There is a new exciting and valuable contribution from the research group around Raj Chetty, Nathan Hendren, and John Friedman, this time on the topic of neighborhood-level social mobility. (Earlier work highlighted measures of the impact on social mobility contributed by university education across the country. This work is presented on the Opportunity Insights website; link, link. Here is an earlier post on that work; link.) In the recently released work Chetty and his colleagues have used census data to compare incomes of parents and children across the country by neighborhood of birth, with the ability to disaggregate by race and gender, and the results are genuinely staggering. Here is a report on the project on the US Census website; link. The interactive dataset and mapping app are provided here (link). The study identifies neighborhoods of origin; characteristics of parents and neighborhoods; and characteristics of children.
Here are screenshots of metropolitan Detroit representing the individual incomes of the children (as adults) based on their neighborhoods of origin for all children, black children, and white children. (Of course a percentage of these individuals no longer live in the original neighborhood.) There are 24 outcome variables included as well as 13 neighborhood characteristics, and it is possible to create maps based on multiple combinations of these variables. It is also possible to download the data.
Children born in Highland Park, Michigan earned an average individual income as adults in 2014-15 of $18K; children born in Plymouth, Michigan earned an average individual income as adults of $42K. It is evident that these differences in economic outcomes are highly racialized; in many of the tracts in the Detroit area there are “insufficient data” for either black or white individuals to provide average data for these sub-populations in the given areas. This reflects the substantial degree of racial segregation that exists in the Detroit metropolitan area. (The project provides a special study of opportunity in Detroit, “Finding Opportunity in Detroit”.)
This dataset is genuinely eye-opening for anyone interested in the workings of economic opportunity in the United States. It is also valuable for public policy makers at the local and higher levels who have an interest in improving outcomes for children in poverty. It is possible to use the many parameters included in the data to probe for obstacles to socioeconomic progress that might be addressed through targeted programs of opportunity enhancement.
(Here is a Brookings description of the social mobility project’s central discoveries; link.)