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Estimating Technological Change Using a Stochastic Frontier Production Function Framework: Evidence from U.S. Firm-Level Data

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  • Rajeev Dhawan
  • Geoffrey Gerdes

Abstract

This paper presents a methodology for estimating an index of technological change using firm-level data in a stochastic frontier production function model that takes into account time-varying technical inefficiency. In contrast to the Solow divisia index approach, econometric estimation of the index with panel data allows the researcher to separate technical progress from the stochastic measurement error. Applying the econometric methodology to a panel of 908 publicly-traded U.S. firms from the COMPUSTAT database, we find evidence of a significant downturn in general technological change for the period, 1970– 1989, whereas the divisia index methodology applied to the same data shows stagnation. When the sample is divided into Manufacturing, Services, and Miscellaneous categories we find that estimates of technological change for the three groups display markedly different stochastic behavior and that the Services group is the source of the downturn. Copyright Kluwer Academic Publishers 1997

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  • Rajeev Dhawan & Geoffrey Gerdes, 1997. "Estimating Technological Change Using a Stochastic Frontier Production Function Framework: Evidence from U.S. Firm-Level Data," Journal of Productivity Analysis, Springer, vol. 8(4), pages 431-446, November.
    • Handle: RePEc:kap:jproda:v:8:y:1997:i:4:p:431-446
    DOI: 10.1023/A:1007792110665
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    Cited by:

    1. Katsuya Takii, 2007. "The Persistence of Differences in Productivity, Wages, Skill Mixes and Profits Between Firms," Discussion Papers in Economics and Business 07-10, Osaka University, Graduate School of Economics.
    2. Lei Li, 2008. "Multinationality and technical efficiency: A neglected perspective," Management International Review, Springer, vol. 48(1), pages 39-64, February.
    3. Katsuya Takii, 2008. "The Persistence of Differences in Productivity, Wages, Skill Mixes and Profits Between Firms in a Rapidly Changing Environment," OSIPP Discussion Paper 08E003, Osaka School of International Public Policy, Osaka University.
    4. Dhawan, Rajeev & Jochumzen, Peter, 1999. "Stochastic Frontier Production Function With Errors-In-Variables," Working Papers 1999:007, Lund University, Department of Economics.
    5. Derek Kruse & Kristie Briggs & Eric J. Neuman, 2022. "Mitigating endogeneity in corporate social responsibility research: An investigation using a neoclassical production function," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 43(1), pages 3-15, January.

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