We estimate rates of "absolute income mobility"-the fraction of children who earn more than their parents-by combining historical data from Census and CPS cross-sections with panel data for recent birth cohorts from de-identified tax records. Our approach overcomes the key data limitation that has hampered research on trends in intergenerational mobility: the lack of large panel datasets linking parents and children. We find that rates of absolute mobility have fallen from approximately 90%for children born in 1940 to 50% for children born in the 1980s. The result that absolute mobility has fallen sharply over the past half century is robust to the choice of price deflator, the definition of income, and accounting for taxes and transfers. In counterfactual simulations, we find that increasing GDP growth rates alone cannot restore absolute mobility to the rates experienced by children born in the 1940s. In contrast, changing the distribution of growth across income groups to the more equal distribution experienced by the 1940 birth cohort would reverse more than 70% of the decline in mobility. These results imply that reviving the "American Dream" of high rates of absolute mobility would require economic growth that is spread more broadly across the income distribution.

Aspiring to do better than one's parents The American dream promises that hard work and opportunity will lead to a better life. Although the specifics of what constitutes a better life vary from generation to generation, one constant is that children expect to do better—or at least to have a good chance at doing better—than their parents. Chetty et al. show that this dream did come true for children born in the middle of the 20th century, but only for half of children born in 1984 (see the Policy Forum by Katz and Krueger). A more even distribution of economic growth, rather than more growth, would allow more children to fulfill their dreams. Science , this issue p. 398 ; see also p. 382

More than 50 years after the Civil Rights Act, black-white family income disparities in the United States remain almost exactly the same as what they were in 1968. This article argues that a key and underappreciated driver of the racial income gap has been the national trend of rising income inequality. From 1968 to 2016, black-white disparities in family income rank narrowed by almost one-third. But this relative gain was negated by changes to the national income distribution that resulted in rapid income growth for the richest-and most disproportionately white-few percentiles of the country combined with income stagnation for the poor and middle class. But for the rise in income inequality, the median black-white family income gap would have decreased by about 30 percent. Conversely, without the partial closing of the rank gap, growing inequality alone would have increased the racial income gap by 30 percent.

After more than a century of convergence, the economic fortunes of rich and poor regions of the United States have diverged dramatically over the last 40 years. Roughly a third of the US population now lives in metropolitan areas that are substantially richer or poorer than the nation as a whole, almost three times the proportion that did in 1980. In this paper I use counterfactual simulations based on Census microdata to understand the dynamics of regional divergence. I first show that regional divergence has primarily resulted from the richest people and places pulling away from the rest of the country. I then estimate the relative contributions to regional divergence of two major socioeconomic trends of recent decades: the sorting of people across metro areas by income level and the national rise in income inequality. I show that the national rise in income inequality is sufficient on its own to account for more than half of the observed divergence across regions, while income sorting on its own accounts for less than a quarter. The major driver of regional economic divergence is national-level income dispersion that has exacerbated preexisting spatial inequalities.

We use data on intergenerational social mobility by neighborhood to examine how social and physical environments beyond concentrated poverty predict children’s long-term well-being. First, we examine neighborhoods that are harsh on children’s development: those characterized by high levels of violence, incarceration, and lead exposure. Second, we examine potential supportive or offsetting mechanisms that promote children’s development, such as informal social control, cohesion among neighbors, and organizational participation. Census tract mobility estimates from linked income tax and Census records are merged with surveys and administrative records in Chicago. We find that exposure to neighborhood violence, incarceration, and lead combine to independently predict poor black boys’ later incarceration as adults and lower income rank relative to their parents, and poor black girls’ teenage motherhood. Features of neighborhood social organization matter less, but are selectively important. Results for poor whites also show that toxic environments independently predict lower social mobility, as do features of social organization, to a lesser extent. Overall, our measures contribute a 76% relative increase in explained variance for black male incarceration beyond that of concentrated poverty and other standard characteristics, an 18% increase for black male income rank (70% for whites), and a 17% increase for teenage motherhood of black girls (40% for whites).

In the 1980s and 1990s, researchers came to understand poor urban neighborhoods as blighted, depopulated areas, based on important ethnographic observations in a handful of cities. This image helped inform influential theories of social isolation and de‐institutionalization. However, few scholars have examined whether those observations were representative of poor neighborhoods nationwide—and whether they are representative today. Based on a descriptive analysis of the largest 100 U.S. metropolitan areas using normalized census tract boundaries, we document an important transformation in the conditions of poor neighborhoods. We find that the depopulation in poor neighborhoods often reported in cities such as Chicago and Baltimore was, in fact, typical across cities in 1990. Today, it is not. Moreover, heterogeneity across cities has increased: The experience of neighborhood poverty is likely to depend more today than in 1990 on the city in question. In fact, the most typically studied cities, such as Chicago, Baltimore, Philadelphia, and Milwaukee, are increasingly atypical in this respect. Addressing today's core questions about neighborhood effects, how and why they matter, requires paying far greater attention to heterogeneity, conducting more ethnographic observation in ostensibly unconventional cities, and addressing the historically extreme conditions in a newly unique subset of cities.

As big urban data usage expands in the social sciences, there remain real concerns about fidelity to on the ground conditions. In this paper, we examine the correspondence between Phoenix metro area restaurants identified by a social media source (http://yelp.com) and those from an administrative source (Maricopa Association of Governments [MAG]). We find that they capture largely disjoint subsets of Phoenix restaurants, with only about one‐third of restaurants in each data set present in the other. Point pattern analyses indicate that the Yelp data is significantly clustered relative to the MAG data. Specifically, restaurants in Yelp are concentrated in certain parts of metro Phoenix, most notably the downtowns of Phoenix, Scottsdale, and Tempe. Further analysis indicates that areas with more Yelp than MAG restaurants tend to have more college‐educated workers and workers employed in the Arts, Entertainment, and Recreation sector. Our comparison highlights the strengths and weaknesses of each data source: Yelp data is far more detailed and comprehensive in certain locations, while MAG data is more consistent across the entire region due to its systematic construction. When combined, administrative and user generated databases seem to provide a more holistic and comprehensive picture of the world than either would provide by itself.

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