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Chapter 3 If Only They Knew: Citizens’ Knowledge of Roll Call Behavior and Evaluations of U.S. Senators Christopher P. Donnelly March 2017 Abstract In order to hold elected officials accountable, citizens must know where these elected officials stand on important issues. This paper assesses how well individuals are able to identify the roll call behavior of their senators and, in turn, the extent to which citizens are able to align their approval of their senators with their issue agreement. I find that citizens are better able to identify party-conforming roll call behavior than party-deviating roll call behavior and, more importantly, that individuals are most likely to misalign their approval and issue agreement when they (unknowingly) disagree with a senator from their own party or (again, unknowingly) agree with a senator from the party opposite their own. Put differently, when a senator deviates from her party, most constituents end up holding the senator “accountable” for something that she did not actually do. The results of such misperceptions are that deviating senators end up with inflated levels of approval among same-party constituents and artificially low levels of approval among opposite-party constituents. These results have important implications for political knowledge, democratic accountability, and citizen cuetaking. Introduction In order to hold elected officials accountable, citizens must know where these elected officials stand on the important issues of the day. Obtaining information about how individual legislators vote, however, can entail high costs (see Downs 1957). One potentially effective shortcut by which individuals can infer their legislators’ positions is through the use of party cues. If an individual knows his legislator’s party affiliation as well as where that party stands on a given issue, then he can forego taking the time to learn how the particular legislator voted on that issue by instead simply inferring that the legislator took whatever her party’s position is. Of course, when a legislator opposes her party’s position on an issue, then such a shortcut for inferring the legislator’s position becomes not only ineffective but actually misleading. In this paper, I ask two questions. First, when a legislator departs from her party on an issue, to what extent do her constituents persist in wrongly assuming that the legislator voted with her party? Second, how–if at all–do such incorrect perceptions of the legislator lead citizens astray? That is, to what extent do citizens who incorrectly perceive a partydeviating legislator’s voting behavior judge the legislator differently than they would have had they been aware of the legislator’s departure from her party? First, I find that citizens are much better able to identify how their legislator voted on a key issue when the lawmaker votes with her party than when she votes against her party. Second, I find that citizens’ perceptions of how a legislator voted, rather than the reality of how a legislator came down on an issue, are the primary driver of citizens’ approval of their legislators. On the one hand, this means that when a legislator votes with her party, citizens are by and large capable of linking their policy agreement with a legislator to their approval of that legislator1 . On the other hand, my results imply that when a legislator departs from 1 By “linking policy agreement with a legislator to approval of that legislator,” I simply mean that one approves of a legislator with whom he agrees and disapproves of a legislator with whom he disagrees. If one approves of a legislator with whom he disagrees or disapproves of a legislator with whom he agrees, then he has failed to link his policy agreement with his approval. 1 the party line, citizens are much less likely to correctly align their policy agreement with their approval of that legislator. While the small minority of constituents who are aware of the legislator’s departure from her own party do prove to hold their legislator accountable for such behavior, I find that, by comparison, those who are not aware of the legislator’s wayward behavior–those are, most of her constituents–end up holding the legislator “accountable” for something that she did not actually do. These results have important implications for political knowledge, democratic accountability, and citizen cue-taking. Classical Representation Theory Meets an Uninformed Citizenry At the foundation of a democratic society is the notion that voters are able to hold their elected officials accountable for the actions they take while in office. Specifically, voters should reward political representatives who they believe are faithfully carrying out their policy preferences and should punish those who are out of step (Downs 1957; Canes-Wrone, Brady, and Cogan 2002). Of course, in order for the responsible citizen to perform such a task, he must be accurately informed about the actions that his elected representatives have taken (Franklin 1991; Nyhan et. al. 2012; Wolpert and Gimpel 1997). When faced with the overwhelming evidence that most Americans are not sufficiently interested in politics to expend the time and energy it takes to become fully informed (Campbell et. al. 1960; Converse 1964; Delli-Carpini and Keeter 1996) many scholars have suggested that a potential remedy to such a threat to the functioning of democracy can be found in the form of information shortcuts or cues (e.g., Boudreau 2009; Lupia and McCubbins 1998; Popkin 1991; Sniderman, Brody, and Tetlock 1991; but see Bartels 1996). An ability or willingness to consume and retain vast amounts of political information, according to these scholars, need not be a necessary condition for voters to make an informed decision. Instead, voters can collect much smaller pieces of information to reach the same decision they would have had they chosen to become “fully informed” (cf. Lau and Redlawsk 2001). While there are a variety of particular informational cues that voters can employ as 2 shortcuts–these include a candidate’s physical appearance, polling strength, interest group endorsements and status as an incumbent or challenger, among others (Lau and Redlawsk 2001, 2006)–partisan affiliation is perhaps the most prevalent type of shortcut available to citizens who are miserly in the amount of time and resources they are willing to commit to obtaining information about politics (Arceneaux 2008, Levendusky 2010). Party Cues for Evaluating Members of Congress One particular context in which party cues might prove a useful shortcut is that of Congress. If voters seek to assess the degree to which their Member of Congress (MC) agrees with them on the major issues of the day, then simply knowing the member’s party affiliation and mapping the issue positions taken by the party onto their MC who claims its affiliation would seem like a generally reliable way to reach an informed decision without incurring the costs associated with learning about this particular MC and the positions she has taken during her time in office (Ansolabehere et. al. 2001, McDermott and Jones 2005, Woon and Pope 2008). Indeed, a systematic, exhaustive analysis of roll call voting by two leading Congressional scholars concludes that the best predictor of how an MC will vote on a given roll call is the party with which she affiliates (Poole and Rosenthal 2007; see also Lee 2009). Yet while party affiliation is a strong predictor of roll call voting, it by no means is a perfect predictor. Indeed, on high-profile, contentious pieces of legislation it is far from uncommon for some legislators, especially those who have incentive to display independence, to vote against a majority of their own party (e.g., Cain, Ferejohn, and Fiorina 1987; Fenno 1978; Mayhew 1974; Sinclair 1995). In this case, party cues not only become useless shortcuts; they could, more perniciously, cause citizens to form incorrect perceptions of an MC’s actions while in office and, should they use such incorrect perceptions in forming their broader, overall evaluations of the MC, could in fact undermine democratic accountability rather than promote this goal (see Dancey and Sheagley 2013). 