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JLEO, V17 N2 477

Comparing Presidents, Senators, and Justices: Interinstitutional Preference Estimation Michael Bailey Georgetown University Kelly H. Chang University of Michigan and University of Wisconsin-Madison

A major challenge in testing spatial, interinstitutional models is placing different sets of actors on a common preference scale. We address this challenge by presenting a random effects, panel probit method which we use to estimate the ideal points of presidents, senators, and Supreme Court justices on one scale. These estimates are comparable across time and institutions. We contrast our method with previously used methods and show that our method increases the ability to study interactions among different institutions.

The complex relations among Congress, the president, and the Supreme Court affect virtually every realm of American policy. In order to examine these relations, a growing number of scholars have developed spatial models of interinstitutional interaction (e.g., Marks, 1988; Gely and Spiller, 1990; Segal, 1997; Moraski and Shipan, 1999). To empirically test these models, we need accurate preference estimates. Estimating preferences within one institution is challenging in and of itself (see inter alia, Adams and Fastnow, 1998; Epstein and Mershon, 1996; Groseclose, Levitt, and Snyder, 1999; Maltzman, 1995; McCarty and Poole, 1995; Poole and Rosenthal, 1997; Zorn, 2001). But estimating preferences across institutions poses further difficulties. Scholars must not only tackle scaling problems within each institution, but they must also calibrate the different scales across institutions. We can say little about the positions of a senator at “5” on a 10-point scale relative to a justice at “2” on a 15-point scale without knowing how the scales relate. The most prominent solution to this problem is to assume that the scales are directly comparable across the institutions (Segal, 1997; Moraski and Shipan, 1999). Given the difficulty of the problem, this is not an unreasonable way to proceed. Given the importance of the problem, however, more

Many thanks to Keith Poole, Nolan McCarty, Sarah Keim, Rudy Espino, Rachel Gorsky, Corrie Potter, Charles Shipan, and several anonymous reviewers. 䊚 2001 Oxford University Press

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The Journal of Law, Economics, & Organization, V17 N2

rigorous methods are necessary to give us confidence in the conclusions we reach based on our empirical analyses. We tackle the problem of estimating interinstitutional preferences with a statistical model that has two key features. The first is the use of “bridge” actors who take positions in all of the institutions. When we estimate ideal points for presidents, senators, and Supreme Court justices, the presidents are the bridge actors because they take public positions both on votes in the Senate and cases before the Supreme Court. The second key feature is the use of a random effects, panel probit framework similar to the approach developed in Bock and Aitkin (1981). Our approach addresses the incidental parameters problem; furthermore, the approach can handle missing data and small numbers of observations for many individuals. The method is applicable to any interinstitutional situation with “bridge” actors who take positions in more than one context. The article proceeds as follows. Part 1 discusses the challenge and the significance of interinstitutional preference estimation. Part 2 explains our approach. Part 3 discusses the data. Part 4 presents the preference estimates. Part 5 reanalyzes Moraski and Shipan’s work on Supreme Court appointments with the estimates from our new method. Part 6 concludes. 1. Interinstitutional Preference Estimation 1.1 The Challenge: Within One Institution

Within a single institution, judicial, legislative, and presidential scholars have long wrestled with the challenges of preference estimation. The most often used measure of preferences is the percent-liberal score—the percent of votes on which a justice or member of Congress chose the liberal alternative. The problems with such measures—identified by Baum (1988), Snyder (1992), and others—have inspired a cottage industry of ideal point estimation methods for the Supreme Court, and more intensely, for Congress (see inter alia Heckman and Snyder, 1997; Poole and Rosenthal, 1997, Groseclose, Levitt, and Snyder, 1999). A fundamental problem of the percent liberal score is its failure to take into account the changing makeup of either the cases or the votes facing the decision makers. Suppose in a given year for the Supreme Court, half of the cases have a cutpoint above “50,” some point in the ideology space of justices, and in the next year only 30% do.1 If a justice’s true ideal point is at 50—and individuals with ideal points higher than the cutpoint vote liberally—then her percent liberal will be 50 in the first year and 70 in the next, a difference due exclusively to varying case makeup. Hence it is essential to estimate vote cutpoints as well as ideal points. The cutpoints calibrate the sets of votes across individuals. Estimating vote

1. Such changes in caseload are not uncommon. For example, in the Burger 6 Natural Court (1981–1985), the percent of civil liberties cases decided in a liberal direction varied from 46.9% to 35.8% (Epstein et al., 1994:168).

Interinstitutional Preference Estimation 479

cutpoints is analogous to adjusting test scores for the difficulty of questions across populations of students who took different versions of a test. But estimating vote cutpoints results in the “incidental parameters” problem: the number of parameters increases with the sample size when one estimates both ideal points and vote cutpoints. Conventional maximum likelihood estimate (MLE) results are not valid in such a situation (Neyman and Scott, 1948; Anderson, 1972; Londregan, 2000). Another issue in the literature is the possibility that preferences change over time. Most approaches yield a single lifetime preference estimate for each justice or legislator. For members of Congress, scholars agree that preferences are quite stable (Poole and Rosenthal, 1997). For justices, however, many agree that at least some justices change substantially over time. Epstein et al. (1998) find that nine of the 16 justices from 1937 to 1993 exhibited significant changes in their views during their careers. For some justices the change is substantial and unambiguous. For example, Warren and Blackmun became substantially more liberal over the course of their careers. Thus estimation routines should allow for changes over time. 1.2 The Greater Challenge: Across Several Institutions

The above issues apply to within-institution estimation efforts. Estimating preferences across institutions is much more challenging since it involves the additional problem of scaling the estimates across the institutions. The example in Figure 1 illustrates these concerns. Suppose we are examining Supreme Court appointments. The top line shows the estimated positions of the president and Senate median. Suppose a new justice, J ∗ , has just been confirmed to a three-person court. The middle line presents one possible scaling of judicial preferences (based, for example, on the assumption that the percentage liberal scores are directly comparable across institutions). The bottom line presents the same scaling shifted by a constant (based, for example, on the assumption that a 50% liberal score in the Supreme Court is equivalent to a 70% liberal score in the Senate). Clearly our conclusions about this appointment will depend on which scaling scheme is correct. If judicial preferences are on the first scale, we would

S J1

P

J*

Court preferences under scaling assumption 1

J3 J1

J*

J3

Court preferences under scaling assumption 2

Figure 1. Possible preference estimates in the face of scaling uncertainty.

