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Unlocking the black box: A comprehensive meta-analysis of the main determinants of within-region income inequality

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A Correction to this article was published on 11 February 2021

This article has been updated

Abstract

Regional income inequality is a topic of increasing relevance worldwide that has received considerable scientific attention. However, a clear-cut, comprehensive view has yet to be put forward of the main determinants of regional income inequality. Indeed, the extant empirical literature on the topic has reported differing results. Thus, this study develops a comprehensive meta-analysis using 33 comparable empirical studies spanning 29 years of research, involving 28 main determinants of which the most frequently mentioned were regional development, human capital, manufacturing/industry share, unemployment, financial development, and trade openness. After adjusting for publication bias and heterogeneity in the results reported by the primary studies, we conclude that the not very frequently addressed institutional related determinants (financial development, fiscal policies and public sector size), substantially contribute to reduce within-region income inequality, particularly in lower-income settings. In a smaller extent, human capital and trade openness also mitigate within-region income inequality. Region level of development, urbanization and, in a lesser extent, technological intensity aggravate within-region income inequality.

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Notes

  1. Between-region inequality usually refers to disparities across regions in real per capita GDP, disposable household income, unemployment rates or nonworking population (Gbohoui et al. 2019).

  2. KEY means that the search was done by Keywords.

  3. AK means that the search was done by Author Keywords.

  4. The search in Google Scholar was done by keywords using the software Publish or Perish by Harzing (2007) Publish or Perish, available from https://harzing.com/resources/publish-or-perish.

  5. The reference date for data gathering was 29th July 2020.

  6. The 33 studies presented 335 variable proxies for the determinants of within-region income inequality. We harmonized these 335 proxies into 63 distinct determinants—Fig. 4 (in Appendix) lists these 63 determinants, indicating the number of average size effects estimates and number of studies analysing each determinant.

  7. In the ‘Electronic Supplementary Document’, we provide a list and detailed account/discussion of all these variables and proxies.

  8. See Fig. 4 in Appendix.

  9. For a detailed discussion of the advantages and disadvantages of the different methods see “Intro—Introduction to meta-analysis” in www.stata.com.

  10. In total, we have 1637 studies from the 33 papers, but such figures vary depending on the determinant in analysis. For instance, for the determinant region’s level of development, we have 167 studies gathered from 27 papers (see Fig. 4 in Appendix).

  11. It is important to note that, this method does not consider the reasons for funnel plot asymmetry other than publication bias, thus it is likely to perform poorly in the presence of high between-study heterogeneity. The simulated meta-analysis results performed by Peters et al. (2007) suggest that in presence of publication bias the use of the estimate from the largest or most precise study could be a reasonable approach; however, when between-study heterogeneity exists these estimates are rather biased. Thus, Peters et al. (2007, p 4544) conclude that “in the presence of publication bias use of the trim and fill method can help to reduce the bias in pooled estimates, even though the performance of this method is not ideal”.

  12. According to Doucouliagos (2011), in absolute terms, a partial correlation coefficient that is less than 0.07 can be considered to have a negligible/very small impact. If equal or higher than 0.07 and less than 0.17 can be considered to have small impact. If higher than 0.17 and less than 0.33 can be considered to have medium impact, and if higher than 0.33. in absolute terms, the impact is large.

  13. Due to the paucity of estimates, we were only able to compute MRA for 16 determinants.

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Acknowledgements

We would like to thank the Editor-in-Chief, Dr. Mikaela Backman, and two anonymous reviewers for their helpful comments and constructive criticisms that help to improve the paper. The usual disclaimer applies.

Funding

The second author further acknowledges the support by the Portuguese public funds through FCT—Fundação para a Ciência e a Tecnologia, I.P., in the framework of the project with reference UIDB/04105/2020.

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  1. Centre for Geographical Studies, Institute of Geography and Spatial Planning (IGOT), University of Lisbon, Lisbon, Portugal

    Diana Barros

  2. Faculty of Economics, CEF.UP, INESC Tec, OBEGEF, University of Porto, Porto, Portugal

    Aurora A. C. Teixeira

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  1. Diana Barros
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  2. Aurora A. C. Teixeira
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Correspondence to Diana Barros.

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Barros, D., Teixeira, A.A.C. Unlocking the black box: A comprehensive meta-analysis of the main determinants of within-region income inequality. Rev Reg Res 41, 55–93 (2021). https://doi.org/10.1007/s10037-020-00149-0

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