Download The Jeffords Switch and Legislator Rolls in the U.S. Senate

The Jeffords Switch and Legislator Rolls in the U.S. Senate
Chris Den Hartog
Department of Political Science
California Polytechnic State University
San Luis Obispo, CA 93407
[email protected]
and
Nathan W. Monroe
School of Social Science, Humanities, and Arts
University of California, Merced
Merced, CA 95211
[email protected]
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.
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)
Congressional scholars sharply dispute the role of the majority party in shaping
US Senate decisions. On one hand, the view that Senate parties exert little influence over
the chamber runs throughout postwar congressional scholarship. The most influential
studies, which focus on Senate procedures and practices such as holds, filibusters, and
nearly unlimited amendment opportunities, portray the chamber as one in which power is
distributed broadly across individual senators and the chamber as a whole as extremely
difficult to manage (Matthews 1960; Ripley 1969; Sinclair 1989; Smith 1989). These
studies are rich in detail and context, but tend to either ignore or explicitly downplay the
importance of parties.1
As partisanship has risen since the 1980s, scholars increasingly have entered party
identification into analyses of Senate behavior—but, rather than consensus, this has led to
divergent views about their effects. Some studies argue that partisanship exacerbates the
gridlock and dysfunction caused by individualism (Binder 1997, 1999, 2003; Binder and
Smith 1997; Oppenheimer and Hetherington 2008; Smith 2005, 2007). From this
perspective, partisan and individual goals act as competing interests, putting enormous
pressure on Senate leadership to deliver partisan advantage in a chamber set up to
empower individual interests (Sinclair 2001).
Other studies, however, point to Senate procedures and processes that work to the
advantage of the majority party (Beth et al. 2009; Campbell 2001, 2004; Den Hartog and
Monroe 2008, 2011; Evans and Oleszek 2001; Evans and Lipinski 2005; Koger 2010;
1
Other models of congressional decision-making leave out parties altogether. See, for
example, Krehbiel 1998; see also Brady and Volden 1998.
Lee 2009; Schiller 1995, 2000, 2001; Wawro and Schickler 2006). Some of these
authors, as well as others, discern different types of pro-majority bias in Senate decisions
(Bargen 2003; Campbell 2001, 2004; Campbell, Cox, and McCubbins 2002; Crespin and
Finocchiaro 2008; Den Hartog and Monroe 2008; Gailmard and Jenkins 2007; Koger and
Fowler 2006; Lee 2009).
We evaluate these competing views by examining how legislative outcomes
varied when Democrats suddenly gained majority status in the middle of the 107th Senate,
owing to Senator James Jeffords’s change in party affiliation. More specifically, we look
at individual senators’ probability of being “rolled”—that is, voting against a bill that
passes—on final passage votes in the periods immediately before and after “the Jeffords
switch” (i.e., the change in majority status), and how these probabilities changed when
Democrats gained and Republicans lost majority status. We conclude that majority status
systematically affects Senate legislative outcomes. Our results show that Republicans
were less successful after the switch than they had been before, and that Democrats were
more successful after the switch than they had been before.2 In some ways, however, the
results also suggest limits on majority party power in the Senate, which we discuss later
in the paper.
The Jeffords switch provides a unique quasi-experimental opportunity for
examining the majority party’s role, because it allows us to isolate the “treatment”
variable—majority status—while holding constant other variables that scholars identify
2
Also, Republicans were more successful than Democrats pre-switch, and Democrats
were more successful than Republicans post-switch.
3
as determinants of Senate decisions. It is the only instance in which majority status
changed without any concurrent change in Senate membership—other changes in
majority status resulted from elections that produced simultaneous changes in other
factors likely to influence legislative outcomes, such as the ideological mix in the
chamber, membership in the House of Representatives, and the expressed preferences of
constituents.3 Many elections also produced changes in control of the House, the White
House, or issue salience. Across the period just before and just after the Jeffords switch,
however, all of these things remained constant.
Yet, this paper’s intended contribution goes well beyond that short period. That
controlled window allows us to understand the nature of majority status in the Senate and
the way majority parties shape Senate outcomes in the modern Senate, not just the 107th
Senate. The Jeffords switch is unique as an opportunity to test competing theories; but
inasmuch as the Jeffords change does produce an effect, we expect that same basic effect
to exist for any change in majority status. The only difference is that for other changes,
we cannot as cleanly detect the causal relationship.
Others also have used the tidy research design produced by the Jeffords switch to
study aspects of American politics. For instance, Nicholson (2005) uses the switch to
study public support for divided government, Jayachandran (2006) uses it to study asset
prices, and Roberts (2007) uses it to illustrate difficulties in drawing inferences about
legislators’ preferences from roll call votes. The work closest to ours is Den Hartog and
3
There has never been a change in majority status in the House without a concurrent
change in membership.
