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Unformatted text preview: Design of Randomized Experiments to Measure Social Interaction Effects Jinyong Hahn UCLA * Keisuke Hirano University of Arizona 27 January 2009 Abstract We consider the use of randomized experiments to measure social interaction effects. Ran domization at two levelsacross groups and within groupscan resolve an omitted variables problem for a linearinmeans model of endogenous social interactions. We examine how the randomization should be carried out to estimate the coefficients of interest most precisely, and calculate the optimal treatment probabilities under different criteria. 1 Introduction There has been considerable work in recent years on estimating models where individuals within a group influence each others behavior. Brock and Durlauf (2001) and Moffitt (2001) survey recent studies of social interaction effects. Models with social interaction effects present severe identification problems, as was pointed out in the seminal paper by Manski (1993). We consider a version of Manskis linearinmeans model of social interactions, and show how a randomization can be used to identify the model. Then we consider the experimental design issue: how should the randomization probabilities be chosen to obtain the most precise estimates of the coefficients of interest? We calculate the optimal treatment probabilities under certain criteria. We find that for some criteria, the design used by Duflo and Saez (2003) is nearly optimal. * Department of Economics, University of California, Los Angeles, Box 951477, Los Angeles, CA 900951477 (hahn@econ.ucla.edu) Department of Economics, University of Arizona, Tucson, AZ 85721 (hirano@u.arizona.edu) 1 2 LinearinMeans Model Suppose we observe N nonoverlapping groups g = 1 ,...,N . For each group g , we sample M g individuals. We assume that the M g individuals form a random subset of the full group, and we assume that all the variables of interest are independent across groups. Consider the following linearinmeans model of social interactions, which is a special case of the model introduced by Manski (1993): y gi = E g [ y gi ] + x gi + gi + gi , (1) where all variables are measured in deviations from sample means, and E g [ ] denotes the mean...
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This note was uploaded on 12/26/2011 for the course ECON 245a taught by Professor Staff during the Fall '08 term at UCSB.
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