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say that the president anticipates the Senate’s preferences; if judicial preferences are on the second scale, we would say that the Senate’s preferences did not affect the president’s choice. Thus scaling problems across institutions can lead to vastly different substantive conclusions, and hence accurate scaling across institutions is crucial to our understanding of interinstitutional interaction. 1.3 The Literature

There are a variety of approaches in the literature which attempt to deal with the challenge of interinstitutional scaling. The first approach is to assume the different scales are directly comparable (Segal, 1997). For justices, Segal uses two measures of ideal points: the Segal and Cover (1989) measure of percent liberal newspaper editorials by the justices before appointment and an annual percent liberal measure in Supreme Court decisions. For Senators, he uses Americans for Democratic Action (ADA) scores.2 Segal then links the scales for the justices and senators by assuming that they are identical. To justify his assumption, Segal asked several public law scholars how the two measures relate; three responded and agreed that a direct comparison of the two sets of scores was better than rescaling one or both measures. As Segal delicately puts it, “this is obviously not a textbook example of scaling” (p. 36); it was, nevertheless, a practical solution to a difficult problem. Moraski and Shipan (1999) follow a very similar strategy in an article on Supreme Court appointments. For justices, they use the Segal and Cover (1989) scores for nominees and percent liberal scores for sitting justices. For presidents and the Senate, they use adjusted ADA scores from Groseclose, Levitt, and Snyder (1999). To link these two measures, they assume that the three sets of preference estimates are on the same scale.3 A second approach provides some statistical or otherwise formal basis for calibrating the scales. One example is Clinton (1998). He provides a threestep method for linking the scales. In the first step he estimates the preferences of justices and presidents in “Supreme Court space.” For justices, these estimates are based on a fixed effect probit model with votes on Supreme Court cases as the dependent variable and justice dummies as independent variables. There are no vote-specific independent variables. Preference estimates for the president in Supreme Court space are based on a percent-liberal score for the president’s solicitor general among cases in which the solicitor

2. For the percent liberal measure, Segal uses predicted scores based on a regression of percent liberal in nonunanimous, constitutional civil liberties cases on a time counter (pp. 35–36). See Epstein and Mershon (1996) for concerns regarding the assumption that preferences are identical across all issue areas. 3. Moraski and Shipan (1999) recognize potential scaling problems and report that their results are unchanged when they are based on other measures of Senate and presidential preferences such as unadjusted ADA scores, NOMINATE scores, and expert assessment-based presidential ratings. Below we present a replication based on our estimates which does produce different results.


general was a petitioner. In the second step, Clinton uses regression to estimate the relationship between the presidential preferences in Supreme Court space and the first dimension of Poole and Rosenthal’s (1997) presidential NOMINATE scores. In the third step, Clinton uses parameters from this second-step regression to generate fitted Senate preference estimates on the same scale as the judicial preference estimates. Although an important step in the literature, there are several limitations to Clinton’s approach. First, the justices’ preferences in Supreme Court space are not necessarily comparable across different natural courts. No case characteristics enter the initial estimation, which means that a liberal vote is a liberal vote no matter what the case makeup. As we pointed out earlier and as Baum (1988) shows, liberal votes across different natural courts are not alike; they depend on the caseload. Second, the method’s multiple steps reduce the efficiency of information use. For example, the estimates of the presidential ideal points in the Supreme Court space exclude information about presidential positions found in Senate votes. This information is relevant for tying down the presidents’ positions as well as those of the senators. In order to make the most use of available information, it is better, if possible, to estimate ideal points for all actors at once. Third, using Poole and Rosenthal’s first dimension may be problematic. Some would consider the second dimension of Poole and Rosenthal scores to be more related to civil liberties, as they appear to tap race and region.4 Another example of a more formal and statistical approach to this problem is Bergara, Richman, and Spiller (2000). They estimate the parameters of a linear transformation which translates Segal’s constitutional scores into the ADA dimension, an approach which is similar to Segal, Cameron, and Cover (1992).5 A related approach to the problem is to link the preferences of senators, justices, and presidents via a set of actors who take positions in more than one institutional environment. Poole and Rosenthal (1997) and Groseclose, Levitt, and Snyder (1999) use members who served in both the House and the Senate to estimate a common space for representatives’ and senators’ preferences. McCarty and Poole (1995) create presidential scores by adding the presidents’ positions on congressional votes to NOMINATE preference estimation routines. Key to these methods is the use of a single data matrix that allows for direct estimation of preferences across institutions. The method we present below develops a similar method to link the three institutions through a common actor, the president. The president votes, in a manner of 4. Clinton also uses cases in which the United States is a party, a set of cases for which the U.S. may be forced to participate in a manner most conducive to winning the case. As will become clear later, we focus on solicitors’ general amicus filings, which are voluntary and therefore allow more latitude to act ideologically. 5. Spiller and Gely (1992) impute ADA scores for the Supreme Court as a whole from a proxy for the Court’s preferences, the proportion of justices who are Democrats. As their approach does not yield individual justice estimates, it is not comparable to the approaches discussed and developed here.

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speaking, in both the Supreme Court and Congress by taking public positions on matters before the two branches. 1.4 The Significance

What kinds of problems are relevant for interinstitutional preference estimates? Here we discuss two examples of separation of powers analyses which highlight the centrality of interinstitutional preference estimation. The first concerns the extent to which justices are directly constrained in their decision making by other institutional actors. Formal models show that the president and Congress can constrain the Court to make decisions within the pareto set defined by the president’s and Congress’ preferences (Gely and Spiller, 1990; Ferejohn and Weingast, 1992). If the Court pushes policy outside these bounds, the president and Congress can overturn the Court’s decisions, potentially passing policies far from the Court’s preferences. Therefore strategic, policy-oriented justices should occasionally vote against their short-term preferences in order to preempt Presidential and congressional involvement. To date, the empirical support for the separation of powers model is mixed. Segal (1997) and Clinton (1998) find no support for the model’s predictions.6 However, the methodological problems discussed above cast doubt on Segal’s and Clinton’s results. In addition, Spiller and Gely (1992) find evidence of a constrained judiciary on labor issues, and Bergara, Richman, and Spiller (2000) find that congressional preferences substantially constrain Supreme Court behavior. The second example involves the Supreme Court appointment process. There are two main views about the nature of this process. On the one hand, some emphasize the power of the president. Scigliano (1971:200), for example, concludes that “Presidents have had the dominant part in appointing (Supreme Court) members.” In a more general context, Moe (1985) argues that there is a senatorial norm of deference to the president. This norm is only violated if a candidate possesses some “smoking gun” characteristic that is likely to cause public outcry (e.g., smoking marijuana or allegations of sexual harassment). Other scholars argue that much of the purported evidence for presidential dominance—evidence such as frequent unanimous confirmation votes— is consistent with presidential anticipation of Senate desires (Hammond and Hill, 1993; Nokken and Sala, 1996; Snyder and Weingast, 2000; Chang, this issue). The practice of senatorial courtesy for lower court appointees is an explicit example in which the Senate’s unanimous votes reflect not senatorial deference, but presidential deference.

6. Segal’s (1997) derivation of the separation of powers model contains some errors; as Groseclose and Schiavoni (2000) point out, and Segal (1998) acknowledges, fewer justices are constrained than originally thought by Segal in his 1997 article.


Moraski and Shipan (1999) present a hybrid approach in which the president dominates under some conditions, the Senate dominates in other situations, and neither dominates under still other conditions (see also Hammond and Knott, 1996). They argue that presidential nominations are subject to two kinds of constraints. First, a president appointing a single person to a nine-person court may be limited in the extent to which he or she can influence the Court’s decisions with that sole appointment. Second, the Senate may not approve the president’s choice unless the choice makes its median member better off. Formalizing these constraints, Moraski and Shipan identify three regime types. When the president and Senate median are on the same side of the court median, and the president is relatively close to the court median, the president nominates an individual who shares his preferences. When the president is on the same side of the court median as the Senate median, but relatively far from the court median, the president is constrained to nominate a person acceptable to the Senate. When the president is on the opposite side of the court median from the Senate median, the only possible nominee is one who maintains the status quo by being located at the court median. Moraski and Shipan provide empirical evidence that the predicted actors are influential and that their influence is of the predicted magnitude. However, measurement issues cloud the conclusions, as Moraski and Shipan make problematic assumptions about scaling across institutions. It is possible that their results are driven by misaligned preference estimates as in our example from Figure 1.

