Estimation of Firm-Level Productivity in the Presence of Exports: Evidence from China's Manufacturing

50 Pages Posted: 2 Feb 2020

See all articles by Emir Malikov

Emir Malikov

University of Nevada, Las Vegas

Shunan Zhao

Oakland University - Department of Economics

Subal C. Kumbhakar

State University of New York (SUNY) at Binghamton - Department of Economics

Date Written: January 8, 2020

Abstract

Motivated by the longstanding interest of economists in understanding the nexus between firm productivity and export behavior, this paper develops a novel structural framework for control-function-based nonparametric identification of the gross production function and latent firm productivity in the presence of endogenous export opportunities that is robust to recent unidentification critiques of proxy estimators. We provide a workable identification strategy, whereby the firm's degree of export orientation provides the needed (excluded) relevant independent exogenous variation in endogenous freely varying inputs, thus allowing us to identify the production function. We estimate our fully nonparametric IV model using the Landweber-Fridman regularization with the unknown functions approximated via artificial neural network sieves with a sigmoid activation function which are known for their superior performance relative to other popular sieve approximators, including the polynomial series favored in the literature. Using our methodology, we obtain robust productivity estimates for manufacturing firms from twenty eight industries in China during the 1999-2006 period to take a close look at China's exporter productivity puzzle, whereby exporters are found to exhibit lower productivity levels than non-exports.

Keywords: ANN sieves, control function, export, nonparametric, productivity, proxy, regularized estimation, TFP

JEL Classification: D24, F10, L10

Suggested Citation

Malikov, Emir and Zhao, Shunan and Kumbhakar, Subal C., Estimation of Firm-Level Productivity in the Presence of Exports: Evidence from China's Manufacturing (January 8, 2020). Available at SSRN: https://ssrn.com/abstract=3516398 or http://dx.doi.org/10.2139/ssrn.3516398

Emir Malikov (Contact Author)

University of Nevada, Las Vegas ( email )

4505 S. Maryland Parkway
Las Vegas, NV 89154
United States

Shunan Zhao

Oakland University - Department of Economics ( email )

Rochester, MI 48309-4401
United States

Subal C. Kumbhakar

State University of New York (SUNY) at Binghamton - Department of Economics ( email )

Binghamton, NY 13902-6000
United States

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