3 Party Cues vs. Specific Knowledge: A Theory of Accountability At its starting point, my theory posits that because obtaining information about a specific legislator’s record is costly, most individuals will use the shortcut of party label to infer a legislator’s position on a particular issue. From this theoretical claim, I can derive a couple of key empirical predictions. First, if my claim that individuals are drawing on their knowledge of the positions that each of the two major parties take on an issue as a shortcut for inferring legislators’ roll call behavior is true, then one direct implication is that individuals should be much better able to correctly identify the position that their legislator took on a specific vote when the legislator has voted with her party as opposed to against her party. A second, indirect implication of my theory is that citizens who hold correct perceptions about how a legislator voted–something that I posit is much more likely to occur when a legislator has voted with her party than when not–should be better able to align their issue agreement with their approval of that legislator. This second claim is best illustrated by several examples. Such examples will allow me to develop and ultimately put forth a more general theory about cue-taking and accountability. Suppose there is a Democratic citizen who, like most citizens affiliating with his party, favors government funding for embryonic stem cell research. Suppose further that this individual is represented by a Republican legislator who, contrary to her own party, favors stem cell research. The individual citizen, however, believes (incorrectly) that his GOP legislator opposes stem cell research, because he is using the party cue to infer her position. As a result, the voter, when asked whether he approves or disapproves of the job that his Republican legislator is doing, states that he disapproves because of his (wrongly) perceived disagreement with her in regards to stem cell research. My theory posits that, had this citizen foregone the use of the party cue and instead learned of the fact that his Republican legislator voted contrary to her party in favor of stem cell research, his probability of approving of that legislator would be significantly greater than in the case I have posited whereby he is unaware of the legislator’s deviation. Further, recall that my theory also surmises that rates 4 of misinformation among constituents will be much greater when a legislator deviates from her party than when she takes a position congruent with her party. As such, in the case of a deviating legislator, there are likely to be many individuals who are mistaken about how the legislator voted, thus calling into question the degree to which the constituency as a collective group will be able to hold the legislator accountable. Moving to an alternative scenario, let us imagine a Republican citizen who, also like most of his co-partisans, opposes government funding for embryonic stem cell research. Suppose further that this individual is represented by the same Republican legislator referenced above–specifically, a Republican legislator who deviates from her party on stem cell research, casting a vote in favor of such funding. Let us imagine that our party-line-subscribing Republican citizen, however, wrongly uses the party cue to infer that his GOP legislator voted against stem cell research funding, as a majority of Congressional Republicans in fact did. Opposing stem cell research funding himself, our hypothetical citizen should, when asked whether he approves of the job that his co-partisan legislator is doing, state that he approves of his legislator, as he perceives himself to be in agreement with her on this issue. My theory posits that had this citizen eschewed use of the party cue and instead taken the time to learn his deviating legislator’s actual position, the probability that he would approve of her would dramatically decrease. In order to be thorough in explicating this paper’s theory, it is also important to consider rank-and-file citizens who themselves depart from the party line on particular issues. Continuing with our example of embryonic stem cell research funding, suppose we have a Republican individual who, contrary to a majority of the rank-and-file in his own party, supports funding for embryonic stem cell research. Suppose further that this rank-and-file Republican is represented by the previously-referenced pro-stem cell research Republican legislator. If he uses the party cue to infer that his legislator voted against such funding, then this citizen should be less likely to approve of his co-partisan legislator than he would had he been informed of the legislator’s true position. It is straightforward to see that we 5 could apply this same logic to a Democratic citizen who deviates from his party on stem cell research funding and is represented by a Republican legislator who herself deviates (so in this case, we have an anti-stem cell research Democrat being represented by a pro-stem cell research Republican). In such a case, use of the party cue would lead this citizen to believe that he is in agreement with his opposite-party legislator, while specific knowledge of the vote would allow this citizen to know that he in fact disagrees with his opposite-party legislator. Thus far, I have only discussed examples in which an individual is represented by a legislator who deviates from her party. What should we expect when an individual is represented by a legislator who votes with her party on an issue? In such a case, I argue that the accountability should work quite smoothly. Specifically, most individuals, regardless of whether they agree or disagree with their legislator and regardless of whether they share the legislator’s party label, will use the party cue to arrive at a correct inference about how their legislator voted and, in turn, will approve of legislators with whom they agree and disapprove of legislators with whom they disagree. To be clear, those who do misperceive their legislators’ roll call behavior in the case of a party-conforming legislator should have just as much difficulty aligning their issue agreement with their approval as those who misperceive their legislators’ roll call behavior in the deviating case. That said, the proportion of citizens who hold incorrect beliefs about their legislators’ roll call record when the legislator votes with her party should be far smaller than those holding incorrect beliefs in the deviating case, and thus should be less of a cause for concern in the party-conforming case than the deviating case. As such, approval evaluations of deviating legislatorss are the main interest of this paper, while approval evaluations of party-conforming legislatorss will come up only as a point of comparison. Generalizing from these examples, then, I can state the following theoretical expectation resulting from my first claim: • Citizens will be much more adept at correctly identifying a legislator’s roll call position 6 on an issue when that legislator has voted with a majority of her party as opposed to an issue on which the legislator voted against a majority of her party. This is due to the use of party cues for inferring legislative behavior. In addition, several empirical implications result from my second claim: • When a legislator deviates from her party on a vote, constituents who disagree with the deviation–those are, same-party constituents who do not deviate from their party on the issue as well as opposite-party constituents who deviate from their own party–and are aware (i.e. who drew on specific knowledge of that legislator’s position as opposed to the party cue) that they are in disagreement will be more likely to disapprove of the job that the legislator is doing than will their counterparts who are unaware (i.e. either do not know how the legislator voted or drew on the party cue to reach the wrong conclusion about how the legislator voted) of the legislator’s position and, in turn, unaware of their disagreement with the legislator. • When a legislator deviates from her party on a vote, constituents who agree with the deviation–those are, same-party constituents who themselves deviate from their party on the issue, along with opposite-party constituents who take their own party’s position on an issue–and are aware (i.e. who drew on specific knowledge of that legislator’s position as opposed to the party cue) they are in agreement will be more likely to approve of the job that the legislator is doing than will their counterparts who are unaware (i.e. either do not know how the legislator voted or drew on the party cue to reach the wrong conclusion about how the legislator voted) of the legislator’s position and, in turn, unaware of their agreement with the legislator. • As a result, citizens who are aware of party-deviating behavior by legislators are more likely to align their issue agreement with their legislator with their approval of that legislator. 