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Can Women Empowerment Explain Cross-Country Differences in Inequality? A Global Perspective

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Abstract

This paper investigates the relationship between women empowerment and income inequality. In particular, we examine whether empowering women in terms of raising their political, social and economic rights reduce income inequality. Furthermore, this paper also sheds light on the effects of women empowerment on income inequality in different economies characterized by income differences. Using data for 134 countries from 1945 to 2015, we estimate a panel data model with treatment for endogeneity, controlling for savings rate, arable land rate and age-dependency ratio. Our results indicate that women empowerment in all three aspects (social, political and economic) plays an important role in reducing income inequality. This effect is more prominent in lower-middle and upper-middle income countries and is robust to different measures of inequality and women empowerment, and an alternative estimation technique that takes care of endogeneity.

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Notes

  1. The panel data for Gini is unbalanced; the longest time span is from 1980 to 2015, there are about 100 countries used in the actual regressions (so maximum around 3,500 observations). We have 2,504 actual observations for gini, which is about 71% of the maximum possible. It should be noted that some missing data between years within a single country are interpolated.

  2. The Hausman specification test used here compare MLE with FE. The null hypothesis is that MLE is an efficient and consistent estimator compared with FE. For the two tests involving \({W}_{econ}\) and \({W}_{polity}\), the \({\chi }^{2}\) statistics equals 8.47 and 6.01 respectively, the corresponding p-values are 0.2930 and 0.5386 respectively, suggesting we fail to reject the null and that MLE is indeed an efficient and consistent estimator compared with FE. For the test involving \({W}_{social}\), the \({\chi }^{2}\) statistics and associated p-value equal 14.49 and 0.0431 respectively, suggesting the null is rejected and that FE is a better estimator compared with MLE.

  3. It should be noted that polity controls such as left of center, center, and right of center governments could also be included as additional controls for inequality, since Cornia (2014) showed that a shift towards left leaning governments reduce inequality. However, given the global nature of this study, it’s hard to define “left” and “right” politics consistently across countries with very different characteristics. For example, China has no “right” leaning politics according to western definition, but it nevertheless experienced significant increase in inequality. Therefore, this polity variable may not be a feasible control in a global study.

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    Authors and Affiliations

    1. Department of Economics, Macquarie University, Sydney, Australia

      Cong Wang

    2. Department of Business Development and Technology, Aarhus University, Herning, Denmark

      Amjad Naveed

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    Appendices

    Appendix

    1.1 A1

    See Table

    Table 8 Correlation table for core variables
    Full size table

    8

    A2: Countries in the Regressions (Core Sample)

    Non-OECD Countries: Afghanistan*, Albania*, Algeria, Andorra, Angola*, Armenia*, Argentina, Bahamas, Bahrain, Bangladesh, Belarus*, Belize, Benin*, Bolivia, Botswana, Brazil, Brunei, Bulgaria*, Burkina Faso, Burundi, Cambodia*, Cameroon, China*, Colombia, Comoros, Congo*, Costa Rica, Cuba*, Cyprus, Djibouti*, Dominica, Dominican Republic, Ecuador, Egypt*, El Salvador, Ethiopia*, Fiji, Gabon, Gambia, Georgia*, Ghana*, Guatemala, Guinea*, Guyana, Haiti, Honduras, India, Indonesia, Iran, Iraq*, Ivory Coast, Jamaica, Jordan, Kazakhstan*, Kenya, Kiribati, Kyrgyz Republic*, Laos*, Latvia*, Lebanon, Lesotho, Liberia, Libya*, Liechtenstein, Lithuania*, Madagascar, Malawi, Malaysia, Maldives, Mali*, Mauritania*, Mauritius, Mongolia*, Morocco, Nepal, Nicaragua, Nigeria, Pakistan, Paraguay, Peru, Philippines, Romania*, Russia*, Rwanda, South Africa, Sri Lanka, Tanzania, Thailand, Trinidad and Tobago, Ukraine*, Venezuela, Vietnam, Yemen*, Zambia*, Zimbabwe. OECD Countries: Australia, Austria, Belgium, Chile, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hong Kong, Hungary, Iceland, Ireland, Israel, Italy, Japan, Korea Rep, Luxembourg, Mexico, Netherlands, New Zealand, Norway, Poland, Portugal, Spain, Sweden, Switzerland, Turkey, United Kingdom, United States.

    *Transitioning Economies.

    A3: Data Sources

    Women Empowerment Variables (Economic Rights, Political Rights and Social Rights) taken from Cingranelli and Richards (2010). Control variables (\(age, sav, arable\)) from World Bank data’s World Development Indicators (WDI). Inequality variables (\(gini, top10, top20\)) from World Bank data’s World Development Indicators (WDI).

    3.1 A4

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    Table 9 Women Empowerment indicators and definitions of their rights.
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    9

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    Wang, C., Naveed, A. Can Women Empowerment Explain Cross-Country Differences in Inequality? A Global Perspective. Soc Indic Res 158, 667–697 (2021). https://doi.org/10.1007/s11205-021-02721-8

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