public opinion and senate confirmation of supreme court

Public Opinion and Senate Confirmation ofSupreme Court Nominees

Jonathan P. Kastellec Princeton University

Jeffrey R. Lax Columbia University

Justin H. Phillips Columbia University

Does public opinion influence Supreme Court confirmation politics? We present the first direct evidence that state-level public opinion on whether a particular Supreme Court nominee should be confirmed affects the roll-call votesof senators. Using national polls and applying recent advances in opinion estimation, we produce state-of-the-artestimates of public support for the confirmation of 10 recent Supreme Court nominees in all 50 states. We find thatgreater home-state public support does significantly and strikingly increase the probability that a senator will voteto approve a nominee, even controlling for other predictors of roll-call voting. These results establish a systematicand powerful link between constituency opinion and voting on Supreme Court nominees. We connect this findingto larger debates on the role of majoritarianism and representation.

The judiciary is the branch of the federalgovernment most insulated from public influ-ence. Federal judges are unelected and have

lifetime appointments. The justices of the SupremeCourt need not even worry about promotions to ahigher court. This leaves them largely unconstrainedin their decision making, which ultimately reachesthe most controversial policy areas. Judicial inde-pendence has obvious advantages, leaving the justicesfree from improper influence, free to make impartialdecisions, and free to protect the rights of unpopularminorities. But ‘‘too much’’ independence from thepublic can raise foundational concerns of counter-majoritarianism.

Scholars have long debated whether SupremeCourt justices are influenced by public opinion(Flemming and Wood 1997; Giles, Blackstone, andVining 2008; Hoekstra 2000; Mishler and Sheehan1993; Norpoth et al. 1994;). Less noticed has been thepossibility that the public might influence who sits onthe Court in addition to how they vote. The decisionto seat a justice is in the hands of the presidents andsenators, but electoral incentives, particularly forsenators, can tie the Court back to the public. Giventhese incentives, does the public indeed play a key rolein confirmation politics? Or does a senator’s partisan-ship and ideology trump constituent preference?

Given the visibility of roll-call votes on SupremeCourt nominees, and the stakes for controversialpolicies at the heart of recent elections, we expectreelection-minded senators to pay close attention tothe views of their constituents. Whether they do soremains an open question. Twenty years ago,Caldeira (1988–89) urged an assessment of the roleof ‘‘organized and unorganized’’ interests—includingthe public at large—in nomination and confirmationpolitics. Using various proxies for state public opin-ion, the few existing assessments of the public rolehave reached conflicting conclusions (cf. Segal,Cameron, and Cover (1992) with Caldeira andWright (1998)). More recent work has studied thechanging dynamics of nomination politics, settingaside any possible effects of public opinion.

In this paper, we use a direct measure of state-level public opinion to study whether senators areactually responsive to the views of their constituentson a particular nominee when casting their votes onthe confirmation of that nominee. We begin byproducing state-of-the-art estimates of the public’ssupport for each nominee in all 50 states. To do so,we make use of recent advances in multilevel model-ing to generate highly accurate estimates from na-tional polls asking about support for 10 recentSupreme Court nominees. These estimates of opinion

The Journal of Politics, Vol. 72, No. 3, July 2010, Pp. 767–784 doi:10.1017/S0022381610000150

� Southern Political Science Association, 2010 ISSN 0022-3816

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are a significant improvement over earlier measures:they can be generated for a broader range of nomi-nations than was previously possible; they account forgeographic variation among poll respondents; andthey specifically capture state-level support for con-firmation. Such estimates allow us to move beyondstudying simple correlations between roll-call votingand more generic measures such as state demographicpercentages or diffuse constituent ideology. As a result,these estimates have a big payoff: they allow us topresent the first evidence that senators do respond tonominee-specific, state-specific support. No previousstudy has shown such a direct relationship. Morebroadly, we can study representation with morenuance than previously possible.

We find that greater home-state public supportsignificantly and strikingly increases the probabilitythat a senator will vote for confirmation, evencontrolling for other predictors of roll call voting:ideological distance between the senator and thenominee, the party of the senator, and the qualityof the nominee. Public opinion, on average, mattersmore than any predictor other than the senator’s ownideological differences with the nominee. The impactof opinion varies with context, with a greater effecton opposition party senators, on ideologically op-posed senators, and for weak nominees. Thus, whilepublic opinion matters a great deal, a senator retainssome leeway when voting on nominees.

These findings speak to larger debates aboutrepresentation and responsiveness in the U.S. Senate,such as the tradeoff between ideological representa-tion and choice-specific public opinion; the balancingof a legislator’s personal policy preferences with thoseof his or her constituents; and the degree of shirkingin representative democracy.

Opinion, Representation, andConfirmation Votes

Three links in the chain are necessary for a mean-ingful connection between the public and who sits onthe Court: knowledge, salience, and senatorial atten-tion. First, does the public know enough to play arole in confirmation politics, particularly with respectto senatorial voting? It is commonly thought that theAmerican public has only minimal knowledge of theSupreme Court (discussed in Caldeira and Wright1998). We now know that this conclusion is over-stated, if not simply incorrect. As Gibson andCaldeira (2009) show, the public’s knowledge of the

Court is much more impressive than previouslythought.1

Of course, general knowledge might be lessimportant than whether citizens pay attention toSupreme Court nominations—in fact, they do. Bythe time a nominee comes up for a vote, mostAmericans can say where they stand. For instance,in the periods around Justice Thomas’s and JusticeAlito’s confirmations, 95% and 88%, respectively,held an opinion about confirming them (Gibson andCaldeira 2009; Gimpel and Wolpert 1996).

The second link is salience—if the public did notcare about confirmation votes, then lawmakers mightnot pay attention to their constituents’ views. How-ever, many Americans do care about such votes(Hutchings 2001). For example, during the Alitonomination, 75% of Americans thought it importantthat their senators vote ‘‘correctly’’ (Gibson andCaldeira 2009). History contains ominous warningsfor senators who ignore such concerns. Despite beingvirtually unknown, Carol Moseley Braun defeatedincumbent Senator Alan Dixon in the Illinois Dem-ocratic primary, principally campaigning against hisvote to confirm Clarence Thomas (McGrory 1992).Using 1992 Senate election data, Wolpert and Gimpel(1997) showed that many voters nationwide factoredtheir senator’s confirmation vote into their own votechoice. Such findings suggest that Americans knowfar more about the Court, pay far more attention toconfirmation politics, and hold their senators farmore accountable for confirmation votes than hasoften been assumed.

Finally, do senators, in turn, monitor the public’spulse? Theories of legislator responsiveness to con-stituent opinion would suggest that the answer is‘‘yes.’’ While the goals of members of Congress aremultifaceted, the desire for reelection has long beenestablished as a powerful driver, if not the primarydriver, of congressional behavior (Mayhew 1974).Although six-year terms provide senators with greaterinsulation than representatives, a reelection-mindedsenator will constantly consider how his votes,particularly highly visible ones, may affect approvalback home (Arnold 1990).2 While the outcomes of

1For example, the authors demonstrate that the National ElectionStudy’s standard question asking respondents to identify theChief Justice—‘‘What about ‘‘John Roberts’’: What job orpolitical office does he now hold?’’—systematically understatesactual knowledge about the Supreme Court. Their findings addto a growing literature arguing that specific recall questions arenot the key to understanding citizen informational capacity.

2We use ‘‘she’’ to denote justices and ‘‘he’’ to denote senatorsthroughout the paper.

768 jonathan p. kastellec, jeffrey r. lax, and justin h. phillips

many Senate votes, such as spending bills or themodification of a statute, are ambiguous or obscuredin procedural detail, the result of a vote on a SupremeCourt nomination is stark: either the nominee isconfirmed, allowing her to serve on the nation’shighest court, or she is rejected, forcing the presidentto name another candidate. In this process, noteWatson and Stookey, ‘‘there are no amendments, noriders and [in recent decades] no voice votes; there isno place for the senator to hide. There are nooutcomes where everybody gets a little of what theywant. There are only winners and losers’’ (1995, 19).

