Abstract
On May 24, 2001 United States Senator James Jeffords announced that he was switching from Republican to independent and would vote with Democrats on organizational matters (i.e. votes deciding party membership and majority party status), effectively taking majority party control of the Senate from the Republicans and giving it to the Democrats. This created an unusually well controlled quasi-experimental opportunity for learning about the role of parties in the Senate—it held most important variables constant while one variable, majority status, changed. We use roll call data to evaluate the probability of individual members of each party being rolled on Senate final passage votes, before and after the switch. We find that, contrary to conventional wisdom on the Senate, majority status is an important factor in Senate decision-making. Our results show that Republicans were more likely to be rolled after the switch than they had been before, and that Democrats were less likely to be rolled than they had been before.
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Notes
Also, Republicans were more successful than Democrats pre-switch, and Democrats were more successful than Republicans post-switch.
There has never been a change in majority status in the House without a concurrent change in membership.
A possible objection at this point is that Jeffords’s policy preferences might have changed, and that such a change in preferences explains our results, rather than the change in majority status. We maintain the assumption that his preferences did not change; we discuss reasons for this assumption later in the paper, when we report our empirical results.
Jeffords’s given reason for switching was that the increasingly conservative Republican caucus made it difficult for him to remain moderate; as part of the switch, Democratic leaders guaranteed that they would be tolerant of his moderate positions, and also gave him a committee chairmanship (Jeffords 2001; Daschle and D'Orso 2003).
The Senate was in the midst of working on the tax bill at the time of Jeffords’s announcement; part of his deal with Democrats was that the switch would not occur until after the Senate passed the bill (Jeffords 2001).
One might also consider committee assignments as a fourth stage, in the sense that the committee assignment process might be seen as a quality filtering system that biases legislative outcomes (Crain 1990). However, we omit this stage and instead treat personal as fixed during a given legislative session.
Senate Rule XIV allows senators to bypass committee consideration altogether and place measures directly onto the calendar; but bringing the bill up for floor consideration is all but impossible if the majority leader objects (Gold 2008; Sinclair 2007). Moreover, Rule XIV allows majority leaders to circumvent a committee if they do not trust it to do the party’s bidding—a practice that has grown more common, especially for high-priority items (Evans and Oleszek 2001; Oleszek 2004).
Roll and roll rates (the ratio of rolls to final passage votes) are used widely in combination with one-dimensional spatial models to study negative agenda power. Carson et al. (2011), Cox and McCubbins (2005) and Jenkins and Monroe (2013) establish theoretical underpinnings of using rolls in this way (but see Krehbiel 2007 for a dissenting view).
Neither M nor Mi represents the median member of their respective party caucus.
This assumption reflects the idea that the majority’s procedural advantages, described in the previous section, make it less costly for the majority party to propose measures and push them toward final passage than it is for the minority party.
More minimally, any partisan theory that assumes that the majority party has negative agenda control and that the minority party lacks complete negative agenda control is consistent with this argument.
The set of votes used here is each Senate final passage vote on a Senate or House bill, a Senate or House joint resolution, an executive nomination, or a conference report. There were 26 votes in the pre-switch period and 18 votes in the post-switch period. Not all senators voted on every vote, although most voted on most of the votes.
We use senators’ NOMINATE scores from the 107th Congress. Given that NOMINATE scores are derived from legislators’ roll call votes in a given Congress, and that our dependent variable is a roll call-based measure, our use of the NOMINATE-based Distance variable on the right hand side of the equation raises the concern that our findings might result from, essentially, using roll call votes to explain roll call votes. An alternative approach would be to use NOMINATE scores from the previous or subsequent congress as a way to circumvent this issue. However, turnover in Senate membership in the 2000 and 2002 elections was sufficiently large that many senators would drop out of our analysis if we used scores from the 106th or 108th Congress. Moreover, our use here is defensible: the Distance variable is a control variable rather than a test variable, so our conclusions do not rely on the Distance variable’s significance. When we estimate the model without the Distance variable the results of our hypothesis tests remain the same, with the slight caveat that the significance level of the Post-Switch Hypothesis test coefficient is 94.8 percent rather than 97.1 percent.