7 • When a legislator votes with her party on a vote, the same logic put forth above for the deviating case applies. In other words, when a constituent who agrees (disagrees) with the legislator’s actual position is aware of this agreement (disagreement), he will be much more likely to approve (disapprove) of the job she is doing than if he is unaware, in which case he is more likely to form an approval evaluation of his legislator that is incongruent with his issue agreement. • But because citizens will be largely capable of identifying party-conforming behavior– again, due to the use of party cues discussed previously–there will be relatively few instances of party-conforming behavior by a legislator, when compared to instances of party-deviating behavior by a legislator, in which a citizen does not align his approval of his legislator with his issue agreement with that legislator. It should be stated up front that for many–probably most–citizens, it is unlikely that agreement on any single issue taken in isolation will be the sole determinant of legislator approval. It seems much more plausible that a citizen will judge his legislator on her overall record across a host of issues. Having said that, one should note that my theory states only that for each issue, the probability that one aligns his issue agreement with his legislator’s record should vary conditional on his knowledge of the legislator’s position. In order for this to be the case, a single issue (and one’s knowledge about the legislator’s position) need only play a part in determining whether one aligns his approval of his legislator with his issue agreement with that legislator. As I will discuss subsequently in this paper, my empirical analysis examines issues that are fairly high-profile and salient. Thus, the assumption that each issue might influence to some degree legislator approval–or more specifically, alignment of one’s issue agreement with one’s legislator approval–is not a demanding assumption to make in the context of this study. Having put forth a theory that combines legislative accountability with citizen cue-taking and from which I derive testable implications, I now proceed to discuss previous research in 8 this area, its limitations, and how I plan to address such shortcomings in a way that offers a valuable contribution to our understanding of the conditional nature of accountability. Limitations of Previous Research In the past, my two claims–those are, that citizens will better be able to identify partyconforming roll call behavior than party-deviating roll call behavior and that citizens who can correctly identify a legislator’s vote should be more likely to align their issue agreement with their approval of that legislator–have been difficult to directly test due to data limitations and measurement error (for a thorough analysis of the early literature on issue accountability and representation, see Stone 1979). In order to test my claims in a way that is precise and that thoroughly addresses the measurement error–namely, the inability to place survey respondents and legislators on exactly the same scale, as well as the inability to ascertain the accuracy of citizens’ perceptions of their legislator’s actions–present in early works on representation, one would need 1) measures of individuals’ own preferences with respect to major roll call votes taken by Congress; 2) measures of how citizens perceive their legislators to have voted on these roll calls taken by Congress; and 3) measures of citizens’ approval of their legislators (Ansolabehere and Jones 2010: 585). Most previous research on legislative accountability (e.g., Achen 1978; Clinton 2006; Erikson 1978, 1990; Franklin 1991; Miller and Stokes 1963; Nyhan et. al. 2012) has not had at its disposal data that meets all three of these conditions; the small handful that have used data meeting all the criteria have been limited to single roll call votes (Alvarez and Gronke 1996, who look at the 1991 vote by the U.S. House of Representatives to authorize force in the Persian Gulf; Wilson and Gronke 2000, who analyze the House’s vote on the President Bill Clinton’s 1994 crime bill; and Wolpert and Gimpel 1997, who examine perceptions of senators’ behavior on then-Judge Clarence Thomas’s 1991 confirmation vote to the U.S. Supreme Court). The one notable exception, however, is a relatively recent article by Ansolabehere and Jones (2010), in which the authors use 2005 data from the MIT 9 Public Opinion Research Training Lab (PORTL), as well as the MIT Module of the 2006 Congressional Cooperative Election Study (CCES), both of which contain for several U.S. House roll call votes the three metrics proscribed above. Namely, voters are asked how they would have come down on a total (across the two studies) of ten specific roll calls taken by the U.S. House, how they believe their own U.S. House member to have voted on each of these issues, and to rate their approval of their current U.S. House member. Using these data, Ansolabehere and Jones (2010) find that individuals’ perceptions of House members’ voting track well with the reality of how House members actually vote and, in turn, allow citizens to make informed approval evaluations of their representatives. There are, however, some limitations to Ansolabehere and Jones’ (2010) analysis, both in terms of the data that are used as well as the way by which the authors arrive at their findings. First, with respect to their data: each of the two datasets from which the authors draw conclusions are limited to roughly 1,000 respondents or, put differently, an average of approximately two respondents per congressional district. Ideally, any data used to test my basic propositions about constituents’ roll call perceptions and approval evaluations would contain far more than two observations per legislative constituency. There is also a limitation to Ansolabehere and Jones’ (2010) empirical analysis, perhaps stemming in part from their data constraints. Specifically, while the authors do demonstrate that citizens are more likely to form incorrect perceptions when a House member deviates from her party than when not (see Ansolabehere and Jones 2010: Table 2, 588), they do not take any steps in their multivariate analysis–that is, the crux of their paper’s empirical assessments–to account for instances of deviating by legislators. As such, these authors do not explore the possibility that their findings might be conditional on whether a legislator has voted with her party on an issue. To explain, it might be the case that citizens are able to hold legislators accountable when the legislator’s party and policy position coincide but are less able to do so when a legislator takes a position contrary to her own party. Unfortunately, if one were to examine the previously-referenced Table 2 in Ansolabehere and Jones’ (2010) 10 article, she would find that for each issue, the raw number of respondents represented by a party-deviating House member ranges from 27 to 114 (depending on the issue) and typically hovers at about 70, providing little statistical leverage for discerning whether the authors’ findings are conditional upon whether a representative voted with her party. The contribution of this paper is therefore three-fold. First, as I discuss below, I will be drawing from a much larger dataset than Ansolabehere and Jones (2010) do. Where Ansolabehere and Jones (2010) are, as mentioned previously, working with an average of just over two respondents per congressional district, the data employed for my analysis will, as I discuss below, average at least a few hundred respondents per legislative constituency, thus lending greater statistical power to the results at which I arrive. Second, I explicitly consider the possibility that citizens’ ability to hold legislators accountable is, at least when the issue is a partisan one, conditional on whether the legislator’s views align with those of her own party. Ansolabehere and Jones (2010) only peripherally examine this possibility, while this paper confronts it forcefully and directly. Thirdly, I situate my analysis in a novel theory of partisan cue-taking and accountability, while Ansolabehere and Jones (2010) consider the mechanism of party cues for inferring legislator positions (see p. 585) but do not integrate it explicitly into their theory. In these ways, I am building upon the foundation laid by Ansolabehere and Jones (2010) with respect to the study of dyadic representation. Data and Research Design With the aforementioned concerns about recent congressional accountability literature in mind, the 2006 Cooperative Congressional Election Study (CCES) Common Content data proves for several reasons to be an ideal vehicle by which to assess the degree to which citizens correctly perceive party-conforming, as opposed to party-deviating, legislative behavior as well as the extent to which they align their issue agreement with their approval of their legislators. Similar to the 2005 data from PORTL and the Harvard/MIT Module of the 2006 CCES, respondents are asked about how they would have voted on seven particular 11 issues that were brought before the United States Senate, six of which were considered in the 109th Congress (2005-2006) and one of which was considered in the 108th Congress (20032004)2 : the Partial-Birth Abortion Ban (denoted “PBA Ban”); a bill authorizing federal funding for stem cell research (denoted “Stem Cell $”); an amendment to the 2005 defense funding bill which would have required President George W. Bush’s administration to provide benchmarks for the withdrawal of active-duty American combat troops from Iraq (denoted “Iraq”); final passage of the Senate’s version of a comprehensive immigration reform plan that provided a pathway to citizenship for undocumented immigrants (denoted “Imm.”); an increase in the federal minimum wage (denoted “Min. Wage”); a reduction in the capital gains tax rate (denoted “Cap. Gains”); and, finally, ratification of the Central American Free Trade Agreement (denoted “CAFTA”). Respondents are also asked how they believe each of their two senators to have voted on each of these seven measures. Given that the issues on which respondents are queried are indeed taken from actual roll calls, responses can be objectively coded as falling into one of three categories: 1. Correct 2. Don’t Know 3. Incorrect The 2006 CCES Common Content differs from the PORTL and Harvard/MIT Module, however, in the