Hostname: page-component-7b7567c8c-kwt9d Total loading time: 0 Render date: 2026-11-13T16:13:32.979Z Has data issue: false hasContentIssue false
  • English
  • Français

BIASED TECHNICAL CHANGE, SCALE, AND FACTOR SUBSTITUTION IN U.S. MANUFACTURING INDUSTRIES

Published online by Cambridge University Press:  15 January 2016

Xi Chen
Xi Chen*
Affiliation:
STATEC
*
Address correspondence to: Xi Chen, STATEC (Institut national de la statistique et des études économiques du Grand-Duché du Luxembourg), B.P. 304, L-2013 Luxembourg; e-mail: xi.chen@statec.etat.lu.
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the 'Save PDF' action button.

Although a realistic characterization of the production function is critical to macroeconomic analysis, estimating the function's characteristics is hampered by both data limitations and methodological difficulties. In this paper, I develop a new empirical strategy for estimating the CES production function with biased technical change. The proposed method extends the control function approach to the CES specification to address endogeneity concerns and is able to retrieve sector-specific and time-varying estimates of technical change. Using data from U.S. manufacturing industries, I find evidence that (i) the production technology exhibits nonincreasing returns to scale, (ii) the elasticity of substitution between capital and labor is below unity, and (iii) technical change is generally labor-augmenting along the balanced growth path.

Keywords

Elasticity of SubstitutionReturns to ScaleBiased Technical ChangeControl Function Approach

Information

Type
Articles
Information
Macroeconomic Dynamics , Volume 21 , Issue 2 , March 2017, pp. 488 - 514
Copyright
Copyright © Cambridge University Press 2016 

Footnotes

This paper is based on Chapter 3 of my Ph.D. thesis at the University of Strasbourg. I would like to thank Bertrand Koebel, François Laisney, the Editor, and two anonymous referees for very helpful comments.

