Dynamic Elite Partisanship: Party Loyalty and Agenda Setting in the US House

René Lindstädt; Ryan J. Vander Wielen

doi:10.1017/S0007123413000173

Dynamic Elite Partisanship: Party Loyalty and Agenda Setting in the US House

Published online by Cambridge University Press: 24 September 2013

René Lindstädt and

Ryan J. Vander Wielen

Article contents

Abstract
Dynamic Elite Partisanship
Data and Methods
Results
Discussion and Conclusions
Footnotes
References

Rights & Permissions

Abstract

Legislators and legislative parties must strike a balance between collective and member-level goals. While there are legislative and reputational returns to co-ordinated behavior, partisan loyalty has a detrimental effect on members’ electoral success. This article argues that members and parties navigate these competing forces by pursuing partisan legislation when the threat of electoral repercussions is relatively low – when elections are distant. This study tests our theory by examining US House members’ likelihood of voting with their party on both partisan and non-divisive votes during the course of the election cycle in order to assess whether members strategically alter their levels of party loyalty as elections approach. It also explores whether majority parties strategically structure the agenda according to variation in members’ electoral constraints. This approach allows elite partisanship to follow a dynamic process, which is referred to here as dynamic elite partisanship. The results demonstrate that as elections approach, members are less likely to cast party votes, and parties are less inclined to schedule votes that divide the parties. At the same time, the study finds no evidence of strategic variation in members’ voting behavior on broadly consensual votes with election proximity.

Information

Type: Articles
Information: British Journal of Political Science , Volume 44 , Issue 4 , October 2014, pp. 741 - 772
DOI: https://doi.org/10.1017/S0007123413000173 [Opens in a new window]
Copyright: Copyright © Cambridge University Press 2013

‘Americans should know where their Representatives stand on the issues before going into the voting booth. But Speaker Pelosi and Senator Reid have delayed dealing with a number of far-reaching and controversial issues until after Election Day precisely so Democrats do not have to reveal to the electorate their support for more trillion dollar deficits, tax hikes on families and small businesses, and a job-killing national energy tax.’

–Statement by Rep. Tom Price (R-GA) in support of a resolution he introduced to block the use of the lame-duck session to pass non-emergency legislation

Political parties have a conflicted existence in many democratic systems.Footnote ¹ On the one hand, voters rely heavily on party labels at the voting boothFootnote ² and reward parties for legislative successes.Footnote ³ Thus there are clear incentives for party coalescence. Yet on the other hand, voters punish individual legislators for partisan behavior,Footnote ⁴ which in turn discourages party co-operation. How, then, do legislators navigate these countervailing incentives? We theorize that legislators, both individually and collectively, balance these competing demands by adopting a dynamic approach to partisanship.

In this study, we investigate the effects of competing demands on elite partisanship in the context of the US House of Representatives. We find that legislators, at the individual and collective levels, balance competing demands by strategically adjusting their levels of partisanship depending on the time to the next election. Specifically, legislators place greater weight on partisan goals when elections are distant, and are increasingly attentive to constituency demands as elections approach. While our substantive focus is the US House, this should not detract from the broader theoretical argument: the presence of countervailing incentives encourages partisan behavior that is sensitive to the variable costs and benefits of co-operation over time. This basic framework provides leverage to help understand legislative behavior in numerous other contexts. Indeed, recent comparative studies have offered evidence that politicians across various political systems face analogous competing demands that place party and electoral goals at odds with one another.Footnote ⁵ The comparative research on party switching also supports the notion that legislators strategically adjust their policy positions for electoral gain.Footnote ⁶ Finally, there is strong reason to believe that even in electoral systems with a negligible personal vote component, parties must still balance competing demands from voters and other principals.Footnote ⁷

Our empirical findings point to two related forms of dynamic partisanship in the US House: (1) decreasing party loyalty among individual members and (2) corresponding conflict avoidance in the selection of roll-call votes by majority parties as elections approach.Footnote ⁸ As a result, parties in the US House start out with a high level of conflict at the beginning of the election cycle, which dissipates as elections approach and the costs of partisan behavior rise. These findings directly contribute to the important and growing literature on the linkages between elections and legislative behavior.Footnote ⁹ At the same time, we identify dynamics that have important implications for policy formation.

The article proceeds as follows. The next section places our study within the research on congressional parties. We believe that the literature has overlooked an important form of partisan variation – changes occurring between congressional elections. In particular, we make the theoretical case for time-dependent variation in partisanship and derive testable hypotheses. In the second section, we describe our data and methods for the member-level and agenda setting analyses. We then examine partisan behavior as a function of election proximity. The empirical evidence shows that members are less likely to exhibit partisan behavior, and parties are less likely to schedule votes that divide the parties, as elections approach. We offer some concluding remarks in the final section.

Dynamic Elite Partisanship

One of the central puzzles of legislative research is the varying role of parties in the US Congress. The influence and cohesion of congressional parties varies greatly over time,Footnote ¹⁰ across issues and vote types,Footnote ¹¹ and between members.Footnote ¹² In the United States, changes in the prominence of parties over time have been attributed to variation in the internal homogeneity of party members’ policy preferences and the level of disagreement between the parties.Footnote ¹³ According to the conditional party government thesis, these factors have important implications for party organization in general and the influence of party leaders in particular.Footnote ¹⁴ Another perspective, which emerged in response to criticism of the conditional party government framework,Footnote ¹⁵ highlights legislators’ electoral incentives to co-operate with their party and to empower party leaders.Footnote ¹⁶ By this account, parties do not seek to maximize policy returns per se, but rather attempt to advance the electoral fortunes of their members by cultivating a favorable party ‘brand’.

Regardless of whether one conceptualizes parties as primarily legislative or electoral coalitions, the prominent partisan accounts acknowledge the importance of both policy and electoral goals to members and parties alike.Footnote ¹⁷ Despite the well-documented tension between the individual and collective pursuit of these goals,Footnote ¹⁸ in which collective efforts (via parties) may prove detrimental to the realization of these goals at the individual level and vice versa, most studies do not examine the implications that these potentially conflicting forces have for partisan behavior.Footnote ¹⁹ In fact, Smith contends that partisan theories that fail to fully account for the existence of multiple goals “do not capture central activities of congressional parties.”Footnote ²⁰ It is then quite possible that, in addition to other catalysts of partisan change (for example, preference homogeneity), partisan behavior reflects the strategic balancing of these goals over time. According to this logic, parties shift emphasis from collective to individual goals and vice versa as a function of the comparative costs of pursuing these goals at any given point in time (by which, crucially, we mean not just across Congresses, but also within congressional terms). Below, we make the case for studying changes in partisanship in a more explicitly dynamic fashion than previously explored in the literature.

In particular, we argue that variation in elite partisanship between elections is the product of both individual and collective considerations. Member-level party support is likely to wane on partisan measures as elections approach, due to individual electoral motivations. Moreover, we also expect parties to reinforce this behavior by strategically setting the agenda to accommodate their members’ concerns about casting difficult votes when the electoral costs of doing so are highest.

As a step toward understanding the time dependency of elite partisanship, we begin by considering the various constraints present in member-level decision making. In particular, members encounter multiple – potentially competing – demands in the pursuit of their goals.Footnote ²¹ Foremost among these are demands by voters, to whom individual members must appeal in order to gain reelection. Political parties also play a central role in members’ decision-making calculus by serving as the collective units that (1) facilitate policy goals via repeated co-ordinationFootnote ²² and (2) forge reputations that offer members collective electoral benefits.Footnote ²³ Party loyalty is also a key determinant of institutional advancement,Footnote ²⁴ which further bolsters members’ legislative and electoral successes. Despite these advantages of party support, there is compelling evidence that party loyalty has damaging effects on electoral prospects at the individual-member level.Footnote ²⁵ Thus members are forced to strategically balance their levels of partisanship over time so as to capitalize on the returns to partisan behavior without incurring the associated electoral sanctions.

Moreover, the costs and benefits of partisan behavior are perhaps most pronounced on votes that generate interparty disagreement. On the one hand, members stand to reap the greatest legislative (that is, policy) and electoral (that is, reputational) returns from party successes on contested votes.Footnote ²⁶ Yet on the other hand, members’ decisions on votes that divide the parties weigh heavily in constituent assessments of their party loyalty.Footnote ²⁷ Therefore the tension created by incentives that both promote and discourage party coalescence is palpable on divisive votes. Of course, members whose districts have clear partisan tendencies that align with their party are somewhat less susceptible to these countervailing forces. However, even under such harmonious conditions, we would still expect cross-cleaving issues to arise as parties struggle to pass (or block) major initiatives (for example, health care reform in 2009–10), enact legislation that is necessary but not popular (for example, the stimulus package in 2009 or the ‘fiscal cliff’ deal in 2012–13), satisfy major interest groups aligned with the party or follow through on intraparty log rolls. Furthermore, even constituents with strong party affiliations remain punitive when presented with a representative who exhibits what they perceive to be excessive partisan behavior.Footnote ²⁸

How members balance these competing forces is logically related to the proposition that electoral penalties for partisan behavior are dynamic. Specifically, a legislator's cost for partisan behavior is likely to be higher when voters more closely monitor her legislative activities.Footnote ²⁹ Generally speaking, monitoring by voters is imperfect due to collective action problems, information costs and memory decay.Footnote ³⁰ However, elections increase the visibility of legislative behavior, which in turn facilitates monitoring by reducing the associated costs.Footnote ³¹ This is a variation on the ‘What have you done for me lately?’ principle identified by Shepsle et al.Footnote ³² Just as voters pay more attention (and give more credit) to legislators for pork barrel projects unveiled when elections are near, they pay more attention to legislative votes as elections draw near.Footnote ³³ Previous research has also shown that voters assign greater weight to more recent votes when assessing a member's performance.Footnote ³⁴ Accordingly, voters may not only recall recent legislative activity more easily; they might also consider recent votes a more reliable measure of a member's future behavior than earlier actions. Thus, we arrive at the assumption that monitoring of legislative voting by voters increases as the time until the next election decreases.Footnote ³⁵

Therefore, since voters respond negatively to party loyalty and are more attentive to (and aware of) their representatives’ legislative behavior as elections approach,Footnote ³⁶ we conclude that individual electoral considerations will increasingly supplant the collective benefits of partisanship in members’ decision-making calculus over the course of the election cycle. The following Party Support Proposition summarizes this time-dependent dynamic at the individual level.

Party Support Proposition: As the time to election decreases, legislators will be less likely to side with their party on votes that divide the parties.

By extension, we also conclude that member behavior on votes that do not divide the parties should be less affected by election proximity. Quite simply, interparty consensus mitigates the tension between the individual and collective goals we theorize about, in part because non-divisive votes convey little information about a member's partisanship. Consequently, there is less incentive for members to strategically moderate their party support on non-divisive votes as elections approach. In its strongest version, the corollary to the Party Support Proposition suggests that the likelihood that members will support their party on non-divisive votes should be constant throughout the election cycle. A slightly weaker prediction is that any decline in members’ party support on non-divisive votes should be significantly less pronounced than that observed for votes that generate interparty division. We summarize our prediction about non-party votes in the following corollary.

Party Support Corollary: Compared to votes that divide the parties, the likelihood that legislators will side with their party on non-divisive votes will be relatively constant throughout the election cycle.

