Issue Attention
29
JONATHAN WOON
University of Pittsburgh
Issue Attention and
Legislative Proposals
in the U.S. Senate
This analysis of bill sponsorship across a variety of issues and Congresses
shows that committee membership is the single most important factor shaping a
senator’s level of issue attention. Constituency demand is of secondary importance.
Ideology, partisanship, and national conditions play little or no role. Consistent with
a theoretical cost-benefit framework, the results suggest that senators are motivated
by the prospect of electoral and policy rewards from successful legislation rather
than from mere position taking. The findings attest to the enduring importance of the
committee system in a highly individualistic and increasingly partisan Senate.
Legislative institutions play a central role in establishing the
relative importance of numerous public concerns. Every two years,
members of Congress introduce several thousand bills and collectively
whittle these down to a few hundred public laws, elevating some issues
while lowering others. The earliest stages of this process require
legislators to shape policy alternatives through effort-intensive
activities: gathering information, drafting bills, building coalitions, and
keeping pace with the actions of various interests. Bauer, de Sola Pool,
and Dexter emphasize the importance of the early stages of legislating
compared to the final roll-call stage:
The decisions most constantly on [a member’s] mind are not how to vote, but what to
do with his time, how to allocate his resources, and where to put his energy. There are
far more issues before Congress than he can possibly cope with. (1972, 405)
Thus, a fundamental aspect of lawmaking and representation is issue
attention—determining which issues to pursue and how much to pursue
them. But how do legislators decide which issues are worth their
valuable time and effort? How much is issue attention influenced by
policy preferences, constituency and electoral concerns, or organizational factors, such as committees and parties? Does any one factor
dominate, or do pressures vary from issue to issue?
LEGISLATIVE STUDIES QUARTERLY, XXXIV, 1, February 2009
29
30
Jonathan Woon
To answer these questions, I analyzed bill sponsorship in the
Senate. My data and research design have important advantages over
those used in previous research, providing new insights about the role
that institutions play in shaping legislative behavior. First, my data
have a panel structure that helps disentangle alternative influences on
issue attention, distinguishing, for example, constituency interests from
institutional positions and partisanship from majority status. Previous
analysis of Senate behavior has tended to look only at overall levels of
specialization and has concluded that committees are important
(Matthews 1960) or that the importance of committees and specialization has diminished over time (Sinclair 1989), without accounting for
alternative explanations of committee effects, such as self-selection.
Second, because the bills in my data are classified by issue, I can
account for senators’ issue-specific investments of time and effort.
Previous research on legislative entrepreneurship failed to take into
account the diversity of issues (Schiller 1995; Wawro 2000). Aggregation may mask the possibility that different factors influence attention
to different issues. For example, attention to agriculture might be highly
influenced by constituency interests, attention to tax policy might be
driven more by ideological concerns, and attention to low-salience
issues like science policy might be induced by committee membership. More important, by studying bill sponsorship in an issue-specific
way and making comparisons across issues, I can offer insights into
why legislators engage in the earliest stages of the legislative process.
Third, although Matthews’s (1960) classic work on legislative
activism pertained to the Senate, more recent and sophisticated analyses
have focused primarily on the House (Frantzich 1979; Hall 1987, 1996;
Wawro 2000). Thus, theoretical developments satisfactorily explain
House but not Senate behavior. In the House, institutional rules,
procedures, and organizational rewards can be used to encourage
representatives’ legislative efforts. For instance, regardless of whether
one believes that restrictive rules enforce gains from trade (Weingast
and Marshall 1988) or induce committees to acquire information
(Gilligan and Krehbiel 1987; Krehbiel 1991), such rules protect the
work of committees, and this power, in turn, motivates committee
members to participate in legislative work. Wawro (2000) has also
shown that representatives’ entrepreneurial efforts are rewarded neither
by their constituents nor by interest groups but instead by the institution,
which promotes legislators to prestigious and powerful committee and
party positions. These incentives are weak, at best, in the Senate, which
does not use restrictive rules to protect committee work, lacks a
germaneness rule for non-appropriations legislation, and offers most
Issue Attention
31
majority party senators some sort of leadership position, say, as a
subcommittee chair.1 Investigating bill sponsorship in the Senate therefore provides an important contrast to previous work on the House.
For nine different issues during the 101st through 106th Senates
(1989–2000), my central finding is that a senator’s committee position
is the single most important factor shaping the senator’s issue-specific
attention. Across issues, committee members pay more attention to
issues than do non-committee members. Somewhat less consistently
across issues, committee leaders tend to pay more attention than rankand-file members. Constituency interest plays a secondary role. The
importance of the committee system is quite surprising in light of the
contemporary Senate’s reputation as a highly decentralized institution
in which individualism runs rampant and policy specialization is no
longer the norm (Sinclair 1989). The results suggest that the advantages of committee positions in terms of agenda influence and specialization are significant enough that they shape senators’ attention to
issues and outweigh the potential benefits of circumventing the regular
committee process on the floor.
Theoretical Framework
Legislators possess finite time, energy, and resources with which
to carry out their jobs and pursue their goals. A very simple, but useful,
point of departure for understanding issue attention is to assume that
legislators are rational, goal-oriented actors and to think of the
legislator’s decision problem as one of constrained optimization. That
is, a legislator chooses to pay attention to the set of issues that yield
the highest net benefits subject to institutional and strategic constraints.2
This conceptualization is consistent with the rational-choice, costbenefit frameworks employed by Hall (1996), Schiller (1995), Wawro
(2000), and others.
To understand issue attention and bill sponsorship, we can partition
the benefits of introducing a bill into two broad categories defined by
whether or not obtaining the benefits depends on the bill’s passage.
According to Mayhew, “the electoral payment is for positions rather
than for effects” (1974, 132), so there may be position-taking benefits
that do not depend on a bill’s passage. Simply by introducing bills on
issues that concern their constituents, demonstrating that they share the
same issue priorities, legislators may enhance their reelection prospects.
When legislation successfully passes all the requisite
procedural hurdles and obtains sufficient approval to become public
law, it changes public policy. Legislators might also have personal
32
Jonathan Woon
preferences about “good public policy” (Fenno 1973) and therefore
benefit when policies are brought closer to what they believe to be
ideal. This is a standard assumption in prevailing theoretical explanations of lawmaking (see, for instance, Krehbiel 1998). If constituents
and campaign contributors also care about policy effects (rather than
merely positions), then successful legislation has electoral benefits
beyond mere position taking.3
The attainment of benefits from policy outcomes is subject to
two types of interrelated constraints that affect a bill’s passage: institutional (rules and procedures) and strategic (the actions of other
legislators). A legislator deciding how to allocate issue attention at the bill
sponsorship stage of the legislative process therefore faces uncertainty
about final policy outcomes and, ex ante, will take into account a bill’s
expected policy benefits. Because the outcome is uncertain, the value of
policy benefits in the cost-benefit calculus is less than the value of
benefits that would actually be received upon the bill’s passage.
