This different mobility between selected counties was not only attributed to lockdown order, residents in these level-1 metropolitan counties could also have higher awareness to prevent transmission due to the denser population. Chetty dedicated a particular part of his presentation to discussing HUD’s Office of Policy, Development & Research Moving to Opportunity program, a unique experimental design which provided randomized housing vouchers to low-income families to move to lower poverty neighborhoods. This landmark experiment starting in 1994 provided vouchers to 4,600 families with children living in public housing. The results of the study have been analyzed in long-term and short-term impacts, and utilized in numerous reports and policy briefs.


We also show that using simpler measures of SES, such as median household income in an individual’s ZIP code, produces very similar results to those reported below. A person with fewer opportunities faces personal, physical, mental or health-related conditions that make participation in the project/mobility action not possible without extra financial or other support. Higher education institutions that have selected students and/or staff with fewer opportunities can apply for additional grant support to the National Agency in order to cover the supplementary costs for their participation in the mobility activities. For participants with fewer opportunities, in particular those with physical, mental or health-related conditions, the grant support may therefore be higher than the maximum individual grant amount set out below. Higher education institutions will describe on their website how students and staff with fewer opportunities can request and justify such additional grant support. For mobility projects supported by external policy funds, the organisational support grant will be shared by the partners concerned on a mutually acceptable basis decided upon by the participating institutions.

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The widely used Penn State index63 of participation in civic organizations has a correlation of 0.06 across counties with upward mobility. There are similarly weak associations of upward mobility with our measures of the density of civic organizations and volunteering rates. The difference between these findings and previous work that has found stronger associations between civic engagement and economic mobility is primarily because we weight our correlations by the number of children with below-national-median parental income. As a result, rural areas—where civic engagement is more strongly correlated with mobility—receive lower weight in our correlations (Supplementary InformationC.2). Going beyond average income levels, previous research has also shown that in counties where people of different incomes or racial backgrounds live in separate neighbourhoods, levels of economic mobility are generally lower.

social distancing

We are able to assign parental SES ranks for 31% of the individuals in our primary analysis sample. We do not use any information from an individual’s friends to predict their SES, which ensures that errors in the SES predictions are not correlated across friends, which would bias our estimates of homophily by SES. We also do not use direct information on individuals’ incomes or wealth, as we do not observe these variables at the individual level in our data. However, we show below that our measures of SES are highly correlated with external measures of income across subgroups.

A) Funding rules applying to all mobility activities

These neighborhoods would rank high in mobility, as well as school quality and civic engagement. This figure shows binned scatter plots of children’s mean SES ranks in adulthood against their own parents’ SES ranks. Each point plots the mean SES rank of children who have parents at a given percentile of the SES distribution. The series in circles is based on data from Facebook, with SES rank calculated as described in the Variable Definitions section of Methods.

The results in Mobility And Engagement Index Table 3 show that economic connectedness remains highly correlated with economic mobility even conditional on race, which implies that segregation by race is unlikely to be the primary driver of the observed correlation between EC and mobility overall. Relationships between mobility and other measures of social capital also remain similar when restricting the sample to areas in which one race forms an overwhelming share of the population (Supplementary Fig. 5). When we regress measures of upward mobility on standardized versions of all of the social capital measures together, EC remains the strongest predictor of upward mobility by a significant margin. By contrast, measures of civic engagement and network cohesiveness have coefficients near zero (Fig. 3b).

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