Collectively, votes that generate interparty disagreement confer benefits to political parties, and the majority party in particular. Not only are these votes the outgrowth of parties pursuing the legislative goals of a majority of their members, but they are also central to the parties’ collective reputations.Footnote ³⁷ Notwithstanding these goals, party leaders must be sensitive to member-level constraints.Footnote ³⁸ In particular, party leaders, as agents of the rank-and-file membership, must be judicious in demanding legislative behavior that is contrary to members’ electoral interests.Footnote ³⁹ The majority party is likely to incur electoral and/or legislative losses if its party leaders make excessive and indiscriminate requests for party support from rank-and-file members, not to mention the possibility that party leaders will lose their coveted leadership posts. Thus party leaders, like individual members, have to balance competing forces by adopting a strategy that maximizes the collective gains from party support while minimizing the member-level repercussions.Footnote ⁴⁰

In 2010, for instance, the Democratic leadership quite openly postponed consideration of an inevitably contentious vote on extending the Bush tax cuts until after the election, in an effort to protect party members from having to make a potentially unpopular decision with elections right around the corner.Footnote ⁴¹ Instead, the issue was voted on soon after the elections. Rep. Tom Price's (R-GA) call to abandon the lame-duck session (quoted at the beginning of this article) was made in response to the Democratic leadership's strategic agenda setting decisions with respect to the Bush tax cuts and other legislative initiatives. Price's comments reflect an awareness of the time sensitivity of legislative decisions relative to elections. We summarize the time-dependent dynamic at the party level in the following Agenda Setting Proposition.

Agenda Setting Proposition: As elections approach, majority party leaders will be less likely to schedule proposals that divide the parties.

In the next section, we discuss the empirical models we use to test the Party Support Proposition, the Party Support Corollary and the Agenda Setting Proposition. Our empirical approach is based on studying roll-call votes to show that partisan behavior, both individually and collectively, is highly dynamic.

Data and Methods

We examine temporal variation in House members’ support for their party, and search for evidence of corresponding agenda setting adjustments made by House majorities between the 84th and 108th Congresses (1955–2004). We proceed with separate member- and party-level analyses so as to evaluate whether there are independent (yet reenforcing) individual and collective dynamics at work, as suggested by the above theoretical propositions. This approach not only uncovers the existence of dynamic elite partisanship, but also identifies the sources of partisan variation. We address member-level effects in the following subsection before turning our attention to the agenda setting effects.

Member-level Analysis

As a first step, we explore whether members modify their party support according to election proximity. In particular, we test for variation in party support that reflects an awareness of the collective benefits and electoral costs of party loyalty. While our empirical analysis includes all available roll-call votes during the period of analysis, we distiguish between divisive and non-divisive votes to account for the difference in predictions made by the Party Support Proposition and its corollary. Therefore we identify roll-call votes in which a majority of one party votes in opposition to a majority of the other party (that is, party votes). Based on our theoretical predictions, we expect that a member's support for her party on party votes will decline with election proximity, while her party support on non-party votes will be largely unaffected by electoral dynamics.

Party votes are widely used as the basis of various measures of congressional partisanship (for example, party unity scores). Unlike other votes, they establish discernible and conflicting party positions. Since party votes generate party divisions, which signify core party differences (that is, issues motivating party coalescence),Footnote ⁴² the outcomes of these votes have meaningful implications for the parties’ collective reputations. For both policy and electoral reasons, party leaders have an incentive to exert greater pressure on rank-and-file members on these votes than on less divisive ones. Yet we also know that party votes significantly influence voters’ appraisals of members’ partisanship, and increasing aggregate party support on these votes has been shown to negatively affect members’ electoral prospects.Footnote ⁴³ Therefore members’ voting behavior on party votes offers valuable insight into the balance that members strike between competing collective (party) and individual (constituent) demands over the course of an election cycle.

To explore temporal variation in party support, we construct our dependent variable by identifying whether members voted with or against the majority of their party for all recorded roll-call votes in the US House during the period of analysis (n = 20,450), coding party support as 1 and defection as 0.Footnote ⁴⁴ The key independent variable(s) throughout this and later stages of the analysis are polynomial terms for the duration of time (measured in days) between the vote and the next election.Footnote ⁴⁵ We also include a dummy variable for party votes, defined as votes in which a majority of one party votes in opposition to a majority of the other party, and interact this Party Votes dummy variable with the polynomial terms of time. Jointly, these variables provide information regarding the extent to which party support varies with election proximity across party and non-party votes (testing the Party Support Proposition and its corollary). To identify the appropriate functional relationship between party support and the timing of votes, we use the Akaike Information Criterion (AIC) to determine the order of the polynomial for days until the next election that best fits the data.Footnote ⁴⁶ We note that, despite using the same evaluation process across all models, the optimal order of the time polynomial is permitted to vary across models due to differences in the underlying data structure. We further note that using splines instead of polynomials yields functional forms on the predicted probabilities of party support that are substantively similar to the results shown below (results from alternative specifications are available in the online appendix).

We also include a number of control variables that account for differences in members’ levels of electoral insulation/vulnerability. The variable Retirement identifies members who decided to retire during the Congress of observation. When members decide to retire, they sever both electoral and partisan connections that may have previously compelled them to behave differently than they would in the absence of such constraints.Footnote ⁴⁷

In addition, we include a variable that taps members’ ideological extremism (Ideological Extremism), operationalized as the absolute value of their first-dimension DW-NOMINATE score.Footnote ⁴⁸ The variable accounts for the different policy costs that members incur when voting with their party on divisive votes. Since the first dimension is most closely associated with interparty conflict, it effectively captures how (in)consistent a member's (potentially induced) preferences are with the center of her party. Members situated near the center of the policy continuum have preferences that are at odds with the majority of their fellow partisans. Conversely, we would expect ideological extremists to have fewer electoral constraints associated with party voting, given the natural congruence between their policy preferences and their party's policy positions.

The variable Seniority measures a member's chamber seniority and accounts for the possibility that members enjoy greater electoral insulation with more years of service. We also include a number of variables that capture the competitiveness of a member's previous election. Lagged Vote Share measures the incumbent's percentage share of the two-party vote in the previous election. Lagged Quality Challenger is a dichotomous measure indicating whether the incumbent faced a quality challenger – defined as a candidate who has held previous elective officeFootnote ⁴⁹ – in the previous election. Lagged Spending Gap is measured as the natural logarithm of challenger expenditures less the natural logarithm of incumbent expenditures.Footnote ⁵⁰

Lagged District Partisanship is measured as the share of the two-party vote that the presidential candidate belonging to the member's party received in her congressional district in the previous presidential election. This is an often-used measure of district partisanship.Footnote ⁵¹ While voters broadly oppose intemperate forms of party loyalty,Footnote ⁵² we account for district partisanship, since some legislators are surely more susceptible to reprisal than others. We also include an indicator variable, In-party Midterm, which accounts for membership in the president's party in midterm election cycles. This captures any adjustments in partisan behavior that in-party members make in anticipation of the well-documented midterm loss.Footnote ⁵³ Each of these member-level variables can be considered a signal to members regarding their relative electoral security.

In one of the member-level models reported below, we use a composite factor score of the control measures, Member-level Characteristics (Factor Score), instead of including each of the individual variables. Increasing values of this measure indicate increasing electoral insulation.Footnote ⁵⁴ To explore whether electoral vulnerability enhances the effect of time on the probability that a member will support her party (on party and non-party votes), we interact the composite factor score with the constitutive (un-interacted) polynomial terms of time, the Party Votes variable and the polynomial terms interacted with Party Votes. This approach makes the results easier to interpret, since it significantly reduces the number of interaction terms needed (that is, we avoid having to interact every member-level measure with the aforementioned variables).Footnote ⁵⁵

Since one might suspect that party adjustments of the agenda that are consistent with our expectations could affect our measurement of member-level behavior, we include additional controls to account for variation in the agenda. In other words, if parties, as hypothesized, schedule more consensual votes as elections approach, then it is conceivable that we might observe declining party support on party votes as a result of the types of votes being considered. However, using observed voting divisions to control for variation in the divisiveness of the agenda would, by definition, obscure the effect we seek to examine.

We account for the possible effects of agenda change by exploiting the fact that some types of votes are more likely than others to generate interparty disagreement. Suspension of the rules, for instance, requires two-thirds support for passage, which is why these votes tend to be held on measures that are relatively non-controversial. We include dummy variables (Vote-type Fixed Effects) for the six vote type categories (minus a reference category) introduced by Crespin, Rohde and Vander Wielen:Footnote ⁵⁶ regular passage of bills, passage under suspension of the rules, miscellaneous passage (final passage of measures that do not require the president's signature), amendments, partisan procedural votes (for example, special rules and motions to recommit) and miscellaneous procedural votes.Footnote ⁵⁷ Crespin, Rohde and Vander Wielen show that these categories are substantially related to levels of observed interparty conflict. By controlling for vote type, as opposed to controlling for observed voting behavior, we employ a measure of interparty conflict that is exogenous to the behavior of interest. Moreover, it has also been shown that variation in party cohesion is related to changes in the issue content of the agenda,Footnote ⁵⁸ and so we likewise include dummy variables (Issue-type Fixed Effects) for the nineteen major topic categories identified by the Policy Agendas Project.Footnote ⁵⁹

Finally, we include dummy variables for Congresses (Congress Fixed Effects) in order to capture any systematic differences in partisan behavior that might be due to circumstances specific to particular Congresses.Footnote ⁶⁰ Including Congress fixed effects also allows us to explore whether legislative behavior systematically changed over the period of analysis.

We estimate the probit model of member i's party support on vote v, shown in Equation 1, both with and without control variables, where α is the intercept term, x _i denotes the vector of control variables for member i (with corresponding vector of coefficients, β) and z _v the vector of control variables for vote v (with corresponding vector of coefficients, $$\[--><$>\bigamma<$><$>\bixi$$$ in Equation 1.

$$no {\quad\quad\quad{\rm Pr}\,{{(\rm PartySupport}_{i,v}} = \,1)\, = &#x0026; \,\rPhi (\alpha \, + \,\mathop{\sum}\limits_{k\, = \,1}^3 {{\zeta }_k}\,{\rm{DaysToElection}}\,_{v}^{k} \, + \,\theta \,{\rm{PartyVotes}}{{\:}_v} \cr &#x0026; \, + \,\mathop{\sum}\limits_{k\, = \,1}^3 {{\delta }_k}\,{\rm{DaysToElection}}\:_{v}^{k} \times \,{\rm{PartyVotes}}{{\:}_v} + \:\;{\bibeta} {{{\bf {{x}}}}^{\prime}_i} \cr &#x0026; \, + \,\bigamma {{{\bf {{z}}}}^{\prime}_v}\, + \,\bixi \,{\bf{Congress}^{\prime}}_v) $$

Since we find evidence of systematic changes in party support related to election proximity (the results are discussed below), the next step in the empirical analysis is to investigate whether majority parties structure the agenda by scheduling divisive (consensual) votes when members are most (least) insulated from the negative electoral effects of partisan behavior.

Agenda Setting Analysis

Next, we explore the proposition that the occurrence of divisive votes and time to the next election are positively related. To study this question, we examine all House votes during the period of analysis (n = 20,450).Footnote ⁶³ We begin by exploring the timing of both divisive and consensual votes. The dependent variable for one model is a dichotomous measure of whether a vote resulted in party voting. This is a natural extension of our member-level analysis, since the above analysis examines voting behavior but not the timing of votes (by design). In a separate model, we construct a dichotomous dependent variable that measures whether a vote resulted in at least 90 percent of the membership voting in the same fashion (hereafter referred to as ‘ultra-consensual’ votes). We would expect to see a decreasing probability of party votes and an increasing probability of ultra-consensual votes as elections approach. As an additional gauge of changes in interparty division relative to the time to the next election, we also examine trends in the differences across parties’ vote distributions (hereafter referred to as the ‘disagreement score’). We measure the disagreement score as the absolute difference in the proportion of participating Democrats and Republicans voting ‘yea’; values approaching 1 on the continuous scale indicate increasing interparty disagreement. If majority parties schedule divisive votes according to election proximity, then the disagreement score should decrease as elections approach.

