Accumulated evidence identifies discernible gender gaps across many dimensions of professional academic careers including salaries, publication rates, journal placement, career progress, and academic service. Recent work in political science also reveals gender gaps in citations, with articles written by men citing work by other male scholars more often than work by female scholars. This study estimates the gender gap in citations across political science subfields and across methodological subfields within political science, sociology, and economics. The research design captures variance across research areas in terms of the underlying distribution of female scholars. We expect that subfields within political science and social science disciplines with more women will have smaller gender citation gaps, a reduction of the “Matthew effect” where men’s research is viewed as the most central and important in a field. However, gender citation gaps may persist if a “Matilda effect” occurs whereby women’s research is viewed as less important or their ideas are attributed to male scholars, even as a field becomes more diverse. Analysing all articles published from 2007–2016 in several journals, we find that female scholars are significantly more likely than mixed gender or male author teams to cite research by their female peers, but that these citation rates vary depending on the overall distribution of women in their field. More gender diverse subfields and disciplines produce smaller gender citation gaps, consistent with a reduction in the “Matthew effect”. However, we also observe undercitation of work by women, even in journals that publish mostly female authors. While improvements in gender diversity in academia increase the visibility and impact of scholarly work by women, implicit biases in citation practices in the social sciences persist.

When a researcher suspects that the marginal effect of [Formula: see text] on [Formula: see text] varies with [Formula: see text], a common approach is to plot [Formula: see text] at different values of [Formula: see text] along with a pointwise confidence interval generated using the procedure described in Brambor, Clark, and Golder to assess the magnitude and statistical significance of the relationship. Our article makes three contributions. First, we demonstrate that the Brambor, Clark, and Golder approach produces statistically significant findings when [Formula: see text] at a rate that can be many times larger or smaller than the nominal false positive rate of the test. Second, we introduce the interactionTest software package for R to implement procedures that allow easy control of the false positive rate. Finally, we illustrate our findings by replicating an empirical analysis of the relationship between ethnic heterogeneity and the number of political parties from Comparative Political Studies.

This article introduces a web-based tool that scholars can use to assess the gender balance of their syllabi and bibliographies. The citation gap in political science is described briefly as well as why under-citing women relative to men is a problem that should be addressed by the field. The Gender Balance Assessment Tool (GBAT) is presented as a way to make assessing gender balance easier with the aim of remedying the gender gap. This is followed by an outline that explains in nontechnical terms how the tool identifies author names and then predicts their gender to produce a single document-level percentage of women authors. Finally, best practices for diversity in syllabi and bibliographies are discussed, and various public sources that can be used to find scholarly work by women, as well as scholars of color, are listed.

Political science, like many disciplines, has a “leaky-pipeline” problem. Women are more likely to leave the profession than men. Those who stay are promoted at lower rates. Recent work has pointed toward a likely culprit: women are less likely to submit work to journals. Why? One answer is that women do not believe their work will be published. This article asks whether women systematically study different topics than men and whether these topics may be less likely to appear in top political science journals. To answer this question, we analyzed the content of dissertation abstracts. We found evidence that some topics are indeed gendered. We also found differences in the representation of “women’s” and “men’s” topics in the pages of the top journals. This suggests that research agendas may indeed be gendered and that variation in research topic might be to blame for the submission gap.

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The adage “All politics is local” in the United States is largely true. Of the United States’ 90,106 governments, 99.9% are local governments. Despite variations in institutional features, descriptive representation, and policy-making power, political scientists have been slow to take advantage of these variations. One obstacle is that comprehensive data on local politics is often extremely difficult to obtain; as a result, data is unavailable or costly, hard to replicate, and rarely updated. We provide an alternative: crowdsourcing this data. We demonstrate and validate crowdsourcing data on local politics using two different data collection projects. We evaluate different measures of consensus across coders and validate the crowd’s work against elite and professional datasets. In doing so, we show that crowdsourced data is both highly accurate and easy to use. In doing so, we demonstrate that nonexperts can be used to collect, validate, or update local data.

Existing research has found that American politicians benefit from trying to attract investment and creates jobs. In this paper, we build on this work by describing the drivers of Americans' attitudes toward inward foreign investment (FDI). We posit that foreign and Chinese investment are different than domestic investment in the public imagination and that nationalism and proximity to deindustrialization interact to shape public opinion about them. We propose and test two theories of this interaction using a survey experiment that randomizes whether a respondent is responding to a statement about “business investment,” “foreign business investment,” or “Chinese business investment”. We find that (1) Americans are skeptical of business investments by Chinese, and, to lesser degree, “foreign” firms; (2) the gap in enthusiasm for generic business investment and foreign/Chinese business investment rises with local trade-related job losses; and (3) the distinction between nationalists' and non-nationalists' attitudes toward FDI declines in local job losses.

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