| Peer-Reviewed

The Cointegrating Relationship of Regional Growth in China

Received: 26 November 2015     Published: 26 November 2015
Views:       Downloads:
Abstract

Since the open door policy declared in the late 1970s, the economic growth of China has been rapidly developed and accumulated. However, the widening regional economic development disparity has been brought to the concern by the government. It is doubtful that the regional economic growth would tend to be equivalent by spillover effects from some more-developed regions to other less-developed ones. The goal of this paper is to examine the long-run relationship of Chinese provincial economic performance with the consideration of the characteristic of cross-sectional dependence in the panel data covering 30 provinces in China over the period 1990-2012. We find a strong spatial dependence over in China’s regional production function. After a cointegrating relation is confirmed using the methodology of Kao (1999) and Pedroni (1999), a spatial error correction model is further applied. We find the local cointegration term is significantly negative, suggesting a long-run convergence relation for the Chinese regional economic growth.

Published in Humanities and Social Sciences (Volume 3, Issue 5)
DOI 10.11648/j.hss.20150305.23
Page(s) 249-255
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2015. Published by Science Publishing Group

Keywords

Regional Cointegration, Spatial Error Correction Model, Regional Covergence

References
[1] Anselin, L. (1995), Local indicators of spatial association—LISA , Geographical analysis, 27 (2), 93-115.
[2] Anselin, L. (1988), Spatial econometrics. Dordrecht, the Netherlands: Kluwer Academic.
[3] Anselin, L. (2005), Exploring Spatial Data with GeoDaTM: A Workbook, Center for Spatially Integrated Social Science.
[4] Baker, D., Merkert, R. and Kamruzzaman, M., (2015) Regional aviation and economic growth: cointegration and causality analysis in Australia, Journal of Transport Geography, 43, pp 140-150.
[5] Baltagi B.H. and C. Kao (2000), Nonstationary Panels, Cointegration in Panels and Dynamic Panels: A Survey, in Badi Baltagi, Thomas B. Fomby and R. Carter Hill, editors, Vol. 15, 7-51.
[6] Baltagi, B.H. (2005), Econometric Analysis of Panel Data, 3nd edition New York: Wiley.
[7] Baltagi, B.H., G. Bresson, and A. Pirotte (2007), Panel Unit Root Tests and Spatial Dependence, Journal of Applied Econometrics 22, 339-360.
[8] Basile, R., M. Costantini and S, Destefanis (2007), Unit Root and Cointegration Tests for Cross-Sectionally Correlated Panels-Estimating Regional Production Functions, Working Paper.
[9] Beenstock, M. and D. Felsenstein (2007), Spatial Vector Autoregressions, Spatial Economic Analysis 2(2), 167-196.
[10] Beenstock, M. and D. Felsenstein (2010), Spatial Error Correction and Cointegration in Nonstationary Panel Data: Regional House Prices in Israel, Journal of Geographical Systems, Vol. 12, No. 2, pp 189-206.
[11] Breusch, T. and A. Pagan (1979), A Simple Test for Heteroskedasticity and Random Coefficient Variation, Econometrica 47, 1287-1294.
[12] Chang, Y. (2002), Nonlinear IV Panel Unit Root Tests with Cross-Sectional Dependency, Journal of Econometrics 110, 261–292.
[13] Chiang, Min–Hsien and Chihwa Kao (2002), Nonstationary panel time series using NPT 1.3 – A user guide. Center for Policy Research, Syracuse University.Choi, I. (2001), Unit Root Tests for Panel Data, Journal of International Money and Finance 20, 249–272.
[14] Corbae D, Durlauf SN, Hansen B (eds.). Cambridge University Press: Cambridge, UK; 311–333.
[15] Elhorst, J. P. (2001), Dynamic Models in Space and Time. Geographical Analysis 33, 119–140.
[16] Elhorst, J. P. (2003). Specification and Estimation of Spatial Panel Data Models, International Regional Science Review 26. 244–268.
[17] Elhorst, J. P. (2004), Serial and Spatial Dependence in Space–Time Models. In Spatial Econometrics and Spatial Statistics, edited by A. Getis, J. Mur, and H. G. Zoller. New York: Palgrave.
[18] Elhorst, J. P. (2009), Spatial Panel Data Models. In Fischer MM, Getis A (Eds.) Handbook of Applied Spatial Analysis, Ch. C.2. Springer: Berlin Heidelberg New York.
[19] Fingleton, B., (1999), Spurious Spatial Regression: Some Monte Carlo Results with a Spatial Unit and Spatial Cointegration, Journal of Regional Science 39(1), 1-19.
[20] Geary, R., (1954), The contiguity ratio and statistical mapping, The Incorporated Statistician 5, 115–145.
[21] Hadri, K. (2000), Testing for Unit Roots in Heterogeneous Panel Data, Econometrics Journal 3, 148-161.
