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 A Framework that Exploits Compromises in Social Experiments to Identify Causal Effects 




[Paper]   [Appendix]        ( last update  01/09/2019)

Noncompliance is a pervasive problem in social experiments which hinges the identification of causal effects. This paper offers a framework in which noncompliance is not portrayed as a drawback, but a key ingredient of identification analysis. The method uses revealed preference analysis to exploit the incentives generated by the design of social experiments in order to nonparametrically identify causal parameters. The framework is used to evaluate the Moving to Opportunity, the largest housing experiment in the US.  Moving to Opportunity was designed to investigate the casual effect of relocating disadvantaged families from high-poverty neighborhoods to low-poverty communities. Substantial noncompliance prevents the evaluation of neighborhood effects, that is the causal effect of residing in different neighborhoods types. Nevertheless, noncompliance still allows for the evaluation of voucher effects,  that is the causal effect of being offered a voucher. Previous literature shows that voucher effects on labor market outcomes are not statistically significant. This paper exploits the incentives of the MTO intervention to identify neighborhood effects. Although voucher effects are not statistically significant, neighborhood effects are. The result reconciles MTO with a growing literature attesting the impact of neighborhood quality on economic well-being. The framework can be broadly applied to exploit economic incentives in multiple choice models with heterogeneous agents and categorical instrumental variables. I show that all causal parameters can be estimated by standard 2SLS applied to suitable data transformations.