Using Monotonicity Restrictions to Identify Models with Partially Latent Covariates

Minji Bang, Wayne Gao, Andrew Postlewaite and Holger Sieg

No 28436, NBER Working Papers from National Bureau of Economic Research, Inc

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.

JEL-codes: C01 J24 L0 (search for similar items in EconPapers)
Date: 2021-02
Note: IO LS PR TWP
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Published as Minji Bang & Wayne Yuan Gao & Andrew Postlewaite & Holger Sieg, 2022. "Using monotonicity restrictions to identify models with partially latent covariates," Journal of Econometrics, .

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Journal Article: Using monotonicity restrictions to identify models with partially latent covariates (2023)
Working Paper: Using Monotonicity Restrictions to Identify Models with Partially Latent Covariates (2022)
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