calling fouls against players. What impressed me at the time is the same thing that always impresses me: an interesting question, the discovery of some randomness that allows one to plausibly provide some evidence relevant to that question, and the collection of interesting data. Joe and Justin hand collected box scores of every NBA game with specific fouls among other statistics of the players combined with the names (and races) of the officiating referees at each game. While they could not link a referee to a foul called, they used a measure of the percentage of the officiating staff that was White and non-White as a proxy. With random variation in the racial composition, supported by both institutional details and a series of regression analyses, they looked at whether a higher share of White referees “caused” a Black or White player to have a foul called against them more or less often conditional on player fixed effects. You can read the abstract to learn what they find, but given the controversy and antagonism it generated within the NBA, I suppose you can also guess.
But it is another paper of his, a solo authored one, at the Journal of Human Resources, that I have always found to be a truly beautiful piece of economics. “Parent-Child Quality Time: Does Birth Order Matter?” was for a long time my favorite empirical paper I’d ever read. It was a simple idea really. Lower birth order, particularly the first born, typically had better academic and labor market outcomes, despite coming from the same family. Sandy Black and coauthors had written about this in a 2005 QJE using Scandinavian registry data, but the mechanisms were largely speculative. Joe’s paper was not so much conclusive as it was a clever descriptive paper showing that lower birth order children received more high quality time from their parents using the American Time Use Survey, which is a time diary and in my opinion one of America’s more interesting repeated cross sections. The patterns he found fit a rule that was well intentioned but likely led to inequities within the family — first borns received all their parents’ time; second borns received half their parents’ time, third borns received a third of their parents time, and on and on. In other words, equity rules with each stage over quality time, or simply budget constraints themselves, leads to American families to spend less time overall in early years with each new child simply because quality time is a scarce resource. Becker might say that instead of equity, we should aim for optimized time spent with children — spent quality time up to the point where marginal benefit equals marginal cost across all children. But such rules, while sensible economists, are likely unethical because of ironically strong bonds of kinship where parents love their children the same.
These kinds of questions over deviations from optimizing behavior where emotion and quick thinking drives decision making, as opposed to pure economic calculation, was a hallmark of Joe’s work, but more recently he transitioned into a very ambitious project of using Machine Learning and large genealogical databases to link people with other large datasets like the Census, to track them over time and create a large family tree of what he calls the Human Family. This is the Joe Price I have come to know — a deeply curious man, a man with deep endowments in the skills of our professions, a hard worker (you will not find him on social media), a mentor and a man of vision. To say that I hold him in the highest esteem is an understatement. And because of his character, the lack of guile and a positive and egalitarian spirit, he was for a long time an economist my age whose productivity did not cause me insecurity. I was simply honored and amazed by what a good economist he was and tried, as I often have, to see if I could crack the algorithm that made him so successful.
So, it is my pleasure to introduce you to one of my friends who I consider to be a special member of economics of larger story. Special in many ways, but one way being he would likely tell me that we are all special. Thank you again for supporting the podcast. If you like it, consider supporting it by subscribing below. I am Scott Cunningham the host of the Mixtape with Scott!
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This week’s episode of the Mixtape with Scott is with a man I have gotten to become friends somewhat unexpectedly: Miles Kimball (Wikipedia). Miles is currently the Eugene D. Eaton Jr. professor in the economics department University of Colorado Boulder. And we got to know each other through a mutual friend, and discovered that we had many of the same somewhat eccentric interests around mental health, self improvement and a desire to serve the profession in our own personal ways.
This is a long interview, but I found it fascinating. I wanted to talk to Miles about his growing up because he grew up in a famous family — his grandfather was named Spencer Kimball, the twelfth President of the Church of Latter Days Saints. Not being Mormon myself, it took me some research to understand the significance, but here’s a wikipedia article about who the President is in the Church of Latter Day Saints. It was an office originally held by the Church’s founder, Joseph Smith, and is their highest governing body. Members of the church consider the President to be also a revelatory person — a prophet and seer — and so I was fascinated both by that background, but also Miles’s own personal story as he left the Mormon church over 20 years ago, an act that I had to imagine was consequential for his life, and very difficult to summarize what it meant for him.
Miles research productivity and interests are diverse. It ranges from the furthest parts of our tradition with topics in macroeconomics, the zero lower bound, theoretical elements of human decision making, subjective measures of well being and happiness, measurement, and more. But this is exactly the kind of person I have come to associate with Miles — his passions (and they are passions) range a very broad topic area. He is one of these renaissance types who goes broad and deep — not either/or; rather both/and. He is also, like me under what can only be described as a sense of calling to something bigger than himself to help economists with improving mental health by providing free life coaching “pods” — small groups who meet regularly over zoom going through life coaching curriculum, led by trained life coaches. Given the high rates of depression, anxiety and loneliness documented among our students, I am grateful for him.
So let me now introduce you to Miles on this journey through his life. I hope you enjoy it. Thanks again for all your support of the podcast and me. Remember to like, share, follow — all that stuff — if you find these interviews about our economists and the profession interesting.
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This week’s episode of the Mixtape with Scott is from 2019. It is an interview with the late Dr. William Spriggs, an economist who died in June 2023. He was a longtime professor of economics at Howard University and Chief Economist for the AFL-CIO. It was from an old series I wanted to do called “What Economists Do” — the premise being more or less what evolved into my current podcast: tell the stories of living economists and in aggregate hope that the collective story of economics is told. And Dr. Spriggs was the first I reached out to. This was filmed at a conference for mentors and mentees hosted by the American Economics Association and we were both there, so I asked him if he’d be willing to let me interview him and he graciously said yes.
For two hours, we talked about Dr. Spriggs’ life — all of which was new to me, as he didn’t know me and I only knew of him by reputation, but not about his personal life. If you aren’t familiar with him, he was a man with a resume. Professor of Economics at Howard University for many years, chief economist to the AFL-CIO, and Assistant Secretary for the Department of Labor in the Obama administration. His scholarly focus was labor economics and public policy, both with an eye towards inequality and persistent structural racism. He was vocal about these things in the world, but also the profession, and he spoke with real courage and so much moral force that it made a real impression on me every time I’d been in his vicinity. Every now and then it seems there is someone like that in America, and Dr. Spriggs was definitely one of those “someones”, at least within our profession.
After I did this interview in the summer of 2019, I forgot about it. I guess I wasn’t quite ready to do the series which back then was going to be called “What Economists Do”. I put it in a dropbox folder, but then after a computer switch, had selected to sync it to the cloud rather than locally and so out of sight out of mind. So when he passed away, I searched for it, thinking it must be somewhere, then last week remembered the cloud and there it was. So, join me on this journey back to 2019, to my fascinating conversation with Dr. William Spriggs.
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Three people were awarded the 2021 Nobel Prize in economics: Josh Angrist, Guido Imbens and David (“Dave”) Card. I have interviewed the first two, and today I have the pleasure of posting the last interview with Dr. Card himself.
To most economists, Dr. Card needs no introduction and to be honest I’m really not even sure what to say. I will just say that one time I was having dinner with a well known labor economist who had been on the market the same year as Card, and this economist over dinner without any hint of exaggeration said simply that Card was the greatest labor economist of his generation, bar none.
Other than that, I will just say some of the things about his work that has meant a lot to me. Card is “real economist”. Even more than that, he is “real labor economist”, which is the highest praise I know to give people. His knowledge of labor economic theory is deep and expansive. It rolls off his tongue effortlessly. You poke him, he bleeds income elasticities and a myriad of models that he holds to with a light grip.
But he was one of the booster rockets on the “credibility revolution”, too, that launched the social sciences into a new level of empirical work. When he began working, labor was in the throes of a fairly deep empirical crisis, and we discussed that in this interview. I learned many things I didn’t know, and he also corrected things I took for granted to be fact, like how I interpreted Bob Lalonde’s job market paper and what it meant. Many of his studies seemed to be lightning rods on multiple levels — both because they were unexpected null results of prevailing neoclassical wisdom, but also because the studies forced the profession to have deeper conversations about epistemology. What is a model? What is evidence? What does it mean to believe something? When are beliefs justified? What makes them warranted? These were not topics that I think Dr. Card himself seemed particularly interested in, but it’s very hard not to see in the anger that surrounded him and those studies people in the throes of being unable, unwilling or incapable of changing their mind even a small bit.
This is in fact the story of the practical empirical work of data workers, though — marshaling convincing evidence, going up against a strong scientific blockade, and successful persuasion looking one way at the time that looks very different later. We saw a complete rejection of the facts with Semmelweis’s hand washing hypothesis, and John Snow’s germ theory, for instance. Both men published work that looking back is so obviously correct but at the time seemed to not move the needle on policymaker and scientist’s opinion. I’m not saying that Dr. Card had that experience with his classic works on the minimum wage or immigration — he did after all win the John Bates Clark award and the Nobel Prize. But listening to his story about what he and his colleague and coauthor Alan Krueger experienced at the time when it was published, I can only say that I think sometimes we forget how intense these academic fights can be. We talk a little at different times about this speech he did in 2012 at Michigan about “design vs model based identification”, also, and if you want to read that, it’s here.
I hope you enjoy this interview as much as I enjoyed being a part of it. It’s around 90 minutes long, but it felt like 30 minutes. At the 60 min mark, I told him well I guess we need to stop and he graciously gave me another half hour. He also makes an announcement in the interview that I think wasn’t public knowledge, making me feel a little like Matt Drudge with breaking news. But no spoilers — you’ll have to listen for yourself. Thank you again for tuning in. If you like these interviews, please share them! And if you really like them, consider supporting them with a subscription. But no worries if you don’t want to. Have a great rest of your week! And remember — clear eyes, full hearts, can’t lose.
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Marina Della Guista is a pioneer in the economics of Sex Work who started her career at the University of Reading and is now a professor at the University of Turin. And when I first started studying sex work, I went looking for what papers economists had written. There weren’t many, but the ones that had been written were fascinating. Many, though not all, were applied theory papers. I remember with great fondness studying new models gaining rich insight into how other economists thought this niche subject in labor economics.
One of the studies that left an incredible mark on my orientation in studying sex work was “Who is Watching” by Marina della Giusta, Maria di Tommasso and Steiner Strøm. It was a paper of supply and demand for sex work in which stigma was part of the cost structure but interestingly stigma was also endogenous and determined jointly in equilibrium with the size of the work force and clientele as well as wages paid and received. As the market grew, as the more people engaged in this illicit activity expanded, the stigma penalty itself declined suggesting to me in my own work that if the internet was expanded sex work, or if it was legalized, the stigma under prohibition and clandestine markets might lift some. The degree to which it did would depend on the elasticities in the world.
It was the sort of Becker style reasoning that I found so attractive — the idea that things we think of as exogenous and unchanging may be endogenous, governed by formal processes and that technology may shape those norms, for good or bad.
Since then, I have become friends with all three authors, and one of them very close. I met all three last November when I visited Turin to do a workshop and serve on a committee. But Marina and I have been friends even before then. She was active on Twitter when I had been too, so we’d deepened our friendship there, but even beyond that I think we just had made regular communication a part of our life.
Marina is an excellent labor economist with both sides of the applied skill set — empiricism and applied theory. She has continued to steadfastly worked on stigma in sex work as well as studying the so-called Nordic model, a leading contender in augmenting standard prohibition by lifting the bands on supply but maintaining the prohibition on demand. Traditional tax theory say the impact on wages and the distribution of burden is the same whether you target supply or demand though being the dutiful empiricist she along with Maria have attempted to determine to what degree the end demand approach changes risk attitudes and any evidence of behavioral change using survey data in UK.
In this podcast she shares her journey through labor and gender, and not being an American, it reminds I hope everyone that the United States economics community is rich and spreads around the globe. If you find these podcast interviews interesting and valuable, please share it with colleagues and students, and consider following and subscribing and supporting it. These interviews are an oral history of the profession as told through the personal stories of economists.
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This week’s episode of The Mixtape with Scott is an interview with an old professor of mine from when I was at the University of Georgia, Dr. George Selgin. George was one of several really interesting professors I was fortunate enough to get to know while a PhD student there. One anecdote of the impression he made on me was that he was a handful of people who ever read my dissertation. He gave back to me a massive marked up document full of suggestions and a lot of red underlines — not just to the job market paper, but all three chapters. Which was remarkable for two reasons: he was not an applied microeconomist and he wasn’t even on my dissertation committee. He also came to my defense and was perhaps the single most vocal one there. Being able to answer his questions was one of my happier moments of that late end of that period, as it felt like George took me very seriously and treated me as a peer. He did that with everyone. And that could be a bit intimidating since being George’s peer usually meant some pretty serious conversations.
But George was like that — he was extremely engaged in my education, but also to many others as well. I never took any courses from him, because I early on sorted into labor and econometrics, but I watched George closely all the time and interacted with him a lot. He gave each person his full attention, read their papers very closely no matter the field, and in seminars was always on top of everything. It was a lesson in areas I found to be valuable like individuality as an economist and taking ideas serious enough to battle with them.
George is a monetary economist and economic historian. He was one of a handful of reasons I decided to go to Georgia at all. He had written a short pamphlet I’d somehow found in college on something obscure (to many anyway) called “praxeology”. That’s something from Austrian economics, and originally I really thought economics was Austrian economics. Coming from a literature background, I’d never had any economics classes, so what I knew, I knew from reading classical liberals like Hayek, Mises, Milton Friedman and a couple others. I was spent a lot of time reading people from the Austrian tradition and the Chicago tradition, from the early to mid 20th century. I knew about George because of that praxeology pamphlet which I read backwards and forwards, over and over, trying to understand everything I could. Imagine my surprise when first year coursework did not involve any praxeology! “I was told there would be no math” I often thought to myself.
So George is a hero of sorts of mine. He is an excellent writer, a very careful thinker, a wonderful economist and an inspiring professor. And he’s going to be part of a longer series I’d like to do on what I’ll just be calling “the two wings of the profession: Austrian economics and the heterodox traditions”. Although that’s a mouthful. I am hoping to do interviews with places like George Mason University, as well as U Mass Amherst, the economists from the old Notre Dame economics department (now defunct), Riverside and more. These are important parts of our profession’s history, with many interesting stories, and I don’t think many people know them. But I’m hoping you will find them interesting. They’ll be trickling in as I continue making progress towards them, but expect them to be scattered across season 2 and 3.
If you like the podcast, consider liking, following, sharing, and subscribing! The podcast is a labor of love. I love the stories of our profession and the people in our profession a lot. I know others do too and I hope these stories speak to you as you continue navigating your own journey, wherever you are and whoever you are. Peace!
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On this week's episode of my podcast, The Mixtape with Scott, I got to meet and talk with the labor economist, Steven Pischke, a distinguished professor at the London School of Economics. Many listeners will automatically recognize that name for his joint venture with Josh Angrist in creating what is probably, without a doubt, the textbook of my generation in causal inference and applied micro econometrics — Mostly Harmless Econometrics. Like Angrist, Pischke earned his PhD in economics from Princeton, contributing significantly to my ongoing exploration of the Industrial Relations Section, one of a handful of ground zeroes for the “credibility revolution” within microeconomics, from the 70s, 80s, and 90s. Pischke, through his personal scholarship and the influential Mostly Harmless Econometrics, had a pivotal role in pushing out this change in the profession. In this episode, Pischke takes us on his journey from Europe to Princeton, sharing his shift from macro to micro (a road less traveled for sure). We explore the years that shaped his life and academic pursuits, providing a unique insight into his personal and professional development.
For those new to the podcast, though, here’s the premise: this podcast sets out to weave an oral tapestry of the economics profession over the past half-century. It's not just a history lesson; it's primarily a podcast of the personal voyages of economists, their lives recounted from being a kid through their career. I choose themes that I find interesting obviously, but those interests change over time. For a while, I’ve been engrossed in the 2021 Nobel Laureates context — Princeton and Harvard, faculty, collaborators, and students. But mainly I am interested in the people, not for their inputs in a larger story, but rather as people themselves. The oral history in my mind is a collection of swatches that make up a patchwork quilt. The oral history is just a story of the people who have their stories and I think you can’t understand one without the other. But ultimately what matters are the people. It’s just that economists seem to more often be interviewed for their papers or their opinions than their lives, but their lives to me matter far more than their papers or opinions. So this dual focus tries to ride a fine line between the historical context and the individual narratives, considering every personal story not just as an integral part of a larger narrative, but also as a standalone almost infinitely valuable treasure.
The creation of this podcast stems from my firm belief in the intrinsic value of every person's journey, a belief that these shared stories are as meaningful to those people as it is others listening. My hope is that not only will some listener out there learn from these personal accounts but may also, by hearing someone else’s story, be helped in navigating their own life journey — your life journey. So please here I have a chance to share with you about Steve Pischke!
As always, thank you for your support. If you like the podcast, share it with others, and even considering supporting it through subscription. But it’s meant to be freely given so don’t feel obligated either.
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A person I had always wanted to get to know Dr. Elizabeth Stuart, a professor at Johns Hopkins in their biostatistics department. I knew about her for a long time before I met her because of her expansive work on a variety of issues in the area of “matching” and unconfoundedness. She did her PhD, as it turned out, at Harvard at the end of the 1990s and early 2000s around the time when Guido Imbens was still there in the economics department, and Don Rubin in the statistics department. At Harvard she worked with people like Don Rubin, her dissertation adviser, as well as Gary King, one of her collaborators and someone else I’ve interviewed on the podcast, and so I wanted to talk to her to try and piece together more of the progression of causal inference throughout the social sciences in the late 20th and early 21st century, not just through writing, but maybe even moreso through students and faculty placements at departments around the world.
But these big ideas are in many ways just the “hook”, as I have said, to build a mental map of why I select certain people for the podcast. Dr. Stuart is an important scholar in her own right. She has spent a career being driven by questions about health and selected into statistics as a way of enhancing her own ability to contribute fruitfully to large and important policy questions regarding health. After graduating from Harvard in 2004, she went to Mathematica before then moving to Johns Hopkins school of public health where she steadily moved forward through tenure to associate then full professor. She is now a professor in the department of mental health, the department of biostatistics, and the department of health policy and management at Johns Hopkins. And she is now leading up pioneering new curriculum options for students there as well as moving into a new administrative position within the university.
I learned things I didn’t know, such as her brief flirtation with going to Princeton’s economics program (the economics students, though, seemed miserable so she opted against it). Since I’ve been also obsessed with trying to better understand Princeton’s economics program throughout the 1970s to 1990s, I was surprised to again realize what a small world it was that Dr. Stuart herself skipped over that like a stone over water before landing at the center of the causal inference universe itself — Harvard’s statistics department. So this was a fun interview. And I hope you enjoy learning more about Dr. Stuart’s life.
If you enjoy this podcast interview, or any of the others, please share it, as well as follow, like and even consider subscribing! The substack goes to subsidizing the cost of paying “my guy” who turns the raw interviews into usable podcast and YouTube videos. Thank you again for all your support!
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Interview with Jason Furman
It has been a common story throughout the last two seasons that while not every economist entered economics with a burning desire to affect public policy, a large number had. But of those that had said that usually had in mind scholarship as the primary mechanism by which policy was affected. In this week’s episode, I am joined by an economist who has spent his career very close to the machinations of economic policy itself — Dr. Jason Furman. Jason, currently a professor in Harvard's Kennedy School, took the road less traveled from being a Harvard student who left “all bug dissertation” to work with Joe Stiglitz in the Clinton administration, came back, then went back to Washington to the Obama administration, then back to Harvard again, this time as a professor!
Our conversation moved from Jason's personal journey as a kid through high school and then carving his own path within and through the economics profession. It’s the stories like Jason’s that I’m trying to learn by listening to the personal stories of living economists and the hope that over time, through the collection of hundreds of them over the next several years, create a large collage of the profession’s story. An oral history of the profession told through the personal stories of economists. And this week’s story is Jason’s.
As always, if you enjoy the show, please don't forget to like, share, and follow me on your preferred platform (especially Substack!). If you haven't yet, do consider subscribing to the podcast, so you don't miss out on any of these incredible stories. Your support helps bring these narratives to life.
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Derek Neal interview
It’s Tuesday which is usually the day of the week I release a new episode for season two of the Mixtape with Scott. These interviews consist of me interviewing an economist, though sometimes I deviate and interview other social scientists or authors. The idea of the podcast is a little out there:
“to be an oral history of the economics profession, focusing selectively on topics from the last 50 years, by listening to the personal stories of the economists themselves”
Topics include things like causal inference and econometrics, Princeton Industrial Relation Section in the 80s and 90s, economists in the tech sector, Gary Becker’s former students, and “public policy” more generally. Each episode is about an hour though sometimes they go longer, and one time it went on for 3 hours (I haven’t posted that one yet). We start when they were little and usually end with where they are now, pausing often to discuss some of the more memorable work they have done.
This week I interviewed Derek Neal, a labor economist and professor of economics at the University of Chicago. If I had to summarize one thing that described this interview, and what I learned from Derek's life, it would be that he has been riding on a knife edge of close calls and good luck. Take for instance how fortunate he was that his economics professor at small college in Georgia where he grew up had been denied tenure at Kentucky. Arriving at this college, he took it upon himself to prepare students for grad school by teaching them not just economics, but through independent studies tons of the math that they did not have access to. And Derek was one of them. Or Bill Johnson, his adviser at Virginia, who helped him learn about the important craft of writing. Or the famous Sherwin Rosen who took Derek under his wing at Chicago the second he arrived there as an assistant professor.
Derek was generous in our interview. He peeled back the curtain a little and walked me through his life through all this serendipity, the “unmerited grace”, as he calls it, to where he is now. Unmerited grace tends to create within the recipient a sense of calling to do the same for others, and the sense I get, and the rumors I hear from others, is that Derek works hard to be for others what his mentors had been for him. I was told by a former student of his just this week that Derek was an incredible adviser, “but very tough”. A description I’ve heard from others whose papers he edited when he was editor at the Journal of Political Economy, too.
For people, like me, who love the stories of the old economists at the University of Chicago, hearing more about people like Sherwin Rosen (who hasn’t come up before on the show) and Gary Becker (who has) should delight you. It was also good to have a southerner whose drawl matched my own. You be the judge who carries it better — me or Derek.
I hope you find this emerging mosaic of stories of our profession of the last 50 years as interesting as me. I am appreciative of all these people giving me an hour of their time and sharing their stories and the stories around them as they followed their own path. I hope you hear their story, but as corny as it sounds, our story and your story too. Hearing stories, listening to stories, and telling stories are important to me, and I’m glad I get to share these with you.
So thanks for listening and tuning in. Don’t forget to like, share, follow and subscribe!
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In this week’s interview on The Mixtape with Scott, I had the opportunity to meet with the James Orin Murfin Professor of Political Science at Princeton University, Dr. Rocío Titiunik. Within the world of applied econometric methodology, Dr. Titiunik is well known for her theoretical work on regression discontinuity design. Her work with coauthors like Sebastian Calonico, Matias Cattaneo, and Max Farrell has shaped the landscape of applied econometrics through their innovative work in econometrics as well as their construction of numerous software packages in R, Stata and now python of practical utility. But she is a dual threat quarterback who is both an important contemporary quantitative methodologist as well as an influential political scientist whose applied work explores the intersection of political institutions and causal inference. That work has been instrumental in expanding our understanding of political participation, legislative behavior, and the intricacies of elections and representation.
However, there's more to Rocío than the accolades on her resume. Beneath the scholarly achievements and methodological innovations is the story of a journey that will, I think, surprise many listeners. We often look at accomplished people and just assume that all the pieces fell into place for them from the moment they stepped foot into academia. But Rocío tells a different story about her path. She talks openly about her first introduction to economics occurring, not through statistics and econometrics, but theory and literature. Her entrance into Berkeley’s celebrated ag Econ PhD program happened almost serendipitously. And even while there, she was unsure how all the different parts of her personality might form within her — or if they ever would. During our conversation, she opened up about the struggles, uncertainties, and the feeling of being lost in the vast tapestry of the economics profession. Her openness and authenticity were refreshing and the interview provided a stark reminder that even the most successful among us grapple with similar doubts and fears, just like the rest of us.
This conversation offers more than just an overview of Rocio's professional accomplishments. It paints a portrait of a person who, despite her status in academia, remains grounded and relatable. Her story is one of perseverance and self-discovery that will resonate with anyone who has ever questioned their path or grappled with finding their unique fit in their chosen field.
Join me in this week's episode as we journey through Rocio's life, her work, and the lessons she's gleaned along the way. As much as it is an exploration of her contributions to political methodology, it is also a celebration of the human experience in all its complexity. And if you want to learn more from Dr. Titiunik’s work, you can come to her upcoming workshop at Mixtape Sessions where for three days she will be teaching about regression discontinuity design. Please remember to like, share and subscribe!
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In this week's episode of the Mixtape with Scott, I’m taking a break from interviewing economists to post a podcast interview with a non-economist, the historian Mike Jay. Mike Jay is a historian of medicine and I interviewed him last year as part of a now somewhat defunct project on the emerging medical reforms in the US and around the world related to "psychedelic medications". I felt that as these were happening fast, it would be good for those health economists and policy advocates to learn more about it, and sometimes that means talking to the non-scientists who have written about it as well as the scientists.
I found Mike because he wrote a fascinating book on the global history of mescaline published through Yale Press who also published my book. I devoured that book during Covid. I spent Covid lock down studying everything I could about contemporary but also historical psychedelic medicine which included the MAPS trials on MDMA, the studies by Roland Griffiths and his colleagues on psychedelics, and others. But I was also interested in the lost work of scientists from the 50s and 60s and the psychotherapies that grew out of it. Mike'ss book on the history of mescaline was absolutely riveting. He’s a great writer and I highly recommend him.
But I also recommend him because he wasn't always a writer (who was?). He aspired to something else and more or less transitioned into it as his career evolved. I thought hearing that type of story might be interesting to others curious about their talents as a writer to hear what it was like for someone else. Mike also has a new book out you may want to check out. I haven’t read it but it’s a continuation of this work he’s been doing on the history of psychedelics. So, again, thanks for tuning it to the Mixtape with Scott. Please like, follow and share! And if you want to support this work, please go over to my substack (causalinf.substack.com) and hit subscribe!
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Podcast interview
I’m going to drop this week’s podcast a day early because Dr. Abadie’s doing a workshop for Mixtape Sessions later this week, and thought it would be better to just give everyone a headsup about it and bundle it with this interview too. Dr. Abadie has had a major influence on me. In 2009, I began studying the legalization of indoor sex work on public health and violence against women, a paper that would land me my first Top 5 publication with Manisha Shah, and from the start I decided to use synthetic control to do it. We were one of the early adopters in fact, and I entered into a long pen pal conversation with Alberto over the years. I asked if I could do the “real podcast interview” with him, the one that’s more of the “oral history of economics; personal stories of economists” and he agreed. (He did a shorter non-themed one abt synth last year for me for a substack I was writing abt synth). So I’m super excited and honored to have a chance to interview Dr Alberto Abadie again on the podcast. And I hope you like it.
Dr. Abadie is at MIT. Before MIT, he was a professor in Harvard Kennedy School where I once heard he got a standing ovation after a lecture on econometrics. The number of econometricians teaching econometrics to non-econometricians who have gotten standing ovations is a very small set is my hunch; we all are trying to nail it, but few get it. He was one of the best speakers for me when I first saw him speak at the Northwestern causal inference workshop a decade ago (which I’m co-directing again this year with Bernie Black for those interested — here for main, here for advanced).
His work spans a lot of topics. He did his doctorate at MIT under Josh Angrist in the 1990s, and then moved into a collaboration with both Josh as well as Guido Imbens with whom he wrote a series of very nice papers on inexact matching. One in econometrica 2006 where they worked out the large sample properties of the method under repeated “matching with replacement”. And another where they worked out a method for using regression adjustment to reduce the bias from inexact matching in a 2011 JASA. I’ve written about both on the substack and they’re great. Over the years, I have come to love them.
The inexact matching method finds matches that minimizes the sum of squared matching discrepancies across all confounders, which is a similar objective function to synth which finds weights on donor pool units (as opposed to M:1 matching). Both methods are imputation methods — using comparison groups to impute missing counterfactuals, only one of them builds on unconfoundedness (matching) and the other on a factor model (synthetic control). Both matching and synth have been very influential, but matching predates Alberto by decades going back to the Rubin and Cochrane in the 1970s and 1960s. Alberto, on the other hand, is the author of synthetic control with Gardazebeal in a 2003 American Economic Review article studying the effect of terrorism in Basque Country on economic variables, like GDP. And it has been used now many times, including my study of sex work.
But he’s done more than that. He’s done work on complier analysis in instrumental variables, a topic that I am noticing becoming more common nowadays with advances made on the leniency design, as well as his new work on clustering under sampling versus design based concepts of uncertainty. He’s a great one, and I hope you like this interview. Here’s the video:
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Plug for Mixtape Sessions
And so this week, starting Thursday and concluding Friday, he will be teaching a 6 hour workshop on synthetic control (Thursday 6-9pm EST) and clustering (Friday 6-9pm EST) at Mixtape Sessions. And I highly encourage you to attend as it’s going to be great!
Synthetic control has been called by Susan Athey and guido imbens the most significant innovation in causal inference of the last two decades. Dr Abadie introduced the estimator with his coauthor Gardazebeal in a 2003 article published in the flagship journal, American Economics Review, to evaluate the effect of terrorism on the Basque Country in Spain. It uses an optimally weighted average of control group units to estimate a treated units counterfactual when randomization did not occur. It’s a powerful estimator that has had considerable influence within both academia as well as industry, with many major tech firms using it routinely. It’s also an estimation framework that has undergone considerable innovations — too much to say here.
The clustering work is different. It has to do with modeling uncertainty of parameter estimates under different sources of uncertainty — design versus sampling. This work is with coauthors Susan Athey , guido imbens and Jeffrey Wooldridge , published in two recent major journal publications of the last few years.
The workshop information is below. Mixtape Sessions deals remain the same. We use price discrimination and scale to help reach as many people as possible with these important innovations in causal inference and applied work. Remember:
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$1 for current residents of low income countries
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$50 for current residents of middle income countries, students of any kind, predocs, postdocs, and anyone in between jobs,
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$595 everyone else
I highly encourage you to take advantage of this opportunity. It’s an unusual opportunity for any of us to study with someone of Abadie’s caliber. And I mean that with genuine earnestness. Here’s the information. There will be Q&A for those who want to ask questions, so come with some questions and we will try to get them to him as best we can. Thanks again for your support of the podcast and Mixtape Sessions. Please don’t forget to like, follow, and share! And even consider becoming a paying subscriber!
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Hello Substack readers,
I'm excited to share with you the insights from my latest podcast episode, where I had the incredible opportunity to interview Yale professor and renowned economist Steven Berry. In this week's edition, we will:
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Introduce you to our esteemed guest, Steven Berry
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Delve into the groundbreaking BLP model
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Recap the fascinating conversation we had
Meet Steven Berry
Steven Berry is the David Swensen Professor of Economics at Yale University, winner of the 1996 Frisch Medal, and a leading figure in the fields of econometrics and industrial organization. With a life that started in the Midwest, Berry's journey into economics was marked by his love for science fiction and the brilliant faculty he studied with at the University of Wisconsin, such as Chuck Manski, Gary Chamberlain, Art Goldberger, John Rust, and many others.
The BLP Model
One of Berry's most significant contributions to the field of economics is the "BLP" model, developed alongside James Levinsohn and Ariel Pakes. Their 1995 Econometrica paper, "Automobile Prices in Market Equilibrium," has had a profound impact on industrial organization and real-world applications.
The BLP model offers a powerful tool for understanding demand in various competitive environments, helping both private companies and public policymakers make better decisions. You can find a link to the BLP paper here.
Our Conversation with Steven Berry
During our interview, we explored Berry's life and his experiences in economics. From his early days in the Midwest to his time at Wisconsin and beyond, we delved into the stories and influences that shaped his career. Berry shared his thoughts on the development and real-world applications of the BLP model, as well as his views on the future of industrial organization and econometrics.
Our conversation with Berry was a fascinating journey through his life and the evolution of economics over the past few decades. For those who are interested in the intersection of econometrics and industrial organization, or simply curious about the personal stories of an influential economist, this interview is a must-listen.
Don't miss the full conversation on this week's episode of the Mixtape with Scott podcast, available on your favorite podcast platform. And as always, be sure to like, share, and subscribe to the podcast, and stay tuned for more "explainers" on econometrics in my Substack.
Until next time,
Scott Cunningham
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Dear Mixtape with Scott listeners,
We are pleased to announce the release of our latest podcast episode, featuring an insightful conversation with Jon Roth, an exceptionally talented young econometrician from Brown University. With only three years since graduating, Jon has already made significant contributions to the field of econometrics, publishing high-profile papers on difference-in-differences in esteemed outlets such as Econometrica, Review of Economic Studies, and AER: Insights. Moreover, he has authored a timely literature review on differential timing and has an R&R at JPE: Micro on staggered rollout. In short, he has hit the ground running with many papers that no doubt will be finding their way into all of our papers soon, if they haven’t already.
In this episode, Jon shares his journey, from growing up in Massachusetts to discovering his passion for economics. He speaks candidly about how his father's accomplishments as a theoretical physicist led him to believe that his potential might lie more in applied labor economics. However, through a series of events, Jon found himself drawn to econometrics, ultimately excelling in the field.
We also discussed Jon's love of sports, his transition from solving problem sets to producing research, and his experience navigating the job market over three years, as he honed his professional identity.
As a special opportunity for our listeners, Jon Roth will be teaching an exclusive workshop on the Mixtape Sessions platform on Friday, April 21st, starting at 9 am EST. Don't miss the chance to learn from one of the brightest minds in the field, as he covers much of his own work and more.