fact that each legislative constituency–in the case of the Senate, each state– has a relatively large number of observations. Specifically, there are 36,421 respondents across the United States who were surveyed for the 2006 CCES Common Content module, with the number of respondents per state ranging from 75 in Vermont to 3,637 in California. States whose populations rank right around the median of the fifty states–states such as Louisiana, Kentucky, and South Carolina–each have about 400 respondents. With a substantially larger 2 The one issue considered in the 108th Congress is the Partial Birth Abortion Ban. 12 pool of respondents both in the aggregate as well as per legislative constituency, I can therefore be more confident of any results that I find in this paper. Another nice property of the data I use is that all of seven of the roll call votes about which respondents are queried are “party votes”–those on which a majority of Republicans line up against a majority of Democrats. Because of this, each senator can be cleanly identified on each issue as having voted “with” her party or “against” her party. Table 1 displays the party breakdown on each of the seven roll call votes. As we see, the number of deviating senators on any given vote ranges from six (in the case of the Capital Gains Tax Cut vote) to 26 (in the case of the Immigration vote). Thus, while all of these votes are ones in which a majority of one party voted against the majority of the other party, they are also all ones on which some senators chose to depart from their party. To reiterate, then, for each issue, senators are categorized as falling into one of two types: 1. Party-Conforming (or Party-Line Voting) 2. Party-Deviating [Table 1 about here] The CCES survey asks respondents to identify their party affiliation on a seven-point scale. Because of the particular political phenomena in which this paper is interested– namely, use of party cues as well as partisan perceptual bias in evaluating elected officials–I exclude from analysis respondents who identify as “pure independent”, which is a relatively small proportion of the survey sample anyhow–about 10%. Ultimately, this paper forms theoretical expectations about how partisans use party cues and how shared partisanship with a senator can influence one’s approval of their senators, thus justifying the exclusion of such strictly unaffiliated respondents (for recent works which follow the same strategy, see Levendusky 2010 and Bullock 2011). Subsequently, I collapse the seven-point scale into a dichotomous measure such that respondents are identified as being either Democratic or Republican, regardless of the strength of their partisan attachment. With previous research 13 suggesting that independent leaners behave very much like strong partisans (see, for instance, Bartels 2000; Greene 2000; Miller 1991; Niemi, Wright, and Powell 1987), such an empirical strategy not only makes analyzing the effect of shared partisanship with one’s senator more tractable but is also theoretically justified. Further, because my theoretical expectations are premised on the notion that citizens are correctly informed about the party affiliation of their senators, I also exclude respondents who did not know or were incorrect when asked to identify the party to which each of their senators belonged3 . Across all respondentsenator observations–of which there are, prior to my exclusions being implemented, (36,421 respondents x 2 senators each = 72,842)–a relatively modest 18% either did not know or were specifically incorrect about their senator’s party affiliation. When such restrictions–the exclusion of pure independents and those who do not know or are incorrect about their senator’s party affiliation–are made, the number of unique respondents drops from 36,421 to 27,717–still a substantial number of respondents on which I am able to conduct analysis. Just as I do with senators, I can also classify respondents as taking their party’s position or taking the position opposite that of their own party. Table 2 displays the party breakdown among respondents across each of the seven issues considered in this paper. While most respondents end up taking their own party’s position on the issues considered, there nonetheless exist a substantial number of deviating respondents. For instance, a near-majority of 49% of Republicans support increasing the minimum wage; similarly, a substantial 40% minority of Democrats take the restrictionist position on immigration, in opposition to all but four of the Democratic senators, while an actual 55% majority of Republicans take the protectionist position on CAFTA, contrary to the pro-free trade stance that most Republican senators took on this issue. Additionally, Figure 1 shows the number of issues on which CCES respondents deviate from their own party. Interestingly, while a mere 7% of party-affiliating respondents deviate on four or more (that is, a majority) of the seven issues, just a 27% 3 Approval evaluations of respondents who are correct regarding the party of one senator but not the other are retained when for the cases in which they evaluate the senator whose party they have correctly identified but dropped for the cases in which they evaluate the senator whose party they did not know or were wrong about. 14 minority of major-party respondents subscribe to the “party line” on all seven of the issues. In sum, there are a substantial number of respondents who, on at least some issues, disagree with their own party. Thus, the first way in which I divide respondents is by whether they take their own party’s position on an issue. In addition, because shared partisanship is such a strong predictor of whether one approves of their senator, respondents are further divided based upon whether they are of the same or the opposite party of the senator whom they are evaluating. For each Respondent-Issue-Senator observation, then a respondent will be classified as one of four types: 1. Same-Party/Party-Line 2. Same-Party/Deviator 3. Opposite-Party/Party-Line 4. Opposite-Party/Deviator [Figure 1 about here] [Table 2 about here] In the CCES data, each respondent is asked to make an approval evaluation for each of their two U.S. senators. Specifically, survey respondents can say that they “Strongly approve”, “Somewhat approve”, “Somewhat disapprove”, or “Strongly disapprove” of the job that their U.S. senator is doing. I collapse the approval scale such that anybody who says that they strongly or somewhat approve of their senator are coded 1, while any individual stating that they strongly or somewhat disapprove are coded as 0. Those who say that they “Don’t know,” a relatively small percentage of respondents, are omitted from analysis. In sum, because the 2006 CCES data contains seven issues on which senators actually voted and about which respondents are asked how they would have voted were it up them, how they believe each of their two U.S. senators to have voted on the issue, as well as whether they approve or disapprove of each of their two U.S. senators, I have all of the ingredients necessary for empirically evaluating my theoretical claims. Moreover, with a 15 very large number of unique respondents–more than 23,000 once various data restrictions are implemented–and an even larger number (close to 300,000) of respondent-issue-senator observations (as I will discuss subsequently in this paper, my data is structured such that each unique respondent can, because he has two senators who he must evaluate across seven different issues, ultimately generate up to 14 distinct Respondent-Issue-senator observations in the dataset), I can be confident in the statistical power of the results that I do find. Results Are Individuals Better at Identifying Party-Conforming Behavior than Party-Deviating Behavior? I begin by evaluating the first of my two claims–that is, that citizens will be better able to correctly identify party-conforming roll call behavior than they will party-deviating roll call behavior. For each of our seven issues, Table 3 displays for each roll call condition (“PartyConforming” and “Party-Deviating”) the percentage of respondents who correctly identify their senator’s vote, the percentage who state that they do not know how the senator voted on the issue, and the percentage who are incorrect in regards to their senator’s vote. As we see, citizens do a much better job at identifying party-conforming behavior than they do at identifying party-deviating behavior. Perhaps the most extreme illustration of this pattern occurs on the Capital Gains Tax Cut vote. On this issue, when a senator votes in line with her party, 72% of respondents are correctly able to identify the senator’s roll call position. When a senator deviates from her party, however, the percentage of respondents who can correctly identify this vote drops precipitously to a mere 12%. The other issues, too, however, display a similar pattern. If we exclude CAFTA, then for no roll call vote does the percentage of respondents correctly identifying party-conforming behavior drop below 60%. Meanwhile, for no issue does the percentage of respondents who are able to correctly identify party-deviating behavior exceed 32%. If we look at the