An important component of my theoretical argument is that legislators’ expectations about the likelihood of a bill’s eventual passage
depend critically on their agenda influence, their access to and influence
over the set of bills that will be considered in committee and on the
floor.4 Although party and committee hierarchies are much weaker in the
Senate than in the House, they nevertheless play critical roles in shaping
agenda influence. For example, theories of strong parties in Congress
emphasize that the majority party controls the legislative agenda
through its scheduling power (Cox and McCubbins 1993, 2005). Thus,
legislative issue attention should yield greater expected benefits for
members of the majority party than for members of the minority party.5
Committee positions also increase the probability of a bill’s consideration and passage, in at least two ways. First, a committee may
recognize that its own members possess greater issue-specific expertise than non-members and may therefore be more likely to consider
legislation sponsored or cosponsored by committee members (Wilson
and Young 1997). Second, a committee member possesses a vote that
may affect whether or not a bill is sent to the floor once it is considered by the committee. Although the probability that the vote will be
pivotal may be small, that voting clout is nonetheless greater than the
power wielded by a non-committee member. Committee leadership—
especially holding the full-committee chair, but also maintaining subcommittee chairs and ranking minority positions—raises the probability
of bill consideration further, since committee leaders possess procedural prerogatives that allow them to establish the committee’s
legislative priorities (Evans 1991; Oleszek 2001).
Issue Attention
33
To complete the theoretical framework, we must weigh positiontaking benefits and expected policy benefits against the cost of bill
sponsorship. These costs will depend on whether the primary motivations for bill sponsorship are driven by policy outcomes or position
taking. If bills are sponsored primarily for their position-taking benefits,
then the costs will be minimal (although nonzero) and should not vary
much from member to member or issue to issue; bills will be introduced with the expectation that they will fail and the exact nature of
their policy consequences will not matter. On the other hand, if policy
outcomes are the primary motivations for bill sponsorship, then the
costs will be much more significant; legislators will likely expend
sufficient effort to ensure that they introduce quality bills that, if enacted
into law, lead to desirable policies. In other words, although drafting
bills involves some costs regardless of legislator motivation, policy
seeking implies that cost variations will be significant enough to affect
sponsorship behavior.
Institutional positions should substantially lower the marginal
costs of such efforts. Committee members may be low-cost specialists
because of their backgrounds or because they have developed expertise
from committee experience (Gilligan and Krehbiel 1987; Krehbiel
1991). According to Hall, committee members also have “transactional
advantages” (1996, 91); they gain political information about each
other as a byproduct of frequent interaction. In addition, committee
leaders control additional resources with which to subsidize bill sponsorship activities. Finally, theories of party in government suggest that
strong majority parties have additional power and resources that can
be used to subsidize attention to issues that are important to the party
(Aldrich 1995; Aldrich and Rohde 1997–98).
Hypotheses
I grouped specific hypotheses about the factors that affect issue
attention into three sets according to how the factors would affect
senators’ cost-benefit decisions (that is, according to position-taking
benefits, policy benefits, agenda influence, or costs related to institutional positions).
Constituency Demand should, in general, influence issue attention
through both position-taking benefits and the electoral benefits from
policy outcomes. More specifically, constituency demand, either in
the form of an issue’s Economic Importance (Adler and Lapinski 1997)
or Problem Severity (Kingdon 1995), should be positively related to
issue attention.
34
Jonathan Woon
Petrocik’s (1996) theory of Party Issue Ownership suggests that
the relevant constituency may be a partisan one. If each party’s core
constituency cares about a different set of issues, then legislators will
receive greater benefits if they pay more attention to their party’s issues.
For example, employment and health care are often considered to be
“owned” by Democrats, while crime and trade are “owned” by Republicans.
An alternative hypothesis is that senators follow an “inoculation
strategy.” Recognizing the issues on which their party is weak, legislators seek to remedy such deficiencies by introducing bills and being
effective in those areas. For example, members of both parties may
devote attention to health care but for different reasons—the Democrats because their core constituency cares about this issue (consider
President Clinton’s health care reform), the Republicans because they
seek to neutralize any electoral advantage that issue ownership conveys
(consider the GOP’s Medicare prescription drug plan). Such inoculation
tactics are consistent with evidence regarding “issue uptake” (Sulkin
2005) and the use of defense bills by women senators to counteract
gender stereotypes and demonstrate their credibility on military affairs
(Swers 2007).
Policy Preferences influence issue attention primarily through
their effects on the benefits obtained from new policy outcomes. These
preferences are ideological, based on legislators’ beliefs about what
constitutes good public policy. I tested a liberalism hypothesis: because
liberal senators prefer government solutions, they should, in general,
sponsor more bills than do conservative senators (Schiller 1995).
Alternatively, the logic of spatial models implies an extremism
hypothesis: since extremists generally have more to gain from policy
changes than do moderates, extremists will pay more attention to issues
and sponsor more bills. Changes in policy are also more valuable when
existing National Conditions are worse and aggregate demand for
policy change is greater. This hypothesis dovetails with both the
empirical macropolitical agenda-setting literature (Baumgartner and
Jones 1993; Kingdon 1995; Walker 1977) and the spatial theories of
gridlock (Krehbiel 1998).
Institutional Positions either raise ex ante expectations of legislative success (and therefore of expected benefits) or lower the costs
of bill sponsorship. First, in terms of committee hierarchies, I expected
Committee Members to pay more attention to issues under their
committee jurisdictions than non-committee members would, Committee
Leaders (both chairs and ranking members) to sponsor more bills than
rank-and-file committee members would, and full-committee leaders
to sponsor more bills than subcommittee leaders.
Issue Attention
35
Although I would argue that committee positions matter because
of strategic and resource advantages, the literature strongly suggests
another possibility. If committees are composed primarily of highdemand preference outliers (Adler and Lapinski 1997; Weingast and
Marshall 1988), then committee membership might matter only because
it is the best proxy for constituency preference. The cost-benefit and
self-selection arguments would seem to be observationally equivalent,
and the appropriate interpretation of any committee effects would
require additional analysis. I will address this inferential problem later
in the article, in part by exploiting the panel structure of the data to
account for unobserved constituency interest.
To continue with institutional positions, I would expect several
effects if the Majority Party wields significant agenda influence. If
the majority party’s agenda influence is greatest in terms of the floor
agenda (rather than at the committee level), then majority party status
should be positively related to bill sponsorship across all issues and
all forms of committee membership. There should also be a positive
and significant interaction between majority party status and committee
positions. Furthermore, if both the issue-ownership and majority-status
hypotheses hold, then there should be an interaction such that Democratic senators pay more attention to employment and health care when
in the majority, whereas Republican senators pay more attention to
crime and trade while in the majority.
