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This paper addresses several shortcomings in the productivity and markup estimation literature. Using Monte-Carlo simulations, the analysis shows that the methods in Ackerberg, Caves and Frazer (2015) and De Loecker and Warzynski (2012) produce biased estimates of the impact of policy variables on markups and productivity. This bias stems from endogeneity due to the following: (1) the functional form of the production function; (2) the omission of demand shifters; (3) the absence of price information; (4) the violation of the Markov process for productivity; and (5) misspecification when marginal costs are excluded in the estimation. The paper addresses these concerns using a quasi-maximum likelihood approach and a generalized estimator for the production function. It produces unbiased estimates of the impact of regulation on markups and productivity. The paper therefore proposes a work-around solution for the identification problem identified in Bond, Hashemi, Kaplan and Zoch (2020), and an unbiased measure of productivity, by directly accounting for the joint impact of regulation on markups and productivity.

Competition Policy --- Enterprise Development and Reform --- Legal Regulation and Business Environment --- Markov Process --- Markups --- Private Sector Development --- Production Function --- Productivity --- Quasi-Maximum Likelihood --- Regulation