percentage of respondents providing positively incorrect assessments 16 as to how their senator voted on a particular roll call, we see that it never exceeds 17% when a senator has voted with her party. In fact, if we exclude CAFTA from consideration, then the percentage offering an incorrect assessment for any issue never exceeds 11%. Yet when a senator deviates from the party line, we find no issue save for CAFTA4 for which the proportion of assessments that are incorrect is below one-third. Taken together, these results suggest that my first claim is strongly supported; citizens are much better at identifying party-conforming roll call behavior than they are at pin-pointing party-deviating roll call behavior. The results also provide support for a key premise of my theory: citizens seem to be at least in part relying on the party cue when forming perceptions of legislators’ issue positions, meaning that they are at least to some extent aware of where each major party stands on each of the issues considered in the data. [Table 3 about here] Are Individuals Who Correctly Identify Roll Call Behavior More Likely to Align Approval with Issue Agreement? Having substantiated my first claim, I now explore the degree to which the second claim put forth in this paper is supported. Recall that this claim states that individuals who are correctly able to identify how their senator voted on a particular issue are more likely to align their issue agreement with their approval of that senator. And because we now know that party-deviating behavior is more challenging for citizens to recognize than is partyconforming behavior, the claim I am making can be understood to suggest that individuals should have a more difficult time aligning their issue agreement with their approval of a senator when the senator has deviated from her party. In order to investigate this hypothesis, I run a linear probability model (LPM)5 (reported in Table 4) in which the dependent variable 4 The fact that only 32% of respondents are incorrect about how their senator voted on CAFTA when the senator has deviated is likely explained by the fact that a staggering 40% of respondents say that they do not know how their senator voted on the issue, meaning that just over one-in-four respondents in the deviating case are actually correct about how their senator voted on CAFTA. 5 Results from a probit model are substantively identical. There are two major reasons I use an LPM over a probit, however. First, as I will discuss subsequently, my model includes senator-specific fixed effects. Fixed- 17 is whether a respondent approves or disapproves of his senator (1 if respondent approves; 0 if respondent disapproves; missing if respondent answers “Don’t know”); the main independent variables are: whether a respondent agrees with their senator’s vote (1 if respondent agrees; 0 if respondent disagrees; missing if respondent does not take a position on issue), whether the senator deviated from her party on the vote (1 if the senator deviated; 0 if the senator voted with her party; missing if senator was not serving at the time of the vote or was absent from the vote), whether a respondent shares a party affiliation with the senator (1 if from the same party as the senator; 0 if from the opposite party)6 , and whether a respondent’s perception as to how his senator voted was correct, incorrect, or neither (i.e., the respondent did not know) (1 if correct; 0 if the respondent does not know; -1 if incorrect)7 . I also include controls for whether the respondent is white (1 if yes, 0 if no), interacted with whether the senator being evaluated is a Republican; the respondent’s income (measured on a 14-point scale), again interacted with whether the senator under evaluation is a Republican; and finally, the respondent’s level of education (measured on a 6-point scale), also interacted with whether the senator being evaluated is a Republican8 . From the LPM I have outlined above (and that is displayed in Table 4), I generate predicted probabilities of senator approval for respondents, conditional on whether they agree with their senator, whether they share the senator’s party label, whether the senator effects probit models can produce biased estimates (Greene 2011). Second, while I am only using the model to generate predicted probabilities of senator approval for various covariate profiles–predicted probabilities which are substantively identical to those produced when a probit is employed–coefficients produced by an LPM are much more readily interpretable than those estimated by a probit. 6 Recall that “pure independents” are excluded from analysis. 7 These four variables comprise a four-way interaction term, along with all appropriate three-way interactions, two-way interactions, and constitutive terms, hence why I choose, in the results section of this paper that will follow, to discuss predicted probabilities of approval for various covariate profiles rather than attempt to walk through the model coefficient by coefficient. As mentioned previously, however, readers who are interested in examining the results of the LPM from which predicted probabilities are generated should consult Table 4. 8 The reason for employing these interaction terms is that I expect the relationship between race, income and education to point in a negative, negative, and positive direction, respectively, for approval of Democratic senators, while I expect the direction of all three of these relationships to reverse for approval of Republican senators. Due to the fact that some respondents are missing data with respect to race, income, or education, inclusion of these control variables drops the number of unique respondents from 27,717 to 23,894 but does virtually nothing to alter the predicted probabilities or their confidence intervals across various covariate profiles when control variables are held at their means. 18 deviated on a particular vote, and whether the respondent was either correct, incorrect, or claimed not to know with respect to how their senator voted on the issue at hand, as well as conditional on race, income, and education, all of which are interacted with the senator’s party and, when generating predicted probabilities, held at their mean values. The unit of analysis in the model is one of Senator-Issue-Respondent. To explain, because each respondent has two senators and because there are seven issues on which respondents are questioned, this means that each respondent is observed as many as (2 x 7 = 14) times in the data. Senator-Issue-Respondent observations for which the respondent did not, for that issue, take a position (i.e. stated “Don’t Know” when asked how they themselves would have voted) are omitted from analysis; similarly, Senator-Respondent observations for which the respondent stated “Don’t Know” when asked whether they approve of their senator are also excluded9 . Finally, Senator-Issue-Respondent observations for which the senator abstained from voting on the issue at hand (or was not a member of the Senate at the time of the vote) are dropped from analysis. Thus, while some–in fact, many–individuals are observed less than 14 times in the data, it is important to note that all but 17 respondents are observed more than once in the the data as I construct it. Because of this, the LPM I employ clusters its standard errors by each individual respondent; additionally, in order to capture the surely unmeasurable idiosyncrasies of individual lawmakers, fixed effects dummies for individual senators are included but not reported10 . As a technical point worth reiterating, all predicted probabilities of senator approval that are reported are those derived when the previously-identified control variables, along with the senator-specific dummies, are held at their mean values. 9 This does not guarantee, however, that a respondent is dropped from analysis entirely, as he or she may decline to make an approval evaluation for one of his or her senators but be willing to evaluate the other of the two senators, allowing half of his or her Senator-Issue-Respondent observations to remain in the dataset. 10 I also ran an LPM without the senator-specific dummies and across the various combinations of roll call condition, issue agreement, shared partisanship with senator, and knowledge of the senator’s roll call position obtained predicted probabilities of senator approval that were virtually identical to those obtained from the model in which the senator dummies are included. The major consequence of including the senatorspecific indicators is to inflate the size of the errors around the estimated probabilities of senator approval, thus making for a more conservative test of my claims, as it becomes more difficult for covariate profiles to become statistically distinguishable from one another. 