A key assumption underlying the policy-preference and institutionalposition hypotheses is that the benefits from policy outcomes (possibly
including electoral rewards) are a significant factor motivating behavior.
Because the probability of any one bill’s passage is typically very low,
however, it is quite reasonable to doubt the claim that expected policy
benefits are significant enough to affect a legislator’s issue-attention
decisions. This skepticism suggests an alternative pure position-taking
hypothesis: if the benefits of position taking outweigh expected policy
benefits, then only the constituency-demand factors should affect issue
attention and the policy preferences and institutional positions should
have no effect on issue attention.
Similarly, although the theoretical framework describes how
various factors may affect issue attention and implies a number of
testable hypotheses, I do not have strong a priori expectations that
every factor will matter for every issue. For this study, I assumed that
legislators pursue multiple goals (Fenno 1973), but the exact influence
of each factor on legislators’ issue-attention decisions is ultimately an
empirical question.
36
Jonathan Woon
Data and Methods
The data consist of public bills introduced by senators during the
101st through 106th Congresses (1989–2000) and were obtained from
the Library of Congress’s THOMAS online database. Only senators
who served nearly complete terms are included in the dataset, with
594 senator-Congress cases resulting.6 My analysis covers periods of
both Democratic and Republican control of the Senate. An important
benefit of the panel structure is that it potentially allows one to distinguish between party and majority-status effects in ways that previous
studies have not been able to do.7
Bill Sponsorship
The dependent variable is the number of bills introduced for a
particular issue, so the unit of analysis is a senator-Congress-issue
triple. To classify bills by issue, I used the “top term” assigned by the
Congressional Research Service, which describes the primary topic of
a bill. The empirical analysis focuses on nine issues. Three are matters
of broad public concern: crime, employment, and health care. The other
six issues, although they episodically capture the attention of the
broader public, are more typically of special concern to narrower
economic interests: agriculture, banking, communications, energy,
trade, and transportation. This set of issues represents a mix of broad
and narrow concerns. Table 1 presents summary statistics for bill
sponsorship. For each issue, the average senator introduces very few
bills, and many senators sponsor no bills at all. Nevertheless, a nontrivial fraction of senators does pay attention to each issue and,
occasionally, a senator introduces up to ten times more bills than the
average senator.
The disaggregation of bill counts by issue is an important advantage of my data, for at least two reasons. First, disaggregration by
issue allows me to determine whether there are issue-specific motivations for bill sponsorship or if certain factors affect behavior across
the board. Previous studies have only looked at overall levels of activity
(Anderson, Box-Steffensmeier, and Sinclair-Chapman 2003; Koger
2003; Schiller 1995). Second, if the average complexity of bills differs
across issues (for instance, if the average trade bill is relatively easier
to write and introduce than the average health care bill), then it would
be inappropriate to combine or compare bill counts for different issues.
In contrast, examining issue-specific bill counts does not require any
assumptions about cross-issue comparability.
Issue Attention
37
TABLE 1
Summary Statistics for Bill Sponsorship
Issue
Mean
Std. Dev.
Max
Percent of Cases
with No Bills
N
Agriculture
Banking
Communications
Crime
Employment
Energy
Health
Trade
Transportation
0.78
0.62
0.46
1.36
0.79
0.84
1.75
2.42
1.04
1.59
1.53
1.23
2.36
1.59
1.68
2.93
4.84
1.70
12
16
12
17
17
18
23
48
13
66
71
74
51
64
59
43
45
54
594
594
594
594
594
594
594
594
594
Total
1.12
2.48
48
59
5,346
Independent Variables
Constituency demand may have one of three possible sources:
economic importance, problem severity, and party issue ownership. I
measured Economic Importance using several variables calculated from
data on gross state product (GSP). First, I calculated issue-specific
measures by computing the proportion of GSP for industries relevant
to each issue. For example, I used the percentage of GSP from farming
for agriculture and the percentage of GSP from natural resources and
utilities for energy. Second, I calculated measures that capture general
characteristics of a state’s economy. I used Economy Size (in terms of
the log of GSP) as a measure of overall demand. I also measured the
Economy Concentration by calculating a Herfindahl Index from the
GSP data. The index ranges from 0 to 1, with higher values indicating
greater concentration in fewer industries; this measure is inversely
related to demand.
I used state-level policy indicators to measure Problem Severity.
The relevant measures are issue specific, and problem indicators are
available for the broad issues but not the narrow economic ones. The
data come from various annual government publications, and I
computed the average for each two-year Congress. For State Crime
Rate, I used the crime rate (specifically, the incidence of serious violent
and property crimes) from the Federal Bureau of Investigation’s Crime
38
Jonathan Woon
in the United States. For Unemployment Rate, I referred to unemployment rates calculated by the Bureau of Labor Statistics. For health
policy, I used data from the Current Population Survey on the
percentage of the state population Uninsured (not covered by either
private or public health plans, which should be positively related to
attention) and the percentage covered by Government Health Care
programs (which should decrease issue attention, because coverage
implies lower demand for new programs).8
To test the role of Party Issue Ownership, I used a simple party
dummy variable. I hypothesized that Democratic legislators would pay
more attention to employment, because of the importance of labor in
their core constituency, and to health issues, because of their association
with Medicare. Republican legislators should pay more attention to
crime, having traditionally been seen as more successful on “law and
order,” and to trade, since they have been associated with both free
trade and greater foreign policy expertise. Following Petrocik (1996),
I support these issue-ownership characterizations with survey data.9
To measure policy preferences, I created two dummy variables:
Extreme Liberal is coded as 1 if a senator is in the most liberal quartile
of senators within a Congress, as measured by Poole and Rosenthal’s
DW-NOMINATE scores; Extreme Conservative is coded as 1 if a senator
is in the most conservative quartile.10 This coding allows me to distinguish between the extremism and liberalism hypotheses, because it
allows for the possibility (but does not assume) that the relationship
between ideology and bill sponsorship is linear or nonlinear. For
national conditions, I used national-level versions of the state-level
problem indicators for crime, employment, and health care.