2. Methodology 2.1 Intuition

Our approach to interinstitutional preference estimation has two key features. The first is the use of “bridges” across the institutions: individuals who vote in different institutional contexts. In our case, presidents provide the bridges by voting both in the Senate via public positions on legislation and in the Court via public positions on cases. Table 1 provides an illustration of the logic behind our approach. In this example the justices vote on Supreme Court cases, the senators vote on bills, and the president takes public positions on both cases and bills (“1” is a liberal vote, “0” a conservative vote). President 1 is in office for the first four votes, and then president 2 takes over. If there were no error in mapping ideal points to votes, we could make many interinstitutional inferences. Senator 3 is more liberal than all the justices since he was more liberal than president 1 on vote 4, and the president was more liberal than all the justices on vote 1. Senator 1 is more conservative than justice 3, since she was more conservative than president 2 on vote 6, and president 2 was more conservative than justice 3 on vote 5. Of course, this is a toy example, and stochastic variance would make us quite

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Table 1. Voting by Three Types of Actors in Two Institutions

Justice 1 Justice 2 Justice 3 President 1 President 2 Senator 1 Senator 2 Senator 3

1

2

3

4

5

6

0 0 0 1 NA NA NA NA

0 0 1 1 NA NA NA NA

NA NA NA 1 NA 0 1 1

NA NA NA 0 NA 0 0 1

0 0 1 NA 0 NA NA NA

NA NA NA NA 1 0 1 1

tentative in these conclusions. However, as we add justices, presidents, senators, votes, and cases, this kind of logic allows us to make interinstitutional inferences with greater confidence. The second key feature of our approach is the use of a random effects model to deal with the incidental parameters problem. We cannot treat all ideal points and vote parameters as fixed effects due to the small number of observations per Supreme Court case. For each case, we will never have more than nine observations—one for each Supreme Court justice—to estimate the two parameters associated with each decision. Londregan (2000) shows that with small numbers of observations, the vote parameter estimates are inconsistent (see also Franklin and Londregan, 2000). Londregan’s results relate to Neyman and Scott’s (1948) more general point that conventional maximum likelihood estimates are inconsistent when the number of parameters increases with sample size. 2.2 Theory and Estimation

In this section we present the statistical model underlying the above intuition for our method. The approach is based on Bock and Aitkin (1981) and Mislevy (1987). In the model, political actors (justices, presidents, and senators) vote on bills or cases based on general orientations toward civil rights and random shocks. The ideal point for an individual on vote t is it = i + i1 p + i2 p2 + it = ¯ip + it

(1) (2)

where p is the number of years the individual has served until the vote, and it is a random shock distributed N 0 i . For notational simplicity, we let ¯ip = i + i1 p + i2 p2 . The parameters i1 and i2 capture the potentially dynamic nature of preferences in terms of both linear and/or quadratic trends. The structural form is similar to that of Poole and Rosenthal (1997). Based on Poole and Rosenthal’s findings of relatively stable Senate preferences, we do not estimate dynamic preferences for senators. Based on Epstein et al. (1998),


we do estimate dynamic preferences for the subset of justices most likely to exhibit change over time: Frankfurter, Douglas, Black, Clark, Warren, White, Blackmun, Powell, Stevens, Kennedy, and Souter. For justices with well-known, stable ideological preferences such as Thomas, Scalia, Marshall, and Brennan, we estimate fixed ideal points. There are several advantages to estimating dynamic preferences. First, if we strongly suspect preference changes over time, incorporating such changes in the estimation process enhances the accuracy of all the estimates. Second, estimating dynamic preferences allows us to determine whether the dynamic patterns found by Epstein et al. persist even when we measure preferences with a more explicit statistical model. Third, estimating dynamic preferences allows us to extend the analysis of preference dynamics beyond the period covered by Epstein et al., who did not analyze any justices appointed after Lewis Powell. Our model allows us to capture two sources of shocks. The first is a shock to an individual’s ideal point, it , as in Equation (2). As previously mentioned, it is distributed N 0 i . The shock has to do with how individuals differ in their ideological consistency. For any two individuals with the same mean preferences, one may exhibit much more variance in his or her voting. We capture this feature by estimating a parameter for each individual. Poole (2000) is, to our knowledge, the first to estimate such a parameter in political science. The second source of shocks is vote specific. The idea behind this shock is that some votes may divide individuals cleanly along ideological lines, while others may feature ideologically jumbled coalitions. Specifically, we assume that person i perceives the location of vote alternative 1 to be it1 = t1 + it , where it is a random shock distributed N 0 t . To keep matters manageable, we draw one shock for both alternatives.7 We can write the utility of actor i as a loss function of the actor’s ideal point and the vote location: ui t1 = −it − it1 2

(3)

= −¯ip + it − t1 − it 2

(4)

Since actor i votes for the alternative which yields higher utility, if we assume

it and it are independent of one another,8 and let yit = 1 if i votes for t1 over t0 , then Pryit = 1 = Pui t1 > ui t0 2 2 − t1 = P −2¯ip t1 − t0 + t0 − 2it − it t1 − t0 > 0

(5)

(6)

7. The spatial locations of each alternative are not identified. Therefore, as will become clear below, we estimate only one cutpoint parameter between the spatial locations of the two alternatives. 8. See Zorn (2001) for estimates of cross-individual correlation on Supreme Court voting.

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+ t0 = P it − it < ¯ip − t1 2 ¯ ip − t = t + i

(7) (8)

t0 9 where t1 > t0 , and t = t1 + . 2 The probability of observing the data is

PY =

N T

¯ 1−yit ¯ ip − t ip − t yit 1−

i + t

i + t

(9)

If we observe the vote parameters ( and ), we could easily estimate the individual parameters ( and ) via nonlinear maximization. However, the vote parameters are not observable. Therefore we follow the marginal maximum likelihood (MML) literature in treating these vote parameters as random effects and integrating them out of the likelihood: PY =

¯ N T ip − t yit

i + t 1−yit ¯ ip − t × 1− PPdd

i + t

(10)

where P() and P() are the densities of these variables. We also make several assumptions which identify and bound the parameters. First, we assume that the s are distributed uniform on 0 3, and the s are distributed uniform on −3 3. The lower bounds maintain positive variance estimates; the upper limits prevent unbounded estimates, an issue in MMLE models (see, e.g., Baker, 1992:97 and Mislevy and Bock, 1990:8). Second, we identify the s by fixing two individuals. We fix a liberal [Senator Burdick (D-ND)] at −15 and a conservative [Senator McClellan (D-AR)] at 1.5. Third, for the same reasons discussed with regard to the s, we assume the i s come from a uniform [0, 3] distribution.10 Fourth, we assume a Bayesian prior for the ideal points. A common problem in many ideal point estimation procedures is that ideal point estimates are unbounded for individuals who always vote liberally or conservatively. Instead of removing

9. See Poole (2000) for a critique of logit error distributions. An alternative way to specify heteroskedasticiy is to write the denominator as i t (Poole, 2000). In our formulation, the identification of the i ’s builds from rewriting the denominator in Equation (8) as ˜ i + ˜ t , where

˜ i = i − min i and ˜ t = t + min i . These are the estimates produced by the estimation procedure. To convert them to the parameters of direct interest, we assume that there is at least one vote on which t = 0. Then min˜ t = min i . Hence, ˆ i = ˜ i + minˆ t . 10. The larger the estimate of i , the less meaningful the value of i ; eventually, for very large i the estimate of i will simply become the estimated mean of the distribution. If the model is estimated without a limit on i , this occurs for fewer than 10 individuals.