4
Monroe (2008), which also uses the switch to study partisan influence on Senate
outcomes. But their dependent variable—stock returns for Democrat-supported and
Republican-supported energy companies—assesses the impact of the switch by
examining outcomes external to Congress. Their research design relies on the premise
that investors’ reactions to the switch accurately reflected shifting party power within
Congress and the consequences such a shift would have on energy stocks. Put differently,
to interpret their results as evidence of majority party bias in the Senate, one must believe
that the market correctly anticipated the legislative consequences of the switch.
We use a research design that is similar but relies on no such premise. Our
dependent variable, a senator’s probability of being rolled, is similar to those used by
Carroll and Kim (2010), Carson, Monroe, and Robinson (2011), Cox and McCubbins
(2005), Lawrence, Maltzman, and Smith (2005, 2006), and Smith (2007)—each of which
uses some measure of individual legislators’ legislative successes or failures and
compares the success (failure) of majority party legislators to the success (failure) of
minority party legislators to draw inferences about party power. A common element of
these other studies, however, is that they examine time series that stretch across multiple
congresses, with one observation for each member in each congress, and changes in
majority status resulting only from elections. The test in this paper has the novel twist
that the period we study includes a change of majority status without a simultaneous
change in other important variables.4
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
4
5
In the following sections we discuss the Jeffords switch, theories of majority
power and the hypotheses we draw from them, our research design for testing those
hypotheses, and empirical results of our tests. We end with a comparison of the
Republican and Democratic majorities’ success, and concluding thoughts.
The Jeffords switch
Following the 2000 election, the Senate was divided evenly between Democrats
and Republicans, leaving the Vice President as the tie-breaking vote and de facto
determinant of which party enjoyed majority status. From January 3 through 20, Al Gore
remained Vice President and Democrats were the majority party—although, knowing that
they were about to lose majority status, they took little action. When Dick Cheney was
sworn in as Vice President on January 20th, Republicans assumed majority status and,
with it, unified control of the government. Realizing that they faced an extraordinary
situation, Democratic leader Tom Daschle and Republican leader Trent Lott agreed at the
outset to a so-called “power-sharing agreement” that, among other things, divided seats
on all committees evenly between Republicans and Democrats, while making
Republicans the chairs of each committee.
This continued for the next few months, during which the Bush administration
defied many predictions by pushing a conservative agenda, to the consternation of some
moderate Republican senators, including Jeffords (Martinez 2001). On May 24, he
announced that he would switch from Republican to independent and would vote with
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.
6
Democrats on organizational matters. These organizational votes, in and of themselves,
are not especially interesting in that they are virtually always straight party-line votes.
They are, however, very important in that their outcomes determine which party will
control committees and other aspects of Senate process that, in effect, give the majority
party an institutional advantage. Thus, Jeffords’s intent to abandon the Republicans and
vote with the Democrats on these organizational matters had the effect of giving
Democrats a 51-49 advantage, thereby making them the majority party.5
The Senate recessed on May 26, after passing Bush’s tax bill;6 when the session
resumed in June, Democrats assumed majority status; Daschle became majority leader,
Democrats became chairs of each committee, and (after a period of bargaining and
procedural wrangling). Democrats assumed one-seat majorities on each committee. The
new alignment was still in place a few months later when the terrorist attacks of
September 11 put a temporary end to politics-as-usual.
Theories of Majority Party Effects on Legislation
Partisan theories of congressional legislation focus heavily on majority parties’
ability to affect outcomes by shaping the agenda—meaning, roughly speaking, affecting
which measures do and do not get final floor votes. But Senate scholarship has long been
rife with skepticism about the Senate majority’s ability to shape the chamber’s agenda.
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 2003).
6
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).
5
7
Before discussing theories or hypotheses, therefore, we discuss Senate procedures that
plausibly could give the majority the ability to affect the set of measures subject to final
votes.
Majority party procedural tools
Agenda setting can be thought of as occurring at three different stages of the
Senate’s legislative process: the committee, scheduling, and floor stages.7 What follows
is a brief characterization of claims found in the literature on the Senate about ways that
the majority party can affect outcomes at each stage.
Committee chairs, who are all members of the majority party, exercise substantial
power to block their committees from considering bills that the chair does not want
considered, and to bring up their own version of a bill (the “chair’s mark”) for
consideration if they prefer it to the version originally referred to the committee (Evans
1991). The power to block consideration of a proposal and the power to make the first
proposal are often cited as important sources of agenda-setting power (Cox and
McCubbins 2005; Finocchiaro and Rohde 2008; Romer and Rosenthal 1978). Chairs also
have considerable sway over other committee members, especially from their own party,
which they often use to ensure that their committees report measures that the chair wants
reported (Evans 1991; Rawls 2009).8
7
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.
