THe HUNT FOR PARTY DISCIPLINE IN CONGRESS
Introduction
The
past several years have seen renewed scholarly investigation of how political
parties and their leaders influence legislative institutions and behavior (see
Aldrich 1995, Cox and McCubbins 1993, Rohde 1991, and Sinclair 1995). Much of this contemporary research
investigates how parties solve the collective action problems that are inherent
in the legislative and electoral processes.
Cox and McCubbins, who conceptualize political parties as “cartels” that
direct legislative activity to enhance the collective electoral fortunes of
their members, provide a typical variant on this theme, but by no means the
only one. The primary function of a
cartel is to build a collective reputation for its members to run under. They argue, however, that without strong
leadership members have individual incentives to engage in legislative
activities (such as pork) that diminish the collective reputation.
This
focus on the problems of collective action has generated much interest in the
cohesiveness of parties as floor coalitions.[1] The principal prediction is that a party
produces more cohesive coalitions of its members than would be possible if the
members were to act on the basis of their individual preferences. Rohde (1991) uses evidence of the increase
in party cohesion since 1975 to demonstrate an increasing role of party in the
post-reform House. Aldrich, Berger, and
Rohde (1999) also use party voting as the main dependent variable to test the
predictions of “conditional party government,” and Cox and McCubbins (1993) use
member support on leadership votes to test for the role of leaders in creating
voting coalitions. Furthermore, some
scholars, including Rohde (1991), see a reassertion of party strength behind
the increased cohesiveness and polarization of congressional parties since the
mid-1970s (for alternative explanations, see Poole and Rosenthal 1984; McCarty,
Poole, and Rosenthal 1997; and King 1998).
As
Krehbiel (1993, 1998) points out, however, these empirical studies of party
voting suffer from the problem that the patterns of behavior that have been
uncovered are consistent with both theories of strong, influential parties and non-partisan models where member
preferences are sorted along party lines.
This dilemma is exacerbated by the problem of measuring legislative
preferences. Ideally, one would like
some exogenous measure of these preferences to test party theories. Voting behavior under the null hypothesis of
no party influence could then be compared with actual voting behavior. The problem is that our usual measures of
legislative preferences are derived from the voting behavior itself.
In
this paper, we attempt to untie this Gordian knot. We begin by reviewing the evidence from work that analyzes
congressional roll call votes under the maintained hypothesis of sincere
spatial voting. We discuss how the
evidence from this work in fact suggests the presence of some party discipline. We next develop the party discipline model
of Snyder and Groseclose (2000). We
argue that their estimation method both seriously biases the estimate of ideal
points for ideological moderates and overestimates the extent of party
discipline. In the text, we provide a
compelling theoretical illustration of the bias. We detail our case further in Appendix B.
To
assess party discipline (a.k.a. pressure) properly, we propose an alternative
approach. The basic idea is very simple.
We start with Krehbiel’s (1993, 1998) proposal that the spatial model of
purely preference-based voting is the appropriate benchmark for evaluation of
models that incorporate party effects.
In one-dimension, the spatial model asserts that, on each roll call,
“Yea” and “Nay” voters are separated by a cutpoint on the liberal-conservative
continuum. Now assume that the
Republicans apply “pressure” to their membership. This will cause some moderate Republicans to the left of the
“sincere” cutpoint to vote with the conservative wing of the party. Republicans will have a cutpoint to the left
of the sincere cutpoint. Similarly, if
the Democrats apply pressure, the cutpoint for Democrats will be to
the right of the sincere cutpoint. That
is, when one or both parties apply pressure, the voting patterns should look as
if there were separate cutpoints for each party, with the Democrat cutpoint
being to the right of the Republican
cutpoint. Consequently, if pressure is
important, we should find a better fit to the data when we estimate two
cutpoints than when we estimate a single cutpoint.
To
keep the analysis as simple as possible, we use non-parametric optimal
classification analysis where legislator ideal points and roll call cutpoints
are jointly rank ordered to maximize predictive success on roll call votes
(Poole, 2000). By classifying the
voting of each party independently and then comparing the results to
classifying both parties together, we can evaluate the maximum possible
improvement in correct classification attributable to party discipline. An
advantage of the cutpoint approach is that it does not require any assumptions
about which specific roll calls are subject to party pressure. A particular advantage of the non-parametric
approach is that it assumes only that the amount of pressure applied to
individual members does not change the order of their induced ideal
points. It does not require making
parametric assumptions about how pressure varies with the ideal point of the
individual member, such as equal pressure being applied to all. On the basis of our optimal classification
analysis, we conclude that allowing for party discipline affords only a very
marginal improvement over the sincere spatial model, particularly in recent
Congresses.
