Nobel prizes in economics are only given to living people. In addition, because it often takes some time to determine whether certain research is truly important, the Nobel prize of goes to older scholars: for example, the 2014 Nobel laureate Jean Tirole was 61 at the time of the award: the ages of the 2013 winners, at the time they won the prize, were Eugene Fama, 74; Lars Peter Hansen, 61; and Robert J. Schiller, 67; while the ages of the 2012 winners at the time of the award were Alvin Roth, 61, and Lloyd Shapley, 89
So what\’s the top research prize going to younger economists? The John Bates Clark medal is a prize given by the American Economic Association \”to that American economist under the age of forty who is judged to have made the most significant contribution to economic thought and knowledge.\” In other words, it highlights a scholar who has already been doing the kind of work for which–who knows?–a Nobel prize might be awarded in a few more decades. As you might imagine, the leading contenders for the Clark medal are usually in their late 30s, rather than a few years younger. in part because the extra few years means that there has been more time to publish research, and in part because there is a feeling that those their early 30s, even if already worthy of the award, will still be eligible in a few more years. Thus, it is a sign of the high regard in which Raj Chetty\’s work is already held that he was awarded the Clark medal in 2013 when he was 33 years old.
For an very short overview of Chetty\’s work, you can read a \”report\” given to the Clark medal committee here. Martin Feldstein write a more in-depth but still fairly short and readable treatment of Chetty\’s work on the occasion of his receiving the Clark medal in the Spring 2014 issue of the Journal of Economic Perspectives. Douglas Clement interviews Chetty in the December 2014 issue of The Region, published by the Federal Reserve Bank of Minneapolis. Here, I\’ll offer Chetty\’s comments on a couple of his recent areas of prominent work: the importance of teacher quality, and the level of intergenerational economic mobility in the United States.
The Importance of Teacher Quality
\”How can we measure and improve, possibly, the quality of teachers in public schools in America? We tackled that question by getting data from one of the biggest urban school districts in the United States, on 2½ million children over a 20-year period, during which they wrote 18 million tests.
\”We take that data, which tells us how students did in math and English, what teachers they had, which classrooms they were assigned to and so forth, and link that to administrative records from tax returns and social security databases on students’ earnings, college attendance outcomes and various other markers of success later in life. So, essentially, the type of question we are able to ask is, how did the third-grade teacher that you had affect your success 25 years later? … If you’re assigned a high value-added teacher in third grade—that is, the teacher who is systematically improving test scores—and I happen to get a low value-added teacher, does that impact last? Are you, in fact, doing better many years later, or are we both doing as well as each other?
\”The prior literature in education would lead us to think that these impacts are not that long lasting. Many studies have shown that test score gains tend to “fade out” over time. What that means is that if a child is assigned to a better teacher in third grade, we see her doing better on third grade tests, but a lot of that gain shrinks by the end of fourth grade and virtually disappears by fifth or sixth grade. Based on that evidence, you might have thought, well, by the time we’re looking at people’s earnings years later, so many other things have happened in their lives, and we’re not really going to find a meaningful effect of these teachers. … And so going into this work, our prior assumption was we might find something, but more likely we might not find any lasting impact, which would also be useful to know. So we were very curious to look at the data.
\”Much to our surprise, it immediately became evident that students who were assigned to high value-added teachers showed substantially larger gains in terms of earnings, college attendance rates, significantly lower teenage birth rates; they lived in better neighborhoods as adults; they had higher levels of retirement savings. Across a broad spectrum of outcomes, there were quite substantial and meaningful impacts on children’s long-term success, despite seeing the same fade-out pattern for test scores.\”
\”How has intergenerational mobility changed over time in America, and how does it vary across places within the U.S.? There’s a popular conception that the U.S. once was a great land of opportunity and that that’s no longer true today. Unfortunately, we’ve had relatively little data to actually be able to study the degree of social mobility systematically in the United States, so it is has been hard to know whether this conception is accurate or not.
\”When we actually looked at the data over the past 30 to 40 years or so—a period for which we have good information from de-identified tax returns on children’s parents’ income as well as their own income—we find that, much to our surprise, there isn’t that much of a difference in social mobility in the United States today relative to kids who were entering the labor force in, say, the 1970s or 1980s. That is, children’s odds of moving up or down in the income distribution relative to their parents have not changed a whole lot in the past few decades.
\”We find that where there is much more variation is across space rather than over time. … For example, for children growing up in places like Salt Lake City, Utah, or San Jose, California, the odds of moving from the bottom fifth of the national income distribution to the top fifth are more than 12 percent or even 14 percent in some cases, more than virtually any other developed country for which we have data. In contrast, in cities like Charlotte, North Carolina, Atlanta, Georgia, or Indianapolis, Indiana, a child’s odds of moving from the bottom fifth to the top fifth are less than 5 percent—less than any developed country for which we currently have data. …
\”We’re studying 20 million families that moved with their kids between metro areas of the United States. We ask if you move, say, as a 5-year-old, from Atlanta to Salt Lake City, do your outcomes improve? Do you look more like the kids who grew up in Salt Lake City and did really well? And secondly, how does that play out, depending uponwhen you moved? If you moved when you were 10 years old or 15 years old, rather than as a 5-year-old, do you get less of the benefit? One of the intriguing preliminary findings from this work is that there’s a linear “exposure effect.” Every extra year you spend in a better environment, your own outcomes improve and converge to the outcomes of the prior residents. This type of evidence strongly suggests that the differences in upward mobility across places are actually a causal effect of growing up in, say, Salt Lake City rather than Atlanta, as opposed to just differences in the types of people who live in Salt Lake City versus Atlanta.\”
The interview introduces some other prominent work by Chetty, as well. For example, there was the study of tax \”salilence,\” to see whether people pay attention to sales taxes. The researchers labelled a selection of about 1,000 products in a story with a label that showed the price plus the sales tax. In theory, this should make no difference to consumer behavior: after all, doesn\’t everyone already know that you will need to pay sales tax at the register? But in practice, showing the higher price with the sales tax caused sales of that product to decline. This finding also suggests that sales taxes might change purchasing behavior less than one might expect–because a lot of people are largely ignoring them.
How might one distinguish between these hypotheses? Chetty looked at data on the savings of the unemployed. Imagine a people in different states who have differing levels of unemployment benefits, but some have higher savings and some have lower savings. It turns out that when those with more saving have more generous unemployment insurance, the negative effect on their finding a job is much lower. Chetty says: \”I end up concluding that something like two-thirds of the relationship between unemployment benefits and unemployment rates, is actually due to a liquidity effect, rather than a distortionary moral hazard effect.\” In other words, many of the unemployed are looking for a good job match, but if they have little savings, they are more likely to run out of funds before they can find it. More generous unemployment insurance helps the unemployed wait a little longer for a better job match, which is ultimately better for the worker and the economy.