IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/32507.html
   My bibliography  Save this paper

Productivity in China's high technology industry: Regional heterogeneity and R&D

Author

Listed:
  • Zhang, Rui
  • Sun, Kai
  • Delgado, Michael
  • Kumbhakar, Subal

Abstract

This paper analyzes the impact of Research and Development (R&D) on the productivity of China's high technology industry. In order to capture important differences in the effect of R&D on output that arise from geographic and socioeconomic differences across three major regions in China, we use a novel semiparametric approach that allows us to model heterogeneities across provinces and time. Using a unique provincial level panel dataset spanning the period 2000-2007, we find that the impact of R&D on output varies substantially in terms of magnitude and significance across different regions. Results show that the eastern region benefits the most from R&D investments, however it benefits the least from technical progress, while the western region benefits the least from R&D investments, but enjoys the highest benefits from technical progress. The central region benefits from R&D investments more than the western region and benefits from technical progress more than the eastern region. Our results suggest that R&D investments would significantly increase output in both the eastern and central regions, however technical progress in the central region may further compound the effects of R&D on output within the region.

Suggested Citation

  • Zhang, Rui & Sun, Kai & Delgado, Michael & Kumbhakar, Subal, 2011. "Productivity in China's high technology industry: Regional heterogeneity and R&D," MPRA Paper 32507, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:32507
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/32507/1/MPRA_paper_32507.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Rachel Griffith & Stephen Redding & John Van Reenen, 2004. "Mapping the Two Faces of R&D: Productivity Growth in a Panel of OECD Industries," The Review of Economics and Statistics, MIT Press, vol. 86(4), pages 883-895, November.
    2. Asaftei, Gabriel & Parmeter, Christopher F., 2010. "Market power, EU integration and privatization: The case of Romania," Journal of Comparative Economics, Elsevier, vol. 38(3), pages 340-356, September.
    3. Schankerman, Mark & Pakes, Ariel, 1986. "Estimates of the Value of Patent Rights in European Countries during the Post-1950 Period," Economic Journal, Royal Economic Society, vol. 96(384), pages 1052-1076, December.
    4. Jacques Mairesse & Pierre Mohnen, 2005. "The Importance of R&D for Innovation: A Reassessment Using French Survey Data," The Journal of Technology Transfer, Springer, vol. 30(2_2), pages 183-197, January.
    5. Zvi Griliches, 1998. "Productivity, R&D, and Basic Research at the Firm Level in the 1970s," NBER Chapters, in: R&D and Productivity: The Econometric Evidence, pages 82-99, National Bureau of Economic Research, Inc.
    6. Orietta Marsili, 2001. "The Anatomy and Evolution of Industries," Books, Edward Elgar Publishing, number 2272.
    7. Hu, Albert Guangzhou & Jefferson, Gary H., 2004. "Returns to research and development in Chinese industry: Evidence from state-owned enterprises in Beijing," China Economic Review, Elsevier, vol. 15(1), pages 86-107, January.
    8. Hans Loof & Almas Heshmati, 2006. "On the relationship between innovation and performance: A sensitivity analysis," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 15(4-5), pages 317-344.
    9. Bronwyn H. Hall, 2010. "Measuring the Returns to R&D: The Depreciation Problem," NBER Chapters, in: Contributions in Memory of Zvi Griliches, pages 341-381, National Bureau of Economic Research, Inc.