Where,
then, is party discipline? We argue
that the main influence of party discipline is not on the votes on specific
roll calls but on the choice of ideal point made by the representative. The smoking gun is provided by the great
changes in ideological position demonstrated by those few legislators who have
switched parties. Wayne Morse and Strom
Thurmond are two well-known examples in the post-war Senate. The Democrats who defected to the
Republicans after the 1994 election made equally dramatic shifts. Our finding that parties shape ideal points
ends our hunt for party discipline in roll call voting.
Independent Voting on the
Floor: The Evidence from the Spatial Model
The
well-known standard spatial model provides a benchmark approach to independent floor voting. Poole and Rosenthal (1991, 1997)
demonstrated that the spatial model is quite successful in accounting for floor
decisions. With two dimensions, one can
correctly predict roughly 85% of the individual decisions -- even on close roll
calls -- for the period 1789-1985.
McCarty, Poole, and Rosenthal (1997, p. 7) report additional results for
the period 1947-1995.[2] In recent Congresses, a one-dimensional
model classifies nearly 90% of the individual decisions (see figure 5).
The
spatial estimates present a strong suggestion that party influence underpins
much of this remarkable classification success:[3]
n In
Congresses where voting is largely one-dimensional, party-line votes are along
the main dimension. The distribution of
ideal points is strongly bimodal. The
two parties appear as two very distinct “clouds”. The clouds, particularly in recent years, barely overlap. [As an
illustration, see McCarty, Poole, and Rosenthal (1997, p. 11).] The presence of a “channel” between the
clouds suggests that party affiliation may discipline the roll call voting
behavior of members. The main dimension
of political conflict clearly appears to reflect partisan conflict. Parties perhaps also influence their
members’ votes on specific roll calls.
n In
Congresses where voting is two-dimensional, there are also two distinct clouds
separated by a channel. Party-line
votes are no longer on the main dimension, but a blend of the first and second
dimensions. [See Poole and Rosenthal,
1991, p. 233 or 1997, p. 44 for an example.]
An interpretation of such plots is that ideal points projected onto roughly
a 45° line represent
the ideological (liberal-conservative) dimension. The orthogonal projection, roughly at –45°, represents a
party loyalty or valence dimension.
Most votes occur along the main, 0°
dimension. On these votes, the
legislator’s decision depends both on ideology and on party loyalty.
Although this evidence shows that
the structure of voting coalitions in Congress coincides strongly with party
affiliation, it does not prove that party per
se has any influence on voting behavior.
Party-line voting is, of course, consistent with both strong party
models and ideological models where preferences are sorted by parties. In the sections that follow, we review a
recent attempt to separate partisan effects from preferences and propose a method
of our own.
The Snyder-Groseclose Model of Party Discipline
One
of the inherent problems in identifying the effects of party is that we observe
only behavior, which is presumably a mix of individual preferences and party
influence. This problem is particularly
acute with congressional voting data.
If party discipline is exercised on floor votes, the ideal points
estimated on the assumption of independent spatial voting might be very biased
estimates of legislator preferences. If
party influences these estimates, it is inappropriate to use them as controls
for preferences when testing for a party effect. Snyder and Groseclose (2000) noted this potential for bias. They proposed both a method for first
estimating unbiased ideal points and then for using the unbiased ideal points
to estimate the effect of party discipline.[4]
The basics of the
one-dimensional Snyder-Groseclose model are as follows:
n On
roll call j, a legislator i, if a Republican, has induced ideal
point xij = xi
n On
roll call j, a legislator i, if a Democrat, has induced ideal
point xij = xi + gj
In
other words, the true ideal points of the Democrats, the xi, are displaced by the amount of party pressure given
by gj.[5] It turns out that only the relative amount
of party pressure matters in the model, so the ideal points of the Republicans
can just be given by their true values.
For the difference in pressure to be consistent with discipline, we
would expect that pressure must move Democrats in a liberal direction relative
to Republicans. Thus, pressure works to
increase the separation of the parties.
If preferences are scales such that left is liberal and right is
conservative, then we would expect g
to have a negative sign
Snyder
and Groseclose argue that, because there would be little need to apply party
discipline on votes not expected to be close, ideological position-taking could
occur on lopsided votes. These votes,
for example, those with margins over 65-35, could be used to estimate the true
ideal points. On these votes, the gj
would be zero. The true ideal points
could then be used to estimate the gj
on close votes, say those with margins less than 65-35.
