More on the Influence of Law Clerks
on Feb 1, 2007 at 12:58 pm
The following is by Professor David Stras of the University of Minnesota Law School. Professor Stras will occasionally provide commentary on the Court’s business and alert readers to significant academic developments regarding the Supreme Court.
A few months ago, Todd Peppers and Christopher Zorn posted a paper on SSRN, see here, entitled “Law Clerk Influence on Supreme Court Decisionmaking.†You may recognize Professor Peppers as the author of “Courtiers of the Marble Palace,†one of the two books recent books that address the influence of Supreme Court law clerks. I will be publishing a review essay in the Texas Law Review later this spring, see here, reviewing Peppers’ book and “Sorcerer’s Apprentices†by Artemus Ward and David Weiden, and providing some empirical data on the influence of the Court’s cert pool.
Peppers and Zorn take a stab at another aspect of clerk influence—that is, whether the law clerks influence the Justices’ votes on the merits of the cases. Although there are some serious limitations to their approach, their paper represents the first attempt to systematically and empirically assess the issue. Using the responses on completed surveys by 639 former clerks, each clerk is coded as belonging to either the Republican (coded 1) or Democratic (coded 0) party at the time of their clerkships. The authors then use the valuable United States Supreme Court Judicial Data Base, which is compiled and updated by Harold Spaeth and others, to code every Supreme Court case as either reaching a liberal or conservative result. The other major variable in their model is Justice Ideology, in which the authors employ Segal-Cover scores, which assign a value between 0 and 1 for every Justice depending on their judicial ideology. For example, Justice Scalia scores a 0 because he is viewed as the most conservative Justice using those scores, while Justices Brennan and Marshall are on the other end of the ideological spectrum. To alleviate concerns about selection bias with respect to the return of surveys, they also run various statistics that permit them to predict the survey responses of nonrespondents. A logit regression model with both their combined measure of clerk partisanship, which includes both predicted and actual responses on partisanship, and Justice ideology using the Segal-Cover scores, predicts that a one-unit change in clerk partisanship (from homogenously Democratic clerks to homogeneously Republican ones) decreases the odds of a liberal vote by 40%. Three of their other logit regression models are also statistically significant and suggestive that clerks influence the merits votes of their Justices. One major weakness (among others) of their approach, of course, is that it uses the law clerks’ party affiliation as a proxy for conservativism or liberalism, which may skew the results some.
What I find most striking about the paper is a point that they do not discuss much, if at all. According to their data, the party affiliation of the law clerks as a group is tilted decidedly toward the Democratic Party. In figure 3, it shows that the predicted probability of any particular clerk having a Republican affiliation for Antonin Scalia, the most conservative Justice, is just over 50%. Meanwhile, that same statistic for the most liberal Justices is just under 20%. These statistics strike me as unsound, especially with respect to recent law clerk hiring. As Peppers himself points out in his book, Justices have been increasingly relying on “feeder judges†for their hiring, and my experience is that many of these judges examine ideology prior to the hiring of a clerk. If that is true, I wonder whether their figures are temporally dependent—that is, the probability of a conservative Justice hiring a conservative clerk today is greater than the numbers for a comparable Justice during the 1950s. I have not seen the survey, but I also wonder whether some conservative clerks self-identified as libertarians, which would explain the decidedly liberal slant to their data. In any case, I highly recommend this paper, and look forward to your comments on it.