    10. Racine, Jeff & Li, Qi, 2004. "Nonparametric estimation of regression functions with both categorical and continuous data," Journal of Econometrics, Elsevier, vol. 119(1), pages 99-130, March.
    11. Nadiri, M Ishaq & Prucha, Ingmar R, 1996. "Estimation of the Depreciation Rate of Physical and R&D Capital in the U.S. Total Manufacturing Sector," Economic Inquiry, Western Economic Association International, vol. 34(1), pages 43-56, January.
    12. Tor Jakob Klette & Samuel Kortum, 2004. "Innovating Firms and Aggregate Innovation," Journal of Political Economy, University of Chicago Press, vol. 112(5), pages 986-1018, October.
    13. Qi Li & Jeffrey Scott Racine, 2006. "Density Estimation, from Nonparametric Econometrics: Theory and Practice," Introductory Chapters, in: Nonparametric Econometrics: Theory and Practice, Princeton University Press.
    14. Wakelin, Katharine, 2001. "Productivity growth and R&D expenditure in UK manufacturing firms," Research Policy, Elsevier, vol. 30(7), pages 1079-1090, August.
    15. Hall, Bronwyn H. & Mairesse, Jacques, 1995. "Exploring the relationship between R&D and productivity in French manufacturing firms," Journal of Econometrics, Elsevier, vol. 65(1), pages 263-293, January.
    16. Theofanis P. Mamuneas & Andreas Savvides & Thanasis Stengos, 2006. "Economic development and the return to human capital: a smooth coefficient semiparametric approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(1), pages 111-132, January.
    17. Griliches, Zvi, 1980. "R & D and the Productivity Slowdown," American Economic Review, American Economic Association, vol. 70(2), pages 343-348, May.
    18. Li, Qi, et al, 2002. "Semiparametric Smooth Coefficient Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 412-422, July.
    19. Li, Qi & Racine, Jeffrey S., 2010. "Smooth Varying-Coefficient Estimation And Inference For Qualitative And Quantitative Data," Econometric Theory, Cambridge University Press, vol. 26(6), pages 1607-1637, December.
    20. Zvi Griliches, 1998. "Issues in Assessing the Contribution of Research and Development to Productivity Growth," NBER Chapters, in: R&D and Productivity: The Econometric Evidence, pages 17-45, National Bureau of Economic Research, Inc.
    21. Cai, Zongwu & Fan, Jianqing & Yao, Qiwei, 2000. "Functional-coefficient regression models for nonlinear time series," LSE Research Online Documents on Economics 6314, London School of Economics and Political Science, LSE Library.
    22. Gary Jefferson & Bai Huamao & Guan Xiaojing & Yu Xiaoyun, 2006. "R&D Performance in Chinese industry," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 15(4-5), pages 345-366.
    23. Valentina Hartarska & Christopher F. Parmeter & Denis Nadolnyak, 2010. "Economies of Scope of Lending and Mobilizing Deposits in Microfinance Institutions: A Semiparametric Analysis," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 93(2), pages 389-398.
    24. Qi Li & Jeffrey Scott Racine, 2006. "Nonparametric Econometrics: Theory and Practice," Economics Books, Princeton University Press, edition 1, volume 1, number 8355.
    25. Kuen‐Hung Tsai & Jiann‐Chyuan Wang, 2004. "R&D Productivity and the Spillover Effects of High‐tech Industry on the Traditional Manufacturing Sector: The Case of Taiwan," The World Economy, Wiley Blackwell, vol. 27(10), pages 1555-1570, November.
    26. Stevenson, Rodney, 1980. "Measuring Technological Bias," American Economic Review, American Economic Association, vol. 70(1), pages 162-173, March.
    27. repec:fth:harver:1487 is not listed on IDEAS
    28. Albert G. Z. Hu & Gary H. Jefferson & Qian Jinchang, 2005. "R&D and Technology Transfer: Firm-Level Evidence from Chinese Industry," The Review of Economics and Statistics, MIT Press, vol. 87(4), pages 780-786, November.
