The changing geography of social mobility in the United States
Edited by Susan Hanson, Clark University, Worcester, MA, and approved October 13, 2020 (received for review May 20, 2020)
Significance
Intergenerational social mobility in the United States has declined over the last century, sparking a national debate about how to improve equality of opportunity. By analyzing data spanning the 20th century, we demonstrate strong temporal patterns operating across regions. Some areas of the United States have witnessed significant declines in social mobility, while others have had persistent low levels all along. Thus, the contemporary national picture is shaped by both powerful forces of change that reduce intergenerational mobility in some regions and deeply entrenched long-term forces generating persistence in others. It follows that improving social mobility will be challenging, as policy would need to respond to both forces and do so according to their varying mixture across different regions.
Abstract
New evidence shows that intergenerational social mobility—the rate at which children born into poverty climb the income ladder—varies considerably across the United States. Is this current geography of opportunity something new or does it reflect a continuation of long-term trends? We answer this question by constructing data on the levels and determinants of social mobility across American regions over the 20th century. We find that the changing geography of opportunity-generating economic activity restructures the landscape of intergenerational mobility, but factors associated with specific regional structures of interpersonal and racial inequality that have “deep roots” generate persistence. This is evident in the sharp decline in social mobility in the Midwest as economic activity has shifted away from it and the consistently low levels of opportunity in the South even as economic activity has shifted toward it. We conclude that the long-term geography of social mobility can be understood through the deep roots and changing economic fortunes of places.
Data Availability
Geographical estimates of intergenerational mobility data and replication files have been deposited in the repository of the Inter-university Consortium for Political and Social Research (ICPSR), available at https://doi.org/10.3886/E125701V1.
Acknowledgments
We thank our thoughtful anonymous editor and reviewers. This work was supported by a generous Donald J. Treiman Research Fellowship through the California Center for Population Research at the University of California Los Angeles (UCLA), and a National University of Ireland (NUI) Travelling Studentship awarded by the NUI. For their comments and suggestions, we thank Leah Boustan, Dora Costa, Judith Seltzer, Rob Mare, Donald Treiman, David Rigby, Jamie Goodwin-White, Billie Lee Turner II, Gerald Mills, Trisalyn Nelson, Sebastien Breau, Peter Kedron, Amy Frazier, Cameron Campbell, and Fabian Pfeffer. We also received helpful comments from talks at the UCLA Economic History proseminar, the Broom Center for Demography at the University of California, Santa Barbara, the School of Geographical Sciences and Urban Planning at Arizona State University, the Department of Geography at the University of Colorado Boulder, the Department of Geography at the University of Hong Kong, the Division of Social Sciences at the Hong Kong University of Science and Technology, the Department of Geography at Umea University, and the annual meetings of the Population Association of America and the American Association of Geographers. Mia Bennett provided excellent advice on visuals.
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Cover image: Pictured is the behavior of last-layer feature means and linear classifiers of a deep neural network during the terminal phase of training, showing that the feature means and linear classifiers converge to a simple and highly symmetric form. Empirically derived artificial neural networks have greatly improved over the past decade, but their mechanisms of action remain poorly understood. The Colloquium on The Science of Deep Learning aims to enrich understanding of highly advanced deep-learning systems and their use as research tools. See the Introduction to the Colloquium by Richard Baraniuk, David L. Donoho, and Matan Gavish on pages 30029–30032. Image credit: X. Y. Han (Cornell University, Ithaca, NY) and Vardan Papyan (University of Toronto, Toronto, Canada).
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© 2020. Published under the PNAS license.
Data Availability
Geographical estimates of intergenerational mobility data and replication files have been deposited in the repository of the Inter-university Consortium for Political and Social Research (ICPSR), available at https://doi.org/10.3886/E125701V1.
Submission history
Published online: November 16, 2020
Published in issue: December 1, 2020
Keywords
Acknowledgments
We thank our thoughtful anonymous editor and reviewers. This work was supported by a generous Donald J. Treiman Research Fellowship through the California Center for Population Research at the University of California Los Angeles (UCLA), and a National University of Ireland (NUI) Travelling Studentship awarded by the NUI. For their comments and suggestions, we thank Leah Boustan, Dora Costa, Judith Seltzer, Rob Mare, Donald Treiman, David Rigby, Jamie Goodwin-White, Billie Lee Turner II, Gerald Mills, Trisalyn Nelson, Sebastien Breau, Peter Kedron, Amy Frazier, Cameron Campbell, and Fabian Pfeffer. We also received helpful comments from talks at the UCLA Economic History proseminar, the Broom Center for Demography at the University of California, Santa Barbara, the School of Geographical Sciences and Urban Planning at Arizona State University, the Department of Geography at the University of Colorado Boulder, the Department of Geography at the University of Hong Kong, the Division of Social Sciences at the Hong Kong University of Science and Technology, the Department of Geography at Umea University, and the annual meetings of the Population Association of America and the American Association of Geographers. Mia Bennett provided excellent advice on visuals.
Notes
This article is a PNAS Direct Submission.
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Competing Interests
The authors declare no competing interest.
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