[22] Hsiao, C. (1986), Analysis of Panel Data, Cambridge, Cambridge University Press.
[23] Hsiao, C., M. H. Pesaran, and A. K. Tahmiscioglu (2002), Maximum likelihood estimationof fixed effects dynamic panel data models covering short time periods. Journal of Econometrics 109, 107–150.
[24] Im, K. S., Pesaran, M. H., and Y. Shin (2003), Testing for Unit Roots in Heterogeneous Panels, Journal of Econometrics 115, 53-74.
[25] Kao, C. (1999), Spurious Regression and Residual–Based Tests for Cointegration in Panel Data, Journal of Econometrics 90 (1), 1–44.
[26] Kao, C., M. Chiang, and B, Chen (1999), International R&D Spillovers: An Application of Estimation and Inference in Panel Cointegration, Oxford Bulletin of Economics and Statistics, 61, 691-709.
[27] Kanbur, R. and X. Zhang (2005), Fifty Years of Regional Inequality in China: a Journey Through Central Planning, Reform, and Openness, Review of Development Economics 9(1), 87–106.
[28] Kosfeld, Reinhold and Jorgen Lauridsen, (2004), “Dynamic spatial modeling of regional convergence processes,’ Empirical Economics 29, pp. 705-722.
[29] Kuo, C.C. and C.H. Yang (2008), Knowledge Capital and Spillover on Regional Economic Growth: Evidence from China, China Economic Review 19, 594-604.
[30] Lauridsen, J. and R. Kosfeld (2006), A Test Strategy for Spurious Spatial Regression, Spatial Nonstationairy, and Spatial Cointegration, Paper in Regional Science 85, 363-377.
[31] Lauridsen, J. and R. Kosfeld (2007), Spatial Cointegration and Heteroscedasticity, Journal of Geography Systems 9, 253-265.
[32] LeSage, J. P. (1999). The theory and practice of spatial econometrics, econometrics toolbox for MATLAB, available on the internet at http://www.spatial-econometrics.com/.
[33] Levin, A., C.F. Lin and C.S.J. Chu (2002), Unit Root Tests in Panel Data: Asymptotic and Finite-Sample Properties, Journal of Econometrics 108, 1-24.
[34] Liao, F. H., Wei, Y. D. (2015), Space, scale, and regional inequality in provincial China: A spatial filtering approach, Applied Geography, 61, 94-104.
[35] Liu, X., P. Burridge, and P. J. N. Sinclair (2002), Relationships between economic growth, foreign direct investment and trade: evidence from China, Applied Economics 34, 1433-1440.
[36] Maasoumi, E. and L. Wang (2008), Economic reform, growth and convergence in China, Econometrics Journal 11, 128–154.
[37] Maddala, G. and S. Wu (1999), A Comparative Study of Unit Root Tests and a New Simple Test, Oxford Bulletin of Economics and Statistics 61, 631-652.
[38] Mankiw, N.G., D. Romer, and D. N. Weil (1992), A Contribution to The Empirics of Economic Growth, The Quarterly Journal of Economics, 407-437.
[39] McCoskey, S. and C. Kao (1998), A Residual-based of the Null Hypothesis of Cointegration in Panel Data, Econometrics Reviews, 17, 57-84.
[40] Moran, P. A. P. (1948). The interpretation of statistical maps. Biometrika 35. 255-260.
[41] Narayan, P. K. and R. Smyth (2004), Temporal Causality and the Dynamics of Exports, Human Capital and Real Income in China, International Journal of Applied Economics 1(1), 24-45.
[42] Newey, W. and K. West (1994), Autocovariance lag selection in covariance matrix estimation, Review of Economic Studies 61, 613–653.
[43] Pedroni, P. (1999), Critical Values for Cointegration Tests in Heterogeneous Panels with Multiple Regressors, Oxford Bulletin of Economics and Statistics 61, 653-70.
[44] Pedroni, P. and J. Y. Yao (2005), Regional income Divergence in China, working paper.
[45] Pesaran, M.H. (2004), General diagnostic tests for cross section dependence in panels. CESifo Working Papers No.1233.
[46] Pesaran, M. H. (2007), A Simple Panel Unit Root Test in The Presence of Cross- Section Dependence. Journal of Applied Econometrics 22, 265–312.
[47] Solow, R.M. (1957), Technical Change and the Aggregate Production Function, Review of Economics and Statistics, 9, 312-320.
[48] Xu, L. C. and H.-F. Zou (2000), Explaining the changes of income distribution in china. China Economic Review 11, 149–70.
[49] Yao, S. (2006), On Economic Growth, FDI and Exports in China, Applied Economics 38, 339-351.
[50] Zhou, Y., Li, N., Wu, W., Wu, J., (2014), Evolution of spatial-temporal pattern of county economic development in China during 1982-2010, Progress in Geography, 33(1), 102-113.
[51] Zou ,W., Zhuang, Z., Zhou, H. and Song, H. (2008), Measuring divergence in provincial growth in China: 1981–2004, Journal of Economic Policy Reform 11(3), 215–227.
Cite This Article
  • APA Style