In brief, their
procedure is:
Stage 1. Use votes with margins greater than 65-35 to
estimate the ideal points, xi.
Stage
2. On the
remaining votes, for each roll call j,
estimate the following OLS (ordinary least squares) model:
(1)
where Di = 1, if legislator i is a Democrat, = 0 if Republican; and Yij = 1, if i votes Yea, = 0 if i votes Nay. In the
Appendix A, we show that if the underlying spatial utility is quadratic, then
. (2)
As
we noted above, the party pressure model predicts a negative estimate for g when
preferences are scaled with Democrats on the left (as we assume they are). Consequently, the two estimated b’s
should be of opposite sign.
Why the Method Overestimates Party
Discipline
This method is
likely to generate the inference that party pressure is substantial even when
all voting is preference based.
Consider, for example, a six-member legislature with the party
affiliations and spatial preferences given in figure 1. If all
voting in this legislature is spatial without error, there are only 12 possible
voting configurations, which are given in figure 2.
Stage
1 of the Snyder-Groseclose method estimates a preference score using only
voting patterns 1-10. But, since voters
3 and 4 cast identical votes in each of these patterns, any scaling procedure
will estimate voters 3 and 4 as having the same position. Thus
stage 1 provides biased estimates of the preferences of moderates. There is not enough information in the
lopsided votes to discriminate “left” moderates from “right” moderates. The preference ordering that maximizes
the classification of votes is shown in figure 3.
In stage 2, the preferences in figure 3 and
party affiliation are used to explain vote patterns 11 and 12. The votes of legislators 1, 2, 5, and 6 are
correctly classified on the basis of the preference estimates, but the votes of
legislators 3 and 4 cannot be. However,
since 3 and 4 are members of different parties, adding party to the model
increases its explanatory power even
though voting is purely preference driven.
Our example extends naturally to larger
legislatures. In general with perfect
spatial voting, a first stage based only on lopsided votes will produce identical
preference estimates of all members in the interval between the 35th
and 65th percentiles. The
second stage will therefore produce a spurious party effect so long as party
and ideology are correlated within this interval. Given the assumptions of no voting error and no overlap of
preferences between the parties, this example is somewhat special. However, in Appendix B, we present Monte
Carlo evidence that shows how this result extends to large legislatures where,
as in the Snyder and Groseclose approach, there is some error in voting and the
distributions of preferences of each party overlaps. We now present an alternative procedure that maintains the
essential features of their model of discipline.
A Non-Parametric Model
All
specifications of a spatial model of voting have two critical elements: ideal
points for the legislators and cutpoints (or separating hyperplanes) for the
roll calls. The Snyder-Groseclose
model, with a discipline parameter to each roll call, is isomorphic with one
where each party has its own cutting line.
(See Appendix A.) That is, moving the ideal points for all Democrats to
the left by a magnitude gj is equivalent to
moving the cutpoint for Democrats to the right by the same amount. Party discipline generally involves getting moderates to
vote with extremists.[6] Consequently, if there is party discipline,
the cutpoint for the Democrats should be to the right of the cutpoint for
Republicans.[7]
Consider
a one-dimensional spatial configuration.
If the cutpoint is constrained to be the same for both parties, this
produces the standard spatial model.
For example, in figure 4, with a common cutpoint, there are three
classification errors, legislators 3, 11, and 15. When each party can have its own cutpoint, this produces a model
that allows for party discipline.
Moderate Democrats to the right of some Republicans can vote with the
majority of their party. Moderate
Republicans to the left of some Democrats can vote with the majority of their
party. The best cutpoint for the
Republicans in figure 4 remains the common cutpoint. Legislator 15 is the only R classification error. But the best cutpoint for the Democrats is
to the right of the common cutpoint.
The D cutpoint leaves only legislator 3 as a classification error for
this party. Rather than estimate either
the one-cutpoint model or the two-point model via a metric technique, such as
Poole and Rosenthal's (1991) NOMINATE or Heckman and Snyder's (1997) method,
one can simply find the joint rank order of legislators and cutpoints that
minimizes classification error. Poole
(2000) presents an efficient algorithm that very closely approximates the
global maximum in correct classification.[8] Note that this method, in contrast to
equation (1), does not require a uniform adjustment in the ideal points of all
members of a party. Only moderates
would need to be disciplined. All that
is required is a displacement of the cutpoint.