Accordingly, a vote on a Supreme Court nomineepresents a situation in which a senator is likely toconsider constituent views very carefully. Cameron,Cover, and Segal set forth this logic nicely:

[W]e imagine senators asking themselves, ‘‘Can I usemy actions during the confirmation process to gainelectoral advantage? ... [C]an they be used against me?What is the most electorally expedient action for me tohave taken?’’ ... The senator can generally expect to gainelectorally (or at least not to lose electorally) fromvoting as constituents wish and can expect to incurlosses from flouting constituents’ desires, regardless ofthe actual outcome of a vote. (1990, 527)

Electoral gains or losses may not manifest immedi-ately. For instance, in a bid to unseat PennsylvaniaSenator Arlen Specter in the 2004 Republican pri-mary, challenger Pat Toomey invoked Specter’s voteagainst Robert Bork 17 years earlier (Babington2004).

Senators’ concerns about mass public opinion onSupreme Court nominees arose following the shift todirect election of senators in 1914, and the subse-quent increase in the transparency of the confirma-tion process. Just two years later, following the firstpublic hearings during a Supreme Court confirma-tion, public support of Louis Brandeis helped bluntRepublican opposition to his nomination, easing hispath to the bench (Maltese 1998, 51). In 1930, theRepublican Senate majority was so concerned aboutrising public opposition to the appointment ofCharles Evans Hughes for Chief Justice that hissupporters blocked further hearings and moved to aquick vote, before public opinion could shift anyfurther (Maltese 1998, 55). President Hoover’s nextnominee would not fare so well—it is thought thatstrong opposition to John Parker among the AfricanAmerican community led several Republican senatorsto vote against a nominee of their own party,ensuring his narrow defeat on the Senate floor.

How do senators take the pulse of their constit-uents on Supreme Court nominees? Public opinion

polls help inform senators, as do more direct forms ofcommunication such as phone calls and letter writ-ing.3 Segal, Cameron, and Cover (1992) and Caldeiraand Wright (1998) argue that interest groups play animportant role both in shaping constituency prefer-ences and informing senators of these preferences:‘‘Interest groups attempt to mold senators’ percep-tions of the direction, intensity and electoral impli-cations of constituency opinion’’ (Caldeira andWright 1998, 503). It is thus likely that most senatorswill have a good idea of where their constituentsstand when voting on a Supreme Court nominee.

Given this, it is no surprise that presidents often‘‘go public’’ in the hope of shifting public opinion ontheir nominees (Johnson and Roberts 2004). Forexample, Richard Nixon’s White House actively workedto shift public opinion on Clement Haynsworth andRonald Reagan’s White House launched a ‘‘major(though largely unsuccessful) public relations offensiveto build support for [Robert Bork]’’ (Maltese 1998, 87–88). Indeed, Gibson and Caldeira argue that ‘‘one of thecrucial elements in confirmation strategies concernshow public opinion will be managed and manipulated’’(2009, 1).

Measuring Constituency Opinion: PreviousResearch

Analysis of roll-call votes on Supreme Court nomi-nees (e.g., Segal, Cover and Cameron 1988–89;Songer 1979) has proceeded along two overlappingtracks. The first follows from the pioneering work ofCameron, Cover, and Segal (1990), finding that roll-call voting was affected mainly by nominee qualityand the ideological distance between a senator andthe nominee. Senators will likely approve a nomineeif she is ideologically close or if she is of high legalquality; otherwise, the probability of approval dropsrapidly. Partisanship and the political environmentare also important: all else equal, senators tend toapprove nominees of a president of the same partyand of a president who is ‘‘strong’’ in that his partycontrols the Senate and he is not in his fourth year ofoffice. Updating both the methodology and thenumber of nominations evaluated, Epstein et al.(2006) and Shipan (2008) agreed that this modelaccurately captures roll-call voting, but added thatthe influence of both ideological distance and parti-sanship seems to have grown over time.

3See, e.g., Marcus (1987) and Clymer (1991) for accounts of theintensity of letter writing during the Bork and Thomas nomi-nations, respectively.

public opinion and supreme court nominees 769

The second track incorporates constituency pref-erences and lobbying interests. Doing so, however,involves several methodological challenges, especiallywith respect to measuring constituency preferences.These challenges arise from a harsh constraint—thelack of sufficient and comparable state-level pollingsamples for nominees. Scholars have therefore pur-sued several alternatives to ‘‘direct’’ estimates ofstate-level opinion. The most ambitious attempt tomeasure constituency opinion is that of Segal,Cameron, and Cover (1992, 109), who generatedstate-level constituent ideology scores using predic-tions from regressions of congressional voting scoreson state presidential election results and indicatorsfor Democrats and Southern Democrats. Then, usingscaling procedures to place nominees, senators, andconstituents on the same scale, they found that‘‘confirmation voting is decisively affected by theideological distance between senators’ constituentsand nominees.’’ Interest group activity—measured atthe nominee-level rather than the senator-level—alsoaffected votes. The linkage between constituencyideology and senators’ votes was robust to theinclusion of such effects.

To be sure, this method of estimating constitu-ency ideology is innovative. There are, however,drawbacks. First, the measure constitutes a broadevaluation of state ideology—not opinion on thenominee specifically or even nominations in general.Moreover, because the predictions are generatedusing only a few presidential elections, the stateestimates are static in many periods, meaning thatthe estimates for all nominees in a given period willbe the same (and indeed the same for ‘‘opinion’’ onany other issue). For example, the estimates of con-stituent position on Harry Blackmun and ClementHaynsworth are the same, despite their vastly differ-ent profiles. Lastly, because constituency ideology isestimated from voting scores, untangling the influ-ence of senator ideology and constituent pressurerequires extreme confidence in our ability to accu-rately place them on the same scale.

Given these limits, one can only show the degreeand direction of correlation between the diffuseconstituent ideology score and senator vote. Withoutaccurate measures of how constituents want thesespecific votes to be cast, without a common metric foropinion and choice, the inferences we can draw arepotentially quite limited (Erikson, Wright, andMcIver 1993, 92). Even if votes and state ideologyare highly correlated, we cannot tell if vote choice isover- or underresponsive to opinion itself, or if votechoice is biased for or against the nominee. That is,

we can only tell whether more liberal (conservative)constituents lead to more liberal (conservative) votes;we cannot tell whether confirmation votes are theprecise votes desired by constituents.

A more contextual proxy for constituent opinionis employed by Overby et al. (1992; 1994), to studyThurgood Marshall and Clarence Thomas. As thepercentage of blacks in a senator’s home stateincreased, he was less likely to support Marshall,but more likely to support Thomas. They attributedthis to the changing dynamics of Southern politics:whereas in the 1960s Southern Democrats resistedcivil rights measures and were reluctant to offendwhite supporters by endorsing Marshall, by the 1990sSouthern Democrats were dependent on black votesto gain office, which led many to support the Thomasnomination despite opposition by most other Dem-ocratic senators. While these studies do suggest thatsenators are mindful of their constituents, thisapproach is difficult to generalize and relies on theassumptions stated. (It also suffers from the correla-tion problem discussed above.)

These problems show how important it is to havenominee-specific opinion measures. The most recentattempt to estimate constituency opinion—and theone that most resembles the method we use—isCaldeira and Wright (1998), which did createnominee-specific measures (for the Bork, Thomas,and Souter nominations). The authors gathered na-tional polls and estimated individual-level models ofopinion, regressing survey respondents’ views of thenominees on race, partisanship, ideology, and ruraldwelling. (The next step is, methodologically, one ofthe main points of departure between their methodand MRP.) They then used the mean level of thesevariables (race, etc.) by state, combined with theparameter estimates from the response models, togenerate state-level estimates of opinion. Conductingseparate models of confirmation voting on the threenominees, they found that state opinion did not havea statistically significant effect on senators’ roll callvote. (Nor did senator ideology, in contrast to earlierwork; on the other hand, lobbying for and against anominee, measured at the senator level, did matter).