8
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
8
The majority’s influence over scheduling (i.e., bringing a bill up for consideration
on the floor) flows largely from the majority leader’s right to be recognized before any
other senator, which gives the majority leader an effective monopoly over the offering of
either a motion to proceed or a unanimous consent request that proposes to bring the bill
up in the form that he prefers (Tiefer 1989). This monopoly allows the majority leader to
block scheduling of bills merely by doing nothing.
Of course, there is no guarantee of the chamber approving the majority leader’s
scheduling proposals, which are often thwarted by filibusters, threats of filibusters, or
objections to consent requests. But, any senator other than the majority leader faces the
same constraints, while also lacking the right of first recognition. Given that every
senator has legislation that he or she wants enacted, the majority leader’s proposal
monopoly can create bargaining leverage that sometimes induces opponents into
acceding to scheduling of a bill that they oppose (Ainsworth and Flathman 1995), and
perhaps also into refraining from filibustering or voting for cloture.
The Senate’s open amendment process has the potential to negate the majority
party’s committee and scheduling advantages, but the majority is not without means of
fighting back against unwanted amendments. Floor amendments are restricted in some
circumstances, such as when the majority leader fills the amendment tree (Beth et al.
2009; Campbell 2004; Oleszek 2004; Schiller 2000). And amendments must be germane
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).
9
once cloture is invoked, leading majority leaders often to file for cloture as early as
possible in order to limit amendments (Oleszek 2004; Sinclair 1997; Tiefer 1989).
When unwanted amendments are offered, the majority can try to kill them by
making and approving motions to table (which are non-debatable and require only a bare
majority for approval), by ruling them out of order (Den Hartog and Monroe 2011), or by
simply voting them down on an adoption vote.9 None of these is a surefire means of
killing amendments, but each clearly is effective in many cases.
As at the scheduling stage, the majority’s ability to move its bills forward is
subject to the constraints of filibusters and objections to consent requests, which
unquestionably can reduce the majority’s success in passing its policy priorities. But, as
noted above, the minority party faces the same constraints and then some—and the
majority leader’s proposal power sometimes creates leverage that allows the majority to
overcome obstruction.
In sum, the picture of majority power that emerges is not one of a dominant
majority party that steamrolls the minority. Rather, it is one in which the majority fights
constantly to shape the agenda, sometimes without success, but succeeds in many other
instances. Moreover, whatever the majority party’s limitations, the minority party faces
the same limitations, plus additional obstacles, when trying to shape the agenda.
Partisan theories and individual roll rates
9
Various Senate procedural experts describe votes on motions to table as instances in
which senators are strongly predisposed to vote the party line, making tabling motions a
tool primarily of majority leaders (Gold 2008; Marshall, Prins, and Rohde 1999; Oleszek
2004; Tiefer 1989).
10
The concept of being “rolled” (an actor voting against passage of a measure on
the vote in which the chamber passes the measure) is useful for evaluating which actors
(e.g., parties, committees, individual legislators) wield “negative” agenda power,
meaning the ability to block passage of measures. A roll indicates a lack, or failure, of
blocking power on the part of the entity being rolled.10 In the context of the Senate, the
significance of rolls lies in their ability to shed light on whether the majority party
successfully shapes Senate legislative decisions.
Partisan agenda setting theories generally assume that majority party leaders have
negative agenda power, and predict that they will use it to block proposals that would
lead to rolls of the party as a whole, or of most party members, leading to substantial
overlap in partisan models’ predictions regarding roll patterns. In this section we sketch
Den Hartog and Monroe’s (2011) “costly-consideration” model, which is specifically
tailored to the Senate, to illustrate the predictions that these models make about the
likelihood of individual legislators being rolled.
This model defines consideration as getting something to a final passage vote,
and posits that getting a measure to a final passage vote is costly—in the sense that it
requires scarce resources to move a bill forward through the legislative process. The
model’s basic setup is as follows (and shown in Figure 1): Three actors—majority
10
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, Monroe, and Robinson (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).
11
proposer (M), minority proposer (Mi), and floor median (F)—have linear, symmetric,
singled-peaked preferences, with ideal policy points denoted by M, Mi, and F,
respectively.11 The sequence of play is, M moves first by either proposing a or not (~a).
If M proposes a, then it pays a cost, denoted by c. Next, Mi can either propose b or not
(~b) and pay a cost, denoted by k. Assume that c < k.12 Finally, depending on the actions
of M and Mi, F will decide between adopting proposal a, b, or maintaining the status quo
(SQ).
Figure 1 here
Without going into gory detail about the model, we can characterize its intuition
and how it applies to the Jeffords switch by examining how the location of two different
status quos produce two unique “roll zones” for individual members in the policy space.
That is, we look at areas of the policy space where members whose ideal points reside in
that part of the space would be rolled.
Note that the “minority no-offer zone” is defined as the area of policy space
wherein the minority will not make a proposal or counter-proposal to move the SQ. NOL
and NOR establish the left and right-most bounds of the minority no-offer zone. In our
first example, the SQ is located at Mi’s ideal policy point. Given this SQ, M will propose
a* that mirrors the original SQ but is located at NOL. Mi will follow by not making a
11
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.
