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Using Monotonicity Restrictions to Identify Models with Partially Latent Covariates

Minji Bang, Wayne Yuan Gao, Andrew Postlewaite, Holger Sieg

arXiv 14 Jan 2021 · Econometrics · publishedJournal of Econometrics (2022)

arXiv:2101.05847 · PDF · DOI · OpenAlex · Extracted main text

Abstract

This paper develops a new method for identifying econometric models with partially latent covariates. Such data structures arise 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 a new application that focuses on the production functions of pharmacies. We find that differences in technology between chains and independent pharmacies may partially explain the observed transformation of the industry structure.

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Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
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5Levinsohn, J. and A. Petrin (2003) Estimating production functions using inputs to control for unobservables0.87452100%
6Newey, W. K (1994) The asymptotic variance of semiparametric estimators0.8229356%
7Ridder, G. and R. Moffitt (2007) The Econometrics of Data Combination0.73732100%
8Abowd, J. and F. Kramaz (1999) THe Analysis of Labor Markets Using Matched Employer-Employee Data0.64422100%
9Abrevaya, J. and S. G. Donald (2017) A GMM approach for dealing with missing data on regressors0.64422100%
10Angrist, J. D. and A. B. Krueger (1992) The effect of age at school entry on educational attainment: an application of instrumental variables with moments from two samp…0.64422100%

Showing the top 10 of 58 scored citations.