Education policy research looking at gender imbalances in technical fields often relies on observational data or small N experimental studies. Taking a different approach, we present the results of one of the first and largest randomized controlled trials on the topic. Using the 2014 Political Methodology Annual Meeting as our context, half of a pool of 3,945 political science graduate students were randomly assigned to receive two personalized emails encouraging them to apply to the conference (n = 1,976), while the other half received nothing (n = 1,969). We find a robust, positive effect associated with this simple intervention and suggestive evidence that women respond more strongly than men. However, we find that women's conference acceptance rates are higher within the control group than in the treated group. This is not the case for men. The reason appears to be that female applicants in the treated group solicited supporting letters at lower rates. The contributions from this research are twofold. First, our findings are among the first large-scale randomized controlled interventions in higher education. Second, and less optimistically, our findings suggest that such "low dose" interventions may promote diversity in STEM fields, but that they have the potential to expose underlying disparities when used alone or in a non-targeted way.
Although understanding the role of race, ethnicity, and identity is central to political science, methodological debates persist about whether it is possible to estimate the effect of something ``immutable.'' At the heart of the debate is an older theoretical question: is race best understood under an essentialist or constructivist framework? In contrast to the ``immutable characteristics'' or essentialist approach, we argue that race should be operationalized as a ``bundle of sticks'' that can be disaggregated into elements. With elements of race, causal claims may be possible using two designs: (1) studies that measure the effect of exposure to a racial cue and (2) studies that exploit within-group variation to measure the effect of some manipulable element. These designs can reconcile scholarship on race and causation and offer a clear framework for future research.
This study presents an exploration of trends in the American Bar Association ratings of minority judicial candidates over time. Notably, the demographics of minority candidates have changed over time, with minority candidates increasingly resembling white candidates in terms of their educational and professional profiles. However, minority candidates are still more likely to receive lower ratings from the ABA than their white counterparts.
In this article, we consider whether personal relationships can affect the way that judges decide cases. To do so, we leverage the natural experiment of a child's gender to identify the effect of having daughters on the votes of judges. Using new data on the family lives of U.S. Courts of Appeals judges, we find that, conditional on the number of children a judge has, judges with daughters consistently vote in a more feminist fashion on gender issues than judges who have only sons. This result survives a number of robustness tests and appears to be driven primarily by Republican judges. More broadly, this result demonstrates that personal experiences influence how judges make decisions, and this is the first article to show that empathy may indeed be a component in how judges decide cases.
This paper uses two new datasets to investigate the reliance by political actors on the external vetting of judicial candidates, in particular vetting conducted by the nation's largest legal organization, the American Bar Association (ABA). First, I demonstrate that poorly rated lower-court nominees are significantly more likely to have their nominations fail before the Senate. However, I also show that minority and female nominees are more likely than whites and males to receive these lower ratings, even after controlling for education, experience, and partisanship via matching. Furthermore, by presenting results showing that ABA ratings are unrelated to judges' ultimate reversal rates, I show that these scores are a poor predictor of how nominees perform once confirmed. The findings in this paper complicate the ABA's influential role in judicial nominations, both in terms of its utility in predicting judicial "performance" and also in terms of possible implicit biases against minority candidates, and suggest that political actors rely on these ratings perhaps for reasons unrelated to the courts.
The recent subprime mortgage crisis has brought to the forefront the possibility of discriminatory lending on the basis of race or gender. Using the over 10 million observations collected by the federal government in 2006 through the Home Mortgage Disclosure Act, this paper explores these claims causally. In so doing, the paper explores two possible theories of discrimination: (1) that any discriminatory lending patterns are picking up the fact that minority borrowers went to different lenders, perhaps as a result of predatory lending, and (2) the possibility that individual lenders discriminated against identically situated borrowers. The results presented provide limited evidence for the idea that borrowers of different races went to different lenders, but only in certain regions of the country and only for certain minority groups. In addition, many of these results are sensitive to missing confounders – e.g., financial data like credit scores and down payments, which the federal government does not collect. Ultimately, the results’ sensitivity suggests that more data gathering is in order before definitive assertions can be made by legal and policy actors.