12
12
counter proposal. Finally, F will adopt a*, which results in the creation of a roll zone,
depicted by boxes with a dashed outline, of minority members.
Figure 2 here
Now, consider a second SQ, as described in Figure 2, which is located at M’s
ideal point. Following a switch of majority status from M to Mi, Mi will propose a* that
is located exactly at NOL. In doing so, a roll zone of former majority members is created.
The key implication is that if majority status switches to the other party, then the
lopsidedness of the roll zone changes from one side of the space to the other; thus, a
change in majority status will change the probability of a roll for individual members.
Jeffords switch hypotheses
This expectation is not, however, unique to the costly-consideration model. Cox
and McCubbins (2005) and Chiou and Rothenberg (2003) offer spatial models that yield
similar predictions. Other partisan theories predict that some level of majority party
agenda power, such as conditional party government (Aldrich and Rohde 2000, 2001;
Rohde 1991) or strategic party government (Lebo, McGlynn, and Koger 2007) theories,
can be easily interpreted as implying similar predictions. We emphasize that we are not
deriving hypotheses that distinguish between party models; rather, the hypotheses
distinguish between party-centric and party-less models.
A common thread linking these models is the premise that, by moving forward
bills favored by its caucus members and by hindering bills opposed by its caucus
members, the majority increases the likelihood that bills passed by the Senate will be to
13
its liking—and thereby decreases caucus members’ probability of being rolled.13 Thus,
partisan theories predict members of the majority caucus are less likely to be rolled than
members of the minority caucus, all else constant. Combined with the Jeffords switch,
this is the basis for the predictions that we test:
Republican Change Hypothesis: Republican senators’ probability of being rolled on a
final passage vote will be higher after the Jeffords switch than before it, ceteris paribus.
Democratic Change Hypothesis: Democratic senators’ probability of being rolled on a
final passage vote will be lower after the Jeffords switch than before it, ceteris paribus.
Pre-Switch Hypothesis: In the period before the switch, Republican senators’ probability
of being rolled on a final passage vote will be lower than Democratic senators’
probability of being rolled on a final passage vote, ceteris paribus.
Post-Switch Hypothesis: In the period after the switch, Republican senators’ probability
of being rolled on a final passage vote will be higher than Democratic senators’
probability of being rolled on a final passage vote, ceteris paribus.
Note that the first two hypotheses serve as the basis for our key quasiexperimental tests, since they capture the effects of changes in majority status (i.e., the
treatment) on roll probabilities for each party; partisan theories predict changes across the
switch, but non-partisan theories predict no changes across the switch.
The latter two hypotheses, taken individually, lead to weaker, supplementary
tests. They present opportunities to falsify the partisan hypotheses; but, under some
13
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.
14
conditions, non-partisan theories also predict majority senators as having lower roll
probabilities, so evidence consistent with these hypotheses does not allow us to
differentiate partisan and non-partisan theories.
Taken jointly, however, these supplementary hypotheses constitute a stronger test,
since non-partisan models predict lower roll probabilities for members of one caucus in
the pre-switch period are unlikely to change in ways that allow non-partisan models to
predict lower roll probabilities for members of the other caucus in the post-switch period.
For example, in a given time interval, a non-partisan model might predict that the lower
roll probabilities for majority caucus members if most status quos are to the minority’s
liking. In such a case, minority members would oppose proposals to move policy toward
the floor median unsuccessfully, thereby being rolled, while majority members would
favor the shift and would not be rolled. In the context of the Jeffords switch, however, the
status quos would have to shift suddenly from one side of the political spectrum to the
other—and at the same time that Jeffords switched—for a non-partisan model to predict
that the last two hypotheses both be true at the same time.
Data and research design
We test our hypotheses using data on final passage votes from part of the 107th
Senate prior to September 11, 2001. Recall that the Republican and Democratic change
hypotheses offer predictions about how each party’s senators’ probability of being rolled
should have changed following the Jeffords switch. Importantly, note that we do not
separate these hypotheses across parties to get at substantive differences between
Democrats’ and Republicans’ performances as the minority and majority, and vice versa.
15
Rather, these hypotheses allow us to employ a particularly strong research design,
wherein we can see both the effect of the treatment being removed from one group (the
Republican Change Hypothesis) and of the treatment being applied to another group (the
Democratic Change Hypothesis). Thus, we can in essence assess the same underlying
mechanism—the advantage of being in the majority—in two different groups. If, instead,
we were to simply pool the pre-Jeffords Republicans with the post-Jeffords Democrats
and look for the common treatment effect across those groups, we lose some of the power
of our quasi-experiment.
