IDEAS home Printed from https://ideas.repec.org/a/kap/jproda/v60y2023i2d10.1007_s11123-023-00683-2.html
   My bibliography  Save this article

Firm heterogeneity, biased technological change, and total factor productivity: Evidence from China

Author

Listed:
  • Qizheng Gao

    (Wuhan University)

  • Jianqing Zhang

    (Wuhan University)

  • Guo Chen

    (Hubei University)

Abstract

Biased technological change is a crucial factor contributing to the growth of total factor productivity (TFP). In this paper, we jointly estimate the demand and production function based on the factor-augmenting CES function, calculate TFP and decomposes it into its biased and neutral components. Using data from Chinese industrial firm, we have three main findings. The cross-sector averages for price elasticity and elasticity of substitution are −8.92 and 0.37, respectively. Capital-augmenting and labor-augmenting technologies grow at a faster rate than the material-augmenting technology. The annual growth in aggregate TFP is 2.19% from 1998 to 2007, and biased technological change accounts for almost 30% of it.

Suggested Citation

  • Qizheng Gao & Jianqing Zhang & Guo Chen, 2023. "Firm heterogeneity, biased technological change, and total factor productivity: Evidence from China," Journal of Productivity Analysis, Springer, vol. 60(2), pages 147-177, October.
    • Handle: RePEc:kap:jproda:v:60:y:2023:i:2:d:10.1007_s11123-023-00683-2
    DOI: 10.1007/s11123-023-00683-2
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11123-023-00683-2
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s11123-023-00683-2?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ulrich Doraszelski & Jordi Jaumandreu, 2018. "Measuring the Bias of Technological Change," Journal of Political Economy, University of Chicago Press, vol. 126(3), pages 1027-1084.
    2. Charles I. Jones, 2002. "Sources of U.S. Economic Growth in a World of Ideas," American Economic Review, American Economic Association, vol. 92(1), pages 220-239, March.
    3. Ezra Oberfield & Devesh Raval, 2021. "Micro Data and Macro Technology," Econometrica, Econometric Society, vol. 89(2), pages 703-732, March.
    4. Daron Acemoglu & Fabrizio Zilibotti, 2001. "Productivity Differences," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 116(2), pages 563-606.
    5. Lucia Foster & John Haltiwanger & Chad Syverson, 2008. "Reallocation, Firm Turnover, and Efficiency: Selection on Productivity or Profitability?," American Economic Review, American Economic Association, vol. 98(1), pages 394-425, March.
    6. Moro, Alessio, 2012. "Biased Technical Change, Intermediate Goods, And Total Factor Productivity," Macroeconomic Dynamics, Cambridge University Press, vol. 16(2), pages 184-203, April.
    7. Marc J. Melitz & Sašo Polanec, 2015. "Dynamic Olley-Pakes productivity decomposition with entry and exit," RAND Journal of Economics, RAND Corporation, vol. 46(2), pages 362-375, June.
    8. Andrew Young, 2004. "Labor's Share Fluctuations, Biased Technical Change, and the Business Cycle," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 7(4), pages 916-931, October.
    9. Lucia Foster & John Haltiwanger & Chad Syverson, 2016. "The Slow Growth of New Plants: Learning about Demand?," Economica, London School of Economics and Political Science, vol. 83(329), pages 91-129, January.
    10. Zhang, Hongsong, 2019. "Non-neutral technology, firm heterogeneity, and labor demand," Journal of Development Economics, Elsevier, vol. 140(C), pages 145-168.
    11. Fernando Rios-Avila, 2015. "Feasible fitting of linear models with N fixed effects," Stata Journal, StataCorp LP, vol. 15(3), pages 881-898, September.
    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. Chang-Tai Hsieh & Peter J. Klenow, 2009. "Misallocation and Manufacturing TFP in China and India," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 124(4), pages 1403-1448.
    14. Weber, William L. & Domazlicky, Bruce R., 1999. "Total factor productivity growth in manufacturing: a regional approach using linear programming," Regional Science and Urban Economics, Elsevier, vol. 29(1), pages 105-122, January.
    15. Daniel A. Ackerberg & Kevin Caves & Garth Frazer, 2015. "Identification Properties of Recent Production Function Estimators," Econometrica, Econometric Society, vol. 83, pages 2411-2451, November.
    16. Brandt, Loren & Van Biesebroeck, Johannes & Zhang, Yifan, 2012. "Creative accounting or creative destruction? Firm-level productivity growth in Chinese manufacturing," Journal of Development Economics, Elsevier, vol. 97(2), pages 339-351.
    17. Devesh R. Raval, 2019. "The micro elasticity of substitution and non‐neutral technology," RAND Journal of Economics, RAND Corporation, vol. 50(1), pages 147-167, March.
    18. 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.
    19. Charles I. Jones, 2005. "The Shape of Production Functions and the Direction of Technical Change," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 120(2), pages 517-549.
    20. Antonelli, Cristiano, 2016. "Technological congruence and the economic complexity of technological change," Structural Change and Economic Dynamics, Elsevier, vol. 38(C), pages 15-24.
    21. Daron Acemoglu, 2015. "Localised and Biased Technologies: Atkinson and Stiglitz's New View, Induced Innovations, and Directed Technological Change," Economic Journal, Royal Economic Society, vol. 0(583), pages 443-463, March.