The key independent variables for each of these models are the polynomial terms for the duration of time (again measured in days) between the vote and the next election. The order of polynomial is determined using the model selection process described above. We find that each of these models is best fit using a quadratic function of time. Since we cannot expect every vote to generate equivalent interparty divisions, we again include control variables for the vote-type categories introduced by Crespin, Rohde and Vander Wielen.Footnote ⁶⁴ In a separate model, we also include issue-type fixed effects, which further account for differences across votes in terms of their propensity to produce interparty disagreement.

We might also anticipate some variation in the occurrence of divisive/consensual votes on the basis of the preference composition of the membership. For one, we would expect partisan disagreement to rise naturally with increasing party polarization. In addition, it has been argued elsewhere that rank-and-file members grant central party leaders broader license to pursue partisan outcomes as the two legislative parties become increasingly polarized (that is, as intraparty homogeneity and interparty distance increase).Footnote ⁶⁵ Therefore it is important that we account for the preference distribution of partisans as we examine variation in the scheduling of divisive/consensual votes. We estimate a separate model that includes the measure of polarization introduced by Vander Wielen and Smith,Footnote ⁶⁶ which is shown in Equation 2. This measure accounts for the polarization conditions articulated by Rohde,Footnote ⁶⁷ while avoiding collinearity between the separate components during the period of analysis. This measure of polarization increases with distance between party medians and as standard deviations for the parties get smaller, all else equal. Stated differently, the greater the interparty disagreement and intraparty cohesion, the greater the value of the polarization measure. We measure the input variables (party medians and standard deviations) using first-dimension DW-NOMINATE scores.Footnote ⁶⁸

$$\frac{{{\rm{|Party}}\;{\rm{Median}}{{\,}_{{\rm{Majority}}}}\, - \,{\rm{Party}}\,{\rm{Median}}{{\,}_{{\rm{Minority}}}}{\rm{|}}}}{{\sqrt {\left( {\sigma _{{{\rm{Majority}}}}^{{\rm{2}}} \, + \,\sigma _{{{\rm{Minority}}}}^{{\rm{2}}} } \right)/{\rm{2}}} }}$$

Equations 3 and 4 show the models for this step of the analysis, where z _v denotes the vector of control variables for vote v (with corresponding vector of coefficients γ). The scalar coefficient for Polarization is denoted $$\[--><$>\beta<$><!--$$ . Since the party and ultra-consensual vote models have the same (non-linear) specifications, we present them in the same equation (Equation 3), where y denotes the occurrence of the operative votes. Following from our theoretical proposition, as the time until the next election decreases, we expect the likelihood of party votes to decrease, the likelihood of ultra-consensual votes to increase and the disagreement score to decline.

Given the hierarchical nature of the data, with votes nested within Congresses, we estimate hierarchical logit models for the analysis of party and ultra-consensual votes, and a hierarchical linear model for the analysis of disagreement scores. In particular, we group by Congresses in the hierarchical models to account for changes in the agenda that result from variation in the composition of the membership over time.Footnote ⁶⁹ In each case, we include a random intercept to permit different baseline effects across Congresses (α_j _[ _v _] in Equations 3 and 4). We also account for differences across Congresses in the effect of election proximity on the dependent variable of interest by including random slope coefficients for the polynomial terms of time (ζ_j _[ _v _] _k in Equations 3 and 4). This allows for the possibility that the effect of time on, for example, the probability of a party vote is not the same in each Congress. The standard deviations corresponding to the random intercept and random slope coefficient(s) provide information regarding the variation in effects across Congresses, with higher standard deviations signifying greater cross-Congress differences. We include only those random slope terms that improve the fit of the model, using the aforementioned model-specification approach.Footnote ⁷⁰

$$no{\quad\quad\quad\quad\quad\quad\quad\quad{\rm Pr}\,({{y}_v}\, = \,{\rm{1}})\, = &#x0026; \,logi{{t}^{{\rm{ - 1}}}} \big ({{\alpha }_{j[v]}}\, + \,\mathop{\sum}\limits_{k\, = \,{\rm{1}}}^{\rm{2}} {{\zeta }_{j[v]k}}\:{\rm{DaysToElection}}_{v}^{k} \cr &#x0026; + \beta \,{\rm{Polarization}}{{\:}_j}\, + \,\bigamma {{{\bf {{z}}}}^{\prime}_v}) $$

$$\left( {\matrix{ {{{\alpha }_j}} \cr{{{\zeta }_j}} \\\end{}} }\right)\, \sim \,N\left( {\left( {\matrix{ {{{\mu }_\alpha }} \cr{{{\mu }_\zeta }} \\\end{}}} \right),\left\bigg( {\matrix{ {\sigma _{\alpha }^{{\rm{2}}} } &#x0026; {\rho {{\sigma }_\alpha }{{\sigma }_\zeta }} \cr\ {\rho {{\sigma }_\alpha }{{\sigma }_\zeta }} &#x0026; {\sigma _{\zeta }^{{\rm{2}}} } \\\end{}}} \right\bigg)} \right),\,{\rm{for}}\,j\, = \,{\rm{1}}, \ldots, {\rm{25}}$$

$$ no{\quad\quad\quad\quad\quad\quad\quad{\rm{DisagreementScore}}_v\, = &#x0026; \,{{\alpha }_{j[v]}}\, + \,\mathop{\sum}\limits_{k\, = \,{\rm{1}}}^{\rm{2}} {{\zeta }_{j[v]k}}\,{\rm{DaysToElection}}\,_{v}^{k} \cr &#x0026; + \beta \,{\rm{Polarization}}{{\,}_j}\, + \,\bigamma {{{\bf {{z}}}}&#x0027;_v} $$

$$ \left( {\matrix { {{{\alpha }_j}} \cr{{{\zeta }_j}} \\\end{}}} \right) \sim N\left( {\left\bigg( {\matrix {{{\mu }_\alpha } \cr{{{\mu }_\zeta }} \\\end{}}} \right),\left\bigg( {\matrix { {\sigma _{\alpha }^{{\rm{2}}} } &#x0026; {\rho {{\sigma }_\alpha }{{\sigma }_\zeta }} \cr\ {\rho {{\sigma }_\alpha }{{\sigma }_\zeta }} &#x0026; {\sigma _{\zeta }^{{\rm{2}}} } \\\end{}}} \right\bigg)} \right),\,{\rm{for}}\,j\, = \,{\rm{1}}, \ldots, {\rm{25}} $$

Since Equations 3 and 4 examine observed voting divisions, temporal variation in member-level party support surely influences the timing of interparty disagreement. For instance, a sufficient decrease (increase) in members’ party support that is consistent with expectations could produce a corresponding decrease (increase) in the occurrence of party votes under conditions of a static agenda. Therefore we take the additional step of examining the timing of votes that we have a priori reason to believe are systematically divisive/consensual. This extension avoids reliance on vote outcomes, which are affected by members’ party support, for uncovering strategic manipulation of the agenda. We know that certain votes are predisposed to high/low levels of interparty disagreement. That is, some votes systematically occur on matters of high/low interparty conflict. For instance, amendments are more likely to expose partisan conflict over the content of the bill than final passage votes that offer a choice between the bill and the status quo.Footnote ⁷¹ Furthermore, evidence suggests that party leaders strategically consider the implications of vote types on interparty disagreement.Footnote ⁷²

We study the occurrence of four vote types that have clearly identifiable associations with interparty conflict: regular passage votes, suspensions, amendments and partisan procedural votes.Footnote ⁷³ Both regular passage and suspension votes customarily occur on measures that are relatively non-controversial, whereas amendment and partisan procedural votes occur on more divisive measures.Footnote ⁷⁴ We examine whether the occurrence of these votes systematically varies over the course of election cycles. For that purpose, we estimate a hierarchical logit model for each of the vote categories, in which the dependent variable is a dichotomous measure of whether the vote is in the vote category of interest. As with earlier models, the key independent variables are the polynomial terms of the number of days until the next election. We also estimate separate models that include the polarization measure and issue-type fixed effects. We again include random intercepts and random slope coefficients for the polynomial terms of time. The order of the polynomial of time and the random components are determined using the model fit specifications discussed above.

$$ no{\quad\quad\quad\quad\quad\quad\quad\quad{\rm{Pr}}({{y}_v}\, = \,{\rm{1}})\, = &#x0026; \,logi{{t}^{{\rm{ - 1}}}} \big({{\alpha }_{j[v]}}\, + \,\mathop{\sum}\limits_{k\, = \,{\rm{1}}}^K {{\zeta }_{j[v]k}}\,{\rm{DaysToElection}}\,_{v}^{k} \cr &#x0026;+ \beta \,\:{\rm{Polarization}}{{\:}_j} + \:\;\bigamma \:\:{{\bf IssueType}}&#x0027;{{\:}_{\!\!v}})$$

$$ \left( {\matrix {{{{\alpha }_j}} \cr{{{\zeta }_j}} \\\end{}}} \right) \sim N\left( {\left( {\matrix {{{{\mu }_\alpha }} \cr{{{\mu }_\zeta }} \\\end{}}} \right),\left\bigg( {\matrix { {\sigma _{\alpha }^{{\rm{2}}} } &#x0026; {\rho {{\sigma }_\alpha }{{\sigma }_\zeta }} \cr\ {\rho {{\sigma }_\alpha }{{\sigma }_\zeta }} &#x0026; {\sigma _{\zeta }^{{\rm{2}}} } \\\end{}}} \right\bigg)} \right),\,{\rm{for}}\,j\, = \,{\rm{1}}, \ldots, {\rm{25}} $$

The notation for the models is shown in Equation 5, where K = 1 for the regular passage and partisan procedural models and K = 2 for the other vote types. If majority parties strategically adjust the agenda, as suggested by our theoretical proposition, then we should see more regular passage votes and suspensions and fewer amendments and partisan procedural votes as elections approach. Such a finding would constitute additional evidence that majorities schedule more consensual votes in response to members’ electoral constraints.

Results

We begin our discussion of the results by interpreting the member-level effects. We then use these results to make a case for why member-level effects are only part of the dynamic elite partisanship story. Finally, the agenda setting effects are discussed.

Evidence for Dynamic Member-level Partisanship

Table 1 presents the results for the model shown in Equation 1, both with and without member and vote-specific control variables included.Footnote ⁷⁵ Notably, in these estimations we find that all of the coefficients for the polynomial terms interacted with the Party Votes dummy variable are statistically significant, with the first and third degrees exhibiting positive signs and the second degree a negative sign.Footnote ⁷⁶ This finding points to a decreasing probability of party support on votes that divide the parties as elections approach (relative to the baseline of non-party votes). Moreover, the magnitudes of the coefficients are quite consistent across the estimations, implying that the effects are robust to the inclusion of myriad controls. Of course, since this is a non-linear model with multiple interactions, a more detailed interpretation of the effects demands the calculation of predicted probabilities, which we provide below.