    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. Subal C. Kumbhakar & Kai Sun & Rui Zhang, 2016. "Semiparametric Smooth Coefficient Estimation of a Production System," Pacific Economic Review, Wiley Blackwell, vol. 21(4), pages 464-482, October.
    2. Delgado, Michael S. & McCloud, Nadine & Kumbhakar, Subal C., 2014. "A generalized empirical model of corruption, foreign direct investment, and growth," Journal of Macroeconomics, Elsevier, vol. 42(C), pages 298-316.
    3. Gong, Binlei, 2018. "Agricultural reforms and production in China: Changes in provincial production function and productivity in 1978–2015," Journal of Development Economics, Elsevier, vol. 132(C), pages 18-31.
    4. Geng, Xin & Sun, Kai, 2019. "Gradient estimation of the local-constant semiparametric smooth coefficient model," Economics Letters, Elsevier, vol. 185(C).
    5. Jinfa Li & Ruijie Qin & Hongbing Jiang, 2022. "Measurement of Innovation Efficiency in China’s Electronics and Communication Equipment Manufacturing Industry-Based on Dynamic Network SBM Model," Sustainability, MDPI, vol. 14(3), pages 1-18, January.
    6. Hasan, Mostafa Monzur & Cheung, Adrian (Wai-Kong), 2018. "Organization capital and firm life cycle," Journal of Corporate Finance, Elsevier, vol. 48(C), pages 556-578.
    7. Lin, Shoufu & Lin, Ruoyun & Sun, Ji & Wang, Fei & Wu, Weixiang, 2021. "Dynamically evaluating technological innovation efficiency of high-tech industry in China: Provincial, regional and industrial perspective," Socio-Economic Planning Sciences, Elsevier, vol. 74(C).
    8. Xie, Hualin & Wang, Wei & Yang, Zihui & Choi, Yongrok, 2016. "Measuring the sustainable performance of industrial land utilization in major industrial zones of China," Technological Forecasting and Social Change, Elsevier, vol. 112(C), pages 207-219.
    9. Alemayehu, Fikru K. & Kumbhakar, Subal C. & Landazuri Tveteraas, Sigbjørn, 2022. "Estimation of staff use efficiency: Evidence from the hospitality industry," Technological Forecasting and Social Change, Elsevier, vol. 178(C).
    10. Zhang, Gongyi & Zhao, Shukuan & Xi, Yujuan & Liu, Na & Xu, Xiaobo, 2018. "Relating science and technology resources integration and polarization effect to innovation ability in emerging economies: An empirical study of Chinese enterprises," Technological Forecasting and Social Change, Elsevier, vol. 135(C), pages 188-198.
    11. Chen, Wei & Shen, Yue & Wang, Yanan & Wu, Qun, 2018. "How do industrial land price variations affect industrial diffusion? Evidence from a spatial analysis of China," Land Use Policy, Elsevier, vol. 71(C), pages 384-394.
    12. Anaya, Karim L. & Pollitt, Michael G., 2017. "Using stochastic frontier analysis to measure the impact of weather on the efficiency of electricity distribution businesses in developing economies," European Journal of Operational Research, Elsevier, vol. 263(3), pages 1078-1094.
    13. Wang, Ya & Pan, Jiao-feng & Pei, Rui-min & Yi, Bo-Wen & Yang, Guo-liang, 2020. "Assessing the technological innovation efficiency of China's high-tech industries with a two-stage network DEA approach," Socio-Economic Planning Sciences, Elsevier, vol. 71(C).
    14. Shan, Siqing & Jia, Yingwei & Zheng, Xianrong & Xu, Xiaobo, 2018. "Assessing relationship and contribution of China's technological entrepreneurship to socio-economic development," Technological Forecasting and Social Change, Elsevier, vol. 135(C), pages 83-90.
    15. Marian Rizov & Xufei Zhang, 2014. "Regional disparities and productivity in China: Evidence from manufacturing micro data," Papers in Regional Science, Wiley Blackwell, vol. 93(2), pages 321-339, June.

