IDEAS home Printed from https://ideas.repec.org/a/spr/empeco/v60y2021i6d10.1007_s00181-020-01922-3.html
   My bibliography  Save this article

Estimation of firm productivity in the presence of spillovers and common shocks

Author

Listed:
  • Shunan Zhao

    (Oakland University)

  • Man Jin

    (Oakland University)

  • Subal C. Kumbhakar

    (State University of New York
    University of Stavanger, Business School)

Abstract

Productivity is largely estimated ignoring the potential impact of spillovers and common shocks in the literature, and therefore, the estimates may be subject to the omitted variable bias and internal inconsistency. In this paper, we estimate a nonparametric production function, in which technology spillovers and common shocks have persistent effects on productivity and are controlled for through spatial networks and a factor structure in the productivity evolution process. We synthesize the proxy variable method to structurally identifying the production functions using the semiparametric common correlated effect estimator. The proposed model is then applied to the Chinese computer and peripheral equipment firms. We find that the annual productivity growth rate in this high-technology sector is about 15%. While firms are cross-sectionally dependent via both spatial and non-spatial connections, the productivity growth is largely explained by firms’ own effort, and mildly explained by the neighbors’ activities. Productivity is found to be higher in the areas of agglomeration, and the common shock effects on productivity are not necessarily correlated with the spatial variables.

Suggested Citation

  • Shunan Zhao & Man Jin & Subal C. Kumbhakar, 2021. "Estimation of firm productivity in the presence of spillovers and common shocks," Empirical Economics, Springer, vol. 60(6), pages 3135-3170, June.
  • Handle: RePEc:spr:empeco:v:60:y:2021:i:6:d:10.1007_s00181-020-01922-3
    DOI: 10.1007/s00181-020-01922-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00181-020-01922-3
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s00181-020-01922-3?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. Kelejian, Harry H & Prucha, Ingmar R, 1999. "A Generalized Moments Estimator for the Autoregressive Parameter in a Spatial Model," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 40(2), pages 509-533, May.
    2. Ciccone, Antonio & Hall, Robert E, 1996. "Productivity and the Density of Economic Activity," American Economic Review, American Economic Association, vol. 86(1), pages 54-70, March.
    3. Markus Eberhardt & Christian Helmers & Hubert Strauss, 2013. "Do Spillovers Matter When Estimating Private Returns to R&D?," The Review of Economics and Statistics, MIT Press, vol. 95(2), pages 436-448, May.
    4. Jin, Man & Zhao, Shunan & Kumbhakar, Subal C., 2019. "Financial constraints and firm productivity: Evidence from Chinese manufacturing," European Journal of Operational Research, Elsevier, vol. 275(3), pages 1139-1156.
    5. Malikov, Emir & Sun, Yiguo, 2017. "Semiparametric estimation and testing of smooth coefficient spatial autoregressive models," Journal of Econometrics, Elsevier, vol. 199(1), pages 12-34.
    6. Vadym Volosovych & Carolina Villegas Sanchez & Bent Sorensen & Sebnem Kalemli-Ozcan, 2017. "Foreign Investment and Domestic Productivity: Identifying Knowledge Spillovers and Competition Effects," 2017 Meeting Papers 1194, Society for Economic Dynamics.
    7. Musolesi, Antonio, 2007. "Basic stocks of knowledge and productivity: Further evidence from the hierarchical Bayes estimator," Economics Letters, Elsevier, vol. 95(1), pages 54-59, April.
    8. Gonçalves, Sílvia & Perron, Benoit, 2020. "Bootstrapping factor models with cross sectional dependence," Journal of Econometrics, Elsevier, vol. 218(2), pages 476-495.
    9. Shunan Zhao & Ruiqi Liu & Zuofeng Shang, 2021. "Statistical Inference on Panel Data Models: A Kernel Ridge Regression Method," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(1), pages 325-337, January.
    10. Benjamin Moll, 2014. "Productivity Losses from Financial Frictions: Can Self-Financing Undo Capital Misallocation?," American Economic Review, American Economic Association, vol. 104(10), pages 3186-3221, October.
    11. Emir Malikov & Shunan Zhao & Subal C. Kumbhakar, 2020. "Estimation of firm‐level productivity in the presence of exports: Evidence from China's manufacturing," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(4), pages 457-480, June.
    12. M. Hashem Pesaran, 2021. "General diagnostic tests for cross-sectional dependence in panels," Empirical Economics, Springer, vol. 60(1), pages 13-50, January.
    13. Hou, Zhezhi & Jin, Man & Kumbhakar, Subal C., 2020. "Productivity spillovers and human capital: A semiparametric varying coefficient approach," European Journal of Operational Research, Elsevier, vol. 287(1), pages 317-330.
    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. Vidoli, Francesco & Canello, Jacopo, 2016. "Controlling for spatial heterogeneity in nonparametric efficiency models: An empirical proposal," European Journal of Operational Research, Elsevier, vol. 249(2), pages 771-783.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Kassoum Ayouba, 2023. "Spatial dependence in production frontier models," Journal of Productivity Analysis, Springer, vol. 60(1), pages 21-36, August.
    2. Minjie Huang & Shunan Zhao & Andreas Pape, 2023. "Estimating Case‐based Individual and Social Learning in Corporate Tax Avoidance," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(2), pages 403-434, April.

