Research and Data Priorities for Improving Economic and Social Mobility Proceedings of a Workshop (2022) / Chapter Skim
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6 Data Infrastructure for Studying Mobility
6 Data Infrastructure for Studying Mobility
Pages 69-84
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• Using educational data and neighborhood data to understand patterns of inequality can push interventions forward and provide intermediate-level out comes to measure success. (Daniel Botting)
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Combining these two sources of data, said Genadek, adds demographic characteristics, socioeconomic variables, and detailed family information to administrative data, as well as additional outcomes of occupation, educational attainment, and housing. Genadek identified four recent examples of research that links administrative data with survey data.4 Commonly used sources of administrative data include Social Security Administration records and tax records; commonly used sources of survey data include the Census, the American Community Survey, the Current Population Survey, and the Survey of Income and Program Participation.
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Another new resource is representative survey samples linked to lifetime Social Security Administration data.5 These data have existed for years, but are now available to everyone, she said. Also available are largescale U.S.
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All of these data are available through the Federal Statistical Research Data Centers. NONTRADITIONAL DATA SOURCES School and Neighborhood Data At this workshop, speakers have discussed the utility of educational data for examining social mobility, and how neighborhoods have a large impact on social mobility, said Daniel Botting (Impact Tulsa)
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SOURCE: Workshop presentation by Daniel Botting, February 15, 2022. social mobility.
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Greig walked workshop participants through three examples of ways in which banking data can be used to study social mobility: the evolution of household cash balances during COVID-19, income growth during business cycles, and understanding the sources and consequences of racial wealth gaps.
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Income Growth During Business Cycles Greig's second example focused on how macroeconomic environments and policies play into matters of inequality. Comparing the rates at which household incomes grew by income quartile between 2013 and 2021, Greig explained that after the Great Recession, high-income families experienced faster income growth than low-income families, but by the time the pandemic hit, this pattern had reversed (see Figure 6-5)
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Compared with White and Hispanic borrowers, Black borrowers are less likely to be making progress on their student loans, with sizable percentages either making no payments or on track to never pay off the loan. By pairing credit bureau data with checking account data, said Greig, it became clear that loan repayment is a "family affair." Nearly 40 percent of individuals who are making student loan payments are doing so on someone else's behalf (e.g., spouse, child, parent)
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On the other hand, government data tend to have representative samples, whereas administrative data require using benchmarking to assess representativeness. DATA GOVERNANCE There are competing goals for the data held by federal statistical agencies, including survey, census, and administrative data, said Katharine Abraham (University of Maryland, College Park)
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For microdata, methods include coarsening categorical variables, top-coding continuous variables, noise infusion, and data swapping. For tabular releases, approaches include cell suppression, noise infusion, swapping in underlying microdata, and cell value rounding.
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In order to address these weaknesses, the U.S. Census Bureau has begun to adopt disclosure avoidance methods based on differential privacy.
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Census Bureau struggled to conduct tests of new systems that were planned for the 2020 Census. During this period, there has been growing reliance on outside researchers, and initiatives that encourage researchers to access federal data and to share data across agencies.
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Census Bureau population data -- particularly if linked with other data -- have the potential to greatly advance research on mobility and racial inequalities. Pfeffer asked speakers to comment on the potential for cooperation between agencies, and between agencies and outside researchers, given concerns about privacy.
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Most Critical Needs Snipp asked speakers to briefly identify their top priorities for data or methods that could advance the study of social mobility. Ideas included the following:
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• Consistent state-level education data with a streamlined process for gaining access. (Botting)
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