Unobservable Productivity Shocks and Bias of Production Function Estimators: The Control Function Approach

Document Type : Scientific paper

Authors

1 islamic azad uni firoozkuh

2 Department of Economic, Firoozkooh Branch, Islamic Azad University, Firoozkooh, Iran

3 Associate Professor at IAU

4 Assistant Professor, Department of Economics, Firoozkooh Branch, Islamic Azad University, Firoozkooh, Iran

10.22080/mrl.2025.27720.2118

Abstract

The ordinary least squares (OLS) estimators will be biased If there is a relationship between observable input levels and unobservable productivity shocks in the production function. Along instrumental variables (IV) and generalized moments method (GMM) approaches, the control function approach is the most widely used in production function estimation to solve this problem. In this article, the production function was estimated by the conventional panel data method and the control function approach and compared with using Iran’ provinces manufacturing industries data of in the period of 2011-2018. The findings showed that the OLS estimator in the panel data methods overestimates the free variables coefficients (labor force), which this is consistent with the argument of Levinson and Petrin (LP) (2003). In the control function approach methods, Wooldridge (WR) method estimators are more efficient than LP method. Among the optimization algorithms, the Nelder and Mead (NM) algorithm results are more efficient than others. By assuming that the capital stock is endogenous, the results indicated that the ACF correction method (by controlling the labor force functional dependence) is more efficient than LP method. Finally, estimators’ efficiency was increased by using dynamic panel instruments in the MR approach. The Article findings point out that the labor force functional dependence and the endogeneity of the capital stock can be important issues in the production function estimation that should not be ignored. Thus, it seems that GMM nonlinear models in general, and control function models, namely OP, LP with ACF and MR correction in particular, should be used more in the production function estimation for controlling unobservable productivity shocks.

Keywords

Main Subjects


Statistical Center of Iran (various years). Survey plan for industrial workshops with 10 or more employees.
 
 
 
Ackerberg, D., Benkard, C. L., Berry, S., & Pakes, A. (2007). Econometric tools for analyzing market outcomes. Handbook of econometrics6, pp. 4171-4276.
Ackerberg, D. A., Caves, K., & Frazer, G. (2015). Identification properties of recent production function estimators. Econometrica83(6), pp.  2411-2451.
Blundell, R., & Bond, S. (1998). Initial conditions and moment restrictions in dynamic panel data models. Journal of econometrics87(1), pp. 115-143.
Bond, S., & Söderbom, M. (2005). Adjustment costs and the identification of Cobb Douglas production functions (No. 05/04). IFS Working Papers.
Cobb, C. W., & P. H. Douglas. (1928). A theory of production. American Economic Review 18 (Suppl. 1), pp. 139–165.
Levinsohn, J., & Petrin, A. (2003). Estimating production functions using inputs to control for unobservables. The review of economic studies70(2), pp. 317-341.
Marschak, J., & Andrews, W. H. (1944). Random simultaneous equations and the theory of production. Econometrica, Journal of the Econometric Society, pp. 143-205.
Mollisi, V., & Rovigatti, G. (2018). Theory and practice of total-factor productivity estimation: The control function approach using Stata. The Stata Journal18(3), pp. 618-662.
Olley, S. and Pakes, A. (1996). The Dynamics of Productivity in the Telecommunications Equipment Industry. Econometrica, 64, pp. 1263-1295.
Petrin, A., Poi, B. P., & Levinsohn, J. (2004). Production function estimation in Stata using inputs to control for unobservables. The Stata Journal4(2), pp. 113-123.
Robinson, P. M. (1988). Root-N-consistent semiparametric regression. Econometrica: Journal of the Econometric Society, pp. 931-954.
Wooldridge, J. M. (1996). Estimating systems of equations with different instruments for different equations. Journal of Econometrics74(2), pp. 387-405.
Wooldridge, J. M. (2009). On estimating firm-level production functions using proxy variables to control for unobservables. Economics letters104(3), pp. 112-114.