Using Monotonicity Restrictions to Identify Models with Partially Latent Covariates.

Autor/inn/en	Bang, Minji; Gao, Wayne Yuan; Postlewaite, Andrew; Sieg, Holger
Institution	National Bureau of Economic Research
Titel	Using Monotonicity Restrictions to Identify Models with Partially Latent Covariates.
Quelle	Cambridge, Mass.: National Bureau of Economic Research (2021) PDF als Volltext (1); PDF als Volltext (2) Link als defekt meldenVerfügbarkeit
Reihe	NBER working paper series. w28436
Beigaben	Illustrationen
Sprache	englisch
Dokumenttyp	online; Monographie; Graue Literatur
DOI	10.3386/w28436
Schlagwörter	Korrelation; Schätzung; Bildungsertrag; Bildungsinvestition; Handel; Industrieökonomie; Markt; Produktionsfunktion; Pharmazie; Arbeitspapier; Struktur
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.
Erfasst von	ZBW - Leibniz-Informationszentrum Wirtschaft, Kiel
Update	2021/3