such individuals from the dataset, we impose our prior belief that is distributed normally across the individuals with a mean and variance which we estimate.11 With these priors we incorporate our doubt that any individual’s ideal point is extremely far away from the central tendencies of the others. We use normality for convenience. An expectation-maximization (EM) algorithm simplifies the estimation (see the appendix).12 An EM algorithm is an iterative estimation method that allows us to avoid more challenging direct Newton-type maximization methods. It proceeds as follows.13 First, we write down a likelihood as if we observe and ; this is called the “complete data likelihood.” If we know and , we can formulate the likelihood in terms of these parameters, and estimating the ideal points would be a straightforward exercise in nonlinear maximization. Since we do not actually observe the vote parameters and , in the “E step,” we use what we do know about the vote parameters based on the provisional ideal point parameter estimates. More precisely, we use Bayes’ theorem to calculate the expected distributions of and given our provisional and estimates. We then formulate the likelihood in terms of these expected distributions of the vote parameters. In the “M step,” we use this expected likelihood as the basis for finding the ideal point parameters which maximize the expected likelihood. From this maximization we have a new set of provisional ideal point estimates that we use in another E step. We repeat these steps until the estimates no longer change from iteration to iteration. More details on the EM estimation process are in the appendix and in Bailey (2001). 3. Data 3.1 Connecting Presidents and Justices

As an indicator of the president’s positions on court cases, we use solicitor general amicus filings [Segal (1989) and Stimson, MacKuen, and Erikson (1995) make this assumption as well]. Several institutional and historical factors support the use of these filings for this purpose. The overt sources of presidential influence on the solicitor general are clear: “the clearest and most important institutional linkage is with the President. It is the president who, by statute, nominates the Solicitor General and at whose pleasure he serves. Should he care to, the president has the coercive language to direct the activities of even a reticent Solicitor General” (Cooper, 1990:7).

11. Information exists even in the votes of individuals who always vote in a particular direction. Compare, for example, the ideal points of an individual who voted conservatively twice (and never liberally) to those of an individual who voted conservatively 50 times (and never liberally). Our approach will estimate the latter to be more conservative. 12. The seminal work on this method is Dempster, Laird, and Rubin (1977). Bock and Aitkin (1981) apply it to random effects models such as ours. We follow the approach presented in Tsutukawa and Lin (1986) and Baker (1992). 13. See Wu (1983) and McLachlan and Krishnan (1997) for more on this process.

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These institutional powers may lead to influence even if overt influence is rare. First, solicitor general appointees often reflect the preferences of the appointing president. As one of Reagan’s former solicitors general, Charles Fried, said, “I have no trouble saying what the Attorney General and his crew want me to, because I’m more conservative than they are” (Cooper, 1990:7). Reagan surely anticipated Fried’s preferences before appointing him. Second, even when the preferences of the solicitor general and the president diverge, the solicitor general may choose to do the president’s bidding, out of deference or out of a desire to avert explicit intervention by the president. In general, there is evidence that the behavior of solicitors general reflects the desires of presidents. Meinhold and Shull (1998) found that presidential policy statements were statistically significant predictors of solicitor general amicus curiae briefs, indicating that solicitors general are responsive to the policy attentiveness and the ideological preferences of the chief executives who appoint them. More specifically, historical examples abound of presidents explicitly guiding solicitor general activities. Fried unabashedly pushed Reagan’s policy goals before the Court (Norman-Major, 1994). Presidents Clinton and Bush both ordered their solicitors general to change positions on cases (Fraley, 1996:22). President Kennedy had frequent contact with Solicitor General Cox (Segal, 1989:142), and President Eisenhower personally added several sentences to the government’s brief in Brown v. Board of Education (Days, 1995:5). In addition, when solicitors stray, presidents push them out. Reagan essentially fired Solicitor General Lee when he expressed reluctance in pursuing Reagan’s agenda (Norman-Major, 1994). Nixon forced out Solicitor General Griswold in 1972 due to a perception that Griswold was too liberal (Salokar, 1992:41). Finally, solicitors general have much more latitude to pursue ideological goals with regard to amicus filings than in cases to which the United States is a party. When the United States is a party to a case, precedent, the stakes of winning, the sometimes nonvoluntary participation, or other nonideological factors may be behind the position taken by the solicitor general. The data on amicus filings come from two sources. First, Gibson (1997) provides data on amicus curiae briefs filed from 1953 through 1987. Second, we created our own dataset of amicus filings for the post-1987 period based on the Supreme Court Briefs database in Lexis-Nexis Academic Universe. In order to ensure the closest possible fit between solicitor general filings and presidential preferences, we limited the sample to cases on which the current president’s solicitor general filed an amicus brief.14 We also 14. For example, only amicus briefs filed by Eisenhower-appointed Solicitors General Sobeloff and Rankin were counted as reflecting Eisenhower’s preferences. The other presidents and the solicitors general they appointed are Kennedy (Cox), Johnson (Marshall, Griswold), Nixon (Bork), Reagan (Lee, Fried), Bush (Starr) and Clinton (Days). An exception is made for President Ford who did not appoint a solicitor general. For him we used the briefs filed by Solicitor General Bork subject to the other conditions we discuss.


excluded cases which were decided during presidential transition periods— periods between November after a change-inducing presidential election and February of the next year.15 The main source of data on the justices’ votes is Spaeth (1997). He provides each justice’s votes on all Supreme Court decisions from 1953 through 1995. In addition, Spaeth (1999) provides the votes on cases during an earlier period, 1946–1953. While votes can obscure important doctrinal distinctions found in the written opinions, they reflect, on the whole, general ideological differences across the justices. In order to match the presidents and justices, we used cases for justices on which the solicitor general filed an amicus brief. Because the solicitor general data are not evenly distributed over time, we augmented the data with randomly selected civil liberties cases from underrepresented terms. Inclusion of these cases provides us with a total of 394 cases, ensuring that we have at least 22 votes for each justice. We have substantially more for most justices, with an average of 113 observations for each justice. 3.2 Connecting Presidents and Senators

As an indicator of the president’s position on Senate votes, we selected Senate civil rights roll calls on which the president took a position and for which a liberal and conservative position were clearly defined according to the definition for liberal and conservative from Spaeth (1997). The selection of civil rights votes was based on issue codes in the Poole and Rosenthal dataset. As with the data for the solicitors general and the justices, we augmented the data on which the president took a position with votes on which the president did not take a position. For example, because Eisenhower did not take a position on any civil rights votes in the 83rd congressional session (1953–54), we added eight civil rights votes from this session to obtain information on senators who did not serve in subsequent sessions. For President Johnson there were more than a hundred votes on which he took a position. In the interest of obtaining a representative but not unwieldy sample, we used a random sample of 20 votes under Johnson.

3.3 Issues in Comparing Preferences Across Institutions and Time

Our underlying statistical model is not a fully specified model of strategic choice. In this section we discuss some implications of this fact for the estimation. First, the selection of court cases may reflect strategic calculations in relation to Congress. In the separation of powers model, justices’ behavior is influenced by strategic considerations about how Congress and the president

15. We used analogous time frames for the transitions from Kennedy to Johnson and Nixon to Ford. We also excluded cases that were unanimously decided, since in such cases the model simply sets the cutpoint very high or very low and adds little information about the ideal points of the individuals.