To test the institutional-position hypotheses, I included a set of
dummy variables concerning committee positions and majority party
status. To test the committee-hierarchy hypotheses, I included dummy
variables for committee membership, subcommittee leadership, and
full-committee leadership. These variables are not mutually exclusive;
the full-committee chair is coded as both a member of the committee
and a full-committee leader. The leadership variables are coded as 1
for both chairs and ranking members. To code these variables, I used
data from the Congressional Directory, and I determined the relevance
of a committee for an issue according to the pattern of bill referrals.11
I included separate Democratic Majority and Republican Majority
variables to test the majority party hypothesis while allowing for a
possible interaction with issue ownership. For example, if there is an
interaction for health policy, then Democratic Majority should be positive
and significant and Republican Majority should have no effect. If there is
Issue Attention
39
a majority-status effect without the issue-ownership interaction, then both
Democratic and Republican Majority should be positive and significant. Similarly, I interacted the separate majority-status variables with each
of the committee-position variables to allow for a three-way interaction
between majority status, committee position, and issue ownership.
Statistical Model
Since the dependent variable is the number of bills introduced
by a senator in an issue area, a count model is more appropriate than a
linear regression model. Count models take into account the discrete
and non-negative characteristics of the dependent variable in order to
obtain unbiased estimates of the effects of each independent variable.
There are many zero observations in the data, reflecting senators who
did not introduce any bills on an issue, so there may be greater
dispersion than in a Poisson count model. I used a negative binomial
regression model because it allows for the possibility of overdispersion
through an additional parameter in the model.
Results
I estimated the negative binomial regression model separately
for each issue by maximum likelihood. Table 2 summarizes the
hypotheses that are supported for each issue. Tables 3, 4, and 5 present
the full results in terms of baseline expected values and first differences
for all of the independent variables, which are more substantively
meaningful than the coefficient estimates.12 I calculated the baseline
by holding all continuous variables at their means and the dummy
variables at 0. Substantively, the baseline is an ideologically moderate
Republican senator who is in the minority, is not a member of a relevant
committee, and hails from an average state. Each first difference
describes a marginal effect: how a change in one variable affects the
number of expected bill introductions relative to the baseline. For
continuous variables, the change is an increase of one standard deviation, and for dummy variables, the change is from 0 to 1. Note that the
estimated dispersion parameter for every issue is significantly greater
than 0, which suggests that the negative binomial model is, in fact,
more appropriate than a Poisson model.13
The central finding of my analysis is that committee membership plays a consistently important role in increasing attention. For all
but one of the nine issues (health policy), the effect of committee
membership is positive and statistically significant. The effects also
–
Party Issue Ownership
–
National Conditions
Democratic
Subcommittee
Chair
–
–
Y
–
–
–
–
GSP
HHI
Employment
Democratic
Subcommittee
Chair
–
Full
–
% Government
Insured
Liberal
–
–
Industry
Health
–
–
–
Y
n/a
–
n/a
n/a
Industry
GSP
Agriculture
–
–
–
Y
n/a
–
n/a
n/a
–
Banking
Subcommittee
Chair
–
–
Y
n/a
–
n/a
n/a
–
Commerce
–
–
Full
Y
n/a
–
n/a
n/a
–
Energy
Republican
Chair
–
–
Y
n/a
–
–
n/a
Industry
GSP
Trade
–
–
Full
Y
n/a
–
n/a
n/a
GSP
HHI
Transportation
Notes: A dash indicates the hypothesis is not supported. If a hypothesis was tested using more than one variable, then only the significant variables are
listed. If there were alternative versions of a hypothesis (e.g., for ideology, leadership, or majority status), then only the supported version of the
hypothesis is listed.
Majority Status Interactions –
–
Full
Committee Leadership
Majority Status
Y
Committee Membership
Institutional Positions
–
Ideology
Policy Preferences
–
GSP
Crime
Problem Severity
Economic Importance
Constituency Demand
Variables
TABLE 2
Summary of Supported Hypotheses
40
Jonathan Woon
[ 0.68, 1.32]
[–0.22, 0.02]
[ 0.34, 0.87]
[–0.11, 0.13]
[–0.66, 0.25]
[–0.15, 0.62]
[–0.31, 0.30]
[–0.33, 0.05]
[–0.47, 0.33]
[–0.69, 0.09]
[ 0.44, 3.77]
[–0.20, 3.64]
[–0.49, 1.77]
[–0.51, 0.96]
[–0.99, 0.14]
[–0.53, 2.64]
[1.09, 12.13]
[–0.75, 3.61]
[–0.72, 4.18]
(SE = .11)
–0.10
0.57**
0.01
–0.20
0.18
0.00
–0.13
–0.14
–0.28
1.72**
1.09
0.29
0.05
–0.55
0.48
4.78**
0.41
0.61
0.69**
–824.51
594
95% C.I.
0.96
Crime
Estimate
[–0.16, 0.25]
[–0.15, 0.31]
[ 0.47, 1.77]
[–0.28, 0.18]
[–0.21, 0.42]
[–0.15, 0.89]
[ 0.02, 4.35]
[–0.29, 2.74]
[–0.02, 1.70]
[–0.19, 3.00]
[–0.31, 1.66]
(SE = .15)
[–0.05, 0.14]
0.04
0.00
0.08
1.01**
–0.10
0.01
0.21
1.26*
0.45
0.55
0.67
0.22
0.59**
–620.70
594
[ 0.14, 0.92]
[–0.19, 0.09]
[ 0.03, 0.19]
[–0.18, –0.06]
[–0.28, 0.16]
[–0.05, 0.08]
[ 0.26, 0.57]
0.44**
–0.05
0.10**
–0.11**
–0.06
0.01
0.39
Employment
Estimate
95% C.I.
–0.85**
0.42
0.49
1.32
0.23
–0.46
3.12*
0.50
3.00**
1.55
0.36
0.85**
–953.83
594
0.16
–0.31**
0.87**
–0.88**
0.05
0.05
0.37**
0.11
–0.07
–0.14
1.47
[–1.27, –0.48]
[–0.32, 1.18]
[–0.14, 1.30]
[–0.06, 3.76]
[–0.54, 1.50]
[–1.02, 0.25]
[ 0.22, 9.00]
[–0.84, 3.28]
[ 0.65, 7.07]
[–0.63, 6.52]
[–1.04, 3.58]
(SE = .10)
[–0.09, 0.51]
[–0.56, –0.11]
[ 0.16, 1.89]
[–1.35, –0.51]
[–0.15, 0.28]
[–0.13, 0.25]
[ 0.12, 0.69]
[–0.07, 0.32]
[–0.26, 0.11]
[–0.83, 0.53]
[ 1.04, 2.02]
Health Policy
Estimate
95% C.I.
*p < .05; **p < .01 (for coefficient estimates); expected values, first differences, and confidence intervals computed using Clarify (King, Tomz, and Wittenberg 2000).