    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. Subal Kumbhakar & Raquel Ortega-Argilés & Lesley Potters & Marco Vivarelli & Peter Voigt, 2012. "Corporate R&D and firm efficiency: evidence from Europe’s top R&D investors," Journal of Productivity Analysis, Springer, vol. 37(2), pages 125-140, April.
    2. Raquel Ortega-Argilés & Lesley Potters & Marco Vivarelli, 2011. "R&D and productivity: testing sectoral peculiarities using micro data," Empirical Economics, Springer, vol. 41(3), pages 817-839, December.
    3. Ortega-Argilés, Raquel & Piva, Mariacristina & Vivarelli, Marco, 2011. "Productivity Gains from R&D Investment: Are High-Tech Sectors Still Ahead?," IZA Discussion Papers 5975, Institute of Labor Economics (IZA).
    4. Raquel Ortega-Argilés, 2013. "R&D, knowledge, economic growth and the transatlantic productivity gap," Chapters, in: Frank Giarratani & Geoffrey J.D. Hewings & Philip McCann (ed.), Handbook of Industry Studies and Economic Geography, chapter 11, pages 271-302, Edward Elgar Publishing.
    5. Hall, Bronwyn H. & Mairesse, Jacques & Mohnen, Pierre, 2010. "Measuring the Returns to R&D," Handbook of the Economics of Innovation, in: Bronwyn H. Hall & Nathan Rosenberg (ed.), Handbook of the Economics of Innovation, edition 1, volume 2, chapter 0, pages 1033-1082, Elsevier.
    6. Ortega-Argilés, Raquel & Piva, Mariacristina & Potters, Lesley & Vivarelli, Marco, 2009. "Is Corporate R&D Investment in High-Tech Sectors More Effective? Some Guidelines for European Research Policy," IZA Discussion Papers 3945, Institute of Labor Economics (IZA).
    7. Raquel Ortega‐Argilés & Mariacristina Piva & Marco Vivarelli, 2014. "The transatlantic productivity gap: Is R&D the main culprit?," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 47(4), pages 1342-1371, November.
    8. repec:ipt:iptwpa:jrc85346 is not listed on IDEAS
    9. Heshmati, Almas & Kumbhakar, Subal C. & Sun, Kai, 2014. "Estimation of productivity in Korean electric power plants: A semiparametric smooth coefficient model," Energy Economics, Elsevier, vol. 45(C), pages 491-500.
    10. Pedro Andres Garzon Delvaux & Heinrich Hockmann & Peter Voigt & Pavel Ciaian & Sergio Gomez y Paloma, 2018. "The impact of private R&D on the performance of food-processing firms: Evidence from Europe, Japan and North America," JRC Research Reports JRC104144, Joint Research Centre.
    11. Henderson, Daniel J. & Kumbhakar, Subal C. & Li, Qi & Parmeter, Christopher F., 2015. "Smooth coefficient estimation of a seemingly unrelated regression," Journal of Econometrics, Elsevier, vol. 189(1), pages 148-162.
    12. Giulio Cainelli, 2008. "Spatial Agglomeration, Technological Innovations, and Firm Productivity: Evidence from Italian Industrial Districts," Growth and Change, Wiley Blackwell, vol. 39(3), pages 414-435, September.
    13. Ugur, Mehmet & Trushin, Eshref & Solomon, Edna & Guidi, Francesco, 2016. "R&D and productivity in OECD firms and industries: A hierarchical meta-regression analysis," Research Policy, Elsevier, vol. 45(10), pages 2069-2086.
    14. Bronwyn Hall & Francesca Lotti & Jacques Mairesse, 2009. "Innovation and productivity in SMEs: empirical evidence for Italy," Small Business Economics, Springer, vol. 33(1), pages 13-33, June.
    15. Li, Xibao, 2011. "Sources of External Technology, Absorptive Capacity, and Innovation Capability in Chinese State-Owned High-Tech Enterprises," World Development, Elsevier, vol. 39(7), pages 1240-1248, July.
    16. Castellani, Davide & Piva, Mariacristina & Schubert, Torben & Vivarelli, Marco, 2019. "R&D and productivity in the US and the EU: Sectoral specificities and differences in the crisis," Technological Forecasting and Social Change, Elsevier, vol. 138(C), pages 279-291.
    17. Zongwu Cai & Qi Li, 2013. "Some Recent Develop- ments on Nonparametric Econometrics," Working Papers 2013-10-14, Wang Yanan Institute for Studies in Economics (WISE), Xiamen University.
    18. Bettina Becker, 2013. "The Determinants of R&D Investment: A Survey of the Empirical Research," Discussion Paper Series 2013_09, Department of Economics, Loughborough University, revised Sep 2013.
    19. 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.
    20. Jarle Møen & Helge Sandvig Thorsen, 2017. "Publication Bias in the Returns to R&D Literature," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 8(3), pages 987-1013, September.
    21. Kafouros, Mario I., 2008. "Economic returns to industrial research," Journal of Business Research, Elsevier, vol. 61(8), pages 868-876, August.

    More about this item

    Keywords

    China; Research and Development; Productivity; Semiparametric smooth coefficient model;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • L00 - Industrial Organization - - General - - - General

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:pra:mprapa:32507. 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: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

    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.