Thought of in these terms, our pre- and post-switch hypotheses are also not to be
conceptualized as substantive comparisons between Republicans and Democrats, but
instead, should be seen as comparisons between the treated group and the untreated group
(or control group) in each period. Note that our prediction is simply that in each period,
the treated group should do better than the untreated group—not that the treated group in
one period should do better or worse than the treated group in another period. We have
no theoretical expectations about that comparison, and thus our commentary in that vein
will be purely speculative.
To compare senators’ probability of being rolled across the switch, we use a
dataset with one observation for each senator in each period (i.e., one observation before
the switch and one observation after it). The pre-switch period runs from the beginning of
the 107th Congress, in January 2001, through the switch; the post-switch period that we
use runs from the switch through September 10, 2001. We thus compare two periods of
roughly comparable length (3-4 months), and avoid the possible confounding influence of
September 11th. Indeed, one of the advantages of using such a short time span is that it
16
greatly reduces the possibility that an unobserved factor, such as changing political issues
or alignments, is actually responsible for whatever changes we observe.
For each observation, we calculate the number of final passage roll call votes on
which the given senator voted in the given period, as well as the number of votes on
which the senator was rolled.14 We also code whether the observation is for the pre- or
post-switch period, and whether the senator was a Republican or Democrat. Using this
dataset, we estimate the following model using extended beta-binomial regression
(discussed below):
RollProbabilityit =  + 1Demi + 2PostSwitcht + 3Demi*PostSwitcht + 4Distancei + it,
where:
RollProbabilityit is the proportion of final passage votes on which senator i was rolled—
i.e., voted against a bill that passed—in period t,
Demi is a dummy, coded one if senator i is a Democrat,
PostSwitcht is a dummy, coded one for the post-switch period, and
Distancei is the absolute value of the difference between i’s ideal point and the floor
median’s ideal point, using the first dimension common-space NOMINATE scores
(Poole 1998; Poole, McCarty, and Rosenthal 1997; Poole and Rosenthal 1997).
The dummy variable PostSwitcht captures the change in Republicans’ probability
of being rolled—and thus is the key variable for testing the Republican Change
14
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.
17
Hypothesis, which predicts a positive coefficient for this variable (indicating that
Republicans’ probability of being rolled increased after the switch). The PostSwitcht and
Demi*PostSwitcht variables jointly capture the change in Democrats’ probability of being
rolled—and thus are the key variables for testing the Democratic Change Hypothesis,
which predicts that the sum of the coefficients for these variables will be negative
(indicating that Democrats’ probability of being rolled declines after the switch). The
variable Demi captures how Democrats’ pre-switch probability of being rolled compares
to Republicans’ pre-switch probability of being rolled; the Pre-Switch Hypothesis
predicts that will be positive. Finally, the Demi and Demi*PostSwitcht variables jointly
capture how Democrats’ post-switch probability of being rolled compares to
Republicans’ post-switch probability of being rolled; the Post-Switch Hypothesis predicts
that will be negative.
In addition, we add the Distancei variable as a control to account for the
ideological “extremeness” of each senator.15 In prominent models of legislative agenda
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 NOMINATEbased 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.
15
18
setting (Cox and McCubbins 2005; Krehbiel 1998), a legislator’s probability of being
rolled is (weakly) greater as the member’s ideal point is farther from the floor median’s
ideal point.16 Thus, while most possible confounding factors are unlikely to vary across
the short period of time we examine, we include this control to account for ideologydriven heterogeneity in senators’ probability of being rolled. 17
The dependent variable in our hypotheses is a senator’s probability of being rolled
on a final passage vote. One possible estimation method for the model we present above
is a binomial model—but this approach assumes that each vote on which senator i could
be rolled is independent of all of the other votes on which i could be rolled, and that
senator i’s underlying probability of being rolled is the same across all such votes (King
1998, pp. 119-121). If these assumptions are not met, “Standard errors will be incorrectly
estimated, comparisons of means or parameter values can be misleading, and estimates
will not be efficient” (Palmquist 1999, pp. 2).
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.
17
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 (Plümper 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.
16
19
Since the assumptions likely do not hold for our data, we instead use extended
beta-binomial regression (King 1998; Palmquist 1997, 1999), which makes neither of the
restrictive assumptions. Nor does it assume that the underlying probability of being rolled
is constant across senators (Palmquist 1999, pp. 13). Using Prentice’s (1986) extended
beta-binomial distribution, the method estimates an additional parameter, γ, which
models over- or under-dispersion that results from the types of dependence and
heterogeneous underlying probabilities just discussed.
In practice, extended beta-binomial bears some similarity to an event count
model. The dependent variable is the number of positive trials for each observed unit—in
our case, the number of rolls for an individual senator in the period (i.e., pre- or postJeffords switch) to which the observation corresponds. One also specifies the number of
“trials” (votes on which a senator voted) and right-hand-side variables. The method
estimates non-linear maximum-likelihood coefficients that, as with logit and other
methods, can be transformed into an estimated probability of a positive trial (a roll) for
different values of the independent variables.18
Results
Before discussing the regression results, we first look at the pattern of change by
examining how each senator’s “roll rate” (i.e., the proportion of votes on which i was
rolled) for the post-switch period compares to his or her roll rate for the pre-switch
18
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.