Join me, your host Scott Cunningham, as we dive into the life and work of Jon Roth in this engaging episode of Mixtape with Scott. We hope you enjoy the conversation as much as we did. Youtube video below.
As always, we appreciate your support for Mixtape with Scott. If you enjoy our podcast and want to get even more from our community, consider becoming a paying subscriber to our Substack. I am working hard on trying to provide exclusive value for all paying subscribers, so stay tuned. For now it’s just the warm glow you’ll get from knowing you’re contributing!
If you haven't already, please take a moment to share this episode with your friends, colleagues, and anyone you think would enjoy it. Your recommendations are incredibly valuable and help us grow our audience. Remember to also like, follow, and subscribe to Mixtape with Scott on your preferred podcast platform to stay updated on our latest episodes. And leave us a review on Spotify or Apple; those always help.
Thank you for being a part of our journey as we continue to explore the fascinating stories of the people shaping the world of economics. We look forward to sharing more thought-provoking conversations with you.
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This week’s episode is an interview with Joseph Doyle. Joe is the Erwin H. Schell Professor of Management and Applied Economics at the MIT Sloan School of Management who has had a distinguished career as a labor economist studying a range of topics that most outside of economics do not always associate with the field — like child welfare and foster care, juvenile incarceration and its effect on high school completion and adult incarceration, and more. The welfare of children, as it turns out, has been a longstanding research focus of Dr. Doyle’s, and because I’ve written on foster care myself, and because his paper with Anna Aizer study the causal effect that juvenile incarceration has on high school completion and adult incarceration is one of my favorite applied papers ever written by economists, I have constantly gravitated back to him and his work.
Dr. Doyle someone I’ve always looked up to for a variety of reasons, not just topics, but also his ingenious approaches to identification of causal effects outside the purely randomized controlled trial. After all, no one would ever entertain the possibility of randomly assigning children to incarceration even if the question of what effect it has on life outcomes is of supreme importance. And so we are dependent on the work of people like Dr. Doyle who care about the topic too, but also have the skill and seriousness of mind and heart to develop plausible strategies to answer the question — not because the methods are cute, but because the question is so vital and important that it begs to be answered. I enjoyed this hour with Dr. Doyle, and hope you do too. Please remember to share, subscribe and like the episode!
And apologies the video below is messed up; I got a new computer and the Zoom wasn’t working right. So unlike usually seeing us side by side, it’s one person at a time. And I am off on naming the order of episodes — this is episode 8, not 10.
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In this week’s podcast episode, The Mixtape with Scott, I am interviewing Paul Oyer. Paul Oyer is a labor economist at Stanford University and author of several books, including "Everything I Needed to Know about Economics I learned from Online Dating", which is one of my favorite "popular general interest books explaining what economics is", as well as "An Economist Goes to the Game" which is about sports and economics. Links below for both. He's a fun, funny and interesting guy whose work in labor economics and personnel economics follows many of my own interests -- how firms hire, what they pay, discrimination, and platforms, just to name a few. I had a lot of fun interviewing Paul and hope you like it too. Thanks for your support, but I welcome even more of it by becoming a subscriber! And of course share this with you friends, family, loved ones, and especially those people you hate. Really rub it in their face with how good your taste in podcasts is.
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In this week's episode of the Mixtape with Scott, I interviewed Mike Luca, the Lee J. Styslinger III Associate Professor of Business Administration at Harvard Business School. Mike studies a variety of topics of high relevance to the underlying economic organization of the online sector, such as the design of online platforms, applied causal inference and data science. More to the point, his research helps organizations consume data or all types and produce insights that help them become more informed and about managerial obstacles and solutions. He is also the coauthor of "The Power of Experiments: Decision-Making in a Data Driven World" with Max Bazerman.
This is part of my longer series on what I’m calling "economists in tech", which includes my interviews with:
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Susan Athey (first chief economist at Microsoft, John Bates Clark award winner, and much more)
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Michael Schwarz (current Microsoft chief economist),
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John List (former chief economists at Uber and Lyft),
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Chris Nosko (VP Head of Science and Analytics at Uber),
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Kyle Kretschman (Head of Economics at Spotify),
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Ronnie Kohavi (computer scientist with a long career in tech spanning decades, early promoter of A/B testing)
This series is, I hope, particularly relevant to those PhD economists and adjacent workers, like data scientists and machine learners, within tech, but also those outside of tech wanting to learn more about the long story of the demand for and supply of PhD economists in tech — which I consider to be the result of a very disruptive, particularly important, technological shift that increased the value of the work that PhD economists do and can do, causing a long march of PhD economists into industry (which is likely to continue growing for a while). Which is itself part of my long term project to collect interviews that when pieced together in the longrun help tell at least a small sliver of the oral history of the last 50 years of economists across many disciplines, many parts of the world, many types of work, many departments, and beneath many influential watershed people and movements that I consider to be particularly interesting (to me).
But in many ways, the oral history element, while meaningful to me, is also a convenient hook for me to listen to and help broadcast the personal stories of real people. Real people matter, and their stories matter, not because of how they connect to some larger thing, but as people in and of themselves. And as I say often at the start of each podcast, I am firmly in the camp of those who believe that we navigate our own historical lives through stories, and for many of us, that includes the personal stories of others. These stories, as I call them, function as models that help us understand ourselves as well as compasses as we try to plot out where we are in our journeys, and inform our decision making under uncertainty as we try to navigate our lives in a way that is consistent with our values and help us find our place in society.
So thanks for tuning in! I hope you find this interview with Mike illuminating.
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Opening music by Wes Cunningham.
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What a pleasure it is this week to introduce my guest on the Mixtape with Scott, Dr. Pedro Sant’Anna. Had you asked me a few years ago the likelihood I’d make such a good new friend this late in life, I would not have guessed it, but from countless conversations on social media, and even more in DM on our Slack channel with two other close friends, Pedro Sant’Anna has become one of my favorite people in life. A constantly upbeat, friendly, energetic man, patient to a fault to explain every single detail of econometrics, and enjoying himself as does so, he is one of the best in the profession. He is as many of you know one of the half dozen important young econometricians that have made major contributions to the difference-in-differences research design. His productivity is intense so I can’t name them all, but the two I know best, almost by heart, are:
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Callaway and Sant’Anna (2021), “Difference-in-differences with multiple time periods” Journal of Econometrics
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Sant’Anna and Zhou (2020), “Doubly-robust difference-in-differences estimators”, Journal of Econometrics
The first one has over 2000 cites and it was only published a little over a year ago. He also has an Econometrica with Jon Roth on issues related to functional form and parallel trends in diff-in-diff and a review article (also with Jon Roth, but also with John Poe and Alyssa Bilinski) for anyone who wants to in one stop learn everything you need to know about diff-in-diff.
In this mixtape episode, though, we learn more than just his papers. Pedro shares his story with me. I hope you like it and I hope as always you come to value both his story, but also the contemporary ongoing series I’m doing on the many stories of economists. Because to quote, Sue Johnson:
“We use stories to make sense of our lives. And we use stories as models to guide us in the future. We shape stories, and then stories shape us.”
Consider subscribing, sharing and possibly even supporting the substack as I continue to try and accumulate enough stories of living economists that we have those stories to help us make sense of our lives, but also an oral history of the profession. Thank you again for your support! Youtube below!
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This week’s episode of the Mixtape with Scott is a little out of order. Season two’s episodes are going to be a little out of order, based on what feels like the best next episode to present at that time. So I decided after doing my interview with University of Arizona professor of economics, Hide Ichimura, that I wanted to release it because I had such a delightful time talking with him. Dr. Ichimura is an econometrician whose work I’ve gotten to know more recently because it’s been experiencing a little bit of a revival (though it’s always remained very popular over the years) within the difference-in-differences literature thanks, in part, to the Sant’anna and Zhao (2020) Journal of Econometrics on robust diff-in-diff, Callaway and Sant’anna (2021) paper on differential timing, and in many ways, other papers that conduct certain kinds of imputations and estimations that are similar in spirit like Borusyak, et al’s (2022) robust efficient imputation estimator, and even Abadie and Imbens (2011) selection bias adjustment method if you squint your eyes.
I had a wonderful experience talking with Dr. Ichimura today. This is sort of part of my broader interest, as I say in the intro, in interviewing econometricians who were active in the 1990s working on topics in causal inference, and to that end, I had in mine two Restuds by Dr. Ichimura with Heckman and Todd (1997) and a 1998 one in Restud also by Heckman and Todd (and the identical title!!), both on program evaluation. But I also just in general wanted to hear his story, and I’m so glad I did and that he would share it.
At the end of the episode, I asked him to share with me a paper that, maybe isn’t his favorite, but that has always stuck in his mind. He shared with me Stephen Nickell’s 1979 article in Econometrica entitled “Estimating the Probability of Leaving Unemployment”.
As always, opening introduction music is by Wes Cunningham (no relation).
And don’t forget to subscribe, share and maybe even support this! This podcast is subsidized by your donations and my workshops!
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In this week’s interview on the Mixtape with Scott, I had the pleasure of interviewing Clair Brown, a labor economist at the University of California - Berkeley. Dr. Brown’s career has spanned several topics like discrimination, industrial economics, and climate. Dr. Brown’s late career has made several turns into environmental economics, particularly climate, but also a re-envisioning of the field of economics with her book Buddhist Economics. Dr. Brown’s work has always focused on issues around welfare that are often massaged out of her models, like meaning, community and fairness in labor markets. I thoroughly enjoyed our time together, and I hope you find it interesting too. Please remember to subscribe and share!
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Time for a new podcast episode! In this week’s episode, I have the pleasure of sitting down with Dr. Beatrice Cherrier, a widely recognized expert in the history of economic thought, and one of my personal favorite economists around. Beatrice is an associate professor of economics at the Center for Research in Economics and Statistics in France. She is a font of wisdom and insight about more parts of the field of economics than is found in most people who specialize in even one of those areas. But she is also a very endearing person and a pleasure to talk to, and I hope you enjoy this interview. Please remember to share and subscribe!
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In this week’s episode of The Mixtape with Scott, I introduce you to the one and only, Dan Hamermesh. Dan is professor emeritus now at the University of Texas where he spent the last part of his career. He’s part of the story of Princeton that I’ve been interested in telling, as he was there from 1969 to 1973 before heading to Michigan State where he was until 1993. He then went down the street from me to Austin at University of Texas until 2014.
Dan did his undergraduate in economics from the University of Chicago where he got to see some of the big labor economists of that era up close, like H. Gregg Lewis, who was also Gary Becker’s adviser. It was really interesting to hear Dan’s impressions of Lewis; for the first time, I wondered if all of the ethos and worldview we associate with Becker (“thinking like an economist” where economics was “everywhere”) might in fact be attributable to Lewis, even though Lewis was in many ways a traditional labor economist, and you could say Becker was a bit more nontraditional since he took economics in an imperialistic fashion into areas like the family and discrimination, among others. Dan provides some interesting perspective.
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Dan has been a premier labor economist his entire life, and is an important part of the story of labor and empirical micro of the last quarter of the 20th century and the first quarter of the 21st century. An unusually productive economist who blended well the price theory of Chicago with contemporary empiricism that we closely associate with labor economics in general. A very funny and energetic man, he is also one of those you tend to say “they’ve forgotten more economics than I’ll ever know”. I thoroughly enjoyed talking with Dan, but I always do. So please welcome MC Hammer himself, Dan Hamermesh.
Opening music credits: Wes Cunningham
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Season two of the Mixtape with Scott is up and boy do I have a dynamite first guest. None other than the man himself, Dr. Jeffrey Wooldridge! Jeff, as I say in the opening, is the author of two phenomenally popular books (here, here and here’s the solutions) in econometrics that has raised an entire generation of economists. We have a great conversation about his life and career and I hope you enjoy it!
Expect new episodes every Tuesday morning. Thanks to my good friend, Wes Cunningham (no relation), for the amazing opener music he made for me — it’s perfect. And thanks to Arslan Yaqoob who set the music to the awesome montage of last season’s guests and has been producing them for me this entire time. Check out the YouTube video below if you enjoy watching more than listening.
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My guest this week on the podcast is Phillip Levine, the Katharine Coman and A. Barton Hepburn Professor of Economics at Wellesley College in Massachusetts. I’ve only personally met Phil once — at a conference on the family many years ago and just briefly. But I have been a huge admirer of him for many reasons for a long time, ever since graduate school, and I wanted to interview him for a lot of reasons. First, he attended Princeton in the 1980s at that heady time when Orley, Card, Krueger, Angrist and so many others were there. The birth place of the credibility revolution is arguably the Princeton’s Industrial Relations Section where a shift in empirical labor took place that eventually ran through the entire profession and placed it on a new equilibrium. Phil was there, colleagues and students with those people, and himself part of that “first generation” of labor economists who thought that way and did work that way and I wanted to hear about his life and how it passed through, like a river bending and turning, the Firestone library and beyond.
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But I also have a special interest in Phil. I actually first learned difference-in-differences from a book that Phil wrote on abortion policy entitled Sex and Consequences (Princeton University Press). I graduated from the University of Georgia in 2007, but the job market had started in 2006, and around the spring when I had accepted my job at Baylor, I was finishing my dissertation. I had one chapter left and it was going to be an extension of Donohue and Levitt’s abortion-crime hypothesis to the study of gonorrhea. My reasoning was that if abortion legalization had so dramatically changed a cohort by selecting on individuals who would have grown up to commit crimes, then it should show up in other areas too. My argument was relatively straightforward and I’ll just quote it here from the article I later published with Chris Cornwell in the 2012 American Law and Economics Review.
“The characteristics of the marginal (unborn) child could explain risky sexual behavior that leads to disease transmission. For example, Gruber et al. (1999) show that the child who would have been born had abortion remained outlawed was 60% more likely to live in a single-parent household. Being raised by a single parent is a strong predictor of earlier sexual activity and unprotected sex, evidenced by the higher rates of teenage pregnancy among the poor.”
It’s funny the order in which things go. I think I somewhat understood what I was doing because I already had planned to do my study before reading Phil’s book. I was going to use the early repeal of abortion in 1969/1970 in five states (California and New York being two of them) followed by the 1973 Roe v. Wade as this staggered natural experiment to see whether abortion legalization led to a drop in gonorrhea a generation later. I had adapted a graph I’d seen by Bill Evans to illustrate how the staggering of the roll out would lead a visual “wave” of declines in gonorrhea in the repeal stages among an emerging cohort that would last briefly until the Roe cohort entered. Visually, I believed you should see a drop in gonorrhea for 15yo starting in 1986 that would get deeper until 1988, flatten, and then disappear completely by 1992.
The design for this idea came from a paper I just linked to above — by Phil Levine. It was entitled “Abortion Legalization and Child Living Circumstances: Who is the “Marginal Child”?” coauthored with Doug Staiger and Jon Gruber, published in the 1999 QJE. It came out two years before Donohue and Levitt’s 2001 QJE on abortion and crime and arguably really set the stage for that paper. The two papers are very different — Phil, Staiger and Gruber are looking at who was aborted using instrumental variables with the five “repeal states” as the instrument. The abstract is worth reading:
“Cohorts born after legalized abortion experienced a significant reduction in a number of adverse outcomes. We find that the marginal child would have been 40–60 percent more likely to live in a single-parent family, to live in poverty, to receive welfare, and to die as an infant.”
They used, in other words, instrumental variables whereas Donohue and Levitt used a lagged abortion ratio measure, if I recall correctly. Phil’s paper really struck me as the more credible design at that time because the staggering of legalization gave such precise predictions — something about the timing, something about the location. It just really haunted me for a long time.
Well, while I was preparing for that project, reading the literature on the economics of abortion, continuing my ongoing interest in the economics of sexual behavior, Phil has a chapter where he sets up for the reader a table explaining something called “difference-in-differences”. While econometrics was my field, I couldn’t recall hearing what that was, because it wasn’t really best I could tell an estimator. Rather it was what we now call a research design. I don’t have the book here at the house, but the table made a huge impression on me because if you just walk through the before and after differencing, even without potential outcomes, you can see with your own eyes exactly why difference-in-differences identifies a causal effect. I have a version of the table in my book, which I’ll produce below.
Once I saw that, it was easy to understand triple differences — a design that many people find very confusing if they only think of it in terms of regression equations. Almost immediately after I understood Phil’s DiD table, I adapted it to my repeal versus Roe context and imagined “Well, what if there were other things happening in these repeal states later? Is there an untreated group I could imagine was affected by those unseen things but which wasn’t treated?” And I thought “Let me use a slightly older group of individuals in the same states as the within-state controls”. That approach — the triple difference — can be seen below in a table I mocked up for a lecture in which I teach triple difference using Guber’s 1994 paper that introduced the design for the first time.
And so I wrote the chapter, and of all my chapters, it was the only one I ever published.
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Where am I going with this? I guess what I’m saying is that as luck would have it, I made a monumental jump in my understanding of this “way of thinking” about doing empirical work from a single table in a short little book on abortion policy by Phil Levine. That one table so completely captivated my mind that ever since I have only wanted to learn more about causal inference in fact. As odd as it may sound, something about difference-in-differences really unlocked for me what the whole empirical enterprise was about. As Imbens said, there is something about potential outcomes that just makes crystal clear what we mean by causality, and many of the research designs that have over time been fully mapped onto potential outcomes — difference-in-differences being one — extend that clarity for a lot of us. Phil’s work has consistently been part of the broader education of labor economists about what the Princeton tradition left us — make clear where the variation in the data is coming from, make clear who is and is not functioning as the counterfactual, “clean identification”, carefully collected data, on questions that matter.
Phil has had a very interesting life; I caught only a peek of it from this interview. He opened up and shared about being a young man growing up middle class where family experiences during difficult economic times appeared to cause inside him an interest in labor. He gravitated towards law but a chance research class in college placed him on a new trajectory. His professors encouraged him to go to Princeton because, to put it bluntly, that was in their opinion where the best labor economics was at the moment. So he did. He alluded to graduate school being very hard — something many of us can identify with — but he survived, graduated, and took a job at Wellesley College where he’s been ever since. We discussed his interest in topics in labor economics, his emerging interest in abortion policy, his coauthorships with several people he calls close friends, and his favorite project of all time — a 2019 AEJ: Applied study with Melissa Kearney, a longtime collaborator, on the effect of Sesame Street on educational outcomes, finding strong effects for boys. We also discussed the nonprofit he founded called MyInTuition which is an online calculator that shows the projected cost of college once financial aid is factored in. This topic around the opaque pricing of higher education is something Phil cares deeply about and has a new book on the topic too.
All in all, Phil is an exemplary labor economist and someone I admire greatly. Not just for his careful empirical style and approach, but also because as you can see throughout his life a deep care for people. I have a deep admiration for the labor economists. Most of us are after all workers. We buy the things we need to survive using money we earned from work. Throughout human history, we have lived at the break even condition of survival, many of us not having enough calories to even make it through the day. The researchers who study work, be it economists or not, are studying poverty, one of the most dangerous plagues that has ever been around, far more dangerous than Covid or the plague. In Phil I see someone whose entire life has been about trying to better understand the causes of the wealth of nations, to quote Adam Smith, be it his early work on unemployment insurance, or his later work on children’s television shows. It was a pleasure to talk to him and I hope you enjoy this interview as much as me. Forgive me for this rambling essay.
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Chris Nosko is a PhD economist. He did his PhD in economics at Harvard in the mid 2010s before going to Chicago Booth take a job as an assistant professor. But for a year prior to taking that job, between Harvard and Chicago, he did a postdoc fellowship at eBay where he, Thomas Blake and Steve Tadelis met and worked together on a project involved a serendipitous event at the company in which eBay quit paying for branded key words (e.g., “eBay Volvo decals”, “eBay typewriters”) on some but not all search engine auctions. They asked for the data on traffic to the site before and after eBay quit paying for branded keywords for all search engines (both those they kept paying and those they didn’t), ran a simple event study diff-in-diff and found evidence that search engine marketing at eBay was perhaps not causing increased traffic to the site. They convinced management to field a large RCT which confirmed their diff-in-diff results, and that study was published in Econometrica. Not a shabby way to start a career as an economist.
For many of us, a PhD in economics from Harvard, a successful partnership with eBay resulting in a study destined for a Top 5 and a tenure track job at Chicago Booth meant staying at Booth and having a career as an academic. No one outrightly says that the only meaningful life you can have as an economist is to be an academic, as it’s vulgar, opinionated and obviously false to talk that way about how someone else should live their life, but the norms are pretty powerful nonetheless. Well, starting around the time that Chris got his job at Booth, tech began experiencing a surge in hiring of PhD economists, largely driven by Amazon’s nearly insatiable appetite for them. Talking with people at Amazon, I have learned that behind this push was Pat Bajari, and behind Pat Bajari was Jeff Bezos who had long believed economics, and economists more specifically, had unique value. As Susan Athey said to me, though, in an interview earlier, Bajari though had to do pull a rabbit out of a hat. Whereas the first wave of economists to tech — people like Hal Varian, Susan Athey, Preston McAfee — had largely been micro theorists helping craft the foundations of a business model through auctions and advertising that would support search engines, arguably the core arteries of the internet itself — Bajari would have the task of bringing in young people, fresh out of grad school, and in Athey’s words, make them productive. And one of the people Bajari would ultimately tap do that was Chris Nosko, an assistant professor at Chicago Booth and someone trained in structural industrial organization, one of the economics’ more interesting experiments of fusing deep microeconomic theory with econometric estimation.
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Nosko was a Ariel Pakes student at Harvard and was well versed in so many different parts of economics and modern technology that it almost seems predestined that he would ultimately leave Chicago Booth permanently and go to Amazon when Bajari finally convinced him to, but that’s all selection on the dependent variable reasoning. When we look back in time at decisions we made, our mind tends to forget that there was a moment when we could’ve gone left instead of right. The same with Chris — there was a decision that had to be made to leave a career as an academic. The decision materialized into what it materialized, but to pretend it was easy, or that it didn’t have risk, or that Chris didn’t try to manage that risk in some ways is really unfair to our earlier selves or even our future selves who are in situations facing, not probabilistic risk but more like Knightian uncertainty in which no one truly has a clue what possibly could happen.
But Chris did leave. Sort of. He took “a leave of absence” from Booth in 2015 and took a job at Amazon, then permanently left Booth in 2016. He spent four years at Amazon before leaving for Uber, one of the more impressive firms to ever exist for creating an actual open marketplace solving two sided matching problems through algorithms and prices. Algorithms, prices and rules — three ways, no doubt there are others, in which modern economies coordinate productive activity. Is it really so surprising that economics might be valued by tech firms given the complex coordination they try to solve using all three?
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Chris has been at Uber for four years. He is now Vice President and Head of Science and Analytics for Uber Product there. Within tech, economists sort into tons of different jobs with titles that to an academic don’t make a ton of sense — just like so much of what academics’ lives takes place within administrative units that make little sense to anyone else. If Chris isn’t the chief economist, though, at Uber, I figure he’s probably up there. And he’s my guest this week on The Mixtape with Scott as part of my longer, unfolding series I call “Economists in tech”. Our conversation covered a lot of ground. We talked about growing up in rural Oregon, falling into programming early on and working a few years between high school and college during the early wave tech boom of the late 1990s and early 2000s as a programmer. It wasn’t exactly what he would do later, as that was more web design and less machine learning and statistics, but the aptitude of programming is very portable and his deep knowledge of tech sectors was anyway established or at least re-invested in while there. We talked about his love for his liberal arts education at the University of Chicago where he did his undergraduate degree, and his broad navigation of economics as a field and a career.
All in all, it was a fun opportunity to talk to Chris, to learn more about his own path, about the world out there outside of academia, what economists do in tech, and how all of these things fit together for both economics but maybe more importantly just for Chris himself. I think a lot of people are going to find Chris’s story very interesting and personally intriguing as they may see him in themselves. You can read some of Chris’s work here. Thanks again for tuning in! I hope you enjoy this week’s interview as much as I did! If you are enjoying these, please consider supporting me by sharing the podcast and/or becoming a paying subscriber!
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THE EARLIER PODCAST WAS MISSING TEN MINUTES SO I HAD TO REPOST
Brigham Frandsen is a professor at BYU’s economics program. He did his undergrad at BYU double majoring in physics and economics where he coauthored two articles — one in physics on lasers, one on the distribution of income with his professor, the famed James McDonald. In this interview, we discuss a lot of things about his life, BYU’s own production function at producing future economists through careful and intensive mentoring of undergraduates, his time at MIT where he worked with Josh Angrist, and his own research as a labor economist and applied econometrician.
I found this to be a really enjoyable talk as I learned more about topics I really wasn’t expecting to learn about. I think one of the themes I see emerging in econometrics over the last few decades that is now becoming a little more salient to me as time passes is the issue of heterogenous treatment effects. Heterogenous treatment effects for instance is at the core of the local average treatment effect literature that Angrist and Imbens were involved in (as well as others at the time). You see it too in the problems with twoway fixed effects and difference-in-differences with staggered adoption. And it’s in Brigham’s work too — from his earliest paper with James McDonald on income distribution, to his newer work with Lars Lefgren on bounds. I think when the story is written, we will see that this heterogeneity and selection have been focal points for econometricians and applied researchers and Brigham will be one of many people I think who helped pushed that forward.
If you want to learn more from Brigham, you can though — he’s teaching a workshop on machine learning and causal inference at Mixtape Sessions Oct 27-28. You’ll get to pick up some python probably while you’re at it — a twofer!
Thanks for tuning in for the podcast. Apologies for the double posting on this — apparently my software refused to convert the MP4 video to more than 45 minutes no matter what I did. But I have a new workflow and it won’t happen again.
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Background stuff about causal inference
Josh Angrist once quipped (on my podcast!) that a paper he wishes he had written was written by his classmate, Bob LaLonde. It was LaLonde’s job market paper, later published in the AER, that arguably helped bring to broader attention some of the empirical problems around causal inference within applied labor at the time. It was very ingenious too. LaLonde took a job trainings program conducted as an RCT, showed that the causal effect of the program was around $800-900, then dropped the experimental control group. He then pulled in six datasets from nationally representative surveys of Americans (3 from Current Population Survey, 3 from Panel Survey Income Dynamics). He reran the analysis with and without covariates adjustment. Not surprising to modern readers, the estimates were severely biased. Not only were the magnitudes off, most of the time the results showed a negative result. This is noteworthy mainly because of the RCT because the RCT established the ground truth of the trainings program — the truth was the program caused an average return of $800-900. So if using typical methods couldn’t even get close to that — well, that’s a problem. And they didn’t, and one more spark of many sparks that lit the fuse that became the credibility revolution occurred.
LaLonde’s paper was published in 1986. Angrist would graduate in 1989 and take a job at Harvard where he’d meet Guido Imbens. During the time together at Harvard in the 1990s, Imbens and Angrist would meet with Don Rubin, the head of the stats department, and between the three of them, several breakthrough contributions to instrumental variables were born.
Rajeev Dehejia Revisits Lalonde
In the midst of this time were Angrist, Imbens and Rubin were all at Harvard, there was a young graduate student in the economics department named Rajeev Dehejia. Rubin and Imbens one semester co taught an innovative new class on causal inference and Rajeev was one of the students who took it that year. Together with his classmate, Sadek Wahba, the two students decided after the class concluded to not so much replicate Lalonde, but rather extend the analysis using the more up-to-date methods learned from Imbens and Rubin. They chose the propensity score and published two papers reevaluating the Lalonde data — one in 1999 JASA and one in 2002 Restat. The propensity score analysis ultimately did much better than what Lalonde’s analysis had done. A lot of gains were made simply from recognizing the serious common support violations rampant in all six of those datasets. One value of the propensity score is, after all, the dimension reduction you get from taking for instance 10 variables and collapsing into one scalar (the propensity score). Once they did, they saw how bad the negative selection was. A huge number of people on the non experimental controls had propensity scores with so many zeroes after the decimal it was like the data was saying “these people in the CPS wouldn’t appear in that treatment group in a million years!”
That’s how I knew of Dehejia for years — the author of two papers showing that propensity score analysis might have promise for program evaluation with deep negative selection baked into the data. I saw him as one of the earliest researchers in the broader credibility revolution trained by that next wave of people connected to Princeton Industrial Relations Section like Angrist as well as Imbens and Rubin who began reshaping our applied practices in paper after paper. So it is a great pleasure to introduce him to you this week in my podcast The Mixtape with Scott.
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Shoshana Grossbard, Economist, Professor, Editor, Becker’s Student
I recently volunteered to teach a new class on the history of economic thought and it has been profoundly rewarding for me. I love economics but I also love the stories of its players, my tribe, the economists. This person critiquing that person, this idea twisting around that other persons idea. The idea I might get paid to study people like Adam Smith and Thomas Malthus? Pinch me — I must be dreaming.
What’s been interesting to me is how much I recognize. Even though I have never read Malthus theory of gluts before, it’s somehow eerily familiar. But it’s more than just seeing the traces of later theories in these early writers. Reading history is also helping reframe the broad story of economics itself — the story of capitalism, its tensions, the conflicts between people, the appropriation and division of surplus, and the organization accomplished through markets and exchange. Reading history helps makes sense of me and others when I see the full sweep of the times and the debates.
But some people are remembered, some people aren’t. Some ideas were dropped and some hung around. Sometimes they were omitted because the theories while useful and intriguing explanations then were not as useful as another explanation that would replace it. That process of what we remember and what we forget happens at all levels, big and small, and it too is part of the story of economics in a way.
This idea that now long gone economists were once in their offices working earnestly on ideas we have collectively chosen to ignore and forget is not itself tragic, though. Nor is what was selected to persist glorious. Both simply are. Most of the things I have chosen or done in fact no one will ever know. Most of the words I have said, no one was present to hear. History has forgotten more than it has remembered.
Nevertheless I want to know. I want to learn all the stories, all the people, all the ideas, all the ways that ideas are forged and changed and kept and passed around. I know that it sounds a bit dramatic to say “My people”. What a strange way to self identify. And yet it is genuine. I am an economist. It will say in my tombstone that I was an economist even. I am more an economist than I am almost any other thing. So perhaps that’s why I care about the stories of the economists — the people and the ideas. I care about the people too. I care especially about the stories that for one reason or another would simply never be told were we not to ask and listen with open mind and accepting hearts, the hallmark of curiosity.
I see my podcast as a way to collect the stories of people whose stories don’t show up in the footnotes of our textbooks. I do it around topics I care about like causal inference, economists in tech, and public policy. And one of the series I have been doing I call “Becker’s students”. I chose Gary Becker because it was Becker’s Nobel prize speech more than any other intellectual experience I had that prompted me to get a PhD in economics. He has cast a massive shadow over me. And my series so far has included interviews with two of his former students from when he was a professor at Columbia University: Robert Michael and Michael Grossman.
But this week I am talking with one of his students from the University of Chicago where he spent the majority of his career. My guest this week is Shoshana Grossbard, professor of economics at San Diego State University, and editor of Review of Economics of the Household.
This interview was one of the best interview experiences I have had yet. Shoshana was honest, warm, and most of all very candid about her career, about the history of household economics, the things that had major impacts on her, but also the discouragements in her career. That she would share both the highs and lows as well as her thoughts about economics as a science and its practitioners so transparently with me of all people was deeply humbling for me.
You will learn that Becker was an important figure for her, not surprisingly as he was her advisor, but like many important people to us who we’ve known for years, the relationship was also a complicated one. His influence cast a long shadow over who she chose to become, and I appreciated that she was so forthright. I hope you like this interview too. Please tell others about it! Don’t forget to subscribe and if you like it, consider supporting it!
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From the earliest days of economics as a formal science, economists have been trying to understand the causes of the wealth of nations. In the field of development economics, the introduction of the randomized controlled trial has become an important tool in the broader toolbox of economists for trying to understand the many things that may cause the improvement of human welfare in lower income countries, too. This work has been ground breaking and recognized by the Nobel committee for its lasting importance. Like many others, I have been inspired by the development economists relentless effort to address poverty through rigorous causal inference and program evaluation.
While most people know that randomization is an important ingredient in causal inference, what is not as widely known is the Stable Unit Treatment Value Assumption, or SUTVA for short. We know that randomized treatments can eliminate selection bias and thus allow us to use realized outcomes in place of potential outcome. SUTVA requires that the potential outcomes themselves be stable and unchanging when treatment assignments of other units change, which brings to mind complex problems with externalities between people and interference when designing experiments, but as Imbens and Rubin note in their 2015 book, SUTVA also requires that treatments not vary unknowingly across units. Such “hidden variation in treatment” can make causal interpretation difficult if not possible.
But SUTVA also brings to mind the problems of external validity. When someone reading of a study’s large gains in a field experiment, they might then decide to roll out such a program at large scale. Assume for simplicity constant treatment effects for a moment — can they expect the same thing to happen in their community what happened in this particular trial? They can insofar as they do not inadvertently change the treatment itself by unknowingly varying the inputs in important ways. If the RCT found large literacy gains using a particular type worker, but to bring it to scale, the policymaker foregoes using those same inputs, then it becomes a new empirical question as to whether the effect found in the lab will in fact scale, even with constant treatment effects. But we have known this for centuries. Concepts like production functions and cost functions directly speak to these very things.
Successful policy requires evidence but also a set of skills that maybe aren’t there when designing or evaluating an RCT. It requires both cognitive skill, and perhaps even moreso non cognitive skill, as often the economist then must wear both a scientist hat, a manager hat, and an entrepreneur hat. And not every economist has those skills, or maybe even is interested in venturing into the messy world of building socially impactful policy. But I have also been inspired by a small group of applied development economists like Noam Angrist, Co-founder and Executive Director of Youth Impact (formerly Young 1ove), and Paul Niehaus at GiveDirectly, who create organizations that try to bring effective programs to larger scale while simultaneously committing themselves to constant evaluation of themselves and their programs. Whether it’s a trend or not, I don’t know, but I have been intrigued.