We explain more thoroughly below, but webriefly note two limitations of the approach above,which might explain negative findings. First, it takesinto account only demographic variation betweenrespondents, and not geographic variation, which canbe much larger (Erikson, Wright, and McIver 1993).Second, using the mean values of each demographicvariable within a state only approximates the correctweighting of demographic influences on opinion. It


does not make use of the true frequencies of eachrespondent type, which is crucial given that, evensetting aside geographic differences, demographicvariables interact with each other to influence opin-ion at the individual level. True frequencies requirethe full set of cross-tabulations, and not just aggregatepercentages (e.g., knowing how many black womenthere are, not just how many blacks and how manywomen). Fortunately, given recent advances in esti-mating state-level opinion from national poll data, itis now possible (as it was not when the studies abovewere performed) to overcome both of these limita-tions, while improving accuracy in other ways. And,crucially, we show that these improved measuresyield a different finding than this previous work.

Data and Methods

The most intuitive way to measure state publicopinion on Supreme Court nominees would be togather all possible national polls on a particularnominee, then break down responses by state, hopingto get sufficiently many within each state to yield anaccurate sample. Such a plan would follow the‘‘disaggregation’’ approach pioneered by Erikson,Wright, and McIver (1993), who pooled polls over25 years (thus achieving adequate sample sizes) todevelop estimates of each state’s overall liberalism.Unfortunately, given the relative paucity of polls onSupreme Court nominations, there are simply notenough respondents in many states to generatereliable estimates of public opinion usingdisaggregation.4

Fortunately, an alternative method exists, onethat generates estimates of state opinion using na-tional surveys. Multilevel regression and poststratifi-cation, or MRP, is the latest implementation of sucha method, and rigorous assessments of MRP dem-onstrate that it performs very well (Gelman and Little1997; Lax and Phillips 2009a; 2009b; Park, Gelman,and Bafumi 2006). It outperforms disaggregation,even for very large samples, and it yields resultssimilar to actual state polls. A single national poll andsimple demographic-geographic models (simplerthan we use herein) can suffice for MRP to producehighly accurate and reliable state-level opinion esti-mates (Lax and Phillips 2009b).

There are two stages to MRP. In the first stage,individual survey response is modeled as a functionof demographic and geographic predictors, withindividual responses nested within states nestedwithin regions, and also nested within demographicgroups. The state of the respondents is used toestimate state-level effects, which themselves aremodeled using additional state-level predictors suchas region or state-level aggregate demographics.Those residents from a particular state or regionyield information as to how much predictions withinthat state or region vary from others after controllingfor demographics. MRP compensates for smallwithin-state samples by using demographic and geo-graphic correlations. All individuals in the survey, nomatter their location, yield information about dem-ographic patterns which can be applied to all stateestimates.

The second stage is poststratification: the esti-mates for each demographic-geographic respondenttype are weighted (poststratified) by the percentagesof each type in actual state populations, so that wecan estimate the percentage of respondents withineach state who have a particular issue position. Inshort, MRP improves upon simpler breakdowns ofopinion by state by not throwing away informationabout demographic and geographic correlations inthe response data, and by using highly accurateweighting data.

To produce estimates for as many nominees aspossible, we searched the Roper Center’s iPoll ar-chive. Not until recently were polls systematicallyconducted on Supreme Court nominees. We foundsufficient polling data for 10 nominees: O’Connor,Rehnquist (for Chief Justice), Bork, Souter, Thomas,Ginsburg, Breyer, Roberts, Alito, and Miers. Of these,all but Miers received a vote on the floor of theSenate. For nominees who featured in only a handfulof polls, we gathered every poll containing sufficientdemographic and geographic information on indi-vidual respondents. For nominees with a large num-ber of such polls, we only used the polls closest totheir confirmation vote. For Clarence Thomas, weonly retained polls taken after the Anita Hill allega-tions surfaced. This procedure helped ensure as muchas possible that our estimates would tap state opinionas it stood at the time a senator cast his vote.5

For each nominee (separately), we then modelsurvey response, specifically a multilevel logisticregression model, estimated using the GLMER

4Consider the eight polls on John Roberts, the nominee with thelargest number of polls. In 15 states there were fewer than 50 totalrespondents per state (e.g., there were only nine from Wyoming).The problem is even more severe for other nominees.

5A complete list of polls and question wordings can be found onthis article’s replication website.


function (‘‘generalized linear mixed effects’’) in R(Bates 2005). For data with hierarchical structure(e.g., individuals within states), multilevel modelingis generally an improvement over classical regression.Rather than using ‘‘fixed’’ (‘‘unmodeled’’) effects, themodel uses ‘‘random’’ (‘‘modeled’’) effects, for somepredictors. These modeled effects (e.g., state effects)are related to each other by their grouping structureand thus are partially pooled towards the groupmean, with greater pooling when group-level var-iance is small and for less-populated groups (this isequivalent to assuming errors are correlated within agrouping structure; Gelman and Hill 2007, 244–65).The degree of pooling within the grouping emergesfrom the data endogenously. They can be modelednot only in terms of this ‘‘shrinkage’’ (the assumptionthat they are drawn from some common distribu-tion) but also by including group-level (e.g., state-level) predictors.

We model response as a function of six race andgender types (males and females broken down intoblack, Hispanic, or white/other); one of four agegroups (18–29, 30–44, 45–64, and 65+); one of foureducation groups (less than a high school education,high school graduate, some college, and collegegraduate); 16 groups capturing the interaction be-tween age and education; state-level ideology (up-dated from Erikson, Wright, and McIver 1993); poll;state; and region (Washington, D.C., as a separate‘‘state’’ and separate region, along with Northeast,Midwest, South, and West).6 These are standardopinion predictors and are employed widely (see,e.g., Gimpel and Wolpert 1996).

We start by coding explicit support for thenominee (y

yesi 5 1) against other responses (y

yesi 5 0

for an explicit negative response, ‘‘don’t know,’’ or‘‘refused’’). This captures explicit positive supportamong all respondents, not simply those expressingan opinion. For individual i, with indexes r, k, l, m, s,and p for race-gender combination, age category,education category, region, state, and poll, respec-tively, we estimate the following model:

Prðyyesi 5 1Þ5 logit�1ðb0 þ a

race;genderr½i� þ a

agek½i�

þ aedul½i� þ a

age;eduk½i�;l½i� þ astate

s½i� þ apollp½i� Þ ð1Þ

The terms after the intercept are modeled effects forthe various groups of respondents (modeled as drawnfrom a normal distribution with mean zero andendogenous variance):

arace;genderr

e

Nð0;s2race;genderÞ; for r ¼ 1; . . . ; 6

apollpe

Nð0;s2pollÞ; for p 5 1; . . .

aageke

Nð0;s2ageÞ; for k 5 1; . . . ; 4

aedule

Nð0;s2eduÞ; for l 5 1; . . . ; 4

aage;edul

e

Nð0;s2age;eduÞ; for k 5 1; . . . ; 4 and

l 5 1; . . . ; 16

The state effects are modeled as a function of theregion into which the state falls, percent religiousconservative, and state-level ideology; and the regioneffects are modeled as drawn from a normal distri-bution with mean zero and endogenous variance:

astatese

Nðaregionm½s� þ brelig � religs

þ bstate:ideo � state:ideos; s2stateÞ; for

s 5 1; . . . ; 51

aregionm

e

Nð0;s2regionÞ; for m 5 1; . . . ; 5

In the second stage, we use the coefficients that resultfrom this estimation to calculate predicted proba-bilities of nominee support for each demographic-geographic type. There are 4,896 combinations ofdemographic and state values (96 within each state).Let j denote a cell from the set of demographic-geographic types. For any j, the results above allow usto make a prediction of pro-nominee support, uj, whichis simply the predicted probability given by the resultsfrom equation (1). We next poststratify according topopulation frequencies derived from the ‘‘5-PercentPublic Use Microdata Sample’’ in the Census.7 That is,the prediction in each cell needs to be weighted by theactual population frequency of that cell, Nj. For eachstate, we then can calculate the percentage who supportthe nominee, aggregating over each cell j in state s. Let g

denote an estimate of nominee support in a given

state s. Then, gs 5+

j2sNjuj

+j2s

Nj.

