Abstract
The current paper empirically explores the geopolitical risk influence on income inequality. It assumes that geopolitical risk’s effects change over time. That is because the geopolitical events’ reoccurrence and the interaction with unobserved variables impose permanent adjustments in the decision process. Therefore, we employ the two-way fixed Prais-Winsten regression with panel-corrected standard errors to estimate this relationship on a yearly basis. Our paper extracts evidence from 18 countries during the period (2007–2020). It finds that the effect of geopolitical risk on income inequality is positive or geopolitical risk impairs income inequality. Moreover, geopolitical risk and its interaction with the unobserved variables can mitigate or expand income inequality, though it will not abolish it. Our results’ robustness check using the Palma ratio as the dependent variable instead of the Gini coefficient produces similar results. Our paper presents a group of policy implications extracted from our results.
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Source: Caldara and Iacoviello (2022)
Source: The World Income Inequality Database (WIID)
Source: The World Income Inequality Database (WIID)
Source: computed by the author
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Notes
This report is produced by the World Inequality Database (WID) and available at WID website: https://wid.world/document/world-inequality-report-2022/.
It is measured by constant 2015 US$.
This data was generated by The United Nations University World Institute for Development Economics Research (UNU-WIDER). Available at https://www.wider.unu.edu/database/world-income-inequality-database-wiid.
Multicollinearity is a serious issue if VIF is greater than 10.
The estimated parameters in Eq. (3) are 52.
These countries are Australia, China, and Israel.
These test statistics are the Lagrange Multiplier LM Test, Likelihood Ratio LR Test, Wald Test, and the Modified Wald test.
The total number of these coefficients is 30. They are available upon request.
We used the “testparm” command.
The result of the time-specific disturbances was Chi2 (13) = 630.92, (Prob. = 0.000), and for the country-specific disturbances was Chi2 (17) = 2.7e + 05, (Prob. = 0.000).
For instance, the ultimate impact of geopolitical risk on the Gini index in 2008 = the coefficient of geopolitical risk in the reference year (2007) + the parameter of the interaction between geopolitical risk and the yearly dummy variable in 2008, if the interaction term is statistically significant. Nevertheless, if the interaction term is insignificant, its value will equal zero.
These empirical studies used at least one of these variables as an explanatory variable in their models.
The result of the time-specific disturbances was Chi2 (13) = 1023.65, (Prob. = 0.000), and for the country-specific disturbances was Chi2 (14) = 16,266.81, (Prob. = 0.000).
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Sweidan, O.D. The Effect of Geopolitical Risk on Income Inequality: Evidence from a Panel Analysis. Soc Indic Res 167, 47–66 (2023). https://doi.org/10.1007/s11205-023-03093-x
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DOI: https://doi.org/10.1007/s11205-023-03093-x