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When Do the Advantaged See the Disadvantages of Others? A Quasi-Experimental Study of National Service

Published online by Cambridge University Press:  23 August 2018

CECILIA HYUNJUNG MO  and
KATHARINE M. CONN
CECILIA HYUNJUNG MO*
Affiliation:
University of California, Berkeley
KATHARINE M. CONN*
Affiliation:
Columbia University
*
Cecilia Hyunjung Mo is an Assistant Professor of Political Science, University of California, Berkeley, 210 Barrows Hall #740, Berkeley, CA 94720-1950 (cecilia.h.mo@berkeley.edu).
Katharine M. Conn is a Senior Research Scientist, Consortium for Policy Research in Education, Teachers College, Columbia University, 525 West 120th Street, New York, NY 10026-6696 (kmc2169@columbia.edu).
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Abstract

Are there mechanisms by which the advantaged can see the perspectives of the disadvantaged? If advantaged individuals have prolonged engagement with disadvantaged populations and confront issues of inequality through national service, do they see the world more through the lens of the poor? We explore this question by examining Teach For America (TFA), as TFA is a prominent national service program that integrates top college graduates into low-income communities for two years and employs a selection model that allows for causal inference. A regression discontinuity approach, utilizing an original survey of over 32,000 TFA applicants and TFA’s selection data for the 2007–2015 application cycles, reveals that extended intergroup contact in a service context causes advantaged Americans to adopt beliefs that are closer to those of disadvantaged Americans. These findings have broad implications for our understanding of the impact of intergroup contact on perceptions of social justice and prejudice reduction.

Type
Research Article
Information
American Political Science Review , Volume 112 , Issue 4 , November 2018 , pp. 721 - 741
Copyright
Copyright © American Political Science Association 2018 

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Footnotes

We are grateful to representatives from Teach For America, especially Raegen Miller, Yoon Ha Choi, Tameka Brigham, and Johann von Hoffmann, for their assistance in collecting the data necessary for this project. We thank representatives from Teach For All, especially Laura Lewis, Robbie Dean, Alonso Sanchez, and Leigh Kincaid, for their partnership. We also acknowledge the excellent research assistance of Allison Archer, Claire Evans, Virginia Lovison, Laura Sellers, Joseph Stigall, and Bryce Williams-Tuggles. The authors gratefully acknowledge the financial support from Vanderbilt University's Discovery Grant Program and the World Bank's Development Grant Facility. We would also like to thank Brooke Ackerly, Larry Bartels, Jonathan Bendor, Joshua Clinton, Marc Hetherington, Martin Gilens, Cindy Kam, Brenton Kenkel, David Lewis, Neil Malhotra, Bruce Oppenheimer, Efrén Pérez, Alan Wiseman, Hye Young You, Elizabeth Zechmeister, and participants of the Annual Meetings of APSA, ISPP, MPSA, and WPSA, Brigham Young University’s Political Science Seminar, Columbia University’s Political Economy Workshop, Princeton University’s Kahneman-Treisman Center for Behavioral Science and Public Policy Behavioral Policy Speaker Series, Princeton University’s Center of the Study of Democratic Politics Seminar, Stanford University’s Political Psychology Research Group, Vanderbilt University’s RIPS Lab Research Group, Washington University in St. Louis’s Political Science Seminar, the Toulouse School of Economics IAST Political Economy and Political Science Conference, UC Berkeley’s Research Workshop in American Politics Colloquium, and Yale University’s CSAP Summer Conference for their helpful comments and feedback. The data, code, and any additional materials required to replicate all analyses in this article are available at the American Political Science Review Dataverse within the Harvard Dataverse Network, at https://doi.org/10.7910/DVN/VTUSLV.

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