Improving Opportunities for Economic Mobility: New Evidence and Policy Lessons
The American dream is a complicated concept, but I’d like to distill it down to a simple statistic that we are able to measure with data: the probability that a child born to parents in the bottom fifth of the income distribution makes the leap all the way to the top fifth of the income distribution.
In the United States, children born to parents in the bottom fifth of the income distribution have a 7.5 percent chance of reaching the top fifth. That compares with about 9.0 percent in the United Kingdom, 11.7 percent in Denmark, and 13.5 percent in Canada. When some people initially see these numbers, they sometimes react by saying, “Even in Canada, which has the highest rates of upward mobility, the rate of success doesn’t look all that high. You only have a 13.5 percent chance of reaching the top if you start out at the bottom.” It is important to remember that, unfortunately, no matter what you do, you can’t have more than 20 percent of people in the top 20 percent. As such, these differences are actually quite large. One way to think about it is this: Your chances of achieving the “American dream” are almost two times higher if you’re growing up in Canada relative to the United States.
These differences across countries have been the focus of much policy discussion. But what should also be given attention is that upward mobility actually varies substantially even within the United States. In recent work, my colleagues and I calculate upward mobility for every metro and rural area in the United States using anonymous earnings records on 40 million children and their parents (Chetty et al. 2014).
What results from that analysis is a map (Figure 1) that shows the geography of intergenerational mobility in the United States. In this map, we’re computing the same statistic mentioned previously: your chances of reaching the top fifth of the national income distribution conditional on starting in the bottom fifth for 741 metro and rural areas in the United States.
What you can see in this map is that there is substantial variation in the United States. For places in the top decile—the lightest colored places on this map—your odds of reaching the top fifth conditional on starting in the bottom fifth exceed 16.8 percent, higher than the numbers we saw for Denmark and Canada. In contrast, at the other end of the spectrum—the darkest red colors—in the southeastern United States, for instance, that number is lower than 4.8 percent, which is lower than any developed country for which we currently have data. To provide an example, if you’re growing up in San Jose, your odds of moving up the income ladder are three times as high as if you’re growing up in a place like Charlotte or Atlanta or Indianapolis.
What’s even more striking in some ways is that a lot of this variation is extremely local. While in this map you initially see the broad regional variation, let’s take the case of the Washington, D.C., metro area, which on average has an 11 percent rate of upward mobility, and look now at the data by county (Figure 2).
You can see that if you’re growing up in the city of Baltimore, you unfortunately have only a 3.5 percent chance of making that leap from the bottom fifth to the top fifth. That compares with 4.7 percent in D.C. If you go to some of the more suburban counties, you see much higher rates of upward mobility: Prince George’s County, 9.2 percent; Charles County, 14.2 percent. This illustrates that even in areas that are quite near each other, you see substantial differences in rates of social mobility in the United States.
Now, naturally the question of interest both to academics and policymakers is why does upward mobility differ so much across areas and, ultimately, what can we do about it? The first clues for us as researchers came from the fact that this spatial variation emerges at very early ages. In high mobility areas like Salt Lake City or San Jose, children from low-income families are more likely to attend college, and they’re less likely to have a teenage pregnancy. By the time they’re 16, 17 or 18 years old, a lot of these patterns have already emerged. The reason that’s important is that it points to factors that affect children not just once they’re in the labor market but before they start working. It suggests that childhood environment could be extremely important here.
Further evidence for that view comes from families who move across areas. In recent work, my colleague Nathan Hendren and I looked at families who move across areas to document the importance of childhood environment (Chetty and Hendren 2015). The first thing we show is that there is clear evidence of childhood exposure effects. Moving to an area of higher upward mobility at a younger age increases children’s earnings in adulthood.