20
period, in Figure 3. Each plotted point represents the difference between the post-switch
roll rate and the pre-switch roll rate for a given senator. We plot this difference (on the yaxis) against the senator’s first-dimension common-space NOMINATE score (on the xaxis), with triangles denoting Republicans and squares denoting Democrats. Note the
horizontal line across the middle of the figure, which marks a zero roll rate change;
individuals below this line (i.e., with a negative change) had higher roll rates before the
switch than after, while individuals above this line (i.e., with a positive change) had
higher roll rates after the switch than before. Thus, if our hypotheses are correct, we
should see Democrats tend to be below the line, and Republicans tend to be above the
line. Clearly, this is the pattern we observe. Of the 51 Democrats (counting Jeffords as a
Democrat, which we have done in Figure 3), only two—Russ Feingold, at the far left of
Figure 3, and Jeffords himself, who is at –0.26 on the x-axis—are above the line, while
one more—Zell Miller, in the middle of the figure—had a change of zero (his roll rate
was zero in each period). The other 48 Democrats all had lower roll rates after the switch
than before. In sharp contrast, 40 of the 49 Republicans are above the line, indicating that
their roll rates were higher after the switch than before. Of the other nine, four are below
the line and five are on the line, indicating no change. For Republicans, the mean change
in roll rate is 0.054, while for Democrats the mean change is -0.158; in each case, a
single-sample test allows us to reject the null hypothesis of no change with p-values
smaller than 0.0001. Thus, it clearly seems that Democrats did better after the change
than before, and that the reverse is true for (most) Republicans.
Figure 3 here
21
We now turn to the regression results, shown in Table 1. The coefficient for
PostSwitch, which captures the change in Republicans’ probability of being rolled and is
predicted by the Republican Change Hypothesis to be positive, is significantly positive
(the coefficient is 1.390 with a standard error of 0.157).19 This indicates that Republicans’
probability of being rolled did indeed increase after the switch.
Table 1 here
The results also support the Democratic Change Hypothesis, which predicts that
the sum of the coefficients for PostSwitch and Dem*PostSwitch will be negative. A linear
combination test shows that this sum is significantly negative (the sum of the coefficients
is -1.437, with a standard error of 0.121; the coefficient for Dem*PostSwitch is -2.827,
with a standard error of 0.198).20 This indicates that Democrats’ probability of being
rolled declined significantly after the switch.
As mentioned, we can also look at which party’s members did better in each
period. The coefficient for Dem captures the difference between Democrats’ and
Republicans’ probability of being rolled in the pre-switch period. The positive coefficient
(2.558, with a standard error of 0.139) indicates that, as expected, Democrats were more
likely than Republicans to be rolled before the switch.
19
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.
20
The linear combination test is a z-test of the proposition that sum of the coeficients
equals zero.
22
In the post-switch period, the difference between Democrats’ and Republicans’
probability of being rolled is the sum of the coefficients for Dem and Dem*PostSwitch.
This sum is negative (-0.268, with a standard error of 0.142) as predicted,21 indicating
that Republicans’ probability of being rolled was higher than Democrats’ after the switch,
all else constant.22
Table 2 shows the substantive content of these results, casting them in terms of
the predicted probability of being rolled. Each cell shows the estimated probability that a
final passage vote will result in a roll for an individual senator, differentiated by party and
period.23 The probability for Republicans goes from 0.026 before the switch to 0.098
after the switch, while for Democrats it goes from 0.214 to 0.061. In addition, we see that
Democrats’ probability (0.214) was higher than Republicans’ (0.026) pre-switch, but that
Democrats’ probability was lower than Republicans’ post-switch. All of these results are
consistent with our hypotheses.
Table 2 here
Could a change in Jeffords’s preferences explain our results?
A potential problem with the research design used in this paper is that the results
presented here are a function of a change in Jeffords’s preferences, rather than of the
21
In a one-tailed test, the significance level is 97.1%; in all other cases discussed here,
the significance level is over 99.999%.
22
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%.
23
Distance is held constant at its median value for members of each party.
23
change in majority party. In other words, changing preferences might be an important
omitted variable. In this section, we address and reject this possibility.
Obviously, we cannot actually observe or perfectly measure Jeffords’s
preferences, but we can estimate them using roll call based measures such as
NOMINATE (Poole and Rosenthal 1997)—and, at first blush, some evidence seems to
suggest that Jeffords’s preferences did change. Using various approaches to estimating
preferences, Clinton, Jackman, and Rivers (2004), Nokken and Poole (2004), and Poole
and Rosenthal’s NOMINATE data all indicate that Jeffords’s voting became distinctly
more liberal after he switched. Nonetheless, there are multiple reasons to believe that a
significant change in Jeffords’s preference does not explain our results.