Baseline Expected Bills
First Differences
Constituency Demand
State Crime Rate
State Unemployment Rate
State Uninsured
State Government Health Care
Economy, % Health Care
Economy Size
Economy Concentration
Democrat
Policy Preferences
Extreme Liberal
Extreme Conservative
U.S. Crime Rate
U.S. Unemployment Rate
U.S. Uninsured
U.S. Government Health Care
Institutional Positions
Democratic Majority
Republican Majority
Committee Member
x Democratic Majority
x Republican Majority
Sub Com Leader
x Democratic Majority
x Republican Majority
Full Com Leader
x Democratic Majority
x Republican Majority
Dispersion Parameter
Log Likelihood
N
Variables
TABLE 3
Negative Binomial Regression Results for Bill Sponsorship on Crime, Employment, and Health Policy
Issue Attention
41
[ 0.15, 0.45]
[ 0.02, 0.19]
[–0.01, 0.15]
[–0.05, 0.44]
[–0.08, 0.27]
[–0.12, 0.21]
[–0.26, –0.01]
[–0.24, 0.10]
[ 0.06, 1.13]
[ 0.02, 2.30]
[–0.12, 1.31]
[–0.23, 0.38]
[–0.31, 0.55]
[–0.21, 1.74]
[–0.22, 1.15]
[–0.06, 9.23]
[–0.07, 8.43]
(SE = .17)
0.27**
0.09*
0.06
0.19
0.06
0.05
–0.13*
–0.07
0.46*
0.74*
0.34
0.00
–0.03
0.35
0.21
2.12
1.99
0.82**
–604.28
594
0.58**
–0.06
1.37**
–0.03
0.04
–0.13
1.71*
1.52
0.43
1.69
1.58
0.91**
–524.00
594
0.16
–0.20**
0.03
0.01
–0.24**
–0.01
[ 0.16, 1.23]
[–0.28, 0.14]
[ 0.50, 2.72]
[–0.27, 0.48]
[–0.27, 0.77]
[–0.35, 0.23]
[ 0.06, 5.99]
[–0.02, 5.65]
[–0.18, 1.88]
[–0.14, 7.67]
[–0.22, 7.70]
(SE = .21)
[–0.04, 0.48]
[–0.38, –0.06]
[–0.04, 0.11]
[–0.06, 0.08]
[–0.44, –0.07]
[–0.07, 0.07]
[ 0.26, 0.61]
0.41
0.37
[ 0.25, 0.54]
Banking
Estimate
95% C.I.
Agriculture
Estimate
95% C.I.
0.02
0.07
0.51**
–0.07
–0.06
–0.04
4.44**
4.48*
0.50
0.57
1.37
0.82**
–459.97
594
0.06
–0.01
0.02
–0.03*
0.00
0.06
0.19
0.06]
0.00]
0.03]
0.20]
[–0.07, 0.15]
[–0.06, 0.20]
[ 0.23, 0.94]
[–0.17, 0.08]
[–0.16, 0.11]
[–0.19, 0.33]
[0.18, 20.30]
[0.13, 21.00]
[–0.02, 1.82]
[–0.11, 3.07]
[–0.02, 6.06]
(SE = .24)
[–0.04, 0.21]
[–0.11, 0.08]
[–0.01,
[–0.07,
[–0.04,
[–0.07,
[ 0.11, 0.31]
Communications
Estimate
95% C.I.
*p < .05; **p < .01 (for coefficient estimates); expected values, first differences, and confidence intervals computed using Clarify (King, Tomz, and Wittenberg 2000).
First Differences
Constituency Demand
Economy, % Farming
Economy, % Financial Services
Economy, % Communications
Economy Size
Economy Concentration
Democrat
Policy Preferences
Extreme Liberal
Extreme Conservative
Institutional Positions
Democratic Majority
Republican Majority
Committee Member
x Democratic Majority
x Republican Majority
Sub Com Leader
x Democratic Majority
x Republican Majority
Full Com Leader
x Democratic Majority
x Republican Majority
Dispersion Parameter
Log Likelihood
N
Baseline expected bills
Variables
TABLE 4
Negative Binomial Regression Results for Bill Sponsorship on Agriculture, Banking, and Communications
42
Jonathan Woon
Issue Attention
43
tend to be substantively large: the increase in the expected number of
bills is always at least as large as the baseline itself, and for four issues,
the increase is more than twice that amount. For example, membership on the Judiciary Committee leads to an increase of 1.72 more
crime bills than the baseline of 0.96, membership on the Labor and
Human Resources Committee increases employment-related bills by
1.01 over the baseline of 0.39, and membership on the Finance
Committee leads to 5.16 more trade-related bills than the baseline of
1.37. In addition, these effects are larger than the effects of any of the
statistically significant constituency variables yet to be discussed.
The data support several of the other hypotheses, but less consistently across issues. The second most consistently important factor
affecting issue attention is constituency demand. Specifically, the
Economic Importance hypothesis is supported for six issues but takes
on a slightly different form for each one. Industry variables are statistically significant and positive for three of these (health policy, agriculture, and trade), state Economy Size is significant and positive for
five issues (crime, employment, agriculture, trade, and transportation),
and Economic Concentration is significant and in the expected direction
for two (employment and transportation). In substantive terms, however, the effect of Economic Importance is a distant second when
compared to Committee Membership. For example, a one standard
deviation increase in the size of a state’s economy only leads to an
increase of 0.57 crime bills, 0.10 employment bills, 0.77 trade bills,
and 0.14 transportation bills.
In terms of other institutional positions, the effects of fullcommittee leadership positions are positive and statistically significant for four issues (crime, health policy, energy, and transportation).
Although these leadership effects are less consistent across issues, their
sizes tend to be as large as, or slightly larger than, the committee effects.
A full-committee leadership position increases expected bill sponsorship by 4.78 bills for crime, 3.00 bills for health policy, 1.69 bills for
energy, and 1.30 bills for transportation. None of the basic subcommittee leader dummy variable effects are statistically significant,
but a few of the subcommittee and majority party interaction variables
do have positive and statistically significant effects. There is, however,
no consistent pattern across issues to support specific hypotheses.14
Taken together, the results for the various leadership variables suggest
that leadership positions increase issue attention, but in more idiosyncratic ways than committee membership. A reasonable interpretation
of this finding is that even though leadership positions provide
additional agenda influence and resources, senators may not always
[ 0.38, 0.73]
[–0.06, 0.09]
[–0.10, 0.04]
[–0.09, 0.05]
[–0.11, 0.02]
[–0.35, 0.07]
[–0.02, 0.48]
[–0.36, –0.03]
[–0.07, 0.46]
[–0.28, 0.15]
[ 0.30, 1.70]
[–0.31, 0.63]
[–0.25, 0.78]
[–0.17, 0.78]
[–0.09, 2.55]
[–0.13, 2.53]
[ 0.35, 4.33]
[–0.12, 4.68]
[–0.34, 2.47]
(SE = .12)
0.54
0.01
–0.03
–0.02
–0.05
–0.13
0.18
–0.18*
0.16
–0.06
0.87**
0.02
0.11
0.18
0.76
0.70
1.69**
1.25
0.46
0.40**
–646.78
594
Energy
Estimate
95% C.I.
