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Firm‐Level Productivity Spillovers in China's Chemical Industry: A Spatial Hausman‐Taylor Approach

Author

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  • Badi H. Baltagi
  • Peter H. Egger
  • Michaela Kesina

Abstract

This paper assesses the role of intra-sectoral spillovers in total factor productivity across Chinese producers in the chemical industry. We use a rich panel data-set of 12,552 firms observed over the period 2004-2006 and model output by the firm as a function of skilled and unskilled labor, capital, materials, and total factor productivity, which is broadly defined. The latter is a composite of observable factors such as export market participation, foreign as well as public ownership, the extent of accumulated intangible assets, and unobservable total factor productivity. Despite the richness of our data-set, it suffers from the lack of time variation in the number of skilled workers as well as in the variable indicating public ownership. We introduce spatial spillovers in total factor productivity through contextual effects of observable variables as well as spatial dependence of the disturbances. We extend the Hausman and Taylor (1981) estimator to account for spatial correlation in the error term. This approach permits estimating the effect of time-invariant variables which are wiped out by the fixed effects estimator. While the original Hausman and Taylor (1981) estimator assumes homoskedastic error components, we provide spatial variants that allow for both homoskedasticity and heteroskedasticity. Monte Carlo results show, that our estimation procedure performs well in small samples. We find evidence of positive spillovers across chemical manufacturers and a large and significant detrimental effect of public ownership on total factor productivity.
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Suggested Citation

  • Badi H. Baltagi & Peter H. Egger & Michaela Kesina, 2016. "Firm‐Level Productivity Spillovers in China's Chemical Industry: A Spatial Hausman‐Taylor Approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(1), pages 214-248, January.
  • Handle: RePEc:wly:japmet:v:31:y:2016:i:1:p:214-248
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    Cited by:

    1. Anna M. Ferragina & Giulia Nunziante, 2018. "Are Italian firms performances influenced by innovation of domestic and foreign firms nearby in space and sectors?," Economia e Politica Industriale: Journal of Industrial and Business Economics, Springer;Associazione Amici di Economia e Politica Industriale, vol. 45(3), pages 335-360, September.
    2. Xu, Yuhong & Yang, Zhenlin, 2020. "Specification Tests for Temporal Heterogeneity in Spatial Panel Data Models with Fixed Effects," Regional Science and Urban Economics, Elsevier, vol. 81(C).
    3. Li, Hao & Guo, Huanxiu, 2021. "Spatial spillovers of pollution via high-speed rail network in China," Transport Policy, Elsevier, vol. 111(C), pages 138-152.
    4. Shahnazi, Rouhollah, 2021. "Do information and communications technology spillovers affect labor productivity?," Structural Change and Economic Dynamics, Elsevier, vol. 59(C), pages 342-359.
    5. Edoardo Baldoni & Roberto Esposti, 2021. "Agricultural Productivity in Space: an Econometric Assessment Based on Farm‐Level Data," American Journal of Agricultural Economics, John Wiley & Sons, vol. 103(4), pages 1525-1544, August.
    6. Paola Cardamone, 2018. "Firm innovation and spillovers in Italy: Does geographical proximity matter?," Letters in Spatial and Resource Sciences, Springer, vol. 11(1), pages 1-16, March.
    7. Vujanović Nina, 2021. "Technological Trends in the Manufacturing and Service Sectors. The Case of Montenegro," South East European Journal of Economics and Business, Sciendo, vol. 16(1), pages 120-133, June.
    8. Peter H. Egger & Yulong Wang, 2025. "Estimating Export-productivity Cutoff Contours with Profit Data: A Novel Threshold Estimation Approach," Papers 2502.03406, arXiv.org, revised Jan 2026.
    9. Kutlu, Levent & Tran, Kien C. & Tsionas, Mike G., 2020. "A spatial stochastic frontier model with endogenous frontier and environmental variables," European Journal of Operational Research, Elsevier, vol. 286(1), pages 389-399.
    10. Li, Cunfang & Li, Danping & Dong, Mei, 2019. "The spillage effect of the transfer behavior of coal resource-exhausted enterprises and science and technology projects," Resources Policy, Elsevier, vol. 62(C), pages 385-396.
    11. Qi Li & Vasilis Sarafidis & Joakim Westerlund, 2021. "Essays in honor of Professor Badi H Baltagi," Empirical Economics, Springer, vol. 60(1), pages 1-11, January.
    12. Anastasia Sherubneva, 2025. "Impact of COVID-19 on outputs of small and medium enterprises in Russia: A spatial view," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 79, pages 121-143.
    13. Baltagi, Badi H. & Egger, Peter H. & Kesina, Michaela, 2017. "Determinants of firm-level domestic sales and exports with spillovers: Evidence from China," Journal of Econometrics, Elsevier, vol. 199(2), pages 184-201.
    14. Li, Qi & Sarafidis, Vasilis & Westerlund, Joakim, 2020. "Essays in Honor of Professor Badi H Baltagi: Editorial," MPRA Paper 104751, University Library of Munich, Germany.
    15. Peter Egger & Marko Koethenbuerger, 2016. "Hosting multinationals: Economic and fiscal implications," Aussenwirtschaft, University of St. Gallen, School of Economics and Political Science, Swiss Institute for International Economics and Applied Economics Research, vol. 67(01), pages 45-69, February.
    16. Xingang Wang & Sholeh A. Maani & Alan Rogers, 2021. "Economic Network Effects and Immigrant Earnings," The Economic Record, The Economic Society of Australia, vol. 97(316), pages 78-99, March.
    17. Baltagi, Badi H. & Egger, Peter H. & Kesina, Michaela, 2019. "Contagious exporting and foreign ownership: Evidence from firms in Shanghai using a Bayesian spatial bivariate probit model," Regional Science and Urban Economics, Elsevier, vol. 76(C), pages 125-146.
    18. Zhaoying Lu, 2021. "Human capital spillovers from Special Economic Zones: evidence from Yangtze Delta in China," Discussion Papers in Economics and Business 21-02, Osaka University, Graduate School of Economics.
    19. Badi H. Baltagi & Peter H. Egger & Michaela Kesina, 2018. "Generalized spatial autocorrelation in a panel-probit model with an application to exporting in China," Empirical Economics, Springer, vol. 55(1), pages 193-211, August.
    20. Farha Fatema & Zhaohua Li & Mohammad Monirul Islam, 2017. "Trade Liberalization and Gender Inequality in Emerging Economies - from the Perspective of Sustainable Development Goals," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 7(11), pages 1075-1092, November.
    21. Glass, Anthony J. & Kenjegalieva, Karligash, 2019. "A spatial productivity index in the presence of efficiency spillovers: Evidence for U.S. banks, 1992–2015," European Journal of Operational Research, Elsevier, vol. 273(3), pages 1165-1179.
    22. 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.

    More about this item

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
    • L65 - Industrial Organization - - Industry Studies: Manufacturing - - - Chemicals; Rubber; Drugs; Biotechnology; Plastics

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