The Mixtape with Scott

** 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.

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.

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!

Scott's Substack is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

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!

Scott's Substack is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

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:

* Did immigrants of the past pull themselves up “by their bootstraps” as the stories are often told to us and remembered?

* Did the children of immigrants move up America’s economic ladder as fast as their “peers” — children, in other words, of established residents?

* 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”.

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 comp…

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 i…

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!

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 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, ju…

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.

Scott's Substack is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

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!