0.90*
–0.51*
5.16**
–0.03
–0.42
1.79
1.61
–0.15
–0.65
1.37
70.29**
1.66**
–1,073.54
594
–0.36
0.75*
0.77**
–0.06
–0.21
–0.29**
0.35**
1.37
[ 0.13, 2.01]
[–1.04, –0.06]
[ 2.75, 8.51]
[–0.79, 1.44]
[–1.04, 0.57]
[–0.71, 8.46]
[–1.28, 12.85]
[–1.54, 4.67]
[–1.43, 0.90]
[–1.30, 11.36]
[1.65, 373.64]
(SE = .15)
[–0.77, 0.07]
[ 0.11, 1.46]
[ 0.40, 1.28]
[–0.29, 0.17]
[–0.86, 0.40]
[–0.52, –0.08]
[ 0.11, 0.66]
[ 0.92, 1.95]
Trade
Estimate
95% C.I.
0.13
–0.04
1.20**
0.23
–0.05
0.61
1.11
0.21
1.30*
0.79
2.38
0.56**
–755.60
594
0.09
–0.27*
–0.03
0.14*
–0.10*
–0.26
0.83
[–0.17, 0.51]
[–0.34, 0.25]
[ 0.47, 2.20]
[–0.33, 1.23]
[–0.46, 0.65]
[–0.12, 1.85]
[–0.22, 3.92]
[–0.57, 1.92]
[ 0.02, 3.74]
[–0.51, 4.21]
[–0.16, 9.06]
(SE = .12)
[–0.18, 0.45]
[–0.52, –0.05]
[–0.12, 0.08]
[ 0.03, 0.28]
[–0.19, 0.00]
[–0.55, 0.02]
[ 0.62, 1.10]
Transportation
Estimate
95% C.I.
*p < .05; **p < .01 (for coefficient estimates); expected values, first differences, and confidence intervals computed using Clarify (King, Tomz, and Wittenberg 2000).
Baseline expected bills
First Differences
Constituency Demand
Economy, % Resources
Economy, % Utilities
Economy, % Farming
Economy, % Manufacturing
Economy, % Transportation
Economy Size
Economy Concentration
Democrat
Policy Preferences
Extreme Liberal
Extreme Conservative
Institutional Positions
Democratic Majority
Republican Majority
Committee Member
x Democratic Majority
x Republican Majority
Sub Com Leader
x Democratic Majority
x Republican Majority
Full Com Leader
x Democratic Majority
x Republican Majority
Dispersion Parameter
Log Likelihood
N
Variables
TABLE 5
Negative Binomial Regression Results for Bill Sponsorship on Energy, Trade, and Transportation
44
Jonathan Woon
Issue Attention
45
utilize these advantages if the increase in net expected benefits is not
sufficiently large to motivate a change in their behavior.
The results show that disaggregating bills by issue and estimating
separate models for each (in contrast to Schiller’s 1995 and Wawro’s
2000 aggregate analyses) is methodologically consequential. One of
the more obvious consequences is that health policy very clearly stands
apart from the other issues. It is the only issue for which committee
membership itself does not matter (although full-committee leadership
is important), and it is the only issue for which the policy-preference
hypotheses receive any support. The liberalism hypothesis is supported:
Extreme Liberal has a positive effect and Extreme Conservative has a
negative effect, so that the total difference in bill sponsorship between
an extremely liberal senator and an extremely conservative one is 1.75
bills, which is more than the baseline of 1.47.15 Similarly, the national
policy conditions hypothesis receives some support, since a one
standard deviation increase in national Government Health Care leads
to a decrease of 0.31 expected bills, but this change is substantively
rather small.
The subtler consequence of disaggregation is that, although the
basic qualitative findings (that committee membership is the primary
influence and that committee leadership and constituency demand are
secondary influences) generalize across the other issues, the quantitative results indicate significant parameter heterogeneity. For example,
baseline expected values and the effects of Committee Membership
range from 0.37 and 0.46 for agriculture, respectively, to 1.37 and
5.16 for trade. As noted earlier, committee leadership positions do not
affect bill sponsorship for every issue, and constituency demand does
not matter for banking, communications, or energy. For those relatively
narrow issues, committee positions are the only significant influences
on bill sponsorship. A pooled analysis would lead to substantively
different conclusions about the importance of factors such as ideology
and majority status—but such conclusions would be inappropriate,
since a likelihood ratio test shows that a pooled model can be rejected.16
The data provide very little or no support for the remaining
hypotheses across all issues. The importance of policy preferences
(ideology and national conditions) is limited to health policy, and none
of the party-related hypotheses receive support from these issue
analyses. The lack of a majority party effect is consistent with the
view of the Senate as a much less partisan institution than the House,
and the lack of support for the issue-ownership hypothesis for any of the
issues suggests that the inoculation strategy may be at play (although we
cannot make a conclusive determination from a null finding).
46
Jonathan Woon
Overall, the results support the cost-benefit model of issue
attention while providing a more refined picture of the specific factors
that affect those costs and benefits. Not only do “committees matter,”
a finding consistent with the literature on congressional behavior
(Fenno 1973; Frantzich 1979; Hall 1996; Matthews 1960; Schiller
1995), but committee membership matters the most. In addition to
committee membership, committee leadership also lowers the costs of
bill sponsorship and raises expected benefits by increasing the probability of bill passage, even if committee leaders do not always exercise
their influence. Constituency demand is the primary source of benefits
for most issues, and the benefits of policy-based preferences are limited
to a single issue (health policy). Although the results do not provide
direct numerical estimates of the mix of benefits due to position taking
versus policy outcomes, the importance of committee factors implies
that we can rule out the pure position-taking hypothesis and strongly
suggests that policy outcomes (in terms of the electoral benefits of
successful legislation) are quite significant.
Committee Effects:
Institutional Advantages or Self-selection?
To the extent that committee assignments are governed by selfselection and senators’ preferences for committee assignments are
driven primarily by constituency interests, the presence of committee
effects might not be due to greater agenda influence and lower costs of
attention, as I claim, but might arise because committee membership
is a better indicator of constituency interest than the independent
variables used in this analysis. There are several reasons, however, for
discounting this interpretation.