490


will react (Gely and Spiller, 1990; Ferejohn and Weingast, 1992). One implication is that justices may vote to hear a case only when its probable outcomes will not be overturned by Congress [see Epstein and Knight (1998:84) for a discussion of the separation of powers model with regard to the certiorari process]. Our model allows vote parameters to vary across votes and time, which partially accounts for this problem. However, we do not explore this issue in depth here; a more detailed analysis that distinguishes between constitutional and statutory cases would be an interesting extension. Second, the endogenous nature of the Court’s agenda could skew the cutpoint distribution in the liberal or conservative direction. In some years the Court may pick cases which it will likely decide conservatively or liberally, creating an uneven distribution of vote cutpoints. This is not a problem for our approach, as the approach explicitly incorporates the differences in cutpoints across votes. Third, differences in agenda setting may skew the dimensionality of the issues being considered across the different institutions. For example, it is possible that the Court ends up considering different kinds of cases than the Senate. In response to such concerns, we tailor our analysis as narrowly as possible, looking at only civil liberties cases in the Court and civil liberties votes in the Senate. We also note that there is generally high correlation across dimensions (Poole and Rosenthal, 1985), making it likely that a single preference dimension measures fundamental preferences.16 Fourth, it is possible that justices act differently on the solicitors general amicus filing cases due to some kind of deference to the solicitor general. We examined whether there are systematic differences between our estimates and estimates based on a random sample of all cases. We found that estimates of justices’ ideal points based on the solicitor general cases correlate at 0.95 with those from the entire sample of cases; thus oversampling cases for which the solicitor general filed an amicus brief does not appear to skew the results. Finally, if all actors shift to the right by the same amount in a given year, and the votes remain the same across years, our estimation procedure will provide fixed ideal point estimates and different cutpoint estimates. This issue also arises in Poole and Rosenthal (1997) and Groseclose, Levitt, and Snyder (1999), as well as other works in which each individual has unchanging preferences. An intriguing alternative would be to fix the cutpoints of certain votes and estimate preferences relative to the votes. Adams, Bailey, and Fastnow (2000) pursue this alternative in the legislative context by assuming cutpoints are fixed across chambers for conference reports with identical language. However, this option is not available in our context, as there is no comparable set of votes on identical legislation or identical cases across multiple years. 16. Sophisticated voting could also complicate efforts to estimate preferences. However, Poole and Rosenthal (1997: chap. 7) show that sophisticated voting is not a problem if everyone engages in such behavior and that regardless, there is little evidence that it occurs on a wide scale in Congress.


4. Results and Analysis We combined the Court and Senate datasets into a single database similar to that in Table 1. The data matrix contains 561 votes and 422 individuals. We estimated the random effects model using S-Plus code (available from the authors upon request). In this section we present and discuss the results of our method. We first lay out the results and discuss them in general terms. In the next section we replicate Moraski and Shipan (1999) using our estimates and find that the strength of their results depends importantly on the preference estimates. 4.1 Ideal Point Estimates

Random effects ideal point estimates and percent-liberal scores are presented in Tables 2–4. For the ideal point estimates, lower values indicate liberalism and higher values indicate conservatism. Table 2 and Table 3 present the results for the presidents and senators. The results are in accordance with expectations. Reagan is by far the most conservative president, followed by Bush, Ford, Nixon, Eisenhower, Carter, Clinton, Johnson, and Kennedy. Helms and Thurmond are very conservative, while the Kennedys are quite liberal. Table 4 presents the results for the Supreme Court justices. The table presents i , i1 , and i2 for the justices with dynamic preferences. The ideal point for justice i on any given vote t is it = i + i1 p + i2 p2 + it , where p indicates the number of years the justice has served until that time. We also provide ideal points for the first and last years of the justices with dynamic preferences. The justices with fixed preferences have one estimate for i . The estimates for the justices are also in accordance with expectations. Among justices with fixed preferences, Thomas, Scalia, and Rehnquist are strongly conservative, while Marshall and Brennan are firmly liberal. Figure 2 displays the ideal points over time for the justices with dynamic preferences. Ideal points are on the y-axis, and terms served on the Court are on the x-axis. Most justices exhibited more or less monotonic patterns over the courses of their careers; Powell and White became more conservative Table 2. Presidential Ideal Points and Voting Patterns

President Eisenhower Kennedy Johnson Nixon Ford Carter Reagan Bush Clinton

Percent Liberal

Ideal Point

Court Cases

Senate Votes

−059 −297 −259 −049 −032 −099 276 220 −121

0% 100% 73% 0% 36% 50% 9% 14% 47%

90% 100% 100% 67% 83% 86% 5% 30% 71%

Abdnor Abourezk Abraham Adams Aiken Akaka Allen, J Allen, M Allott Anderson Anderson, W Andrews Armstrong Ashcroft Baker Barrett Bartlett Bartlett2 Bass Baucus Bayh Beall Beall2 Bellmon, H Bender Bennett

Senator

146 −293 184 −105 −058 −194 159 173 −046 −061 −153 −006 226 184 011 021 100 −121 −082 −057 −125 −047 −109 −048 −033 030

Ideal Point

Carroll Case, C Case, F Chafee Chavez Chiles Church Clark Clark, J Clements Coats Cochran Cohen Conrad Cook Cooper Cordon Cotton Coverdell Craig Cranston Crippa Culver Curtis D’Amato Danforth

Senator −166 −319 −006 −184 −002 001 −117 −294 −113 −189 175 061 −093 −111 −052 −082 106 032 087 089 −350 137 −283 050 033 −007

Ideal Point Gambrell Garn George Gillette Glenn Goldwater Goodell Gore Gore, Al Jr Gorton Graham Gramm Grams Grassley Gravel Green Gregg Griffen Griswold Gruening Gurneye Hansen Harkin Harris Hart Hart, G

Senator

Table 3. Senate Ideal Points and Voting Patterns, 1953–1996

087 216 120 −094 −155 054 −102 021 −115 073 −055 153 184 096 −164 −062 087 −043 −020 −165 055 106 −155 −193 −393 −402

Ideal Point Jenner Jepsen Johnson, E Johnson, L Johnston Johnston, B Jordan, B Jordan, L Karnes Kassebaum Kasten Keating Kefauver Kempthorne Kennedy, J Kennedy, R Kennedy, E Kerr Kerrey Kerry Kilgore Knowland Kohl Krueger Kuchel Kyl

Senator 055 023 148 −006 243 −005 165 005 122 007 056 −177 −008 083 −223 −188 −202 036 −080 −145 −318 007 −124 −029 −093 184

Ideal Point McNamara Mechen Melcher Metcalf Metzenbaum Mikulski Miller Millikin Mitchell Mondale Monroney Montoya Morgan Morse Morton Moseley-Braun Moss Moynihan Mundt Murkowski Murphey Murphy Murray Murray, P Muskie Neely

Senator −192 056 −153 −441 −217 −115 −016 017 −163 −268 −038 −075 −009 −495 −017 −228 −102 −251 018 051 148 014 −051 −067 −115 −391

Ideal Point Schmitt Schoeppel Schweiker Scott Scott2 Scott, W Seymour Shelby Shelby(Rep) Simon Simpson Simpson2 Smathers Smith Smith, B Smith, H Smith, M Smith, R Snowe Sparkman Specter Spong Stafford Stennis Stevenson Stevens

Senator −041 022 −055 193 −127 223 075 −004 030 −302 058 054 083 −075 −099 −007 −056 186 −041 185 −038 022 −089 156 −218 −002

