There are large inequalities between men and women throughout the world. Past research has attempted to explain differences of income between countries via certain well-established theories. However, early theories tended to ignore gendered differences and only focused upon a narrow view of inequality. This dissertation advances recent attempts to incorporate women into research on global inequality by broadening the focus of influential factors to the political order.
Theories of global inequality include modernization, dependency, and world system. The latter theories critiqued modernization theory, but neglected to consider the gendered nature of development and inequality. The women in development, and gender and development theories responded to this theoretical need by incorporating women into analysis on global inequality. Still, research to date has tended to neglect the political realm in favor of economic explanations, and to define inequality by a single, narrow indicator.
In this dissertation, I utilize country-level data drawn from 127 countries, including measures of economic dependency and development, culture, and politics. These variables are analyzed via multivariate regression analysis with full information maximum likelihood. This method allows researchers to include cases that have missing data that would otherwise be excluded. I estimate the effect of these factors upon women’s status cross-nationally by the gender empowerment measure and gender inequality, both derived from the United Nations. The analysis includes descriptives, correlations, and multivariate regression.
The results indicate that dependency factors do not predict women’s status in accordance with the dependency/world system framework. The political order variables are significant predictors of women’s status, even when controlling for economic and cultural factors. Specifically, government size and women’s rights are significant predictors of gender empowerment, and democracy is a significant predictor of gender inequality. These findings contribute to the research literature on global gender inequality in two principle ways. First, the dissertation considers well-established factors as well as under-considered variables like political characteristics of states. Second, the statistical method employed for multivariate regression allows for the incorporation of more countries that are often excluded from research due to missing data.