Working Paper: NBER ID: w28436
Authors: Minji Bang; Wayne Gao; Andrew Postlewaite; Holger Sieg
Abstract: This paper develops a new method for identifying econometric models with partially latent covariates. Such data structures arise naturally in industrial organization and labor economics settings where data are collected using an “input-based sampling” strategy, e.g., if the sampling unit is one of multiple labor input factors. We show that the latent covariates can be nonparametrically identified, if they are functions of a common shock satisfying some plausible monotonicity assumptions. With the latent covariates identified, semiparametric estimation of the outcome equation proceeds within a standard IV framework that accounts for the endogeneity of the covariates. We illustrate the usefulness of our method using two applications. The first focuses on pharmacies: we find that production function differences between chains and independent pharmacies may partially explain the observed transformation of the industry structure. Our second application investigates education achievement functions and illustrates important differences in child investments between married and divorced couples.
Keywords: No keywords provided
JEL Codes: C01; J24; L0
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| latent covariates (C20) | identification of latent covariates (C20) |
| monotonicity restrictions (D11) | identification of latent covariates (C20) |
| firm type (L20) | productivity (O49) |
| parental investment (J13) | child outcomes (J13) |
| divorced fathers (J12) | child quality (J13) |