Table 1

Likelihood of Casting a Vote Supporting Party

Note: The dependent variable is a dichotomous measure of whether members voted with the majority of their party. Each model applies the polynomial order of time that offers the optimal fit. The models cluster on unique members, adjusting the variance-covariance matrix to account for repeated measurements. Standard errors in parentheses. *p ≤ 0.05.

As expected, we also find a strong, positive effect of Ideological Extremism (Models 2 and 3). Namely, members whose preferences are more aligned with their party's position (that is, members who are located away from the center of the ideological continuum) exhibit a higher probability of party support, as predicted. We also find that Lagged District Partisanship is statistically significant and negative (Models 2 and 3), which at first glance seems somewhat counterintuitive. However, we note that Lagged District Partisanship is highly collinear with Ideological Extremism, and when we remove Ideological Extremism from the models, Lagged District Partisanship becomes positive and statistically significant, as expected. The negative and statistically significant effect of In-party Midterm Election (Models 2 and 3) has a straightforward interpretation: members of the president's party are less likely to support their party in a midterm election cycle. This is an intuitive result, which suggests that members attempt to escape the midterm decline by exhibiting greater independence from their party. The effects on both Ideological Extremism and In-party Midterm Election are highly robust.

The other member-specific control variables are not statistically significant when correcting the standard errors for clustering.Footnote ⁷⁷ Model 4 in Table 1 builds on Model 3 and replaces the various member-level variables with a single composite factor score. In addition, we include two-way interaction terms between the factor score and the polynomial terms of time to investigate the possibility that the slope for non-party votes is dependent on electoral insulation, and three-way interaction terms between the factor score, polynomial terms and Party Votes to gauge possible slope effects for party votes. Stated differently, it is conceivable that the effect of time on party support is functionally dependent on a member's electoral circumstances. As indicated by the failure of the interacted polynomial terms to achieve statistical significance, there is no such variation in the effect of time across levels of electoral insulation. However, the composite factor score on its own is negative and statistically significant for non-party votes, and positive and statistically significant for party votes. Since higher values indicate greater electoral insulation, it is not surprising that more secure members are afforded greater liberty to support their party on party votes. This finding confirms our supposition that a member's predisposition toward party support is contingent on her electoral circumstances.

To facilitate interpretation of the central findings, we simulate the 83.5 percent confidence intervals for the probability of party support on party and non-party votes across the two-year election cycle for selected models in Table 1 (see Figure 1).Footnote ⁷⁸ Since we wish to make statistical inferences regarding the differences in predicted probabilities across vote categories (party and non-party votes) at a given time during the election cycle as well as within vote categories over time, 83.5 percent confidence intervals have been shown to be appropriate for attaining conventional type I error rates of 5 percent (that is, 95 percent confidence).Footnote ⁷⁹ Panel A in Figure 1 shows a member's predicted probability of supporting her party on a party and non-party vote by the number of days to the next election for Model 3, holding all other variables at their mean. Panel B shows the analogous predicted probabilities for Model 4, holding all other variables at their mean. Panel C shows the predicted probabilities for Model 3 for the Congresses with the highest (103rd Congress) and lowest (96th Congress) predicted baseline party support, holding all other variables at their mean. Panel D shows the predicted probabilities for Model 4 for the 99th percentile of the factor score (insulated member) and the 1st percentile of the factor score (vulnerable member), holding all other variables at their mean. Although Models 1 and 2 yield similar predicted probabilities, we pay particular attention to Models 3 and 4 since they offer superior fits to the data.

Fig. 1

Predicted probability of party support on party and non-party votes by distance from election Note: Panel A shows a member's predicted probabilities of supporting her party on a party and non-party vote by the number of days to the next election for Model 3; Panel B shows the analogous predicted probabilities for Model 4; Panel C shows the predicted probabilities for Model 3 for the Congresses with the highest (103rd Congress) and lowest (96th Congress) predicted baseline party support; and Panel D shows the predicted probabilities for Model 4 for the 99th percentile of the factor score (‘insulated member’) and the 1st percentile of the factor score (‘vulnerable member’). All other variables are held at their mean, and 83.5 percent confidence intervals are used throughout (see Maghsoodloo and Huang Reference Maghsoodloo and Huang2010).

We find an appreciable drop in members’ probability of supporting their party on party votes with proximity to election across each of the models. For Models 3 and 4 in Table 1 (Panels A and B in Figure 1, respectively), the average member is expected to decrease her party support on party votes by almost 8 percentage points during the course of the election cycle. Moreover, the figures demonstrate that this decline in predicted probability is statistically significant. Conversely, we find compelling evidence that members’ likelihood of supporting their party on non-party votes is nearly constant over the course of the election cycle. Specifically, there are no two distinct points in time during the election cycle at which the confidence intervals of predicted probabilities for non-party votes are mutually exclusive in Model 3, thus indicating statistical stability. In Model 4, there is only a small number of paired days in which the confidence intervals are non-overlapping, and even then by minuscule margins. In fact, the largest difference in the mean predicted probabilities of party support on non-party votes between any two points in time during the entire electoral cycle is only about 1 percentage point in these models.

As a secondary finding, we also identify discernible variation in baseline party support across Congresses and levels of electoral insulation for both party and non-party votes (see Panels C and D in Figure 1, respectively). Given that the predicted probabilities generated by the models are non-linear (that is, logistic), variation in the baseline party support also affects the rate of change in party support over time. For instance, the predicted probabilities of party support on party votes for an average member in the Congress with the highest baseline party support (103rd Congress) vary from approximately 0.922 (when elections are most distant) to 0.849 (when they are most proximate). We compare this to the Congress with the lowest baseline party support (96th Congress), in which the analogous predicted probabilities are 0.89 and 0.8, respectively (see Panel C).Footnote ⁸⁰ For both Congresses, the general relationships regarding member behavior on party and non-party votes discussed above hold. That is, members exhibit a noteworthy decline in their party support on party votes during the election cycle, but remain statistically constant in their party support on non-party votes. For an insulated member (99th percentile of the factor score), the mean predicted probabilities of party support for party votes range from 0.97 to 0.931 during the course of the election cycle, whereas the mean predicted probabilities for a vulnerable member (1st percentile of the factor score) range from 0.871 to 0.769 (see Panel D). Here again, we find remarkable stability in party support on non-party votes over time and across levels of electoral security.

In each of the models, the functional form of the relationship between party support and proximity to election on party votes suggests that party support is characterized by an equilibrium level that is interrupted by low levels when elections are proximate, and by high levels when elections are distant.Footnote ⁸¹ This is a rather plausible functional form that is broadly consistent with the conjecture that partisan behavior is inversely related to voter monitoring. Specifically, the most rapid reduction in party support occurs during the first year of the election cycle, with additional reductions occurring shortly before the next election. Arguably, this trend follows the attentiveness of the most politically astute in the earliest stages of the election cycle and the less politically engaged in the latter stages. In fact, it has been shown elsewhere that House incumbents invite challenges from experienced politicians when they lend excessive party support in the first session of a term.Footnote ⁸² Given that the entry of well-supported challengers is a crucial determinant of electoral competitiveness in House races,Footnote ⁸³ it is not entirely surprising that members temper legislative behavior in the first year of the election cycle that creates voter discontent and generates demand for strong challengers. Moreover, the decline in members’ party support when elections are proximate surely reflects the disproportionate level of voter attentiveness that occurs immediately before an election.Footnote ⁸⁴

These findings offer robust evidence in favor of both the Party Support Proposition and its corollary. We observe a considerable decline in members’ support for their party on party votes as elections approach. Therefore members exhibit behavior that reflects the strategic balancing of collective- and individual-level considerations – they seek collective returns when individual electoral demands are minimal and curtail their partisan support as elections approach. Conversely, when the tensions between collective- and individual-level motivations are suppressed (that is, on non-party votes), members exhibit stability in their partisan loyalty.

To further explore the member-level effects identified in the above analysis, and to more directly account for alternative explanations, we examine only votes that occur during the period extending from two months preceding an election to the end of a Congress. We select this period because it provides a limited and approximately equal amount of time before and after an election within a given Congress.Footnote ⁸⁵ Because this approach examines only votes that are held at the end of a Congress, we can better account for the possibility that the model results are an artifact of end-of-Congress effects. It could be argued, for instance, that the diminishing party support on party votes as elections approach is a result of legislators making concessions in an effort to advance legislation before the end of the Congress. If this explanation were indeed driving our results, then we would expect the likelihood of party support on post-election party votes to be either statistically indiscernible from (or perhaps even lower than) pre-election party votes.

We note that the above analysis attempts to account for this alternative explanation by measuring days to the next election rather than to the end of the Congress. Nonetheless, we believe that this extension is a valuable validity check of our central findings. We replicate the above models using a dummy variable that measures whether the given vote occurred prior to the election. We use this measure in lieu of the polynomial terms of time, since the period of analysis does not provide sufficient variation in the number of days to the next election. We also include a dummy variable to account for party votes as well as an interaction term between these variables. For each of the models, we find that members exhibit a statistically significant increase in party support on party votes following election (results are available in the online appendix). This finding suggests that we are not observing an end-of-Congress effect, but rather a strategic adjustment of legislative behavior relative to elections.

The Duality of Dynamic Elite Partisanship

Our finding that the likelihood of party support on party votes, both in terms of baseline levels and marginal change across time, varies predictably across members according to their electoral vulnerability [see Figure 1(d)], provides support for the claim that observed variation in party unity over time is, at least in part, a function of individual-level behavior and not simply agenda manipulation. However, it remains of interest to assess whether variation in party unity is solely a product of systematic changes in member-level party support on party votes.Footnote ⁸⁶ To better understand the contribution of member-level effects to the observed change in party unity over time, we conduct a simulation based on the above analysis.

To begin, we consider the nature of variation in party unity over time. Figure 2(a) graphically presents the conventional measure of party unity – measured as the percentage of roll-call votes that are party votes over two-year congressional terms.Footnote ⁸⁷ We then look at the differences in party unity scores between the first year following a House election and the year preceding the next election [see Figure 2(b)]. Based on Figure 2(b), we conclude that there is substantial and systematic change in party unity scores across years in election cycles. Scores in the first year tend to be considerably higher than in the second year, and the differences often achieve statistical significance. In fact, the changes in party unity across years in election cycles identified in Figure 2(b) are often larger than the changes across Congresses identified in Figure 2(a). Here, we are interested in assessing the amount of reduction in party unity across the first two years of the election cycle in Figure 2(b) that could plausibly be attributed to variation in member-level support.

Fig. 2

Change in party unity over time Note: Panel A shows the percent of party votes over two-year congressional terms, and Panel B shows changes in the percent of party votes over time within election cycles. The election cycle is divided such that the first year extends from November of the even-numbered year to October of the odd-numbered year, and the second year extends from November of the odd-numbered year to October of the even-numbered year. Changes that achieve two-tailed statistical significance at the 0.1 level are denoted in Panel B.

To do so, we first generate, for each Congress separately, the predicted probabilities of party support on party votes for every member at the mean number of days to the next election in year one (time t) and year two (time t + 1) of the election cycle using Model 3 from Table 1, since it offers the best fit of all the member-level models.Footnote ⁸⁸ We use the members’ observed characteristics (for example, ideological extremism, seniority, lagged vote share, etc.) to calculate their predicted probabilities. We then simulate 500,000 roll-call votes at time t, in which each member's likelihood of casting a party vote is equal to her predicted probability of party support at time t. We conduct the same simulation at time t + 1. Thus we arrive at distributions of party support at time t and t + 1 that we can difference to arrive at a single distribution of predicted vote loss across the years of the election cycle.

