Joshua Angrist GS ’89 was awarded the 2021 Nobel Prize in Economics. He won half of the prize jointly with Guido Imbens “for their methodological contributions to the analysis of causal relationships.”
Angrist, along with David Card GS ’83, who won the other half of the prize, was one of the five Princeton affiliates to be awarded a Nobel Prize this year, a record-high number in the University’s history.
In an interview with The Daily Princetonian, Angrist discusses his upbringing, Nobel-prize-winning work, and Princeton’s role in his success. The following transcript has been edited for clarity and concision.
The Daily Princetonian: First of all, many congratulations on winning the Nobel Prize in economics.
Joshua Angrist: Thank you.
DP: Let’s start with a bit of history. Would you please describe the environment in which you grew up? How do you think that environment has impacted who you are today as an economist?
JA: Well, I grew up in Pittsburgh, Pennsylvania. And my parents were academics, though they left academia when I was still young. I wasn’t a very good student. I wasn’t particularly interested in school, but we all read a lot. Maybe one of the most important things about my family that I remember is that almost without fail we ate dinner together, and we had lots of interesting dinner table discussions.
DP: So where did your interest in economics come into the game?
JA: Well, we would talk about economics. My father, in particular, was interested in financial markets, even though he was a mechanical engineer. He was interested in commodities markets. He wrote some books about it, in fact. But when I went to Oberlin College, I didn’t know what I would major in. I thought maybe I would major in psychology because I had left high school early and had been working with people with intellectual disabilities in mental institutions.
And then I took a psychology class; it wasn’t very interesting, in fact. I took an econ class, and I loved it. So I saw right away that economics was for me. Partly, I had a very charismatic teacher — Bob Piron. He had a teaching style that I’ve sort of adopted where he’s cold calling, and it’s a very lively and somewhat tense atmosphere; people have to stay on their toes. I thought that was just a lot of fun, and I loved the material. So in the fall of my freshman year I took Econ 101. Then after that, I couldn’t get enough.
DP: And then you moved to Princeton to receive a master’s degree?
JA: So I first visited the London School of Economics (LSE) as a junior; that was my first time overseas. That was kind of an important year for me since the type of study there was more journal articles, so there was an increase in the maturity of it.
Then, I didn’t know what I wanted to do. I had worked as a research assistant for Alan Meltzer who, unfortunately, has passed away, but he’s a very distinguished macroeconomist. I had done some empirical work for Alan. But I wanted to do something else, so I moved to Israel. I thought that was exciting. I had visited Israel when I was at the LSE. I have lots of family in Israel, but I’d never been there. My grandparents’ generation had moved there, with the exception on my mother’s side of her parents. But all their siblings had moved there in the 1930s to escape the Nazis.
First I was a Master’s student at Hebrew University. So that I did for one year. I met my future wife, Mira — that was a success. But I didn’t do very well at Hebrew University, so I dropped out of Hebrew University after one year. But I became an Israeli citizen. I served for two years in the Israeli army. Then Mira and I decided to get married. So when I got out of the army — this is by now three years after I’d graduated — I wrote Orley [Ashenfelter GS ’70] a letter, and I said, ‘Orley, can I come and do my Ph.D. at Princeton?’ And this is like May already. And he said, ‘sure, come in the fall.’ So Mira and I decided, okay, we’ll do that. We enrolled. Orley also gave me a job. You know, I needed to earn a little money.
DP: So you must have impressed him a lot.
JA: I guess. He certainly took a gamble on me. But I did go to Princeton because of him, and I took his labor economics class, and I was pretty sure I wanted to work with him. I found his material very engaging. I also met Dave Card, another thesis advisor who is a [2021 Nobel Economics Prize] co-winner. And just everything that was going on there in labor economics, again, it was like my college experience: I just knew it was for me.
And then Orley had the idea for my thesis, which was to use the draft lottery in the 1970s to study the effects of military service on men who served in Vietnam in the Vietnam era. And that’s a very clever idea because people who serve in the military are not randomly selected; they might be special in some way. On one hand, they’re healthier. On the other hand, maybe they have poor civilian opportunities. That wouldn’t be true today, but it would be true in the Vietnam era perhaps. I worked with Orley and David Card and Whitney Newey, who was then a faculty at Princeton and is now my colleague here at MIT, and with them, I learned a lot of econometrics.
DP: That’s great. So do you have any special memories from your time at Princeton?
JA: Well, we were graduate students. We lived in graduate student housing. We lived in Lawrence apartments. Do you know where that is? Is that still there?
DP: Yes, it is.
JA: I remember mostly liking it. It was full of graduate students. We had a little apartment first. And then our daughter was born, so we moved into an apartment for families that was bigger, in the Lawrence low-rise. It was a kind of paradise for young families.
I remember just being very happy to go to work every day. The early part of the Ph.D, I didn’t love as much; the coursework was very difficult. I didn’t like macro. [But] I loved all of econometrics. And I had wonderful teachers, and we just had a very nice life. We had good friends. In retrospect, maybe we left too soon. I finished my Ph.D. in four years. Today, people take six.
DP: As for your work that was awarded the Nobel Prize, how would you explain to someone who might not be necessarily familiar with economics what your work is?
JA: Well, we’re interested, broadly speaking, in causal inference. We want to know the effects of things. It could be public policy. It could be events in economic life, like the arrival of large numbers of immigrants. It could be the effects of choices you make, like getting lots of schooling or going to college.