Theoretically, even if preferences (that is, demand) for committee
assignments are perfectly determined by constituency interests, the
assignment process itself (supply) is constrained by committee size
and party rules (for example, rules that preclude two senators from the
same state of the same party from serving on the same committee). In
other words, senators might want to be on committees for constituency reasons, but they can’t always get what they want. The fact that
senators regularly transfer to new committees attests to the strength of
the supply constraint and to the reality that senators’ committee preferences are never entirely satisfied. Moreover, there are good reasons
(including the theory of progressive ambition) to believe that senators
may leave constituency committees for more powerful and policyrelevant committees, such as Judiciary and Finance. In fact, Stewart
Issue Attention
47
and Groseclose (1999) have shown that constituency-oriented
committees are generally ranked last in terms of desirability, compared
to prestige or policy committees.
Several additional empirical analyses more convincingly rule out
the possibility that committee assignments simply mask underlying or
imperfectly measured constituency interest. First, I reestimated the
models for agriculture and trade after omitting the industry-specific
Economic Importance variables. These issues represent the best-case
scenarios for the face validity of the industry variables as measures of
constituency demand. By omitting them and observing how the
committee effects changed, I assessed the extent to which omittedvariables bias affects the committee coefficients for the other issues.
For agriculture, the effect of committee membership (first difference)
increased from 0.46 additional bills to 0.66; for trade, the effect increased from 5.16 additional bills to 5.91. In both cases, the difference
is minor, which suggests that any potential inflation of the coefficients
due to omitted-variables bias is small for other issues.
Second, I examined changes in bill sponsorship before and after
committee transfers. This exercise takes advantage of the panel structure
of the data and is essentially a natural experiment in which constituency
demand is held constant and committee membership is the treatment
variable. The self-selection interpretation implies that there should be
no difference in bill sponsorship before and after committee transfers.
Table 6 presents the effects of committee transfers in terms of estimated
first differences from negative binomial regression models. There are
relatively few transfers for several issues, so I pooled the data and controlled for issue-specific fixed effects. For transfers to a committee, we
can reject the hypothesis that the new committee assignment has no effect,
for issues pooled together as well as disaggregated into broad and narrow
concerns. For transfers off of a committee, the transfer has no effect when
all issues are pooled together and when only broad issues are considered.
The lack of a transfer-off effect for broad issues is consistent with the
notion that senators (or their personal staffs) acquire expertise, thus
permanently lowering the cost of bill sponsorship on those issues. For
narrow issues, however, there is a statistically significant decrease in bill
sponsorship after a senator transfers off a committee. This decrease is
consistent with the notion that the cost of acquiring technical information
relative to the small payoffs of success on these issues leads members
to rely more heavily on the expertise of committee staff. When senators
leave these committees, they lose this resource advantage and their
bill sponsorship declines accordingly. Overall, the transfer analysis
favors the cost-benefit interpretation of committee effects.
48
Jonathan Woon
TABLE 6
Effects of Committee Transfers on Bill Sponsorship
Issue Range
Transfer to
Estimate
95% C.I.
Transfer from
Estimate
95% C.I.
All issues
0.67**
[0.25, 1.45]
–0.34
[–1.14, 0.28]
Broad issues
2.52**
[1.07, 4.99]
0.77
[–0.27, 3.12]
Narrow issues
0.32*
[0.05, 0.81]
–1.24*
[–2.97, –0.13]
*p < .05; **p < .01 (for coefficient estimates).
Notes: Each estimate is a first difference from a separate negative binomial regression model
that includes a set of issue dummy variables. Estimated using Clarify (King, Tomz, and
Wittenberg 2000).
Third, I estimated alternative specifications of the models presented in Tables 3, 4, and 5 to include additional covariates intended
to control for alternative explanations of the committee effects.17 Since
party rules or the supply of committee assignments sometimes preclude two same-state senators from serving on the same committee,
the presence of at least one senator on a committee may be a good
indicator of state-level constituency demand. I therefore included a
dummy variable indicating if at least one senator from a state was a
member of the relevant committee. In that case, I interpreted the committee effect as an additional effect above and beyond constituency
interest. Alternatively, I could (and did) take advantage of the data’s
panel structure to control for unobserved constituency interest by including a set of state dummy variables (that is, state fixed effects).
Finally, the relevant constituency may not be a geographic one but
rather a set of high-demand interest groups. When data were available
(from the Center for Responsive Politics), I controlled for campaign
contributions from relevant industries (health care, agriculture, banking, communications, energy, and transportation). Although I found
some minor differences in the point estimates for the committee membership coefficients across alternative specifications, the main results
of my original analysis remained intact: committee membership is the
dominant factor influencing senators’ issue-attention decisions. All of
the additional analyses favor the cost-benefit framework over the selfselection interpretation.
Issue Attention
49
Conclusion
Most observers would characterize the United States Senate as
an institution in which individualism exerts a much stronger force on
legislative behavior than do partisan or committee organizations,
especially when compared with the House (Binder 1997; Evans and
Lapinski 2005; Ripley 1969). Procedures such as unanimous-consent
agreements, the filibuster, and the ability to offer nongermane floor
amendments give individual senators tools with which to circumvent
the normal committee process. According to Sinclair (1989), the level
of individualism has increased along with the decline in specialization
and the importance of committees since the earlier eras described by
Matthews (1960) and Fenno (1973).
My findings, in contrast, attest to the enduring importance of
committee assignments and leadership positions in shaping senators’
legislative activities. Since the Senate does not provide the same kind
of valuable selective incentives as the House, such as restrictive rules
(Krehbiel 1991, 1997) or promotions to powerful institutional positions (Wawro 2000), it may seem puzzling that senators would engage
in the “highly detailed, dull, and politically unrewarding” (Matthews
1960, 64) work of the Congress. Describing such legislative efforts as
public goods, Hall (1996, 50) asks, “Why would a member participate
in their production? Why not free ride? Why not let Representative
Schmuck do it?” It would seem that Senate individualism would further
exacerbate this problem. The production of collective benefits by an
activity such as bill sponsorship does not mean, however, that the level
of the activity will be zero (Olson 1965). As I have shown, there can
be substantial private benefits from bill sponsorship for members of
the Senate.
Although the costs and benefits of legislative issue attention
cannot be observed directly, my theoretical framework implies several
testable hypotheses about their sources. The results suggest that many
of the benefits of issue attention are related to constituency demand
and, occasionally, policy preferences. Early-stage legislative efforts
are thus not entirely public goods. My analysis suggests that there
may be substantial personal rewards in terms of electoral benefits.
Moreover, the rewards cannot be solely due to position taking, since,
if that were the case, committee positions would be irrelevant. Thus,
even though the probability of bill passage may be very low, the data
suggest that the potential benefits of new policy outcomes for
committee members are sufficiently large to weigh heavily in bill
sponsorship decisions.