First, the results are based on a set of roll call votes that covers a wide range of
issues; for a change in preferences to explain all this pattern of change across many
issues, Jeffords would have to have abruptly and simultaneously changed his positions on
many issues. This seems to lack face validity, and also conflicts with anecdotal accounts
of the rift between Jeffords and his party—most of which emphasize that moderate
Senate Republicans like Jeffords increasingly had become marginalized within their own
caucus as it moved to the right.
Second, a change in observed voting behavior does not necessarily imply a
change in preferences, which is only one of many factors, such as constituency and party
loyalty that contribute to voting behavior. Trying to parse the effects of each on a
legislator’s roll call behavior is notoriously difficult, and estimation measures such as
24
NOMINATE inherit this ambiguity24—that is, they produce a single estimate for a
legislator that is a product of preferences, constituency, partisan, and other effects. Given
that partisan theories presume that party members will exhibit at least some degree of
party loyalty on at least some votes, a change in Jeffords’s NOMINATE score is
consistent with partisan theories—and, after all, Jeffords publicly announced that he
would change his voting behavior on organizational votes. It is thus unsurprising from a
partisan viewpoint that Jeffords’s voting pattern differed somewhat after the switch.
Third, regardless of whether we take roll call-based estimates as measures only of
preferences, evidence suggests that the change in Jeffords’s roll call behavior did not
occur during the period we examine—and therefore cannot explain our results. The
estimates mentioned above use roll call data from longer time spans than the severalmonth window that we examine in our analysis, including at least the entirety of the 107th
Congress. Using Poole and Rosenthal’s W-NOMINATE program,25 we estimated WNOMINATE scores for each day across the period from March 2001 until September 10th
2001. In other words, for the first day in this time series, we began by estimating scores
for each Senator based on all roll calls that had occurred in the 107th Congress up to that
point. For the next day on which there was at least one additional roll call, we then reestimated the scores, incorporating the new roll call data. For each day in the time series,
we updated the scores in this fashion, so that we have a time series of estimated (firstdimension) W-NOMINATE scores across this period.
24
See Smith (2007) for an overview of the problems with parsing effects.
25
The W-NOMINATE program is available at Voteview.com.
25
Figure 4 here
Jeffords’s first-dimension score, shown in Figure 4, bounces around some,
especially early when the scores reflect fewer votes. But, overall, the trend is relatively
flat and centrist across the window as a whole. These cardinal estimates of his
preferences may be misleading, since this methodology does not account for whether or
how the estimated policy space itself bounces around across this period. In other words,
Jeffords’s relatively constant scores could be consistent with him becoming more liberal,
if the policy space itself were moving to the right. To account for this possibility, we have
also examined Jeffords’s ordinal ranking on each day across this period. Viewed from
this perspective, the results provide fairly compelling evidence that Jeffords was not
moving to the left relative to other senators. He was the 49th, 50th, or 51st most
conservative senator for every day in the time series. In other words, he was at or very
near the median position across the pre- and post-switch periods we examine.26
Finally, even if Jeffords’s preferences did change, it is unlikely to have changed
the location of the median legislator enough to explain the pattern of roll rates we observe
across a range of issues. In preference-based theories such as Krehbiel’s (1998) pivot
26
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 preand 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).
26
model, the location of the ideological median legislator is a key determinant of legislative
outcomes. In Poole and Rosenthal’s DW-NOMINATE estimates for the entire 107th
Congress, Jeffords’s score shifts from -0.006 as a Republican to -0.34 as an Independent,
in the process changing from the 51st to the 30th most liberal senator. However, the
chamber median hardly shifts, from -0.029 to -0.062, which seems unlikely to explain the
observed changes in roll probabilities for Republicans and Democrats.
Do Republicans do better than Democrats?
One intriguing aspect of these findings, which goes beyond our hypotheses but
suggests an interesting possible characteristic of Senate decision-making, is that the postswitch Democratic minority clearly did not seem to do as well as the pre-switch
Republican majority—nor did the post-switch Republican majority seem to do as badly
as the pre-switch Democratic majority.
From the point of view of partisan theories of legislative decision-making, there
seem to be two explanations seem to be most likely. First, from a Conditional Party
Government perspective, one might immediately suspect that the Republicans were more
homogeneous in their preferences than were the Democrats, leading to more delegation to
party leaders among the Republican majority – and thus more ability to control the Senate
floor agenda – than among the Democratic majority. Though we make no pretense of
answering this question definitively, we can take a crude look at whether this conjecture
holds up to scrutiny by comparing the heterogeneity of preferences among senators of
each party in the 107th Congress. We do so in two ways—by taking the standard
deviation of first-dimension common space NOMINATE scores for senators of each
party, and by calculating the range between the minimum and maximum score for each
27
party (we exclude Jeffords from each party).27 For Democrats, the standard deviation is
0.134 and the range is 0.651; for Republicans, the standard deviation is 0.142 and the
range is 0.653. There is thus little evidence that greater Republican unity produced the
Republican majority’s seemingly higher level of success.