We
now turn to our empirical analysis.
This analysis involves both the testing of implications of our
methodological critique of Snyder and Groseclose as well as testing the
implications of their model of party discipline utilizing our two cutpoint
model. We begin by presenting evidence
on three methodological predictions. In
each case, these predictions are
consistent with the mismeasurement of preferences in the
Snyder-Groseclose framework under the hypothesis of purely spatial voting. In only one of the cases is the prediction
also consistent with their theoretical model.
Therefore, verification of these relationships illustrates the inability
of Snyder-Groseclose to distinguish party pressure from mismeasurement of
preferences. The three methodological
predictions are as follows:
M1.
Estimate the rank order of ideal points
by one-dimensional optimal classification first using all roll call votes and
then using only lopsided votes. The
correlation of the all votes rank orders and the lopsided votes rank orders
will be greater for extremists (the first and last thirds of the all votes
distribution) than for moderates (the middle third). While this prediction is consistent with the Snyder-Groseclose
assertion that party pressure primarily affects moderates, it also follows from
our claim that, if there is preference-based voting, ideal points of moderates
will be inaccurately recovered if only lopsided votes are used to estimate
ideal points.
M2.
Similarly, when the rank order is
estimated first on all roll call votes and second on only close votes, the
correlation of the all votes ranks and the close ranks will be greater for
moderates than for extremists. The
motivation for this prediction is similar to that of the first. If there is preference-based voting, the
ideal points of extremists will be inaccurately recovered if only close votes
are used to estimate ideal points. This
prediction is inconsistent with Snyder and Groseclose as they implicitly assume
that extremists will have similar preference estimates on pressured votes as
they do on unpressured votes.
M3.
The close-all correlations for moderates will
be high if there is preference-based voting, lower if there is party
discipline. The reason is that, if
there is discipline only on close votes as claimed by Snyder and Groseclose,
all votes estimates will mix preference-based lopsided votes and disciplined
close votes. The close votes estimates
will have more distortion of the true ideal points.
After
these tests concerning the effects of Snyder and Groseclose’s procedure on
ideal point estimates, we turn to testing hypotheses from the party discipline
model. In all cases, the null model of
preference-based voting predicts no difference.
H1.
Classification should be substantially
higher with a two-point model than with a one-point model. Note that classification cannot be lower
with the two-point model.
H2.
The improvements in classification should
be greater on close votes. Since
Snyder-Groseclose predict that rational parties will whip close votes, the
incremental predictive power of the two-cutpoint model should be accordingly
higher on those votes.
H3.
The rank order of the legislators should
disclose more separation of the parties in the one-point model than in the
two-point model. The reason is that the
one-point model ignores party discipline.
Moving Democrats to the left and Republicans to the right should pick up
some of the effects of party pressure.
In contrast, in the two-point model, each legislator's ideal point can
take on its true rank order position, because the cutpoints can pick up the
effects of party discipline.
H4.
The separation of the cutpoints should be
greater on close votes. The identifying
assumption of the Snyder-Groseclose model is that party pressures are more
likely on close votes. Therefore, under
their assumptions, the distance between the Democratic and the Republican
cutpoint should be greatest on those votes.
H5.
The estimated cutpoint for the Democrats
should be to the right of the estimated cutpoint for the Republicans.
A Caveat
Some
instances of party pressure may be masked.
Consider a legislature with no party overlap. All Democrats are to the left of all Republicans. Suppose that, were there no pressure, a
Republican Party bill would be rejected by a majority composed of all Democrats
and some moderate Republican defectors.
If the Republicans then apply “pressure” to the defectors, resulting in
a party-line vote, the vote will still appear to be a vote consistent with
preference-based voting. Thus when
ideal points are estimated correctly, the true explanatory power of party may
be masked. In fact, when there is no
overlap in the distribution of party ideal points and there is errorless
spatial voting, it is impossible to identify party pressure effects.