50
Jonathan Woon
More important, my analysis indicates that committee membership and leadership positions have the greatest effect on increases in
the expected net benefits of issue attention. This finding supports the
argument that committee positions provide greater influence over the
agenda than non-members wield and lower the marginal costs of bill
sponsorship relative to those non-members face. These advantages,
coupled with the relatively small size of committees and subcommittees,
in turn lead senators to engage rationally in early-stage legislative
activities that they might not otherwise have accepted. Fenno nicely
describes the importance of the committee system in this regard with
his account of the remarkable transition of Dan Quayle from a lazy
and inattentive member of the House to a reasonably accomplished
Senate subcommittee chair:
Nothing in Quayle’s previous experience would have led anyone to expect him to be
especially interested in—much less influential in—this area of policy. . . . Once he
had positioned himself on a committee and on a subcommittee, the subject of job
training fell into his legislative lap. (1989, 2)
Thus, by dividing labor, lowering transaction and specialization costs,
and distributing agenda influence, the committee system provides
senators with both the opportunities and the motivation to legislate,
even when personal or constituency interests are insufficient.
Jonathan Woon <[email protected]> is Assistant Professor of
Political Science, University of Pittsburgh, 4600 Wesley W. Posvar
Hall, Pittsburgh, PA 15260.
NOTES
I thank Stephen Ansolabehere, Keith Krehbiel, three anonymous reviewers, and
Editor Larry Evans for their encouragement and their many helpful suggestions. This
research was supported by the Stanford Law School’s John M. Olin Program in Law
and Economics Summer Research Fellowship.
1. Approximately four out of every five majority party senators has chaired at
least one committee or subcommittee (Ornstein, Mann, and Malbin 2000). The comparable figure for the House is two out of five representatives.
2. Mathematically, the net (expected) benefits can be described by the expression
pBO + BP – C, where p = probability of passage (representing strategic and institutional
constraints on agenda influence), BO = outcome-based benefits, BP = position taking
benefits, and C = costs. Each term is described and justified in the text. A senator will
pay attention to an issue if the net benefits outweigh the opportunity costs of alternative uses of that legislator’s time and effort. For analytical purposes, we need only
assume that there is some threshold that net benefits must exceed for legislative issue
attention and bill sponsorship to be nonzero.
Issue Attention
51
3. Although there is little direct evidence regarding the positive correlation
between electoral and legislative success (see Padró i Miquel and Snyder 2006 for
evidence from the North Carolina state legislature), Box-Steffensmeier and Grant (1999)
have found that legislative effectiveness is rewarded by higher contributions from
political action committee, which, in turn, should enhance reelection prospects.
4. See my 2008 work for a formal model and empirical analysis that shows
how agenda influence affects the ideological content of bills that legislators introduce
in both the House and the Senate. Like the framework presented here, my formal model
assumes that legislators’ preferences combine position taking and policy seeking (Woon
2008).
5. Although strong-party theories rely on procedures specific to the House,
Campbell, Cox, and McCubbins (2002) have suggested that the majority party in the
Senate may, in practice, have similar control of the floor agenda.
6. Senators who left office between February of the first session and November of the second session and their replacements are excluded.
7. See Cox and Terry (2008) regarding the benefits of using panel data for
analyzing legislative productivity in the House.
8. Alternatively, one might hypothesize that increases in government coverage
should increase demand for legislative oversight and corresponding legislation.
9. Using Gallup Poll data, I computed Democratic advantage scores (the
proportion of respondents who think the Democrats are better at an issue minus the
proportion who think the Republicans are better) averaged across polls during the
1989–1998 period. Positive scores indicate Democratic ownership; negative scores
indicate Republican ownership. For Democrat-owned issues, the average Democratic
advantage is 17.3 for health (n = 16, min = –5, max = 43) and 18.5 for employment (n
= 6, min = –7, max = 38). For Republican-owned issues, the advantage is –3.5 for
crime (n = 14, min = –23, max = 8) and –13.3 for trade (n = 6, min = –20, max = –6).
The range of the scores helps indicate the extent of issue ownership: for example, the
Democrats never have an advantage on trade, but for crime the magnitude of their
advantage is never as large as the Republicans’ best score.
10. I chose this specification instead of the scores themselves because treating
the scores as cardinal requires several restrictive assumptions regarding the shapes of
legislators’ utility functions and the distribution of the random errors in the random
utility model (see Krehbiel 1998, 74, note 28, for similar reservations about the cardinal
meaning of the scores). The dummy variable specification instead uses the ordinal
properties (i.e., rankings) of NOMINATE scores, without relying on the method’s exact
numerical estimates.
11. See the Appendix on the LSQ website <http://www.uiowa.edu/~lsq/
Woon_Appendix> for details on the correspondence between issues and committees
according to bill-referral data from the THOMAS database. There is one committee
with primary jurisdiction over each issue except health policy, which comes under the
jurisdiction of both the Finance Committee and the Labor and Human Resources
Committee. Accordingly, the variables for the health policy issue rely on whether or
not a senator holds a position on either of the two committees.
12. I estimated these quantities using King, Tomz, and Wittenberg’s (2000) Clarify
software (using 25,000 simulations for each model). The maximum likelihood coefficient
estimates can be found in the Appendix on the LSQ website.
52
Jonathan Woon
13. Another approach to modeling overdispersion is to use a zero-inflated model,
which assumes that the covariates that explain the decision to introduce any bills at all
are different from the covariates that explain the number of bills. To determine if a
zero-inflated model might be appropriate, I restricted the dependent variable to 0 (no
bills) and 1 (at least one bill) and reestimated all of the regressions as probit models.
The results are reported in the online Appendix. The qualitative results from the probit
estimates are substantially similar to those from the negative binomial regressions,
suggesting that a zero-inflated approach is not necessary.
14. Democratic subcommittee chairs introduce more bills than rank-and-file
committee members do for employment, health policy, and banking issues. Although
results for employment and health bills are consistent with a Majority Party–Party
Issue Ownership interaction, there is no other evidence for either the majority party or
issue-ownership hypotheses, so I do not interpret the results as support for the interaction
hypothesis. Subcommittee chairs of both parties introduce more bills than do rankand-file committee members for communications, but, as with the other subcommittee
chair variables, I do not interpret this result as support for the majority party hypothesis,
since it lacks additional support in the data.
15. There are a few statistically significant coefficients scattered across the issues, but there is no issue for which both ideology variables have a significant and
positive effect, so the extremism hypothesis receives no support.
16. The pooled model restricts the coefficients for the independent variables to
be the same across all issues. Even when one allows for issue-specific heterogeneity
using fixed effects for both the constant and the dispersion parameter, one can reject
the pooled model with a likelihood ratio test. See the online Appendix for results from
the pooled model.
17. I thank the anonymous reviewers for these suggestions. The results of the
additional analysis appear in the online Appendix. The campaign contribution data
come from the Center for Responsive Politics <http://www.opensecrets.org>.
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