References

REFERENCES

Acemoglu, Daron (2002) Directed technical change. Review of Economic Studies 69, 781–809.Google Scholar
Acemoglu, Daron (2003) Labor- and capital-augmenting technical change. Journal of the European Economic Association 1, 1–37.Google Scholar
Ackerberg, Daniel, Caves, Kevin, and Frazer, Garth (2006) Structural Identification of Production Functions. MPRA paper 38349, University Library of Munich.Google Scholar
Antràs, Pol (2004) Is the U.S. aggregate production function Cobb–Douglas? New estimates of the elasticity of substitution. BE Journal of Macroeconomics 4, 1–36.Google Scholar
Arrow, Kenneth J., Chenery, Hollis B., Minhas, Bagicha S., and Solow, Robert M. (1961) Capital–labor substitution and economic efficiency. Review of Economics and Statistics 43, 225–250.Google Scholar
Bartelsman, Eric J. and Gray, Wayne B. (1996) The NBER Manufacturing Productivity Database. NBER technical working paper 0205, National Bureau of Economic Research.Google Scholar
Basu, Susanto (2008) Returns to scale measurement. In Durlauf, Steven N. and Blume, Lawrence E. (eds.), The New Palgrave Dictionary of Economics. London: Palgrave Macmillan.Google Scholar
Basu, Susanto and Fernald, John G. (1997) Returns to scale in U.S. production: Estimates and implications. Journal of Political Economy 105, 249–283.Google Scholar
Cameron, Colin A. and Trivedi, Pravin K. (2005) Microeconometrics: Methods and Applications. Cambridge, UK: Cambridge University Press.Google Scholar
Chirinko, Robert S. (2008) σ: The long and short of it. Journal of Macroeconomics 30, 671–686.Google Scholar
Dupuy, Arnaud (2006) Hicks neutral technical change revisited: CES production function and information of general order. B.E. Journal of Macroeconomics 6, 1–26.Google Scholar
Farmer, Roger E.A. and Guo, Jang-Ting (1994) Real business cycles and the animal spirits hypothesis. Journal of Economic Theory 63, 42–72.Google Scholar
Grossman, Gene M. and Helpman, Elhanan (1991) Innovation and Growth in the Global Economy, Vol. 1. Cambridge, MA: MIT Press.Google Scholar
Heckman, James J. and Robb, Richard (1985) Alternative methods for evaluating the impact of interventions: An overview. Journal of Econometrics 30, 239–267.Google Scholar
Heckman, James J. and Vytlacil, Edward J. (2007) Econometric evaluation of social programs, part I: Causal models, structural models and econometric policy evaluation. In Heckman, James J. and Leamer, Edward E. (eds.), Handbook of Econometrics, Vol. 6, Chap. 70. Amsterdam: Elsevier.Google Scholar
Hicks, John R. (1932) The Theory of Wages. London: Macmillan.Google Scholar
Imbens, Guido W. and Newey, Whitney K. (2009) Identification and estimation of triangular simultaneous equations models without additivity. Econometrica 77, 1481–1512.Google Scholar
Jones, Charles (2005) Growth and ideas. In Aghion, Philippe and Durlauf, Steven N. (eds.), Handbook of Economic Growth (1st ed.), Vol. 1, Part B, Chap. 16, pp. 1063–1111. Amsterdam: Elsevier.Google Scholar
Jorgenson, Dale W. (2001) Information technology and the U.S. economy. American Economic Review 91, 1–32.Google Scholar
Karabarbounis, Loukas and Neiman, Brent (2014) The global decline of the labor share. Quarterly Journal of Economics 129, 61–103.Google Scholar
Kennedy, Charles (1964) Induced bias in innovation and the theory of distribution. Economic Journal 74, 541–547.Google Scholar
Klump, Rainer, McAdam, Peter, and Willman, Alpo (2007) Factor substitution and factor-augmenting technical progress in the United States: A normalized supply-side system approach. Review of Economics and Statistics 89, 183–192.CrossRefGoogle Scholar
Klump, Rainer, McAdam, Peter, and Willman, Alpo (2008) Unwrapping some euro area growth puzzles: Factor substitution, productivity and unemployment. Journal of Macroeconomics 30, 645–666.Google Scholar
Klump, Rainer, McAdam, Peter, and Willman, Alpo (2012) The normalized CES production function: Theory and empirics. Journal of Economic Surveys 26, 769–799.Google Scholar
Kmenta, Jan (1967) On estimation of the CES production function. International Economic Review 8, 180–189.Google Scholar
León-Ledesma, Miguel A., McAdam, Peter, and Willman, Alpo (2010) Identifying the elasticity of substitution with biased technical change. American Economic Review 100, 1330–1357.Google Scholar
León-Ledesma, Miguel A., McAdam, Peter, and Willman, Alpo (2015) Production technology estimates and balanced growth. Oxford Bulletin of Economics and Statistics 77, 40–65.Google Scholar
Levinsohn, James and Petrin, Amil (2003) Estimating production functions using inputs to control for unobservables. Review of Economic Studies 70, 317–341.Google Scholar
Li, Tailong, Pan, Shiyuan, and Zou, Heng-fu (2015) Directed technological change: A knowledge-based model. Macroeconomic Dynamics 19, 116–143.Google Scholar
McAdam, Peter and Willman, Alpo (2013) Medium run redux. Macroeconomic Dynamics 17, 695–727.Google Scholar
Moro, Alessio (2012) Biased technical change, intermediate goods, and total factor productivity. Macroeconomic Dynamics 16, 184–203.Google Scholar
Oberfield, Ezra and Raval, Devesh (2014) Micro Data and Macro Technology. NBER working paper 20452, National Bureau of Economic Research.Google Scholar
Olley, Steven G. and Pakes, Ariel (1996) The dynamics of productivity in the telecommunications equipment industry. Econometrica 64, 1263–1297.Google Scholar
Piketty, Thomas (2014) Capital in the Twenty-First Century. Cambridge, MA: Harvard University Press.Google Scholar
Robinson, Peter M. (1988) Root-n-consistent semiparametric regression. Econometrica 56, 931–954.Google Scholar
Samuelson, Paul A. (1965) A theory of induced innovation along Kennedy–Weizsacker lines. Review of Economics and Statistics 47, 343–356.Google Scholar
Solow, Robert M. (1964) Capital, labor, and income in manufacturing. In National Bureau of Economic Research, The Behavior of Income Shares: Selected Theoretical and Empirical Issues, Studies in Income and Wealth, Vol. 27, pp. 101–142. Princeton, NJ: Princeton University Press.Google Scholar
Theil, Henri (1957) Specification errors and the estimation of economic relationships. Revue de l'Institut International de Statistique 25, 41–51.Google Scholar
Thursby, Jerry G. and Lovell, C.A. Knox (1978) An investigation of the Kmenta approximation to the CES function. International Economic Review 19, 363–377.Google Scholar
Young, Andrew T. (2013) U.S. elasticities of substitution and factor augmentation at the industry level. Macroeconomic Dynamics 17, 861–897.Google Scholar