We can interpret this difference distribution as giving us information about the likely decline in party support on an individual divisive vote that occurs across years in the election cycle solely because of the member-level effects identified in Table 1. Next we assume that all of the votes in the second year of the election cycle that missed classification as party votes by a margin equal to or smaller than the 95th percentile of the vote loss distribution would have been party votes if not for the decline in member-level support over time. Therefore we control for member-level effects by determining the percentage of party votes that would have occurred in the second year of the election cycle if these (near-miss) votes were instead classified as party votes. This gives us a rather generous estimate of the member-level contribution to vote losses over time. Stated differently, by using such a generous estimate (the 95th percentile of the vote loss distribution), we are biasing the analysis against finding any residual causes of declining party unity after having accounted for member-level effects. If the decline in party unity can be entirely attributed to member-level effects, then majority party leaders bear no responsibility for reducing party votes by means of manipulating the agenda. On the contrary, if the member-level effects fail to fully explain the reduction in party unity over time – especially when we are intentionally overstating the member-level effects – then there is compelling reason to believe that agenda setting plays an independent role.

Figure 3 presents the simulated effect that member-level variation in party support has on party unity across years in election cycles that exhibit a statistically significant decrease in party unity [see Figure 2(b)]. The dots at the base of the bars indicate the observed decrease in percent of party votes across years. The tops of the bars correspond to the change in party unity across years after accounting for the 95th percentile of vote loss predicted by the empirical model. If a bar does not cross zero on the y-axis, then member-level effects fail to account for all of the observed decrease in the percent of party votes.

Fig. 3

Contribution of member-level effects on change in party unity Note: Figure shows the simulated effect that member-level variation in party support has on the decrease in party unity over time in election cycles that exhibit a statistically significant decrease [see Fig. 2(b)]. Dots at the base of the bars indicate the observed decrease in percent of party votes over time. The tops of the bars correspond to the change in percent of party unity after accounting for the 95th percentile of vote loss predicted by the empirical model. If a bar does not cross zero on the y-axis, then member-level effects do not account for all of the observed decrease in percent of party unity. All but one election cycle (1984–86) is bounded away from zero. Dotted lines in the bars indicate the level of vote loss overestimation in the election cycle 1984–86.

We find that all but one election cycle (1984–86) is bounded away from zero, and many of the election cycles still exhibit substantial decline in party unity across years. In fact, we find that, on average, nearly half of the decline in party votes across election cycle years cannot be explained by (a generous assessment of) member-level effects. This simulation, at the very least, suggests that the role of majority party agenda manipulation is not a trivial part of this story. We now turn our attention to the Agenda Setting Proposition.

Evidence for Dynamic Agenda Setting

The results of the models in Equations 3–4 are shown in Table 2 and comport with our expectations.Footnote ⁸⁹ Specifically, we find that there is a marked decrease in both the probability of a party vote and the disagreement score as elections approach. Conversely, we observe a rise in the probability of ultra-consensual votes as the next election draws nearer. We also find that the vote type variables have the predicted effects: regular passage and suspension votes depress interparty voting conflict, and amendments and partisan procedural votes have the opposite effect. This finding supports the notion that certain vote types are predisposed to particular levels of conflict, which motivates the analysis that follows.

Table 2

Timing of Divisive/Consensual Votes

Note: Included in this analysis are all roll-call votes that occurred between the 84th and 108th Congresses (1955–2004). The dependent variable in the ‘Party Vote’ model is a dichotomous measure of whether the vote resulted in a majority of Democrats opposing a majority of Republicans. The dependent variable in the ‘Ultra-Consensual’ model is a dichotomous measure of whether the vote had at least 90 percent of the membership vote in the same way. The dependent variable in the ‘Disagreement Score’ model measures the absolute difference in the percent of Democrats and Republicans voting yea. The baseline vote type category is Miscellaneous Procedural Votes. Each model applies the polynomial order of time that offers the optimal fit. Standard errors in parentheses. *p ≤ 0.05.

We also note that the results are robust to the inclusion of the Polarization variable and the issue-type fixed effects. Polarization has the expected (positive) effect for both the party vote and disagreement score models, but is not statistically significant for the ultra-consensual model. In other words, cross-Congress levels of interparty disagreement are positively related to polarization.

Figure 4 shows the predictions for each of the complete models (with issue-type fixed effects) in Table 2 on an average vote throughout the election cycle.Footnote ⁹⁰ The predicted changes in these votes with respect to election proximity are noteworthy. The probability of a party vote falls by over 12 percentage points as time to the next election decreases, while the probability of an ultra-consensual vote rises by roughly 8 percentage points. Moreover, the disagreement score falls by approximately 9 percentage points over the course of the election cycle. Each of these findings suggests a substantial decrease in the consideration of divisive measures as elections approach. Therefore we find initial support for the Agenda Setting Proposition.Footnote ⁹¹

Fig. 4

Predictions for the timing of divisive/consensual votes Note: Panel A shows the predicted probability of a party vote by the number of days to the next election (results from the complete ‘Party Vote’ model in Table 2); Panel B shows the predicted probability of an ultra-consensual vote by days to the next election (results from ‘Ultra-Consensual’ model in Table 2); and Panel C shows the predicted absolute difference in the percent of Democrats and Republicans voting yea by days to the next election (results from ‘Disagreement Score’ model in Table 2). All other variables are held at their mean, and 83.5 percent confidence intervals are used throughout (see Maghsoodloo and Huang Reference Maghsoodloo and Huang2010).

Finally, we examine the occurrence of vote types that are associated with particularly high/low levels of interparty disagreement. Again, the advantage of this analysis is that we have a priori expectations for divisiveness levels on these votes that do not depend on observed levels of voting conflict. Table 3 shows the results for the models in Equation 5. The coefficients of the polynomial terms in these models are consistent with our expectations that votes that are predisposed to generate conflict decrease with election proximity and traditionally consensual votes increase as elections approach. All of the polynomial terms achieve statistical significance except in the partisan procedural model. Inclusion of the Polarization variable does not substantively alter the results, and Polarization is statistically significant and in the expected direction for both the regular passage and amendments models.Footnote ⁹² The results are also robust to the inclusion of the issue-type fixed effects. Figure 5 illustrates the predicted probabilities for the occurrence of the vote categories over the course of the election cycle using the complete models (with issue-type fixed effects).Footnote ⁹³

Table 3

Timing of Vote Types

Note: Included in this analysis are all roll-call votes that occurred between the 84th and 108th Congresses (1955–2004). The dependent variables indicate whether the vote was a Regular Passage, Suspension, Amendment or Partisan Procedural vote. Each model applies the polynomial order of time that offers the optimal fit. Standard errors in parentheses. *p ≤ 0.05.

Fig. 5

Predictions of timing of vote types Note: Panel A shows the predicted probability of regular passage votes by the number of days to the next election (results from the complete ‘Regular Passage’ model in Table 3); Panel B shows the predicted probability of suspension votes by days to the next election (results from ‘Suspensions’ model in Table 3); Panel C shows the predicted probability of amendment votes by days to the next election (results from ‘Amendments’ model in Table 3); and Panel D shows the predicted probability of partisan procedural votes by days to the next election (results from ‘Partisan Procedural’ model in Table 3). All other variables are held at their mean, and 83.5 percent confidence intervals are used throughout (see Maghsoodloo and Huang Reference Maghsoodloo and Huang2010).

These findings constitute additional evidence in support of the Agenda Setting Proposition. That is, we find that majority parties are decreasingly likely to schedule divisive votes as elections approach, which suggests that House majority party leaders respond to their members’ electoral pressures by adjusting the agenda accordingly. These findings point to an agenda setting strategy in which majorities seek to maximize collective gains when member-level electoral constraints are low (when elections are distant) and promote members’ electoral fortunes when constraints are comparatively high (when elections are near).Footnote ⁹⁴

Discussion and Conclusions

There is abundant evidence that voters across democratic systems have conflicted views of parties. While they rely on party labels to cast their votes and reward parties for policy successes, voters loathe excessive partisanship by elites. Voter ambiguity regarding partisanship, therefore, presents parties with countervailing incentives. Strong partisanship will bring desired policy successes and present voters with a clear party brand, but voters will punish legislators at election time if they are overly partisan. The question is then: how do parties solve this dilemma?

We argue that elite partisan behavior is time dependent. A key factor in this story is that voter monitoring of legislative activity varies systematically over time. Parties and elected politicians exhibit behavior that reflects an awareness of the time sensitivity of partisanship, as they opportunistically pursue party goals when the costs of partisan behavior are relatively low (when voter attentiveness is low). As such, this study suggests that elite partisanship is best characterized as a highly dynamic process, even over the short term, as opposed to the conventional perspective, which holds that partisanship changes incrementally over long periods of time.

We believe these findings have important implications for multiple related literatures. Our work suggests that a complete picture of variation in elite partisanship may require analysis over more refined measures of time. In fact, partisan variation appears to be significantly more dynamic than previously considered. Therefore, studies that measure the cohesiveness and/or polarization of congressional parties over extensive periods (for example, two-year congressional terms) may obscure meaningful information. Moreover, a finding that members adopt systematically variable voting positions relative to the timing of the next election adds to recent evidence that members’ policy positions are strategically adjusted,Footnote ⁹⁵ but contradicts observations of voting stability over time.Footnote ⁹⁶

Our findings also contribute to the expansive literature that addresses the legislative conditions related to policy change.Footnote ⁹⁷ Our work suggests that the timing and nature of legislative output may not only be a function of structural features of Congress or the types of issues that arise, but also of when in the election cycle these issues surface. We believe these results point to two possible policy implications. First, inevitably divisive measures may well be postponed until after elections. The story of the Bush tax cuts briefly detailed above provides some anecdotal evidence to this effect. Secondly, the majority party may be less inclined to pursue partisan manipulation of legislation when elections are near. The decline in consideration of divisive measures as elections approach offers some circumstantial evidence of this possibility. A thorough examination of the policy implications of these findings is, however, beyond the scope of this article, so we leave this question for future study.

Our finding that House members and congressional parties recognize the electoral consequences of party support and strategically adjust their behavior relative to election proximity has important application beyond the study of Congress. It certainly has direct import for any electoral system that has a personal vote component. Yet we believe that these partisan dynamics can likewise be triggered by the presence of other countervailing incentives, such as conflicting demands between government coalition partners and partisan voters. Therefore the existence of a personal vote is not, in our view, a necessary condition for these dynamics. Furthermore, the strategic between-elections behavior we uncover among US House members is also generally consistent with findings from comparative studies on party switching.Footnote ⁹⁸

Footnotes

René Lindstädt is Professor, Department of Government, University of Essex (email: rlind@essex.ac.uk); Ryan J. Vander Wielen is Associate Professor, Department of Political Science, Temple University (email: vanderwielen@temple.edu). The authors wish to thank Jamie Carson, Gregory Koger, Matthew Lebo and Everett Young for sharing the data used for some control variables in the member-level models, as well as John Aldrich, Kevin Arceneaux, David Rohde, the BJPolS editor, three anonymous reviewers and participants of the 2012 Philadelphia-Area American Politics Workshop, Philadelphia, PA, USA. An online appendix with supplementary materials is available at http://dx.doi.org/10.1017/S0007123413000173. The data used in this article, which is part of a larger, ongoing project on dynamic elite partisanship, will be made available at http://journals.cambridge.org/action/displayJournal?jid=JPS within two years of the date of publication.

