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Dynamic Factor Demand Models and Productivity Analysis

In: New Developments in Productivity Analysis

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  • M. Ishaq Nadiri
  • Ingmar Prucha

Abstract

In this paper we discuss recent advances in modeling and estimating dynamic factor demand models, and review the use of such models in analyzing the production structure, the determinants of variable and quasi-fixed factors, and productivity growth. The paper also discusses the traditional approach to productivity analysis based on the Divisia index number methodology. Both approaches may be seen a complementary. The conventional index number approach will measure the rate of technical change correctly if certain assumptions about the underlying technology of the firm and output and input markets hold. Furthermore, the conventional index number approach is appealing in that it can be easily implemented. However, if the underlying assumptions do not hold, then the conventional index number approach will, in general, yield biased estimates of technical change. The econometric approach based on general dynamic factor demand models allows for a careful testing of various features of a postulated model. Furthermore it not only provides for estimates of technical change, but can also yield a rich set of critical information on the structure of production, the dynamics of investment in physical and R&D capital, the effects of spillovers, the depreciation rate of capital, the impact of taxes, expectations, etc. The paper also explores in terms of a Monte Carlo study how estimates of important characteristics of the production process can be affected by model misspecification. The results suggest adopting a simple specification for reasons of convenience may result in serious biases.
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  • M. Ishaq Nadiri & Ingmar Prucha, 2001. "Dynamic Factor Demand Models and Productivity Analysis," NBER Chapters, in: New Developments in Productivity Analysis, pages 103-172, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberch:10125
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    21. Jeffrey Bernstein & Theofanis Mamuneas, 2008. "Public infrastructure, input efficiency and productivity growth in the Canadian food processing industry," Journal of Productivity Analysis, Springer, vol. 29(1), pages 1-13, February.
    22. Dumont, Michel, 2004. "The Impact of International Trade with Newly Industrialised Countries on the Wages and Employment of Low-Skilled and High-Skilled Workers in the European Union," Thesis Commons bmxag, Center for Open Science.
    23. Khayyat, Nabaz T. & Lee, Jongsu & Lee, Jeong-Dong, 2014. "How ICT Investment Influences Energy Demand in South Korea and Japan?," MPRA Paper 55454, University Library of Munich, Germany.
    24. Wojciech Szewczyk & Anna Sabadash, 2013. "Macroeconomic Modelling of Public Expenditures on Research and Development in Information and Communication Technologies," JRC Research Reports JRC82943, Joint Research Centre.
    25. David Greenstreet, 2007. "Exploiting Sequential Learning to Estimate Establishment-Level Productivity Dynamics and Decision Rules," Economics Series Working Papers 345, University of Oxford, Department of Economics.

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    JEL classification:

    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • D2 - Microeconomics - - Production and Organizations

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