We next code explicit disapproval of the nominee(yno

i 5 1) against other responses (ynoi 5 0 for a

positive response, ‘‘don’t know,’’ or ‘‘refused’’),repeating the process above. We then have estimates,for each state, of the probability of an explicit yes andof an explicit no—with the remainder being the‘‘don’t know,’’ or ‘‘ refused’’ category. We can thencalculate the percentage of those in each state that say

6Estimates are highly robust to variations in this specification.

7For nominees whose nominations do not fall on Census yearsand occurred before 2000, we weight the Census data to reflectthe results from the two closest decennials. For nominations after2000, we use the 2000 census.


yes of those with an opinion (the first categorydivided by the sum of the first two).8 Throughoutthe rest of the paper, we focus on the level of supportamong opinion holders, but the results we presentbelow are robust to using overall opinion.

Visualizing State-Level Opinion

We explore the resulting estimates of state-levelopinion in Figure 1. The left plot depicts the supportfor each nominee in each state (the full list ofopinion estimates is given in Table 1). The lightercolors indicate lower support; darker colors indicategreater support. States are ordered by overall lib-eralism, with more liberal states towards the top ofthe y-axis. Within each plot, separating Democraticand Republican nominees, nominees are orderedleft to right from lower mean support to highermean support. We can see a relationship between stateideology and support for confirmation, since the barstend to darken moving downwards (for Republicannominees). Ideology, however, does not fully explainsupport for confirmation, or the grayscale woulddarken smoothly from top to bottom. Still, and notsurprisingly, citizens in more liberal states are morelikely to support Democratic appointees and opposeRepublican appointees, and vice versa.

The right plot in Figure 1 depicts the distribu-tions of estimated state support for each nominee.The vertical dashed lines show mean support foreach nominee. Bork and Miers were the two mostunpopular nominees, on average, while Souter,O’Connor, Ginsburg, and Breyer enjoyed widespreadsupport. O’Connor, for example, had roughly 90%support across the board. Bork was the only nomineefor whom the balance of public opinion in asignificant number of states was opposed to hisnomination. The bottom histogram depicts supportfor all nominees combined, revealing that most ofthe distribution of opinion falls between 60% and80% support. Despite the overall tendency to sup-port a nominee, the histograms show widespreadvariation in estimated state support for severalnominees.

Roll-Call Voting and State-Level Opinion

We now examine the relationship between ourestimates of public opinion and voting on nominees.This analysis excludes Harriet Miers, since her nom-ination was withdrawn before she received a Senatevote (we return to her nomination below, however).The remaining nine nominees in our sample wereeach voted on by the full Senate, for a total of 891confirmation votes (nine senators abstained, in total),75% of which were to approve the nominee.9 Webegin our analysis by studying the bivariate relation-ship between estimated public opinion and voting.For each nominee, the lines in Figure 2 present theestimated logit curves from a logistic regression ofroll-call votes on state public opinion. The last plotshows the logit curve from pooling all nominees. Thehash marks at the top and bottom of each panel depictstate opinion for ‘‘yes’’ and ‘‘no’’ votes, respectively.The correlation is strong: as a senator’s constituentsbecome more supportive of a nominee, he is morelikely to vote affirmatively. For the more controversialnominees, the relationship between voting and opin-ion is stronger, with variation across nominees. Theslope of the curve is most steep for the Roberts vote.

Can we conclude that public opinion influencesroll-call voting, rather than simply aligning with it? Toanswer this question, we turn to a multivariate analysisof roll call voting on Supreme Court nominees, sothat we can control for other influences. We buildon existing studies, which model voting on SupremeCourt nominees as a function of nominee quality, theideological distance between a senator and a nominee,partisanship, and presidential strength. These studiesshow that senators are likely to support high qualitynominees regardless of ideological distance, but thatthe probability of approval is lower for low qualitynominees, especially for nominees who are ideologi-cally distant. Senators are also more likely to supportnominees appointed by presidents of the same party,and by presidents with greater popular support. Withthis in mind, we use the predictors below.

d Lack of quality: The degree to which a nominee isunqualified to join the Court (according to anideologically balanced set of newspaper editorials(Cameron, Cover, and Segal 1990)). It ranges from0 (most qualified) to 1 (least).

d Ideological distance: The ideological distance be-tween the senator and nominee, measured using aninstitutional bridging technique between Common

8Dropping those without an opinion would eliminate random-ness within type. To accurately capture support among thosewith an opinion, we must run two separate models. While thepredictors we use vary slightly across nominees depending on theinformation available in the survey data, each model takesroughly the form above. The results of Lax and Phillips(2009b) suggest that such minor variations are irrelevant. Thegoal is the best predictive model possible. To compare effects ofpredictors across nominees, we would use the same predictors ineach model. Model details are available upon request.

9Roll call and other data for all nominees except Alito come fromEpstein et al. (2006). We collected data on Alito.


FIGURE 1 (a) Support for nominees, by state. Every state-nominee block depicts the level of supportamong opinion holders in the respective group, with lighter colors indicating lower supportand darker colors indicating greater support. The states are ordered by overall liberalism, withmore liberal states towards the top of the y-axis and more conservative states towards thebottom. Within each plot, nominees are ordered left to right from lower mean support tohigher mean support, except for clarity we offset the two Democratic nominees (Ginsburg andBreyer) from the seven Republican nominees. (b) The distribution of nominee support.Nominees are ordered by party and then increasing mean support. The dashed vertical linedepicts mean support.


