Female Leaders and Foreign Policy



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This dissertation is a first-cut exploration into the examination of foreign policy behavior of female leaders. We know a lot about in general Female politicians, their substantive and descriptive representation, how gender stereotypes influence their election and behavior. Yet how gender stereotypes influence the conduct of the foreign policy of female executives remains unclear. This dissertation offers to fill this gap by specifically examining factors that help to alleviate the negative side of gender stereotypes for female leaders. The three chapters, while written as individual papers, contribute to a single story that deepen our understanding of gendered decision-making at the executive level. In the first chapter, I jointly explore two factors, namely the gender of the leader and the preferences of parties, that have not been considered together to analyze whether the gender of the executive leader makes a difference in the allocation of foreign aid. Through analysis of 34 OECD donor countries from 1960 to 2015, I find that female leaders, supported by internationalist parties, are more likely to increase foreign aid. In the second chapter, I shift focus to the voters' evaluation of foreign aid policy and test the partisan argument experimentally. The results show that voters are less likely to stand behind the foreign policy agenda of a female leader that involves aid increase, compared to similar agenda by male leader. Yet, the support of co-partisans shields women from the negative effects of stereotypes. The last chapter examines how stereotypes influence speeches and the voting behavior of female leaders at a global institution. Analysis of speeches reveals that female executives are more likely than their male colleagues to focus on women, on the issues of development and cooperation, and their implications for women in their annual statements. However, countries voting records at the UNGA do not change depending on the leader's gender.



Female executives, foreign policy, text-analysis