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Black-White Differences in Intergenerational Economic Mobility in the U.S.

December 2011

Written by: Bhashkar Mazumder

Working Paper Number:

CES-11-40

Abstract

Traditional measures of intergenerational mobility such as the intergenerational elasticity are not useful for inferences concerning group differences in mobility with respect to the pooled income distribution. This paper uses transition probabilities and measures of 'directional rank mobility' that can identify inter-racial differences in intergenerational mobility. The study uses two data sources including one that contains social security earnings for a large intergenerational sample. I find that recent cohorts of blacks are not only significantly less upwardly mobile but also significantly more downwardly mobile than whites. This implies a steady-state distribution in which there is no racial convergence in income. A descriptive analysis using covariates reveals that test scores in adolescence can explain much of the racial difference in both upward and downward mobility. Family structure can account for some of the racial gap in upward mobility but not downward mobility. Completed schooling and parental wealth also appear to account for some of the racial gaps in intergenerational mobility.

Document Tags and Keywords

Keywords Keywords are automatically generated using KeyBERT, a powerful and innovative keyword extraction tool that utilizes BERT embeddings to ensure high-quality and contextually relevant keywords.

By analyzing the content of working papers, KeyBERT identifies terms and phrases that capture the essence of the text, highlighting the most significant topics and trends. This approach not only enhances searchability but provides connections that go beyond potentially domain-specific author-defined keywords.
:
statistical, black, hispanic, longitudinal, white, segregation, wealth, disadvantaged, percentile, household, racial, race, generation, socioeconomic, mobility, intergenerational, family, poorer, transition

Tags Tags are automatically generated using a pretrained language model from spaCy, which excels at several tasks, including entity tagging.

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:
Social Security Administration, Center for Economic Studies, Federal Reserve Bank, National Longitudinal Survey of Youth, Survey of Income and Program Participation, Social Security Number, PSID, Detailed Earnings Records, Summary Earnings Records, W-2

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