TABLE 1 Estimates of state support among opinion holders, by nominee

State O’Connor Rehnquist Bork Souter Thomas Ginsburg Breyer Roberts Alito Miers

Alabama 88 64 52 81 68 69 77 76 69 57Alaska 91 66 56 85 67 74 78 77 70 56Arizona 91 67 54 83 66 77 81 74 68 56Arkansas 88 64 51 77 68 71 80 75 69 55California 91 62 43 71 58 83 87 63 58 38Colorado 91 63 46 74 61 81 86 67 61 45Connecticut 91 48 44 71 67 84 88 65 60 44D.C. 90 28 15 31 62 92 93 31 30 23Delaware 89 63 46 71 67 78 84 70 64 49Florida 89 69 52 78 70 79 82 74 67 54Georgia 88 63 47 76 71 73 80 71 64 53Hawaii 91 62 47 73 62 81 86 69 62 44Idaho 91 71 63 89 68 71 75 82 75 64Illinois 90 56 49 70 68 83 85 67 66 51Indiana 90 61 56 77 71 78 81 74 73 57Iowa 91 62 55 74 66 80 83 74 72 52Kansas 91 56 56 77 70 79 82 74 72 54Kentucky 88 68 49 72 67 75 83 74 67 52Louisiana 88 64 50 81 73 70 77 74 68 61Maine 91 59 54 81 70 77 83 76 69 55Maryland 89 56 39 63 67 81 86 63 57 40Massachusetts 91 52 41 67 65 83 89 64 58 45Michigan 90 55 48 68 68 85 85 68 66 50Minnesota 91 57 54 74 68 82 83 73 71 54Mississippi 87 63 50 82 73 69 75 74 68 61Missouri 90 52 54 76 71 79 82 73 71 56Montana 91 65 49 76 63 81 85 72 65 46Nebraska 91 64 59 80 71 78 81 77 75 60Nevada 91 63 49 77 64 80 84 70 64 49New Hampshire 91 56 52 80 69 80 84 74 68 50New Jersey 91 51 42 69 66 84 88 63 58 44New Mexico 91 63 52 80 64 78 82 70 66 53New York 91 45 41 69 68 84 88 62 59 43North Carolina 88 67 51 79 68 74 79 75 68 54North Dakota 91 66 66 86 73 72 76 82 79 65Ohio 90 53 49 69 71 84 84 69 68 53Oklahoma 88 73 61 86 69 72 75 82 76 63Oregon 91 57 46 73 62 81 86 69 62 42Pennsylvania 90 52 48 77 70 81 85 71 66 50Rhode Island 90 51 42 68 67 85 89 65 59 44South Carolina 88 65 49 79 73 71 78 73 67 56South Dakota 91 65 64 84 72 72 77 80 78 62Tennessee 88 65 50 75 69 72 81 74 67 56Texas 89 68 55 82 70 73 77 76 71 60Utah 91 65 62 89 69 71 75 81 74 64Vermont 91 56 51 78 69 80 85 73 67 50Virginia 88 62 49 77 67 77 80 73 67 55Washington 91 60 45 71 62 82 87 67 60 41West Virginia 88 66 47 68 68 76 86 72 65 50Wisconsin 91 60 52 70 69 82 85 71 70 52Wyoming 91 65 54 82 65 77 81 76 69 54


Space scores (Poole 1998) and Segal-Cover nominee-ideology scores (Segal and Cover 1989).

d Same party: Coded 1 if the senator is a copartisan ofthe president.

d Presidential capital: We use two measures to cap-ture presidential capital. The first, ‘‘strong presi-dent,’’ is coded 1 if the president was not in hisfourth year of office and his party controlled theSenate at the time (Cameron, Cover, and Segal1990). The second is public approval of the presi-dent, using the last Gallup poll preceding the vote.10

d State voter ideology: We control for the possibilitythat senators respond to diffuse state-level ideology(rather than nominee-specific opinion) by includ-ing updated scores created by Erikson, Wright,and McIver (1993). We recode this variable tomatch whether nominees are liberal or conservative(i.e., nominated by Democratic or Republicanpresidents, respectively), such that higher valuesindicate greater ideological support for the nomi-nee. Higher values should increase the probabilitythat the senator votes to confirm a nominee.

Our key expectation is that constituent opinionwill play a strong role in driving the votes of senators.Of course, we still expect that the other variablesnoted above will continue to have an independentcontribution to explaining senator votes on nomi-nees. For all the models we present, the continuouspredictors have been standardized by centering (atzero) and dividing by two standard deviations—as aresult, the coefficients for all continuous and binarypredictors are comparable on roughly the same scale(Gelman 2008). A one-unit change in a continuouspredictor covers two standard deviations of thatpredictor. Such linear transformations cannot affectstatistical significance; rather, they make it easier tocompare relative magnitudes across predictors.

Results

Before turning to our full regression analyses, wedocument the basic relationships between votes, opin-ion, partisanship, and ideological distance. Figure 3depicts state-level support on the x-axis and ideolog-ical distance on the y-axis; the open circles denote

FIGURE 2 Correlation between state opinion androll call voting. For each nominee, theblack line depicts the estimated logitcurve from regressing senators’ voteson state public opinion. Hash marksindicate votes of approval (‘‘1’’) andrejection (‘‘0’’) of nominees, while thenumbers in the right-hand corner ofeach plot denote the overall vote tallyby the Senate. The bottom plot poolsall nominees together.

0.25.5

.751

Bork42−58

0.25.5

.751

Rehnquist65−33

0.25.5

.751

Alito58−42

0.25.5

.751

Thomas52−48

0.25.5

.751

Roberts78−22

0.25.5

.751

Souter90−9

0.25.5

.751

Ginsburg96−3

0.25.5

.751

Breyer87−9

0.25.5

.751

O'Connor99−0

40 50 60 70 80 90 1000

.25.5

.751

State support for nominee

Allnominees

Pr(

Vot

ing

Yes

)

10Fine-grained measures of presidential capital might be pref-erable for some research questions (Johnson and Roberts 2004).However, because we are evaluating only nine nominees selectedby only four presidents, our ability to gain leverage on thedynamics of presidential capital are greatly limited.


‘‘no’’ votes, while the dark circles denote ‘‘yes’’ votes.From the top panel, which depicts this information forall senators, it is evident that few senators vote againstnominees who have a high degree of public support.For nominees with less public support, senators arelikely to vote ‘‘yes’’ if the nominee is ideologicallyclose to him. By contrast, senators facing a nomineewho is less popular in his state and is ideologicallydistant from him will usually vote against confirma-tion. ‘‘Yes’’ votes and ‘‘no’’ votes can be roughlydivided by a diagonal cut-line: the dashed line in eachplot is the estimated cut-line, based on a simplelogistic regression invoking distance and opinion aspredictors, showing where the estimated probabilityof a positive confirmation vote is 50%.

How does partisanship affect these relationships?The second panel in Figure 3 depicts only senators ofthe president’s party. As the graph makes clear, ‘‘no’’votes by in-party senators are very rare, but are under-taken only when the nominee is relatively unpopular inhis state. Senators from the opposition party almostalways reject unpopular nominees. For moderately tohighly popular nominees, ideological distance is crucial:more moderate members of the opposition party dosupport nominees with moderate support, while moreideologically distant members often vote ‘‘no.’’

We now turn to more complete models of theprobability that a senator will vote to confirm anominee. The first five models in Table 2 are regularlogit models. Models 6–10 are parallel regressions(i.e., Model 6 contains the same predictors as Model1, etc.) using multilevel modeling, accounting for thegrouping of votes by nominee. Each of these modelsincludes varying intercepts for each nominee (i.e.random effects), assumed to be drawn from a normaldistribution with mean zero and endogenous varianceestimated. These intercept shifts capture average var-iation across nominees not captured by the otherpredictors.11 Models 1 and 6 replicate the logit modelin Epstein et al. (2006) for our subset of nominees (wesuccessfully replicated it for all the nominees theyevaluated). The remaining models bring in state-levelopinion and use various specifications.

The coefficient on Opinion is statistically signifi-cant and of a sizeable magnitude in every model.These results demonstrate that public opinion has arobust influence on Supreme Court confirmationpolitics—as state support for a nominee increases,senators are more likely to vote for the nominee, even

FIGURE 3 Public opinion, ideological distance,partisanship, and roll call voting onSupreme Court nominees. The opencircles denote ‘‘no’’ votes, while theclosed circles denote ‘‘yes’’ votes. Thedashed line shows an estimated cut-line between ‘‘yes’’ and ‘‘no’’ votes(based on a logistic regression usingjust distance and opinion aspredictors).

= no vote= yes vote

= no vote= yes vote

= no vote= yes vote

All senators

lacigoloedIecnatsi

D

40 50 60 70 80 90

Near

Far

ecnahc %05etov sey fo

In−party senators

lacigoloedIecnatsi

D

40 50 60 70 80 90

Near

Far

Out−party senators

lacigoloedIecnatsi

D

40 50 60 70 80 90

Near

Far

State opinion11We checked that no single nomination drives our results byrunning Models 8 and 9 nine times, leaving out each nominee inturn. The coefficient on Opinion remained substantively andstatistically similar.


after controlling for other predictors of the vote. Wereturn shortly to the substantive effect of publicopinion on roll call voting.