This week on The Mixtape with Scott, I have the pleasure of interviewing one of these economist entrepreneurs, Noam Angrist. Noam is, as I said, the co-founder and executive director of Youth Impact, a non-profit focused on improving the welfare of young people around the world through education and health programs. I met him for the first time several years ago because I noticed what he was doing and had done and wanted to introduce him to my students. So I wrote and asked him if he would be willing to be the de facto keynote speaker at a conference I was helping organize on causal inference. He graciously agreed and spoke with us about the organization he had helped found and the work they were doing in developing countries scaling rigorously evaluated interventions to reach thousands of youth around the world. I was very intrigued because of the blend of causal inference and economics with such creative entrepreneurial work.
Given the explosive success of the credibility revolution at changing hearts and minds, I suppose it was only a matter of time before economist-entrepreneurs trained in that way of thinking would begin moving outside of academic departments and into other parts of the world. We have seen it with economists in tech. Noam Angrist is an example of the contemporary economist-entrepreneur who works closely with governments, academia and even commerce to bring the best of all things to help achieve the age-old quest of economics of improving the well being of humans alive today.
In this interview, Noam and I talked about growing up in Massachusetts and the looking back serendipitous injury in high school that put him on a path to studying economics at MIT. He’s since embarked on an original career as an economist where his competency as a leader, his creativity and curiosity as a scientist and his innate commitment to public service and community is shaping the type of work and the way that work is done at Youth Impact. I hope you enjoy this interview as much as I did. Please follow, subscribe and consider supporting The Mixtape with Scott!
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Who is Leah Boustan?
Leah Boustan is a professor of economics at Princeton University and this week’s guest on The Mixtape with Scott. Her research has to date largely focused on two of the largest demographic events in US history: the Great Migration of African-Americans from the rural South to industrial cities in the North and West in the mid-twentieth century, and a period of mass migration from Europe to the US from 1850-1920. She is author of two books related to both topics: Competition in the Promised Land (Princeton University Press, 2017) and Streets of Gold: America’s Untold Story of Immigrant Success (PublicAffairs, 2022) with Ran Abramitzy.
Leah’s work with Ran on immigration to the US takes advantage of large digitized records from the Census which they linked together so that they could follow individuals over decades. This allowed them to trace out the fortunes of migrants across multiple waves of the Census to ask and attempt to answer several fundamental questions like:
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Did immigrants of the past pull themselves up “by their bootstraps” as the stories are often told to us and remembered?
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Did the children of immigrants move up America’s economic ladder as fast as their “peers” — children, in other words, of established residents?
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Does assimilation today by immigrants happen at a similar or different speed as those in the past?
The conversation was enriching for me, as all of my interviews with Leah are. In Leah you see, also a unique story of entrance into economics — through high school debate, not mathematics, where she grew to love studying the nuances of public policy from an objective yet passionate research-oriented point of view. The roads we take through our lives look like a straight line in hindsight but as we’ve seen with other guests are anything but at the time. Leah became an economist the way she became an economist, but I think it is a story nonetheless that many can identify with.
And an article in economics that she thinks about a lot? Goldin and Katz 2002 JPE, “The Power of the Pill: Oral Contraceptives and Women’s Career and Marriage Decisions”.
Get full access to Scott's Substack at causalinf.substack.com/subscribe
In this week’s episode of The Mixtape with Scott, I had the pleasure of interviewing Kyle Kretschman, Head of Economics at Spotify. It was a great opportunity for me because Kyle is one of the first economists I have spoken to who didn’t enter tech as a senior economist (e.g., John List, Susan Athey, Michael Schwarz, Steve Tadelis). Kyle entered tech straight out of graduate school. He spent much of his career at Amazon, a firm that has more PhD economists than can be easily counted. Under Pat Bajari’s leadership there, Kyle grew and his success was noticed such that he was then hired away by Spotify to lead up their economics team. At the end of the interview, I asked Kyle an economics article that has haunted his memories and he said “BLP”, which is affectionate shorthand that “Automobile Prices in Market Equilibrium” by Berry, Levinsohn and Pakes 1995 Econometrica goes by. I really enjoyed this interview, and despite the less than ideal sound quality at times, I hope you will too.
But before I conclude, I wanted to share some more of my thoughts. This series I’ve been doing on “economists in tech”, which has included interviews with John List, Susan Athey, Michael Schwarz and Steve Tadelis, comes from a complex place inside me. First there is the sheer curiosity I have about it as a part of the labor market for PhD economists. As I have said before on here, the tech sector has exploded in the last decade and the demand for PhD economists has grown steadily year over year. Tech demand selects on PhD economists with promising academic style research inclinations. There is substantial positive selection in this market as firms seek out strong candidates can be produce value for them. This is reflected in both junior market salaries, but also senior. Job market candidates are economists with technical skills in econometrics and economic theory, not to mention possess competent computer programming skills in at least one but often several popular coding languages. They are also candidates who were often entertaining careers within academia at the time they entered tech, and in those academic careers, they envisioned themselves writing academic articles about research they found personally and scientifically important and meaningful. Going into tech, therefore, would at least seem to involve choice that may go far beyond merely that of taking one job over another. It may involve a choice between a career in academia and a career outside it, which for many of us can feel permanent, as though we are leaving academia. And for many economists, it may be the first time they have ever contemplated such a thing. If they do internalize the story that way, if they do see taking a job in tech as “leaving academia”, then I can imagine that for at least some economists, that may be complicated, at least.
But there’s another reason I have been wanting to talk to economists in tech and that is I am very concerned about the welfare of our PhD students. In a recent article published in the Journal of Economic Literature, economists interviewed graduate students in top economics programs. They found there incredibly high rates of depression, anxiety, loneliness and even suicidality. This is a common feature of graduate studies, but it is interesting that PhD economists have incredibly good employment opportunities and yet the depression and anxiety plague there too. One of the things that struck me in that study was the disconnect between what graduate students felt about their work and what their advisors felt about their own work. Many students, for instance, do not feel they are properly supported by advisers, do not believe their advisers care about their research success and do not even care about them as a person. Whereas most Americans (and faculty) feel that their work has a positive impact on society, only 20% of PhD students in economics feel that way. (I discussed the article as well as my own research on the mental health of PhD students here.)
I suppose part of me feels a great sigh of relief to see the labor market for PhD economists expanding in light of those troubling statistics. If students know that life is full of infinite possibilities, then perhaps they can begin to process earlier what they want to do in the short years they have on this small spinning ball of rock we call Earth. If students do not in the end want to become professors, if they do not have the opportunities to become one, they should know that there is no “failure” involved there. Careers are just that — careers. They do not tell us who we are. The sooner a student can detach from the unhelpful story that our value is linked to a vita listing our accomplishments, the sooner they can begin their own life work of choosing their meaning. Can having more labor market opportunities with more employers competing for them help do that? Well no, not really. At least, not exactly. It can disrupt certain equilibrium, but then the new equilibrium can just as easily cover that up too. Still, I do like the idea that to keep students in academia, universities and departments must fight harder for them, pay attention to them, and invest in them as people. I like the idea that students have more options and that the options are diverse. Will it help their depression? Well, that’s another matter, as that’s complex. And presumably the economists in the survey I mentioned were themselves well aware of the career options they had since they were coming from the nation’s top 10 PhD programs in economics.
I suppose my point is that ultimately, the burden of life really cannot be resolved with money or career. We are trained to look there because we have boundless appetites. But ultimately the hard work of navigating life can only be helped so much by a job. We must still decide for ourselves what meaning we will choose for ourselves. But one thing I know, and one thing which I think our profession is profoundly bad at saying out loud, is that if we make our identity connected to vitas, we will not just be miserable, we will be hopeless, and probably poisoned. Such a mindset leads to endless laps on a brutalizing treadmill of meaningless performance in which a person chases for first place in a race they don’t remember signing up for and which they cannot win. They compare themselves with others running, not knowing that they too are brutalized by their own treadmill, not realizing that it is impossible to catch up with someone else as there is always someone else ahead of us. The sooner we learn that the joy we long for will not come when we get a top 5, the sooner we can look elsewhere. It has taken me many years to relearn a lesson I learned decades ago — I am whole now. I am complete now. I still run, and I still chase, but I am not chasing completeness. I am not chasing my own wholeness. Being whole and complete has nothing to do with a career. Careers are ultimately orthogonal to hope, which does not mean they do not matter — they absolutely matter. But if asked to deliver meaning, we will find that our jobs are as weak as wet spaghetti at such a task as that.
So, I suppose in some ways I simply want to announce — there are incredible opportunities for economists inside government, commerce and academia. But the weight of this life is not likely to be lighter in any one of them, for the weight we feel in life is largely self imposed, inside us, in the stories we tell about who we are and for many of us who we are not. Those stories are real, because we feel them and because we believe them, but they are not true. All stories are wrong, but some are useful, and the story that our lives can only matter if we have certain types of jobs or certain types of success, while it may be useful to getting a paper out or accomplishing something important, in a much bigger sense it is hollow at best and pure poison at worst.
TRANSCRIPT
This transcript will be updated once the more complete transcript is finished; for now it was transcribed using voice-to-text machine learning.
Kyle Kretschman:
Might not have prepared myself well enough to be attractive for some of the most pop most top tier schools.
Scott Cunningham:
In this week's episode of the mix tape with Scott, I had the pleasure of interviewing Kyle kretchma the head of economics at the streaming platform. Spotify. Before I dive into the interview, though, I wanted to give you a bit of a heads up about the sound quality. Unfortunately, the sound quality in the interview on Kaza side is a bit muffled. We discussed refilming. It tried to find a way to tweak it, but there were certain constraints on the actual sound itself that kept us from being able to do it. And we didn't feel that refilming, it would be good because we thought that the interview had a lot of serendipitous kind of spontaneous tangents and things spoken about that. We thought students and people in academia would want to know, would need maybe even need to know. And I doubted that I could recreate it, cuz I don't even know why it happened.
Scott Cunningham:
So I'm gonna post a video version of this at my subs, for those who feel that a video version would help them kind of follow it in so far as the audio might be at times challenging. So check out the subst for those of you that wanna watch, watch it instead of just listen to it, hopefully that'll help. I won't say much here by way of introduction, except to say a few things about Kyle, because I wanted to let Kyle tell you his story in his own words, cuz it's his story to tell. And it's an interesting story. Kyle's a PhD economist though from the university of Texas Austin, which is down the road from where I live and work at Baylor, where he wrote on topics in graduate school and applied econometrics, empirical industrial organization or empirical IO and public choice after graduating, Kyle went to Amazon, not academia.
Scott Cunningham:
In fact, given we might start the boom of tech hiring PhD economists in the early to mid 20 2010s. You could say Kyle maybe was sort of one of the earlier hires among that second wave of PhD economists that went there. He worked for several years at Amazon before being hired away by Spotify to head up and lead a new economics team there, perhaps this is part of a broader trend of tech firms building up more internal teams, not just of data scientists, but like Amazon departments of economists who knows recall though from an earlier interview with Susan athe where, when I asked Susan why she said pat Maja had done something amazing at Amazon, she said he made economists productive. And in time he made many of them productive and very in productive from what I've been able to follow. And Kyle is from what I can gather someone whose skills matured and deepened under the leadership of Papa jar at Amazon and other leaders at and other economists at Amazon.
Scott Cunningham:
And he was ultimately hunted down by a major tech term to create an economics team there I'm by no means an expert on the labor market for PhD economists. I just have been very intrigued and curious by the, the, the Mar the labor market for PhD economists in tech, because well, partly because of realizing first that cause of inference was really valued in tech, but then to sort of realize that there was just this very large community of economists there, but I don't think it's controversial to say over the last 10 to 15 years, the tech industry really has been disruptive in the labor market for PhD economists. They continue to hire at the junior and senior market in larger and larger volume selecting more and more on people who likely would've gone into academia into tenure track or tenured positions. They pay very high wages, some of the very, some of the highest wages in the country, both at the junior level and especially at the, at the higher end at the, at the more advanced levels, people can earn compensation packages by the, in the, by the time they're in their thirties, that many of us didn't know were possible.
Scott Cunningham:
It's in my mind, historically novel, and I might be wrong about this, but it, it seems historically novel that the PhD economists who likely would've produced academic research papers in tenured and tenure track jobs have begun to branch out of academia, but maintain those skills and maintain that research output. It's partly driven best. I can tell, buy Amazon, I might be wrong, but by Amazon and paja, as well as Jeff Bezos own view, that economists are what I guess we would just say value added for many firms. Therefore I'm continuing to wanna speak with economists in tech to help better trace out the story. This interview with Kyle follows on the back of earlier interviews with people in tech like John list, you know, a, a distinguished professor of economics at the university of Chicago, but also the former chief economist that Lyft and Uber now Walmart Michael Schwartz, former professor of economics at Harvard. Now, chief economist at Microsoft and Susan athe former chief economist at Microsoft professor at Stanford and now chief economist at the DOJ. I hope you find this to be an interesting dive into the industry. Learn a little bit more about economists there, but by, by learning the about one particular important economist, there a, a young man named Kyle crutch, head of economics at Spotify, my name's Scott Cunningham. And this is the mix tape with Scott.
Scott Cunningham:
Well, it's my pleasure today to have, as my guest on the mix tape with Scott, Kyle crutch, Kyle, thanks so much for being on the call.
Kyle Kretschman:
Hey Scott, thanks for having me really appreciate the time to talk
Scott Cunningham:
Well before we get started with your career and, and everything. I was wondering if you could just tell us your name and your title and where you work.
Kyle Kretschman:
Sure. Yeah. As you said, I'm Kyle kretchma, I'm the head of economics at Spotify,
Scott Cunningham:
Head of economics at Spotify. Awesome. Okay. I can't wait to talk. So let me, let me, let's get started. I was wondering if you could just tell me where you grew up.
Kyle Kretschman:
Sure. So most of the time I grew up in outside of Pittsburgh, Pennsylvania, about an hour north of the city, real real small town probably had one stop light. And maybe the, the funny story that I can share is what I took my wife there. She asked where's the Starbucks. And I said, no Starbucks here. There's no
Scott Cunningham:
Starbucks.
Kyle Kretschman:
Yeah. So pretty small town called Chippewa township in Pennsylvania.
Scott Cunningham:
Oh, okay. Is that near like Amish stuff or anything like that?
Kyle Kretschman:
No, that's the other side of the state. So this would be Western Pennsylvania about near the end of the turnpike, about five minutes from the Ohio border.
Scott Cunningham:
Oh, okay. Okay. You said, but you, did you mention, you kind of grew up in different places?
Kyle Kretschman:
Yeah. So before that, my father worked in civil engineering and so would do build roads and bridges basically across every, across the nation. So I was actually born in Louisiana, lived there with, I think for a whole two, three weeks. I don't quite remember. Cause I was pretty young obviously, but then Michigan and then spent some time in Philadelphia before moving out to Pittsburgh around second grade.
Scott Cunningham:
Oh, that's kinda like, that's like when people described their parents being in the military, just kind of moving around a lot.
Kyle Kretschman:
Yeah. A little bit. So, but
Scott Cunningham:
Then you settled in the second grade
Kyle Kretschman:
That's right. Yeah. So outside of Pittsburgh and then stayed in Pittsburgh through high school and even through undergrad.
Scott Cunningham:
Oh, okay. Oh, you went to undergrad in Pennsylvania.
Kyle Kretschman:
Yeah, I did. So I went to undergrad at the university of Pittsburgh. Oh, okay. It was, yeah. If, I guess maybe continuing the story growing up in a town with no Starbucks. I was, I was pretty intrigued by going to a city. Yeah. And find out that lifestyle and yeah, we might have lived pretty close, like an hour away, but we didn't go down to the city very much. So Pittsburgh was just really, really enticing for a city to, for, to go to undergrad in. And so I basically looked at all schools that were in cities and so the proximity plus then the, the ability to just spread my wings and explore what it's like to be in a city was really, really enticing.
Scott Cunningham:
Did any of your friends go to pit with you?
Kyle Kretschman:
Yeah, so there's probably, I grew, I graduated from a class of about a little over 200 people in high school and I think there was like five or six people from high school that went to pit for my class. So definitely had some really good friends who went and kept in touch with, through undergrad.
Scott Cunningham:
Mm. Yeah. So it wasn't, were you sort of an early generation or you weren't, were you a first generation college student in your family or did your parents go to college
Kyle Kretschman:
Combination? So my dad went to Penn state civil engineer, as I mentioned, me and my mom actually graduated from undergrad the same week. So my mom went back to school later in life after me, after we went to school. And so yeah, we, we were able to celebrate graduation cuz she went to a small private school right outside of the city also.
Scott Cunningham:
Oh, okay. Okay. Yeah. Well, so what did you like to do in high school?
Kyle Kretschman:
So I played a lot of sports before high school and then I kind of switched into, and this was a traditional sports of football, basketball, baseball, but then I switched into tennis in high school. And so that kept me busy, but along with a lot of academics and really, really liked computer science. So played a lot of video games growing up, really enjoyed like that aspect in combination.
Scott Cunningham:
What games were your, were you, did you play on a, on a video game, plat platform? Like an Nintendo or did you play?
Kyle Kretschman:
Yeah, no, we played a lot of plays very much into like role playing games. Some of the arcade games like Marvel versus Capcom. So yeah. Yeah. Very, very interested in gaming. Yeah. Maybe I was a little too early for that. Cause you know, every, everybody in the 1990s was like, oh, I could make pu money playing video games, which wasn't true back, which wasn't true back then, but that's right. You know, nowadays
Scott Cunningham:
You can that's right. Yeah. You know, that's right. You can do it. There's all kinds of ways you can make money doing things today that nobody knew was possible 10, 10 or 15 years ago. Even
Kyle Kretschman:
My
Scott Cunningham:
That's cool. Yeah. I, I, it's funny, you know, computer games can keep a, keep a kid in high school going, you know, like especially I think they're kind of misunderstood. I, I had a lot of friends that, well, I mean, I, I, I had, when I didn't have a lot of, we moved from a small town in Mississippi to Memphis and I, those, those that first year when I didn't have a friends, I did bulletin boards and played Sierra online games like Kings quest. And it's like, it's like, you know, not intertemporal smoothing, but like inner temporal socializing, smoothing, you know, so that you just kind of get through some periods that would otherwise be a little lonelier.
Kyle Kretschman:
Yeah, for sure. And I mean, I mean for this audience, like most video games are some sort of form of constrained optimization. So there was, there was the inkling that I, I liked understanding how economies worked in high school through this and yeah. Going back to my mom, my mom always said like she encouraged it and she encouraged education. And there was actually kind of like that nexus, whenever I took economics in high school, it was like, oh, you know, some of these games really are full economies that are constrained and constrained in a way that you can understand and complete in, you know, under a hundred hours. Right. But there was that combination that was kind of showing itself of computer science, computer gains and economics of putting itself together.
Scott Cunningham:
So you were kind of thinking even in high school about economics in that kind of like, you know, optimizing something and like, like almost that modern theory that we get in graduate school.
Kyle Kretschman:
I think more, I had the intuition when I didn't have know how to say what it was in high school because my high school was pretty forward and that it offered both advanced computer science courses that could get you through definitely through first year of undergrad, maybe even through second year with advanced placement. And then they also offered advanced placement economics. And so I, I ended up taking advanced place in economics my junior year when most people took senior year. And so whenever I was going small
Scott Cunningham:
Town, even in that small town, they had, you had good your high school. Good econ.
Kyle Kretschman:
Yeah. It was a real, it was a really good high school that would put together good curriculum that did a lot of college preparatory work though. They, wow. They really leaned into the advanced placement, the AP courses to get students ready to go to school.
Scott Cunningham:
Wow. Wow. So even at, as a junior, you're taking AP econ, you know, you don't have to take AP econ. That kind of is say that, that sounds like somebody that was kind of interested in it.
Kyle Kretschman:
Yeah, very much. Yeah. And again, as soon as I, I definitely didn't get to the graduate level of understanding, like, you know, LaGrange multipliers, but the, the micro and macro sequence just made intuitive sense to me. It was like, it was kind of where I was like, yeah, this fit. And this is how I think. And some people might criticize me now that I think too much like an economist. Right. Like, but at the same time, it just like, it started to put together that language and even more so some of the frameworks that really kind of drew me into it.
Scott Cunningham:
Well, did you, did you, did you notice that you had this interest in computer science and this interest in economics and that they might be one, did you get a feeling that they could be in conversation with each other?
Kyle Kretschman:
Not
Scott Cunningham:
At first, our ancestors a hundred years ago. Didn't, you know, those economists didn't think that way, but now it's just so natural for this generation of economists to be almost one half, you know, one third mathematician, one third economist, one third computer scientist.
Kyle Kretschman:
Yeah. So not at first, but I, I feel like I made have like lucked into it, honestly, because whenever I chose to go to Pitt, I chose to start as computer science because I knew what that pass was. I was inspired by my older brother, the great teacher in high school. And like, I was definitely like, okay, a software software development engineer career is great. It's cutting edge. It's there. But after probably like the first year, it just didn't feel that end state didn't feel right. And so I made kind of the hard decision to choose, honestly, to switch into economics as a major, because I wasn't sure what the end state would be, where I was going with it. Cuz it was definitely felt more amorphous, you know, it's a social science, so yeah. It didn't feel like it was gonna be as clear cut and as, and have as much certainty. But pretty quickly, like after a year was like, oh, well we're doing, we're using E views at the time. All right, this is coding. I know how to do this. This is great. Right. And starting, starting to see some of that in undergrad was like the, kind of the aha moment that like, yeah, this is, this is a place where I can apply this love of coding and problem solving, but problems and solutions that I find really, really hard and interesting.
Scott Cunningham:
It was because of econometrics though. It was in that.
Kyle Kretschman:
Exactly. Yeah, yeah.
Scott Cunningham:
Yeah. Wow. That's, that's really interesting because you know, I think it's still the case that, you know, you can easily end up with an econometrics class that remains purely theoretical and doesn't end up, you know, exposing the student with a lot of actual coding, but it sounds like your professors were, were getting you into working with data.
Kyle Kretschman:
That's correct. Yeah. Both. Both within the class. So like I said, we used E views at the time. Yeah. And again, kind of like learning as a go, I, I don't think I really knew what I was doing whenever we were typing commands and E views, but the computer scientist in me was like, okay, well this is a function. I know functions. Didn't put outputs, but definitely didn't understand necessarily things that were going under the hoods or you know, all of the theory that goes with it. Oh, right, right, right. So it was, you
Scott Cunningham:
Knew the coding part, you knew you were coding, but you did, but like the, the actual statistical modeling was kind of the new part, but that was a way for you to kind of engage it a little bit.
Kyle Kretschman:
Yep, exactly.
Scott Cunningham:
Oh, that's interesting. That's interesting. Well, so what were you gonna have to choose between a computer science and an econ major did or did you end up doing both?
Kyle Kretschman:
So I chose an econ major, but then I had what I would call basically minors or concentrations in computer science, but then also in statistics and also in math, because once, once I had an internship at a bank and was doing data entry and I was like, eh, I don't think this is what I wanna use my economics degree for. Yeah. I had a couple professors at pit named Steve Houston and Frank Giani who brought me on as a research assistant, an undergrad to start being part of some of like their survey projects and data collection. And even, even one of 'em I don't, Steve was crazy, but he even let me TA classes on undergrad, so oh, wow. But he kinda, I mean, I, I say that jokingly because it was formative for me, it was like, okay, this is great. How do I do more of this? And he was like, well, you go get your econ PhD. And I was like, so I can be a teacher with computer science and doing economics altogether. He goes, yeah, let's do that. And so it was with the help and support of some of these really good professors and education to kind push me on this path consider to get Ancon PhD.
Scott Cunningham:
Mm. And that's when you were like, so how, how, what, what year would you have been in your program?
Kyle Kretschman:
Probably. I think I was in my junior year where I was starting to explore this. And then in my senior year is where I was like, okay, I'm actually gonna be doing more more of this and applying to grad school because going back, as I said, I entered with some credits. So my senior year was very, I didn't need a full course load. So I was looking for other things to keep me busy, which maybe, maybe that's one of the themes of this conversation is I kinda kind of like the variety and really have variety seeking behavior too. Yeah,
Scott Cunningham:
Yeah, yeah. Yeah. So you graduate, was there like a field that you were mostly interested in?
Kyle Kretschman:
I thought I would be going into macro economics. Macro. Yep. Yeah, because Steve worked on the council of economic advisors and I was really inspired by that and the application of economics within, within policy and just again, always applied economics, not necessarily theoretical. So yeah. Then again was, that would be sort of like labor and macro was like the initial idea, but finally Scott, I didn't do all my homework and like, think about like what grad school looked like or all it looked like. I kind of went a little bit more naive than I think other people with, again, ideas of how I could become like a teacher, an educator with some of these tools versus like how disciplined and single thread you need to be on research to be within an econ PhD program and to see that.
Scott Cunningham:
So you, so you kind of were like, so when you were thinking about graduate schools, what, how, what, what did you sort of, can you walk me through like what you were thinking and how you went about trying to apply to graduate school and where you ultimately chose?
Kyle Kretschman:
Yeah, sure. So applied probably the, the top 10 and the top 10 probably said no thanks. But also then was targeting specific schools that we had relationships with that I knew would provide computer science and macros. So university at the Iowa at the time, this was 2000 and had a really strong macro program. And then also at the university of Texas with Dean Corbe there, they also had one in Russ Cooper. And so those were like the two that I was like targeting at outside of what the top schools were. But yeah, as I, I kind of mentioned, I, I might not have prepared myself well enough to be attractive for some of the most pop with top tier schools because kind of, you know, as I said, bounced around and would be yeah, a little bit working on it a little bit different things and have computer science versus being solely focused on like economics and math and things that might be more of what the top tier schools were looking for.
Scott Cunningham:
Yeah. Yeah. You know, you know, it's like the, I mean, I'm the same way. I didn't ha have any econ classes in college. I was a English major, but the, the, the diff there's so many students that sort of seem to almost for whatever reason, know a lot sooner what they want to do and then like make those choices. And then there's just many of us that are, you know, in a process of search yeah. That when you're in a process of search, well, you, you know, by definition, that's like you're using that time to search.
Kyle Kretschman:
That's exactly right. As
Scott Cunningham:
Opposed to saying, I've gotta take, I've gotta become a triple major computer science, math, econ, and have to do like, you know, these set of these set of steps that, you know, there's no way I could even have known to do it unless somebody had told me it's weird. I mean, it's just funny how the little things can have such big repercussions for your whole life, but it's, but it, it worked out great. So you end up, where do you end up going?
Kyle Kretschman:
I went to the university of Texas at Austin.
Scott Cunningham:
Yeah. Yeah. What year was that? And
Kyle Kretschman:
So, so this would've been 2002.
Scott Cunningham:
Oh, okay. So you go to oh 6 0 7.
Kyle Kretschman:
Okay. And so ended up working. So I ended up working a lot with Jason, Ava. Yeah. And who came in and became the, the head of the department. Yeah. Applied econometrician who just did an amazing job going back to whenever I said, I didn't know how things worked under the hood, in those formulas. He didn't even let us use those formulas. So anytime we were doing applied econometric econometrics with them, not only we learning to teach, we're learning the theory, but he said, you have to code it yourself. You have to do the matrix algebra, you have to calculate standard errors. You can't really call those functions. So that was probably again, that wasn't until the third year, but yeah, in the first year to go back a little bit,
Scott Cunningham:
I, that played to your strengths though. I bet that played to your strengths. Yeah. Just at the end of the day, wanting to be someone that, that wrote down the raw code.
Kyle Kretschman:
That's exactly right. And, but the first year I didn't play my strength. Yeah. Yeah. So the first year I felt, I felt a little bit outta water and I was like, this is, I remember when we were proving what local non association. And I was like, this is, this is one hard, but also like, again, going back to like, that is this actually how I wanna be spending my time and right. I, I was like, yes, I do. But I was like, I, I knew that I needed to get to those applied applications. Yeah. And so that's, again, why I was thankful to be able to work with Jason and Steve Trayo and a few other, they applied econometricians at Texas that really encouraged me to explore starting in the second year. They didn't us like pin it down. And so I, I thought I, at the second year I worked like wrote the first, a paper on school choice and trying to see if I could find some sort of instrument on school selection on public versus private. And again, so that led to like that idea of like applied econometrics was really, really the thing that like, I was like, okay, now this fits again. Once we got into second and third year
Scott Cunningham:
Was, was picking up that intuition, that kind of like labor style identification, causal inference kind of approach. Was that something you picked up from Jason or was that just like from your labor people? Oh, okay.
Kyle Kretschman:
Yeah. That's yeah. From Jason and Steve a lot. They did a great job of doing that. And yeah. So then, yeah. Then I, then I threw in, I knew threw a little bit of a switch in there also, and my co-author Nick master and Arti and closest friend and classmate in Texas was very theoretical and very interested in applied empirical IO. And so we started working in that field also together. And so then I got to work with the Han me vet and Ken Hendrix on using empirical IO. So, oh, wow. Yeah. And so again,
Scott Cunningham:
This is the more structural, more structural econometric. So you've got this like reduced, you've kind of got this like traditional labor reduced form type of, part of your brain. And then you've got this empirical IO structural part of your brain kind of emerging at the same time.
Kyle Kretschman:
That's right. That's exactly right. Yeah. And then we threw, we threw everybody for a loop. I also saying we wanted to study study politics and how money turns into vote using both using all these tools. So yeah, I can see here kind of saying in hindsight, like it all makes sense in this story that I'm telling you, but at the time it was more of what you were talking about. It was searching. It was, I wanna be working on really interesting applied problems. I love the toolkit that economics provides in framing. And yeah. I have to be coding to be able to utilize these tools that I've had built up in the past.
Scott Cunningham:
Yeah, yeah. Yeah. So, so matching with Nick was really important
Kyle Kretschman:
Very much.
Scott Cunningham:
And why, if you hadn't to match with Nick, I mean, just kind of outta curiosity, if you could articulate the value added of that whole partnership, what was it?
Kyle Kretschman:
Yes. Sure. So, so we matched basically from math camp going into, going into the first year because Nick came both from the pure math and physics background and also had some experience in the air force. So the air force was sending him to Texas and he, we were, we were definitely, we definitely didn't have a lot of vend overlap on the fact. He's like, well, I would have the intuition and some of the computer skills, Nick would have the theoretical math skills,
Scott Cunningham:
The theoretical math skills. Yep.
Kyle Kretschman:
And then we just had, we had the common factor that we wanted to work hard together and learn together and we're willing to, we're willing to intellectually hash out really tough things together. Yeah. So yeah, he huge credit to him through being able to put up with me. And he says, he says the same thing once in a while. But again, matching with somebody that had the, the more real analysis proof based understanding of math was so valuable for me. And especially,
Scott Cunningham:
I think some empirical IO, especially empirical IO, just being able to, you know, think like an economist in the area of IO is thinking real deep about, you know, a rich set of models and modeling approaches.
Kyle Kretschman:
That's
Scott Cunningham:
Exactly right. That's definitely not what you're learning in your econometrics classes, even though they might go together.
Kyle Kretschman:
Yep. So, so yeah, it was just a, it was a really good match from the beginning. And so we complimented each other and we're, we're able to build a strong enough relationship to be able to be able to hash out, have really long nights yelling at each other, we say in the office, but it never, it was always for educational purposes and lifting each other up.
Scott Cunningham:
Was that different than what you thought grad school was gonna be like?
Kyle Kretschman:
Yeah. So I knew the research component a little bit. I just didn't under understand the unstructured research on how that was gonna go and like the cadence and where it was gonna and how that was gonna be so required to develop your own viewpoint. Yeah. I thought it would be more directed cuz as a 22 year old, that was the experience I had generally. So that was the big one was the undirected and I liked it, but it was also very difficult.
Scott Cunningham:
How would you describe what you're talking about to your college self? Who kind of like, you know, he, he doesn't really, he doesn't even have the vocabulary for what you're describing. What would you say? It was like,
Kyle Kretschman:
I think you use a good term. You have to be not only wanting to search, you have to be willing to search, but you also, then you have to put in the guardrails yourself to keep it focused because you're not necessarily gonna have those external guardrails that you will have from an alternative path of going to either like a master's program that's gonna be more structured or going in an industry or going to get a job. Right. Like I mentioned at a bank for like a 22 year old where entry level jobs are gonna be more structured. Yeah. So yeah, I just, I, I probably knew it, but I didn't know what it meant to be and what, what it meant to experience it.
Scott Cunningham:
So how did Jason and, and Steve kind of, and any other faculty, how, how did they, how did they, I, so I did this interview with Susan athe and she was saying that, you know, the amazing thing that pat Maja did at Amazon was he managed to make economists productive, which kind it was kind of a weird, weird way of saying it. And so in a way it could, in a way you could imagine a department that sort of has like a, you know, this idea of like research has got to come. There's like a, there's like a, a journey that a graduate student has to come on to just to basically make a decision to be a researcher. Yeah. You know, and you could imagine that creating the conditions for that is, is involves faculty member, doing stuff that's not necessarily obvious. What, how did they, how do you think they contributed to that for you personally?
