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Identification and Estimation of Group-Level Partial Effects

Kenichi Nagasawa

Papers from arXiv.org

Abstract: This paper presents a new identification result for causal effects of group-level variables when agents select into groups. The model allows for group selection to be based on individual unobserved heterogeneity. This feature leads to correlation between group-level covariates and unobserved individual heterogeneity. Whereas many of the existing identification strategies rely on instrumental variables for group selection, I introduce alternative identifying conditions which involve individual-level covariates that "shift" the distribution of unobserved heterogeneity. I use these conditions to construct a valid control function. The key identifying requirements on the observable "shifter" variables are likely to hold in settings where a rich array of individual characteristics are observed. The identification strategy is constructive and leads to a semiparametric, regression-based estimator of group-level causal effects, which I show to be consistent and asymptotically normal. A simulation study indicates good finite-sample properties of this estimator. I use my results to re-analyze the effects of school/neighborhood characteristics on student outcomes, following the work of Altonji and Mansfield (2018).

New Economics Papers: this item is included in nep-ecm and nep-ure
Date: 2018-11
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