The results for other predictors match those fromprevious studies. Senators are more likely to supporta nominee appointed by a president of the sameparty, ideologically near to him, and of higherquality. Higher presidential strength also increasesthe chances of a ‘‘yes’’ vote. (There is less precisionon the estimates of group-level predictors, those suchas presidential approval that do not vary within agiven nominee.) While Models 4 and 9 show thatdiffuse state-voter ideology does affect voting, themagnitude of its estimated coefficient is dwarfed bythat of state-specific nominee opinion.

Next, Models 5 and 10 assess the degree to whichthe relationship between opinion and voting may be

conditioned by a senator’s proximity to his reelectionbid (c f. Overby et al. 1992, 1994). We interact stateopinion with an indicator variable, Reelection, coded1 if a vote on a nominee took place within two yearsof the senator’s next reelection. The coefficient onOpinion in Models 5 and 10 gives the estimated effectof public opinion on senators who were not facingreelection: it is unchanged from Models 4 and 9. Thecoefficient on the interaction term is small and notstatistically different from zero, indicating that thereis no additional effect of opinion among senatorsfacing reelection. Thus, as a general matter, weconclude that the effect of opinion is more relatedto senators’ long-term interests in maintaining con-stituent support, rather than a more short-term focuson whether a vote contrary to such support will haveimmediate negative consequences.

TABLE 2 Explaining roll call voting. The first set of models are regular logistic regressions. The second setare corresponding multilevel models, with intercepts varying by nominee. For all models, *indicates p , .05 (one-tailed tests). All continuous predictors in the models have beenstandardized by centering and dividing by two standard deviations—as a result, the coefficientsfor the continuous and binary predictors are comparable on roughly the same scale. A one-unitincrease in a standardized predictor captures a two-standard deviation increase in theunderlying variable. AIC denotes the Akaike Information Criterion, with lower values denotingimproved model fit, taking into account the number of predictors. N 5 891 for all models.

Logit MLM

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)

Opinion – 3.3*(.5)

3.8*(.5)

3.0*(.5)

3.0*(.6)

– 4.1*(.8)

4.3*(.8)

3.1*(.7)

3.1*(.8)

State voter ideology – – – .6*(.3)

.6*(.3)

– – – .7*(.4)

.7*(.4)

Lack of quality 21.1*(.3)

2.9*(.3)

22.1*(.4)

23.0*(.5)

23.0*(.5)

21.8(1.2)

21.4(1.0)

22.5*(1.2)

23.2*(.8)

23.2*(.8)

Ideological distance 24.7*(.4)

23.5*(.5)

23.1*(.5)

24.0*(.5)

24.0*(.5)

24.9*(.5)

24.2*(.6)

24.1*(.6)

24.1*(.6)

24.1*(.6)

Same Party 1.1*(.4)

2.5*(.6)

3.0*(.6)

2.9*(.6)

2.9*(.6)

1.8*(.5)

2.5*(.6)

2.6*(.6)

2.7*(.6)

2.8*(.6)

Strong president 1.5*(.3)

.7*(.3)

– 2.4*(.5)

2.5*(.5)

2.1*(1.2)

.6(1.1)

– 2.5*(.8)

2.5*(.8)

Presidential approval – – 1.5*(.4)

3.1*(.5)

3.1*(.5)

– – 1.7(1.1)

3.3*(.9)

3.2*(.9)

Reelection – – – – 0.0(.4)

– – – – .1(.4)

Opinion 3 Reelection – – – – .1(.8)

– – – – .2(.8)

Intercept .8(.2)

1.2(.3)

1.6(.2)

.4(.3)

.4(.4)

.6(1.0)

1.7(.9)

2.1(.5)

.5(.6)

.5(.6)

AIC 419 351 342 313 317 347 317 315 311 315Standard deviation of

justice effects– – – – – 1.6 1.3 1.1 .6 .6

% Correctly Classified 90.8 92.4 92.1 92.5 92.6 93.3 93.4 93.6 93.4 93.4% Reduction in Error 63.4 69.6 69.6 70.1 70.5 73.2 73.7 74.6 73.7 73.7


In terms of model performance, model fit im-proves significantly when public opinion is includedas a predictor. Akaike’s information criterion (AIC)shows that within each set of models, Models 4 and 9perform the ‘‘best.’’12 Each multilevel model, whichallows the intercepts to vary by nominee, performsbetter than its regular logit counterpart, demonstrat-ing that it is important to account for the grouping ofvotes by nominee.

Substantive Importance of Public Opinion

To flesh out our findings about the role of publicopinion in confirmation politics, we calculate andgraph predicted probabilities of a senator voting yesunder a variety of conditions. Given that marginalprobabilities in a logit model vary across predictorvalues, such displays will help us understand howthe impact of public opinion varies given the valuesof the other predictors, as well as how the impact ofthese other predictors varies given different levels ofpublic opinion. All predictions use the following base-line unless otherwise noted: continuous predictors areset to their mean (zero, by construction), party is set tothe opposition party, and the random effect is that foran average nominee (zero). For continuous predictors,we set ‘‘low’’ values to be those one standard deviationbelow the predictor mean, and ‘‘high’’ values to bethose one standard deviation above the mean. We useModel 8 for all predicted values in the text.

In Figure 4, we show the effects of varying state-level public opinion on the nominee, given differentlevels of the other predictors. The graphs use pointpredictions from the logits, which resemble thosecalculated using simulations, but yield smootherplots. Each panel highlights a shift in a differentpredictor. Public opinion is on the x-axis in eachpanel, ranging from 35% to 95% support (theapproximate range of the opinion data used). Thenon-shaded regions depict the range of public opinionbetween low opinion (one standard deviation belowthe mean) and high opinion (one standard deviationabove)—that is, the range where most observationsfall. The predicted probability of voting yes is shownon the y-axis in each panel. Across curves, at a givenlevel of opinion, we can compare the effect ofchanging the predictor noted in the panel description.

Public opinion and nominee quality. In the toppanel of Figure 4, we show how the effect of publicopinion varies given quality. One might suspect that

public opinion simply mirrors nominee quality, yetwhile the two are positively correlated, the probabilityof a ‘‘yes’’ vote varies substantially across publicopinion levels even for nominees of similar quality.(In addition, recall from Figure 1 that there issubstantial variation in state-level opinion withineach nominee; this variation, of course, cannot beexplained by nominee quality.) For popular nomi-nees, quality has almost no effect; a ‘‘yes’’ vote is nearcertain. For less popular nominees, however, theeffect is substantial. Low-quality and unpopularnominees are much less likely to get a ‘‘yes’’ votethan either popular or high-quality nominees. Qual-ity levels also condition the impact of opinion. For ahigh-quality nominee, roughly 50% public support ina state yields a 50-50 chance of a ‘‘yes’’ vote from thatstate’s senator. A low quality nominee needs roughly65% support to have the same chance.

Public opinion and partisanship. The secondpanel in Figure 4 shows predicted probabilities forsame- and opposite-party senators. Same-party sen-ators are already highly likely to support a nominee,at least over the central range of opinion. There is adrop-off in same-party senator support only once thenominee is significantly unpopular. For opposite-partysenators, however, public opinion strongly influencesvoting; a ‘‘yes’’ vote only approaches certainty amongmore popular nominees. To put this another way,same- and opposite-party senators (holding ideologyconstant) react similarly to high-opinion nominees,but low-opinion nominees are very vulnerable tosenator opposition, especially among members of theopposition party. (To be clear, this is not due to anexplicit interaction term, but rather a function of theintercept shift for same-party senators.)

