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Globalization and spatial inequality: Does economic integration affect regional disparities?

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Abstract

This article examines the link between economic globalization and spatial inequality in a panel of 142 countries over the period 1992–2012. Our instrumental variables estimates reveal a strong causal effect of the degree of economic integration with the rest of the world on spatial inequality, indicating that the advances in the process of globalization currently underway contribute to significantly increasing regional income disparities. This means that globalization leads to the emergence of losing and winning regions within countries and that the group of losing (winning) regions tends to be made up of low (high-)-income regions. This result has to do with the regressive spatial impact of actual economic flows, while existing restrictions on trade and capital do not exert a significant effect in this context. Our findings are robust to the inclusion in the analysis of different covariates that may be correlated with both spatial inequality and globalization and are not driven by a specific group of influential countries. Likewise, the observed relationship between economic integration and spatial inequality does not depend on the measures used to quantify the magnitude of regional income disparities within the various countries. At the same time, our estimates suggest that the spatial impact of globalization is contingent on the level of economic development.

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Notes

  1. Related to this literature, the last years have seen the publication of various analyses on the spatial implications of Brexit, which can be considered as the first example in the modern era of a reversal of an agreement of trade integration (Head and Mayer 2017). For further details, see, for example, Los et al. (2017), Capello et al. (2018) or Chen et al. (2018).

  2. The night-time lights data are drawn from meteorological satellites of the US Air Force, which orbit the Earth 14 times per day measuring the intensity of lights between 8:30 and 10:00 pm local time. In order to capture only man-made lights, these data are processed by scientists at the National Oceanic and Atmospheric Administration’s (NOAA) National Geophysical Data Center (NGDC), removing observations for places experiencing the bright half of the lunar cycle, the summer months when the sun sets late, auroral activity and forest fires. Finally, a satellite-year dataset is obtained by averaging data from all orbits of a specific satellite in a given year over all valid nights. See Henderson et al. (2012) for further details.

  3. The reliability of the regional income predictions based on luminosity data is confirmed by various robustness tests performed by Lessmann and Seidel (2017, pp. 128–131). Importantly, these authors report a positive and high correlation between their predictions and observed regional GDP per capita, which is especially strong in low- and high-income countries. Furthermore, Lessmann and Seidel (2017) find no evidence that the potential selection bias in the observed data on regional GDP affects their results. Indeed, these authors suggest that their predictions are more appropriate for an analysis of real income disparities within countries than observed income data, which are usually based on nominal values.

  4. A comprehensive list of papers using the KOF index of globalization can be found at http://globalization.kof.ethz.ch/. See also the survey paper by Potrafke (2015).

  5. For further details on the method of calculation used to obtain the index, see http://globalization.kof.ethz.ch/.

  6. The list of countries included in the analysis is ultimately determined by data availability to calculate the KOF index of economic globalization. In particular, in order to facilitate comparison, countries with missing data for some of the two subindices of economic integration mentioned in the prior section were removed from the analysis. See the online Appendix for further details.

  7. Taking into account that the study period is relatively short, our empirical approach exploits the variation in annually repeated cross-country data in order to maximize the number of observations, thus reducing the collinearity among explanatory variables and improving the efficiency of the estimates (Baltagi 2001). An alternative strategy would be to divide the time span under analysis into five-year periods (Lessmann and Seidel 2017; Hirte et al. 2017). Although this latter approach may be preferable to minimize the potential effects of the business cycle (Lessmann 2014), the employment of five-year periods comes at the cost of ignoring valuable information on changes in both spatial inequality and economic globalization within any given five-year interval. In any case, Table A1 in the online Appendix shows that the main results of the paper remain qualitatively unaltered when we divide the study period into five-year periods to estimate model (2).

  8. The sources and definitions of all the control variables used throughout the paper are presented in the online Appendix. Likewise, Table A2 in the online Appendix shows several descriptive statistics.

  9. Following the same strategy described above, the 2SLS regressions in columns 4-6 of Table 3 use the (weighted) average of actual economic flows and restrictions in neighbouring countries as instruments for the two components of the KOF index of economic globalization.

  10. Note that \(GE(\delta )\) can be transformed into a subclass of the widely employed Atkinson index with \(\varphi =1-\delta \) for \(0 \le \delta < 1\), where \(\varphi \) is the (relative) inequality aversion parameter (Lessmann and Seidel 2017).

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Authors and Affiliations

  1. Department of Economics and INARBE, Universidad Pública de Navarra, Campus de Arrosadia s/n. 31006, Pamplona, Spain

    Roberto Ezcurra

  2. Department of Economics, Universidad Pública de Navarra, Pamplona, Spain

    Alba Del Villar

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  1. Roberto Ezcurra
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Correspondence to Roberto Ezcurra.

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Ezcurra, R., Del Villar, A. Globalization and spatial inequality: Does economic integration affect regional disparities?. Ann Reg Sci 67, 335–358 (2021). https://doi.org/10.1007/s00168-021-01050-5

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