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A Bayesian space-time approach to identifying and interpreting regional convergence clubs in Europe

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  • Manfred M. Fischer
  • James P. LeSage

Abstract

This study suggests a two-step approach to identifying and interpreting regional convergence clubs in Europe. The first step calculates Bayesian probabilities for various assignments of regions to two clubs using a general stochastic space-time dynamic panel relationship between growth rates and initial levels of income as well as endowments of physical, knowledge and human capital. This approach produces club assignments that are unconditional on specific parameter estimates. The second step uses the club assignments in a dynamic space-time panel data model to assess long-run dynamic direct and spillover responses of regional income levels to changes in initial period endowments for clubs that were identified. Correctly determining the dynamic partial derivative impacts of changes in initial endowments on regional income levels is an important contribution of our study. The dynamic trajectories of regional income levels over time allow us to draw inferences regarding the timing and magnitude of regional income responses to changes in (physical capital, human capital and knowledge capital) endowments for the clubs that have been identified in the first step. We find different responses to endowments by regions in two clubs that appear consistent with low- and high-income regions as clubs.
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  • Manfred M. Fischer & James P. LeSage, 2015. "A Bayesian space-time approach to identifying and interpreting regional convergence clubs in Europe," Papers in Regional Science, Wiley Blackwell, vol. 94(4), pages 677-702, November.
    • Handle: RePEc:bla:presci:v:94:y:2015:i:4:p:677-702
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    5. Weili Zhang & Jibin Fu & Quan Ju, 2020. "A study on the model of economic growth convergence in developing regions: an empirical analysis from Henan Province, China," Empirical Economics, Springer, vol. 59(2), pages 547-567, August.
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    7. Demidova, Olga, 2021. "Methods of spatial econometrics and evaluation of government programs effectiveness," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 64, pages 107-134.
    8. Demidova, Olga, 2014. "Spatial-autoregressive model for the two groups of related regions (eastern and western parts of Russia)," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 34(2), pages 19-35.
    9. Eleonora Cutrini, 2023. "Postcrisis recovery in the regions of Europe: Does institutional quality matter?," Journal of Regional Science, Wiley Blackwell, vol. 63(1), pages 5-29, January.
    10. Pintera, Jan, 2024. "Regional convergence in the European Union – Factors of growth between the great recession and the COVID crisis," Economic Systems, Elsevier, vol. 48(1).
    11. Rios, Vicente & Gianmoena, Lisa, 2018. "Convergence in CO2 emissions: A spatial economic analysis with cross-country interactions," Energy Economics, Elsevier, vol. 75(C), pages 222-238.
    12. Fiaschi, Davide & Gianmoena, Lisa & Parenti, Angela, 2018. "Spatial club dynamics in European regions," Regional Science and Urban Economics, Elsevier, vol. 72(C), pages 115-130.
    13. Piribauer, Philipp & Glocker, Christian & Krisztin, Tamás, 2023. "Beyond distance: The spatial relationships of European regional economic growth," Journal of Economic Dynamics and Control, Elsevier, vol. 155(C).
    14. Olga A. Demidova, 2014. "The asymmetric spatial effects for eastern and western regions of Russia," HSE Working papers WP BRP 50/EC/2014, National Research University Higher School of Economics.
    15. Weili Zhang & Wei Xu & Xiaoye Wang, 2019. "Regional convergence clubs in China: identification and conditioning factors," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 62(2), pages 327-350, April.
    16. Paolo Postiglione & Maria Simona Andreano & Roberto Benedetti, 2017. "Spatial Clusters in EU Productivity Growth," Growth and Change, Wiley Blackwell, vol. 48(1), pages 40-60, March.
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    More about this item

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • O47 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Empirical Studies of Economic Growth; Aggregate Productivity; Cross-Country Output Convergence
    • O52 - Economic Development, Innovation, Technological Change, and Growth - - Economywide Country Studies - - - Europe

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