Public opinion and ideological distance. Thelast two panels of Figure 4 display the impact ofideological distance, first among opposite-party sen-ators and then among same-party members. Publicopinion is most important for ideologically distantsenators; they are only likely to support distantnominees who are popular in their state. Moremoderate senators of the opposition party, on theother hand, are likely to support nominees with weakto moderate public approval. For same party senators,we see that ideological distance only influences theirvotes among very unpopular nominees. As a nominee’sstate approval exceeds 60%, a ‘‘yes’’ vote by an in-partysenator approaches certainty. When the senator andnominee are ideologically close, a swing from lowopinion to high increases the probability of a yes votefrom 85% to nearly 100%. For ideologically distantsenators/nominees, the spread is from under 10% to

12AIC rewards goodness of fit, but discourages overfitting. LowerAIC values indicate the preferred model variant.


nearly 80%. Thus, ideologically distant senators of theopposition party are very sensitive to public opinion,and ideologically compatible ones are far less so.

Counterfactuals

One additional way to assess the importance ofpublic opinion in confirmation politics is to makecounterfactual ‘‘predictions’’ had the public feltdifferently about the nominees. We ask three ques-tions based on such counterfactuals.

Should Bork blame the public? Robert Borkreceived far less public support for confirmation thandid Samuel Alito (see Figure 1). What if Bork hadreceived as much public support as Alito? We appliedthe coefficients from Model 9 to predict votes foreach of the senators who voted on Bork’s confirma-tion, but using the state-by-state opinion estimatesfrom Alito instead of from Bork (leaving all else thesame). Bork received only 42 votes in his favor (givenactual opinion on his nomination, we would havepredicted 43). If he were as popular as Alito, however,with the state-by-state popularity of Alito, we predictthat he would have been confirmed with 54 votes.

Justice Alito’s confirmation too seemed at risk fora time. He eventually received 58 votes (we wouldhave predicted 59), the same number of votes castagainst Bork. We asked whether Bork would havebeen confirmed if as popular as Alito—what aboutthe reverse? With state-by-state opinion at Bork’slevels, we predict that Alito would have lost somesupport, but still would have been confirmed with 54votes. This suggests that attempts by the Democratsto investigate Alito further and shift the public’s

FIGURE 4 The predicted effects of opinion onroll call voting. Each panel shows thepredicted probability of a senatorvoting yes on confirmation, across therange of state-level public opinion, fordifferent levels of the other predictors.All curves are derived from Model 8 inTable 2, but results are similar for all.The default value of each continuousvariable is its mean. ‘‘Low’’ values areone standard deviation below this;‘‘high’’ values are one standarddeviation above. We assume unlessotherwise noted that the senator is ofthe opposite party, that the president isweak, and that the nominee isotherwise average (random effect set tozero). The nonshaded regions depictthe range of public opinion betweenlow opinion (one standard deviationbelow the mean) and high opinion (onestandard deviation above)—that is, therange where most observations fall.

40 50 60 70 80 900

.25

.5

.75

1Panel A: High vs. low quality

High quality Avg.qual.

Low quality

Pr(

Vot

ing

Yes

)

40 50 60 70 80 900

.25

.5

.75

1Panel B: Same vs. opposite party

Opposite party

Same party

Pr(

Vot

ing

Yes

)

40 50 60 70 80 900

.25

.5

.75

1Panel C: Ideological distance (opposite party)

Close tonominee Average

distance

Far fromnomineeP

r(V

otin

g Y

es)

40 50 60 70 80 900

.25

.5

.75

1Panel D: Ideological distance (same party)Close tonominee

Averagedistance

Far fromnominee

Pr(

Vot

ing

Yes

)

State opinion


stance on confirmation might have proved futile.Bork and Alito had similar quality levels and, onaverage, were roughly as compatible ideologicallywith the senators, but otherwise the situations werequite different. Alito faced a Senate with 12 moreRepublicans than did Bork, giving him a larger baseof support against any decline in public opinion. Interms of confirmation (rather than individual senatorvotes), the partisan distribution of the Senatetrumped the effects of opinion—Alito’s nominationmight have suffered a different fate if it had takenplace after the Democrats took control of the Senatefollowing the 2006 elections.

Did the public confirm Justice Thomas? JusticeThomas also faced a tough confirmation fight,eventually being confirmed with 52 votes (the sameas we would have predicted) after Anita Hill’sallegations nearly derailed him. Thomas was morepopular a nominee on average than was Bork, and abit more popular than Alito. Did this make a differ-ence in his confirmation vote? What if he had been asunpopular as Bork? Our prediction, applying Bork’sstate-by-state opinion level instead of his own, is thatThomas would have received only 40 votes—a ‘‘land-slide’’ vote against confirmation. Public opinion, itseems, was crucial to his successful confirmation.

Could Harriet Miers have won confirmation?The nomination of Harriet Miers was unique in manyways, particularly in the manner in which senatorsof the president’s party signaled their opposition.Nevertheless, her nomination is still useful for explor-ing the potential magnitude of opinion effects. InOctober 2005, President Bush nominated Miers toreplace Justice O’Connor. Three weeks later, he with-drew the nomination, after vocal opposition fromRepublicans. Miers was one of the least popular nomi-nees in our sample; her average state-level supportwas 52% among those with an opinion, ranging froma low of 38% in California to 65% support in NorthDakota. On average, her support was similar to Bork’s,with less variation across states. Her lack of qualityscore was .64, higher than any of our other nominees.Neither her quality nor opinion levels would be goodomens for a successful confirmation, as compared toAlito, for example. On the other hand, because she wasmore ideologically moderate than Alito, her averageideological distance from senators (.14) was slightlyless than the average across our nominees (.18) andclearly less than the average for Alito (.21) (her distancewas on par with Souter or Ginsburg’s average ideolog-ical compatibility with the senators). This factor wouldpush in her favor in comparison to Alito (who, to repeat,was confirmed with 58 votes).

We temporarily set aside any idiosyncratic fea-tures of her nomination and assume she was other-wise an average nominee (zero nominee effect,maintaining other predictors as is). Our best pre-diction is then that she would have squeaked by 51votes, all from Republican senators. Of course, theopposition from members of her own party, as well asher poor performances in meetings with senators(Greenburg 2007, 278–81), indicate that Miers was awell below average nominee. We take this weaknessinto account by attributing to her the same negativenominee effect as Alito, while keeping her actualpublic opinion as is. Given this, our best predictionwould be that she would only have received 32votes—a landslide against confirmation. Couldgreater public opinion have saved her nomination?To answer this, we next predicted senator votesassuming the public had supported Miers to thesame extent they did Alito, while still capturing herweaknesses by maintaining the negative nomineeeffect. We predict she would have gained confirma-tion with 53 votes. The gain in public opinion wouldapproximately offset the negative nominee effect.

Such counterfactuals help show the pivotal roleof public opinion in confirmation politics. Publicopinion can mean the difference between a JusticeBork and a Justice Kennedy.

Justice Sotomayor: A Robustness Check

As a final robustness check, we attempt to ‘‘predict’’the confirmation votes for Justice Sotomayor (wecompleted the analysis for this paper before she wasconfirmed in August 2009). We can use the votes onher nomination as an out-of-sample prediction totest the robustness of our model. Sotomayor was oneof the least popular nominees among those westudied, with an average state-level opinion of 58%,placing her above only Bork and Miers. This stands instark contrast to the widespread support for Breyerand Ginsburg, the other two Democratic nominees inour sample. While Breyer and Ginsburg were largelyuncontroversial nominees, Sotomayor was attackedpersisently by Republicans, likely driving down heroverall popularity as a nominee.

We predicted each senator’s vote on Sotomayor’snomination (using the estimates from Model 9), andcompared those predictions to senators’ actual votes.(The Senate voted to confirm her 68 to 31.) Wepredicted 93 of 99 votes correctly, a rate that evenimproves upon our in-sample predictions from Table 2.Thus, our model works well to explain a nominationthat was not the subject of our original analysis. We also


reran our regression models with Sotomayor included.Results were statistically and substantively the same.