Kyle Kretschman:
For me personally, at the time, again, it goes back to encourage the exploration versus mandating or saying that I need to be on one path. So like even Nick and I at the time explore the idea of a private company and how, what, what that would be into like pinching, pitching a venture capitalist on, on that. So all those things, again, in grad school, they, they were encouraged, but they weren't structured at the time. Yeah. So yeah, I can, I can, I understand Susan's comment because I was, I was one of those economists who started pretty early with pat and we, we have a lot of good mechanisms that we've learned and built at Amazon when I was there at the time through pat, through lay other people who were willing to make the jump into this entrepreneurial space that hit the election and the, of coalesce of economists doing open book, empirical research, along with data science. Right. Just becoming more and more valuable and applicable, but is kind of what Susan piloting that we can, we can talk more about if you
Scott Cunningham:
Want. Yeah. I do wanna talk about that. I wanna talk about the, the decision though, you know, to, to be, because you, you sort of started off in college, you know, you said things like, oh, you can become an educator and then you've gone in this non-academic direction and you know, it, it, and that's like a, that's a more common story now, you know, right. Of, of top talent, very talented PhDs that you could have easily seen 20 years ago, would've been an academia. Their counterfactuals are, are following you. And so, you know, it's, it's a, it's a big part of our, you know, collective story as economists that this, this new labor market that didn't, that didn't exist historically now exists and draws in so much talent. And I was just curious in a way you're kind of like a, a first generation person like that, you know, when you think about it, right. Cause text's not very old, right. Facebook, Facebook, what it's like 2007. And so, you know, so you've got this, you, you, you've got this, this chance to kind of say like, it must have been, so I don't wanna put words in your mouth, but I guess I was just wondering, what were the feelings like as you considered not taking an academic track and when did it start to be something in your mind that you thought that's gonna be something I'm explore
Kyle Kretschman:
Probably pretty early, because if you wanna really trace the roots of like tech economists back, it starts obviously with Hal varying at Google and me and Nick, actually, we, we sent an email to Hal, probably 2008 saying, do you have any, have any use for some summer interns who can do some empirical IO? And he said, no, not, not at this time, but so, but he
Scott Cunningham:
Answered the email.
Kyle Kretschman:
He did answer the email. Yeah. It was nice, nice of him to answer. Cause we knew he was probably pretty busy, but so it, honestly, when Amazon started hiring economists, I was probably searching for about a year to move into tech. If you wanna move back to the decision point coming outta grad school, honestly it was a challenging labor or a challenging job market for me, somebody who is a lover variety, who is working on empirical IO problems with campaign, policy, campaign, finance reform, policy recognition. That's, that's not fitting a lot of the standard application process. Yeah. Once again, that's so that's probably a theme for me. And again, at the time it was hard. I was, I was in the running for jobs at VA wakes force that I thought would be really good fit because they're the EDU the emphasis would be on education with the research ability to do research and work on problems that were more widely probably policy oriented. Yeah. But neither neither of them came through. So I just always knew that I industry was gonna be an option. And so
Scott Cunningham:
What year is this? What,
Kyle Kretschman:
What, what this would've been in this would've been in
Scott Cunningham:
20 11, 20 11. Okay. Oh, so you moved through the, you moved through the program or kind of relatively quickly. Oh 7, 4, 4, 5 years. Okay.
Kyle Kretschman:
Five years. Yeah. Five years. Yeah. Oh six to 11. Okay. But so for about a year, about six. Yeah. Yeah. And so starting in 2013 is whenever I started applying to the first tech job as a data scientist and got it went great until I talked to the VP who was a business part, like pure business person. When I was talking to the hiring manager at the time, it was a company who was providing college counseling as a software service. And so they would do this at their, their clients were both for profit and not for profit companies. And we were talking like, we'd get into details about treatment effects models and how we could measure the impact of their intervention. It went great. But then I had the flyout scheduled, but then the interview with the VP, he said, well, how am I gonna monetize your algorithm? Right. And I was like, I'm not sure I know what algorithm means, but right. I, I wasn't prepared for that language and that application and how you turn econometric modeling and measurement into, into business impact at the time. Yes. Right. So spent another year looking around with different opportunities like that and honestly learning again. So, so whenever Amazon, so this would've been in 2014 and then Amazon was hiring its first big cohort with pat. So this was a cohort that was about, I think there was about 13 of us. It was a no brainer.
Kyle Kretschman:
Whenever, whenever we did the interview, it just was like, all right, this is exactly right for me. I was hop. I was hoping it was right on the other side. And I could probably tell you some funny stories about the interview process, but I was like, this is, this is what's meant to be. Yeah. So it, it, it was like a 10 year journey from 2004 when I switched outta computer science into 2014 being like this, just this fit.
Scott Cunningham:
Right. Right. Right. So outta curiosity, you know, is, is there, is there something that you think is supposed to be learned by the fact that when you were on the job market and you had that interview with that, that gig and the, and you get to the VP and he articulates questions that are not traditional econ questions, or even econometrics questions like business profitability to act, it's kind of ironic, isn't it like to everybody? That's not an economist. That's actually what we, they think we do, you know, is like, they think we do all that stuff. And then they don't know that we're like, like you said, you know, trying to set up a Lara and solve, solve it, like what's a Lara, but do you think your competition at that time did know how to answer questions like that? Like non-economists in those positions
Kyle Kretschman:
Probably at an inflection point. Yeah. Because this is the same time. Wherever machine learning is becoming more common toolkit with an industry. So there would be like machine learning algorithms that are designed for, you know, prediction, problem sequencing, anything like that that are specifically designed to be used in a business setting to monitor.
Scott Cunningham:
So they, they not only know machine learning, it's like, they also can kind of immediately articulate why this would be profitable.
Kyle Kretschman:
I think so. Yeah, because again, the computer, so it's like in learning the language and this is the language that would probably be more understood within a machine learning computer science version is okay, well, I'm gonna use this to change the recommendation engine right. Is very common one. Yeah. That's obviously gonna be, so how are you gonna monetize it? I'm gonna improve the match and the recommendation engine it's gonna have this. So I think at the time there was a little bit of it, but, you know, hopefully I think, I think I learned pretty quick that you can, you can use econometrics in a similar vein. As I said, it's a flavor of data science,
Scott Cunningham:
Have you had to become a blue collar machine learner?
Kyle Kretschman:
I've had to understand it, but not, I think you mean by blue collar, you mean like implementing it
Scott Cunningham:
And yeah, I just, when I, I usually say blue collar in the sense of like, you know, you, don't like, you know, you basically are picking up these skills, but you weren't like, you know, you didn't get a PhD in computer science. You know,
Kyle Kretschman:
The answer was then that answer is definitely yes. So like as we, as our cohort and as we grew, the economics discipline at Amazon, that was a big part of it is how one could we bring in some machine learning scientist help educate and teach us. Mm. And yeah. So, and even in, sometimes in lecture style, we would do that because it was so important, but then even more so learning to so that you can interact with different stakeholders specifically, like machine learning scientists. Mm. Then understanding when you can actually implement it and marry it within the econometric models was definitely a huge part of the education process.
Scott Cunningham:
So you go to Amazon, is that right? That's like your first entry into tech
Kyle Kretschman:
That's
Scott Cunningham:
Right. Is Amazon, what's your title?
Kyle Kretschman:
So Scott
Scott Cunningham:
A scientist or economist.
Kyle Kretschman:
I, it was something like business intelligence engineer. There wasn't an economist job family. There was, as you said, it was kinda the forefront. I think it was this. Yeah. I think that's what it was, but
Scott Cunningham:
Cause it is now right. Baja has a that's
Kyle Kretschman:
Right.
Scott Cunningham:
He created a job title called economist.
Kyle Kretschman:
That's right. Yeah. And that got set up about a year in, so like, and I was part of the group. So we would set these, we would set up like these people and process mechanisms that allow economists to be so influential and productive within Amazon.
Scott Cunningham:
Mm, okay. So how is he doing it? Why, why is Susan saying he performed a miracle by making economist productive? Can you kind of describe, like, if you had to just guess at like the counterfactual, if it hadn't been, you know, pat, it hadn't even been an economist that was hired into Pat's position. Like, what is it that he, what, what is it that he, or Amazon or whatever is making you go transform and become this new version of yourself?
Kyle Kretschman:
There's, there's a lot of factors and I could probably spend an hour on this, but I'll, I'll try to, I'll try to reduce it down to like some key mechanisms and ideas. The first is that Amazon is probably the most data driven company. I know. Mm. They are so focused on measurement, both of things you can directly measure. And, but they are. So they were very early interested in economic measurements that are UN observables either coming from like coming from econometric models. That, that was whenever pat demonstrated some of those that was like the light bulb went off the, so, because again, it, Amazon was run by and still generally is people with operation science background. And so this over index on measuring as, as coly and as precisely as possible, well that's that's economics. So that, that was part of it. Another part of it is culturally Amazon operates that makes decisions based on six page white papers, you wanna make some economists really productive, have them write a six page white paper instead of giving them a presentation, especially to people like who may be in the background with MBAs or other people who have a comparative advantage, we economists have a care advantage in writing.
Kyle Kretschman:
So it was little bit of like a surprise, but you might hear these anecdotes where it's true. Like whenever you go into a, a decision making meeting, you come in with your six page white paper that says here's the business decision to be made here is my recommendation. And here's why, and people sit there and it can be a room for five people can be a room of 25 executives. They sit and read the paper and they read the whole thing. Is there an append that can go on forever depending on how big the meeting is. Sure. But that structure of, of data driven decision making, combined with how you're presenting your argument is written seems like, seems like economists should be pretty good at that. Right?
Scott Cunningham:
Is that a pat thing? He came up with work, the work he made,
Kyle Kretschman:
What was the six page idea was from Jeff Bezos. And so that was, would
Scott Cunningham:
Those be circulated throughout the, throughout the, the, the firm,
Kyle Kretschman:
The stakeholders who needed to be part of the decision making they be circulated. But again, this is every, like everybody's writing six pages. PowerPoint is basically outlawed at, at Amazon. And again, that happened mid 2000. Sometimes people can Google it to find out, but that six page culture and decision making culture, just again, fit economists.
Scott Cunningham:
So how is a six page paper similar to the kinds of writing that, you know, you sort of associate with economists and how is it different?
Kyle Kretschman:
So its I'll start with the differences. So one with the six page versus like a 30 page academic, you are not going to be able to share the research process. You are not supposed to share the research process. You're supposed to share the clear recommendation and how you got to that recommendation. Right? So if you think about like a 30 page academic paper XT, be condensed down into those six pages. In my view, they're just, that's just not how the industry operates, but you probably would know better than me on that where, but so again, where it's the same is again, it's a data driven argument. The purpose of this paper, the abstract here is the hypothesis that I have that and here's how I tested it. And here's how I'm making my conclusion. So what I always found really honestly easy was I felt like I was doing the scientific process. Like I felt I, I was with business decision making it generally work within what is the hypothesis? How are we doing this? How are we testing it? What are we think some alternative conclusions could be, but what are we making towards it? So yeah, yeah. Again, it was closer to what I felt like would be a scientific paper in and that hold of day driven mindset is again, that's more, it's very common. Amazon have a common Spotify now
Scott Cunningham:
Has that been influential throughout, throughout industry? Has that, how have you noticed Amazon influencing
Kyle Kretschman:
Some
Scott Cunningham:
Yeah. Like most people don't understand.
Kyle Kretschman:
Yeah. There there's some companies who definitely have completely adopted it. There's some companies who haven't, but the, the six pager again, that's, this is not a, this isn't a concept just to economist and tech. This is the concept is, is held up as one of the key mechanisms for all of Amazon.
Scott Cunningham:
Mm mm Hmm.
Kyle Kretschman:
One other.
Scott Cunningham:
How often were you writing those?
Kyle Kretschman:
Depends on what level you were farther in my career. That's the only thing I did was write six page papers and it would be part of like, my team would help, but again, anytime you have a key business decision to be made or an update, like you're gonna be writing the six page. So yeah, it's again, the farther, the more seniority you have though, the more that becomes your job is to communicate side and guide through these business decisions.
Scott Cunningham:
Do they, to you,
Kyle Kretschman:
They belong to the team because it's always
Scott Cunningham:
Put 'em on a, you can't they're like proprietary though to Amazon.
Kyle Kretschman:
Oh, correct. Yeah. No, they, they're not publicly available. They're
Scott Cunningham:
Proprietary. Like it must is it what's that feel like to do something? What's it, what's it feel like to, to do something that creative in that kind of like scientific that's siloed within the firm? Does that feel strange?
Kyle Kretschman:
No, it didn't. Because what it enables is to be able to work on some of the hardest questions without having to worry about without having to worry about com communication strategies or right. For press release. So no, it felt like we were able, and this is going back to like some of the things that pat and we did at Amazon make successful. We worked on some of the hardest problems at Amazon from a very early stage because we said that it wouldn't be publicly available. Right. So that's gonna do that. And
Scott Cunningham:
That's been a key part. Yeah. Because okay. I get it. Okay. That, that makes a lot of sense. Yeah. So who did you discover? You were, go ahead. Sorry, Kyle.
Kyle Kretschman:
No, I was gonna say maybe the last me to highlight. Cause again, I, I, we could probably spend this whole interview on this, but the, the other key mechanism that pat pioneered was the proliferation of economists as a job family was not pat saying and us saying, go do this. And I can give through my own personal example. It was the other business executives, seeing the measurement, seeing the results on product, just saying, okay, I want that. So it really was a demand, AKA demand, internal demand for more economists, that was gonna say, I want this with my business decision making process and want these people who can do this and collaborate across the difference. It was not a, oh, we're gonna put economist in the siloed function that everybody's gonna come here. And that was, that was my story. But the very first year I worked on projects directly for the consumer CFO, basically the whole year. It wasn't necessarily by design, but it was what happened. And at the end of the year, year and a half, the, the VP of finance said, come over here and do this with me and come build, come build an economics team and an economics function here within my organization. And that's really is again, that's the real key was it was business decision makers, demanding the ability to understand this and demanding the skill set, just like they would data science, machine learning because of demonstrated value.
Scott Cunningham:
What were they witnessing with their own eyes that was so compelling that they would Inc that it would increase demand.
Kyle Kretschman:
So both I'll call it like ad hoc economic analysis on maybe big strategy projects, but also then the introduction of econometric systems into product.
Scott Cunningham:
Mm. What does that mean? Introduction of econometric systems into products.
Kyle Kretschman:
So say you have a product that is gonna, let's go back to the recommended system. And I use that again as an abstract, but within there you might make a change to it and you might make a change with the recommender system. That's gonna cause a treatment effect. Right. So, okay. So we can do that one off to estimate that, but you could also then build an economic system. That's gonna measure those treatment effects and changes like an AB platform or things like that. So maybe people might be more common and familiar with like experimental platforms. This would also be then econom. This would be sub out the AB part of it and sub in an economic model, that's going to be doing always on measurement sometimes at a, you know, service level. So sometimes within like individual pages, sometimes it's gonna be at a monthly level, but the integration of econometric models into the product.
Scott Cunningham:
Right, right. Wow. So how are you a different economist because of that experience at Amazon, if you had to guess, what was it the treatment effect?
Kyle Kretschman:
Oh, it mean it was, it was incredibly formative because it to tie like it put the fit together with the application to where I could understand and really to where it is, my job is to take a business question, turn it into a scientific process that can be solved with econometrics. And then also be thinking about, is this a problem that needs a scalable solution? Right. So, so Amazon taught me business integration taught me so many different languages, taught me leadership and management taught me how to work with stakeholders in collaborative ways, but then even more so how to deliver the value through econometric measurement, both again, as I said, not only, not only just in ad hoc research papers or one off analysis, but also then where does this fit directly within the products that we build in tech?
Scott Cunningham:
Yeah. So where'd you go, seems like people don't stay very long in tech. That's like normal. Whereas like, is, is that right? People kind of like, it, it's less normal to stay your whole career at Amazon unless is that wrong or,
Kyle Kretschman:
I mean, it's got it still do. So it's probably tough to say that because really the, the field started, like you said, really proliferated in 2012. So I stayed at Amazon for six years and I thought I'd be staying even longer. But Spotify came with the opportunity to one work on something I care very deeply about, which is the music industry. I'm a huge music fan. They also came with the idea to build again. So, you know, that was the part that really enticed me was Spotify did not have any PhD economists who were in an and, and economist roles. They had like one in a data science role, but they didn't have the structured economic discipline that they were seeing that Amazon was proliferating. And also then going into like Uber, Airbnb and the other tech companies. And so they said, can you build again?
Kyle Kretschman:
And I said, yeah, I'm, I'm excited to build. And then last one, all these there's definitely personal considerations here too. And Spotify just really did a great job showing how the company as a whole has Swedish cultures and values. And at the time I had a nine month old and they said, this is a great place to come be a father with the balance and that, and I said, all right, let's make the jump and come to Spotify. And so now I've been here about two years. So cuz I, I actually went to Spotify in may of 2020.
Scott Cunningham:
So remind me again, your job title at Spotify.
Kyle Kretschman:
So I'm head of economics.
Scott Cunningham:
Is, is that the, is that, is that like chief economist? I, I feel like I see different, different job titles and I don't know exactly what, what everything,
Kyle Kretschman:
Yeah. It, it it's on the path to it. So I'm, I'm the highest ranking PhD economist at Spotify.
Scott Cunningham:
I see. Okay. I've been there for two years. Okay, go ahead. Sorry.
Kyle Kretschman:
Yeah. Cause again, that's what I was brought into build was to build, like we did at Amazon was overall integration of PhD economists within the different business units.
Scott Cunningham:
So this is the part I'm, I'm having some hard time, like, you know, putting, visualizing or putting in my own words. What exactly will it look like if you have been successful in five years at that goal and what would it look like if you had been a complete, complete bust? What are the two things that are like empirical that I would be able to, to observe?
Kyle Kretschman:
Yeah. A complete bust is probably that an economics discipline is not, is not part of Spotify and there's not, there's not a job family. So a complete bus would've been, I, I moved to Spotify, an economics discipline. I either in, or I'm working data science job, what success looks like is actually what we put first from a, so I'll talk about the people in process, discipline success. We, I came into was
Scott Cunningham:
Real quick. So
Kyle Kretschman:
Foundation on basically. Yeah.
Scott Cunningham:
So, so failure actually would mean that the economist community within Spotify just never materialized, is that what you're saying? And that, and that means like this, having groups of economists that, that think and use the kinds of training we had in graduate school, but in a way that is actually productive in the firm is, is that, is that right?
Kyle Kretschman:
So, so yeah, and again, that's,
Scott Cunningham:
The job is successful if you're able to actually create internal demand for economists.
Kyle Kretschman:
Yep. That's right. And that's, that's what I would say against from the process side. And then from the product side, that's using econometric research in the ways that I've been talking about it's using it both not only for individual analysis, but also then building econometric measurement systems that improve the product to get towards Spotify's mission of, of billion listeners and fans who can connect with over a million creative artists who are making a living. So that's, so it's a combination, it's the combination people process. Do we have the people set up? Do we have this integrated system of economists working alongside all these different types of stakeholders along with the product side of, do we have these measurement techniques that we're applying in a way that is important to Spotify's not only Spotify's business, but all the stakeholders that have an interest in Bon life.
Scott Cunningham:
So I feel like, you know, I think to academics that, that, and, and maybe even to some degree students, maybe I'm, maybe I'm completely an outlier here and I'm wrong, but you know, I think there's this like really shallow is a negative word. It, I mean, shallow, literally more and just like, it's just the thinnest knowledge possible of what exactly, you know, the, the, the core skillset of a successful economist is in tech. You know, and for many people they think, I think they, they think it's such a primitive level. They're like, it needs to be somebody that can code, you know, it's a data scientist, but, but it, but it, but that's not what I associate with economics. Right. So what would you, what would you articulate? It is,
Kyle Kretschman:
So it's the ability to do econom applied econometric research. That's applied to business problems. Mm. So within that is coding. Yes.
Scott Cunningham:
Right, right. Within that is coding.
Kyle Kretschman:
I, the vast majority, I won't say everyone, but the vast majority of tech economists are gonna have some level of coding and maybe they're not coding anymore. Like I'm not doing any coding anymore, but like they, they have that ability. So that's just again, that's, that's a skillset, but the real ability is doing long-term economic research. Because the questions that we get asked are very hard and difficult, and they are maybe in the academic setting, maybe they are publication worthy, takes that take three years, four years to actually solve with the right model. Yeah. But it's the ability to take that three year research roadmap and make it progress. So when you're doing that, you need to have your summary statistics that the business can see, understand, give feedback on because that accelerates the research process and also accelerate the business impact. So, one, I guess one comparative skillset that I've learned is what I call research with an open book. You shouldn't really go a month without talking to your stakeholders. You should be showing where the research is. You need to be the person who owns that three year measurement roadmap, but you're the person who's gonna be having to take the feedback at a consistent basis. And that's, that's really the different part. So, but again, that goes back to the applied econometrics part
Scott Cunningham:
Of it. So what is the, so walk me through your, walk me through typical days in your job now.
Kyle Kretschman:
So my typical day is much more generally now on the management people side. So it's definitely going to be, as I said, building this discipline because we have the creator economics team who's focused on the supply side. We spun up the ads economics team that has a completely separate unit again. So that's like part of the people process, but then it's creator economics team has a hybrid data science and economics team. That's doing all these things that I'm talking about. So with that right now, you know, we're working on long term roadmaps for both product and research and being there. And then also giving I say, honestly, translating a lot of the work that the team is doing into the actual business decision making. So, so I kind of work if you think of the research process. And I think this is kind of generally true, most managers or at least tech economist managers, they're working at that hypothesis generation and then communication of results where then the team itself is working with the data analysis, the statistical models along that research loop to integrate. So it's more of an, my, my role really is an integration of being a translator of economic ability and measurement into the business.
Scott Cunningham:
So, you know, so you've clearly have a comparative advantage evidence by, you know, your, your successful Mo creating of a career. Right. And, but I was curious, what do you think your, what do you think that is? You know, how would you explain that to like, you know, like on a subreddit, explain to me like I'm five, how, how would you like explain, explain to me what you see as your compared to advantage now in life, you know, that you, that you, that you have the, that your presence as this value added for the firm. And, and was that now that you look back, when you think about who you were as a young person back at UT, or even at pet, do you see signs of that and what, what were the signs
Kyle Kretschman:
The, yes I do, because really my comparative advantage now is as I mentioned, I seek variety and specifically I seek variety on really hard, interesting problems. Mm. So that's, that's gonna be it, you can abstract away from the economist role. You can abstract away anything else, but I like to work on things that are hard to solve, which is that's gonna be valuable in so many different contexts. And then specifically in the economist frame, again, we're, we're probably comparing to other economists, I'm much more of a variety speaker. So I wanna work on a variety of problems. And I mean, you're
Scott Cunningham:
People that are not like that.
Kyle Kretschman:
I mean, right. So there would be people who are gonna be much more focused on changing the methodology for, I mean, Scott, you work in, cause inference, you tell me, what are you, what are you working on? That's gonna be changing some sort of estimator. Right, right. That's that just doesn't hold that much appeal to me, I'll say it. What does appeal to me is this entangling a problem, making it less, making a super ambiguous problem solvable. Right. And then having people dive in and solve that. So, yeah. Yeah. I guess the Reddit theme variety speaking on ambiguous problems,
Scott Cunningham:
It's funny, you know, like maybe you heard at some point in your career, I'm just guessing someone, or I could imagine someone saying you're not focused.
Kyle Kretschman:
Yeah, I
Scott Cunningham:
Did. And, and in fact that might have been true and really not relevant. Right. Well,
Kyle Kretschman:
It was probably, it was probably relevant feedback to certain end goals. But again, once I, once I realized what the end goals were, it's all of this to tied them all together, all of this led up to where and economi and tech is exactly where yeah, I was, I was meant to be helping quote.
Scott Cunningham:
Right. Right. Well, you know, I think like one thing that I, I feel like is really positive about the, the growth of the, of tech. And in many ways I see now at universities are now in direct competition with tech and I, and I wouldn't, I don't think that was true 50 years ago that maybe, maybe universities were in competition with government. I don't even know if that was exactly like it is now, you know? And it, it seems like what I see with you is, is a person who, who, who took ownership of their life. Right. And just sort of said, I don't wanna put words in your mouth, but it, but it seems like one of the things that it's like, you know, I think young people can be encouraged by or should hear is that, you know, here's a person that took ownership of their life. This was their career. It's the only life they had. This was their career. And they, they, they had the fortitude and the resilience and the, you know, to, to be themselves and make a career. Is that true? You think,
Kyle Kretschman:
I appreciate you saying that. And it's probably true again, I kind of like hesitate because again, during, in my twenties and the searching phase, it definitely didn't feel that
Scott Cunningham:
Way. Didn't feel that way.
Kyle Kretschman:
Right. But now in the decade of my thirties, yeah, it did. Because, but I was, I was more than willing to explore different avenues. Yeah. Because that's what, because I wasn't, I wasn't willing to settle, I guess I'll say that, like, I wasn't willing to settle for something that I didn't feel was the right fit for me. Yep. And so that's, that's what a big driver that's been able to help me get here for
Scott Cunningham:
Sure. Yep. Yep. I think that, I think the, the truth is economics is a really valuable PhD and it supports a, it, it supports many personalities with it and it, but you know, like I do think sometimes one of the skills of that, of people that are in that stage of searching is just to be able to be, to wait. Yeah. You know, you're like, so you don't always know that you're planting seeds and, and you know, you can't harvest seeds when they're seeds, you know, you can't harvest plants when they're seeds, you have to wait. Yeah. But you have to be disciplined and continue to water it too, though.
Kyle Kretschman:
Yeah. And also be, be willing to take some risks once in a while
Scott Cunningham:
And willing to take some calculated
Kyle Kretschman:
Risks. But yeah. Specifically like for tech economists, now there is a lot more information. And I will say probably five years ago, I probably cautioned some people from going to get their PhD in economics if they wanted to be a tech economist, because it was still, you know, the majority of people who jumped into it were risk takers, willing to do that. There wasn't this. But now I believe the head of NABE has said that there's over 1500 economists in tech. Now we have a great conference that we spun up while we're, while, while I was at Amazon, that I was a part of that now NABE tech in November. So if people wanna learn, now, the ability is there. So in grad school, like come to NA tech, come see, come look at the program, come see exactly. Cuz it was, it's a conference that's designed by tech economists for tech
Scott Cunningham:
Economist. Yeah. Susan mentioned it too. A B E national association, business economics. I actually, it's a huge conference and I actually don't think in academia, a lot of people know about it.
Kyle Kretschman:
Yeah. That's cause
Scott Cunningham:
We just know about the as SSA.
Kyle Kretschman:
Right. And that's one of the reasons I kind of wanted to plug it here, honestly. Cause again, it, it, it started out probably 50 people. I forget what year, but now it's up to, I think they're expecting over 500. And so that is, so some of this discovery is searching that if people, if people who are in their PhD wanna learn more about, that's gonna be the best place.
Scott Cunningham:
I only have two more questions and then I'm gonna let you go. One of 'em is kind of touchy, feely, but okay. So, so, and I, and I I'll just admit, as I ask this question, you're gonna immediately know, I must think this way. And so that does not mean that you think this way, but you know, I love being an economist. I love economics. I love being an economist. And if I was to go get a job and the job title said, didn't say economist on it. I would be, I would feel, I, I can already tell that would be something I would have to work through. Cuz I feel so connected to the tribe. And I was just wondering, you know, do you think that economists and tech feel connected to the tribe?
Kyle Kretschman:
It, it gets to which tribe
Scott Cunningham:
Do you think they feel like a part of the broader American economics association?
Kyle Kretschman:
My I'll say my personal view is probably not because that's not the colleagues that we've grown up with over the past 10 years. Do we feel part of the tech economist community? Yes. There is one there very much is one. And do we feel part of that tribe very much. Do I feel part of the academic economist tribe? Probably not. Yeah. But to tie it all together, like do I feel like I'm an economist? Yes. Yeah. A hundred percent. Yes. And again, we're making differentiations probably for your audience, but in the grander scheme of things, if you look at, if you look at anybody in tech, who's an economist they're gonna to, to non they're, they're economists
Scott Cunningham:
They're economist. Right? They, they, they, they, they think different.
Kyle Kretschman:
Yeah. I mean, I'll give a quick anecdote, like one of my, one of my good friends and colleagues at Amazon Neil go, she's not there anymore. His wife and my wife just would always be like, they just respond to the, the question in the exact same way.
Scott Cunningham:
Yeah. Right.
Kyle Kretschman:
So that's like, they very much are just so rational with their responses in general, you know, there's exceptions, but they, they would compare notes and we'd have the same general view and outlook on personal finances. Let's say so. Yeah. But I think what you're heading at is, is their different tribes that are growing up. Yeah. Because I think there's probably always been different tribes. And so tech economists is a tribe that is, I mean is more than growing up as again, we think there's probably hook going on 2000 of them and there's very strong connections within that.
Scott Cunningham:
Yeah. I guess I wonder sometimes I'm like, what's the point? The point is community. The point is not for the, the goal is not for the AA to have everybody. The goal is for, you know, I think the goal is for people to thrive and feel connected to their broader, to a broader community. And if that's grown and ly in tech, that's good. I just always kind of think to myself, like, but at the end of the day, the universities are staffed by faculty in the AEA and it, it just seems like, and so it's like, it, it seems like at the graduate training, you know, many people can feel job dissatisfaction because they know they're not gonna become an academic, you know? And that, that can be a, that might be a source of some of the struggles. There's a new paper in the general economic literature about mental health struggles amongst PhD students and economics.
Scott Cunningham:
And there would, you could, if you read between the lines, if you read between the lines, there was a real disconnect between them and their advisor. They, the, the, you know, you don't know causality, cuz it was just a survey, but like, you know, the, the students were like, they just seemed to have job dissatisfaction. They didn't think their work mattered. You know? And, and I just kept thinking in my mind, you know, do they know about all their options? Are any of those options stigmatized? You know, because like, if you are on, if you've held it up to students that the only meaningful career that you could possibly have is at a, is tenured at a, at a university writing papers that get into top fives and you, and you sense inside your heart, you know, that just, I don't think that that's, that's not doing it.
Kyle Kretschman:
Yeah. I think it ties together what you said earlier that economics welcomes a whole bunch of different viewpoints of world views, but maybe doesn't reward the diversity of the way it should in academic. And so like from a diversity viewpoint, these are different career paths, but even more shows should welcome more ideas. So like that's probably where the economics field needs to continue to lean in is with the diversity to enable people, to not to know different paths, to raise up people on different paths and have those opportunities. And I think tech is providing a huge pathway for
Scott Cunningham:
That. Yep. Yep. I do too. I am very grateful for it. It seems really exciting. It seems like a world of just like working on I'm sure that the way we think about, you know, gossip at Guinness coming up with, you know, students tea of this story of a, of a person in industry making these major contributions, we're just at the beginning of, of that, you know, with just the sheer volume of, of social scientists in tech, working on fascinating important topics. My last question, what is your favorite paper in economics or no, let me say it this way. What is a paper in that has, that has, for some reason stuck in that has stuck around in your head
Kyle Kretschman:
BLP so yeah, it, it has, because that was probably the first time that I felt like the wow moment of we can, we can estimate substitution effects with observational data. Don't need experiments. We have this method that obviously has proliferated into so many different ways and that stuck with me because throughout all my stuff, I've generally been super in substitution effects. So yeah. Yeah. I would put that in that research thread.
Scott Cunningham:
Yeah. I, I, I don't think that that's that you having that empirical IO structural, you know, having written in your head.
Kyle Kretschman:
Yeah. Right,
Scott Cunningham:
Right. That's great. Well, it is a real pleasure to, to get to walk through. I appreciate you walking me through your, your life and tell me about your, your career. It's really nice pleasure to meet.
Kyle Kretschman:
Yes. Thanks Scott. I really appreciate the.
Get full access to Scott's Substack at causalinf.substack.com/subscribe
I first met Peter Arcidiacano, professor of economics at Duke University, while I was a PhD student at the University of Georgia and I have followed his work since from a distance. I originally followed Peters work because he’d written several articles about sex from a two-sided matching perspective. I was struck by the fact that we both saw thinking about sexual relationships in terms of a matching problem. Two sided matching perspectives focus on the assignment mechanisms that bring people together, and when it comes to sexual relationships, the relative supply of possible partners and competition for those partners will in equilibrium result in pairings, some of which may become the most life sustaining and defining partnerships of those peoples lives. Peter’s work was gratifying to read, and I have often looked up to him for his successful merging of theory and econometrics to study topics I cared about.
The economic way of thinking is not about topics, nor is it is not about data, even though economists tend to have particular topics they study intensely and use data usually to do so. The economic way of thinking does though typically involve careful study of allocation mechanisms, such as prices and markets, that bring the productive capacity of communities into existence. These things are important as they animate humans to work together, produce output that manages the production itself, and increasingly towards the end of history, left surplus for humans to enjoy. Who ends up in what activities doing what types of specialized work ultimately shapes that which is made, how much and how it is distributed. The allocations end up not only shaping our lives, but our children’s lives. Starting conditions can cast a long shadow lasting centuries even causing certain groups to creep ahead as more and more of the surplus mounts and accrues to them, while others watch as a shrinking part of the growing pie flows to them.
In the United States, in the 21st century, one of the key institutions in all of this assignment of love and commerce has been the university. And within the university system, there are gradations of institutional pedigree and at the top of the pack sits elite institutions whose students seem practically destined to shape and receive the surplus. Given the path dependence in wealth, and how it has interacted with race, it is therefore no wonder that policymakers and economists have for decades sought to refine the rules by which schools can select high school applicants for admission. In many ways, our country’s fight over the use of race in selecting students into college is the old debates about capitalism and the self adjusting market system writ large.