Ideal Point

492 The Journal of Law, Economics, & Organization, V17 N2

Bennett2 Bentsen Bible Biden Bingaman Boggs Bond Boren Boschwitz Bowring Boxer Bradley Brady Breaux Brewster Bricker Bridges Brock Brooke Brown Brunsdale Bryan Buckley

167 −011 006 −212 −085 −108 136 −051 −027 163 −194 −103 −018 −007 −185 021 019 076 −271 034 026 −053 011

Daniel Daschle DeConcini Denton Dewine Dirksen Dixon Dodd Dodd, C Dole Domenici Dominick Dorgan Douglas Duff Durenberger Durkin Dworshak Eagleton East Eastland Ellender Engle

167 −113 −026 286 184 −029 −012 −173 −112 019 017 −037 −099 −391 017 −209 −127 037 −121 286 211 176 −177

Hartke Haskell Hatch Hatfield Hatfield, P Hathaway Hawkins Hayakawa Hayden Hecht Heflin Heinz Helms Hendrickson Hennings Hickenlooper Hickey Hill Hoblitzel Hodges Hoey Holland Hollings

−160 −057 153 −231 −162 −294 146 159 005 234 −006 −054 279 106 −391 055 −035 159 019 −162 064 119 −003

Laird Langer Lausche Lautenberg Laxalt Leahy Lehman Lennon Levin Lieberman Long, E Long, O Long, R Lott Lugar Lusk Mack Magnuson Malone Mansfield Martin Martin, E Martin, T

−033 −188 −050 −191 213 −191 −390 097 −168 −059 −170 −210 089 155 129 −075 109 −106 053 −061 017 007 007

Nelson Neuberger Neuberger 2 Nickles Nunn O’Mahoney Packwood Pastore Payne Pearson Pell Percy Potter Pressler Prouty Proxmire Pryor Purtell Quayle Randolph Reid Revercomb Reynolds

−160 −178 −157 155 −006 −006 −132 −098 007 −060 −158 −100 007 100 −047 −117 −062 −041 133 −079 −039 007 137

Steward Stone Symington Symms Taft Talmadge Thomas Thompson Thurmond Thye Tobey Tower Trible Tsongas Tunney Tydings Upton Wallop Walters Warner Watkins Weicker Welker Continued

049 −026 −082 262 −073 181 089 184 176 007 −020 090 084 −159 −288 −075 163 237 194 101 007 −189 120 Interinstitutional Preference Estimation 493

Bumpers Burdick Burke Burns Bush Butler Butler, H Byrd Byrd, H Byrd, R Campbell Campbell (Rep) Cannon Capehart Carlson

Senator

Ideal Point

−069 −150 −128 110 −228 037 064 271 176 −005 −030 −002 −004 007 003

Table 3. Continued

Ervin Evans Exon Faircloth Fannin Feingold Feinstein Ferguson Flanders Fong Ford Fowler Frear Frist Fulbright

Senator 165 −081 −005 087 057 −432 −041 106 021 −138 −005 −083 054 184 070

Ideal Point Hruska Huddleston Hughes Humphrey, G Humphrey, H Humphrey, M Humphreys Hunt Hutchison Inhofe Inouye Ives Jackson Javits Jeffords

Senator 041 005 −215 368 −285 −162 −033 064 147 089 −145 −125 −085 −172 −091

Ideal Point Mathews Mathias Matsunaga Mattingly Maybank McCain McCarran McCarthy McCarthy, J McClellan McClure McConnell McGee McGovern Mclntyre

Senator −028 −304 −167 146 062 047 247 −182 120 150 230 160 −060 −147 −112

Ideal Point Ribicoff Riegle Robb Robertson Rockefeller Roth Rudman Russell Russell2 Saltonstall Sanford Santorum Sarbanes Sasser Saxbe, W

Senator −167 −106 −060 167 −086 028 035 085 166 −059 −116 165 −127 −079 −054

Ideal Point Wellstone Wiley Williams Williams2 Wilson Wirth Wofford Wofford2 Yarborough Young, M Young, S Zorinsky

Senator −250 −026 048 −165 005 −114 −092 124 −053 036 −295 141

Ideal Point

494 The Journal of Law, Economics, & Organization, V17 N2


Table 4. Supreme Court Ideal Points and Voting Patterns Ideal Point Justice Black Blackmun Brennan Breyer Burger Burton Clark Douglas Fortas Frankfurter Ginsburg Goldberg Harlan Jackson Kennedy Marshall Minton Murphy O’Connor Powell Reed Rehnquist Rutledge Scalia Souter Stevens Stewart Thomas Vinson Warren Whitaker White

1

2

1st Year

Last Year

−210 219 −280 −163 215 270 019 −191 −302 359 −078 −289 349 274 381 −353 291 −372 211 112 317 404 −390 300 586 015 069 425 184 171 135 −008

−070 −021 000 000 000 000 055 −047 000 −138 000 000 000 000 −072 000 000 000 010 022 000 000 000 000 −375 −015 000 000 000 −077 000 014

003 000 000 000 000 000 −003 001 000 010 000 000 000 000 005 000 000 000 −002 −001 000 000 000 000 042 −001 000 000 000 003 000 000

−276 198 −280 −163 215 270 072 −237 −302 230 −078 −289 349 274 313 −353 291 −372 219 133 317 404 −390 300 253 −001 069 425 184 097 135 005

220 −300

000 −686 524

105

−039 192

−149 −644

−247 158

Percent Liberal

Segal–Cover Score

73% 56% 83% 71% 18% 25% 29% 93% 79% 43% 59% 92% 23% 28% 30% 92% 27% 86% 34% 23% 21% 7% 95% 15% 58% 73% 42% 6% 27% 73% 31% 34%

088 012 100 048 012 028 050 073 100 067 068 075 088 100 037 100 072 — 042 017 073 005 — 005 033 025 075 016 — 075 050 050

over time, while Blackmun, Clark, Douglas, O’Connor, Souter, Stevens, and Warren became more liberal. Others, however, exhibited nonmonotonic change; Black and Frankfurter became more liberal before moving in a conservative direction, while Clark became conservative before moving in a liberal direction. Looking across the institutions, the results are also reasonable and interesting. Not only are Justices Rehnquist, Thomas, and Scalia conservative, they are much more conservative than Presidents Reagan and Bush. Similarly, Justices Brennan, Marshall, and Stevens (in his later years) are substantially more liberal than the most liberal presidents, Presidents Kennedy and Johnson. In terms of recent appointments, we can see that Breyer is more liberal than Clinton, while Ginsburg is slightly more conservative and very near Senator Lieberman.


-4 -8

4 2 0

Ideal Point

4 2 0

Ideal Point

Ideal Point

496

-4 -8

5

10

15

5

10

5

-4 -8

10

15

20

25

30

-8

4

6

8

10

12

14

2

6

8

10

Ideal Point

4 2 0 -4

12

14

10

-8

Stevens

15

1

2

3

20

4

5

6

25

30

Souter

4 2 0 -4 -8

10

-4

15

Ideal Point

Ideal Point

-4

8

4 2 0

Powell

4 2 0

6

-8 5

O'Connor

5

4

Kennedy

-8 4

-4 -8

2

Ideal Point

-4

15

4 2 0

Frankfurter

4 2 0

2

10 Clark

4 2 0

Douglas

Ideal Point

20

Ideal Point

Ideal Point

Ideal Point

-4 -8

Ideal Point

15

Blackmun

4 2 0

5

-4 -8

20

Black

0

4 2 0

4 2 0 -4 -8

5

10

15

0

Warren

5

10

15

20

White

Figure 2. Ideal points of the justices with dynamic preferences.