You might want to take a look at my undergraduate book, “Mastering’ Metrics: The Path from Cause to Effect.” The second chapter is actually about: does it matter if you go to an elite private school like Princeton. Are you going to earn more by virtue of that? You know, you’re spending a lot to go to Princeton or somebody is. Maybe you’ve got loans or scholarship, but somebody is spending a lot for you to go to Princeton. And Princeton is a wonderful place, but it’s not clear that it’s worth it. You know, there’s a lot of good public schools. What state did you grow up in?
DP: I’m actually from Toronto, Canada.
JA: Well, then you could have gone to any of a lot of Canadian schools almost for free. And now you have to go to Princeton and pay tuition, or if you’re on a scholarship, somebody is paying for you. So, you know, is that worth it? So that’s a great example of a causal question, you know, we’re kind of imagining your life in the counterfactual world where you went to the University of Toronto instead of Princeton. Then, we’re looking ahead and saying ‘okay what are your earnings going to be when you’re 40? Are you going to earn more because you went to Princeton? Is that a good investment?’
And, you know, on paper, at least on data, people who go to Princeton tend to earn more. But it’s hard to get into Princeton. It’s harder to get into Princeton than to get into the University of Toronto; even though Toronto is selective, Princeton is way more selective. So the people who go to Princeton may have higher earnings, not because of all the good things that happened to them in Princeton, but just because Princeton picks talented people.
So I want to solve that kind of problem. I want a good experiment for that. And the experiments sometimes come in the form of partial random assignment: something that changes your path, in some way, that is outside of your control. It’s as if you were inserted into an experiment. So a version of this would be, there might be a lot of people who want to go to college and some of them get scholarships and some of them don’t. And that’s done by random assignment. We don’t directly control your decision about where to go, but we observe that there’s some randomness in where you’re offered a place.
The first time I used that idea was in my thesis work on the draft lottery. You know, people choose to serve in the military and the military also screens soldiers on the basis of health and test scores. None of that stuff is random, so it would be very misleading to just compare soldiers to people who aren’t veterans. A good example of that is World War II veterans. People served in World War II and in American cohorts. If you look at men born between 1925 to 1928, people who served in World War II tend to live longer. So you say, okay, well, did their military service actually make them more healthy? No, what’s really going on there is that people that weren’t very healthy were not taken into the military. So we need some sort of randomness to kind of solve that problem. And the solution that econometricians have come up with, and I didn’t invent this but I studied it and I helped develop the tool, is called instrumental variables.
DP: How did you come up with this idea?
JA: So the problem of selection bias, the fact that people aren’t necessarily comparable, is not a new problem. Maybe that’s one of the oldest problems in empirical social science. And I was inspired by the work of my thesis advisors Card and Ashenfelter, who had been working on this problem themselves. They had graduate students like Bob LaLonde, who unfortunately passed away a few years ago, who had studied the quality of econometric evidence when you didn’t have a good experiment, and his thesis very famously concluded that without some kind of experiment, it’s hard to estimate the effects of something like a government training program. So I kind of fell into the milieu where that was the type of question that was in the air. And then with the draft lottery I thought, well here’s one case where I have a great solution, [instrumental variables].
But then it turned out to be more than one [case]. I did some work with Alan Krueger, estimating the effects of schooling. Alan was a faculty member. He died a couple years ago. We used the fact that people born in different times of the year get more schooling or less schooling, and that’s because of the way compulsory attendance laws work. So your quarter of birth becomes an instrument for your schooling. So I sort of got into it with one applied paper and then I saw that the idea was more general and then I got interested in the methodology. And when I went to Harvard, I met Guido Imbens — my co-laureate — and we did more theoretical work.
DP: That’s amazing. And so you’re a pioneer in your field and from my readings, you’re one of the pioneers of the credibility revolution in economics. Pioneers usually face pushback for their innovation. Did you face such obstacles, and if so, how did you overcome them?
JA: We did. You know, every set of ideas, new ideas, meets with some resistance. I can’t complain. In the end, I won the Nobel Prize. So, eventually our ideas were accepted, and I think it’s natural for people, proposing a new way of doing things, to meet some resistance. My work with Guido and also Dave Card’s work, a lot of it with Alan, changed the focus of econometrics and also changed the way econometrics was taught.
I look back through some of the correspondence from that period. A lot of our papers were rejected. [Guido] Imbens and I struggled to publish our first paper. We had a paper that had some results that were never published. Two, in fact; I had one and then we had one together. But in 1994, we did get the local average treatment effects (LATE) paper published, [which showed how causal inferences can be drawn from observational data].
DP: Now to finish up, do you have any advice for Princeton students?
JA: You know, most undergraduates will not go on to a life of research, but I think many undergraduates who haven’t thought about studying economics are missing out, and they should give it a shot. You know, I don’t know if this is true at Princeton, but our largest major at MIT is computer science and data science. And if you ask me, I think that we do all of that stuff particularly well in economics [through] focusing on causal questions. And a lot of the people who are attracted to data science would find economics and econometric analysis just as interesting, maybe more interesting. So they should give it a shot.
Mahya Fazel-Zarandi is a news contributor for the ‘Prince.’ She can be reached through email at email@example.com and @MahyaFazel on Twitter.
Editor’s Note: This article has been updated to include more context regarding Angrist’s statements on his time in Israel.