So it’s in this broader context about work, schools, matching and allocation mechanisms that I think of Peter and his scholarship. When I review the range of topics on Peters vita, I see the signature marks of the modern 21st century labor economist. Someone interested in markets and how they work to connect people into productive cooperation. Someone interested in institutions, someone concerned about inequality and discrimination, someone versed both in economic theory and econometrics, someone at home with a bewildering array of numbers in a spreadsheet. To me, it is natural that Peter has pivoted so fluidly between topics like sex, work, discrimination and higher education because in my mind these are all interconnected topics concerning the assignment mechanisms we use in America to organize society and maintain our collective standard of living.
I invited Peter on the Mixtape with Scott as part of an ongoing series I call “economists and public policy”. The series focuses on how economics and economists think about and attempt to shape public policy. It includes people with a variety of perspectives, and even some who are critics of economics itself. Previous guests on the podcast in the “economists and public policy” series have been Sandy Darity, Elizabeth Popp Berman, Anna Stansbury, Mark Anderson, Alan Manning, Larry Katz, Jeremy West and Jonathan Meer.
Peter has not only produced academic articles in some of economics’ most impactful outlets — he has recently served as expert witness in two major discrimination cases, one of which put him on the opposite side of the stand as David Card, winner of 2021 Nobel Prize in economics. You can read about the cases here. They involve the broader topic of race and affirmative action at universities. The cases more specifically involve whether Harvard and UNC Chapel Hill admissions criteria show signs of discrimination.
One of the things about Peter’s involvement as expert witness that I want to highlight, though, is that his expert testimony was, at its core, an example of the role that econometrics can play in the shaping of public policy. It is more and more the case that economics’ role in the shaping of public policy in the 21st century will involve not merely economic theory, but also statistical analysis of complex datasets too, and I think it is worth pausing and noting that the economist shapes public policy oftentimes these days as much through interpreting data as her counterpart did using pure economic theory. I hope you find this discussion with Peter thought provoking and informative about both his work on these cases, but also about the role of economics and econometrics in forming public policy. But I also hope that the interview will give you a deeper insight into Peter and who he is.
Scott’s Substack as well as The Mixtape with Scott are supported by user subscriptions. Please share this episode to people within and outside economics that you think might be interested. I love doing these interviews and using the substack to do deeper dives into econometrics and the lives of economists and if you find this work valuable, please consider subscribing and supporting it.
Scott's Substack is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.
Transcript
Scott Cunningham:
In this week's episode of Mixtape: the Podcast, I had the pleasure of talking with a professor at Duke University, named Peter Arcidiacono. I can never pronounce it correctly, no matter how many times I try. I first met Peter in graduate school. He was, probably then, an assistant professor at Duke, where he has spent his entire career. I was a PhD student at the University of Georgia. And he had a research paper on a topic that I was also working on, involving marriage markets. He's been an incredibly prolific producer in the area of labor economics and education, as well as affirmative action. And he uses tools in econometrics, that I largely never invested in, structural econometrics and discrete choice modeling. So when I read his work, I usually do it, both, because I'm interested in the paper and the paper topic, but also because I'm hoping that this will be a chance for me to open my mind a little bit more, and pick up on some of that econometric modeling, that I lack.
Peter is also an expert witness in a high profile case, right now, involving affirmative action and racial discrimination at Harvard University, and the University of North Carolina Chapel Hill, both of which have been combined into a single case. As I understand it, it's going for the Supreme court soon. In this interview, we walk through a lot of big and small issues around society's preferences around poverty, inequality, as well as the role that higher education is playing in both. My name is Scott Cunningham, and this is Mixtape: the Podcast.
Scott Cunningham:
Okay. This is great. I don't know if you remember. So this is an interview with professor of economics at Duke University, Peter Arcidiacono. And we're going to be talking about a range of topics. But just to give the reader and the listener a little bit of background, Peter, could you tell me a little bit about yourself, and what your involvement is with a current case, going before the Supreme court, involving University of North Carolina and Harvard University?
Peter Arcidiacono:
Certainly. And thanks for having me on. I've been at Duke now, for over 20 years. This was my first job out of grad school, and stayed here ever since. And a lot of my work has been on higher education, both with regard to choice of college major, as well as affirmative action.
And one of the really dissatisfying things about working on affirmative action, is that universities hide their data. So you can't really get a good sense of how the programs are working, because you typically don't have the data. And I think that that really matters, because to me, so much of the discussion about affirmative action, is in the binary. Either we have it, or we don't have it. But what it means to have it, is something, as economists, we would think about, that's something we would be optimizing over. And so, there's really a large space between race as a tiebreaker in admissions, and what somebody like Abraham Kennedy would advocate for, which would be more of a quota system.
And so, thinking about where you stand on that, to me, I had this opportunity to work on these two cases, two lawsuits. One brought against Harvard, and one against UNC, on the role of race in these admissions processes. And for me, it was an opportunity to look behind the veil, and see how these programs actually operated.
My intent was always to, a feeling as though, if I'm going to be an expert on affirmative action, I should know how these processes actually work. So my intent was always to use this for the purposes of research, as well. And we've written a number of papers out of the Harvard case. Four have been accepted now, and we just released a fifth one on racial preferences of both schools. And we'll see what happens with that. So those lawsuits, I testified in trial, at both those cases. My counterpart in the Harvard case was David Card, who recently won the Nobel Prize. I was wondering how I would respond to that. And actually, my response, I got to go up against a Nobel Prize winner.
Scott Cunningham:
Yeah. Right. Right.
Peter Arcidiacono:
So those experiences were somewhat traumatizing. But both experts, David Card and Kevin Hoxby, are pillars in the field, and people who have been very helpful to me, and who I have a great deal of respect for.
Scott Cunningham:
Yeah. Yeah.
Peter Arcidiacono:
So these cases have now, in both those cases, the side I was on lost at the first round. In the Harvard case, they also lost at the appellate round.
Peter Arcidiacono:
In UNC, it didn't actually go through the appellate round, because-
Scott Cunningham:
Oh, so-
Peter Arcidiacono:
... supreme court merged the cases.
Scott Cunningham:
... Both the Harvard University case and the Chapel Hill case, were already decided, but not at the Supreme Court level.
Peter Arcidiacono:
That's right.
Scott Cunningham:
Okay.
Peter Arcidiacono:
So the decision was appealed. It's now before the Supreme Court.
Scott Cunningham:
Yeah.
Peter Arcidiacono:
I think the Supreme Court scheduled here, arguments in October, and then, we'll see when they release a decision.
Scott Cunningham:
Okay. So, and these are both cases involving affirmative action and racial discrimination amongst particular groups of people? Is that groups of students, is that right?
Peter Arcidiacono:
That's right. Though, in the UNC case, there's actually no claim of Asian American discrimination. So that actually, you only see that at Harvard. You don't see that at UNC. That doesn't mean, I think that Asian discrimination is unique to Harvard.
Scott Cunningham:
Right.
Peter Arcidiacono:
I think it has to do with the fact of there not being that many Asian Americans in North Carolina.
Scott Cunningham:
North Carolina, right.
Peter Arcidiacono:
It's always been a bigger issue at the very top schools.
Scott Cunningham:
And you were called in, as an expert witness, for the plaintiff in both of those cases.
Peter Arcidiacono:
That's right.
Scott Cunningham:
Right. So David Card is the expert witness for Harvard, representing Harvard, against an accusation of, well, what exactly is the accusation against both of these institutions, and who brought these accusations against them?
Peter Arcidiacono:
So the group is called Students for Fair Admissions. And they basically got groups of students to, as their plaintiffs. Though, it's not about those particular students, in terms of remedies. And in Harvard, there's three claims. One, whether or not they're discriminating against Asian American applicants, relative to white applicants.
Scott Cunningham:
Mm-hmm.
Peter Arcidiacono:
Two, whether the size of the preferences given for underrepresented groups, is constitutional.
Scott Cunningham:
Yeah.
Peter Arcidiacono:
And three, whether there were race-neutral alternatives that they could have used. So the Supreme Court has said, "If there is a race-neutral alternative, you should use that."
Scott Cunningham:
Okay.
Peter Arcidiacono:
I'm not really involved at the race-neutral part. We had a different expert for that aspect.
Scott Cunningham:
Yeah.
Peter Arcidiacono:
Though, in both cases, Card and Hoxby actually did the race-neutral part, as well.
Scott Cunningham:
What exactly does the constitution say a admissions committee can use, when drawing up a student cohort?
Peter Arcidiacono:
Well, so I'm not sure what the constitution has to say on it, but I can say what the history of this of the court challenges have been.
Scott Cunningham:
Okay.
Peter Arcidiacono:
So I think, it's Title VI of Civil Rights Act said, "You're not supposed to use race-"
Scott Cunningham:
Race.
Peter Arcidiacono:
"... in these types of things." And there are other categories too.
Scott Cunningham:
Okay.
Peter Arcidiacono:
But race is the focus of this one. Now, the reason they had that, was because of the history of ill treatment of African Americans.
Scott Cunningham:
Right.
Peter Arcidiacono:
And this is obviously going in the other direction-
Scott Cunningham:
Mm.
Peter Arcidiacono:
... with regard to African Americans receiving preferences in the admissions process.
Scott Cunningham:
Mm. Mm.
Peter Arcidiacono:
So, but then, the history was that the original decision, the Bakke case, said, "Look, you can't use race in admissions, because of reparations. You can only use it because of the benefits of diversity." So the state can have an interest in diversity. And that was a compromised position to get that swing justice, to sign onto it.
Scott Cunningham:
Mm.
Peter Arcidiacono:
Since then, there have been a number of cases. I think the ones that are most relevant right now, are the ones that came out of the Michigan cases.
Scott Cunningham:
Yeah.
Peter Arcidiacono:
And there was one at the undergraduate level, which they found that you could not use race as part of an explicit point system.
Scott Cunningham:
Mm.
Peter Arcidiacono:
So you can get points for having a good SAT score, points for being a particular race, you add them up together, then you could rank the-
Scott Cunningham:
I see.
Peter Arcidiacono:
... applicant.
Scott Cunningham:
So there were schools that were doing a point system based on individual characteristics, like race. And that was, at that moment, it was unclear whether that would be legal. It was, I guess, or was it something that schools were, potentially, in a legal, bad situation, when they were using it? Or was it just not known?
Peter Arcidiacono:
I don't think it was clear. And that's where the court ruled. You cannot use it in that way.
Scott Cunningham:
Got it. Okay.
Peter Arcidiacono:
At the same time, there was a case against Harvard's Law School. And on that one, they said that you could use race, holistically. As an economist, I can express anything as a formula. And then, the question is, whether you see all parts of the formula or not.
Scott Cunningham:
Yeah.
Peter Arcidiacono:
So it gets a little tricky. And I think that, from my perspective, I would've rather had the ruling go in the exact opposite way.
[inaudible 00:11:59] on if we're going to find in favor of one or the other.
I would prefer a point system to a holistic one, because then, everything's clear.
Scott Cunningham:
Clear. Yeah. It seemed really precise-
Peter Arcidiacono:
[inaudible 00:12:09], to hide their data.
Scott Cunningham:
... Yeah. It seems like lots of times with the law, the imprecision of this language, as though it's a solution to the problem, is really challenging for designing policy.
Peter Arcidiacono:
I totally agree. Yeah.
Scott Cunningham:
So, okay. I want to set up the reader a little bit, oh, the listener, to know who you are before we dive into this, because I'm loving this thread, but I want people to know who you are. So before we get more into the case, can you tell me where you grew up, and why you got into economics? Your first, what was the touchstone that brought you into this field?
Peter Arcidiacono:
So I grew up in the Pacific Northwest. My first set of years were actually in Ellensburg, Washington, which is a town of 13,000. My dad was a math education professor.
Scott Cunningham:
Oh, okay. What university was he a professor at?
Peter Arcidiacono:
Central Washington University.
Scott Cunningham:
Okay. Okay.
Peter Arcidiacono:
And then-
Scott Cunningham:
Hey, but what'd you say it was? What was it again?
Peter Arcidiacono:
... It was math education.
Scott Cunningham:
Math education.
Peter Arcidiacono:
Yeah. So he was teaching teachers how to teach math.
Scott Cunningham:
Oh. So you've always been, it's in the family to be interested in education?
Peter Arcidiacono:
Yes. And-
Scott Cunningham:
And even this math education part. That's another way of describing an economist that studies education.
Peter Arcidiacono:
... Right.
Scott Cunningham:
Math education.
Peter Arcidiacono:
Well, my parents actually met in linear algebra class, so.
Scott Cunningham:
Oh, that's romantic.
Peter Arcidiacono:
And I've got two brothers, and they were both math majors.
Scott Cunningham:
Oh, wow. Okay.
Peter Arcidiacono:
I was the only non-math major.
Scott Cunningham:
Okay. Okay.
Peter Arcidiacono:
But I came into college, and started out in chemistry. I think, Econ PhD programs are filled with former, hard science majors.
Scott Cunningham:
No joke. Yeah, yeah. They hit organic chemistry, and then, they changed their major.
Peter Arcidiacono:
Right. And I just couldn't stand the lab. It was too social. And one of my good friends, a guy who ended up being the best man at my wedding, was a couple years ahead of me, told me I should take an economics class.
Scott Cunningham:
Mm.
Peter Arcidiacono:
And it was amazing. I think that just the way of thinking, just worked naturally for me.
Scott Cunningham:
Well, so when you say way of thinking, the way of thinking that was, can you tell me what your 19 year old self would've been jarred by? What are the specific things, that economic way of thinking, that he was noticing?
Peter Arcidiacono:
Well, it just fit with a lot of how I operated. So I view economics as a great model of fallen man.
Scott Cunningham:
Uh-huh.
Peter Arcidiacono:
Fundamentally, I was the guy who always looked for the loopholes. So responding really well to incentives. I had a keen eye for how I could game the system.
Scott Cunningham:
Right. Right.
Peter Arcidiacono:
And so, I think a lot about what economics is doing, is the dismal science, right? The reigns on the parade of well-intentioned policies.
Scott Cunningham:
Right. Right.
Peter Arcidiacono:
How are people going to get around the policies? Well, that's where I lived, was figuring out how I could game the system.
Scott Cunningham:
Right. Right. Right. So you were, this idea of that rational choice paradigm, is that what you mean?
Peter Arcidiacono:
Yeah.
Scott Cunningham:
And that-
Peter Arcidiacono:
Yeah.
Scott Cunningham:
... that people would just simply, if they have goals, those goals don't just go away with a policy. They might just continue to try to achieve those goals at lowest cost, even then.
Peter Arcidiacono:
Exactly.
Scott Cunningham:
Right. Right.
Peter Arcidiacono:
And the other studying thing, which I think, really affected why I ended up doing the research that I did, was, for me, the chemistry classes were just way harder-
Scott Cunningham:
Uh-huh.
Peter Arcidiacono:
... than economics classes.
Scott Cunningham:
Yeah.
Peter Arcidiacono:
And I'm not trying to say that any classes are easy.
Scott Cunningham:
Yeah.
Peter Arcidiacono:
But there is definitely large differences-
Scott Cunningham:
Mm-hmm.
Peter Arcidiacono:
... in every university, and what the expectations are-
Scott Cunningham:
Mm-hmm.
Peter Arcidiacono:
... across fields.
Scott Cunningham:
Yeah.
Peter Arcidiacono:
And that distorts people's behavior.
Scott Cunningham:
Mm.
Peter Arcidiacono:
So I view it, that most colleges are subsidizing students, to go into low paying fields. And how do they subsidize them to do that? They offer higher grades-
Scott Cunningham:
Mm.
Peter Arcidiacono:
... and lower workloads, smaller class sizes. All those things work, so that lots of people come in wanting to major in well-paying fields, and switch in, and switch out.
Scott Cunningham:
Right. Right. Right.
Peter Arcidiacono:
And they do so because of the incentives the universities provide.
Scott Cunningham:
Yeah. So you got interested in economics, and that's like, you're describing some sort of price theory, microeconomics. But you've also have made a career out of being such a strong econometrician in this area of structural econometrics and discrete choice modeling. How did you get interested in those topics? What was your first reaction to econometrics?
Peter Arcidiacono:
I had a very strange econometrics background. So my first year econometrics, was taught by Chuck Manski.
Scott Cunningham:
Oh.
Peter Arcidiacono:
The whole year. And so, it was lots of bounds.
Scott Cunningham:
Uh-huh.
Peter Arcidiacono:
And then, my second year, it was all John Rust.
Scott Cunningham:
Mm.
Peter Arcidiacono:
So a complete swing, right? So you go from the non-parametrics, what can you identify under the smallest number of assumptions?
Scott Cunningham:
Mm.
Peter Arcidiacono:
To what can you identify, if you want an answer something really big.
Scott Cunningham:
Right.
Peter Arcidiacono:
You got to make a lot of assumptions to make that.
Scott Cunningham:
Oh, boy. That's an interesting journey, right there.
Peter Arcidiacono:
So I actually never had the mostly harmless econometric-
Scott Cunningham:
Right. Right.
Peter Arcidiacono:
... at all.
Scott Cunningham:
Yeah. Yeah. Yeah. Yeah.
Peter Arcidiacono:
And the econometrics has always been-
Scott Cunningham:
This was Wisconsin?
Peter Arcidiacono:
... That's right.
Scott Cunningham:
Yeah.
Peter Arcidiacono:
That's right.
Scott Cunningham:
What year was this?
Peter Arcidiacono:
In the econometrics, the advances were always more, because I needed to do something to estimate my models.
Scott Cunningham:
Right. This was mid nineties? This would've been the mid nineties, or late nineties?
Peter Arcidiacono:
I'd like to say late nineties. Yeah-
Scott Cunningham:
Late nineties? Okay. Yeah-
Peter Arcidiacono:
... [inaudible 00:19:10].
Scott Cunningham:
... Yeah. Yeah. Yeah. Yeah. Okay, keep going. Sorry.
Peter Arcidiacono:
So I was thinking about my own experience, in terms of choosing a college major, and thinking about, Well, people are learning over time. They start out those STEM classes, and figured out, wow, this is a little bit harder than I expected.
Scott Cunningham:
Yeah. Yeah.
Peter Arcidiacono:
And then, moved through.
Scott Cunningham:
Right.
Peter Arcidiacono:
So I had a mind, I actually had the idea for my job market paper, my first year. And had this idea of a forward looking model, of how people choose their college major.
Scott Cunningham:
Mm-hmm.
Peter Arcidiacono:
And so, then, I go into John Rust's office, because he's my second year econometrics professor, and was describing this problem to him, that people are making decisions today, giving expectations about the future.
Scott Cunningham:
Mm-hmm.
Peter Arcidiacono:
And he says, "Yeah, I think I can help you with that." And I was like, "No, you don't understand. This is a really hard problem." And of course, John Rust had written the [inaudible 00:20:13] paper about how to estimate these types of models-
Scott Cunningham:
Right.
Peter Arcidiacono:
... And he was fantastic with me. [inaudible 00:20:20]. He didn't say idiot. You could at least look at what I do, before you come to my office. He was fantastic with me.
Scott Cunningham:
Yeah. Yeah.
Peter Arcidiacono:
And actually, the funny story about that too, is he's actually the only reason I'm an economics professor, because-
Scott Cunningham:
Oh, yeah?
Peter Arcidiacono:
... I only got into one grad school. Got rejected from much worse places in Wisconsin. It was the only place that accepted me.
Scott Cunningham:
Mm.
Peter Arcidiacono:
And the joke was that that was the year John rusted everybody in. So there were 53 of us to [inaudible 00:20:57].
Scott Cunningham:
That's awesome.
Peter Arcidiacono:
17 got PhDs.
Scott Cunningham:
Wow.
Peter Arcidiacono:
And if you look at another guy, one of my friends, I just actually found out we were actually at a conference in honor of John Rust, this past weekend.
Scott Cunningham:
Yeah.
Peter Arcidiacono:
And it turns out, that was the only place that admitted him, as well. And he's been incredibly successful too.
Scott Cunningham:
The John Rust fixed effect is filled with stories.
Peter Arcidiacono:
That's right.
Scott Cunningham:
That's really cool. That's really cool. I'm curious, thinking about what your, I want get to the Harvard and the Chapel Hill. But before we move on, you could imagine, had you gone to Princeton, or MIT, and worked with, or Berkeley, and worked with these, the treatment effects guys, like Imbens, and Angrist, and Card, and Kruger, and O'Reilly, and all these people. It's not just that your knowledge of econometrics would've been slightly different. Even the kinds of questions, that you would be asking, might be different. So I'm curious, what do you think your training and structural, under Manski and Rust, how has that shaped, not just the way you do your work, but even the types of questions that you ask, that you imagine, you might not have asked? For instance, just even thinking, modeling choice-
Peter Arcidiacono:
[Inaudible 00:22:40].
Scott Cunningham:
I'm sorry. I don't know. Did I lose you?
Peter Arcidiacono:
You froze on me.
Scott Cunningham:
Ah, I froze? Okay-
Peter Arcidiacono:
You're still frozen.
Scott Cunningham:
... I'm still frozen? Okay. There. Okay.
Peter Arcidiacono:
Now, you're back. So you're asking about what types of questions.
Scott Cunningham:
Yeah. What kinds of questions do you think you ended up being really interested in, and working on? Not just the model that you wrote down, but even the actual topics. Because I'm curious, I'm wondering if listeners could really frame their understanding of this structural, versus this causal inference, tradition. Not just in terms of the technical pieces, but like this is practically how, the work a person ends up, that you think you ended up doing, versus if you had got Angrist as an advisor.
Peter Arcidiacono:
Oh, I think it has shaped me quite a bit. I am certain that if I'd gone to a place like Chicago, I would've probably ended up working with Steve Levitt. I am naturally attracted to some of those topics, that are more of a freakaconomics-type nature. And if you look at it, we actually had competing papers-
Scott Cunningham:
Yeah.
Peter Arcidiacono:
... on discrimination in the Weakest Link game show.
Scott Cunningham:
Uh-huh. Yeah.
Peter Arcidiacono:
And I've written a couple of sports papers. So I have that in me, to think about those types of things. If I'm-
Scott Cunningham:
Topics, right? Right.
Peter Arcidiacono:
... Yeah.
Scott Cunningham:
Yeah.
Peter Arcidiacono:
I think that the Manski Rust combination did have a big effect on me, and, in the types of questions that I asked. Which is what structural brings to the table, is thinking about mechanisms.
Scott Cunningham:
Mm.
Peter Arcidiacono:
So when you think about the effect of affirmative action on outcomes, understanding why the effect is what it is, matters.
Scott Cunningham:
Yeah.
Peter Arcidiacono:
How it affects application behaviors. How is affects what majors issues. What would be those counterfactuals? And for that, I think you need some of these structural approaches.
Scott Cunningham:
Right.
Peter Arcidiacono:
Now, one of the things about those structural approaches, to say, typically involve making some pretty big assumptions.
Scott Cunningham:
Yeah.
Peter Arcidiacono:
And I think that that's where the Manski influences had on me, because I also have papers that use subjective expectations data. And I think that that is actually an incredibly promising area of work.
Scott Cunningham:
Mm.
Peter Arcidiacono:
It's quite clear that people don't know as much as they should know, when they make important decisions. Certainly, higher education being a prime example of that.
Scott Cunningham:
Yeah.
Peter Arcidiacono:
COVID really makes that clear, you know? How can it be that the people who are unvaccinated, are least likely to wear a mask? Clearly, they're operating under very different beliefs about-
Scott Cunningham:
Right.
Peter Arcidiacono:
... what's going to happen.
Scott Cunningham:
Right. Right. Right. Right. Okay. So let's move into this Harvard Chapel Hill project. So setting it up, tell me, what is the first event that happens, that makes this a case against Harvard? Not counting alleged discrimination, but the actual historical event, that leads to a need for an expert witness.
Peter Arcidiacono:
Well, I think the need for the expert witness came about, because Harvard had to release their data, in the context of the trial.
Scott Cunningham:
Mm.
Peter Arcidiacono:
So in the context of the lawsuit, the claim was there were some smoking guns that suggested the possibility, for example, of Asian American discrimination
Scott Cunningham:
That would not fit this holistic criteria, that you mentioned earlier?
Peter Arcidiacono:
Well, so, it's an interesting question, right? So you can't have with the holistic criteria, you can take race into account, but the question is whether you could take race into account, in a way that penalizes a group, relative to white applicants?
Scott Cunningham:
Yeah.
Peter Arcidiacono:
So it might be one thing to say, "We're going to give a bump for African Americans, relative to whites."
Scott Cunningham:
Yeah.
Peter Arcidiacono:
Maybe another thing to say, "We're going to give a bump for whites, relative to Asian Americans."
Scott Cunningham:
Yeah. Yeah. Right. Right. Okay. So they've had a lawsuit brought against Harvard. Harvard's had a lawsuit filed against them. What year is-
Peter Arcidiacono:
[inaudible 00:27:32]. Sorry, say it again.
Scott Cunningham:
What year would that have been?
Peter Arcidiacono:
Oh, man. I think it was back in 2015, or something like that.
Scott Cunningham:
- Did anybody see that coming? Or was this odd, this is just inevitable?
Peter Arcidiacono:
I think that, they were advertising for plaintiffs, students who had been rejected. So certainly, there was an intent to file such a lawsuit, for sure. And then, they had to weigh what universities to file it against. And they chose Harvard, because of the patterns on what were going on with Asian Americans. And I think UNC had more to do with the, there was some evidence in the record, from past cases, that race-neutral alternatives would work there.
Scott Cunningham:
Mm-hmm. Okay. So you get involved. How do you get selected as the expert witness? And what's your job, exactly, in all this?
Peter Arcidiacono:
So I think I get selected, I've written a couple of survey articles on affirmative action. And I view it that there are lots of nuances. So the fact that I would actually say there are nuances, as opposed to it being always good-
Scott Cunningham:
Right.
Peter Arcidiacono:
... made it attractive for them, I think.
Scott Cunningham:
Mm-hmm.
Peter Arcidiacono:
And back in 2011, there was actually a protest here, at Duke, over one of my studies.
Scott Cunningham:
Oh, really?
Peter Arcidiacono:
Yes. So that one, we were actually using Duke data, and confronting a tough fact, which is lots of black students at Duke came in, wanted to major in STEM and economics, but switched out. In exploring why they were switching out at such a higher rate, relative to white applicants.
Scott Cunningham:
Mm-hmm.
Peter Arcidiacono:
So for men, it was very extreme.
Scott Cunningham:
Yeah.
Peter Arcidiacono:
8% of white men switched out of STEM and economics-
Scott Cunningham:
Mm-hmm.
Peter Arcidiacono:
... to a non-STEM, non-economics major. Over 50% of black males switched out. And you look at that, you think, that's a problem.
Scott Cunningham:
Mm-hmm.
Peter Arcidiacono:
And once you account for the differences in academic background, prior to Duke, all those racial gaps go away. And I think what, the path to the protest to serve in the long run. So I won't get into all details of that, but I think that they didn't believe the fact at first.
Scott Cunningham:
And what was the fact exactly, that the racial discrimination, the racial bias, the racial differences vanished, once you conditioned on what, exactly?
Peter Arcidiacono:
I conditioned on academic background.
Scott Cunningham:
Oh, I see. Okay.
Peter Arcidiacono:
Course and such like that. But I think even the original effect, they were surprised by, which was that the switch out rates were so different.
Scott Cunningham:
Yeah.
Peter Arcidiacono:
And at that time-
Scott Cunningham:
But why is that a protest against you? What does that have to do with you, if you're just documenting facts?
Peter Arcidiacono:
Well, I think that the negative press headline said, potentially racist study says black students are taking the easy way out. And so-
Scott Cunningham:
Potentially racist study.
Peter Arcidiacono:
Potentially racist study. Yes.
Scott Cunningham:
This study was racist.
Peter Arcidiacono:
That's right. And I think that the issue, it actually makes a lot more sense now, than it did to me at the time. And economists thought this was crazy at the time. It's actually interesting, because I got attacked from people all over the country. It didn't make a major news flash, but within certain circles, it did. And actually, one of the people who wrote about it at the time, was Abraham Kendi. This was before he changed his name. He's not the, he wasn't famous in the same way that he is now. But the fact that I wasn't pointing the finger at the departments, I was pointing the finger, I think it was interpreted as victim blaming. It's their fault that they're switching out because they're not prepared. That's never how I would want to frame it. I would want, to me, this is, the issue is that you're not prepared-
Scott Cunningham:
You think you framed it?
Peter Arcidiacono:
No, I don't think so. But the way economists talk about things is different.
Scott Cunningham:
I know. I think that something, I think we're, a generous view is that we can't, we don't know what we sound like or something. I get into this a lot with my work on sex work, and I've, I work really hard to try to be very factual. And it, the use of words can be so triggering to a group of people. And I can never, I still can't quite articulate what exactly it is, in hindsight, that I, what word I used that was so wrong. But you feel like you would write that paper differently now?
Peter Arcidiacono:
Knowing that non economists would read it? Yes.
Scott Cunningham:
What would you do differently?
Peter Arcidiacono:
Well, I think, you have to be much more, when I say, it counts for the differences in switching behavior.
Scott Cunningham:
Okay.
Peter Arcidiacono:
The way other people hear that, is I'm able to explain why every single person switches their major, and has nothing to do with other factors. That's the reductionist claim against economists, as opposed to, on average, this is occurring.
Scott Cunningham:
Mm.
Peter Arcidiacono:
So I did a radio interview at the time, and one of the people on the show was a blogger from Racialicious, who was a regular on the show. And-
Scott Cunningham:
Yeah.
Peter Arcidiacono:
... I didn't really know anything about the show, going in.
Scott Cunningham:
Yeah.
Peter Arcidiacono:
And she spent, so she got to go first, and she talked about how problematic my study was. And the way she described it, were ways that I did not think was consistent.
Scott Cunningham:
With what the the study was.
Peter Arcidiacono:
Right. And so, my response to that, really, by grace-
Scott Cunningham:
Yeah.
Peter Arcidiacono:
... was to say, if I thought that was what the paper was saying, I'd be upset too. And then, was able to pivot into, look, we're actually on the same side on this. We want black students at Duke to succeed in the majors that they're interested in. And to that point, we need to identify the barriers that are affecting that, and what resources we can provide, to make it so that that would not be the case.
Scott Cunningham:
So what are you going to say to your old, let's say you could go back in time, 10 years to that young economist, writing this paper. Without telling him exactly what specifically to say, you can only say a general principle. As you think about writing this, I want you to think about writing it in a different way. What exactly should you be? I guess, what I'm getting at, is how would you pause, what is, what pedagogically should we be communicating to young economists, about language and audience, that we haven't been doing historically, so that we are not unnecessarily tripping people up and creating confusion?
Peter Arcidiacono:
Yeah. Yeah. I think it's really tricky, because on a lot of things, it's just very hard to have a discussion where the emotions are not involved.
Scott Cunningham:
Yeah. Yeah.
Peter Arcidiacono:
So when you speak about things related to race, and you talk about things in a very matter of factual way-
Scott Cunningham:
Yeah.
Peter Arcidiacono:
... that can be heard as you don't care.
Scott Cunningham:
Yeah.
Peter Arcidiacono:
You are not interested in fixing the problem at all. You're just explaining away why we don't need to do anything.
Scott Cunningham:
Right. Right.
Peter Arcidiacono:
And that's how, there's actually this marriage book, I really like, which is, again, I'm going to say this, it's going to come across as stereotyping. This is obviously distributions overlap, but it's called Men Are Like Waffles, Women Are Like Spaghetti. And the ideas is that men compartmentalize everything. So we're talking about this specific issue, not seeing how it relates to the broader picture.
Scott Cunningham:
Yeah.
Peter Arcidiacono:
The advice, the marriage advice I always give now, is don't try to solve your wife's problems. That's always a mistake.
Scott Cunningham:
Yeah.
Peter Arcidiacono:
And, but that's effectively, as economists, exactly what we do. We are working in the little waffle box.
Scott Cunningham:
Right.
Peter Arcidiacono:
Focused on this particular problem.
Scott Cunningham:
Yeah.
Peter Arcidiacono:
And I don't know how to change that with regard to economics papers. I really try to be very nuanced in my language and such.
Scott Cunningham:
Yeah. Yeah.
Peter Arcidiacono:
Maybe in how you motivate the paper, recognizing the racial inequities and the historical discrimination.
Scott Cunningham:
Yeah.
Peter Arcidiacono:
But there is a sense in which it will not be enough.
Scott Cunningham:
Yeah. Yeah. There's this, I can't, I just now drew a blank on the, I teach it all. I can see the slide in my deck, but there's a famous computer scientist. And he says, this principal about writing code, and he says, "Be conservative in what you do, and be liberal in what you accept from others." And it's this principle of code writing, which I guess is like, he's basically saying, "When you write code, it needs to be, the noise to signal ratio needs to be very, very low. You need to be very clear in what it's doing, in a very efficient choice to minimize this, these unnecessary errors." But when you're receiving the code, either from your earlier part of the code, or for some other foreign source, you have to change your viewpoint in that sense, because really, the goal, when you're on the receiving end of the code, it seems like your goal is to be this antenna.
Scott Cunningham:
And this antenna is trying to extract information from any meaningful information from the noise. And so, you have to have, as a listener, a certain amount of grace that tolerates that this other person may make mistakes, doesn't say it all right, goes really, really to great lengths to try to, you go to great lengths, to try to figure out exactly what the message is, and what it isn't. And it does seem like, successful communication is a, about a sender who is being clear, and a receiver that is being charitable in what they're going to allow the sender to say, unless the goal is conflict.
Peter Arcidiacono:
That's right.