The estimates further indicate that simply assuming measures are comparable across institutions is problematic. For example, Table 2 indicates that percent-liberal measures are not directly comparable across institutions, as there are substantial differences in the president’s percent liberal in court cases versus percent liberal on Senate votes.17 President Johnson, for example, was liberal on 73% of court cases and 100% of Senate votes, indicating that the distribution of cutpoints of the votes facing Johnson was different in the Court and the Senate, thereby rendering direct comparison of percentliberal scores across chambers inappropriate. The same is true of the Court and the Senate. Consider, for example, Whitaker and Minton; both voted liberally nearly 30% of the time, but 17. The difference for Eisenhower is exaggerated because there was only one civil rights amicus filing under his administration.


Means Medians

0.4 Conservative

Ideology

0.2

-0.0

-0.2

-0.4 Liberal

-0.6

1960

1970

1980

1990

Year Figure 3. Senate means and medians, 1953–1995.

their estimated ideal points are quite different, with Whitaker being substantially more liberal. Pairing Murphy and Brennan or Breyer and Black yields similar conclusions. For the Senate, Democrats Estes Kefauver and Jay Rockefeller both voted liberally about 70% of the time, but because they voted on very different sets of votes, their estimated ideal points are quite different: Kefauver is more of a moderate at −008, and Rockefeller is more liberal at −086.18 As a validity check, Figure 3 presents the Senate median and mean for each session of Congress from 1953 to 1995. This pattern is similar to that in Groseclose, Levitt, and Snyder (1999). From 1953, there was a steady liberalization of the Senate until 1981, when the Senate became drastically more conservative. In 1987, another liberalization occurs until 1993 when another conservative swing began. Table 4 also makes it clear that we should be careful to use Segal and Cover scores for their intended purpose only: to test the attitudinal model based on pre-Court preferences (Segal and Cover, 1989; Epstein and Mershon, 1996). They are not appropriate for estimating the preferences of a sitting court when, for example, we need to know the median position of the Court. For example, based on the Segal and Cover scores, one would conclude that Jackson and Harlan are more liberal than Douglas and Goldberg! The content scores reflect perceived positions at the time of appointment [subject to concerns such as those raised in Epstein and Mershon (1996)], but they do not necessarily estimate the preferences of a sitting court. 18. Percent-liberal data for all senators is available upon request from the authors.

498


5. A Reanalysis of Moraski and Shipan As discussed earlier, Moraski and Shipan (1999) present an elegant theoretical framework for understanding Supreme Court nominations. However, their method of calibrating preference estimates across institutions leaves their conclusions open to doubt. Therefore we replicate their analysis with the estimates from our method. Moraski and Shipan formally derive three separate regimes of appointments from a one-dimensional model in which actors have single-peaked preferences. In regime 1, the president is unconstrained, as both the president and Senate median want to pull the Court in the same direction, and the Senate prefers the president’s ideal point to the current status quo. In regime 2, the president and the Senate want to move the Court in the same direction, but the Senate is more moderate, meaning that the Senate prefers the status quo to the president’s ideal point. In this case the president will anticipate the needs of the Senate, picking a nominee who is as close as possible to the president’s ideal point and who makes the Senate indifferent between the status quo and the nominee. In regime 3, the president and Senate want to pull the Court in opposite directions, meaning that the Senate will not accept a nominee other than one that preserves the status quo. Based on this model, Moraski and Shipan begin by estimating the model Nt = 0 + 1 D1 Pt + 2 D2 Ist + 3 D3 Jt + t

(11)

where Nt is the nominee’s position, Dk is a dummy indicator of regime k, P is the president’s ideal point, Is is the indifference point of the Senate, and J is the status quo court median. Their theory is actually much stronger, however, implying not only that the president’s ideal point matters in regime 1, but that it should not matter in regimes 2 and 3. Similarly the Senate indifference point should not matter in regimes 1 and 3 and the Court status quo should not matter in regimes 2 and 3. They therefore estimate a more fundamental model: Nt = 0 + 1 D1 Pt + 2 D2 Ist + 3 D3 Jt + 4 D2 + D3 Pt + 5 D1 + D3 Ist + 6 D1 + D2 Jt + t

(12)

Moraski and Shipan’s theory provides more guidance than simply predicting that the coefficients are positive and different than zero. In both specifications, Moraski and Shipan also expect (and find) the coefficients to be near one, as the model provides a point prediction for each of the regimes. That is, in regime 1, their model predicts the nominee to be at exactly the preferences of the president. In regime 2, their model predicts the nominee to be at exactly the Senate indifference point. Finally, in regime 3, their model predicts the nominee to be at exactly the Court median. The first step in replicating this analysis is to classify nominations by regime type. Based on our preferences estimates, there are many differences as reported in Table 5. For example, Moraski and Shipan’s measures indicate

∗∗ For

1 3 1 1 1 1 1 3 1 3 1 1 1 1 2 1 1 1 2 3 3 1 1

Moraski and Shipan 1 1 1 2 1 3 3 3 3 3 1 1 1 1 1 3 3 2 2 3 3 2 2

Random Effects

Regime Coding

Eisenhower Eisenhower Eisenhower Eisenhower Eisenhower Kennedy Kennedy Johnson Johnson Johnson Nixon Nixon Nixon Nixon Ford Reagan Reagan Reagan Reagan Bush Bush Clinton Clinton

Name

Ideal Point −059 −059 −059 −059 −059 −297 −297 −259 −259 −259 −049 −049 −049 −049 −032 276 276 276 276 220 220 −121 −121

President

year ideal points for justices whose preferences varied over their tenure. promotion to chief justice.

097 349 −280 135 069 005 −289 −302 −353 −302 215 198 133 404 −001 219 404 300 313 253 425 −078 −163

Warren Harlan Brennan Whitaker Stewart White Goldberg Fortas Marshall Fortas∗∗ Burger Blackmun Powell Rehnquist Stevens O’Connor Rehnquist∗∗ Scalia Kennedy Souter Thomas Ginsburg Breyer

∗ First

Ideal Point∗

Justice

Nomination

Table 5. Classifying Appointments by Regime Type

044 008 007 007 −040 −040 −040 −055 −055 −055 −054 −054 −052 −052 −066 004 007 007 −009 −018 −031 −016 −016

Median

Senate

Clark/Burton Frankfurter/Clark Frankfurter/Clark Warren/Frankfurter Warren/Whitaker Warren/Stewart Black/White Black/Clark Warren/Black Warren/Black Stewart Stewart/White Stewart Stewart Stewart/White Blackmun/White Blackmun/White Blackmun/White Blackmun/O’Connor O’Connor/Kennedy O’Connor/Kennedy O’Connor/Kennedy O’Connor/Kennedy

Median

Court

244 152 152 033 −065 −098 −124 −070 −111 −111 069 082 069 069 113 021 021 000 −051 120 071 051 033

