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Can neighbor regions shape club convergence? Spatial Markov chain analysis for Turkey

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Abstract

This study explores the impact of neighbor regions on the club convergence for Turkey. Markov chain analyses are augmented by using the spatial lag conditioning. The central hypothesis is that, having a rich (poor) spatial proximity increases the chances to move towards higher (lower) income classes. Our preliminary evidence covers the 1975–2017 period and points out that Turkish regions are not converging on average rather converging to varying income levels. This signals the formation of convergence clubs. Our augmented analyses highlight that the club convergence process is influenced from the income level of neighbor regions. Those regions whose neighbors belong to high income classes have higher chances to move to higher income classes, whereas the peripheral regions linked with the poor ones are getting more isolated. Our results highlight that regional policy framework and local economic activity has an influence beyond the administrative boundaries of regions. This calls for spatially flexible and smart local policies to combat with regional disparities.

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Notes

  1. Turkey realizes a number of severe financial crises during the sample period. Two economic contractions of the period correspond to the 1994 and 2001 financial crises (− 5.5 and − 6% economic growth respectively). Consequently, in 2009 Turkey is influenced from the 2008 Global Financial Crises and the economy shrinks by 4.7%. Naturally, all these macroeconomic turmoils have possible effects on the regional growth performances. The extent of regional resilience to economic turmoils is not considered within this study. We regard this as a further dimension of regional disparities which could be better handled in a separate research.

  2. Regional GDP data is from Özötün (1980, 1988) and Turkstat (2019a, b).

  3. While there is no universal rule for the right number of income classes, the scholarly literature uses five income groups to implement transition probability analyses (Quah 1993; López-Bazo et al. 1999). Le Gallo (2004) discusses that the exact number of income classes has negligible effect on the results. Still, we replicate all of the transition probability analyses with four income classes. Our results are virtually unchanged. These results are supplied in the Online Appendix.

  4. Similar results from the spatial lag analysis with an inverse distance weight matrix are provided in the Online Appendix.

  5. We would like to thank one of the anonymous reviewers for expressing the importance of the spatial mechanisms and its theoretical foundations.

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Acknowledgements

Earlier versions of the manuscript is presented at the 18th Annual Conference of Turkish Regional Science Association (2018) and Workshop on Regional Inequalities in Turkey: Methodology, Data and Impact of New GDP Revision on Regional Accounts in Turkey (2019) organized jointly by Turkish Regional Science Association, Marmara University Faculty of Economics and Marmara University Center for Regional Development. Author is grateful to the participants, particularly to Yasemin Basturkcu, Alpay Filiztekin, Fatma Dogruel, Suut Dugruel, Tuzin Baycan, Ferhan Gezici and Haluk Levent for useful comments and suggestions. Author would also like to thank to Firat Bilgel for his critical comments and constructive suggestions on the earlier versions of the paper.

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  1. Department of Economics and Finance, Faculty of Economics and Administrative Sciences, Piri Reis University, 34940, Tuzla, Istanbul, Turkey

    Burhan Can Karahasan

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Karahasan, B.C. Can neighbor regions shape club convergence? Spatial Markov chain analysis for Turkey. Lett Spat Resour Sci 13, 117–131 (2020). https://doi.org/10.1007/s12076-020-00248-z

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