    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. Hou, Zhezhi & Jin, Man & Kumbhakar, Subal C., 2020. "Productivity spillovers and human capital: A semiparametric varying coefficient approach," European Journal of Operational Research, Elsevier, vol. 287(1), pages 317-330.
    2. Tran, Kien C. & Tsionas, Mike G. & Prokhorov, Artem B., 2023. "Semiparametric estimation of spatial autoregressive smooth-coefficient panel stochastic frontier models," European Journal of Operational Research, Elsevier, vol. 304(3), pages 1189-1199.
    3. Jingfang Zhang & Emir Malikov, 2023. "Detecting Learning by Exporting and from Exporters," Journal of Productivity Analysis, Springer, vol. 60(1), pages 1-19, August.
    4. Li, Mingyang & Jin, Man & Kumbhakar, Subal C., 2022. "Do subsidies increase firm productivity? Evidence from Chinese manufacturing enterprises," European Journal of Operational Research, Elsevier, vol. 303(1), pages 388-400.
    5. Hou, Zhezhi & Zhao, Shunan & Kumbhakar, Subal C., 2023. "The GMM estimation of semiparametric spatial stochastic frontier models," European Journal of Operational Research, Elsevier, vol. 305(3), pages 1450-1464.
    6. Emir Malikov & Jingfang Zhang & Shunan Zhao & Subal C. Kumbhakar, 2023. "Accounting for Cross-Location Technological Heterogeneity in the Measurement of Operations Efficiency and Productivity," Papers 2302.13430, arXiv.org.
    7. Cem Ertur & Antonio Musolesi, 2017. "Weak and Strong Cross‐Sectional Dependence: A Panel Data Analysis of International Technology Diffusion," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(3), pages 477-503, April.
    8. Lin, Mi & Kwan, Yum K., 2016. "FDI technology spillovers, geography, and spatial diffusion," International Review of Economics & Finance, Elsevier, vol. 43(C), pages 257-274.
    9. Gareth Anderson & Mr. Mehdi Raissi, 2018. "Corporate Indebtedness and Low Productivity Growth of Italian Firms," IMF Working Papers 2018/033, International Monetary Fund.
    10. J. Paul Elhorst & Marco Gross & Eugen Tereanu, 2021. "Cross‐Sectional Dependence And Spillovers In Space And Time: Where Spatial Econometrics And Global Var Models Meet," Journal of Economic Surveys, Wiley Blackwell, vol. 35(1), pages 192-226, February.
    11. Shunan Zhao & Bing Qian & Subal C. Kumbhakar, 2020. "Estimation of productivity and markups with price dispersion: Evidence from Chinese manufacturing during economic transition," Southern Economic Journal, John Wiley & Sons, vol. 87(2), pages 666-699, October.
    12. Orea, Luis & Álvarez, Inmaculada C., 2019. "Spatial Production Economics," Efficiency Series Papers 2019/06, University of Oviedo, Department of Economics, Oviedo Efficiency Group (OEG).
    13. Man Jin & Huiting Tian & Subal C. Kumbhakar, 2020. "How to survive and compete: the impact of information asymmetry on productivity," Journal of Productivity Analysis, Springer, vol. 53(1), pages 107-123, February.
    14. Cern Ertur & Antonio Musolesi, 2012. "Spatial autoregressive spillovers vs unobserved common factors models. A panel data analysis of international technology diffusion," INRA UMR CESAER Working Papers 2012/9, INRA UMR CESAER, Centre d'’Economie et Sociologie appliquées à l'’Agriculture et aux Espaces Ruraux.
    15. Huang, Minjie & Zhao, Shunan & Kumbhakar, Subal C., 2022. "Decomposition of Output, Productivity and Market Structure Changes," European Journal of Operational Research, Elsevier, vol. 303(1), pages 422-437.
    16. Esteban Lafuente & Zoltan J. Acs & Mark Sanders & László Szerb, 2020. "The global technology frontier: productivity growth and the relevance of Kirznerian and Schumpeterian entrepreneurship," Small Business Economics, Springer, vol. 55(1), pages 153-178, June.
    17. Philippe Martin & Thierry Mayer & Florian Mayneris, 2008. "Spatial Concentration and Firm-Level Productivity in France," Sciences Po publications 6858, Sciences Po.
    18. Mohamed Amara & Khaled Thabet, 2019. "Firm and regional factors of productivity: a multilevel analysis of Tunisian manufacturing," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 63(1), pages 25-51, August.
    19. De loecker, Jan & Asker, John & Collard-Wexler, Allan, 2011. "Productivity volatility and the misallocation of resources in developing economies," CEPR Discussion Papers 8469, C.E.P.R. Discussion Papers.
    20. Genthner, Robert & Kis-Katos, Krisztina, 2022. "Foreign investment regulation and firm productivity: Granular evidence from Indonesia," Journal of Comparative Economics, Elsevier, vol. 50(3), pages 668-687.

    More about this item

    Keywords

    Productivity; Technology spillover; Cross-sectional dependence; Agglomeration; Chinese computer and peripheral equipment firms;
    All these keywords.

    JEL classification:

    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
    • R3 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location
    • O14 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Industrialization; Manufacturing and Service Industries; Choice of Technology
    • O53 - Economic Development, Innovation, Technological Change, and Growth - - Economywide Country Studies - - - Asia including Middle East
    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General

    Statistics

    Access and download statistics

    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:spr:empeco:v:60:y:2021:i:6:d:10.1007_s00181-020-01922-3. 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.