Scott Cunningham:
If the goal is conflict, then obviously, you don't do that. What you do with conflict, is you find the most bad, then, it's just bad faith. It's just like, trap a person, win the debate. And sometimes, many of us don't realize who we're talking to. We don't know if we're talking to a good faith or a bad faith person. But there's limits, I think, to what an economist or anyone can do, if the person they're talking to really is not interested in connecting.
Peter Arcidiacono:
That's right. And it's interesting, because I think when I either speak publicly, or even giving seminars to economics audiences, the first part is building trust.
Scott Cunningham:
Totally.
Peter Arcidiacono:
We have the same goals.
Scott Cunningham:
Yeah.
Peter Arcidiacono:
We may have different views about how to get there. And I've got some information that may change your mind on this.
Scott Cunningham:
Yeah.
Peter Arcidiacono:
And the issue is whether they can hear the information I say, or if it's going to be ruled out because I'm a bad person.
Scott Cunningham:
Right. Well, let me ask you something. So these tests for, okay, so you correct me where my thinking is wrong. Testing for racial discrimination in admissions. I could imagine econometrics one, I get the data set from Harvard, and I run a regression of admit onto a race dummy.
Peter Arcidiacono:
Right.
Scott Cunningham:
And then, I interpret the statistical significance on the race dummy. And then, I add in more observables. In what sense is this, philosophically, what we are trying to do in the United States, legally, to detect for whatever it is that's violating the constitution. And in what sense is it a big fat failure, that's not what we're trying to do? Can you elaborate that as a multivariate regression-
Peter Arcidiacono:
Yeah. So I think, how to interpret that beginning coefficient, I don't think that coefficient has much of an interpretation, particularly in admissions, because of who applies. And that was, one of the papers that we published on this, is about Harvard's recruiting practices.
Scott Cunningham:
... Mm.
Peter Arcidiacono:
And Harvard, they recruit a lot of people. And particularly, African Americans, who simply have no chance of admission. And so, you could make it. And that could be part of the reason, right, would be, we want to appear as though when you do just that one regression with that one variable-
Scott Cunningham:
Mm.
Peter Arcidiacono:
... through affecting my applicant pool, I can always make it so that coefficient-
Scott Cunningham:
So what happening? So if I've got a university, just in real simple sense, let's say a university, if they're white, they span their, they basically task to the university, to whoever, and they say, "Get a pool of white applicants, use this rule. Get a pool of black applicants, use this rule." And it's just very, very different rules.
Peter Arcidiacono:
That's right.
Scott Cunningham:
Okay. If I then run a regression, how in the world am I going to detect racial preference in admission, when racial preference was used in the drawing up of the application in the first place?
Peter Arcidiacono:
So I think that's where, I think one of the principles that, it's not randomization for sure.
Scott Cunningham:
Right.
Peter Arcidiacono:
But one of the key principles, is how do you think about selection on observables versus unobservables?
Scott Cunningham:
Yeah. Right, right.
Peter Arcidiacono:
And so, if you can account, in the case we just described, if it was differences in test scores alone, once you account for test scores, then you could see how they were treated differently. Conditional on those test scores.
Scott Cunningham:
Yeah.
Peter Arcidiacono:
And typically, the way that works, is that when you add controls, the coefficient on the discriminated group typically goes down, because there was, because of history discrimination, that there was going to be differences in those things. That was why you had the program in the first place.
Scott Cunningham:
Right. Right.
Peter Arcidiacono:
But what's interesting in the case of Asian Americans, is it tends to go in the opposite direction. Right? So they're stronger on a lot of the observables.
Scott Cunningham:
Right.
Peter Arcidiacono:
You add controls, it looks like the coefficients becomes more negative. For African Americans-
Scott Cunningham:
The coefficient, as in, the, so if I did a regression of admit onto an Asian dummy, nothing else, it'll be positive?
Peter Arcidiacono:
Well, it depends. So it would be positive if you had nothing else, and you excluded legacies-
Scott Cunningham:
Legacies.
Peter Arcidiacono:
... and athletes.
Scott Cunningham:
Okay. So I dropped the legacy and the athletes. I regress admit onto an Asian dummy. Asians are more likely to... So when does the, so what-
Peter Arcidiacono:
When it's slightly positive and insignificant.
Scott Cunningham:
... Okay.
Peter Arcidiacono:
As soon as you add anything related to academic background-
Scott Cunningham:
So then, I put in high school GPA and zip code, and I start trying to get at these measures of underlying academic performance, observable. And that's when it flips?
Peter Arcidiacono:
Oh yeah. Yeah. This is something I just did not appreciate before the Harvard case, is how incredibly well Asian Americans are doing academically.
Scott Cunningham:
Mm.
Peter Arcidiacono:
If you did admissions based solely on academics, over half would be Asian American. That is a stunning number. All groups would go down, and Asian Americans would be the only group that went up.
Scott Cunningham:
Okay. Say that again. I didn't quite follow. So what will astound me? What would it?
Peter Arcidiacono:
So Asian Americans, they're in the low twenties, in terms of their share of admits, or something like that.
Scott Cunningham:
Yeah.
Peter Arcidiacono:
When you look at typical applicants.
Scott Cunningham:
Yeah.
Peter Arcidiacono:
If you had admissions based solely on academics-
Scott Cunningham:
Mm-hmm.
Peter Arcidiacono:
... with some combination of test scores and grades-
Scott Cunningham:
Mm-hmm.
Peter Arcidiacono:
... they would be over half of Harvard's.
Scott Cunningham:
I see. Got it. They're just, it's just such an incredibly selective group. Selective, in terms of the measures of probable performance and success, and all these things. They are, as a group, high... What's the right word? How do you, this is one of these things, we're using the languages, is really careful. I was going to say, I know economists, we have models that say high type, low type. And obviously, it's like, what's the right way to start talking about these young people? These are young people at the beginning of their, everybody comes at a difference. So what's the right, what's the loving, charitable, honest way of talking about people with these underlying differences?
Peter Arcidiacono:
Well, I think that, what happened to them before college, was such, that on average, you see tremendous differences-
Scott Cunningham:
Yeah.
Peter Arcidiacono:
... in the skills that have been accumulated-
Scott Cunningham:
Right.
Peter Arcidiacono:
... prior to college.
Scott Cunningham:
Right. Right. So there appears to be, one way you could describe it, is to say, there appears to be differences in human capital.
Peter Arcidiacono:
That's right. But I think human capital, I guess-
Scott Cunningham:
Unobservable human capital appears to be different, but it's like showing up on these observable dimensions.
Peter Arcidiacono:
... That's right.
Scott Cunningham:
Got it.
Peter Arcidiacono:
And for me, that doesn't, in any way, point the finger, and say there's something wrong-
Scott Cunningham:
Right.
Peter Arcidiacono:
... with the groups that aren't doing well on that.
Scott Cunningham:
Yeah. No.
Peter Arcidiacono:
And in fact, there's some people who argue, look, the differences in test scores, the reason African American score worse on the tests, is because of stereotype threat.
Scott Cunningham:
Mm-hmm.
Peter Arcidiacono:
And that idea is that everybody expects them to do poorly. And so, they do poorly.
Scott Cunningham:
Yeah.
Peter Arcidiacono:
To me, that's giving the K through 12 education system a pass. There are real differences-
Scott Cunningham:
Right.
Peter Arcidiacono:
... in the K through 12 education experience-
Scott Cunningham:
Mm-hmm.
Peter Arcidiacono:
... for African Americans.
Scott Cunningham:
Yeah.
Peter Arcidiacono:
That's what we need to fix.
Scott Cunningham:
Right.
Peter Arcidiacono:
We can't shy away from the real issue. And that's actually one of my big concerns with places like the UC system, saying, "We don't want standardized tests anymore." We're just going to ignore that there's a serious deficiency.
Scott Cunningham:
Yeah.
Peter Arcidiacono:
Not that the people are deficient, that the educational system was deficient-
Scott Cunningham:
Yeah. Yeah.
Peter Arcidiacono:
... for these students.
Scott Cunningham:
It's interesting. It's like, one of the papers I teach a lot, is, I know you're familiar with, is Mark Hoekstra's review of economics and statistics article, on the returns to attending the state flagship school. I've always thought-
Peter Arcidiacono:
Yeah.
Scott Cunningham:
... that this really interesting study, it feels relevant to what you're working on with Harvard and UNC, because it's about, I feel like when I was in graduate school, I came away from my labor courses, just realizing attending college is crucial. College is an anti-poverty program, as far as I can tell. You could see it in my work on crime, with the, you and I actually have some similar backgrounds. We're both interested in sex ratios and marriage markets.
Peter Arcidiacono:
Yeah.
Scott Cunningham:
But you could see the incarceration rate of African American men just plummeting, with college attainment, levels of college enrollment. But so, it's like, I graduated thinking, "Oh, well, the returns to college are important." But then, it's like, Mark's paper highlighted that there was this heterogeneity, even there. Even in these, in terms of the flagship school and Harvard.
Peter Arcidiacono:
Right.
Scott Cunningham:
And the reason why this stuff is important, I feel like it gets into these complicated things with regards to how we've decided to organize America, because the United States, we purchase goods and services using, goods and services go into the utility function. In many ways, that's the, trying to get utility functions that are virtuous and correlated with a life that's worth living, is the big goal. But we buy those goods and services at market prices, using labor income. And so, then, it always wraps back into this issue about something like Harvard or Chapel Hill, which is, some of these schools have imbalanced returns that affect labor income and quality of life, or might arguably, subjective wellbeing, as it's measured by utility. And I guess I'm just sitting here thinking to myself, if you have a group of people who are just for historical, it's not even historical accident, because they were historically discriminated against in the United States.
Scott Cunningham:
But at this point, it's a stock. African Americans have come to the table with this different kind of human capital, that's going to end up shaping all of their labor income. It's going to have massive impacts on labor income, where they go to college. It's like, I don't see how you can separate out the fact that there, we've got to decide, collectively, what exactly is the goal for these different groups of people that live here in the United States, and that one of the existing mechanisms for income, is college. And it all wraps back into this whole issue, about what exactly should the composition of the student body be, given these ridiculously imbalanced returns to each of these individual schools?
Peter Arcidiacono:
That's right. But I think that some of those things could be balanced more, if we were doing the things that were actually successful in changing the human capital-
Scott Cunningham:
Yeah. Yeah.
Peter Arcidiacono:
... upfront. And so, one of the most, it was really disappointing, in my mind, when, after Floyd, I think KIPP Charter schools decided that their motto was no longer appropriate. Be nice, work hard. And I say that, mainly because no excuse charter schools, which no excuse, that's something that you can't really say quite the same way now.
Scott Cunningham:
Right.
Peter Arcidiacono:
These schools were incredibly successful at closing the achievement gap.
Scott Cunningham:
Yeah.
Peter Arcidiacono:
They were actually very successful.
Scott Cunningham:
Right.
Peter Arcidiacono:
We could be doing that. That's where the resources ought to go.
Scott Cunningham:
Right, right.
Peter Arcidiacono:
Instead, what you see in California now, is they're getting rid of advanced classes. There's two ways to deal with an achievement gap, right? You can bring the people who aren't doing as well, up.
Scott Cunningham:
Right.
Peter Arcidiacono:
Or you could bring the people who are doing well, down. The getting rid of the advanced classes, is not bringing, in my mind, those students up.
Scott Cunningham:
Mm-hmm.
Peter Arcidiacono:
And if anything, it's providing huge advantages to people of means, because you cripple the public education system, take the path out for them to develop that human capital.
Scott Cunningham:
Right. Right.
Peter Arcidiacono:
And then, the people with resources send their kids to private schools, so that stuff isn't going to go on.
Scott Cunningham:
Right. Right. Right.
Peter Arcidiacono:
And that's where I think a lot of the discussion, we can talk about affirmative action at Harvard. At the end of the day, that's really about appearances. The people are going to Harvard are all, most of them are coming from an incredibly rich backgrounds.
Scott Cunningham:
Right. Right.
Peter Arcidiacono:
Regardless of what race. There are differences across the races. But that's where the action is.
Scott Cunningham:
Right. Right.
Peter Arcidiacono:
And that's what we typically focus on in education. But where we really need to be doing more, is for the lower income kids.
Scott Cunningham:
Mm-hmm.
Peter Arcidiacono:
And COVIDs is going, we're starting to see that that's going to be a train wreck. Our education for this kids who went to-
Scott Cunningham:
Yeah. Yeah, yeah, yeah.
Peter Arcidiacono:
... public schools.
Scott Cunningham:
Yeah. Yeah. Yeah. There's certain elasticities, that I think COVID highlights, which is that there's a, there are groups of students who, probably, their ability to substitute to the best case scenario in a very difficult situation, was really, they had a very high, they were able to do it. It may not have been, it wasn't a perfect substitute. They were able to continue to do it. And I think for some groups of students, it was a train wreck.
Peter Arcidiacono:
Yeah.
Scott Cunningham:
Just their ability to make those substitutions to whatever was required, could be anything ranging from the access to physical resources, like computing, computers, and wifi that's stable, and all these things, to, just simply, the way your brain works. Just being able to be present. I definitely think that COVID cut a mark through the students, that, it did in our family, completely cut a mark through students in weird jagged way, for sure.
Peter Arcidiacono:
But within your family, you're able to substitute in ways that other families cannot.
Scott Cunningham:
Yeah. Yeah.
Peter Arcidiacono:
And that's the catch. And I think that, I don't work a lot in the K through 12 space, so this is a non-expert opinion on that. But if my read on the studies, is if you find positive effects of, say, charter schools, Catholic schools, smaller class size, if you're going to find positive effects for anyone, it's going to be inner city African Americans. And I think that the reason that you see that, is the way family substitutes, that they're not, their families are not in as good of a position to substitute-
Scott Cunningham:
Yeah. Yeah.
Peter Arcidiacono:
... the way my family is. My kid has a bad teacher, we're going to do the bad effects.
Scott Cunningham:
Right. Right. Right.
Peter Arcidiacono:
So you're going to think, "Oh, the teacher's fine." But no, we even did the effects of that teacher, in ways that other families cannot.
Scott Cunningham:
Right. Right. Right. So what do you think is the smoking gun evidence, that that Harvard University has to... What's the smoking gun fact, that's evidence for, that's the most damning evidence for racial discrimination in admissions, that-
Peter Arcidiacono:
So racial discrimination against Asian Americans, I think that there's a, there's so many damning facts. Well, I'll start with the first one, which is Harvard's own internal offices. They have their own internal research teams. They estimated models of admissions, and consistently found a penalty against Asian Americans.
Scott Cunningham:
... Mm.
Peter Arcidiacono:
You could look at that. You'll hear people say, "Well, those are simplistic models." The fit of those models was incredibly high.
Scott Cunningham:
Yeah. Yeah.
Peter Arcidiacono:
I think. So they were explaining-
Scott Cunningham:
I think people underestimate the shoe leather sophistication that goes on in these admissions office, with developing their own internal models.
Peter Arcidiacono:
... Well, and what was striking, is Harvard's defense of this was, "Well, we really didn't understand the model."
Scott Cunningham:
Mm.
Peter Arcidiacono:
Well, what was interesting, is that those models also had whether or not you were low income, in it.
Scott Cunningham:
Mm.
Peter Arcidiacono:
And they were confident that those models, the same model, showed that they were giving a bump to low income students.
Scott Cunningham:
Mm.
Peter Arcidiacono:
It's like, you're going to interpret the coefficient one way when it's the result you like, and another way, when it's the result you don't like.
Scott Cunningham:
Right. Right. So their own models showed, so what was the penalty? What was it? It was a dummy, a coefficient on a binary indicator for Asian American, or Asian?
Peter Arcidiacono:
That's right. That's right.
Scott Cunningham:
How big was it?
Peter Arcidiacono:
And then, also, it even had stuff on the personal rating. You can see, there was charts from their office that shows, what do you know, Asian Americans on all of Harvard's ratings, are scoring either much better than whites-
Scott Cunningham:
Mm.
Peter Arcidiacono:
... or the same as whites, even on the alumni personal rating. So Harvard has these alumni interview, the students, and even on that, Asian Americans are doing similarly to whites.
Scott Cunningham:
Yeah.
Peter Arcidiacono:
And then, you see their own personal rating, based, not on meeting with the applicants. They do much, much worse.
Scott Cunningham:
Yeah. Yeah, yeah, yeah. Yeah. Well, so what does Harvard have to prove?
Peter Arcidiacono:
Well, I think typically, in something like discrimination cases, well, what they have to prove, probably depends on the judge, I suppose-
Scott Cunningham:
Yeah. Right.
Peter Arcidiacono:
... is the catch. What they were able to say at trial, were things like, "Well, the teachers must be giving them poor ratings. We don't think that Asian Americans are deficient on personal qualities, but maybe the teachers are scoring them poorly." How that is an excuse. I don't-
Scott Cunningham:
Yeah. I don't see what they're trying to... This is, I guess, where it's frustrating, because I'm struggling to know exactly what the objective function for Harvard is, in their own stated goals. What is their objective function? To create a particular kind of cohort? What is the cohort?
Peter Arcidiacono:
... Well, I think you'd get a lot of gobbledygook when it comes to that-
Scott Cunningham:
That's what I was wondering. Yeah. Okay.
Peter Arcidiacono:
... Yeah. So, but I think it is also interesting to think about the counterfactual of, if this case was not associated with affirmative action at all-
Scott Cunningham:
Yeah.
Peter Arcidiacono:
... would it have played out the same way? And to me, I think the answer is no. Honestly, I don't think Card even takes the case.
Scott Cunningham:
Mm.
Peter Arcidiacono:
I think it would've been a much better... Your worse look for Harvard than it was. I think that it was a bad look for Harvard as it was, but because of who brought the case, and because of its ties to affirmative action, that gets back to that waffle analogy, right? If you look at it in the context of the waffle, there's just simply no argument in my mind, for the way they're treating Asian Americans.
Scott Cunningham:
Mm-hmm.
Peter Arcidiacono:
It's a clear cut discrimination case.
Scott Cunningham:
Mm.
Peter Arcidiacono:
And if you just put it in a different context, it would just be completely unacceptable. Imagine Trump Towers having a discrimination suit brought against them by black applicants. And the defense being, "Look, it's not that we're discriminating against black applicants. They just happen to score poorly in our likability rating."
Scott Cunningham:
Mm-hmm.
Peter Arcidiacono:
That would be outrageous.
Scott Cunningham:
Yeah.
Peter Arcidiacono:
There would be protests. This is because it's tied to that third rail of affirmative action.
Scott Cunningham:
Yeah.
Peter Arcidiacono:
But to me, the judge could have ruled, "Look, you can have affirmative action, but you got to stop discriminating against Asian Americans relative to whites.
Scott Cunningham:
So then, if you could fill up half of Har... So is this what the thing is? Harvard, as a university, collectively, however this ends up being decided, collectively, they have a preference over their student composition.
Peter Arcidiacono:
Right.
Scott Cunningham:
And that preference is discriminatory.
Peter Arcidiacono:
Their preference, I think, lines up with Kendi's in some sense. They would like to have their class look like the population.
Scott Cunningham:
They would like to have it look like, that they would like 13% African American, whatever percent, what is it, Asian American is what, five, is single digit?
Peter Arcidiacono:
Yeah.
Scott Cunningham:
Yeah. And they would like to have a balanced portfolio of Americans.
Peter Arcidiacono:
And, but even that, I think, is giving Harvard too much credit, in the sense that, what we choose to balance on, we choose to balance on skin color.
Scott Cunningham:
Right.
Peter Arcidiacono:
You're not balancing on income.
Scott Cunningham:
Right.
Peter Arcidiacono:
You're not balancing on parental education.
Scott Cunningham:
Yeah. Yeah.
Peter Arcidiacono:
A whole bunch of other things you could've balanced on. Why-
Scott Cunningham:
Yeah. There's like an infinite number of character. Every person is a bundle of, just almost an infinite number of characteristics. And it's not practically... Yeah. Yeah.
Peter Arcidiacono:
... If you really want a representative class, then you do a lottery among high school graduates.
Scott Cunningham:
Yeah, yeah, yeah. Exactly.
Peter Arcidiacono:
That would be the only way.
Scott Cunningham:
That would be the only way, the only way it would be to have a randomized student body. Okay.
Peter Arcidiacono:
Do you feel like ask this about, was by somebody from a class at Duke, about how would you make the admissions process more equitable?
Scott Cunningham:
Uh-huh.
Peter Arcidiacono:
And I'm like, it's a selective admissions process. I don't even know what that-
Scott Cunningham:
Right.
Peter Arcidiacono:
... means. Even a process where you did the lottery, why is that equitable, because you've got the winners and the losers? The lottery. We're not equalizing outcomes for everybody. We're equalizing X anti.
Scott Cunningham:
Yeah. It's like, this is all this comp, this is this deep collective choice, social preferences questions about... And it's weird. I guess we're talking about this at Harvard, because we believe that Harvard University will literally change a kid's life, more than going to University of Tennessee, Knoxville, or something like that. Right? That's why we're having this conversation.
Peter Arcidiacono:
Yeah. I think that that's the perception, that it will literally change their kids' lives.
Scott Cunningham:
Yeah. Yeah.
Peter Arcidiacono:
I'm not totally convinced that of there being massive gains-
Scott Cunningham:
Right.
Peter Arcidiacono:
... relative to the counterfactual for-
Scott Cunningham:
Yeah.
Peter Arcidiacono:
... at that level.
Scott Cunningham:
Right.
Peter Arcidiacono:
I think, when you're at the margin of going to college or not-
Scott Cunningham:
Yeah.
Peter Arcidiacono:
... that's the big margin.
Scott Cunningham:
That's the big margin. Yeah. Yeah.
Peter Arcidiacono:
College quality effects, I think get undone a little bit by college major effects.
Scott Cunningham:
Right, right, right.
Peter Arcidiacono:
So yeah. I think that's a real valid question, about whether it's worth it to be paying the huge sums of money to go to just a slightly better school.
Scott Cunningham:
Okay. I want to conclude with this. So you've now spent many years working on this, going deeper into administrative data, about questions that, about phenomenon that we had only speculated about, possibly. What are the top two things that you learned, that, being a career economist that had worked on topics in education and affirmative action, what are the top two things that you learned, that you just literally, it was, you just, would've never learned, had you not been involved in these two cases, that are of real, that you think that matter for other people too?
Peter Arcidiacono:
Right. So I think one of the things, is that the Supreme Court rulings in the Michigan cases, were really a handout to elite private schools.
Scott Cunningham:
Oh, interesting.
Peter Arcidiacono:
Then, when you think about doing holistic admissions, a place like Harvard can do that in a way that UNC cannot.
Scott Cunningham:
Mm.
Peter Arcidiacono:
UNC is effectively, and you can see this in our models, they are more formulaic.
Scott Cunningham:
Mm.
Peter Arcidiacono:
I don't even know how Berkeley's doing admissions now, without test scores. Think about how many applications they get, with the resources that they have. I just don't know. The other part to-
Scott Cunningham:
Wait, [inaudible 01:08:39], Peter, are you basically saying that Harvard and elite universities get, first of all, a profound amount of information about each student, and they get this incredibly right tail applicant, that they don't even need test scores, to find the students that are going to be successful?
Peter Arcidiacono:
Well, I think they can get information in a way that the public schools can't, because of the resources. So you won't have to take those test scores, but they're going to see, you might have things like winning a science fair-
Scott Cunningham:
Right.
Peter Arcidiacono:
... in other proxies.
Scott Cunningham:
Right.
Peter Arcidiacono:
And that's information that the public schools aren't even going to really collect-
Scott Cunningham:
Yeah, yeah.
Peter Arcidiacono:
... because they don't have the resources to do that. You can see that, just in the number of letters Harvard requires versus UNC.
Scott Cunningham:
The fact that any university would ever voluntarily say, "We're not going to collect this informa, or make decisions based on this anymore," you should already just automatically think, the only people that would ever voluntarily do anything, is because they don't need that thing.
Peter Arcidiacono:
Right.
Scott Cunningham:
Right. They're endogenously sorting into something that, probably, they incur almost no cost of doing it. So an elite university that drops some sort of admission criteria, probably, they've got something just as good sitting right there.
Peter Arcidiacono:
Well, I think, I'm not sure if that's something just as good. I think it's also a protection against an Asian American discrimination suit, because Asian Americans are just doing so well there.
Scott Cunningham:
Right.
Peter Arcidiacono:
You need to take it out of the criteria. And that probably brings me up to the second point, which I think is a huge one. I think holistic admissions favors people from privileged backgrounds. And I'd say that even more so, after going through these cases.
Scott Cunningham:
Yeah.
Peter Arcidiacono:
We think about test scores as being unfair, because of coaching, and they're correlated with income. And that, of course, is true.
Scott Cunningham:
Yeah.
Peter Arcidiacono:
But the other things are even more unfair. Harvard has an athletic rating for non recruit athletes, and the people who score best on that are white legacies.
Scott Cunningham:
Mm.
Peter Arcidiacono:
And why is that? Because part of that, is are you likely to walk on to one of Harvard's sports teams? Well, Harvard sports, normally we think about sports as being more of an equal opportunity thing. That's not true at Harvard. Harvard offers more varsity sports than any school in the country.
Scott Cunningham:
Mm.
Peter Arcidiacono:
And what's a marginless sport? It's sailing. So sailing, who does sailing? People who are coming from really rich families.
Scott Cunningham:
Yeah. Right.
Peter Arcidiacono:
That was another stunning thing, is that I think we really need to rethink college athletics. It's one thing to think about Duke's basketball team-
Scott Cunningham:
Right.
Peter Arcidiacono:
... as a pathway where, sure, of course, we're going to admit you. You're... But it's another thing, in my mind, to think about a sailing team as being, I don't have to be particularly strong academically, as long as I'm on the sailing team.
Scott Cunningham:
Right.
Peter Arcidiacono:
That seems like a pathway for the rich.
Scott Cunningham:
Mm. Mm. Well, how much longer do you think you'll be involved in this? This is, you've got now a treasure trove of a data too, right? You have a lot of questions that you're going to be probably mining for a long time, just even your scholarly career, outside-
Peter Arcidiacono:
I wish. If I had access to the raw data.
Scott Cunningham:
Oh, you don't?
Peter Arcidiacono:
I don't.
Scott Cunningham:
What do you, so what do you use? How can you be an expert witness without the raw data?
Peter Arcidiacono:
Well, I had the raw data at the time I wrote my reports.
Scott Cunningham:
Oh.
Peter Arcidiacono:
So all my reports have tables from that.
Scott Cunningham:
Got it.
Peter Arcidiacono:
And so, we've published four papers, effectively, out of my reports and out of other things revealed in the trial.
Scott Cunningham:
Yeah.
Peter Arcidiacono:
They actually tried to get the database admitted into evidence, and not surprisingly, Harvard objected.
Scott Cunningham:
Yeah, yeah, yeah. That would've been, once it's in evidence, anybody can have it.
Peter Arcidiacono:
That's right. My fear with a lot of this stuff, is universities will selectively release their data to people who will get them the answers that they want.
Scott Cunningham:
Mm.
Peter Arcidiacono:
And no one else will be able to look at it.
Scott Cunningham:
Yeah. Yeah. Yeah.
Peter Arcidiacono:
It's a serious problem. It's like pharmaceutical companies being the only ones allowed to evaluate the trials of their drugs.
Scott Cunningham:
I bet you, the Asian American community was, has been, I bet this has been very troubling.
Peter Arcidiacono:
Oh. And confirming for them. So that, I didn't really know about this. But then, I talked to some of my former Asian grad students, and they're like, "Yeah, this is, we've known this has been going on for a long time." And I think what, to me, what's probably more troubling to them, is not that it's going on, but the fact that now that it's been exposed, how comfortable people are with it. That's disappointing.
Scott Cunningham:
Yeah. Yeah. Yeah. It's so nice to talk. We met a long time ago. You came and presented your terms of in, I think it was, no, I don't think you presented the terms of endearment paper. I think that you presented something else, but you were going to. But Chris Cornwell had invited you to Georgia while I was a graduate student. And we, I was in Chris's office with you and, because we had this marriage market. I had a marriage market dissertation, and-
Peter Arcidiacono:
Yeah.
Scott Cunningham:
... and you had this other thing. And it's nice to meet again after probably 16 years.
Peter Arcidiacono:
Yeah. It's been a long time.
Scott Cunningham:
Been a long time. Yeah. Well, good luck with everything. Thanks for sharing everything about this. This has been really interesting for me. I hope for other people too.
Peter Arcidiacono:
Oh, I love doing it. I really appreciate you having me on. When I look at the other people you have on, it's like, wow. This guys, which one of these doesn't belong?
Scott Cunningham:
All right. You have a great day.
Peter Arcidiacono:
You too. Talk to you later.
Get full access to Scott's Substack at causalinf.substack.com/subscribe
This week I have the pleasure of introducing Dr. Anna Aizer, professor of economics at Brown University and editor-in-chief at the Journal of Human Resources. I am a long time admirer of Dr. Aizer’s work and have followed her career with curiosity for a long time. Some of her papers imprinted pretty strongly on me. I’ll just briefly mention one.
Her 2015 article in the prestigious Quarterly Journal of Economics with Joe Doyle on juvenile incarceration, for instance, has haunted me for many many years. It was the first or second paper I had seen at the time that had used the now popular “leniency design” to examine the causal effect of being incarcerated as a youth on high school completion and other outcomes as well as adult incarceration. Simply comparing those outcomes for those incarcerated and those not incarcerated as a kid will not reveal the causal effect of juvenile incarceration if juvenile incarceration suffers from selection bias on unobservable confounders. So Dr. Aizer with Joe Doyle used a clever approach to overcome that problem in which they found quasi-random variation, disconnected from the unobserved confounder, in juvenile incarceration caused by the random assignment of juvenile judges. As these judges varied in the propensity to sentence kids, they effectively utilized the judges’ own decisions as life changing lotteries which they then used to study the effect of juvenile incarceration on high school and adult incarceration. And the findings were bleak, depressing, enraging, upsetting, sad, all the emotions. They found that indeed being assigned to a more strict judge substantially raised one’s chances of being sentenced as a kid. Using linked administrative data connecting each of those kids to their Chicago Public School data as well as Cook County incarceration data, they then found that being incarcerated significantly increased the effect of committing a criminal offense as an adult, and it decreased the probability of finishing high school. The kids, best they could tell, mostly didn’t return after their juvenile incarceration, but if they did return, they were more likely to be given a emotional and behavioral disorder label in the data. My interpretation was always severe — incarceration had scarred the kids, traumatizing them, and they weren’t the same.
The paper would haunt me for various personal reasons as I saw a loved one arrested and spent time in jail on numerous occasions. I would see kids in my local community who had grown up with our kids arrested and think of Dr. Aizer' and Joe Doyle’s study, concluding the most important thing I could do was bail them out. The paper was one of many events in my own life that led me to transition my research to mental illness within corrections and self harm attempts by inmates even.
But there’s other personal reasons I wanted to interview Dr. Aizer. Dr. Aizer went to UCLA where she studied with Janet Currie, Adriana Lleras-Muney and Guido Imbens. Recall that when Imbens was denied tenure at Harvard, he went to UCLA. Currie, who had attended Princeton at the same time as Angrist, Imbens’ coauthor on many papers on instrumental variables in the 1990s, was an original economist focused on the family, but unlike Becker and others, brought with her that focused attention to finding variation in data that could plausibly recover causal effects. The story, in other words, of Princeton’s Industrial Relations Section and design based causal inference, going back to Orley Ashenfelter, was spreading through the profession through the placements of scholars at places like UCLA, which is where Dr. Aizer was a student. In this storyline in my head, Dr. Aizer was a type of first generation member of the credibility revolution, and I wanted to talk to her not only for her scholarly work’s influence on me, but also because I wanted to continue tracing Imbens and Angrist’s influence on the profession through UCLA.
The interview, though, was warm and interesting throughout. Dr. Aizer is a bright light in the profession working on important questions in the family, poverty and public policy. For anyone interested in the hardships of our communities and neighborhoods, I highly recommend to you her work.
Now let me beg for your support. Scott’s Substack and the podcast, Mixtape with Scott, are user supported. If your willingness to pay for the episodes and the explainers (I’m going to write some more I promise!), please consider becoming a subscriber!
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Transcript
Scott Cunningham:
In this week's episode of the Mix Tape podcast, I had the pleasure of interviewing Dr. Anna Aizer, professor of economics at Brown University in Rhode Island and editor in chief of the Journal of Human Resources. I have had a keen interest in Anna Aizer and her career and her work for a couple of reasons. Actually a lot, but here's two. First, she did her PhD at UCLA when Janet Currie was there, as well as when Guido Imbens was there. Imbens taught there after he left Harvard, for those of you that remember that interview I did with him. Recall my overarching conviction that Princeton's industrial relations section, which was where Orley Ashelfeltner, David Card, Alan Kruger, Bob Lalonde, Josh Angrist originated from, as well as Janet Currie.
My conviction that this was the ground zero of design based causal inference. And that design based causal inference spread through economics, not really through econometrics, and econometrics textbooks, but really through applied people. She also worked with Adriana Lleras-Muney, who's also at UCLA now, who was a student of Rajeev Dehejia, who wrote a seminal work in economics using propensity score, who was also Josh Angrist’s student at MIT. So you can see, Anna fits my obsession with a sociological mapping out of the spread of causal inference through the applied community.
