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Social Interactions, Networks and Fixed Effects

Paper Session

Sunday, Jan. 7, 2018 10:15 AM - 12:15 PM

Marriott Philadelphia Downtown, Meeting Room 414
Hosted By: Econometric Society
  • Chair: Brendan Kline, University of Texas

Heterogeneous Endogenous Effects in Networks

Sida Peng
,
Cornell University

Abstract

This paper proposes a new method to identify leaders and followers in a network. Prior works use spatial autoregression models (SARs) which implicitly assume that each individual in the network has the same peer effects on others. Mechanically, they conclude the key player in the network to be the one with the highest centrality. However, when some individuals are more influential than others, centrality may fail to be a good measure. I develop a model that allows for individual-specific endogenous effects and propose a two-stage LASSO (2SLSS) procedure to identify influential individuals in a network. My method allows me to identify leaders and followers through their observed behaviors on the network. I develop robust inference for my estimators including uniformly valid confidence intervals. I extend the analysis to allow for multiple types of connections (multiple networks), and I show how to use the square-root sparse group LASSO to detect which of the multiple connection types is more influential. Simulation evidence shows that my estimator has good finite sample performance. I further apply my method to the data in Banerjee et al. (2013) and my proposed procedure is able to identify leaders and effective networks.

Keeping up With Peers in India: A New Social Interactions Model of Perceived Needs

Arthur Lewbel
,
Boston College
Sam Norris
,
Northwestern University
Krishna Pendakur
,
Simon Fraser University
Xi Qu
,
Shanghai Jiao Tong University

Abstract

We propose a new nonlinear model of social interactions. The model allows point identification of peer effects as a function of group means, even with group level fixed effects. The model is robust to measurement problems resulting from only observing a small number of members of each group, and therefore can be estimated using standard survey datasets. We apply our method to a national consumer expenditure survey dataset from India. We find that each additional rupee spent by one's peer group increases one's own perceived needs by roughly 0.5 rupees. This implies that if I and my peers each increase spending by 1 rupee, that has the same effect on my utility as if I alone increased spending by only 0.5 rupees. Our estimates have important tax policy implications, since the larger these peer effects are, the smaller are the welfare gains associated with tax cuts or mean income growth.

A Semiparametric Network Formation Model With Multiple Linear Fixed Effects

Luis Enrique Candelaria
,
Duke University

Abstract

This paper analyzes a semiparametric model of network formation in the presence of multiple, unobserved, and agent-specific fixed effects. Given agents’ observed attributes, the conditional distributions of these effects, as well as the disturbance terms associated with each linking decision are not parametrically specified. I give sufficient conditions for point identification of the coefficients on the observed covariates. This result relies on the existence of at least one continuous covariate with unbounded support. I provide partial identification results when all covariates have a bounded support. Specifically, I derive bounds for each component of the vector of parameters when all the covariates have a discrete support. I propose a semiparametric estimator for the vector of coefficients that is consistent and asymptotically normal as the number of individuals in the network increases. Monte Carlo experiments demonstrate that the estimator performs well in finite samples. Finally, in an empirical study, I analyze the determinants of a friendship network using the Add Health dataset.

Binarization for Panel Models with Fixed Effects

Irene Botosaru
,
Simon Fraser University
Chris Muris
,
University of Bristol

Abstract

Abstract This paper considers the identification and estimation of a linear transformation model with fixed effects, where the transformation function is unknown, weakly monotone, and time-varying. The model analyzed nests a large number of fixed effects panel models for discrete and continuous outcomes that are used in applied work, such as binary choice, ordered choice, and various transformation models, and their time-varying counterparts. Importantly, we allow for time effects in ordered choice models, as well as for time-varying censoring. The key observation behind our identification results is that the transformation model can be converted to many binary choice models. The relevance of our results to the nonlinear panel literature is threefold. First, we provide a general nonparametric solution to the incidental parameter problem in a fixed-T setting for this large class of models. Second, we show the identification of a menu of partial effects that are time-varying. Additionally, we show how our results can be used in a nonlinear difference-in-differences setting to identify the distribution of the counterfactual outcomes for the treated, as well as the ATT. Third, we provide different estimators for the parameters of interest and derive their large sample properties.
JEL Classifications
  • C3 - Multiple or Simultaneous Equation Models; Multiple Variables
  • C57 - Econometrics of Games and Auctions