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Development Accounting with Spatial Effects

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  • Wilfried Koch

Abstract

Abstract The aim of this paper is to show how the spatial autocorrelation phenomenon often observed in the world distribution of income per capita, can be introduced structurally as the outcome of spillovers effects into a development accounting equation. Neglecting spatial autocorrelation potentially biases our vision of the role played by physical capital in the development process. We show that the total contribution of physical capital accounts for almost 90% of the differences between developing countries and the richest countries.

Suggested Citation

  • Wilfried Koch, 2008. "Development Accounting with Spatial Effects," Spatial Economic Analysis, Taylor & Francis Journals, vol. 3(3), pages 321-342.
    • Handle: RePEc:taf:specan:v:3:y:2008:i:3:p:321-342
    DOI: 10.1080/17421770802353733
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    References listed on IDEAS

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    1. Cem Ertur & W. Koch, 2006. "Convergence, Human Capital and International Spillovers," Post-Print halshs-00250303, HAL.
    2. John W. Kendrick, 1976. "The Formation and Stocks of Total Capital," NBER Books, National Bureau of Economic Research, Inc, number kend76-1, August.
    3. James P. LeSage & R. Kelley Pace, 2008. "Spatial Econometric Modeling Of Origin‐Destination Flows," Journal of Regional Science, Wiley Blackwell, vol. 48(5), pages 941-967, December.
    4. Dale W. Jorgenson, 1995. "Productivity, Volume 1: Postwar US Economic Growth," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262100495, December.
    5. KOCH, Wilfried, 2006. "Growth and Spatial Dependence in Europe," LEG - Document de travail - Economie 2006-02, LEG, Laboratoire d'Economie et de Gestion, CNRS, Université de Bourgogne.
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    Cited by:

    1. Bernard Fingleton & Manfred Fischer, 2010. "Neoclassical theory versus new economic geography: competing explanations of cross-regional variation in economic development," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 44(3), pages 467-491, June.
    2. Badi H. Baltagi & Bernard Fingleton & Alain Pirotte, 2014. "Estimating and Forecasting with a Dynamic Spatial Panel Data Model," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 76(1), pages 112-138, February.
    3. Rosa Bernadini Papalia & Silvia Bertarelli, 2013. "Identification and Estimation of Club Convergence Models with Spatial Dependence," International Journal of Urban and Regional Research, Wiley Blackwell, vol. 37(6), pages 2094-2115, November.
    4. 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.
    5. B. Fingleton & P. Cheshire & H. Garretsen & D. Igliori & J. Le Gallo & P. McCann & J. McCombie & V. Monastiriotis & B. Moore & M. Roberts, 2008. "Editorial," Spatial Economic Analysis, Taylor & Francis Journals, vol. 3(3), pages 269-273.
    6. Glass, Anthony J. & Kenjegalieva, Karligash & Sickles, Robin C., 2016. "A spatial autoregressive stochastic frontier model for panel data with asymmetric efficiency spillovers," Journal of Econometrics, Elsevier, vol. 190(2), pages 289-300.
    7. Jan Ditzen, 2014. "Economic Growth and Migration," SEEC Discussion Papers 1406, Spatial Economics and Econometrics Centre, Heriot Watt University.
    8. Rafael Boix-Domenech & Vicent Soler-Marco, 2017. "Creative service industries and regional productivity," Papers in Regional Science, Wiley Blackwell, vol. 96(2), pages 261-279, June.

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    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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