The Upward Mobility Initiative can appeal to a variety of audiences, including researchers. The Urban Institute identified metrics that measure key predictors of mobility from poverty as part of its Upward Mobility Framework (UMF); these metrics were selected based on several criteria. The UMF includes measures of neighborhood segregation, transportation access, education quality, housing affordability and stability, environmental quality, political participation, public safety and policing, job quality, and more. It offers 26 Mobility Metrics for all counties in the US and all cities with population greater than 75,000.

The Mobility Metrics data are a valuable resource for researchers trying to understand how characteristics of a place either hinder or promote the aspirations of its residents. Urban researchers have recently completed three analyses using these data:

• Kassandra Martinchek (2024) combines Mobility Metrics data on racial segregation, employment, income, educational attainment, and home values by race and ethnicity with credit bureau data and ACS data to explore the extent to which community-level conditions explain racial disparities in young adults’ credit scores. Martinchek finds that a significant portion of racial disparities can be explained by the structural characteristics of a community, namely the degree of racial residential segregation and differences in housing wealth held by racial and ethnic groups.
• Amy Rogin (2024), using Mobility Metrics data on air quality and housing affordability, finds that counties with high levels of air pollution and low supply of affordable housing are clustered along the West Coast and throughout the Southeast and East Coast, while counties with low levels of air pollution and high supply of affordable housing tend to be in a corridor of the Great Plains and upper Rockies. Residents of counties with high pollution and low housing affordability, on average, also experience higher rates of concentrated poverty and elevated rates of crime. 
• Julia Long (2024), using Mobility Metrics data on low birth weight, racial residential segregation, and income among the 350 largest US counties, finds that both greater racial diversity and higher income are associated with a lower incidence of low birth weight among infants born to Black mothers.Therefore, policies that foster inclusive neighborhoods may reduce racial health disparities.

The programming code used to generate the Mobility Metrics data are publicly available in GitHub, and datasets can be downloaded from the Urban Data Catalog, which includes a data dictionary, crosswalk files, and other information to guide users of the data. Urban also provides outlining the evidence on key predictors of mobility from poverty and ideas on how local policies and programs can help move the needle on those predictors.

Urban Institute will release an update to the Mobility Metrics data later this year. The update will include additional years of information, metric refinements, and more detailed breakdowns of the metrics by sociodemographic characteristics. Eight communities have used these data to craft mobility action plans to help their residents rise out of poverty. Urban invites researchers interested in better understanding how community-level factors can boost or impede upward economic and social mobility to access and use these data. High-quality research with high-quality data forms the foundation on which better public policy and practice are built.