We propose methods of estimating the linear-in-means model of peer effects in which the peer group, defined by a social network, is endogenous in the outcome equation for peer effects. Endogeneity is due to unobservable individual characteristics that influence both link formation in the network and the outcome of interest. We propose two estimators of the peer effect equation that control for the endogeneity of the social connections using a control function approach. We leave the functional form of the control function unspecified, estimate the model using a sieve semiparametric approach and establish asymptotics of the semiparametric estimator.
|Number of pages||18|
|Journal||Review of Economics and Statistics|
|Publication status||Published - 2021 May 14|
Bibliographical noteFunding Information:
We thank Bryan Graham and three referees for their helpful and valuable comments and suggestions. We are particularly grateful to one of the referees for suggesting the idea that is presented in section V.B. of the paper. We also appreciate the comments and discussions of the participants at the 2015 USC Dornsife INET Conference on Networks, the 2016 North American Summer Meeting of the Econometric Society, the 2016 California Econometrics Conference, the 2017 Asian Meeting of Econometric Society, the 2017 IAAE conference, the 2018 UCLA-USC Mini Conference, and the econometrics seminars at University of British Columbia and Ohio State University. The first draft of the paper was written while I.J. was a graduate fellow of USC Dornsife INET and H.R.M. was the associate director of USC Dornsife INET. H.R.M. acknowledges that this work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2017S1A5A2A01023679).
© 2019 by the President and Fellows of Harvard College and the Massachusetts Institute of Technology.
All Science Journal Classification (ASJC) codes
- Social Sciences (miscellaneous)
- Economics and Econometrics