The other possibility is that the result is explained by divided government. This is
consistent with Campbell, Cox, and McCubbins’ (2002) finding that divided government
weakens majority party agenda control in the Senate, since Republicans held the House
and the presidency during the post-switch Democratic majority. But, of course, the
Jeffords switch research design that we use throughout this paper, though well suited for
testing our hypotheses regarding the effects of majority status, is not purposed for testing
hypotheses about variations in majority party power. We include this discussion merely
as an aside, partly in hope of prompting future research dealing with the questions we
have raised.
Conclusion
In the literature on Senate decision-making, the roles of parties and majority
status often are murky. We have presented a test of hypotheses predicated on the
assumption that the Senate majority party can systematically and significantly bias the
Senate agenda to its advantage. Our findings support these predictions.
By focusing on a short time frame—just a few months on either side of the
Jeffords switch—our results give us a narrow but potent window for isolating the effect
27
See Aldrich, Berger, and Rohde (2002) and Aldrich, Rohde, and Tofias (2005) on
measuring the extent to which conditional party government conditions are met.
28
of a majority status "treatment" in the Senate. During our period of study, member
preferences (including Jeffords’s preferences, as we have shown), the preferences of
external actors, the legislative agenda, and numerous other potentially confounding
factors remained constant. Yet, Democrats’ probability of being rolled was markedly
worse than that of Republicans during their time as the majority party at the beginning of
the 107th Congress, but those fortunes were reversed following the Jeffords switch. In
contrast with some Senate literature claiming the contrary, it is clear that the Senate
majority party exercises some level of influence over decision-making within the
chamber.
House-based theories of congressional parties do not claim that the majority party
is akin to the strong, cohesive parties represented by the responsible-party Westminster
ideal (Lijphart 1984), and we certainly make no such claim about the Senate majority
party. Rather, we posit that majority party leaders wield various powers—in particular,
powers to influence the agenda—that allow them to manipulate outcomes. The results
presented here seem to offer substantial evidence that the Senate majority party, through
some means, manipulates outcomes for the benefit of its own members, and does so in
ways that have often gone unrecognized by Senate scholarship.
29
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Table 1. Effect of Jeffords switch on senators’ probability of being rolled
Coefficient, (SE), p-value
Dem
(Predicted to be positive)
2.558**
(0.139)
0.000
PostSwitch
1.390**
(Predicted to be positive)
(0.157)
0.000
Dem*PostSwitch
-2.827**
(0.198)
0.000
Distance
2.153**
(0.293)
0.000
Constant
-4.389**
(0.171)
0.000
-0.020**

(0.002)
0.000
Linear Combination z-test:
-1.437**
PostSwitch + Dem*PostSwitch
(0.121)
(Predicted to be negative)
0.000
Linear Combination z-test:
-0.268*
Dem + Dem*PostSwitch
(0.142)
(Predicted to be negative)
0.029
N
198
2
Pseudo R
0.1168
Log-likelihood
-1291.7813
Cells in right-hand column show extended-beta binomial coefficients, standard errors,
and p-values are for one-tailed tests.
** Indicates coefficient significance exceeds 99 percent.
* Indicates coefficient significance exceeds 95 percent.
34
Table 2. Estimated probability of a Senator being rolled on a final passage vote before
and after Jeffords switch, by party
Probability of being rolled on a final passage vote is:
Member’s party
Pre-Jeffords
Post-Jeffords
Republican
0.026
0.098
Democrat
0.214
0.061
Distanceit is set at the median value for members of each party.
35
Figure 1. Modification of Figure 3.6d from Den Hartog and Monroe 2011
Minority no-offer zone
c
M
NOL=a*
F
k
NOR Mi=S
36
Figure 2. Modification of Figure 3.6d from Den Hartog and Monroe 2011
Minority no-offer zone
c
M=SQ NOL=a*
F
k
NOR
Mi
37
Figure 3. Change in senators’ roll rates going from pre- to post-switch
0.4
0.3
0.1
0
-0.1
-0.2
-0.3
Common-space score (first dimension)
Republicans
Democrats
Jeffords
38
0.526
0.48
0.426
0.399
0.381
0.346
0.322
0.3
0.281
0.245
0.186
0.009
-0.057
-0.153
-0.2
-0.219
-0.235
-0.238
-0.279
-0.314
-0.344
-0.364
-0.388
-0.472
-0.4
-0.642
Change in roll rate
0.2
Figure 4. Jeffords’s estimated W-NOMINATE scores on all votes through the given date.
1
Republican
0.8
Independent
W-NOMINATE score
0.6
0.4
0.2
0
-0.2
-0.4
-0.6
-0.8
-1
Days
39