Discussion

While public opinion may be shaped by manyfactors, some idiosyncratic or trivial, the evidenceshows that on average and in the aggregate, opinionis sufficiently meaningful and informed for scholarsand senators alike to take it seriously (see, e.g.,Erikson, Wright, and McIver 1993). And, as we notedat length earlier, voters are far more informed aboutconfirmation politics than has often been assumed orasserted. Our findings are part of a surprisinglylimited body of work tying government choice tochoice-specific public opinion. Studies of confirma-tion politics can take for granted the fit betweenconstituent views and representative behavior—yetthat fit can be questioned and any slack is noteworthyto models of legislative behavior. Whether senatorsshould mirror constituent preferences or insteadexercise independent judgment is an interestingnormative question. Whether they actually do so isan important empirical question. Furthermore, it isalso important to know whether senators listen totheir constituents’ specific preferences or merelyfollow their constituents’ ideological tendencies.

Our paper connects two literatures on SupremeCourt confirmation politics—one examining howpublic opinion is formed on nominees (e.g., Gibsonand Caldeira 2009, Gimpel and Wolpert 1996) and onethat examines why senators decide to approve or rejecta nominee. It also speaks to debates about legislativeresponsiveness in general and senatorial responsivenessmore narrowly. The answer to our main question—does public opinion on a given nominee in senator’shome state drive his or her confirmation vote? —is aresounding and robust yes. Given that we controlledfor state-level voter ideology and senator ideology, ourfindings about the effect of public opinion on roll-callvoting are all the more striking. While senators doseem to be making use of such diffuse ideology as a cuefor how they should vote, the effects of opinion arestrong and indeed far stronger than that of diffuseideology. Moreover, we note that the constituentinfluence we find exists even though elected senatorswill already tend to reflect their constituents’ views.

In addition, the role of presidential calculationsin nominee selection reinforces our findings. Presi-dents surely choose nominees they hope to getthrough the Senate, and frequently make publicappeals in an effort to raise support for their

nominees (Johnson and Roberts 2004; Moraski andShipan 1999). To the extent that presidents take intoaccount the expected public view of a nominee, ourresults would understate the effect of opinion in thelarger nomination and confirmation game.

Senators do respond to other forces besidesnominee-specific opinion, most notably their ownpreferences and partisanship. Our results thus speakto larger debates about the tradeoffs between theseforces. First, we find clear evidence of party effects,consistent with partisan theories of legislative organ-ization and behavior. This suggests that senatorsbalance party pressure with direct constituent pres-sure or that the long-term electoral calculus pushestowards maintenance of the party label throughconfirmation or rejection of the president’s nominee(for copartisans or the opposition, respectively).

Second, that personal preferences still mattersuggests that senators are willing to partially ‘‘shirk’’the desires of their constituents, in pursuit of theirown ideological or other goals. Our results speak tothe empirical literature on responsiveness. Overall,the trend of the literature, to paint broadly, is thatrepresentative democracy works and policy choices areresponsive though imperfectly so. Whether shirkinghappens in any particular voting context is an empiri-cal question. One advantage of our approach is that wewere able to assess representation in a concrete set ofvotes, in contrast to the more common focus onaggregate responsiveness in the existing literature.

Our findings are particularly timely given the closesplit on the Court today between liberals and conserva-tives and the fact that President Obama has had theopportunity to nominate one justice to the Court andmay nominate several more. While the Democraticmajority in the current Senate provides a cushion fornominees with lower public support, even same-partysenators are not immune to the call of public opinion—and the threat of a filibuster still looms. ConservativeDemocratic senators will look to their constituents inthese votes, as will the more moderate Republicans.

Conclusion

Mr. Dooley famously stated that ‘‘ th’ supreme coortfollows th’ illiction returns’’ (Dunne 1901, 26). Senatorsclearly worry that election returns may follow SupremeCourt confirmations. A process thought to be drivenlargely by political elites turns out to be responsive tothe mass public as well. Even the six-year terms ofsenators do not make them invulnerable to publicpressure on an issue of this magnitude and salience.


Constituent opinion is a strong and robustpredictor of a senator’s roll-call vote even aftercontrolling for the strongest known influences onconfirmation votes. This finding establishes a strongand systematic link between constituent opinion andvoting on Supreme Court nominees. Even high-qualitynominees and those named by strong presidents arevulnerable to constituent influence. On the otherhand, constituent opinion plays a larger role in thevote calculus of those positioned to oppose thenominee, whether for partisan or ideological reasons,than for those who will otherwise be likely to supportthe nominee, and for weaker nominees more generally.

These results tie the Court back to majority will.The public’s influence over justices after confirmationmay be unclear, but we find strong evidence ofinfluence over confirmation itself. This means thatthe Court is even less likely to fall outside themainstream of American public opinion than wouldbe the case if the public’s influence over the Court’smembership were realized solely through the rela-tively blunt instrument of election of senators and thepresident (see Dahl 1957).

At the same time, we are not arguing thatconfirmations are simply popularity contests. Rather,the quality of democratic government should bejudged, at least in part, by the responsiveness ofelected officials to the preferences of their constitu-ents. Functioning democracy requires some matchingof governmental choices to public opinion, regardlessof whether public opinion on a nominee is trivializedas mere popularity or reflects a legitimate judgmenton the nominee in question. To some extent, onemight not care what public opinion is based upon,but rather only whether senators respond to it.

Future work could take up where we now leaveoff. We suggest a few avenues here. We did not findresidual differences in the effect of opinion acrossnominees (results available upon request). But evenin the most comprehensive model, with our mostcomplete set of controls, there are residual differencesacross nominees in terms of the varying intercepts(shifting the base probability of a ‘‘yes’’ vote up ordown from the average). If we truly captured all across-nominee variation, we would expect the random effectsby nominee to shrink to zero. These could be idiosyn-cratic, but future work might inquire further. Whatelse separates ‘‘good’’ nominees from ‘‘bad’’?

Next, one might dig deeper into what drivespublic opinion on Supreme Court nominees. Supportitself could be the central dependent variable in sucha project, with our multilevel models of individualsupport unpacked in greater detail or modified to

answer questions focused at that level, rather than thestate level. For example, how does support changeover time? How do people get informed and whatrole do elites play in this process? Third, one couldstudy how presidents can increase public support fortheir nominees and whether it works in the context ofother factors (building on Johnson and Roberts2004). Fourth, one could study the interaction ofthe public and organized interests in pressuring thevotes of senators (see Caldeira and Wright 1998).Finally, one could study whether senators respondmore to their fellow partisans or the median voter incasting their votes on Supreme Court nominees.

This article will surely not be the the last word onthe role of public opinion in Supreme Court con-firmation politics. We hope that this line of inquirywill lead to a deeper understanding of the linkagesbetween the public and the legislators who confirmthe members of the nation’s highest court.

Acknowledgments

We thank Andrew Gelman, Elliot Slotnick, JeffreyStaton, Yu-Sung Su, and members of the CornellCourts Colloquim for helpful comments and sugges-tions, along with the Roper Center and the ColumbiaApplied Statistics Center. We would like to thank LeeEpstein, Rene Lindstadt, Jeffrey Segal, and ChadWesterland for generously making their data publiclyavailable. Replications datasets and code are availableat www.princeton.edu/~jkastell/sc_noms_replication.html

Manuscript submitted 30 July 2009Manuscript accepted for publication 18 January 2010

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Jonathan P. Kastellec is Assistant Professor,Department of Politics, Princeton University, Prince-ton, NJ, 08544.

Jeffrey R. Lax is Associate Professor, Departmentof Political Science, Columbia University, New York,NY, 10027.

Justin H. Phillips is Assistant Professor, Depart-ment of Political Science, Columbia University, NewYork, NY, 10027.


public opinion and senate confirmation of supreme court

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