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The Role of Judge Gender and Ideology in Hiring Female Law Clerks

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Abstract

Federal law clerks play a vital role in the development and implementation of the law. Yet, women remain underrepresented in these positions. We suggest that one reason for this underrepresentation may be differences in hiring practices among judges in the federal judiciary. Specifically, we hypothesize that male judges and conservative judges may be less likely to hire female law clerks than female judges and liberal judges for two reasons. First, gendered attitudes held by judges may make some judges prone to hire women and/or others more resistant to these hires. Second, due to ideological asymmetries between the law clerk pool and judges in the federal judiciary, conservative judges and male judges may be less likely to hire women law clerks. Using data on clerks hired in the federal judiciary between 1995 and 2005, we find support for both mechanisms.

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... Since Peppers' Courtiers of the Marble Palace (2006), many studies have examined the selection of Supreme Court clerks (e.g., Kromphardt 2014;Kaheny et al. 2015;Badas and Stauffer 2023;Badas, Sanders, and Stauffer 2024) and the influence law clerks wield while serving the judge (e.g., Ward and Weiden 2006;Peppers and Zorn 2008;Black and Boyd 2012;Blake, Hacker, and Hopwood 2015;Kromphardt 2015;2017;Mascini and Holvast 2024;Bonica et al. 2019). Similarly, quite a few scholars have explored the influence of attorneys on the US Supreme Court (e.g., McGuire 1995;Johnson, Wahlbeck, and Spriggs 2006;Corley 2008;Black and Owens 2013;Gleason, Jones, and McBean 2019;Gleason 2020;Nelson and Epstein 2022) and other courts (e.g., Haynie and Sill 2007;Kaheny, Szmer, and Sarver 2011;Sheehan and Randazzo 2012;Szmer, Songer, and Bowie 2016). ...
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Many social scientists believe that dumping long lists of explanatory variables into linear regression, probit, logit, and other statistical equations will successfully “control” for the effects of auxiliary factors. Encouraged by convenient software and ever more powerful computing, researchers also believe that this conventional approach gives the true explanatory variables the best chance to emerge. The present paper argues that these beliefs are false, and that without intensive data analysis, linear regression models are likely to be inaccurate. Instead, a quite different and less mechanical research methodology is needed, one that integrates contemporary powerful statistical methods with deep substantive knowledge and classic data—analytic techniques of creative engagement with the data.