19 [Table 4 about here] In order to better elucidate the structure of my data, Table A1 in Appendix A offers an example of what data for a fictionalized but typical CCES respondent might look like across 14 Respondent-Issue-senator observations for that respondent. Readers are encouraged to consult Appendix A in order to understand which variables can and cannot vary across a single unique respondent within our data. Potential Endogeneity Bias It is worth mentioning that the logic behind my theory of cue-taking and accountability takes as given the premise that voters’ perceptions of a senator’s roll call behavior are what condition their approval of the legislator. That the causal arrow might run in the other direction, however, is a non-trivial concern. To explain, it may be the case that constituents have pre-existing opinions about whether they approve or disapprove their senator’s job performance and use those opinions to form their perceptions of the senator’s roll call positions. Specifically, a citizen may assume that a senator of whom he approves voted in line with his own–that is, the citizen’s– preferences and, moreover, might perceive a senator of whom he disapproves to have voted against whatever his (the citizen’s) own opinion on the issue happens to be. In other words, individuals might be projecting their own opinions onto the senators they like and doing the opposite with senators they dislike. One way by which I address this concern is by categorizing respondents by whether they are of the same or different party as the senator whom they are evaluating. By holding constant one key factor that ought to lead citizens to assume agreement with (in the case of same-party individuals) a senator they are predisposed to like or, alternatively, to assume disagreement with (in the case of opposite-party individuals) a senator they are predisposed to dislike, I am able to examine the association between issue agreement and approval by itself. Furthermore, supplementary analysis reported in Appendix B suggests that while projection perhaps tells part of the story about the relationship between one’s 20 knowledge of issue agreement with their senator and one’s approval of that senator, it does not tell the whole story11 . Having said this, you will note that the empirical analysis that follows is cautious about making definitive causal claims. In my view, the strong patterns of associations between knowledge of a senator’s voting behavior and approval of that senator are compelling enough in their own right to be of interest to scholars of representation, despite the fact that one must take care in making causal interpretation. Given the large differences in approval that are associated with correct vs. incorrect knowledge regarding a senator’s voting behavior, one may question whether the true effects would be quite as large absent projection, but would be hard-pressed to suggest that there is no causal connection between knowledge of a senator’s vote and approval. In sum, while reverse causality is a concern worth noting for my analysis, and is something about which I am fully aware and transparent in my analysis, such a concern should not by any means preclude us from examining the differences in a senator’s approval that are associated with differences in individuals’ knowledge about that senator’s roll call behavior. Having outlined the structure of my data, the model used to predict individuals’ approval of senators, as well as the limitations of my data, I now discuss my findings regarding the statistical relationship between knowledge of roll call behavior and approval of one’s senators. Same-Party/Party-Line Respondents Figure 2 displays the predicted probabilities of senator approval for individuals who I categorize as Same-Party/Party-Line respondents (recall the classifications put forth previously), conditional on their knowledge of their senator’s roll call behavior on the issue at hand, as well as whether or not the senator voted with her party on the issue. To be clear, the respondents 11 Appendix B provides for each respondent type the percentage who are correct, incorrect, or unaware when it comes to how their Senator voted, conditional on whether or not the senator being evaluated voted with or against her party. Table B1 displays the results when pooled across all seven issues, while Table B2 provides the results for the stem cell research vote alone, just as an illustrative example. As both tables illustrate, citizens do appear, to some degree, to engage in projection. However, whether a senator voted with her party on an issue is still, on balance, a far better predictor of whether an individual is correct about how that senator voted than is whether one is in agreement or disagreement with their senator, conditional on shared party affiliation. 21 examined in this figure are ones for whom a party-conforming vote should be pleasing, while a party-deviating vote should be displeasing. The findings displayed in Figure 2 conform to expectations. When evaluating a senator who has deviated from her party on an issue, Same-Party/Party-Line respondents have a 90% probability of approving of that deviating senator–so long as they incorrectly believe that senator to have voted the party-line, a belief at which they would arrive were they to use the party cue. When Same-Party/Party-Line respondents are aware of their senator’s departure from the party–a fact that cannot be discerned from using party as a cue–their probability of approving of that senator drops substantially, estimated at 56%. What these results suggest is that when a senator deviates from her party on an issue, a co-partisan constituent who takes the party-line view on the issue and incorrectly believes that their senator has done so as well is approximately 35 points more likely to approve of that senator than is a co-partisan constituent who takes the party-line position and is aware that the senator has deviated. Interestingly, the probability of a party-line-subscribing co-partisan approves of a senator whom the respondent correctly believes to have voted the party line is 92%–statistically indistinguishable from the 90% probability of approval for deviating co-partisan senators who are wrongly thought to have voted the party line! These findings are especially significant given that I have previously shown that most individuals are unable to identify party-deviating behavior, as they suggest that there are many respondents who might reach a different evaluation of their senator– especially when she deviates from her party–if only they were aware as to how the senator actually voted. In sum, when a Same-Party/Party-Line respondent evaluates a deviating senator, use of the party cue to infer her position is associated with a much greater probability of approval than would use of specific knowledge. [Figure 2 about here] 22 Opposite-Party/Party-Line Respondents Now let us examine the approval evaluations of Opposite-Party/Party-Line respondents, as shown in Figure 3. Unlike Same-Party/Party-Line respondents, Opposite-Party/Party-Line respondents should be favorable towards party-deviating behavior and unfavorable towards party-conforming behavior. Looking at Figure 3, we see that, once again, knowledge appears to be strongly associated with one’s approval evaluations. Specifically, when a senator deviates from her party on an issue, Opposite-Party/Party-Line respondents who know about the deviation have a 56% probability of approving of their senator–an impressive figure given that we are looking at opposite-party respondents. What happens when a senator deviates from her party but her opposite-party constituents who are in agreement with the senator’s actual position mistakenly persist in wrongly assuming that the senator voted with her party (perhaps because they are using the party cue to infer her position)? In such an instance, an individual’s probability of approving of the senator plummets from the previously-mentioned 56% to a mere 18%. This is not much greater than the 10% approval predicted for an opposite-party senator who actually voted her party’s line and is known to have done so. As with Same-Party/Party-Line respondents, we find that with Opposite-Party/Party-Line respondents, knowledge highly correlated with approval evaluations; among such respondents, knowing how a deviating senator actually behaved– which, as stated before, cannot be done by using the cue of where the senator’s party stands–is associated with a 38-point increase in approval when compared to those OppositeParty/Party-Line respondents who wrongly believe their deviating senator to have voted the party line. In the case of Opposite-Party/Party-Line respondents, the problem that a deviating senator faces is the reverse of what she faces among Same-Party/Party-Line respondents. With the former group, those who know about the deviation are, because they agree with the position, predicted to be more favorable than those who do not know about the deviation; among the latter group, those who know about the deviation are predicted to be less favorable 23 towards their senator. Thus far, then, my analysis has shown misuse of party cues to be associated with inflated levels of support for deviating senators from their same-party constituents who disagree with their actual position and deflated levels of support from deviating senators’ oppositeparty constituents who agree with their actual position relative to the levels of support deviating senators receive from each of these respective sub-constituencies when individuals are correctly-informed. [Figure 3 about here] Same-Party/Deviator