But putting aside Anna as being instrumentally interesting, I am directly interested in her and her work on domestic violence and youth incarceration among other things. I've followed it super closely, teach a lot of these papers all the time, think about them even more. In this episode, we basically walked through her early life in Manhattan to her time at Amherst College, to her first jobs working in nonprofits, in areas of reform and poverty, to graduate school. We talked about her thoughts about domestic violence and poverty and crime along the way, too. And it was just a real honor and a pleasure to get to talk to her. I hope you like it as much as me. My name is Scott Cunningham and this is Mix Tape podcast. Okay. It's really great to introduce my guest this week on the podcast, Anna Aizer. Anna, thank you so much for being on the podcast.
Anna Aizer:
Pleasure to be here. Thanks so much for inviting me.
Scott Cunningham:
Before we get started, could you tell us obviously your name and your training and where you work?
Anna Aizer:
Sure. I'm a professor of economics at Brown University. I did my PhD at UCLA oh many years ago. Before that actually I got a masters in public health. Sorry. I have a strong public health interest and focus in a lot of my work. I'm also currently the co-director of the NBR program on children. That is a program at the NBR that is focused entirely on the economics of children and families. I'm the editor in chief of the Journal of Human Resources.
Scott Cunningham:
Great. It's so nice to meet in person. I've been a long time reader of your papers because you write about these topics on violence against women. There's not a lot of people in economics that do. And the way that you approach it shares a lot of my own thoughts. I'm going to talk about it later, but it's really nice to meet in person.
Anna Aizer:
Sure. Nice to meet you, too.
Scott Cunningham:
Okay. I want to break up the conversation a little bit into your life. First part, just talk about your life growing up. And then the second part, I want to talk about research stuff. So where did you grow up?
Anna Aizer:
I grew up in New York City.
Scott Cunningham:
Oh, okay.
Anna Aizer:
Yeah, I did.
Scott Cunningham:
Which, borough was it?
Anna Aizer:
Manhattan.
Scott Cunningham:
Oh, okay.
Anna Aizer:
Yeah. Yeah. Upper side. But when I went off to college, I went to rural Massachusetts.
Scott Cunningham:
Yeah.
Anna Aizer:
Yeah. I went to Amherst, which is a very small liberal arts college in the Berkshires. That was a very different experience for me. And believe it or not, I was not an econ major.
Scott Cunningham:
Oh, you weren't?
Anna Aizer:
In fact I was not. I only took one econ course my entire four years in college.
Scott Cunningham:
Oh, wow. Wait, so what'd you major in?
Anna Aizer:
I majored in American studies with a focus on colonial American history and literature.
Scott Cunningham:
Mm. On literature. Oh, that's what I majored in, too.
Anna Aizer:
Yeah.
Scott Cunningham:
Yeah, yeah, yeah. Oh, wow. So early American history. So what, was this was the 1700s or even-
Anna Aizer:
Yeah. So I did a lot of 17, 1800s, a lot of the New Republic period. My undergraduate thesis was actually on girls schooling in the Early Republic.
Scott Cunningham:
Oh wow. What was the deal with girls schooling in the Early Republic?
Anna Aizer:
What was the deal with the girls schooling? Well, it depends. For most of the Northeast, the focused on girls schooling was really this idea that it was a new country, they were going to have to have leaders in this new country, and someone had to educate those leaders. Someone had to educate those little boys to grow up, to go ahead and lead this country. And so the idea was, well, we had to start educating moms so that they could rear boys who could then go on to this great nation.
Scott Cunningham:
I see. Women's education was an input in male leadership?
Anna Aizer:
That's correct.
Scott Cunningham:
Got it. Got it. Wow. Okay. Well, that's interesting. I get that. You start educating women though, I suspect that you get more than just male leaders.
Anna Aizer:
I think that's right. It was an unintended consequence.
Scott Cunningham:
Unintended consequence. They didn't think that far ahead. Okay.
Anna Aizer:
Yeah. That's a very good point to make, because two women who were educated in one of the first schools dedicated to educating women so that they could go on and rear their boys to be strong leaders were Katherine Beecher, who went on to create one of the most important girls schools in Troy, New York. And Harriet Beecher Stowe of course, who wrote Uncle Tom's Cabin.
Scott Cunningham:
They're related?
Anna Aizer:
Yeah. They are sisters. They are sisters.
Scott Cunningham:
Oh, they're sisters.
Anna Aizer:
They were one of the first sets of girls who were educated in this mindset of we need leaders so let's have some educated moms. And they of course had other ideas and they went and formed schools and wrote incredibly important works of fiction that ended up playing a pretty significant role in the Civil War.
Scott Cunningham:
Wow. Was this the thing over in England too? Or was this just an American deal?
Anna Aizer:
I don't know the answer to that.
Scott Cunningham:
Huh. I guess they have a different production function for leaders in England where as we it's very decentralized here or something. Right?
Anna Aizer:
Right. So you're saying in England they already had their system of you go to Eaten, and then you go to Cambridge or Oxford. Right. I think that's probably right. So we didn't have that here.
Scott Cunningham:
Yeah. That's right. I mean, you're creating everything from scratch. And with such a reactionary response to England who knows what kinds of revolutionary approaches you're taking to... That's probably pretty revolutionary, right? Say we're going to teach women even though it's in order to produce male leaders, it's still thinking outside the box a little bit.
Anna Aizer:
Yeah. I suppose that's true. Yeah.
Scott Cunningham:
That's cool. How come you didn't end up in... So you end up at Amherst. As a kid in Manhattan, what were you doing? You were reading books and stuff? You were a big reader?
Anna Aizer:
I suppose. Yeah. I suppose so.
Scott Cunningham:
Is that what drew you to Amherst, a liberal arts college?
Anna Aizer:
I don't really know. I don't think I actually knew what I wanted until much later in life. I was an American studies major, which at the time I learned a lot. It took me a while to gravitate to economics. Once I did, it was clear that that was really the right path for me.
Scott Cunningham:
Yeah. One question I want to leave your kid. So your parents let you ride the subway when you were a little kid?
Anna Aizer:
Oh yes.
Scott Cunningham:
Oh gosh. I bet that was so cool.
Anna Aizer:
Oh yes. I grew up in New York City during the '70s and '80s, which was far more dangerous than it was today. But at that time parents had a much more hands off approach to parenting. I think I was eight years old when I started taking public transportation by myself.
Scott Cunningham:
Oh my gosh. There was latch key parents back then?
Anna Aizer:
Sure.
Scott Cunningham:
So you jump on the subway. Where are you going at eight years old in Manhattan?
Anna Aizer:
You go to school.
Scott Cunningham:
You're just catching the subway to go to school?
Anna Aizer:
Yeah.
Scott Cunningham:
Oh, that's so cool. I bet you had a great childhood.
Anna Aizer:
I have to say it was pretty good.
Scott Cunningham:
Oh man.
Anna Aizer:
I can't complain.
Scott Cunningham:
Yeah. I grew up in a small town in Mississippi, but it was the same kind of thing. Well, it was very different than Manhattan, but just being able to have that level of... It's all survivor bias. The other kids that are getting really neglected and abused. But those of us that made it out a lot it's like, all you have is great memories of being able to do whatever.
Anna Aizer:
Right. Agreed.
Scott Cunningham:
So you wrote this thesis. At Amherst, did everybody write a thesis? Is that real common?
Anna Aizer:
Most people did. I think a third of the students wrote a thesis. It was very common.
Scott Cunningham:
But you're gravitating towards research, though?
Anna Aizer:
Yeah. So it was clear that I really, really enjoyed that a lot. In fact, more recently in my economic research I have done a lot more historical work than I had done initially. So I think that training has really come in handy.
Scott Cunningham:
Yeah. What did you like about that project that you wrote your thesis on? What did it make-
Anna Aizer:
Well, it was really a lot of fun. I focused on two schools in particular. I focused on this school in Lichfield, Connecticut, and another school in Pennsylvania, a Quaker school in Westtown. I focused on those two schools because those two schools, for whatever reason, kept a lot of their records. They have really wonderful-
Scott Cunningham:
Oh my God. You had their records?
Anna Aizer:
Yeah. So you have really wonderful archives where you could just go through and read all about what they were thinking about, when they founded the schools, what the curriculum should be like. And even some of the writings of some of the students and teachers.
Scott Cunningham:
Oh my gosh.
Anna Aizer:
So it was really just a tremendous amount of fun to read all of that stuff, all that primary materials.
Scott Cunningham:
Oh my gosh. Wait. Did you actually have the names of the kids? Did you see their-
Anna Aizer:
Sure. They had all of that.
Scott Cunningham:
Did you have the census records and stuff?
Anna Aizer:
Oh, I guess you could. I mean, this was so long ago before people were doing all that cool linking, but yeah, you absolutely could.
Scott Cunningham:
Oh, that's so neat. I wonder where those kids ended up. What did it make you feel doing that research, that was so original and just being out there in these archives?
Anna Aizer:
Well, it was just amazing how much you could learn by just peeking into people's lives. It was really exciting. It was really fun. And you just felt like you were discovering something new.
Scott Cunningham:
Yeah, yeah, yeah, yeah. So you liked that. But that's interesting because some people would be like, oh, discovering something new. I don't even care about that. When you were discovering something new, you were like, I like this feeling.
Anna Aizer:
Yeah. Yeah. I really did.
Scott Cunningham:
Yeah.
Anna Aizer:
I really did.
Scott Cunningham:
Yeah. So what happened? So you graduate?
Anna Aizer:
I graduated. My first job was actually working for an Alternative To Incarceration program in New York City. So I moved back home. You have to remember, this was early mid '90s, and this was the peak in terms of crime rates in the country, and in New York City in particular. And the jails-
Scott Cunningham:
Before you say this, when you were growing up, did your parents... Was it like people were cognizant... I mean, now you know, oh, it was the peak because it's fallen so much, but what was the conversation like as a kid about crime?
Anna Aizer:
In the '90s in New York City at this time, that was really the crack cocaine epidemic, so there was a lot of talk about that. That really did dominate a lot of the media at the time. It really was a big concern.
Scott Cunningham:
Yeah. Yeah.
Anna Aizer:
As we know, the city and the state, not just in New York, but nationally, really responded with very tough on crime approach, started incarcerating a lot of people. So much so that they were really out of space in the New York City jail. So Rikers Island was at capacity, even upstate prisons were pretty full. The city, not because they were concerned that we were putting too many people in jail, which has... After the fact we know that we did put too many people in jail, that there was a cost to these incredibly high incarceration rates.
Anna Aizer:
At the time, the concern was that we don't have enough space, so what are we going to do? The city funded an Alternative To Incarceration program for youth. It was called the Court Employment Project. It was really focused on kids between the ages of 16 and 21 who were charged with a felony in New York state Supreme Court. And these were kids who were being charged as adults, treated as adults in the system. New York City has since raised the age of majority, but at that time it was 16. So we were focused on really younger 16 to 21. Well then, most of the kids we were working with were 16 to 18.
Scott Cunningham:
What kind of felonies are we talking about? Is this the drug felonies? Or is it [inaudible 00:15:51]?
Anna Aizer:
Yeah. So a lot of it was possession with intent to sell, selling. But also robbery, that was pretty common as well. We were only working with kids that were facing at least six months in adult prison, essentially. That was the rule for our program. Because again, our program was really focused on trying to reduce the number of people who were being detained and incarcerated for long periods of time. So we were only dealing with people who had-
Scott Cunningham:
Wait, real quick. So you're in your early 20s?
Anna Aizer:
Yeah. So I would've been about 23.
Scott Cunningham:
How'd you find this gig? You were just going back to New York City? Or what was the deal?
Anna Aizer:
Yeah. I knew I wanted to go back home. At that time, jobs were advertised in the paper, so you looked through the help wanted ads and you just sent cover letters and resumes by mail to whatever jobs appealed to you. I was interested in those jobs. I was also interested in working with public defenders, so the Legal Aid Society in New York, I applied for a number of jobs there.
Scott Cunningham:
Where's this coming from? What's your values exactly at this time? You're concerned about poverty or concerned about something? What's the deal?
Anna Aizer:
Yeah. I think I guess I already was really worried. I was really concerned about low income kids who were really... I felt already were getting derailed at very young ages in a way that I thought would be very hard for them to recover. I think that in that sense was really confirmed when I started working that these were kids who in a split minute their lives were just totally changed. So certainly in the case of things like robberies, these were often group of kids with not much to do, just getting into trouble, and it just getting too far too quick. And before they knew it, they were facing two to six years. I mean, it was just really tragic.
Scott Cunningham:
Yeah, yeah, yeah. Yeah. I know. Six months. You think about it, too. You're looking at these six months in the program. You start looking at six months and you think, oh, that's six months. The thing is, those things cascade, because six months with a felony record serving prison becomes de facto a cycle of repeated six months, one year, two years.
Anna Aizer:
Sure.
Scott Cunningham:
You just end up... Well, that's going to be a paper that you end up writing, so I'll hold off on that. Okay. So you end up applying, you spray the city with all these resumes. And then this thing. So what is this company? This is a nonprofit?
Anna Aizer:
Yep. So it's a nonprofit that had a contract with the city. They had a contract with the city. Again, they were funded really because the city could not afford to put any more people on Rikers Island.
Scott Cunningham:
So it's like a mass incarceration response almost?
Anna Aizer:
Yeah.
Scott Cunningham:
Capacity constraints.
Anna Aizer:
They were at capacity, so they needed to do something. So what this program was, it was an intensive supervision program. The kids had to come in at least twice a week and meet with a counselor. The counselor would provide counseling services and also check in on them, make sure they were going to school or working or getting their GED. And then they would write up these long reports.
Anna Aizer:
I only worked in the courts, so I wasn't doing any of the counseling myself. I had no qualifications to do that. I worked in the courts, so my job was to screen kids for eligibility for the program, interview them, see if they were good candidates. Then talk to their families, talk to their lawyers. And then talk to the judge eventually about the program and about what we would be doing and why we thought this person was a good candidate. And then once they were in the program, I would then provide updates or reports back to the judge and the defense attorney to let them know how the individual was doing.
Scott Cunningham:
And wait. What is the treatment going to be that things are doing?
Anna Aizer:
Again, so it was really-
Scott Cunningham:
It's a deferment of you're going to go to jail?
Anna Aizer:
Yeah. That's exactly right. It was a six month program. If they made it through after six months, they would be sentenced to probation instead of jail time.
Scott Cunningham:
Yeah. They would refer adjudication type concept.
Anna Aizer:
Exactly.
Scott Cunningham:
Right. Yeah.
Anna Aizer:
Exactly. So that was the idea.
Scott Cunningham:
But it's non random. And I know you're not-
Anna Aizer:
It was, yeah.
Scott Cunningham:
You're not thinking about the future Anna Aizer [inaudible 00:21:17], but it's not random.
Anna Aizer:
No.
Scott Cunningham:
What is it conditioned on? Because you're doing all of it, right?
Anna Aizer:
Right. Right. So you look at a kid's record. You would look at whether or not the kid seem to have support. The downside was if a kid didn't make it through the program they might be sentenced to more time-
Scott Cunningham:
Really?
Anna Aizer:
than they would have... Maybe. I mean, the judge would-
Scott Cunningham:
Why? Because you're getting a new judge or something?
Anna Aizer:
No, it's the same judge. But the judges say, "Look, I'm going to give you a chance. Instead of sending you away now for six to 18, I'm going to give you an opportunity to prove yourself. Six months, stay out of trouble, complete this program. And then I'm going to send you to probation. But if you don't complete the program, I'm going to sentence you more." In the end, they might not have actually done that. They certainly didn't tie their hands in any way.
Scott Cunningham:
What do they doing? Why are they doing that? Why is a judge doing that? They're trying to deal with some sort of adverse selection or something? They don't want people to-
Anna Aizer:
They want to create an incentive for the kid to-
Scott Cunningham:
They're trying to create an incentive for the kid. Got it. Okay.
Anna Aizer:
Yeah. They-
Scott Cunningham:
Like a little scared straight thing?
Anna Aizer:
A little. I mean, the judges always think that. It's not clear that that works. I don't think that really matters so much in the decision making of young people. I think it's-
Scott Cunningham:
Yeah. Totally. Totally.
Anna Aizer:
But that certainly was on the mind I think of many of the judges.
Scott Cunningham:
It's funny though. When I think about this paper that we're going to talk about a little bit, it's like you're already aware of, oh, these judges have a little bit of discretion. They're saying a bunch of stuff that's not in the law. "If you don't do this, I'm going to give you penalize, I'm going to give you really bad grade at the end with another year in prison." Did that cross your mind that you were noticing that judges were... This judge does that and this other judge does not tend to do that, is that something you could have noticed?
Anna Aizer:
Absolutely.
Scott Cunningham:
Oh, wow.
Anna Aizer:
Yeah. So there were many, many judges. So this is Manhattan. This is the main criminal courts in Manhattan, so I had many, many judges, a lot of people. The way it works is once you've been indicted on a felony you come before one of these three judges. They're called conference judges. They try to dispose of the case. Either the case gets dismissed or they take the plea deal. But if that doesn't happen, they reach into a bin, literally a lottery-
Scott Cunningham:
It's like a bingo ball machine?
Anna Aizer:
It's a lottery with all these different judges' courtrooms. They pull out a number, and that's the number of the courtroom you get assigned to. You know right then if you get assigned to certain judges, for sure that kid is going to do jail time. And if you get assigned to other judges, for sure that kid is going to get probation.
Scott Cunningham:
Who knows this? The kids don't.
Anna Aizer:
The kids don't, but they don't know it.
Scott Cunningham:
They can't comprehend.
Anna Aizer:
But their attorney will know it.
Scott Cunningham:
And then maybe their parents.
Anna Aizer:
No, I don't think their parents would know.
Scott Cunningham:
Although, who in a group of kids that maybe their parents aren't as-
Anna Aizer:
I don't think their parents would know it, either. You would know it because you have to remember that all of the judges for the most part were either defense attorneys or prosecutors before they were judges, and you can tell. The judges who would-
Scott Cunningham:
Is that the main source of the discretion that you notice?
Anna Aizer:
I think so. I think so. I think the judges who previously prosecute-
Scott Cunningham:
I mean, they're such different. It does seem like the prosecutors and the defense attorneys are almost cut from a completely different worldview and set of values.
Anna Aizer:
I think that's right.
Scott Cunningham:
I had this friend that was a public defender in Athens and he was like... I think this is what he said. I'm not going to say his name because he probably didn't say this, but I thought he basically said, "I don't like prosecutors because they think they are always guilty."
Anna Aizer:
Yeah.
Scott Cunningham:
And you could tell. The public defender, they were like, "My whole job is to not do that." I could just imagine that shaping... Either there's a lot of selection into that or that just really... You hear that all the time. There's got to be human capital with that.
Anna Aizer:
Yeah. I agree. I think they have a different perspective, which is what draws them to either defense work or prosecutorial work. But then you have to remember their jobs are really very different. So the prosecutor he or she is just dealing with the victims, so that's who they're talking to all day. The defense attorney is talking to the defendant and getting to know them and their families. They really just have very different sympathies. And the judges come from one or the other.
Scott Cunningham:
One or the other.
Anna Aizer:
So you can see it.
Scott Cunningham:
So you're a kid, you're young person. What are you feeling over the course of working with this? Tell me a little bit about your growth and the thoughts that you're thinking about.
Anna Aizer:
Yeah. I really felt like these were kids that just got derailed, that these were kids, they were in a very tough situation. They made a decision and they had no idea what the consequences of that were going to be. Nor should they have. They were 16. It's very hard to know where these things end up. I did feel as though the criminal justice system was way too harsh.
Scott Cunningham:
You could tell. Because the whole point of this nonprofit you're working on is a response to such an excessive amount of penalization. They literally don't have any room.
Anna Aizer:
Yeah.
Scott Cunningham:
Yeah. They don't have any room for anybody.
Anna Aizer:
Yeah. They had no room. That's exactly right.
Scott Cunningham:
We're doing so much punishment we can't even do it right.
Anna Aizer:
That's exactly right. In the juvenile and criminal justice system, more generally, there's a disproportionate involvement of Black and Hispanic youth.
Scott Cunningham:
Yeah.
Anna Aizer:
But they are 100% poor.
Scott Cunningham:
Yeah. Right.
Anna Aizer:
So that's the other thing. And that just seemed incredibly unfair to me.
Scott Cunningham:
Yeah, yeah, yeah. Right.
Anna Aizer:
And it's not the case that not poor kids don't also mess up. They do.
Scott Cunningham:
They just can avoid the 10,000... There's 10,000 events from the mess up to the things that these kids are facing in this program that they have many ways of mitigating it.
Anna Aizer:
Yeah. That's right.
Scott Cunningham:
There's even in terms of parents spending a ton of money, or just saying you can't hang out with these people. There's a bunch of stuff that poor families just are like... So you're feeling heavyhearted.
Anna Aizer:
Yeah.
Scott Cunningham:
You could have gone in a different direction. You could have not gone to graduate school or gone to get this master's. What's the decision criteria where you're thinking I've got to go in a new direction?
Anna Aizer:
Yeah. At a certain point I just felt as though I needed more training. I wanted more of a professional degree, so I got a degree in public health where you learned a lot about the health system and financing and the social determinants of health. I felt like I needed, again, more training. I should say, I went from that job, not directly back to graduate school, but I went and I worked in not a homeless shelter, but a service center for homeless people also in New York City. I went from the criminal justice system to the homeless system. I was there for another year. And then I went back to school.
Scott Cunningham:
To what, two or three years total between Amherst and graduate school?
Anna Aizer:
That's correct. Yeah.
Scott Cunningham:
Yeah.
Anna Aizer:
That's correct.
Scott Cunningham:
It's interesting you go to public health because I think a lot of people that don't know anything about anything, they'll be like, well, she's doing criminal justice so I could have seen her going to law school. Now she's going to the homeless thing. Okay, well, maybe she could do social work. What were the things you were thinking of? And how did you end up choosing public health? Because a lot of people don't associate either of those things with public health. They heard the word health.
Anna Aizer:
Right. So a couple things. One, I thought about law school, but I felt as though lawyers deal with the problem after it's happened.
Scott Cunningham:
Right.
Anna Aizer:
And I felt like maybe we should focus more on preventing.
Scott Cunningham:
Right.
Anna Aizer:
And the other thing, when I worked with homeless people I really did start to feel like this was a homeless individuals... Homeless families are different. I worked with homeless single adults, and for the most part in New York City at that time, all of the homeless single adults had serious mental health problems.
Scott Cunningham:
Yeah. Right.
Anna Aizer:
I really came to see homelessness as a public health problem.
Scott Cunningham:
A mental health problem.
Anna Aizer:
Yeah.
Scott Cunningham:
They hit public health. Got it.
Anna Aizer:
Yeah.
Scott Cunningham:
Right. Right.
Anna Aizer:
So that's really how... I could have done social work, but that's not really what I wanted to do.
Scott Cunningham:
Yeah. But it's funny you say preventative. To me when I hear that I'm thinking, oh, Anna's already starting to think about public policy.
Anna Aizer:
Yeah. I think I was.
Scott Cunningham:
I wouldn't necessarily think that if you were to tell me you went and got a master's in social work.
Anna Aizer:
Yeah. No, I think that's [inaudible 00:31:54]-
Scott Cunningham:
Because that cold be clinical or much more working with the... You would've had that experience and you'd be like, I want to work with these families. But that's not what you thought, so something else is going on. So you're thinking I want to do what?
Anna Aizer:
Yeah. I think I really was interested in policy already then.
Scott Cunningham:
Yeah. And that makes the masters of public health make a lot of sense.
Anna Aizer:
Correct. Yeah.
Scott Cunningham:
I see. So where'd you end up going, Harvard?
Anna Aizer:
I went to Harvard. Yeah. I got a masters in health policy and administration. And then I moved to DC. I worked for Mathematica policy research for two years, and I learned a lot about policy research.
Scott Cunningham:
Are you getting a quantitative training at the master's of public health when you went?
Anna Aizer:
Yeah, so that's where I really took my first micro theory class and my first statistics class. So I took biostatistics and micro theory there. And when I worked at Mathematica, I worked with a lot of economists. So most of the senior researchers at Mathematica were economists by training. That's where I really got exposure to the way economists think about, research and policy evaluation. It was then that I decided I wanted to go back and get a PhD in economics.
Scott Cunningham:
Okay. So what was it? What's the deal? Why do you like economics at this point?
Anna Aizer:
The senior researchers at Mathematica were either economists or sociologists or political scientists. I just felt like the economists had a very clear way in which they set up problems. I think that goes back to economic models of decision making.
Scott Cunningham:
Yeah. Right.
Anna Aizer:
And it just struck me that that was just a very good way to conceptualize almost any problem. I also liked the way they thought about data. I think the people that I worked most closely with and came to admire were all economists. So that's how that-
Scott Cunningham:
And how long were you there? Were you doing public policy stuff at Mathematica?
Anna Aizer:
Yeah. I was doing a lot of evaluations of Medicaid programs. In particular, Medicaid managed care, moving from a different financing model for Medicaid and evaluating that, and various settings, and writing them policy briefs so that... God. It was either two or three years, I can't really remember, maybe three years. I think I was there three years and then I went back to graduate school.
Scott Cunningham:
And then you go to UCLA?
Anna Aizer:
And then I went to UCLA.
Scott Cunningham:
Am I right that you were working mainly with Janet Curry?
Anna Aizer:
Yes. So Janet Curry was my-
Scott Cunningham:
You worked pretty closely with her?
Anna Aizer:
Yeah. She was my main advisor. The other folks I worked with were Joe Huts and Jeff Grogger.
Scott Cunningham:
And who?
Anna Aizer:
Jeff Grogger.
Scott Cunningham:
Oh, Jeff Grogger?
Anna Aizer:
None of whom are there anymore.
Scott Cunningham:
Yeah, yeah, yeah, yeah. Right. Right. I'm just curious. I associate you a lot with... Because I wrote that book on causal inference I'm obsessed with the causal inference stuff in all these weird ways, with all the people. I see Princeton industrial relations section, Card, Angres, et cetera. And then I see Janet Curry. And then I see you at UCLA, and I associate you so much with that methodological approach, especially for some of the papers that I've known really well. Did you get a sense when you were at UCLA, oh, this is causal inference, this is different, this is the credibility revolution? Or was it just really subtle, or this is just how you do empirical work?
Anna Aizer:
That's a great question. So I should also say that my first year econometrics teacher was Hero Inmans.
Scott Cunningham:
Was it, really?
Anna Aizer:
Yeah. Hero [inaudible 00:36:18] UCLA.
Scott Cunningham:
Oh my gosh. I didn't know that.
Anna Aizer:
For a short period of time. I was lucky enough that he was there when I was there. So he taught me in my first and my second years. So of course he was very much big part of this. And actually Enrico Moretti was also at UCLA when I was there, so I took courses with him. I think between Janet, Hero, Enrico and Joe Huts, they were really in the thick of it. That was the way it was done.
Scott Cunningham:
That was the way it was done.
Anna Aizer:
That was the way it was done.
Scott Cunningham:
Yeah. What did you learn? What do you think the salient concepts were that had you... This is a make believe, right? But I'm just saying, had you gone to a different school where you didn't have any of those people, what do you think the salient econometric causal inference kind of things were to you that you were like, oh, this is what I notice I keep doing over and over again, or keep thinking about?
Anna Aizer:
Well, I would say that the method was in service to the question. I feel as though I'm seeing it more these days. People, they find an experiment, a natural experiment, and then they figure out the question. That's not how I remember it. You had the question and then the method was in service to that question. I worry that that's getting a little bit lost these days, that people have the experiment and then they're searching for the question. I think that ends up being less interesting and less important.
Scott Cunningham:
Yeah, yeah, yeah. Yeah. There were certain economists, I think, that were so successful as approaching it that way. It seems like it was cut both ways, because it seems like applied causal inference grew on the back of that kind of natural experiment first, but it almost becomes... To a kid with a hammer, everything's a nail, so it's just like, look through the newspaper, look for a natural experiment. What can I do? How can I do this? How can I [handle 00:38:49]?
Scott Cunningham:
And it is funny. I don't think it's as satisfying too, just even emotionally. I guess you can find discoveries that way, like you were, but it does feel like you don't end up building up all the human capital with the importance of that question. It's almost like, you're like, well, how can I make this question really important? As opposed to it is important.
Anna Aizer:
Right.
Scott Cunningham:
What were you studying? I know what you were studying. At UCLA, what was the question that you were really captivated by?
Anna Aizer:
So I was really focused on health. You have to remember, I'd done a master's in public health and I just worked at Mathematica, so I was really focused on health. So really all of my dissertation was on health. My main dissertation chapter was actually on Medicaid in California. It was on the importance of enrolling kids early in Medicaids. I don't know if you know much about the Medicaid program, but there are many kids, 60% of kids, who are uninsured are actually eligible for the Medicaid program, but not enrolled in the Medicaid program. And that's partly because-
Scott Cunningham:
60%?
Anna Aizer:
Yeah.
Scott Cunningham:
Wow.
Anna Aizer:
We could reduce the number of kids who are uninsured in this country by more than half if you just enrolled all those kids who were eligible for Medicaid in the program.
Scott Cunningham:
Yeah.
Anna Aizer:
And part of the-
Scott Cunningham:
We saw that in that Oregon Medicaid experiment.
Anna Aizer:
Yeah. Oregon was mostly adults. I don't know how these numbers differ for adults and kids. I'm really more focused on kids. It's partly by design because Medicaid is a program. If you show up at the hospital and you don't have insurance and you're eligible for Medicaid, the hospital will enroll you. And most people know that.
Scott Cunningham:
Oh, is that right?
Anna Aizer:
Yeah. I mean, because they have every interest. They want to get paid, so they'll enroll you in the Medicaid program, but there's a cost to that. Because what that means is that kids, if parents know that once they go to the hospital their kid will be enrolled in the Medicaid program should they need hospitalization, they don't end up getting them enrolled prior to that. So they miss out on the ambulatory preventative care that might prevent them from being hospitalized to begin with. And that's partly because of the structure of the program, but that's also because the states made it difficult for kids to enroll in the Medicaid program. In California, there was a big change. The application for Medicaid used to be 20 pages long. Imagine that, right? They cut it down to four.
Scott Cunningham:
What kind of stuff are they asking on those 20 pages?
Anna Aizer:
Who knows? Who knows what they're asking.
Scott Cunningham:
Good grief. I mean, they're wanting them on there. Are they screening them out or are they just-
Anna Aizer:
I think that's partly what they were trying to do, right?
Scott Cunningham:
Screen them out? Because it's expensive.
Anna Aizer:
It's expensive.
Scott Cunningham:
You've got some of these legislators, they're like, this is expensive and I don't even want to do this so add a dozen pages.
Anna Aizer:
Yeah. So just make it hard. Now, what happened in '97 was the child health insurance program, CHIP. And they said, "If you want CHIP money..." So that's federal money to ensure more kids. "If you want CHIP money, federal money, you are going to have to enroll more kids in the Medicaid program. You have to do outreach." So the states actually were forced, and that's actually what prompted California to go from a 20 page application to a four page application. They also spent about $20 million on advertisement and basically training community based organizations in how to complete a Medicaid application. So they train them. "Here, you can help your clients enroll in Medicaid. For every application that you help that ends up getting onto the Medicaid program we'll give you 50 bucks." And this really mattered. A lot of kids started enrolling in the Medicaid program who otherwise wouldn't, particularly Hispanic and Asian American kids.
Scott Cunningham:
Is this what your dissertation ends up being about?
Anna Aizer:
This is what my dissertation is about.
Scott Cunningham:
On both the shortening and the payment?
Anna Aizer:
So it was basically once they started doing this you started seeing big increases in the number of kids who were enrolled in the Medicaid program. And you saw declines in hospitalizations for things like asthma. Asthma is a condition for which if you're being seen and treated on an ambulatory basis, you shouldn't end up in the hospital.
Scott Cunningham:
Oh. Wait. So what's your control group and all this stuff?
Anna Aizer:
What the state did was they targeted different areas, and provided training to those community based organizations in how to complete a Medicaid application. So they gave me all that data.
Scott Cunningham:
Get out of here.
Anna Aizer:
So I had all the data.
Scott Cunningham:
So you're doing some IB thing? You're doing some-
Anna Aizer:
Yeah. It was, basically if you live in a neighborhood where a community based organization had already been trained then you were much more likely to be enrolled in the Medicaid program. So you can see that.
Scott Cunningham:
Oh my gosh. This is so cool. Were you excited when you found that?
Anna Aizer:
I was super excited.
Scott Cunningham:
I bet.
Anna Aizer:
I was super excited. This was so old. I was begging Medicaid to send me this data. Begging, begging, begging. And they weren't really answering. And then one day Janet came in to the office where all the graduate students sit, and she said, "I think I got this fax for you." She handed this 20 page fax that has all the data on what community organization got trained and when.
Scott Cunningham:
Okay. Anna, I want to ask a meta question real quick. You just said, these days people maybe start with natural experiment first, but originally it was question first. Okay. Not devil's advocate, but just a statement of facts. The one reason they may do that is because when you find these kinds of natural experiments or whatever, it almost just feels almost itself random. You're weren't even really looking for it. You read something in the newspaper, you're like, oh my gosh, they're doing this weird thing. And the risk of going question first is, you could have this incredibly important question, like the Medicaid project payment thing, and you're like, if everybody in my department, like Hero Inmans and Moretti and Curry, who are to answer a question either subtly or not so subtly, or to answer a question is going to require this credible design and we really need you to staple this dissertation together. You're going to have to have a-
Anna Aizer:
I think that's why you have lots-
Scott Cunningham:
It seems really risky. It seems really risky.
Anna Aizer:
Yeah. I think you have to have lots of ideas.
Scott Cunningham:
You have to have lots of ideas.