More accurately, a legislator’s probability of being rolled is greater as the member’s ideal point is farther outside the protected interval (assuming, as is common in the literature, that status quos are uniformly distributed). Because different assumptions about the agenda-setter and floor veto players lead to different protected intervals, the appropriate control for ideological extremeness differs from one model to the next. Including all different possible permutations of this control would increase the length and complexity of the text and results—with, we believe, little payoff. In results not reported here, we have experimented with different permutations, and in no case has it made a difference in the inferences that we draw about the hypotheses. We thus report results using only the crude Distance variable defined above.
Another possibility would be to drop the Distance variable and instead include a fixed effect dummy for each senator. With panel data such as ours, however, inclusion of such unit fixed effects “does not allow the estimation of time-invariant variables” and is inefficient (Plumper and Troeger 2007, pp. 124–125). Given that one of our key test variables, the dummy for Democratic senators, is time-invariant, we do not use fixed effects.
Another potential estimation option is a negative binomial event count model. Though they are similar, King (1998) emphasizes that event count data (the number of positive outcomes out of an unknown or infinite number of trials) differs from grouped binary data (the number of positive outcomes out of a known number of trials) and recommends extended beta-binomial for the latter type of data.
We drop subscripts from variable names in the rest of the discussion. The Pseudo R2 is 0.1168, the Log-likelihood is −1291.7813, and the number of observations is 198 because Jeffords is excluded from the analysis, since he was in the majority in each period. Including Jeffords makes no significant difference in the results.
The linear combination test is a z-test of the proposition that sum of the coeficients equals zero.
In a one-tailed test, the significance level is 97.1%; in all other cases discussed here, the significance level is over 99.999 %.
At the suggestion of an anonymous reviewer, we also estimated this model without the Distance variable. The results are very similar, with two small exceptions: the Pseudo-R2 drops from 0.1168 to 0.096, and the significance level of the Dem-plus-Dem × PostSwitch sum drops from 97.1 to 94.7 %.
Distance is held constant at its median value for members of each party.
See Smith (2007) for an overview of the problems with parsing effects.
The W-NOMINATE program is available at Voteview.com.
We also estimated W-NOMINATE scores for each Senator in the 107th Congress across the entire period of our analysis; that is, we use all Senate roll call votes from the beginning of the 107th Congress until September 10th, 2001, so that we get one estimate for each senator (except Jeffords) for the entire pre- and post-switch period. We treat pre- and post-switch Jeffords as two different individuals, in order to contrast his pre- and post-switch voting behavior. The result is an estimated pre-switch (first-dimension) ideal point of .14, and a post-switch ideal point of −.095—which is a very small shift in voting behavior. To put this change in perspective, compare it to the ideal points of a few other prominent Senators from across the political spectrum, estimated across the same time period: Wellstone (−0.969); Kennedy (−0.813); Daschle (−0.719); Baucus (−0.098); Chafee (0.159); Specter (0.289); Lugar (0.715); Santorum (0.825); Helms (1.000).
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Acknowledgments
A previous version of this paper was presented at the 2005 Annual Meeting of the American Political Science Association, September 1–4, Washington, D.C. We thank John Aldrich, Cheryl Boudreau, Andrea Campbell, Dave Clark, Gary Cox, David Epstein, Bob Erikson, Karen Ferree, Clark Gibson, Will Heller, Simon Hix, Henry Kim, Thad Kousser, Gary Jacobson, Jonathan Katz, Jonathan Krasno, Jeff Lax, Michael McDonald, Mat McCubbins, Kathryn Pearson, Rose Razaghian, Dave Rohde, Brian Sala, Greg Wawro, and Nick Weller for helpful comments on previous versions and related projects. We also gratefully acknowledge financial support provided by NSF Grant # SES 9905224 (Principal investigators: Gary W. Cox and Mathew D. McCubbins.
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Den Hartog, C., Monroe, N.W. The Jeffords switch and legislator rolls in the U.S. Senate. Public Choice 165, 25–43 (2015). https://doi.org/10.1007/s11127-015-0289-0
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DOI: https://doi.org/10.1007/s11127-015-0289-0