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Estimating outcomes in the presence of endogeneity and measurement error with an application to R&D

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Published
<mark>Journal publication date</mark>30/04/2023
<mark>Journal</mark>Quarterly Review of Economics and Finance
Volume88
Number of pages17
Pages (from-to)278-294
Publication StatusPublished
Early online date14/02/23
<mark>Original language</mark>English

Abstract

We adopt a Bayesian econometric technique to address issues of endogeneity and measurement error when estimating outcomes while also tackling censoring. We motivate our study based on the theoretical framework laid out by Dasgupta and Stiglitz [1980] to highlight the endogeneity issue by investigating the relationship between market structure and innovation. We apply our method to estimate the R&D expenditures for Chinese manufacturing firms to highlight the importance of the econometric issues. Reduced-form results suggest a nonlinear relationship between market concentration and R&D expenditures, while our approach suggests a strictly positive relationship consistent with canonical theoretical models built on oligopolistic competition.