This
masking of party pressure is inherent to spatial analysis. It would confound a correct
Snyder-Groseclose analysis as well as our optimal classification method. Albeit important, the question we can ask is
limited to “Can allowing for party discipline improve on the classification of
a purely preference-based model?”[9]
With
our optimal classification method, it is possible to calculate an upper bound
for the amount that party pressure can increase vote classification, roughly as
a function of the overlap between the two parties. This upper-bound represents the classification on a strict party
line vote of a two-cutpoint model (perfect classification) minus the
classification of a strict party line vote using a single cutpoint.[10] When there is no overlap between the
parties, a single cutpoint correctly classifies a party line vote so as noted
above their can be no classification gain for the two cutpoint model. However, the greater the party overlap, the
worse a one-cutpoint model does in explaining a strict party-line vote. Thus, the maximum classification gain
increases in the overlap. If we use the
configurations of preferences that emerge from optimal one-cutpoint
classification to measure overlap, the maximum classification gain from
party-pressure consistent cutpoints (i.e. D>R) ranges from 0 in the 80th
House (where there is zero overlap) to 16% in the 92nd House. The average upper bound over all of the
congresses we analyze is 5%. However,
it is important to remember that these upper bounds are simply for roll calls
consistent with party pressure (i.e. Democratic cutpoint to the right of the
Republican cutpoint). Perfect
classification is the upper bound if we allow other cutpoint configurations
(e.g the Republican cutpoint on the right).
Secondly, as we discuss below, optimal classification with a single
cutpoint will underestimate party overlap which would lead to the
underestimation of these upper-bounds.
Tests Using the
Non-Parametric Model
One Dimensional
Classification
We
begin with the three predictions concerning correlations of ideal points. In order to show that the pattern of
estimates we expected would arise in actual data, we first performed optimal
one-point classification using all
the roll call votes in each House from the 80th through the 105th. If the basic spatial model is correct, this
procedure should produce a rank order of legislator ideal points that is very
close to the true order. Next, we did
optimal classification using only lopsided
votes, those with greater than 65-35 margins.
Finally, we did optimal classification using only close votes, those with margins of 65-35 or less.
We
then computed Spearman rank order correlations between the lopsided vote rank orders and the all votes rank orders for left-wingers, the one-third of the
legislators furthest to the left in the all
votes classification; moderates, the middle one-third; and right-wingers, the
one-third furthest to the right. We
would expect these correlations to be high for the left-wingers and
right-wingers but low for the moderates because the lopsided votes provide little information about the ideal points of
moderates (M1). Conversely, when
correlations are made between close votes rank orders and all votes rank orders, we expect the correlations to be high for
moderates but low for left-wingers and right-wingers.[11]
Insert Table 1 about here
The
hypothesized patterns occur, as shown in Table 1. Indeed, for the lopsided-all
comparison, in every post-war House but one, the middle one-third
correlation is lower than both that for the left-wingers and that for the
right-wingers.[12] Table 1 indicates that the middle correlation
is particularly low in the period preceding the passage of the major civil
rights bills of the 1960s. In this
period, there was an important second dimension (Poole and Rosenthal, 1997)
that confounds the recovery of moderate positions on the first dimension. When the second dimension vanishes, even the
middle correlations are reasonably high because the “errors” in voting provide
some information about moderates. That
is, for example, a relatively liberal moderate is still less likely to vote with
the right-wingers than is a relatively conservative moderate, even on lopsided
votes. Nonetheless, as predicted by M2,
correlations for moderates are lower than for extremists.[13]
As
predicted, these results reverse for the close-all
comparison. The moderates always
produce a correlation above 0.9. The
left-winger and right-winger correlations are always below 0.9, usually much
below, and in one case, the correlation is negative.
The
close-all correlations for moderates
are strikingly high, predicted by preference-based voting but not by voting
subject to party discipline (M3). If
the party discipline effect were important, we would expect lower rank order
correlations, particularly for Houses before 1980, when there was still
considerable overlap in the ideal point distributions of the two parties.
Classification with
Two Cutpoints
In this section, we assess the ability of a party discipline model to
improve on a preference-based model.
Our criterion is percentage of votes correctly classified.
To find the highest classification possible for a party discipline model,
there is a simple solution: just classify each party separately. This allows the cutpoint on each roll call
to adjust to pressures internal to the party.
Because the cutpoints can adjust, one
will find the true intra-party rank order of the ideal points. The classification from this model can be
compared to a single cutpoint model.
The results of this exercise appear in figure 5, which shows results for
one-, two-, and six-dimensional models.
We used a six-dimensional model to parallel the high dimensionality used
by Snyder and Groseclose in their empirical work. With multiple dimensions, the cutpoint is replaced with a
separating hyper-plane.
In one dimension, it is apparent that a two-party model adds little, particularly in recent Congresses. The improvement in the earlier Houses is at the level that results when a two-dimensional model with one cutting line is used. In one dim