THE CONSISTENCY OF JUDICIAL CHOICE Paul M. Collins, Jr. Department of Political Science University of Houston Houston, TX 77204-3472 [email protected] ABSTRACT Despite the fact that judicial scholars have developed reasonably well-specified models of the voting behavior of U.S. Supreme Court justices, little attention has been paid to influences on the consistency of the choices justices make. Aside from the methodological problems associated with failure to account for heteroskedasticity with regard to the justices’ voting behavior, I argue that variance in judicial choice is also of theoretical import. Simply put, by uncovering influences on the stability of judicial choice, a more complete understanding of judicial decision making is provided. I explore this possibility by developing a theoretical framework that uncovers influences on the consistency of judicial choice which are then subjected to empirical validation. I show that the stability of judicial decision making is affected by attitudinal and strategic factors, as well as the Court’s informational environment. PAPER PREPARED FOR DELIVERY AT THE 101ST ANNUAL MEETING OF THE AMERICAN POLITICAL SCIENCE ASSOCIATION, WASHINGTON, D.C., SEPTEMBER 1-4, 2005The pursuit of consistency is perhaps the driving force behind all decision making (Heider 1946). Yet, within the context of judicial politics, consistency is often elusive.1 Although confronted with identical argumentation, jurists on collegial courts routinely disagree as to how the law should be applied in any given case.2 With regard to the U.S. Supreme Court, this is perhaps no more evident than in the rise of nonunanimous decisions since the early twentieth century. Still, researchers seeking to explain the behavior of judicial decision makers are necessarily motivated by the search for consistency within the behavior of jurists. Indeed, the concept of consistency is a key aspect of all of science: it is predictability that guides the researcher towards his or her formulation of theories and hypotheses that later allow for generalization (Severin and Tankard 1997: 159). Despite the pivotal role of consistency in understanding judicial behavior, few analyses explicitly deal with the concept.3 This is unfortunate, for both judicial scholars (e.g., Baum 1994) and Supreme Court justices (e.g., Scalia 1997) recognize the desire for consistency in judicial decision making. But, by focusing primarily on how different types of variables influence the ultimate decisions of jurists, we are, in effect, missing a large piece of the puzzle. A more complete understanding of judicial behavior is achieved through the supplementary understanding of the variability in judicial decision making. In other words, instead of only trying to determine whether some Χ causes Υ, a full understanding of the choices jurists make can only be achieved if we also 1 For example, the justices have yet to articulate a clear standard for the alteration of precedent (see, e.g., Justice Scalia, concurring, in Harper v. Virginia Department of Taxation, 509 U.S. 86, at 103 [1993]). 2 Similarly, trial court judges handling different cases touching the same legal concept regularly disagree as to the correct interpretation of the law. 3 For examples of research dealing with variability in decision making on the Supreme Court, see, e.g., Brenner and Spaeth (1995) and Spaeth and Segal (1999), analyzing the justices’ voting behavior in precedent setting cases and cases that challenge those precedents, Epstein et al. (1998), addressing preference change over time, and Zorn and Caldeira (2003), investigating bias in media based measures of Supreme Court preferences (discussed in detail below). 1seek to determine whether some Ζ contributes to the variably in Υ.4 The purpose of this paper is to contribute to an understanding of variability in judicial behavior by analyzing the circumstances under which the decision making of U.S. Supreme Court justices is more and less consistent. An understanding of the consistency of judicial choice is important in several regards. First, an appreciation of the factors that contribute to the stability of judicial decision making in the Court speaks directly to extant models of judicial decision making. For example, Segal and Spaeth (1993: 69) propose that Supreme Court justices are able to “further their policy goals because they lack electoral or political accountability, ambition for higher office, and comprise a court of last resort that controls its own jurisdiction.” Though some may quibble with this characterization of the Court, we are unable to determine – save for a massive overhaul of the Court, itself – whether the attitudinal model would manifest itself with regard to the Court by taking away the justices’ lack of accountably, ambition, or the fact the Court sits atop the apex of the judicial system. However, we can examine the attitudinal model in cases that reach the Court via mandatory appeal. If controlling its own agenda is central to the attitudinal model, we would expect that, for cases reaching the Court on mandatory appeal, the attitudinal model is attenuated, i.e., the justices’ behavior is less consistent (in terms of attitudinal voting) in these mandatory appeals. As will be argued below, an understanding of the variance in judicial choice is equally applicable to judicial decision making based on strategic approaches. Second, there exists an important methodological reason for examining variance in judicial decision making. Virtually all models of the justices’ voting behavior utilize a dichotomous dependent variable (whether it be operationalized as reverse/affirm, liberal/conservative, or by some 4 To more clearly make this point, allow me to draw an analogy to grades. While the mean (median, mode) certainly reveals information regarding the standing of the class on some assignment, the standard deviation also reveals a great deal of information. If we focused only on some measure of central tendency we would greatly reduce our knowledge of student progress by obscuring underlying tendencies in the data that are revealed by the standard deviation (i.e., variance). 2other means) and, accordingly, utilize maximum likelihood techniques for statistical inference. But, if heteroskedasticity exists in a maximum likelihood model, it leads to inefficient estimates of the model’s