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Technology Transfer and Early Industrial Development: Evidence from the Sino-Soviet Alliance

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  • Michela Giorcelli
  • Bo Li

Abstract

This paper studies the causal effect of technology and knowledge transfers on early industrial development. Between 1950 and 1957, the Soviet Union supported the “156 Projects” in China for building technologically advanced industrial facilities. We exploit idiosyncratic delays in project completion and the unexpected end of the Sino-Soviet Alliance, and show that receiving both Soviet technology and know-how had large, persistent effects on plant performance, while the effects of receiving only Soviet capital goods were short-lived. The intervention generated horizontal and vertical spillovers, and production reallocation from state-owned to privately owned companies since the late 1990s.

Suggested Citation

  • Michela Giorcelli & Bo Li, 2022. "Technology Transfer and Early Industrial Development: Evidence from the Sino-Soviet Alliance," CESifo Working Paper Series 9552, CESifo.
  • Handle: RePEc:ces:ceswps:_9552
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    References listed on IDEAS

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    1. Jan De Loecker & Frederic Warzynski, 2012. "Markups and Firm-Level Export Status," American Economic Review, American Economic Association, vol. 102(6), pages 2437-2471, October.
    2. James G. MacKinnon & Matthew D. Webb, 2018. "The wild bootstrap for few (treated) clusters," Econometrics Journal, Royal Economic Society, vol. 21(2), pages 114-135, June.
    3. Stephen Bond & Måns Söderbom, 2005. "Adjustment Costs and the Identification of Cobb Douglas Production Functions," Economics Papers 2005-W04, Economics Group, Nuffield College, University of Oxford.
    4. A. Colin Cameron & Jonah B. Gelbach & Douglas L. Miller, 2008. "Bootstrap-Based Improvements for Inference with Clustered Errors," The Review of Economics and Statistics, MIT Press, vol. 90(3), pages 414-427, August.
    5. Nicholas Bloom & Benn Eifert & Aprajit Mahajan & David McKenzie & John Roberts, 2013. "Does Management Matter? Evidence from India," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 128(1), pages 1-51.
    6. Alberto Abadie & Susan Athey & Guido W Imbens & Jeffrey M Wooldridge, 2023. "When Should You Adjust Standard Errors for Clustering?," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 138(1), pages 1-35.
    7. Klette, Tor Jakob & Griliches, Zvi, 1996. "The Inconsistency of Common Scale Estimators When Output Prices Are Unobserved and Endogenous," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(4), pages 343-361, July-Aug..
    8. Richard Blundell & Stephen Bond, 2000. "GMM Estimation with persistent panel data: an application to production functions," Econometric Reviews, Taylor & Francis Journals, vol. 19(3), pages 321-340.
    9. Amit Gandhi & Salvador Navarro & David A. Rivers, 2020. "On the Identification of Gross Output Production Functions," Journal of Political Economy, University of Chicago Press, vol. 128(8), pages 2973-3016.
    10. Yair Mundlak, 1961. "Empirical Production Function Free of Management Bias," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 43(1), pages 44-56.
    11. Ackerberg, Daniel & Caves, Kevin & Frazer, Garth, 2006. "Structural identification of production functions," MPRA Paper 38349, University Library of Munich, Germany.
    12. Olley, G Steven & Pakes, Ariel, 1996. "The Dynamics of Productivity in the Telecommunications Equipment Industry," Econometrica, Econometric Society, vol. 64(6), pages 1263-1297, November.
    13. Andrews, Donald W.K. & Marmer, Vadim, 2008. "Exactly distribution-free inference in instrumental variables regression with possibly weak instruments," Journal of Econometrics, Elsevier, vol. 142(1), pages 183-200, January.
    14. 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.
    15. Wooldridge, Jeffrey M., 2009. "On estimating firm-level production functions using proxy variables to control for unobservables," Economics Letters, Elsevier, vol. 104(3), pages 112-114, September.
    16. Greevy, Robert & Silber, Jeffrey H. & Cnaan, Avital & Rosenbaum, Paul R., 2004. "Randomization Inference With Imperfect Compliance in the ACE-Inhibitor After Anthracycline Randomized Trial," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 7-15, January.
    17. Stephen Bond & Måns Söderbom, 2005. "Adjustment Costs and the Identification of Cobb Douglas Production Functions," Economics Series Working Papers 2005-W04, University of Oxford, Department of Economics.
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    Cited by:

    1. Gaspar, Raymond, 2022. "Harnessing Foreign Technology to Improve Firm Performance: Evidence from Philippine Manufacturing Enterprises," ADBI Working Papers 1321, Asian Development Bank Institute.

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    More about this item

    Keywords

    industrialization; technology transfer; knowledge diffusion; China;
    All these keywords.

    JEL classification:

    • L20 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - General
    • M20 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Economics - - - General
    • N34 - Economic History - - Labor and Consumers, Demography, Education, Health, Welfare, Income, Wealth, Religion, and Philanthropy - - - Europe: 1913-
    • N64 - Economic History - - Manufacturing and Construction - - - Europe: 1913-
    • O32 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Management of Technological Innovation and R&D
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

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