¹ Carey Reference Carey2007; Lebo, McGlynn, and Koger Reference Lebo, McGlynn and Koger2007.

² Markus and Converse Reference Markus and Converse1979.

³ Bowler, Farrell, and Katz Reference Bowler, Farrell and Katz1999; Cox and McCubbins Reference Cox and McCubbins2007.

⁴ Carson et al. Reference Carson, Koger, Lebo and Young2010; Soroka and Wlezien Reference Soroka and Wlezien2010.

⁵ For example, Carey Reference Carey2008; Tavits Reference Tavits2011.

⁶ For example, Desposato Reference Desposato2006; Heller and Mershon Reference Heller and Mershon2005, Reference Heller and Mershon2008; Laver and Benoit Reference Laver and Benoit2003; Mershon and Shvetsova Reference Mershon and Shvetsova2008.

⁷ For example, Karp and Bowler Reference Karp and Bowler2001.

⁸ Parsons and Weber (Reference Parsons and Weber2011) demonstrate the interplay of electorally motivated individual- and party-level dynamics for European Union issues in national legislatures.

⁹ For example, Ansolabehere, Snyder, Jr., and Stewart, III Reference Ansolabehere, Snyder and Stewart,, III2001; Canes-Wrone, Brady, and Cogan Reference Canes-Wrone, Brady and Cogan2002.

¹⁰ Cooper and Brady Reference Cooper and Brady1981; Rohde Reference Rohde1991; Theriault Reference Theriault2008.

¹¹ Crespin, Rohde, and Vander Wielen Reference Crespin, Rohde and Vander Wielen2013; Snyder, Jr. and Groseclose Reference Snyder and Groseclose2000.

¹² Smith Reference Smith2007.

¹³ Aldrich Reference Aldrich1995; Aldrich and Rohde Reference Aldrich and Rohde2000; Rohde Reference Rohde1991.

¹⁴ Rohde Reference Rohde1991.

¹⁵ For example, Krehbiel Reference Krehbiel1999.

¹⁶ Cox and McCubbins Reference Cox and McCubbins2005, Reference Cox and McCubbins2007; Lebo, McGlynn, and Koger Reference Lebo, McGlynn and Koger2007; Patty Reference Patty2008.

¹⁷ Smith Reference Smith2007.

¹⁸ Canes-Wrone, Brady, and Cogan Reference Canes-Wrone, Brady and Cogan2002; Carson et al. Reference Carson, Koger, Lebo and Young2010.

¹⁹ But see Lebo, McGlynn, and Koger Reference Lebo, McGlynn and Koger2007.

²⁰ Smith Reference Smith2007, 25.

²¹ Maltzman Reference Maltzman1997.

²² Aldrich Reference Aldrich1995; Schwartz Reference Schwartz1989.

²³ Cox and McCubbins Reference Cox and McCubbins2007.

²⁴ Coker and Crain Reference Coker and Crain1994.

²⁵ For example, Carson et al. Reference Carson, Koger, Lebo and Young2010.

²⁶ Hinich and Munger Reference Hinich and Munger1989; Snyder, Jr. and Groseclose 2000.

²⁷ Carson et al. Reference Carson, Koger, Lebo and Young2010.

²⁸ Carson et al. Reference Carson, Koger, Lebo and Young2010.

²⁹ Lindstädt and Vander Wielen Reference Lindstädt and Vander Wielen2011.

³⁰ Bednar Reference Bednar2006.

³¹ For example, Kalt and Zupaan Reference Kalt and Zupan1990.

³² Shepsle et al. Reference Shepsle, Van Houweling, Abrams and Hanson2009.

³³ While we do not further investigate the mechanism responsible for variation in monitoring, we assume that the rise in voter attentiveness as elections approach results from such factors as increased scrutiny from local media and efforts by challengers to draw attention to votes they consider inconsistent with voter preferences.

³⁴ Weingast, Shepsle, and Johnsen Reference Weingast, Shepsle and Johnsen1981.

³⁵ While variation in legislative behavior that reflects a sensitivity to elections has been documented in the Senate (for example, Elling Reference Elling1982), comparatively little research of this variety has studied the House because of the much shorter terms (but see Tien Reference Tien2001). Yet we can study this phenomenon in the House by moving away from the convention of using congressional terms as the unit of analysis and toward a more refined measure of time. An examination of the House arguably presents a more demanding test of our theoretical account, given that conventional wisdom holds that House members ‘must constantly abide by the preferences of their constituents, since the need to secure reelection is always right around the corner’ (Ahuja Reference Ahuja1994, 105). Furthermore, we suggest that a complete picture of the balancing of competing demands must go beyond the traditional focus on member-level voting to also consider collective behavior.

³⁶ Gelman and King Reference Gelman and King1993.

³⁷ Cox and McCubbins Reference Cox and McCubbins2007.

³⁸ Sinclair Reference Sinclair1998.

³⁹ Lebo, McGlynn, and Koger Reference Lebo, McGlynn and Koger2007.

⁴⁰ We suggest that any benefits the minority party receives from the majority party's strategic adjustment of the agenda are merely a by-product of the majority's considerations of its own constraints. After all, the majority party has more to lose than the minority party, both in terms of seats and institutional advantages, by engaging in electorally risky behavior. Moreover, voters tend to penalize the majority party more severely for what they perceive to be unfavorable legislative activity (Jones and McDermott Reference Jones and McDermott2009). Thus the comparative electoral benefits of a more consensual agenda as elections approach would appear far greater for the majority party than for the minority party.

⁴¹ Dixon and Cornwell Reference Dixon and Cornwell2010.

⁴² Poole and Rosenthal Reference Poole and Rosenthal2007.

⁴³ Carson et al. Reference Carson, Koger, Lebo and Young2010.

⁴⁴ We use the roll-call data made available on Keith Poole's Voteview website (at http://voteview.com/) and code absences and other unrecorded activity as missing data, since we cannot definitively determine party support. We detail the data sources used for constructing the dependent and independent variables in an accompanying online appendix at http://journals.cambridge.org/action/displayJournal?jid=JPS.

⁴⁵ We used Poole's Voteview codebooks to collect the dates on which votes occurred, and relied on the House Clerk's website (at http://clerk.house.gov) to determine the dates of elections.

⁴⁶ We also use the Bayesian Information Criterion (BIC) and likelihood ratio tests to confirm the model selections based on the AIC. We allow for non-linear specifications (up to a cubic polynomial) when successive polynomial orders provide improvements in model fit.

⁴⁷ Rothenberg and Sanders Reference Rothenberg and Sanders2000.

⁴⁸ Poole and Rosenthal Reference Poole and Rosenthal2007.

⁴⁹ Jacobson Reference Jacobson1989.

⁵⁰ Use of the natural logarithm captures the non-linear relationship between money and votes identified by Jacobson (Reference Jacobson1980). Spending data are not available before 1978. Excluding this variable from the analysis, however, does not substantively affect the results.

⁵¹ See, for example, Ansolabehere, Snyder, Jr. and Stewart, III Reference Ansolabehere, Snyder and Stewart,, III2001; Carson et al. Reference Carson, Koger, Lebo and Young2010.

⁵² Carson et al. Reference Carson, Koger, Lebo and Young2010.

⁵³ Bafumi, Erikson, and Wlezien Reference Bafumi, Erikson and Wlezien2010.

⁵⁴ Specifically, the composite score has a strong positive relationship to Ideological Extremism, Seniority, Lagged Vote Share and Lagged District Partisanship, and a strong negative relationship to Lagged Quality Challenger and Lagged Spending Gap. The composite score exhibits a considerably weaker (positive) relationship to both Retirement and In-party Midterm.

⁵⁵ We estimate numerous models using a wide variety of control variables, lagging schemes and interactions with the polynomial terms of time, and find that the following results are highly robust to their selection.

⁵⁶ Crespin, Rohde, and Vander Wielen Reference Crespin, Rohde and Vander Wielen2013.

⁵⁷ See the online appendix for additional information on vote categories.

⁵⁸ Lee Reference Lee2008.

⁵⁹ The data used for coding issue types were originally collected by Frank R. Baumgartner and Bryan D. Jones with the support of National Science Foundation grant numbers SBR 9320922 and 0111611. We note that alternatively using the issue type categories identified in the Political Institutions and Public Choice (PIPC) data produces substantively similar results.

⁶⁰ Using fixed effects for election cycles, rather than Congresses, produces substantively similar results. We believe that there is a strong theoretical rationale for accounting for Congresses, since doing so captures variation in both membership and partisan structures.

⁶¹ We find evidence that the probit link offers subtle improvements in model fit compared to the logit link for some of the member-level models, whereas the reverse is true for the agenda setting models. For both sets of models, either specification of the link function arrives at substantively similar results (with the exception of the amendments model, for which the second-degree polynomial loses statistical significance when estimated using probit; although the corresponding logit model provides a considerably better fit than the probit specification in this instance).

⁶² Due to the size of the data matrix, we are unable to estimate a hierarchical model for the entire dataset to account for repeated measures. However, we estimated a hierarchical model for samples of the data, and arrived at substantively similar results to those below.

⁶³ We use the PIPC roll-call database (at http://www.poli.duke.edu/pipc/data.html).

⁶⁴ Crespin, Rohde, and Vander Wielen Reference Crespin, Rohde and Vander Wielen2013.

⁶⁵ Aldrich and Rohde Reference Aldrich and Rohde2000; Rohde Reference Rohde1991.

⁶⁶ Vander Wielen and Smith Reference Vander Wielen and Smith2011.

⁶⁷ Rohde Reference Rohde1991.

⁶⁸ We note that this measure of polarization varies across, but not within, Congresses. It captures important cross-Congress variation in party preferences that theoretically affects agenda setting strategies. While members’ observed policy positions may vary over time due to strategic considerations, it is exceedingly unlikely that their preferences would systematically vary according to elections. Therefore we use first-dimension DW-NOMINATE scores, which are based on a scaling technique that accounts for all recorded votes, to capture member preferences and polarization at the Congress level. Clearly, some of the strategic decisions theorized about could affect members’ scores at the margins. However, we are confident that these scores offer a reasonable basis for assessing Congress-by-Congress variation in polarization.

⁶⁹ As an alternative, we also grouped votes on election cycles – the votes occurring between elections – as opposed to Congresses, and found substantively similar results. This is not entirely surprising, considering that the variables that categorize votes according to Congress and election cycle correlate at 0.9998. Since Congresses capture the bulk of the duration effects, as evidenced by the correlation with election cycles, and avoid (potentially sizable) incongruities in membership, we believe there is a strong theoretical rationale for grouping on Congresses.

⁷⁰ We note that the models are highly robust to alternative specifications of both fixed and random effects.

⁷¹ Roberts and Smith Reference Roberts and Smith2003.

⁷² Finocchiaro and Rohde Reference Finocchiaro and Rohde2008.

⁷³ The miscellaneous categories are omitted from this analysis since they offer less conclusive predictions for partisan divisions.

⁷⁴ Crespin, Rohde, and Vander Wielen Reference Crespin, Rohde and Vander Wielen2013.

⁷⁵ As a robustness check, we also estimated each of the models in our analysis excluding votes that took place during the lame-duck sessions. The results we report below are not substantively changed by excluding lame-duck votes.

⁷⁶ We confirm the marginal effects and statistical significance of the interaction terms using simulations (for a more detailed discussion, see Berry, DeMeritt, and Esarey Reference Berry, DeMeritt and Esarey2010).

