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

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  • Zhang, Hongsong
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    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 fi rms' ’production decisions with biased technological change. This model allows me to identify and estimate the firm-time-specifi c 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 fi rm-level evidence of biased technological change over time and biased technological dispersion across rms. 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 fi rms have a higher capital-labor efficiency ratio and that biased technological dispersion explains a large part of the dispersion of capital-labor ratio across fi rms.

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    Bibliographic Info

    Paper provided by Agricultural and Applied Economics Association in its series 2013 Annual Meeting, August 4-6, 2013, Washington, D.C. with number 150225.

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    Date of creation: 01 Apr 2013
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    Handle: RePEc:ags:aaea13:150225

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    Keywords: Multidimensional Productivity; Technology Bias; Biased Techno- logical Change; Biased Technological Dispersion; Institutional and Behavioral Economics; Research and Development/Tech Change/Emerging Technologies;

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    1. 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, MIT Press, vol. 113(3), pages 903-947, August.
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    7. 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.
    8. Doms, Mark & Dunne, Timothy & Troske, Kenneth R, 1997. "Workers, Wages, and Technology," The Quarterly Journal of Economics, MIT Press, vol. 112(1), pages 253-90, February.
    9. Bhargava, Alok, 1986. "On the Theory of Testing for Unit Roots in Observed Time Series," Review of Economic Studies, Wiley Blackwell, vol. 53(3), pages 369-84, July.
    10. James Levinsohn & Amil Petrin, 2003. "Estimating Production Functions Using Inputs to Control for Unobservables," Review of Economic Studies, Oxford University Press, vol. 70(2), pages 317-341.
    11. Salvador Navarro & David Rivers & Amit Gandhi, 2012. "On the Identification of Production Functions: How Heterogeneous is Productivity?," 2012 Meeting Papers 105, Society for Economic Dynamics.
    12. Stevenson, Rodney, 1980. "Measuring Technological Bias," American Economic Review, American Economic Association, vol. 70(1), pages 162-73, March.
    13. Robert C. Feenstra & Zhiyuan Li & Miaojie Yu, 2011. "Exports and Credit Constraints Under Incomplete Information: Theory and Evidence from China," NBER Working Papers 16940, National Bureau of Economic Research, Inc.
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