Dion, Sumner, and Mitchell (2018) find that a published article is more likely to cite at least one female-authored paper if that article is itself authored by women. To complement their work, we study the number of times that an article in their data set is cited given that it has at least one female author. We find that articles with at least one female author are cited no more or less often than male-authored articles once we control for the publishing journal and the number of authors. The importance of controlling for author count in our model suggests that spurious correlation and/or self-citation might explain at least some of the gender differences found by Dion, Sumner, and Mitchell (2018).

How do political scientists use online tools as part of their scholarly work? Are there systematic differences in how they value these tools by field, gender, or other demographics? How important are these tools relative to traditional practices of political scientists? The answers to these questions will shape how our discipline chooses to reward academics who engage with “new media” such as blogs, online seminars (i.e., webinars), Twitter, and Facebook. We find that traditional tools of scholarship are more highly regarded and used more often than any new media, although blogs are considered most important among new media. However, we also find evidence that these webinars are used and valued at rates comparable to traditional tools when they are provided in ways that meet political scientists’ needs. Finally, we observe that women and graduate students are substantially more likely than men and tenure-track academics to report that webinars and online videos are important sources of new ideas and findings.

Cluster-robust standard errors (as implemented by the eponymous cluster option in Stata) can produce misleading inferences when the number of clusters G is small, even if the model is consistent and there are many observations in each cluster. Nevertheless, political scientists commonly employ this method in data sets with few clusters. The contributions of this paper are: (a) developing new and easy-to-use Stata and R packages that implement alternative uncertainty measures robust to small G, and (b) explaining and providing evidence for the advantages of these alternatives, especially cluster-adjusted t-statistics based on Ibragimov and Müller. To illustrate these advantages, we reanalyze recent work where results are based on cluster-robust standard errors.

How does the structure of the peer review process, which can vary among journals, influence the quality of papers published in a journal? This article studies multiple systems of peer review using computational simulation. I find that, under any of the systems I study, a majority of accepted papers are evaluated by an average reader as not meeting the standards of the journal. Moreover, all systems allow random chance to play a strong role in the acceptance decision. Heterogeneous reviewer and reader standards for scientific quality drive both results. A peer review system with an active editor—that is, one who uses desk rejection before review and does not rely strictly on reviewer votes to make decisions—can mitigate some of these effects.

At the turn of the twenty-first century, an important pair of studies established that greater female representation in government is associated with lower levels of perceived corruption in that government. But recent research finds that this relationship is not universal and questions why it exists. This article presents a new theory explaining why women’s representation is only sometimes related to lower corruption levels and provides evidence in support of that theory. The study finds that the women’s representation–corruption link is strongest when the risk of corruption being detected and punished by voters is high – in other words, when officials can be held electorally accountable. Two primary mechanisms underlie this theory: prior evidence shows that (1) women are more risk-averse than men and (2) voters hold women to a higher standard at the polls. This suggests that gender differences in corrupt behavior are proportional to the strength of electoral accountability. Consequently, the hypotheses predict that the empirical relationship between greater women’s representation and lower perceived corruption will be strongest in democracies with high electoral accountability, specifically: (1) where corruption is not the norm, (2) where press freedom is respected, (3) in parliamentary systems and (4) under personalistic electoral rules. The article presents observational evidence that electoral accountability moderates the link between women’s representation and corruption in a time-series, cross-sectional dataset of seventy-six democratic-leaning countries.

Two contributions in this issue, Grant and Lebo and Keele, Linn, and Webb, recommend using an ARFIMA model to diagnose the presence of and estimate the degree of fractional integration, then either (i) fractionally differencing the data before analysis or, (ii) for cointegrated variables, estimating a fractional error correction model. But Keele, Linn, and Webb also present evidence that ARFIMA models yield misleading indicators of the presence and degree of fractional integration in a series with fewer than 1000 observations. In a simulation study, I find evidence that the simple autodistributed lag model (ADL) or equivalent error correction model (ECM) can, without first testing or correcting for fractional integration, provide a useful estimate of the immediate and long-run effects of weakly exogenous variables in fractionally integrated (but stationary) data.

A state-dependent dynamic system is one in which (1) the marginal effect of x on y at time t () depends on the prior value of the dependent variable , and (2) the persistence of the dependent variable () depends on xt. We present a methodological strategy for dealing with state-dependent dynamic systems and demonstrate the consequences of ignoring state-dependence. As an applied example, we find evidence of state-dependence in the relationship between presidential approval and economic performance: high unemployment rates are most damaging to presidential approval among presidents with the highest initial approval ratings.

In this article, we present a technique and critical test statistic for assessing the fit of a binary-dependent variable model (e.g., a logit or probit). We examine how closely a model's predicted probabilities match the observed frequency of events in the data set, and whether these deviations are systematic or merely noise. Our technique allows researchers to detect problems with a model's specification that obscure substantive understanding of the underlying data-generating process, such as missing interaction terms or unmodeled nonlinearities. We also show that these problems go undetected by the fit statistics most commonly used in political science.

Empirical political science is not simply about reporting evidence; it is also about coming to conclusions on the basis of that evidence and acting on those conclusions. But whether a result is substantively significant––strong and certain enough to justify acting upon the belief that the null hypothesis is false––is difficult to objectively pin down, in part because different researchers have different standards for interpreting evidence. Instead, this article advocates judging results according to their “substantive robustness,” the degree to which a community with heterogeneous standards for interpreting evidence would agree that the result is substantively significant. This study illustrates how this can be done using Bayesian statistical decision techniques. Judging results in this way yields a tangible benefit: false positives are reduced without decreasing the power of the test, thus decreasing the error rate in published results.

Recent research finds that states with more women involved in government are also less prone to corruption (Dollar, Fisman, and Gatti 2001; Swamy et al. 2001). But a review of experimental evidence indicates that “women are not necessarily more intrinsically honest or averse to corruption than men” in the laboratory or in the field (Frank, Lambsdorff, and Boehm 2011, 68). Rather, the attitudes and behaviors of women concerning corruption depend on institutional and cultural contexts in these experimental situations (Alatas, Cameron, and Chaudhuri 2009; Alhassan-Alolo 2007; Armantier and Boly 2008; Schulze and Frank 2003). If women's inclination toward corruption is contextual, then what are the contexts in which we would expect female involvement in government to fight corruption? The answer is important to understand where gender equality initiatives present a cost-effective and politically feasible approach to cleaning up government.

Private information characteristics like resolve and audience costs are powerful influences over strategic international behavior, especially crisis bargaining. As a consequence, states face asymmetric information when interacting with one another and will presumably try to learn about each others' private characteristics by observing each others' behavior. A satisfying statistical treatment would account for the existence of asymmetric information and model the learning process. This study develops a formal and statistical framework for incomplete information games that we term the Bayesian Quantal Response Equilibrium Model (BQRE model). Our BQRE model offers three advantages over existing work: it directly incorporates asymmetric information into the statistical model's structure, estimates the influence of private information characteristics on behavior, and mimics the temporal learning process that we believe takes place in international politics.