    22. Po-Chi Chen & Ming-Miin Yu, 2014. "Total factor productivity growth and directions of technical change bias: evidence from 99 OECD and non-OECD countries," Annals of Operations Research, Springer, vol. 214(1), pages 143-165, March.
    23. Daron Acemoglu, 2007. "Equilibrium Bias of Technology," Econometrica, Econometric Society, vol. 75(5), pages 1371-1409, September.
    24. Mallick, Debdulal, 2012. "The role of the elasticity of substitution in economic growth: A cross-country investigation," Labour Economics, Elsevier, vol. 19(5), pages 682-694.
    25. W. Walker Hanlon, 2015. "Necessity Is the Mother of Invention: Input Supplies and Directed Technical Change," Econometrica, Econometric Society, vol. 83, pages 67-100, January.
    26. Johannes van Biesebroeck, 2003. "Productivity Dynamics with Technology Choice: An Application to Automobile Assembly," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 70(1), pages 167-198.
    27. David H. Autor & Lawrence F. Katz & Alan B. Krueger, 1998. "Computing Inequality: Have Computers Changed the Labor Market?," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 113(4), pages 1169-1213.
    28. Christophe Feder, 2018. "A measure of total factor productivity with biased technological change," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 27(3), pages 243-253, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. James Harrigan & Ariell Reshef & Farid Toubal, 2018. "Techies, Trade, and Skill-Biased Productivity," NBER Working Papers 25295, National Bureau of Economic Research, Inc.
    2. Zhang, Hongsong, 2019. "Non-neutral technology, firm heterogeneity, and labor demand," Journal of Development Economics, Elsevier, vol. 140(C), pages 145-168.
    3. Michele Battisti & Valentino Dardanoni & Stefano Demichelis, 2024. "Inter-firm Heterogeneity in Production," Papers 2411.15980, arXiv.org.
    4. Pham, Hoang, 2023. "Trade reform, oligopsony, and labor market distortion: Theory and evidence," Journal of International Economics, Elsevier, vol. 144(C).
    5. Berkowitz, Daniel & Nishioka, Shuichiro, 2024. "The growth of firms, markets and rents: Evidence from China," Journal of Comparative Economics, Elsevier, vol. 52(2), pages 383-399.
    6. Laurens Cherchye & Thomas Demuynck & Bram De Rock & Marijn Verschelde, 2018. "Nonparametric Production Analysis with Unobserved Heterogeneity in Productivity," Working Papers ECARES 2018-25, ULB -- Universite Libre de Bruxelles.
    7. Dewitte, Ruben & Dumont, Michel & Merlevede, Bruno & Rayp, Glenn & Verschelde, Marijn, 2020. "Firm-Heterogeneous Biased Technological Change: A nonparametric approach under endogeneity," European Journal of Operational Research, Elsevier, vol. 283(3), pages 1172-1182.
    8. Jamil, Nida & Chaudhry, Theresa Thompson & Chaudhry, Azam, 2022. "Trading textiles along the new silk route: The impact on Pakistani firms of gaining market access to China," Journal of Development Economics, Elsevier, vol. 158(C).
    9. Yoonseok Lee & Mary E. Lovely & Hoang Pham, 2023. "Dynamic and non‐neutral productivity effects of foreign ownership: A nonparametric approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(1), pages 24-48, January.
    10. Paul Schrimpf & Michio Suzuki & Hiroyuki Kasahara, 2015. "Identification and Estimation of Production Function with Unobserved Heterogeneity," 2015 Meeting Papers 924, Society for Economic Dynamics.
    11. Jaumandreu, Jordi & Doraszelski, Ulrich, 2019. "Using Cost Minimization to Estimate Markups," CEPR Discussion Papers 14114, C.E.P.R. Discussion Papers.
    12. Michael Rubens, 2023. "Market Structure, Oligopsony Power, and Productivity," American Economic Review, American Economic Association, vol. 113(9), pages 2382-2410, September.
    13. Emir Malikov & Shunan Zhao & Jingfang Zhang, 2024. "A System Approach to Structural Identification of Production Functions with Multi-Dimensional Productivity," Advances in Econometrics, in: Essays in Honor of Subal Kumbhakar, volume 46, pages 211-263, Emerald Group Publishing Limited.
    14. Guangjun Shen & Jingxian Zou, 2023. "Total Factor Productivity in China's Manufacturing Sector in the Aftermath of the Global Financial Crisis," China & World Economy, Institute of World Economics and Politics, Chinese Academy of Social Sciences, vol. 31(2), pages 1-25, March.
    15. Laurens Cherchye & Thomas Demuynck & Bram De Rock & Cédric Duprez & Glenn Magerman & Marijn Verschelde, 2021. "Structural Identification of Productivity under Biased Technological Change∗," Working Papers ECARES 2021-28, ULB -- Universite Libre de Bruxelles.
    16. Eisenberg, Tom, 2024. "Missing data and the effects of market deregulation: Evidence from Chinese coal power," International Journal of Industrial Organization, Elsevier, vol. 93(C).
    17. Geoffrey Barrows & Hélène Ollivier & Ariell Reshef, 2023. "Production Function Estimation with Multi-Destination Firms," CESifo Working Paper Series 10716, CESifo.
    18. Andrés César & Guillermo Falcone, 2020. "Heterogeneous Effects of Chinese Import Competition on Chilean Manufacturing Plants," Economía Journal, The Latin American and Caribbean Economic Association - LACEA, vol. 0(Spring 20), pages 1-60, December.
    19. David Van Dijcke, 2022. "On the Non-Identification of Revenue Production Functions," Papers 2212.04620, arXiv.org, revised May 2024.
    20. Ezra Oberfield & Devesh Raval, 2021. "Micro Data and Macro Technology," Econometrica, Econometric Society, vol. 89(2), pages 703-732, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:kap:jproda:v:60:y:2023:i:2:d:10.1007_s11123-023-00683-2. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.