Anna Aizer:
I think you have lots of ideas. A good friend of mine in graduate school was Enrico Moretti's RA. He told me that Enrico had tons of ideas. Wes, this was my friend, his RA, would just do some really quick takes on all of these ideas. And if there was something there he'd pursue it. But if there was nothing there he'd drop it.
Scott Cunningham:
What does that mean, nothing there, something there? What does that mean?
Anna Aizer:
Either, if you can't find exaggerate variation or the exaggerate variation doesn't actually work, you don't have the first stage, he'd just drop it and move on to something else.
Scott Cunningham:
That's a skill. That's almost some therapeutic skill to be excited about something and willing to let it go.
Anna Aizer:
Yeah. I think that's right. I think that's actually-
Scott Cunningham:
You got a lot of ideas?
Anna Aizer:
I had a lot of ideas. It never worked out.
Scott Cunningham:
Never worked out. And that's normal.
Anna Aizer:
I think that's normal.
Scott Cunningham:
Yeah. That's not a bad thing.
Anna Aizer:
Yeah. I think that's how research should go. In fact, I'm not as good as Enrico, I probably hold on to things for longer than I should.
Scott Cunningham:
Yeah, yeah, yeah, yeah. Boy, where'd you end up publishing that work? I should know this, but I don't know.
Anna Aizer:
That published in Restat Review Economics Institute.
Scott Cunningham:
Oh, cool. So what'd you end up finding?
Anna Aizer:
So what I end up finding is if you pay these organizations to enroll... Well, a couple things. Advertisement, just blanketing the television and radio with information. Sign up for Medicaid, sign up for CHIP, that does not work at all.
Scott Cunningham:
Doesn't work?
Anna Aizer:
No.
Scott Cunningham:
Advertising doesn't work?
Anna Aizer:
It doesn't work. What works is having these communities organizations help families complete the application. That's incredibly important.
Scott Cunningham:
That's a supply demand kind of philosophy that you see in drugs, too. Mark Anderson has this paper on meth. They would post these advertisements of people that were addicted to meth. They look horrible. They lose their teeth and all this stuff. It didn't do anything.
Anna Aizer:
Yeah.
Scott Cunningham:
Maybe I'm wrong, but it seems like you're talking about a group of people. They're like, they need more assistance. They need somebody... You think about that thing you were saying earlier about these kids that are higher income versus lower income. When I said there were 10,000 steps that the higher income people had, it wasn't really like the kids, it was external forces that were investing, going after them.
Anna Aizer:
Yeah. Right.
Scott Cunningham:
It seems like incentives need to be targeted to people to go after. For whatever reason it is not enough to just simply have it. You need people going in and helping along the way.
Anna Aizer:
Right. Agreed. I agree. They need support.
Scott Cunningham:
They need support.
Anna Aizer:
Yeah.
Scott Cunningham:
Okay. So that is amazing. I bet your advisors were so proud of you for that project.
Anna Aizer:
I don't know.
Scott Cunningham:
I think so.
Anna Aizer:
You'd hope so, but that'll be icing on the cake.
Scott Cunningham:
Right. Exactly. Yeah. I guess that's not super important.
Anna Aizer:
Yeah, it is. You do always want your advisor... I mean, I had tremendous respect for all my advisors. So yeah, I'd be very pleased if they liked the work that I did. Basically, states did spend this money to enroll kids early, but it paid off because it meant that they were less likely to be hospitalized. In fact, some of these programs can be very much cost effective.
Scott Cunningham:
Yeah. Yeah. I had told myself, I was like, well, I'm asking Anna about the juvenile incarceration paper with Joe Doyle. And then I was going to ask her about domestic violence. And I feel like I've got to make a hard choice now, because I don't have a lot of time. So I was thinking, well, let's see how this goes. And then we can fit. So domestic violence. First thing I want to ask is, how did you get interested in that topic? And when did it start? In a way I could almost imagine, oh, you've been thinking about domestic violence forever.
Anna Aizer:
Yes. So I actually-
Scott Cunningham:
You've been thinking about women ever since college.
Anna Aizer:
Yeah. That's true. And made that connection. This was basically my first big project after I started at Brown. After my dissertation I was thinking, okay, what's my next big project going to be? And I think that's a very important decision for junior faculty to think about. After you finish publishing your dissertation you got to think about what's my next big project? Because it takes so long to publish anything in economics, that's really going to matter a lot. That might be the only thing you publish before you're coming up for tenure given how long.
Anna Aizer:
I was thinking about it, and I just felt like I didn't have a clear question in mind, but just been looking at the numbers it's incredibly prevalent, domestic violence. But it's also shown some pretty encouraging trends. Domestic violence against women has been declining pretty significantly. In the US, I think about... I haven't looked the number up recently, but it was about 1,000 women a year were being killed, and so many more actually are victims of domestic violence. And if you look at victimization surveys, between one and three and one in four women in the US report ever being the victim of domestic violence. It's really prevalent. And it just struck me, this is a big problem and I don't know how to answer it, but we should know more about it given just how prevalent it is. And so that's how I started.
Anna Aizer:
I have a good friend from high school, and she's a lawyer in New York City. She was working with victims of domestic violence. She's a lawyer by training. She used to say, "These women have nothing. They have no resources. They are so poor." That, to me, just made me think about, okay, I need to start thinking about income and resources and poverty and domestic violence, because clearly that's a big part of this.
Scott Cunningham:
Yeah. Yeah. It's so funny. I feel like you and I ended up responding to the bargaining theory papers in the exact same way. That's when I was studying a lot of my stuff on couples and things and bad behavior on the part of the men, I was always thinking about sex ratios in the marriage market. Why I was thinking about that was the ability to exit the partnership could be really, really important. And I was curious. You can talk about people not having resources and not necessarily be thinking in terms of one of these Nash bargaining, like Manser and Brown, and McElroy and Horn, and Shelly Lundberg kinds of ways of thinking. I was curious, were you thinking about those theory papers a lot? Or am I just projecting?
Anna Aizer:
I had this friend, again, who was working and telling me just how poor many of the women she was working with were. And then once you actually look at the statistics, the survey statistics, it's true that any woman can be a victim of domestic violence, but it is really a poor woman problem. So it's very clear to me that poverty has a lot to do with it. It's because many of these women have no other source of support. They have low levels was in schooling. They have few prospects in the labor market. And they're really stuck. That is ultimately-
Scott Cunningham:
Stuck as in cannot leave.
Anna Aizer:
Cannot leave. I mean, they have a very-
Scott Cunningham:
Because that's the solution. That's one of the most important solutions, which is probably you need to leave the relationship.
Anna Aizer:
Yeah. Or you need to be able to threaten to leave.
Scott Cunningham:
You need to be able to threaten to leave. How important do you think the credible threat is? Because my sense is, that's to an economist, because they're like, you should thinking about unions and stuff. They're like, oh, credible threats. That's all you got to, you have to do it. I feel like, I don't know if that really works. I actually think the truth is you're going to have to leave. And maybe there's some marginal guy. We're talking about the marginal guy, but whatever, that's the info marginal, whatever. The extensive marginal guy, he's got narcissism personality disorder, substance abuse problems.
Anna Aizer:
Yeah. You may be right.
Scott Cunningham:
He's got major, major problems. And that stuff is very inelastic to everything.
Anna Aizer:
Yeah. You may be right. I can't answer this because I don't know for sure. At the same time I remember talking to some folks about this, and their feeling was that it's all a continuum of a bad relationship. Violence may be one extreme, but relationships have ebbs and flows. They can be better at some points and worse at others. So they did feel as though a relationship didn't always have to be violent, that you could have relationships that were violent at one point but then were no longer. Of course, you also have relationships in which that's not the case, and the only solution is to leave. But there could very well be relationships where you can have better and worse periods.
Scott Cunningham:
Yeah. The reason why I bring it up is because I feel like these days you hear a lot about mental health. Well, you hear about mental health period, but in domestic violence there'll be also an emerging story of the narcissist personality disorder. I've been always lately thinking, I've been like, I wonder if this is true. Anecdotally, what you see a lot is how manipulative... And that's like a very judgemental way of putting it, but I don't know how else to say it. How manipulative one of the person can be towards the other where they're like, "Well, if you loved me..." They get all this trepped up stories about love. What love becoming almost this story.
Scott Cunningham:
I've wondered for those people that can't or won't... It's actually won't, right? They can leave. I mean, there are some people they will be literally harmed if they leave, so I'm not talking about those people. But I mean, the person that literally you're watching an equilibrium where they don't leave, I've wondered lately if it's like, the victim is all tangled up with loyalty and love.
Anna Aizer:
Yeah. Sure.
Scott Cunningham:
And it is taken advantage of by a person that no one can tell them not to love this person. That's nobody's business.
Anna Aizer:
Yeah. Yeah. I mean, it is a really complicated thing.
Scott Cunningham:
It is so complicated. It is so complicated. Finding the policies that provide resources to a person. Some of that might be a person that's at those earlier ebbs too, those earlier ebbs in the bad relationship. And you're like, well, some people may not be ready to leave yet.
Anna Aizer:
I mean, this a thing where I do think the right policy response is providing resources to women, but also probably interventions aimed at the assailant is probably going to be just as effective. Sorry. My phone is ringing.
Scott Cunningham:
That's okay.
Anna Aizer:
Hello. Sorry about that. I thought it might be my kids.
Scott Cunningham:
I wonder about these battery courts. Have you heard about these [inaudible 01:00:05] courts?
Anna Aizer:
Yeah. I mean, they're-
Scott Cunningham:
I wonder what you know about those?
Anna Aizer:
Yeah. Not a lot, I would say.
Scott Cunningham:
Yeah. These issues of poverty and mental health and all of these things interacting in order to get healing and healthy meaningful lives to all everyone is... I do think this is something that economists can offer, but it's not something that... I wouldn't say there's a ton of people. You're one of a small number of people working on domestic violence, it seems like.
Anna Aizer:
It's a very hard thing to study. Data's very difficult to come by for obvious reasons, for a good reason. I mean, this is data that needs to be protected. Glenn Ludwig and the crime lab in Chicago, they're doing work around violence reduction more generally. And probably many of those principles and findings probably relate to domestic violence as well, changing the behavior of young people so that they are less quick to react and less quick to react in a violent way. When they do, we would probably have some pretty important spillover to domestic violence as well, I think.
Scott Cunningham:
Yeah. Yeah, yeah.
Anna Aizer:
I think there are ways to reduce violence more generally that would probably apply to the setting of domestic violence.
Scott Cunningham:
Yeah. It's funny, circling back to that judge who threatens with higher penalties. I think economists, when they think about violence and things like that, you're an exception for thinking about outside options and stuff like that, but the shadow of Gary Becker's deterrence hypothesis, it can just be this straight jacket for a lot of people, because they just only think in terms of relative price changes on the punishment margins. When you talk to psychologists, or you read that psychology literature about narcissism or borderline personality disorder or substance abuse, you're talking about a group of people that are, for variety of reasons, have really low discount rates or just have beliefs that things don't apply to them. Or in no uncertain terms, the elasticities of violent behavior with respect to some unknown punishment that you don't even know if it's going to real, it just seems like, we don't really know, but [inaudible 01:03:06] really big.
Anna Aizer:
Yeah. So there was this criminologist named Mark Kleiman. Do you know that name?
Scott Cunningham:
Oh yeah. Mark Kleiman. Yeah. Yeah.
Anna Aizer:
I mean his big thing was, it should be swift, sure and short. That's how we should do punishment. He felt as though that would be far preferable to the system in which there's uncertainty. But if it doesn't work out, you're going to spend a lot of time in jail. He thought that was a fair model.
Scott Cunningham:
The thing is, though, swift certain and did you say short?
Anna Aizer:
Short.
Scott Cunningham:
Yeah. Well, with prison sentences lingering on your record it is by definition never short.
Anna Aizer:
Yeah.
Scott Cunningham:
You face these labor market scarrings and you can't get housing, you can't get jobs and that does not go away. So even if the prison sentence is short, the person... I just feel like this is the tension around violence in the country, which is punishment has so many margins where it is permanent. It's got so many margins. And just being in a cage is only one of them.
Anna Aizer:
Yeah. I mean, particularly for young people, jail is incredibly scarring.
Scott Cunningham:
Incredibly scarring. Incredibly scarring. We've been studying suicide attempts in the jail and we-
Anna Aizer:
Yes, that's right.
Scott Cunningham:
We walked the jail for this one particular jail. I have never in my life seen anything like that. I've been working on this project for four years. I hadn't walked to the jail. I don't know. It's not the first thing that came to my mind. The team finally walked the jail. I spent the whole day there. The jails have so much mental illness in it. They just are in... It's not even cages. A cage has... Air gets in. It's a sealed box. It's like Houdini's box. They stay there, and for a variety of regulatory reasons and so forth, they stay in there. Can't have a lot of materials if they are at risk. If they've come in with psychosis because of substance abuse or underlying mental illness stuff, they might get moved into certain types of physical quarters. I just can't even imagine, just in an hour, let alone... And that's just jail. That's not even prison. It's just absolutely a trauma box.
Scott Cunningham:
Unfortunately, we didn't get to talk about your paper with Joe Doyle on the juvenile incarceration. But every time I teach that juvenile incarceration paper, where kids were incarcerated as a young person, and then end up not going back. It's not even the future prison part, it's the not going back to high school.
Anna Aizer:
Oh, of course.
Scott Cunningham:
And then when they go back, they're labeled with a behavioral emotional disorder. It's really like anybody that's had any exposure to a kid involved in corrections, you're like, oh, I know exactly what that is. They were traumatized. You don't even have to come up with some exotic economic theory. They were traumatized. That's why they come back to school with a behavioral emotional disorder. It is [inaudible 01:06:59].
Anna Aizer:
Yep. That's good.
Scott Cunningham:
That paper is one of the most important papers I have personally ever read. I teach it nonstop. And I've even cried teaching it in class. I get so emotional when I get to that part, because, I don't know about you, but it seems like it's really hard not to come away with... A lot of papers you read, you're like, well, we're not really sure exactly all to make of it. But when I read that paper that you wrote, I just think, especially when you think about the leniency design, I just think these kids probably didn't need to go to prison.
Anna Aizer:
Oh yeah.
Scott Cunningham:
Honestly, what else are you going to say? They end up committing more crimes. And they are not going back to school. How was this the policy goal? What was it like writing that paper when you started to realize what was going on?
Anna Aizer:
Again, when I worked in this Alternative to Incarceration program we had kids come into the program who had spent some time in jail. And we had kids who had spent very little time, maybe just a night. The kids who had spent even just three weeks in jail, they always did worse in the program. Always. It was a known fact. The program knew it. And the question was, well, are these kids somehow different? There was a reason why they were in jail and these other kids weren't. Is that why they do worse in the program? Maybe they're in jail because their family didn't show up for them in court. They couldn't make bail.
Anna Aizer:
Or was it something about spending three weeks in jail that just made it impossible for them to complete the program? This was a big question that was on everybody's mind. We talked about this quite a bit at the program, and we didn't know the answer. When I finally figured out how to do it, working with Joe, I wanted to know the answer to a question that I had been thinking about for over a decade.
Scott Cunningham:
Gosh. Were you emotionally upset when you started to see coefficients get really big?
Anna Aizer:
It really was not surprising. It really wasn't, because these are kids who are only marginally attached to school. These are not the kids who were going to school, doing well in school. These are kids who were not really that attached to school for whatever reason. So you take them out even for a month, they're not going to go back. I mean, it's obvious. We saw that in the program. What they ended up doing was moving a lot of kids from school to GED because they had not been involved in school, they were not involved in school. It just was much more likely that they would be able to complete a GED than actually go back to high school and finish.
Scott Cunningham:
Your paper, it like hit home for personal reasons. We had an event happen. I wrote a professor. I was like, this thing had happened. Anna and Joe find this result. I feel hopeless. There's this kid in town and I raised money for him. Basically, I was like, you just got to do everything in your power to not let them spend an extra minute in jail. And all this scared straight stuff. Parents get into it, too. They're exhausted. They're like, "Well, he's got to learn his lesson." Nobody learns a damn thing in jail. They don't learn a lesson. Because you're just so hopeless. You start grasping at straws. And people will tell you that might happen.
Scott Cunningham:
I just keep thinking to myself, look, just get him out of jail. Whatever the folklore is, let the folklore about how to help a kid happen outside of jail. Just get him out of jail and then just let the next thing. But the thing is, they need so much help. These kids that end up getting tangled up with... They do these petty larceny things. They're high on Xanax and they do these petty larceny things. And it just starts adding up. You get them out and you just realize, you're like, oh, okay. Now where's the massive infrastructure to help them? And you're like, there's not one. So then they just keep getting arrested and they keep getting arrested. Then you're going, well, how many lottery tickets is the optimal number of lottery tickets to always be buying so that you're diversifying, so that one time maybe it clicks? And you're just like, I don't have the budget constraint for it.
Anna Aizer:
Yep.
Scott Cunningham:
I find it hard to be hopeful sometimes when you work on these projects. I think that's part of it. When you work on projects around violence and kids and jail, it can, it can make you feel hopeless. I, even with your paper, just feel like... I'm like, okay, the goal needs to be to avoid this. But then you just think, but nobody's listening to an economist.
Anna Aizer:
Well, a couple things. The rate of juvenile detention has been falling pretty steadily. The trends look very different from adult incarceration and detention, which have only started to fall more recently. Juvenile detention incarceration has been falling. Actually, I was speaking with some policy makers who said that a 2013 National Academy Of Sciences report on juvenile detention was really very impactful, and that it made very clear that detaining and incarcerating youth was a terrible idea. So lots of things have changed in response. The federal policy in this space is to do what everything can be done to reduce the number of kids that are detained and incarcerated.
Anna Aizer:
Now, most of these kids are being detained and incarcerated in local and state jails and prisons. Federal government isn't actually jailing people, so their policy levers are more limited. But policy makers, at least at the federal level and increasingly at the state and local level, do understand that incarcerating youth is a bad idea. And rates have been falling. So that's the good news.
Scott Cunningham:
Right. Right. One of the things in this podcast series I've been gradually beginning to focus on is how so much of changing policy from the perspective of the individual scientist is the long game. You are doing things, and just almost knowing that this is a part of a larger process that takes a long time, and the work matters. It matters. It's important to tell the truth and do good work and do your best. Even to get it published in the top journal so that people take it really seriously, too.
Scott Cunningham:
It is so nice to talk. I really mean it. I've felt just such a strong inspiration and things that I've learned from you and how you do ask questions and how you answer them. I watch you get administrative data. That was the thing with that Joe Doyle paper, I watched you get administrative data. I think, oh, I could get administrative. It's really been for me personally, just looking, I have watched you be an economist and just use it to navigate a little bit about, okay, Anna's doing this and it's possible to do this.
Anna Aizer:
I got to give a shout out to Joe Doyle, because he was the one that was able to access that Chicago data.
Scott Cunningham:
Golly. I mean, there's all that linking. It seems like it's an early linking paper even.
Anna Aizer:
Yeah. Shout out to Joe more generally. He was a great co-author on that project.
Scott Cunningham:
Yeah. Yeah. Well, thank you so much for being on the podcast.
Anna Aizer:
Yeah. Thank you, Scott. It was a pleasure talking to you.
Scott Cunningham:
Yeah. Okay. Bye, bye.
Anna Aizer:
Bye, bye.
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Ronny Kohavi, PhD
“Economists in tech” is a podcast series of mine trying to tell the story of the movement predominantly Economics PhD talent into and throughout the emerging tech sector. Previously interviews have been with Michael Schwarz (Microsoft), Susan Athey (Stanford, now DOJ, formerly Microsoft), and John List (Chicago, Wal-mart). But this week I chose to share an interview I did a month ago with a prominent computer scientist named Ronny Kohavi.
Economists may not know about Ronny. Ronny did his PhD at Stanford in 1995, and was at ground zero to watch major advances happen in tech. His early work was in machine learning, and many of his most cited papers remain in that area too. But something that he has also been instrumentally involved in is from day one in tech being an aggressive evangelist, promoter and guide for the adoption and design of randomized controlled trials now used extensively within tech (called there A/B test not RCT). His recent book with Tang and Hu, "Trustworthy Online Controlled Experiments” discusses in detail his thoughts on this topic.
In a lot of ways, Ronny could just as easily fit in the “causal inference” series, but I chose to pin him in this because I think he is more broadly familiar to the tech sector for pushing for the randomized experimental design, and I thought that might be interesting for those of us who stand outside with curiosity tech. If you want to study with Ronny, he teaches a regular workshop at Sphere on RCTs.
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Anna Stansbury is a professor at MIT's Work and Organization Studies department. I interviewed her as part of my "economics and public policy" mixtape series. We discussed her growing up in England, and what drew her to economics, as well as her thoughts about labor market trends and other stylized facts and what she thinks they mean. I hope you enjoy this as much as I did!
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Robert Michael, professor emeritus at University of Chicago, was a student of Nobel laureate Gary Becker from a productive period when Becker was at Columbia up through the late 1960s and in this interview shares a bit of why that time was so special. As you may recall, I have been doing my only little “mixtape” about Becker’s students and previously interviewed Bob’s old classmate and longtime friend, Mike Grossman. Bob describes a lot about the secret sauce that made Columbia such a special time for people like him, Mike, Bill and Elizabeth Landes, Isaac Ehrlich and many others. It wasn’t merely the chance to be mentored by Becker according to Bob; it was also Jacob Mincer and how complementary those two were — yin and yang, theory and empirical rigor. Bob would go on to helping shape the profession, not merely through his wonderful scholarship, but also through his overseeing of numerous important panel and cross-sectional datasets. The two with which I am most familiar are the National Longitudinal Survey of Youth 1997, which I wrote my dissertation on, and the National Health and Social Life Survey, a 1992 survey which was the first representative survey of adult American sexual behavior.
He co-authored two books about sex in fact — one entitled The Social Organization of Sexuality and Sex in America. Both of these books document the sexual practices of adult Americans from that early 1992 period, riding on the crest of the AIDS epidemic and helping us better understand the basic facts about sex in America. I think you will be deeply moved, though, listening to Bob describe the lengths to which they at NORC went to talk to respondents and learn about their sexual behavior was stunning and not surprising. He notes that some respondents wept during the survey because, as they said, they had literally never talked to anyone about some of these important parts of their lives, some not even their own spouses, therapists or doctors. And yet Bob had with his team at NORC created such a safe, compassionate and respectful environment that not only could he ask intimate questions to strangers, but in fact have a nearly 90% response rate of people willing to share. A true model of science — curious, careful and compassionate.
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In this episode, I introduce listeners to Michael Grossman, an early pioneer in the field of health economics. His dissertation work under at Columbia University on "health capital and demand" became a cornerstone of the modern field of health economics. We discuss his time growing up in New York, his time with Gary Becker at Columbia as his student, how he got into health in the first place, and much more. Mike is a much beloved economist and I hope you will enjoy this interview as much I did.
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In this week's episode of Mixtape the Podcast, I interview Mark Anderson, a health economist at Montana State University. We discuss his time growing up in Montana, his brief stint at Stanford playing football, how he got into economics, cannabis reform, public health at the turn of the 20th century, and the joys of hand collecting data. We also discuss a new course he is teaching for young faculty and students on doing applied research. Enjoy!
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In this week's episode of the Mixtape podcast, I had the pleasure of interviewing Dr. Katy Graddy. Dr. Graddy is a professor of economics and dean of the international business school at Brandeis. She also did her PhD in economics at Princeton in the mid-1990s where as I see it design-based causal inference has its start and is gaining influence. We discussed the joys of collecting data and using it with economic theory to study markets. We discussed fish, art and bereavement, and some of the ways in which creativity manifests as an economist.
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In this week's episode of Mixtape: the Podcast, I have the pleasure of interviewing one of my truly favorite people I've met and learned from, Alvin Roth. Alvin Roth is the 2012 winner of the Nobel Prize in economics and professor of economics at Stanford University. He is a widely regarded and extremely innovative game theorist who uses game theory not only to understand the world but to improve it. Those improvements broadly are grouped under a field we now call "market design", but it has included helping design kidney exchange policies that can help address kidney shortages, helping redesign the allocation of physicians to hospitals and residencies, and much more. A humble man who is as I say in the interview kinder than he is smart, which given he won the Nobel Prize says a lot about both. Always a joy to talk with this man. I hope you feel so too.
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In this interview, I had the opportunity to talk with a wonderful man, Alan Manning. Dr. Manning is a professor of economics at London School of Economics. He is a labor economist's labor economist. He has beat the steady drum of careful empirical work thinking hard about the welfare of workers and to evaluate the presence that market composition has on their overall well being. We discussed a new paper of his in the Journal of Human Resources trying to explain the source of a wage premium in Germany for workers in urban areas, and whether and to what degree that premium is due to local competition of firms. We talked about his whole career and I hope you enjoy it.
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In this interview, I talk with the esteemed economist, Susan Athey, a professor of economics at Stanford University and a recently elected President of the American Economics Association. She was one of a handful of micro theorist pioneers, like Hal Varian to Google and Preston McAfee to Yahoo, who in the early 2000s traveled from academia to work for large technology firms to work on market design elements, such as the design of auctions, that would enhance the productivity of the firms themselves. Dr. Athey did this first as a consultant at Microsoft, then as its first chief economist, then later on the board of more than a half dozen firms. She has since returned to her alma mater, Stanford University, where among her many activities she established a lab on social impact, and has written countless influential articles drawing on the strengths of machine learning methods and approaches at the service of causal inference. Just as Dixit predicted that she would win the John Bates Clark award, I’ll state the obvious that it will not be the last major Prize she wins. I hope you enjoy!
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In this week’s podcast, I had a great time talking with Gary King, the Albert J. Weatherhead III University Professor at Harvard, the Director of the Institute for Quantitative Social Science and founder of several firms specializing in data analytics and education. As a scientist, he has made major contributions to the fields of statistics and political science, but more than that, he is also just one of the most creative, curious and passionate thinkers I’ve had the chance to meet. There is too much to summarize so let me just say I think, like I found him, you will likely be inspired as he shares his thoughts about science, the social order, inference and data. This is Mixtape: the Podcast and I am your host, Scott Cunningham!
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In this episode of Mixtape: the Podcast, I interviewed Petra Todd, professor of economics at University of Pennsylvania. Dr. Todd is a widely regarded and highly influential applied and theoretical econometrician who has written across many topics ranging from developing tests for evaluating racial discrimination in motor vehicle searches, to analysis of large conditional cash transfers (PROGRESA), to making seminal contributions to our understanding of program evaluation methodologies such as regression discontinuity design and matching. She is unique among many who write in the area of program evaluation for merging design based approaches to causal inference with approaches built on economic models, or "structural" methods. In this interview, we discussed her love of economics, her work with and mentorship from Jim Heckman, the early work she did studying the PROGRESA conditional cash transfer program and the value of structural econometrics more generally for applied researchers interested in causal inference and understanding programs. To learn more about the topics we discussed, see this new forthcoming article in the Journal of Economic Literature, coauthored with her former colleague Kenneth Wolpin, entitled “The Best of Both Worlds: Combining RCTs with Structural Modeling.”
http://athena.sas.upenn.edu/petra/papers/surveywkenlatest.pdf
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Michael Schwarz leads economics at Microsoft as Corporate Vice President and Chief Economist. A former professor at Harvard, Michael became an early pioneer in tech as part of a larger trend of top PhD economists moving into industry to work on a variety of real world topics related to market design and causal inference. In this interview, we discuss some of his ground breaking work in micro theory and application and the ongoing relevance and power of economic theory for understanding our social and corporate world.
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In this interview, I had the pleasure of speaking with Dr. Elizabeth Popp Berman, Associate Professor of Organizational Studies at University of Michigan. We spoke about her new book, "Thinking Like an Economist: How Efficiency Replaced Equality in U.S. Public Policy" recently published by Princeton University Press, and her career as a sociologist.
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In this week’s episode of Mixtape: the Podcast, I had the pleasure of interviewing longterm editor of the Quarterly Journal of Economics, Larry Katz. Dr. Katz is a distinguished labor economist and a pillar in the profession as editor of the more impactful and influential journal in our science. He has written a number of classic studies in labor economics ranging from topics like skill based changes in relative wages with Kevin Murphy to the importance of neighborhoods on life outcomes based on the Moving to Opportunity experiment. As with many of the people I have the chance to interview, Dr. Katz has forgotten more economics than I will ever know.
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Peter Hull is young econometrician at Brown University who writes about a variety of applied topics such as education, labor and criminal justice. Most of his work manages to simultaneously reveal something new about a phenomena while also extending our methodological understanding of causal inference. In this episode of Mixtape: the Podcast, Peter and I talk about growing up in Maine as a child spending time near the water and outdoors as well as in mathematics. We talk about the unexpected journey he made into economics as a college student when he saw its potential to meaningfully inform public policy, as well as econometrics' ability to answer causal questions. We talk about his love of instrumental variables in particular, the potential outcomes model, causal inference and a new paper of his with Michal Kolesar and Paul Goldsmith-Pinkham on interpreting regressions with multiple treatment variables.
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Guido Imbens is the Applied Econometrics Professor at Stanford University's economics department and business school, as well as a co-recipient of the 2021 Nobel Prize in Economics for his work on the local average treatment effect and instrumental variables in his 1990s era work with Josh Angrist. In this interview we discuss that time in his life, his influences, his career and collaborations over the last several decades. Dr. Imbens is one of the more enjoyable people I've had the pleasure of meeting in all of economics.
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In this 8th episode of Mixtape: the Podcast, I interviewed Sandy Darity, the Samuel DuBois Professor of Public Policy at Duke’s Sanford School and pioneer in a framework within economics called "stratification economics". Stratification economics focuses on the determinants of group-level inequality rooted in group identity, relative position within society, and historic inequalities that compound over time. But we also discuss his love Tarheels basketball, growing up in the Middle East and the degree to which scarcity should be the foundation of economics or not.
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Episode 7 of Mixtape: the Podcast. I interview Josh Angrist, winner of the 2021 Nobel Prize in economics, Ford professor of economics at MIT, and director of the MIT Blueprint Labs. In this interview, we discuss a range of topics such as being bored and aimless as a young man, his time in the Israeli army as a paratrooper, his time at the 1980s Princeton Industrial Labor Relations group, his collaborations with fellow Nobel laureate Guido Imbens and the late Alan Krueger, as well as the econometric contributions he made to our understanding of causal inference and instrumental variables for which the Nobel Committee awarded him the prize. A pioneer in many ways who through his scholarship, mentoring, and proselytizing of causal inference and applied methodology, Josh Angrist is arguably one of the most important figures in empirical microeconomics of the last 50 years and a delightful person to interview.
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Orley Ashenfelter is arguably the founding father of one of the most influential empirical movements in the modern era -- the so-called credibility revolution. He was the adviser to two Nobel laureates (Josh Angrist and David Card), and guided the Princeton Industrial Relations group for years. Arguably if not one of the most important labor economists of his generation, then at least one of the sharpest. In this interview we talk about his influences, his discovery of the famed Ashenfelter Dip, the popular research design difference-in-differences and more. Check it out!
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When I think of the economics of the minimum wage, I think of Ted Lasso season 2 when we learn of a pretend new book by Brené Brown, "Enter the Arena, But Bring a Knife". The economics of minimum wage is not for the faint of heart as the question of its effect, both in theory and in reality, has been debated fiercely by extraordinarily competent labor economists for decades, and I don't see it ending any time soon. In this interview, I talk with two economists linked to Texas A&M's economics department -- Jonathan Meer and Jeremy West -- an important paper in the minimum wage literature published in a 2016 issue one of the top labor economics journal, the Journal of Human Resources, about their work on the minimum wage. Check it out and prepared to have your priors confirmed and/or challenged about this important program!
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A panic attack spread across empirical social science fields like economics from 2008 to 2022 as a result of a half dozen econometrics articles analyzing the most popular non-experimental methods in causal inference -- the difference-in-differences design. The reason? The way researchers had been used it probably wasn't right because they'd been using the wrong tools to do it. One of those econometricians was the brilliant Sophie Sun, a recent graduate of MIT's famous economics department who with Sarah Abraham worked on the problem of analyzing what are called "event studies" using a traditional version of the ordinary least squares model called "twoway fixed effects". This paper both helped expose problems with that approach, but graciously, also proposed solutions. A shot heard around the world! In this interview, we learn more about Sophie's work on the subject, where the ideas came from, and her own interpretation of what she helped create.
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Alberto Abadie is the creator of one of the most important innovation in causal inference of the last 20 years -- the synthetic control method. Published in 2003, Abadie's model identifies causal effects of broad social interventions when experimentation is practically impossible. He tells the story about how he became interested in terrorism, which was the impetus of the creation of the method in the first place (and which obviously cannot be randomized), as well as his thoughts about econometrics more generally. A brilliant and interesting man, expect him to one day win the Nobel Prize. Get ahead of that future wave by learning more about him now.
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Steve Tadelis is an interesting bird: Harvard PhD applied microeconomics theorist turned experimentalist, he spent some time at eBay as a Distinguished Scientist where he made some interesting discoveries about the effectiveness (or not) of paid search advertising, a key part of search engine giants like Google's underlying business model. In this interview with Steve, we learn about that research, what makes good versus bad ambassadors of economics in tech, and more.
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Scott Cunningham, professor of economics at Baylor University and author of Causal Inference: the Mixtape, interviews John List, professor of economics at University of Chicago, chief economist at Walmart (formerly Lyft and Uber Chief Economist), and author of THE VOLTAGE EFFECT about his life and career as an economist inside and outside academia, as well as the distinction between scientific work focused on narrow empirical questions and the science of scaling programs into their maximum effectiveness. .
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