    Chun-Hung A. Lin, Chia-Ming Li. (2015). The Cointegrating Relationship of Regional Growth in China. Humanities and Social Sciences, 3(5), 249-255. https://doi.org/10.11648/j.hss.20150305.23

    Copy | Download

    ACS Style

    Chun-Hung A. Lin; Chia-Ming Li. The Cointegrating Relationship of Regional Growth in China. Humanit. Soc. Sci. 2015, 3(5), 249-255. doi: 10.11648/j.hss.20150305.23

    Copy | Download

    AMA Style

    Chun-Hung A. Lin, Chia-Ming Li. The Cointegrating Relationship of Regional Growth in China. Humanit Soc Sci. 2015;3(5):249-255. doi: 10.11648/j.hss.20150305.23

    Copy | Download

  • @article{10.11648/j.hss.20150305.23,
      author = {Chun-Hung A. Lin and Chia-Ming Li},
      title = {The Cointegrating Relationship of Regional Growth in China},
      journal = {Humanities and Social Sciences},
      volume = {3},
      number = {5},
      pages = {249-255},
      doi = {10.11648/j.hss.20150305.23},
      url = {https://doi.org/10.11648/j.hss.20150305.23},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.hss.20150305.23},
      abstract = {Since the open door policy declared in the late 1970s, the economic growth of China has been rapidly developed and accumulated. However, the widening regional economic development disparity has been brought to the concern by the government. It is doubtful that the regional economic growth would tend to be equivalent by spillover effects from some more-developed regions to other less-developed ones. The goal of this paper is to examine the long-run relationship of Chinese provincial economic performance with the consideration of the characteristic of cross-sectional dependence in the panel data covering 30 provinces in China over the period 1990-2012. We find a strong spatial dependence over in China’s regional production function. After a cointegrating relation is confirmed using the methodology of Kao (1999) and Pedroni (1999), a spatial error correction model is further applied. We find the local cointegration term is significantly negative, suggesting a long-run convergence relation for the Chinese regional economic growth.},
     year = {2015}
    }
    

    Copy | Download

  • TY  - JOUR
    T1  - The Cointegrating Relationship of Regional Growth in China
    AU  - Chun-Hung A. Lin
    AU  - Chia-Ming Li
    Y1  - 2015/11/26
    PY  - 2015
    N1  - https://doi.org/10.11648/j.hss.20150305.23
    DO  - 10.11648/j.hss.20150305.23
    T2  - Humanities and Social Sciences
    JF  - Humanities and Social Sciences
    JO  - Humanities and Social Sciences
    SP  - 249
    EP  - 255
    PB  - Science Publishing Group
    SN  - 2330-8184
    UR  - https://doi.org/10.11648/j.hss.20150305.23
    AB  - Since the open door policy declared in the late 1970s, the economic growth of China has been rapidly developed and accumulated. However, the widening regional economic development disparity has been brought to the concern by the government. It is doubtful that the regional economic growth would tend to be equivalent by spillover effects from some more-developed regions to other less-developed ones. The goal of this paper is to examine the long-run relationship of Chinese provincial economic performance with the consideration of the characteristic of cross-sectional dependence in the panel data covering 30 provinces in China over the period 1990-2012. We find a strong spatial dependence over in China’s regional production function. After a cointegrating relation is confirmed using the methodology of Kao (1999) and Pedroni (1999), a spatial error correction model is further applied. We find the local cointegration term is significantly negative, suggesting a long-run convergence relation for the Chinese regional economic growth.
    VL  - 3
    IS  - 5
    ER  - 

    Copy | Download

Author Information
  • Department of Industrial Economics, Tamkang University, New Taipei City, Taiwan

  • Division of Marketing, Delta Electronics, Inc., Taipei, Taiwan

  • Sections