The Opportunity Atlas: Mapping the Childhood Roots of Social Mobility

Abstract

We construct a publicly available atlas of children's outcomes in adulthood by Census tract using anonymized longitudinal data covering nearly the entire U.S. population. For each tract, we estimate children's earnings distributions, incarceration rates, and other outcomes in adulthood by parental income, race, and gender. These estimates allow us to trace the roots of outcomes such as poverty and incarceration back to the neighborhoods in which children grew up. We find that children's outcomes vary sharply across nearby areas: for children of parents at the 25th percentile of the income distribution, the standard deviation of mean household income at age 35 is $5,000 across tracts within counties. We illustrate how these tract-level data can provide insight into how neighborhoods shape the development of human capital and support local economic policy using two applications. First, the estimates permit precise targeting of policies to improve economic opportunity by uncovering specific neighborhoods where certain subgroups of children grow up to have poor outcomes. Neighborhoods matter at a very granular level: conditional on characteristics such as poverty rates in a child's own Census tract, characteristics of tracts that are one mile away have little predictive power for a child's outcomes. Our historical estimates are informative predictors of outcomes even for children growing up today because neighborhood conditions are relatively stable over time. Second, we show that the observational estimates are highly predictive of neighborhoods' causal effects, based on a comparison to data from the Moving to Opportunity experiment and a quasi-experimental research design analyzing movers' outcomes. We then identify high-opportunity neighborhoods that are affordable to lowincome families, providing an input into the design of affordable housing policies. Our measures of children's long-term outcomes are only weakly correlated with traditional proxies for local economic success such as rates of job growth, showing that the conditions that create greater upward mobility are not necessarily the same as those that lead to productive labor markets.

Race and Economic Opportunity in the United States: An Intergenerational Perspective

“…The population-weighted interdecile (90-10) range ofȳ cr 25 across tracts within CZs is 7.1 percentiles for black men and 8.4 percentiles for for white men, about as large as the variation between CZs discussed above. 26 The tract-level estimates can be visualized using the Opportunity Atlas (Chetty et al 2018), a searchable, interactive map that is analogous to Figure IX, but at the tract rather than CZ level. This subsection summarizes the key properties of the tract-level estimates, focusing in particular on the black-white gap in upward mobility.…”

Supply Shock Versus Demand Shock: The Local Effects of New Housing in Low-Income Areas

“…Results are consistent across a number of specifications, and remaining bias from unobservables driving the selection of building sites likely pushes our estimates toward zero. In addition, we find that new buildings increase low-income in-migration, implying that this improved affordability can foster more integrated, economically diverse neighborhoods that may provide low-income residents with greater economic mobility (Chetty, Hendren, and Katz 2016;Chetty et al 2018).…”

“…Instead, policymakers should recognize that new market-rate housing has both local and regional benefits and should therefore be an important part of strategies to address the growing affordability crisis. In addition, our migration results suggest that strategies that encourage housing construction also foster more economically integrated neighborhoods, which could promote economic mobility for low-income residents (Chetty, Hendren, and Katz 2016;Chetty et al 2018).…”

Industrial Automation and Intergenerational Income Mobility in the United States

“…Finally, we look at the probability of completing various educational transitions conditional on childhood income: high school, at least some college, and a four-year college degree. Information on educational attainment is from decennial Censuses or the 2005-2015 American Community Survey (ACS) that have been linked to the IRS data underlying the income mobility estimates (Chetty, Friedman, et al, 2018). Educational attainment is as reported by the child, with priority given to more recent ACS data if available, and excluding all respondents younger than age 24 at the time of questionnaire completion.…”