What’s particularly fascinating about this data is comparing siblings within the same family. Take a family that moves from D.C. to Prince George’s County with two kids. We find that the child who was younger at the point of the move to the better area—the area with higher rates of mobility—ends up doing better as an adult. For example, if you move with a 5-year-old and a 10-year-old, we see that the 5-year-old is doing better than the 10-year-old in proportion exactly to that 5-year age gap, because that 5-year-old has an extra five years of exposure to the better environment. We find very clear evidence of linear childhood exposure effects, suggesting that each year in a better childhood environment really matters.
Further evidence for the importance of childhood exposure comes from the Moving to Opportunity experiment. In a reanalysis of data from that experiment, my colleagues and I found that moving to low-poverty census tracts at a young age has substantial impacts on children’s long-term success (Chetty, Hendren, and Katz 2015). It increases their earnings in adulthood by 30 percent, makes them more likely to go to college and so forth.
What is it that places like Salt Lake City or San Jose are doing to generate such high levels of upward mobility? Or at a more local level, Charles County versus the city of Baltimore—what are the differences in the characteristics of these places? We’ve looked at several factors and identified five strong correlates of upward mobility.
The first is the degree of segregation in an area: More mixed-income communities tend to produce better outcomes for kids from disadvantaged backgrounds. The second is income inequality: Areas with less income inequality tend to have higher rates of upward mobility. The third and fourth factors come from the sociology literature. We find that areas with more stable family structures—in particular, areas with fewer single parents—have substantially higher rates of upward mobility. Areas that are more socially cohesive, with large amounts of social capital, also have much higher rates of social mobility. Finally, as you might expect, areas with better public schools tend to have much higher rates of upward mobility.
Lastly, I want to provide a different perspective on why we should be interested in social mobility. The traditional argument for greater social mobility is based on principles of justice, the principle of the equality of opportunity. But improving opportunities for upward mobility can also increase the size of the economic pie, coming back to a point that has been made by Federal Reserve Board Chair Janet Yellen (2015).
In ongoing work, we are studying the lives of inventors—measured using patent records—in the United States. We find that a child’s probability of becoming an inventor is strongly related to his or her parents’ income: Children from rich families are 10 times as likely to become inventors as those from lower-income families. Further examination of these data suggests that a large portion of this innovation gap can, once again, be attributed to differences in childhood environment and exposure between low- and high-income families. These results imply that improving opportunities for social mobility could ultimately increase the rate of innovation in the economy and thereby benefit everyone, not just disadvantaged children. Hence, increasing mobility is of interest not just from the perspective of justice but also from the perspective of economic growth.
Let me conclude by briefly summarizing a couple of policy lessons. First, it’s critical to tackle social mobility at a local and not just a national level. Let’s focus on specific cities such as Atlanta or Baltimore and on neighborhoods within those cities. Second, the childhood environment seems particularly important. Improve neighborhoods and schools; jobs certainly matter, but ladders to opportunities start before children begin to work. Third, and most broadly, as I hope I’ve illustrated, harnessing big data to evaluate policies scientifically and measuring local progress and performance can be incredibly valuable.
Chetty, Raj, and Nathaniel Hendren. 2015. “The Impacts of Neighborhoods on Intergenerational Mobility: Childhood Exposure Effects and County-Level Estimates.” Working Paper. Cambridge, MA: Harvard University and National Bureau of Economic Research (NBER), May. [ back to text ]
Chetty, Raj, Nathaniel Hendren, and Lawrence F. Katz. 2015. “The Effects of Exposure to Better Neighborhoods on Children: New Evidence from the Moving to Opportunity Experiment.” NBER Working Paper No. 21156. Cambridge, MA: National Bureau of Economic Research, May. [ back to text ]
Chetty, Raj, Nathaniel Hendren, Patrick Kline, and Emmanuel Saez. 2014. “Where is the Land of Opportunity? The Geography of Intergenerational Mobility in the United States.” The Quarterly Journal of Economics 129 (4): 1553–623. [ back to text ]
Yellen, Janet L. 2015. “Opening Remarks.” Speech delivered at the Federal Reserve System Community Development Research Conference, Washington, D.C., April 2. [ back to text ]