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Identifying Peer Effects in Networks with Unobserved Effort and Isolated Students

Aristide Houndetoungan, Cristelle Kouame, Michael Vlassopoulos

arXiv 10 May 2024 · Econometrics · publishedJournal of Applied Econometrics (2026)

arXiv:2405.06850 · PDF · DOI · OpenAlex · Extracted main text

Abstract

Peer influence on effort devoted to some activity is often studied using proxy variables when actual effort is unobserved. For instance, in education, academic effort is often proxied by GPA. We propose an alternative approach that circumvents this approximation. Our framework distinguishes unobserved shocks to GPA that do not affect effort from preference shocks that do affect effort levels. We show that peer effects estimates obtained using our approach can differ significantly from classical estimates (where effort is approximated) if the network includes isolated students. Applying our approach to data on high school students in the United States, we find that peer effect estimates relying on GPA as a proxy for effort are 40% lower than those obtained using our approach.

Citation extraction

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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
cpz2009unmatched citation key cpz20091.00053100%
m1993unmatched citation key m19931.00053100%
BramoulleDjebbariFortin2009unmatched citation key BramoulleDjebbariFortin20090.87482100%
bramoulle2020peerunmatched citation key bramoulle2020peer0.64422100%
fruehwirth2013identifyingunmatched citation key fruehwirth2013identifying0.64422100%
boucher2022estimatingunmatched citation key boucher2022estimating0.58531100%
lee2004asymptoticunmatched citation key lee2004asymptotic0.5112250%
BoucherFortin2016unmatched citation key BoucherFortin20160.51121100%
epple2011peerunmatched citation key epple2011peer0.51121100%
kelejian1998generalizedunmatched citation key kelejian1998generalized0.51121100%

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arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

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1Quantile Peer Effect Models10pt10pt For comments and suggestions, I am grateful to Yann Bramoullé, Vincent Boucher, Firmin Doko Tchatoka, Mathieu Lambotte, and Marie Aurélie Lapierre. This research uses data from the National Longitudinal Study of Adolescent to Adult Health (Add Health), a program that is directed by Kathleen Mullan Harris and designed by J. Richard Udry, Peter S. Bearman, and Kathleen Mullan Harris at the University of North Carolina at Chapel Hill, and funded by Grant P01-HD31921 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development, with cooperative funding from 23 other US federal agencies and foundations. Special acknowledgment is given to Ronald R. Rindfuss and Barbara Entwisle for assistance in the original design. Information on how to obtain Add Health data files is available on the Add Health website (www.cpc.unc.edu/addhealth). No direct support was received from Grant P01-HD31921 for this research. An R package, including all replication codes, is available at: https://github.com/ahoundetoungan/QuantilePeer0.40511