It's hard to believe that, in over twelve years of blogging, I have never blogged about any of Raj Chetty's research. That's not because I haven't read it. If anything, it's because it is so detailed that it defies a short blog take. For example, we read two related papers published in the journal Nature (here and here, both open access) in the Waikato Economics Discussion Group back in 2022. Ordinarily, I would follow up with a blog post, but they are so in-depth that I couldn't find the time to summarise them effectively [*]. Three years later, they are sitting in a virtual pile of read-but-not-yet-blogged-about papers [**].
Anyway, Chetty and co-authors have suddenly made it much easier for me to summarise their extensive research on social mobility in the US. That's because you can see the data in action for yourself now, at The Opportunity Atlas. This very cool online tool allows you to see social mobility in action. Social mobility is effectively how much a child's socioeconomic position in adulthood depends on their socioeconomic position when they were growing up.
On The Opportunity Atlas, you can choose from a range of outcomes in adulthood, and see where the mean outcome is, if they grew up in a household at different rankings of parental income (1st, 25th, 50th, 75th, or 100th percentile). You can also look separately by gender and by race (Black, White, Hispanic, Asian, Native American). The interface is quite intuitive to use. For example, here's the basic map of expected (mean) income at age 35, for children who grew up in households at the 25th percentile of parental income:
The red areas, such as the South, have lower social mobility, because children who grew up there in households at the 25th percentile have lower incomes as adults. In contrast, the blue areas (in the north and west) have higher social mobility, because children who grew up there in households at the same 25th percentile have higher incomes as adults.
The tool is very flexible. It's very easy to switch to looking at other outcome variables, and for other percentiles of parental income, as well as zooming in on particular areas. For example, here's the teenage birth rate for Black women who grew up in households at the lowest (1st) percentile of parental income in Los Angeles:
The greyed-out Census tracts are those where there are too few Black women who grew up in the lowest income households for the data to be reported. However, the map shows a band of high teenage birth rates for mothers who grew up in the lowest income households, that stretches from South Central to Compton.
Importantly, the underlying data can be downloaded from the Opportunity Insights website. The cool thing about the data underlying the Atlas is that it is based on the census tract where the child grew up, not the census tract where they live as an adult. That means that the Atlas is showing you the adult outcomes for children who grew up in a particular area, not the adult outcomes of adults who live there today. That is explained in this new article by Chetty (Harvard University) and co-authors, published in the journal American Economic Review (ungated earlier version here).
That article outlines the methods underlying the dataset. In short:
...we use de-identified data from the 2000 and 2010 decennial censuses linked to data from federal income tax returns and the 2005–2015 American Community Surveys to obtain information on children’s outcomes in adulthood and their parents’ characteristics. We focus in our baseline analysis on children in the 1978–1983 birth cohorts who were born in the United States or are authorized immigrants who came to the United States in childhood...
We construct tract-level estimates of children’s incomes in adulthood and other outcomes, such as incarceration rates and teenage birth rates by race, gender, and parents’ household income level—the three dimensions on which we find children’s outcomes vary the most. We assign children to locations in proportion to the amount of their childhood they spent growing up in each census tract. In each tract-by-gender-by-race cell, we estimate the conditional expectation of children’s outcomes given their parents’ household income using a univariate regression whose functional form is chosen based on estimates at the national level to capture potential nonlinearities.
Chetty et al. then go on to show why it matters that we look at social mobility based on the place where children grew up, rather than contemporary poverty rates or adult outcomes, and finally give some short use cases for the dataset. I won't go into detail on those (you should read the paper), but one of the things that Chetty et al. do show is that because the effects change slowly over time, looking at outcomes today for children who grew up in a particular census tract in the 1980s still provides meaningful information that can be used for targeting social programmes today.
It's important to note that the Opportunity Atlas by itself doesn't show us causal estimates of adult outcomes. However, Chetty et al. establish how much of the effect is causal using a couple of different methods: (1) using data from the Moving to Opportunity experiment; and (2) a quasi-experiment that looks at how the effects differ depending on how many years a child was 'exposed' to a particular Census tract). Both methods both imply that roughly 62%) of the observed variation across census tracts reflects causal neighbourhood exposure effects, not just higher-opportunity families sorting into better places.
In the conclusion, Chetty et al. highlight a number of applications where the Opportunity Atlas data has already been used:
For researchers, the Opportunity Atlas data provide a new tool to study the determinants of economic opportunity. For example, recent studies have used the Opportunity Atlas data to analyze the effects of lead exposure, pollution, neighborhood redlining, and the Great Migration on children’s long-term outcomes (Manduca and Sampson 2019; Colmer, Voorheis, and Williams 2019; Park and Quercia 2020; Aaronson, Hartley, and Mazumder 2021; Derenoncourt 2022). Other studies use the Atlas statistics as inputs into models of residential sorting (Aliprantis, Carroll, and Young 2024; Davis, Gregory, and Hartley 2019) and to understand perceptions of inequality (Ludwig and Kraus 2019). The ongoing American Voices Project (https://americanvoicesproject.org/) is interviewing families in neighborhoods with particularly low or high levels of upward mobility to uncover new mechanisms from a qualitative lens.
I can see a number of use cases for this as well. For instance, there is probably a lot of value in using the Opportunity Atlas data alongside the data on racial diversity and segregation from the Mixed Metro project (which also offers data down to the Census tract level). Also related is this from a footnote in the Chetty et al. paper:
Understanding how neighborhood effects change with the composition of the neighborhood is an important question that warrants further work...
This also makes me think (again) that we need more detailed work on social mobility in New Zealand, building on the work of my colleagues Niyi Alimi and Dave Maré (see here). One of the amazing things about Chetty's research is that it is now looking at the neighbourhood (Census tract) level, and that sort of spatial disaggregation offers a lot of opportunity for detailed follow-up research and policy action. And with StatsNZ's Integrated Data Infrastructure, we have the basic framework necessary to do this sort of work in New Zealand as well. We could use that to build our own Opportunity Atlas for New Zealand.
*****
[*] So, in lieu of a separate blog post, here's the short summary of those two papers. In the first paper, Chetty et al. use billions of Facebook friendship links to measure local social capital, especially "economic connectedness" (cross-class friendships). They find that places with higher economic connectedness have much higher upward social mobility. In the second paper, the same group of authors show that cross-class friendship gaps come from both who people are exposed to (whether schools, neighbourhoods, or groups) and "friending bias" (less cross-class befriending even when exposed).
[**] In case you're wondering, there are currently 45 papers in that virtual pile, and it seems to be growing. I'm reading research faster than I'm blogging about it. I might have to start blogging about multiple papers in a single post to keep from falling further behind!
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