Profile of David Card

Quasiexperimental Approach

After taking some graduate-level economics classes as an undergraduate, Card realized he wanted to pursue a PhD. He completed his PhD in economics in 1983 with Orley Ashenfelter at Princeton University. After his PhD, Card briefly taught at the University of Chicago. He was then recruited to join the faculty of Princeton University, where he stayed until his move to UC Berkeley in 1986.

One of Card’s initial research questions involved evaluating programs for disadvantaged workers who needed to be retrained. People who were struggling in the labor market and went into a retraining program seemed to do better after they came out of the program. “But many people who do poorly temporarily will do better in the future no matter what, and the problem was to figure out how much of their recovery is due to the program and how much is just something that would've occurred anyway,” says Card. “We developed methods to compare people who went in the program and people who didn’t, which basically led to a quasi-experimental research methodology,” he says.

Card applied this new methodology to a variety of problems. Among these problems was assessing what happens when immigrant groups are rapidly assimilated, such as during the “Mariel boatlift,” the mass emigration of Cuban immigrants — now known as Marielitos — from Cuba’s Mariel Harbor to the United States in 1980. “The first example that I really worked on was a very simple paper about the economic impacts of the Mariel boatlift and what happened in Miami after the Marielitos mass exodus and arrival in the Miami labor market,” he says (2).

Card compared the economic effects in Miami to those in Atlanta, Houston, Los Angeles, and Tampa, which receive fewer Cuban immigrants. He found that despite a large increase in low-skilled labor in Miami, wages for the low-skilled workers were not significantly affected and overall unemployment rates and wages for the Miami labor market were unchanged. “This paper managed to capture people’s interest, so I think that was probably an important turning point for me,” says Card.

Analyzing the Minimum Wage

At around the same time, Card turned his attention to the minimum wage. “The federal minimum wage was frozen in the 1980s during a period of high inflation, and individual states started to have their own minimum wage, so I became interested in trying to evaluate that,” says Card.

Card and his colleague Alan Krueger compared what happened in fast-food restaurants in New Jersey and Pennsylvania when New Jersey introduced a minimum wage. They found that the minimum wage increase in New Jersey did not result in job reduction in fast-food companies in that state (3). “It was a pretty controversial finding,” says Card.

When Card received the American Economic Association’s John Bates Clark Prize, awarded to the economist under 40 whose work is judged to have made the most significant contribution to the field, “there was a lot of blow-back about that,” he says. However, numerous follow-up studies over the next 30 y validated his findings. “People have done all kinds of analyses, and their results are consistent with what Alan and I found,” says Card.

Card’s innovation was in finding a methodology that could tackle these big questions, whether related to immigration or minimum wage. “The Mariel boatlift paper and the New Jersey minimum wage paper were the two big papers that… [used] the simple methodology of looking at a treatment group and a comparison group to try and make a social science analysis closer to a real experiment,” he says. “I’ve worked on some experimental economics as well, but I'm probably best known for this quasi-experimental approach.”

Screening Gifted Kids

A topic that recently captured Card’s interest was universal screening for gifted kids. “Gifted programs are pretty old, they’ve been around since the beginning of the 1900s, and in some places they have a bad reputation because in some situations they were introduced as a way to segregate White and Black people,” he says. “But most states have gifted and talented programs,” says Card.

Card and his collaborator Laura Giuliano studied a screening test for gifted kids that was being administered by the Broward County School District in Florida. “The district wanted to get more kids into the gifted program, and so… they tested everybody in second grade with a kind of a quick screening test,” he says. “They found a whole bunch of extra gifted kids, almost all of whom were minorities, and put them in gifted education, and it kind of changed the whole system there,” says Card.

The researchers found that the universal screening program significantly increased the number of poor and minority students who met the IQ standards for gifted status (4). This raised the question of whether better identification of qualified students from all backgrounds could improve minority representation in advanced K−12 academic programs. “It's very much a funnel system at the high end, and in order to get somebody into academia and a professorship, you have to identify these talented kids and get them to finish high school and go to a good college,” says Card. “If you’re worried about the lack of representation in academia, you need to be thinking about this issue,” he says.

In his follow-up work, Card is studying the effects of being identified as gifted on high-school performance. “We're finding pretty good effects on the probability that you graduate from high school on time,” he says. “The current education system does a terrible job with boys from disadvantaged family backgrounds, and they end up either in the criminal justice system or they just drop out and are underemployed, and that's kind of a something I've been working on a lot,” says Card.

Gender Gap in Academia

More recently, Card and his colleague Stefano DellaVigna studied how academic journals function as gatekeepers. “We constructed this huge database that included what the referees initially thought of a paper and then how it eventually was cited, kind of like taking early movie reviews and then comparing them to long-run evaluations,” he says.

The researchers then collaborated with Nagore Iriberri and Patricia Funk to analyze gender bias in publication. “We now have a database with about 40,000 economists’ names and their genders, which took many years to build,” says Card. “What we found was that there was some bias against female-authored papers, so they’re less well received by the referees than they will be received in the future,” he says (5).

Card and his collaborators then analyzed gender bias in the election of fellows of the Econometric society, which represents a major honor in the economics field. “What we found was that there was definitely gender bias against women up into the 1960s,” says Card. “By the late 1980s, women and men with the same record were about equally likely to be made fellows, and then around 2010 you can see a big increase in women,” says Card.

In his Inaugural Article, Card and his collaborators used a similar approach to analyze who gets elected to the NAS and AAAS in math, economics, and psychology (1). “We basically found the same pattern that we found in our earlier paper,” he says. Female researchers seemed to be held to a higher bar than males from 1960 to 1979, whereas by the 1990s, both academies’ selection process was approximately gender neutral. The study found a positive preference for female members in the past 20 years, with women 3 to 15 times more likely to be selected as members of AAAS and NAS than men with similar publication and citation records. The results were also similar across all the three fields. “That was interesting as psychology has one of the highest fractions of female researchers in the field, whereas math has the lowest and economics is kind of in between,” says Card.

“What seems to be happening is that the academies are essentially finding women who have extremely good records who should be members of the Academy and selecting them 15 to 20 y ahead of when they might usually be selected, so they’re kind of accelerating the promotion,” says Card. “It's possible that some of that acceleration is because the female scholars are extremely prominent and had to overcome some structural hurdles early on, but some of it I think is also a concern by the academies that the membership is still overwhelmingly male, and if you want to change that then you have to make some marginal changes,” he says.

Card has won numerous honors and accolades over his career, but a particular highlight came in the form of a 2 am phone call in 2021, announcing that he had won a share of the Nobel Memorial Prize in Economics. “I had just been visiting friends in Canada, and then the phone rang and it was a call from Sweden, and I thought it was my friend back in Canada playing a joke on me,” he says. “There are so many of my colleagues in economics that could win a Nobel Prize, I really didn't think there was much chance that somebody like me would ever win it,” says Card.

For all his honors and discoveries, Card says he is most gratified by the 150 PhD students he has trained over the course of his career. “Many are professors, many work in business, government, or elsewhere, and, probably, in the end, this is what I would consider my major contribution to the field.”