In this paper we explore whether countries led by women have fared better during the COVID-19 pandemic than those led by men. Media and public health officials have lauded the perceived gender-related influence on policies and strategies for reducing the deleterious effects of the pandemic. We examine this proposition by analyzing COVID-19-related deaths globally across countries led by men and women. While we find some limited support for lower reported fatality rates in countries led by women, they are not statistically significant. Country cultural values offer more substantive explanation for COVID-19 outcomes. We offer several potential explanations for the pervasive perception that countries led by women have fared better during the pandemic, including data selection bias and Western media bias that amplified the successes of women leaders in OECD countries.

Recent research has shown that natural disasters present political problems for societies, as these events make both citizens and leaders vulnerable. Autocratic leaders use language strategically following natural disasters to maximize their time in office. We introduce a new data set derived from using computational linguistic programs (LIWC and Coh-Metrix) to explore language patterns in the discourse of three prominent political leaders to uncover their strategies for navigating the political and social problems created by natural disasters. Our analysis covers the speeches of Chairman Mao Tse-Tung, Commander Fidel Castro, and President Hosni Mubarak. We show that leaders' language reveals their preferences and strategies for accommodating the social, political, and economic shocks created by natural disasters through blaming and credit-claiming language. Our results provide insight into how autocratic leaders' language reflects these three strategies.

Corpus selection bias in international relations research presents an epistemological problem: How do we know what we know? Most social science research in the field of text analytics relies on English language corpora, biasing our ability to understand international phenomena. To address the issue of corpus selection bias, we introduce results that suggest that machine translation may be used to address non-English sources. We use human translation and machine translation (Google Translate) on a collection of aligned sentences from United Nations documents extracted from the Multi-UN corpus, analyzed with a “bag of words” analysis tool, Linguistic Inquiry Word Count (LIWC). Overall, the LIWC indices proved relatively stable across machine and human translated sentences. We find that while there are statistically significant differences between the original and translated documents, the effect sizes are relatively small, especially when looking at psychological processes.

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