Ideal Point


500


that President Kennedy was unconstrained when appointing Justice White (regime 1), while our measures indicate that the president and Senate were deadlocked (regime 3) since the moderately liberal Senate was opposite the court median to the very liberal Kennedy. For the Harlan appointment, the predictions are reversed; Moraski and Shipan’s measures indicate a deadlock situation (regime 3), while our measures indicate that it was a situation of presidential control (regime 1). In 11 of the 23 cases, our regime classification is different from that of Moraski and Shipan. We should be clear that our replication is based on 23 observations, while Moraski and Shipan base their results on 28 observations. We have five fewer data points due to the exclusion of three failed appointments and two appointments under Truman. We cannot obtain the data our method requires for these cases, as there are no Court votes for the failed appointees, and there are no amicus briefs that meet our requirements under Truman.19 Table 6 presents the results from our replication. Column (a) presents the results for the initial model [Equation (11)]. Two of the three key coefficients (the first three in the table: regime 1, 2, and 3’s predictions) are significantly different from zero, indicating possible support for the theory. However, there are some reasons for caution. First, the sign on 1 is in the wrong direction. Second, the coefficients on regime 2 and regime 3’s predictions are far from their predicted values; using a one-sided t-test, we can reject the null that 2 = 1 at the 0.10 level, and we can reject the null that 3 = 1 at the 0.01 level. Whereas Moraski and Shipan’s theory predicts that the nominee will equal the Senate median in regime 2 and the court median in regime 3, the predicted nominees in this specification are much more extreme, being located at points three times the Senate median and the Court median. The more fundamental specification is in column (b) which corresponds to Equation (12). The theoretical model is again not robust, albeit more obviously. None of the regime prediction coefficients (the first six in the table) are significantly different from zero. The coefficient estimate that is closest to significance is regime 1’s predictions in regimes 2 and 3, which is not predicted to be significant. In sum, the results indicate that Moraski and Shipan’s results do not hold up when our data and methods are used. Our estimates also raise interesting questions about our understanding of Supreme Court appointments. For example, in 20 of 23 cases the nominated justice was not located between the preferred points of the president and the Senate.20 Moraski and Shipan’s theory never predicts this outcome. As currently conceived, it is starkly suboptimal for the president and the 19. Promotions to chief justice do not fit neatly into Moraski and Shipan’s theory. We get the same results as reported below if we exclude them from the analysis. As Moraski and Shipan’s model does not predict failed appointments, excluding failed appointments is entirely reasonable and probably biases the replication in favor of corroborating the model. 20. In Moraski and Shipan’s data, the nominated justice was located outside the pareto set defined by the president and Senate’s ideal point 16 of 28 times.


Table 6. Predicting the Ideology of Supreme Court Appointments

Presidential ideal point (in regime 1 only) Senate’s indifference point (in regime 2 only) Court median (in regime 3 only) Presidential ideal point (in regimes 2 and 3) Senate’s indifference point (in regimes 1 and 3) Court median (in regimes 1 and 2) Intercept Adjusted R2

(a)

(b)

−031 −022 332 215 304 475

−117 −055 119 051 123 076 063 137 −015 −019 −031 −038 076 112 051

113 234 053

Numbers in parentheses are t-statistics. N=23.

Senate to agree to someone outside the line segment connecting their preferred points. Factors outside the current theory may well explain such frequent anomalies. To summarize, our findings suggest limits on the breadth of Moraski and Shipan’s results and point to a need for continued study of interinstitutional bargaining over Supreme Court appointments.

6. Conclusion Estimating comparable ideal points for presidents, justices, and senators is difficult, but it is extremely important if we wish to understand how political institutions interact in the American separation of powers system. In contrast to many methods used in the literature, this article offers a statistically grounded approach to the problem of interinstitutional preference estimation. The method is based on a random effects model that allows us to use the presidents as bridges in calibrating the preferences across the Supreme Court and the Senate. The method is useful in a number of ways. We have demonstrated that the method helps us subject analysis in the literature to more stringent tests based on statistical preference calibration. In addition, the estimates presented here can be useful in a variety of endeavors, ranging from determining the effects of the Supreme Court on elections to studying the effect of public opinion on the courts relative to the Senate. In particular, two research agendas could benefit from the approach presented here. The first is the investigation of separation of powers models in which the Court anticipates the behavior of Congress and the president in

502


its decision making (Gely and Spiller, 1990; Segal, 1997; Bergara, Richman, and Spiller, 2000). Generally the Court is strategically constrained in statutory cases and unconstrained in constitutional cases. We could estimate ideal points for both sets of cases using our method. Finding differences in the two sets would constitute evidence for the separation of powers model; finding no differences would support a pure attitudinal model. A second research agenda concerns Senate action on Supreme Court nominations (see e.g., Cameron, Cover, and Segal, 1990; Segal, Cameron, and Cover, 1992). To understand the roles played by ideology, qualification, and presidential power in the confirmation of justices, we need to have accurate and comparable preference estimates of senators and justices. The method we present here provides such estimates; combined with a model of how the Court status quo and other factors enter into the senators’ voting calculations, we could then further assess this important interinstitutional interaction. The approach presented here is relevant for any context in which political phenomena depend on the interaction of institutionally independent actors. The main requirement is that there are “bridges” across the institutions. These bridges may be simple extensions of those used here. For example, governors may bridge across the courts and legislature in state politics by taking positions on both court and legislative activity. However, there are other possible bridges. For example, they may be legislation ruled on by the courts, where legislators and justices become linked by virtue of voting on the same political issue. With this method and its applications, scholars across a broad range of areas can ground interinstitutional theory testing more firmly in a statistically justifiable framework. In turn, our understanding of politics across institutions will advance with our ability to measure preferences across them. Appendix: Implementation of the EM Algorithm We will call the parameters to be estimated " = # , where ,

, and # are vectors, and the rest are scalars. After choosing provisional starting values of ", the following E-Step and M-Step are repeated until convergence. E-Step: Calculate the expected log of the complete data posterior, given the provisional parameter values. The complete data is C = Y . Bayes’ theorem implies P"C =

PC"P" PC

∝ PC"P" Because we have flat priors on all elements of " except , P" reduces to a constant times P# . In addition, P(C" = PY PP.


Therefore P"C ∝ PY PPP# We are interested in Q, the expected value of the log complete data posterior: Q1 "" 0 = E&log PY + log P + log P + log P# ' To calculate this expectation with respect to and , we integrate over the densities of these parameters, densities that are calculated from provisional parameter estimates of " and Bayes’ theorem: P Y " 0 =

PY ˆ0 ˆ 0 PP PY ˆ0 ˆ 0 PPdd

The expected value of the log complete data posterior is therefore Q1 "" 0 =

log PY P Y " 0 dd + log PP Y " 0 dd + log PP Yi " 0 dd + log P# P Yi " 0 dd

(13)

We approximate the double integrals with product rules using Gauss– Legendre quadrature points (see Stroud 1971:100; Judd, 1998:270). We use 15-point quadrature, meaning the joint probability of and is evaluated at 225 different points for each estimate of the double integral. M-Step: Maximize the expected log of the complete data posterior [Equation (13)] given the data and provisional parameter estimates. The individual specific parameters (i i1 i2 , and ) enter only in the first term of Equation (13). They also enter independently of each other, meaning that they can be estimated one individual at a time. Hence these parameters are estimated by using nonlinear maximization to find the values that maximize the posterior. We use the nlminb function in S-Plus to implement this maximization. The second and third terms in Equation (13) do not contain any parameters to be estimated because of the uniform priors on and . The ideal point

504


distribution parameters are calculated based on the following:

N − # 2 ( (Q1 P Yi " 0 dd − i 2 = (# (# 2 N − # P Yi " 0 dd = = =

N

i P Yi " 0 dd −

N

# P Yi " 0 dd

N

i − N# N i 1 ⇒ #ˆ = N 1

Similar calculations provide ˆ . References

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