Respondents As Table 2 in our analysis has illustrated, there are some issues on which there exist a substantial number of respondents who deviate from their own party’s position. Specifically, while Same-Party/Party-Line Respondents and Opposite-Party/Party-Line respondents make up a combined 77% of Senator-Issue-Respondent observations, that still leaves about 23% of Senator-Issue-Respondent observations which fall into one of the two deviator categories (see Table 5). It is thus important that we look at approval evaluations of such respondents to see how they behave. [Table 5 about here] We begin this portion of the analysis by examining the behavior of Same-Party/Deviator respondents. Keep in mind that if a respondent is a Same-Party/Deviator, he should react positively when a co-partisan senator deviates from her party on an issue and should react negatively when a senator from his own party takes the party-line position on an issue. Does specific knowledge as opposed to (mis)using the party cue correlate with a difference in approval evaluations for these respondents? Figure 4 suggests that it does. When a senator deviates from her party and a Same-Party/Deviator respondent is aware of such a deviation, he has an 87% probability of approving of the senator; yet when a co-partisan senator deviates from her party but the Same-Party/Deviator respondent wrongly believes 24 she voted with her party–against the respondent’s own wishes–his associated probability of approving drops to 67%. Interestingly, when a Same-Party/Deviator respondent evaluates a senator who actually voted the party line–in which case the cue leads to the correct inference– he has a 69% probability of approving of the senator, quite obviously indistinguishable from the 67% probability of approval that he has of a senator who deviated from her party but who he thinks voted the party line! In other words, use of the party cue leads these respondents to the same place, regardless of how the senator actually behaved. Overall, incorrect perceptions among Same-Party/Deviator respondents are associated with a 20% decrease in approval of a same-party senator who has deviated–a somewhat more modest result than what we observed for respondents who belong to the Same-Party/Party-Line category or the Opposite Party/Party-Line category. Nonetheless, these results demonstrate that knowledge certainly correlates with the way in which Same-Party/Deviator citizens evaluate their senators, and that misuse of party cues can lead such citizens, like their non-deviating counterparts, to different conclusions than when specific knowledge is employed. [Figure 4 about here] Opposite-Party/Deviator Respondents Finally, let us examine Senate approval evaluations of Opposite-Party/Deviator respondents. These respondents are particularly interesting in that they should react positively to an opposite-party senator who takes her own party’s line on an issue but should react negatively to an opposite-party senator who deviates from her party on an issue. As Figure 5 shows, use of the party cue as opposed to drawing on specific knowledge to infer a deviating senator’s vote is strongly associated with how Opposite-Party/Deviator respondents evaluate deviating senators. Specifically, we see that when an opposite-party senator deviates–again, this is an action with which an Opposite-Party/Deviator disagrees–those who are aware of the senator’s action have a 21% probability of approving of that senator, while those who believe that the senator did not deviate (perhaps because they have used the party cue to infer her 25 position) approve of the senator with a probability of 46%. Taking these differences in approval between Opposite-Party/Deviator respondents who are correct about how a senator voted as opposed to the Opposite-Party/Deviator respondents who are incorrect, we see that correct knowledge corresponds with a 25% decrease in the probability of approval. In sum, as we saw with Same-Party/Deviator respondents, these differences in approval associated with knowledge are not as stark when compared to Same-Party/Party-Line and OppositeParty/Party-Line respondents but are present nonetheless. [Figure 5 about here] Discussion and Conclusion The findings of this paper are two-fold: first, because citizens use party cues to infer behavior of their senators, they are substantially more adept at identifying party-conforming roll call behavior than they are at recognizing party-deviating behavior; second, as a result of this, citizens are more likely to align their approval of their senator with their issue agreement with that senator when the senator has voted the party line than when this is not the case. As I discussed, the study by Ansolabehre and Jones (2010) upon which this paper seeks to build uses roll call perception data of House members and finds that individuals’ perceptions of a Representative’s roll call record track well with the reality of how the Member actually voted and that, in turn, individual approval of a House member lines up nicely with issue agreement. They conclude: Constituents have preferences about important matters of the day; they have beliefs, formed through whatever means, about their representatives’ policy decisions. In the aggregate, constituents’ beliefs are approximately right. That is, on average, voters see their politicians as taking approximately the general overall position across a variety of roll-call votes as the representative in fact did. And...constituents rely on perceived policy agreement to hold legislators 26 accountable. The electorate rewards those seen to be in agreement with their views, and they punish those seen to be out of step. (596) What I have found in this paper is not inconsistent with the findings of Ansolabehere and Jones (2010). Most of the time, senators vote with their party; in these cases, citizens are largely correct about their legislators’ behavior and, at least when we look at approval, appear to hold them accountable, approving of those with whom they agree and disapproving of those with whom they disagree. Both this paper and Ansolabehere and Jones (2010), however, leave room for the possibility that some of the time, citizens will fail to exercise accountability. Taking as my starting point a theory of party cueing, I have shown the specific conditions under which Ansolabehere and Jones’ (2010) optimistic findings about representation might fail to hold up: specifically, when a legislator departs from her party. To explain, regardless of the issue we examine, a vast majority of constituents are unable to correctly state how their senator voted when she departs from her party and, more importantly, the small minority who do know how the senator voted tend to, probabilistically, be more likely to align their issue agreement and approval in the case of a deviating senator than the vast majority who either do not know at all how the deviating senator voted or, worse, are positively incorrect about how their deviating senator voted. Thus, my findings do not debunk those of Ansolabehere and Jones (2010) but rather illuminate the specific conditions under which they might fail to hold. My findings also suggest some steps ahead for scholars of legislative accountability. As noted previously, one drawback to the use of observational data is that it fails to specifically identify the extent to which projection might explain the results at which we have arrived. Perhaps Opposite-Party/Party-Line constituents who already have a positive view of their opposite-party senator are the ones who are most likely in that subgroup to guess that the senator deviated; the same might be true in the opposite direction for Same-Party/PartyLine constituents–that those co-partisans who happen to have a negative image of their same-party senator are most likely within their subgroup to guess that their senator has 27 deviated. With such concerns in mind, future research should seek to understand what would happen to a deviating senator’s approval if her deviating position was in fact more widely known to her constituents. Results from this paper suggest that if this were to happen, deviating senators would become less popular among their co-partisan constituents (or at least the ones who toe the party line) and better-liked by their opposite-party constituents (again, at least the ones who subscribe to their own party’s view on the issue and thus hold the same position as the deviating opposite-party senator). If there were something truly different about the people who know about a deviating senator’s behavior in the first place, however, then perhaps corrections would do little to move people’s evaluations. We obviously cannot observe counterfactuals, however, and thus future work may want to explore experimentally what would happen to a deviating senator’s approval if some constituents (those in the treatment group) who did not recognize such behavior were corrected on their misperceptions, while others (those in the control group) were not. Would deviating senators end up with less party-polarized support? The results from my observational analysis suggest quite possibly. Extending this work experimentally would help us to better understand the