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Biased Technology and Contribution of Technological Change to Economic Growth: Firm-Level Evidence

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  • Zhang, Hongsong

Abstract

The increasing mean wage-interest ratio and decreasing mean capital-labor ra- tio observed in some Chinese manufacturing industries suggest that technological change is factor-biased. In order to study the nature of technological change and its contribution to economic growth, this paper builds and estimates a structural model of firms' production decisions with biased technological change. This model allows me to identify and estimate the firm-time-specific factor-biased technology using micro data. The basic idea of the estimation is that the choice of inputs contains information about the unobserved productivities; therefore we can invert the inputs demand function to recover the unobserved productivities. I estimate the model from a firm-level data set of four Chinese Manufacturing industries. The empirical results provide firm-level evidence of biased technological change over time and biased technological dispersion across firms. The estimation results show that technological change contributes to the growth of gross output by 1.81%-3.10% annually and value added by 12.67%-21.16%, which is higher than the combined contribution of capital and labor. Capital efficiency grows much faster than la- bor efficiency in China, and the contribution of technological change to economic growth is mainly due to the change of capital efficiency. The results also show that large firms have a higher capital-labor efficiency ratio and that biased technological dispersion explains a large part of the dispersion of capital-labor ratio across firms.

Suggested Citation

  • Zhang, Hongsong, 2013. "Biased Technology and Contribution of Technological Change to Economic Growth: Firm-Level Evidence," 2013 Annual Meeting, August 4-6, 2013, Washington, D.C. 150225, Agricultural and Applied Economics Association.
    • Handle: RePEc:ags:aaea13:150225
    DOI: 10.22004/ag.econ.150225
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    1. Richard Ericson & Ariel Pakes, 1995. "Markov-Perfect Industry Dynamics: A Framework for Empirical Work," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 62(1), pages 53-82.
    2. Antràs Pol, 2004. "Is the U.S. Aggregate Production Function Cobb-Douglas? New Estimates of the Elasticity of Substitution," The B.E. Journal of Macroeconomics, De Gruyter, vol. 4(1), pages 1-36, April.
    3. Qu Feng & William C. Horrace, 2012. "Alternative technical efficiency measures: Skew, bias and scale," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(2), pages 253-268, March.
    4. Mark Doms & Timothy Dunne & Kenneth R. Troske, 1997. "Workers, Wages, and Technology," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 112(1), pages 253-290.
    5. Daron Acemoglu, 2002. "Directed Technical Change," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 69(4), pages 781-809.
    6. Kaddour Hadri, 2000. "Testing for stationarity in heterogeneous panel data," Econometrics Journal, Royal Economic Society, vol. 3(2), pages 148-161.
    7. Amit Gandhi & Salvador Navarro & David Rivers, 2011. "On the Identification of Production Functions: How Heterogeneous is Productivity?," University of Western Ontario, Centre for Human Capital and Productivity (CHCP) Working Papers 20119, University of Western Ontario, Centre for Human Capital and Productivity (CHCP).
    8. James Levinsohn & Amil Petrin, 2003. "Estimating Production Functions Using Inputs to Control for Unobservables," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 70(2), pages 317-341.
    9. Sofronis K. Clerides & Saul Lach & James R. Tybout, 1998. "Is Learning by Exporting Important? Micro-Dynamic Evidence from Colombia, Mexico, and Morocco," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 113(3), pages 903-947.
    10. Miguel A. León-Ledesma & Peter McAdam & Alpo Willman, 2010. "Identifying the Elasticity of Substitution with Biased Technical Change," American Economic Review, American Economic Association, vol. 100(4), pages 1330-1357, September.
    11. Jinghai Zheng & Angang Hu, 2006. "An Empirical Analysis of Provincial Productivity in China (1979-2001)," Journal of Chinese Economic and Business Studies, Taylor & Francis Journals, vol. 4(3), pages 221-239.
    12. Yuhn, Ky-hyang, 1991. "Economic Growth, Technical Change Biases, and the Elasticity of Substitution: A Test of the De La Grandville Hypothesis," The Review of Economics and Statistics, MIT Press, vol. 73(2), pages 340-346, May.
    13. Stevenson, Rodney, 1980. "Measuring Technological Bias," American Economic Review, American Economic Association, vol. 70(1), pages 162-173, March.
    14. Kalt, Joseph P, 1978. "Technological Change and Factor Substitution in the United States: 1929-1967," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 19(3), pages 761-775, October.
    15. Robert C. Feenstra & Zhiyuan Li & Miaojie Yu, 2014. "Exports and Credit Constraints under Incomplete Information: Theory and Evidence from China," The Review of Economics and Statistics, MIT Press, vol. 96(4), pages 729-744, October.
    16. Rainer Klump & Peter McAdam & Alpo Willman, 2007. "Factor Substitution and Factor-Augmenting Technical Progress in the United States: A Normalized Supply-Side System Approach," The Review of Economics and Statistics, MIT Press, vol. 89(1), pages 183-192, February.
    17. Devesh Raval, 2011. "Beyond Cobb-Douglas: Estimation of a CES Production Function with Factor Augmenting Technology," Working Papers 11-05, Center for Economic Studies, U.S. Census Bureau.
    18. Panik, Michael J, 1976. "Factor Learning and Biased Factor-Efficiency Growth in the United States, 1929-1966," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 17(3), pages 733-739, October.
    19. Alok Bhargava, 1986. "On the Theory of Testing for Unit Roots in Observed Time Series," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 53(3), pages 369-384.
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    Keywords

    Institutional and Behavioral Economics; Research and Development/Tech Change/Emerging Technologies;

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