⁷⁷ Most of the member-specific control variables become statistically significant in models that do not account for clustering on individual members. This suggests that intracluster correlation in errors is driving the statistical effects on the control variables in the uncorrected models.

⁷⁸ These simulations are conducted using code written by the authors that implements the algorithms detailed in King, Tomz, and Wittenberg (Reference King, Tomz and Wittenberg2000). This code is available upon request.

⁷⁹ Goldstein and Healy Reference Goldstein and Healy1995; Maghsoodloo and Huang Reference Maghsoodloo and Huang2010; Payton, Greenstone, and Schenker Reference Payton, Greenstone and Schenker2003.

⁸⁰ While we find an increase in the baseline party support across successive Congresses during the period of analysis, which is consistent with the growing party polarization over this period, there is no systematic variation in the functional form of party support on party or non-party votes during the election cycle. We confirm this by including a trend term, and its appropriate interactions, in the models.

⁸¹ We note that there is no statistically significant non-monotonic change in the predicted probabilities for party votes. The predicted probabilities of party support between 200 and 400 days to election (that is, the approximate duration during which the mean predicted probability is slightly decreasing in time) are statistically indiscernible, suggesting that we cannot reject the null hypothesis that the slope for predicted probabilities is flat over this period. Of course, some of the appearance of non-monotonic variation can be attributed to the parametric curve-fitting exercise, in which we find evidence that a cubic polynomial offers the best fit for the data. When using non-parametric methods (for example, splines), we confirm that the predicted probabilities over this middle period are essentially flat.

⁸² Carson Reference Carson2005.

⁸³ Abramowitz, Alexander, and Gunning Reference Abramowitz, Alexander and Gunning2006.

⁸⁴ Gelman and King 1993.

⁸⁵ Reasonable adjustment to the periods of time before and after an election yields substantively similar results.

⁸⁶ Since this simulation aims to identify the amount of temporal variation in party unity scores that is attributable to member-level effects, and party support on non-party votes is statistically constant according to the model used in this simulation (Model 3 in Table 1), we focus our attention on the voting behavior associated with party votes.

⁸⁷ For example, Rohde Reference Rohde1991.

⁸⁸ We confirm this conclusion using various model selection criteria (for example, AIC and BIC).

⁸⁹ We also estimate all member-level and agenda setting models including a variable to account for budget votes. Generally speaking, budget votes must be scheduled and are likely to generate interparty disagreement (more than 68 percent of the budget votes during the period of analysis were party votes). The results we report below are not substantively changed by accounting for budget votes, and the variable does not improve model fit.

⁹⁰ The predictions are based on the complete models since they provide the best model fit, and are generated using the Zelig package (Imai, King, and Lau Reference Imai, King and Lau2008) in R (R Development Core Team 2012). We were unable to simulate from the models with random slopes using Zelig, so the simulations are based on models that include only random intercepts. However, the fixed-part coefficients in these models are strikingly similar in magnitude and statistical significance to the models that include random slopes (as reported in Table 2). Results of the models including only random intercepts are available in the online appendix. Since we are not interested in the random effects for these predictions (we are not, for example, concerned with plotting the differences in baseline or slope effects across different Congresses), but rather in the predictions for a typical Congress, only the fixed effects are relevant. After all, it is typically assumed that the random effects are distributed normally with a mean of 0 and unknown variance (including in Zelig). Consequently, Zelig and other software packages only estimate the variance for the random effects. Therefore, with a sufficient number of simulations, the prediction for a random effect for a typical unit (which we are interested in here) is equal to zero.

⁹¹ Given that the sample size for the analysis of the Party Support Proposition exceeds that of the Agenda Setting Proposition by a factor of more than 137, we are unable to identify the functional relationship between divisive/consensual votes and time with the same level of precision as for the analysis of party support. Therefore, differences in the precise functional behavior across the analyses may well be attributed to the disparity in sample sizes.

⁹² It is not entirely surprising that polarization has a positive and statistically significant effect on the use of suspensions. For one, factors correlated with polarization, such as legislative workload and rules changes, have encouraged the use of suspensions (Carr Reference Carr2005). Moreover, suspensions restrict the minority's ability to amend or delay legislation, which is particularly useful to the majority when party polarization is comparatively high (Binder Reference Binder1997; Moffett Reference Moffett2008). The Polarization variable is in the expected direction for the partisan procedural model, but it fails to achieve statistical significance.

⁹³ See footnote 90 for a discussion of generating predicted probabilities for these models.

⁹⁴ We likewise evaluate whether the results of our agenda setting models (in Tables 2 and 3) are an artifact of end-of-Congress effects. Again we restrict the analysis to votes occurring during the period extending from two months preceding an election to the end of a Congress. As before, we replicate the models using a dummy variable that measures whether the given vote occurred prior to or after the election in lieu of the polynomial term(s) of time. We find that the coefficient on the dummy variable is statistically significant and in the direction predicted by the Agenda Setting Proposition for each of the models, although this variable fails to achieve statistical significance in the amendments and complete suspension models in Table 3. We note, however, that this variable is statistically significant and in the expected direction for all partisan procedural models in Table 3, whereas the original models failed to identify a statistically discernible time effect (results are available in the online appendix). Therefore we find compelling evidence (in seventeen of the twenty-one agenda setting models) that divisive votes are strategically depressed in the period immediately preceding election.

⁹⁵ Bailey Reference Bailey2007; Lindstädt and Vander Wielen Reference Lindstädt and Vander Wielen2011.

⁹⁶ Lott and Bronars Reference Lott and Bronars1993; Poole Reference Poole2007.

⁹⁷ Hurley, Brady, and Cooper Reference Hurley, Brady and Cooper1977; Krehbiel Reference Krehbiel1998.

⁹⁸ For example, Laver and Benoit Reference Laver and Benoit2003; Mershon and Shvetsova Reference Mershon and Shvetsova2008.

References

Abramowitz, Alan I., Alexander, Brad Gunning, Matthew. 2006. Incumbency, Redistricting, and the Decline of Competition in U.S. House Elections. Journal of Politics 68 (1):75–88.Google Scholar

Ahuja, Sunil. 1994. Electoral Status and Representation in the United States Senate: Does Temporal Proximity to Election Matter? American Politics Quarterly 22 (1):104–118.CrossRef Google Scholar

Aldrich, John Rohde, David. 2000. The Consequences of Party Organization in the House: The Role of the Majority and Minority Parties in Conditional Party Government. In Polarized Politics: Congress and the President in a Partisan Era, edited by Jon R. Bond and Richard Fleisher, 31–72. Washington, DC: Congressional Quarterly Press.Google Scholar

Aldrich, John H. 1995. Why Parties?: The Origin and Transformation of Political Parties in America. Chicago, IL: University of Chicago Press.Google Scholar

Ansolabehere, Stephen, Snyder, James M. Jr. Stewart,, III, Charles. 2001. Candidate Positioning in U.S House Elections. American Journal of Political Science 45 (1):136–159.Google Scholar

Bafumi, Joseph, Erikson, Robert S. Wlezien, Christopher. 2010. Balancing, Generic Polls and Midterm Congressional Elections. Journal of Politics 72 (3):705–719.CrossRef Google Scholar

Bailey, Michael A. 2007. Comparable Preference Estimates across Time and Institutions for the Court, Congress, and Presidency. American Journal of Political Science 51 (3):433–448.CrossRef Google Scholar

Bednar, Jenna. 2006. Is Full Compliance Possible? Conditions for Shirking with Imperfect Monitoring and Continuous Action Spaces. Journal of Theoretical Politics 18 (3):347–375.Google Scholar

Berry, William D., DeMeritt, Jacqueline H.R. Esarey, Justin. 2010. Testing for Interaction in Binary Logit and Probit Models: Is a Product Term Essential? American Journal of Political Science 54 (1):248–266.Google Scholar

Binder, Sarah A. 1997. Minority Rights, Majority Rule: Partisanship and the Development of Congress. New York, NY: Cambridge University Press.Google Scholar

Bowler, Shaun, Farrell, David M. Katz, Richard, eds. 1999. Party Discipline and Party Government. Columbus, OH: Ohio State University Press.Google Scholar

Canes-Wrone, Brandice, Brady, David W. Cogan, John F.. 2002. Out of Step, Out of Office: Electoral Accountability and House Members’ Voting. American Political Science Review 96 (1):127–140.Google Scholar

Carey, John M. 2007. Competing Principals, Political Institutions, and Party Unity in Legislative Voting. American Journal of Political Science 51 (1):92–107.Google Scholar

Carey, John M. 2008. Legislative Voting and Accountability. New York, NY: Cambridge University Press.Google Scholar

Carr, Thomas. 2005. Suspension of the Rules in the House of Representatives. Congressional Research Services Report (RL32474).Google Scholar

Carson, Jamie L. 2005. Strategy, Selection, and Candidate Competition in U.S. House and Senate Elections. Journal of Politics 67 (1):1–28.Google Scholar

Carson, Jamie L., Koger, Gregory, Lebo, Matthew J. Young, Everett. 2010. The Electoral Costs of Party Loyalty in Congress. American Journal of Political Science 54 (3):598–616.Google Scholar

Coker, David C.W. Crain, Mark. 1994. Legislative Committees as Loyalty-generating Institutions. Public Choice 81 (3/4):195–221.Google Scholar

Cooper, Joseph Brady, David W.. 1981. Institutional Context and Leadership Style: The House from Cannon to Rayburn. American Political Science Review 75 (2):411–425.Google Scholar

Cox, Gary McCubbins, Mathew. 2005. Setting the Agenda: Responsible Party Government in the US House of Representatives. New York, NY: Cambridge University Press.CrossRef Google Scholar

Cox, Gary McCubbins, Mathew. 2007. Legislative Leviathan: Party Government in the House. 2nd ed. New York, NY: Cambridge University Press.Google Scholar

Crespin, Michael H., Rohde, David W. Vander Wielen, Ryan J.. 2013. Measuring Variations in Party Unity Voting: An Assessment of Agenda Effects. Party Politics 19 (3):432–457.Google Scholar

Desposato, Scott W. 2006. Parties for Rent? Ambition, Ideology, and Party Switching in Brazil's Chamber of Deputies. American Journal of Political Science 50 (1):62–80.Google Scholar

Dixon, Kim Cornwell, Susan. 2010. No Tax Cut Vote before Election: Democrat. Reuters. 23 September.Google Scholar

Elling, Richard C. 1982. Ideological Change in the U.S. Senate: Time and Electoral Responsiveness. Legislative Studies Quarterly 7 (1):775–792.Google Scholar

Finocchiaro, Charles J. Rohde, David W.. 2008. War for the Floor: Partisan Theory and Agenda Control in the U.S. House of Representatives. Legislative Studies Quarterly 33 (1):35–61.Google Scholar

Gelman, Andrew King, Gary. 1993. Why Are American Presidential Election Campaign Polls So Variable When Votes Are So Predictable? British Journal of Political Science 23 (4):409–451.Google Scholar

Goldstein, Harvey Healy, Michael J.R.. 1995. The Graphical Presentation of a Collection of Means. Journal of the Royal Statistical Society A 158:175–177.Google Scholar

Heller, William B. Mershon, Carol. 2005. Party Switching in the Italian Chamber of Deputies, 1996–2001. Journal of Politics 67 (2):536–559.Google Scholar

Heller, William B. Mershon, Carol. 2008. Dealing in Discipline: Party Switching and Legislative Voting in the Italian Chamber of Deputies, 1988–2000. American Journal of Political Science 52 (4):910–925.Google Scholar