trade-offs a legislator faces by voting against her party. Results from this paper also shed light on the limits of cues as substitutes for specific knowledge. When a senator votes with her party, citizens’ use of the party cue in forming perceptions–and, in turn, evaluations of each of their senators–works well. Further, because senators will, most of the time, vote with a majority of their party, cues should allow citizens to “get it right” more often than not. That said, when a senator departs from her party, cues can clearly lead individuals to incorrect perceptions which, in turn, lead the misinformed constituents to evaluate the senator differently than those who are informed about the deviating roll call behavior. In particular, co-partisans who are aware of the senator’s deviation from the party line are much less likely to approve of their senator than are their unaware co-partisan counterparts; at the same time, the opposite-party constituents who are aware of a senator’s deviation are much more likely to approve of their senator 28 than are their uninformed opposite-party brethren. Moreover, rank-and-file partisans who themselves deviate from their own party on an issue also have a difficult time aligning their approval and issue agreement with a deviating senator. In the case of deviating senators, then, specific knowledge becomes a prerequisite for making informed evaluations of that senator, and cues become a poor and misleading substitute. 29 References Achen, C.H. (1978). Measuring representation. American Journal of Political Science, 22, 475-510. Alvarez, M., and Gronke, P. (1996). Constituents and legislators: learning about the Persian Gulf War resolution. 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Information, recall, and accountability: The electorate’s response to the Clarence Thomas nomination. Legislative Studies Quarterly, 22, 535-550. Woon, J., and Pope, J. (2008). Made in Congress? Testing the electoral implications of party ideological brand names. Journal of Politics, 70, 823-836. 32 Tables Table 1: Party Divisions on Key US Senate Roll Calls Vote by Party Republicans Democrats Total # Deviating Sens. Issue PBA Ban Stem Cell $ Iraq Immigration Min. Wage Cap. Gains CAFTA 47-3 17-31 64-34 20 19-36 44-1 63-37 20 1-52 39-6 40-58 7 22-32 40-4 62-36 26 7-46 45-0 52-46 7 51-3 3-41 54-44 6 43-12 12-33 55-45 24 Number of yes votes always provided first 33 Table 2: Mass Party Divisions (%) on Key US Senate Roll Calls Opinion by Party GOP % Democrats % Total % Total % Deviation N Issue PBA Ban Stem Cell $ 86-14 26-74 58-42 20 24,321 33-67 95-5 66-34 18 24,784 Iraq Imm. 22-78 20-80 93-7 60-40 59-41 40-60 14 30 25,197 24,852 Percentages favoring policy always provided first Includes only respondents affiliating with a major political party Percentages are calculated using CCES survey weights 34 Min. Wage Cap. Gains CAFTA 49-51 97-3 74-26 25 25,776 87-13 16-84 51-49 15 24,482 45-55 28-72 36-64 41 21,489 % Table 3: Constituents Knowledge of Senator’s Vote by Senator’s Partisan Behavior Senator Voted... Issue PBA Ban Stem Cell $ Iraq Immigration Min. Wage Cap. Gains CAFTA 67% 11% 22% 75% 5% 20% 72% 8% 20% 60% 11% 29% 68% 9% 23% 72% 6% 22% 47% 17% 36% 29% 39% 32% 26% 47% 27% 19% 59% 22% 32% 34% 34% 23% 49% 28% 12% 61% 27% 27% 32% 40% With Party Correct Incorrect Don’t Know Against Party Correct Incorrect Don’t Know Includes only respondents affiliating with a major political party and who have correctly identified the party of the senator whose roll call position they are evaluating. Percentages are calculated using CCES survey weights 35 Table 4: Explaining Approval of U.S. Senators DV: Respondent Approves of U.S. Senator β (std. error) Senator Deviated x Respondent Agrees x Respondent Same Party as Senator x Respondent Knowledge of Roll Call Position -0.026 (0.018) Deviated x Agree x Same Party -0.0015 (0.017) Deviated x Same Party x Knowledge -0.094∗∗ (0.014) Deviated x Agree x Knowledge 0.066∗∗ (0.014) Agree x Same Party x Knowledge -0.019∗ (0.010) Deviated x Agree 0.029∗∗ (0.013) Deviated x Same Party -0.14∗∗ (0.012) Deviated x Knowledge 0.053∗∗ (0.011) Agree x Same Party -0.0051 (0.0077) Agree x Knowledge 0.24∗∗ (0.0071) Same Party x Knowledge 0.050∗∗ (0.0091) Deviated 0.056∗∗ (0.026) Agree 0.0018 (0.0060) Same Party 0.53∗∗ (0.0072) 36 Table 4 (continued): Explaining Approval of U.S. Senators Knowledge -0.17∗∗ (0.0047) White -0.018∗ (0.034) White x GOP Senator 0.0040 (0.011) Income -0.011∗ (0.0044) Income x GOP Senator 0.0024∗ (0.00094) Education 0.014∗∗ (0.0023) Education x GOP Senator -0.014∗∗ (0.0033) Constant 0.38∗∗ (0.031) n N 271,459 23,894 Coefficients presented are OLS coefficients Standard errors clustered by respondent and shown in parentheses Senator fixed effects included but not reported Unit of analysis is respondent-issue-senator observation Total number of respondent-issue-senator observations denoted by “n” Total number of unique respondents denoted by “N” Coefficient estimates and standard errors calculated using aweight option in Stata 14.0 ∗ p < 0.05 ∗∗ p < 0.01 37 Table 5: Composition of Senator-Issue-Respondent Types Senator-Issue Respondent Type Weighted n Percentage Same-Party/Party-Line Same-Party/Deviator Opposite-Party/Party-Line Opposite-Party/Deviator Total 112,799 33,228 96,629 28,803 271,459 41.6% 12.2% 35.6% 10.6% 100.0% Includes only respondents for whom predicted probabilities of Senator approval are calculated. 38 Figures Figure 1 Issue Deviation Among Mass Partisans 0 10 Percent 20 30 40 2006 CCES Common Content Data 0 1 2 3 4 5 6 Number of Issues on Which Respondent Deviates from Party 39 7 Figure 2 Senator Approval by Roll Call Vote Knowledge Same-Party/Party-Line Respondents Senator Deviated 0 .2 .4 .6 .8 1 Senator Voted Party Line Incorrect DK Correct Incorrect DK Correct Knowledge of Roll Call Vote Predicted Probability of Approving of Senator 40 95% C.I. Figure 3 Senator Approval by Roll Call Vote Knowledge Opposite-Party/Party-Line Respondents Senator Deviated 0 .2 .4 .6 .8 1 Senator Voted Party Line Incorrect DK Correct Incorrect DK Correct Knowledge of Roll Call Vote Predicted Probability of Approving of Senator 41 95% C.I. Figure 4 Senator Approval by Roll Call Vote Knowledge Same-Party/Deviator Respondents Senator Deviated 0 .2 .4 .6 .8 1 Senator Voted Party Line Incorrect DK Correct Incorrect DK Correct Knowledge of Roll Call Vote Predicted Probability of Approving of Senator 42 95% C.I. Figure 5 Senator Approval by Roll Call Vote Knowledge Opposite-Party/Deviator Respondents Senator Deviated 0 .2 .4 .6 .8 1 Senator Voted Party Line Incorrect DK Correct Incorrect DK Correct Knowledge of Roll Call Vote Predicted Probability of Approving of Senator 43 95% C.I. Appendix A Table A1: Hypothetical Example of Single Respondent with 14 Senator-IssueRespondent Observations ID State Sen. Name Same Party Issue Agree Sen. Deviated Knowledge Approve Respondent Type 1,345 1,345 1,345 1,345 1,345 1,345 1,345 1,345 1,345 1,345 1,345 1,345 1,345 1,345 NH NH NH NH NH NH NH NH NH NH NH NH NH NH Gregg Gregg Gregg Gregg Gregg Gregg Gregg Sununu Sununu Sununu Sununu Sununu Sununu Sununu 0 0 0 0 0 0 0 0 0 0 0 0 0 0 PBA Ban Stem Cell $ Iraq Imm. Min. Wage Cap. Gains CAFTA PBA Ban Stem Cell $ Iraq Imm. Min. Wage Cap. Gains CAFTA 1 1 0 1 0 0 1 1 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 1 -1 1 1 0 0 1 1 1 1 0 1 1 -1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 Opp.-Party/Deviator Opp. Party/Party-Line Opp. Party/Party-Line Opp. Party/Party-Line Opp. Party/Party-Line Opp. Party/Party-Line Opp. Party/Deviator Opp. Party/Deviator Opp. Party/Party-Line Opp. Party/Party-Line Opp. Party/Party-Line Opp. Party/Party-Line Opp. Party/Party-Line Opp. Party/Deviator 44 Appendix B Table B1: Roll Call Knowledge by Respondent Type and Whether Senator Voted w/ Party (Pooled Across All Seven Votes) Respondent Type Same-Party/Deviator Opposite-Party/Party-Line Knowledge of Vote Same-Party/Party-Line Senator Voted Party Line Correct Don’t Know Incorrect 69.1% 24.7% 6.2% 33.1% 41.9% 25.0% 78.5% 15.8% 5.7% 53.5% 28.4% 18.1% N 116,976 32,125 97,490 3,712 Senator Deviated Correct Don’t Know Incorrect 26.8% 32.7% 40.5% 36.3% 41.0% 22.7% 18.7% 25.2% 56.1% 37.1% 36.1% 26.8% N 14,197 6,051 13,986 4,717 Includes only respondents affiliating with a major political party and who have correctly identified the party of the senator whose roll call position they are evaluating. Percentages are calculated using CCES survey weights 45 Opposite-Party/Deviator Table B2: Roll Call Knowledge of Stem-Cell Research Vote by Respondent Type and Whether Senator Voted w/ Party Respondent Type Same-Party/Deviator Opposite-Party/Party-Line Knowledge of Vote Same-Party/Party-Line Senator Voted Party Line Correct Don’t Know Incorrect 75.5% 20.9% 3.6% 46.5% 36.5% 17.0% 83.3% 13.5% 3.2% 63.9% 25.7% 10.4% N 18,115 2,996 14,008 3,712 Senator Deviated Correct Don’t Know Incorrect 27.6% 28.3% 44.1% 32.5% 34.6% 32.9% 23.2% 22.0% 54.9% 25.8% 38.7% 35.5% N 2,827 1,342 3,673 208 Includes only respondents affiliating with a major political party and who have correctly identified the party of the senator whose roll call position they are evaluating. Percentages are calculated using CCES survey weights 46 Opposite-Party/Deviator