Hinich, Melvin J. Munger, Michael C.. 1989. Political Investment, Voter Perceptions, and Candidate Strategy: An Equilibrium Spatial Analysis. In Models of Strategic Choice in Politics, edited by Peter C. Ordeshook, 49–68. Ann Arbor, MI: University of Michigan Press.Google Scholar

Hurley, Patricia, Brady, David Cooper, Joseph. 1977. Measuring Legislative Potential for Policy Change. Legislative Studies Quarterly 2 (4):385–398.Google Scholar

Imai, Kosuke, King, Gary Lau, Olivia. 2008. Toward a Common Framework for Statistical Analysis and Development. Journal of Computational and Graphical Statistics 17 (4):892–913.Google Scholar

Jacobson, Gary C. 1980. Money in Congressional Elections. New Haven, CT: Yale University Press.Google Scholar

Jacobson, Gary C. 1989. Strategic Politicians and the Dynamics of U.S. House Elections, 1946–86. American Political Science Review 83 (3):773–793.Google Scholar

Jones, David R. McDermott, Monika L.. 2009. Americans, Congress, and Democratic Responsiveness: Public Evaluations of Congress and Electoral Consequences. Ann Arbor, MI: University of Michigan Press.Google Scholar

Kalt, Joseph P. Zupan, Mark A.. 1990. The Apparent Ideological Behavior of Legislators: Testing Principal-agent Slack in Political Institutions. Journal of Law, Economics, and Organization 33 (1):103–131.Google Scholar

Karp, Jeffrey A. Bowler, Shaun. 2001. Coalition Government and Satisfaction with Democracy: An Analysis of New Zealand's Reaction to Proportional Representation. European Journal of Political Research 40 (1):57–79.Google Scholar

King, Gary, Tomz, Michael Wittenberg, Jason. 2000. Making the Most of Statistical Analyses: Improving Interpretation and Presentation. American Journal of Political Science 44 (2):347–361.Google Scholar

Krehbiel, Keith. 1998. Pivotal Politics: A Theory of U.S. Lawmaking. Chicago, IL: University of Chicago Press.Google Scholar

Krehbiel, Keith. 1999. Paradoxes of Parties in Congress. Legislative Studies Quarterly 24 (1):31–64.CrossRef Google Scholar

Laver, Michael Benoit, Kenneth. 2003. The Evolution of Party Systems between Elections. American Journal of Political Science 47 (2):215–233.Google Scholar

Lebo, Matthew J., McGlynn, Adam J. Koger, Gregory. 2007. Strategic Party Government: Party Influence in Congress, 1789–2000. American Journal of Political Science 51 (3):464–481.Google Scholar

Lee, Frances E. 2008. Agreeing to Disagree: Agenda Content and Senate Partisanship, 1981–2004. Legislative Studies Quarterly 33 (2):199–222.Google Scholar

Lindstädt, René Vander Wielen, Ryan J.. 2011. Timely Shirking: Time-dependent Monitoring and its Effects on Legislative Behavior in the U.S. Senate, 1981–2002. Public Choice 148 (1/2):119–148.Google Scholar

Lott, John R. Bronars, Stephen G.. 1993. Time-Series Evidence on Shirking in the United States House of Representatives. Public Choice 76 (1/2):125–149.Google Scholar

Maghsoodloo, Saeed Huang, Ching-Ying. 2010. Comparing the Overlapping of Two Independent Confidence Intervals with a Single Confidence Interval for Two Normal Population Parameters. Journal of Statistical Planning and Inference 140 (1):3295–3305.Google Scholar

Maltzman, Forrest. 1997. Competing Principals: Committees, Parties, and the Organization of Congress. Ann Arbor, MI: Michigan University Press.Google Scholar

Markus, Gregory B. Converse, Philip E.. 1979. A Dynamic Simultaneous Equation Model of Electoral Choice. American Political Science Review 73 (4):1055–1070.Google Scholar

Mershon, Carol Shvetsova, Olga. 2008. Parliamentary Cycles and Party Switching in Legislatures. Comparative Political Studies 41 (1):99–127.Google Scholar

Moffett, Kenneth W. 2008. Suspended Rules in the Postreform House. Paper presented at the 2008 Annual Meeting of the American Political Science Association, Boston, MA, 28–31 August.Google Scholar

Parsons, Craig Weber, Till. 2011. Cross-cutting Issues and Party Strategy in the European Union. Comparative Political Studies 44 (4):383–411.Google Scholar

Patty, John W. 2008. Equilibrium Party Government. American Journal of Political Science 52 (3):636–655.CrossRef Google Scholar

Payton, Mark E., Greenstone, Matthew H. Schenker, Nathaniel. 2003. Overlapping Confidence Intervals or Standard Error Intervals: What Do They Mean in Terms of Statistical Significance. Journal of Insect Science 3 (34).Google Scholar

Poole, Keith T. 2007. Changing Minds? Not in Congress! Public Choice 131 (3/4):435–451.Google Scholar

Poole, Keither T. Rosenthal, Howard. 2007. Ideology and Congress. New Brunswick, NJ: Transaction Publishers.Google Scholar

R Development Core Team. 2012. R: A Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing. R version 2.15.2.Google Scholar

Roberts, Jason M. Smith, Steven S.. 2003. Procedural Contexts, Party Strategy, and Conditional Party Voting in the U.S. House of Representatives, 1971–2000. American Journal of Political Science 47 (2):305–317.CrossRef Google Scholar

Rohde, David. 1991. Parties and Leaders in the Postreform House. Chicago, IL: University of Chicago Press.Google Scholar

Rothenberg, Lawrence S. Sanders, Mitchell S.. 2000. Severing the Electoral Connection: Shirking in the Contemporary Congress. American Journal of Political Science 44 (2):316–325.Google Scholar

Schwartz, Thomas. 1989. Why Parties? Research Memorandum, July.Google Scholar

Shepsle, Kenneth A., Van Houweling, Robert P., Abrams, Samuel J. Hanson, Peter C.. 2009. The Senate Electoral Cycle and Bicameral Appropriations Politics. American Journal of Political Science 53 (2):343–359.Google Scholar

Sinclair, Barbara. 1998. Legislators, Leaders, and Lawmakers. Baltimore, MD: Johns Hopkins University Press.Google Scholar

Smith, Steven S. 2007. Party Influence in Congress. New York, NY: Cambridge University Press.Google Scholar

Snyder, James M. Jr. Groseclose, Tim. 2000. Estimating Party Influence in Congressional Roll-Call Voting. American Journal of Political Science 44 (2):187–205.Google Scholar

Soroka, Stuart N. Wlezien, Christopher. 2010. Degrees of Democracy: Politics, Public Opinion, and Policy. New York, NY: Cambridge University Press.Google Scholar

Tavits, Margit. 2011. Power Within Parties: The Strength of the Local Party and MP Independence in Postcommunist Europe. American Journal of Political Science 55 (4):923–936.Google Scholar

Theriault, Sean M. 2008. Party Polarization in Congress. New York, NY: Cambridge University Press.Google Scholar

Tien, Charles. 2001. Representation, Voluntary Retirement, and Shirking in the Last Term. Public Choice 106 (1–2):117–130.Google Scholar

Vander Wielen, Ryan J. Smith, Steven S.. 2011. Majority Party Bias in U.S. Congressional Conference Committees. Congress and the Presidency 38 (3):271–300.Google Scholar

Weingast, Barry R., Shepsle, Kenneth A. Johnsen, Christopher. 1981. The Political Economy of Benefits and Costs: A Neoclassical Approach to Distributive Politics. Journal of Political Economy 89 (4):642–664.Google Scholar

Table 1Likelihood of Casting a Vote Supporting Party

Fig. 1Predicted probability of party support on party and non-party votes by distance from election Note: Panel A shows a member's predicted probabilities of supporting her party on a party and non-party vote by the number of days to the next election for Model 3; Panel B shows the analogous predicted probabilities for Model 4; Panel C shows the predicted probabilities for Model 3 for the Congresses with the highest (103rd Congress) and lowest (96th Congress) predicted baseline party support; and Panel D shows the predicted probabilities for Model 4 for the 99th percentile of the factor score (‘insulated member’) and the 1st percentile of the factor score (‘vulnerable member’). All other variables are held at their mean, and 83.5 percent confidence intervals are used throughout (see Maghsoodloo and Huang 2010).

Fig. 2Change in party unity over time Note: Panel A shows the percent of party votes over two-year congressional terms, and Panel B shows changes in the percent of party votes over time within election cycles. The election cycle is divided such that the first year extends from November of the even-numbered year to October of the odd-numbered year, and the second year extends from November of the odd-numbered year to October of the even-numbered year. Changes that achieve two-tailed statistical significance at the 0.1 level are denoted in Panel B.

Fig. 3Contribution of member-level effects on change in party unity Note: Figure shows the simulated effect that member-level variation in party support has on the decrease in party unity over time in election cycles that exhibit a statistically significant decrease [see Fig. 2(b)]. Dots at the base of the bars indicate the observed decrease in percent of party votes over time. The tops of the bars correspond to the change in percent of party unity after accounting for the 95th percentile of vote loss predicted by the empirical model. If a bar does not cross zero on the y-axis, then member-level effects do not account for all of the observed decrease in percent of party unity. All but one election cycle (1984–86) is bounded away from zero. Dotted lines in the bars indicate the level of vote loss overestimation in the election cycle 1984–86.

Table 2Timing of Divisive/Consensual Votes

Fig. 4Predictions for the timing of divisive/consensual votes Note: Panel A shows the predicted probability of a party vote by the number of days to the next election (results from the complete ‘Party Vote’ model in Table 2); Panel B shows the predicted probability of an ultra-consensual vote by days to the next election (results from ‘Ultra-Consensual’ model in Table 2); and Panel C shows the predicted absolute difference in the percent of Democrats and Republicans voting yea by days to the next election (results from ‘Disagreement Score’ model in Table 2). All other variables are held at their mean, and 83.5 percent confidence intervals are used throughout (see Maghsoodloo and Huang 2010).

Table 3Timing of Vote Types

Fig. 5Predictions of timing of vote types Note: Panel A shows the predicted probability of regular passage votes by the number of days to the next election (results from the complete ‘Regular Passage’ model in Table 3); Panel B shows the predicted probability of suspension votes by days to the next election (results from ‘Suspensions’ model in Table 3); Panel C shows the predicted probability of amendment votes by days to the next election (results from ‘Amendments’ model in Table 3); and Panel D shows the predicted probability of partisan procedural votes by days to the next election (results from ‘Partisan Procedural’ model in Table 3). All other variables are held at their mean, and 83.5 percent confidence intervals are used throughout (see Maghsoodloo and Huang 2010).

Lindstädt Supplementary Material

Supplementary Material

PDF 250.7 KB

Article contents

Dynamic Elite Partisanship: Party Loyalty and Agenda Setting in the US House

Abstract

Information

Dynamic Elite Partisanship

Data and Methods

Member-level Analysis

Agenda Setting Analysis

Results

Evidence for Dynamic Member-level Partisanship

The Duality of Dynamic Elite Partisanship

Evidence for Dynamic Agenda Setting

Discussion and Conclusions

Footnotes

References

Lindstädt Supplementary Material

Save article to Kindle

Save article to Dropbox

Save article to Google Drive

Reply to: Submit a response

Your details

You have entered the maximum number of contributors

Conflicting interests