Interest Group Density and Policy Change in
the States
Eric R. Hansen
[email protected]
Department of Political Science
University of North Carolina at Chapel Hill
Caroline Carlson
[email protected]
Department of Political Science
University of North Carolina at Chapel Hill
Virginia Gray
[email protected]
Department of Political Science
University of North Carolina at Chapel Hill
Prepared for delivery at the
American Political Science Association Annual Meeting
Philadelphia, PA
September 4, 2016
Copyright by the American Political Science Association
Abstract
What are the implications of growing interest group populations for public policy? Recent
research has found interest groups to be much more influential in preserving the policy status
quo than securing policy change, but this research has focused primarily on individual groups’
efforts rather than system-level dynamics. We argue that policy change is less likely to occur in
more densely populated interest group systems because the greater diversity of policy-relevant
information exchanged in these systems raises uncertainty and lowers the likelihood of legislative
consensus, which is needed for policy change. As evidence, we observe the outcomes of 250
bills taken in a stratified random sample of all state legislative bills introduced in 2007. We
find tentative evidence that bills introduced in states with more densely populated interest
communities are defeated earlier in the legislative process. These findings imply that as interest
groups continue to proliferate, policy may remain static in state legislatures.
Scholars and journalists alike have documented a rapid proliferation in the number of organized interest groups in the American states over the last two decades (Lowery, Gray, and
Cluverius 2013; Wilson 2015). How will this growth in lobbying affect policymaking in the
states? While popular accounts portray lawmakers as subservient to the demands of special
interests, research has shown that lobbying groups are more influential when mobilizing to
defend the status quo than when pressing for a change in policy (Baumgartner et al. 2009;
Burstein 2014). However, research in this vein has focused mostly on one interest community
(Washington D.C.) or on single groups or issues (Baumgartner and Leech 1998; Hojnacki et al.
2012). This research has tended to ignore system-level variables such as interest group population density, which has been shown to shape relevant legislative actions such as the overall
number of bill introductions (Gray and Lowery 1995) and the content of policy reforms (Gray,
Lowery, and Benz 2013).
To begin to answer the question of how interest group population growth will shape statelevel policy, we study how interest group density affects legislatures’ propensity to change public
policy. We contend that as interest communities grow in size, policy change is less likely to
occur. When more interest groups lobby in a given issue area, lawmakers receive more competing
signals about the consequences of policy change. Greater competition among interest groups
induces both informational and electoral uncertainty among lawmakers. Facing an environment
of uncertainty, lawmakers are less willing to support a change in policy on a given issue. As a
result, bills stall in early stages of the legislative process and changes in public policy become
less likely.
As a preliminary empirical test of our argument, we study the advancement of 250 bills
sampled from 48 state legislatures in 2007. We pair bill topics with state lobbying registration
data collected from the National Institute on Money in State Politics (see Lowery, Gray, and
Cluverius 2013) in order to compare the effects of density across states. We measure the effects
of group density on three dependent variables: bill progress through the legislature, passage
from committees, and enactment. We find that bills on average are defeated earlier in the
legislative process as the size of the interest community in the state increases. We also find that
1
bills are less likely to be reported from committee or be signed into law in states with more
densely populated interest communities.
Our study provides tentative evidence in line with findings elsewhere that interest groups
exert the most influence over policy in preserving the status quo. Our research design allows
us to generalize these previous findings across political environments. It also illuminates the
role of political environment in affecting policy change. Our findings suggest that, if current
trends in interest group formation continue, legislatures will become increasingly hesitant to
alter public policy.
Interest Group Density and Policy Influence
How do organized interests influence the policies that elected officials put into effect? Answering
the question is important to gauging the quality of democratic governance in the United States.
Those worried about bias in policy outcomes due to interest group activity have some basis for
concern. Interest organizations, on the whole, tend to reflect the preferences and pursue the
policy goals of the economically advantaged, due to the material resources required to maintain
organizations (Kimball et al. 2012; Olson 1965; Schattschneider 1960; Schlozman and Tierney
1986). However, it is unclear that bias in the economic composition of interest organizations
begets bias in policy outcomes (Lowery and Gray 2004a). It is unusual for interest groups to
get what they want when lobbying the government for policy change (Baumgartner et al. 2009;
Burstein and Linton 2002). Even in cases where public interests are pitted against well-funded
business interests, public interests often prevail (Gray et al. 2004; Hojnacki et al. 2015; Smith
2000).
In recent research, scholars have examined the success of individual groups or coalitions
of groups in lobbying or spending money to achieve policy change (Baumgartner et al. 2009;
Burstein 2014; Grossmann and Pyle 2013; Lewis 2013). On the whole, these studies have found
that groups are much more successful in achieving their objectives when lobbying to protect the
policy status quo than when pressing for changes to current policy. Status quo preservation is
an easier position to defend because it requires no coordinated effort on the part of lawmakers
2
and because a much smaller number of political actors are needed to block policy changes at
any number of veto points in the legislative process. However, it is also important to examine
what role interest group systems as a whole play in shaping policy outcomes (Gray and Lowery
1996). In densely populated interest group systems, a large number of groups are organized,
monitoring legislative activity, and ready to mobilize at any sign of trouble. Lawmakers take
into consideration the potential reactions of organized groups, even if no direct lobbying activity
occurs.
Our central claim is that legislatures tend to keep the status quo in place when considering
policy changes in political arenas with more densely developed interest group systems. Groups
organize not only to lobby for the passage of new legislation, but also to lobby against unfavorable proposals. In short, when more groups lobby on the issues, there is greater for potential
for uncertainty about a given proposal to be raised and legislation to be defeated.
We begin by assuming lawmakers are motivated to create good public policy and to win
reelection (Fenno 1973). However, lawmakers work in an environment of uncertainty. Even
though they might have a general impression of what good policy looks like or how to maintain
constituency support, they need help from other political operatives to fill in the details.
A vital function of interest groups in democratic political systems is to provide such information to lawmakers (Hall and Deardorff 2006). Lawmakers solicit two types of information
from interest groups in service to their goals: technical and political. First, because they are
rarely experts in the fields they are charged with regulating, lawmakers require technical information about the consequences of specific policy proposals. While staff and legislative research
services are employed to supply policy-relevant information to lawmakers, they do not always
have the resources or expertise to supply all necessary information. Interest groups are eager
to oblige. Lobbyists selectively supply information to lawmakers in a way that helps advance
group goals (Austen-Smith 1993; Hall and Deardorff 2006). Interest groups on occasion participate in the drafting of legislation as well, to help lawmakers formulate the correct legislative
language and ensure their own interests are furthered or protected (e.g. Garrett and Jansa 2015;
Hertel-Fernandez 2014). Second, lawmakers desire political information, which interest groups
also selectively supply. Lawmakers want to know how their stance for a given policy proposal
3
will affect their standing with stakeholders and constituents, then follow their lead in making
policy decisions. If a stance on a given proposal is likely to draw a serious political threat,
lawmakers may decline to champion a policy change (Mahoney and Baumgartner 2015).
As interest groups grow in number, legislators have the potential to receive more selective
information from lobbyists representing a wide range of groups with competing interests. Group
competition arises from resource constraints that require organizations to adapt and fill ever
narrower issue niches (Bosso 2005; Browne 1990). It is rare even in densely populated interest
group systems for there to be multiple organizations dedicated to achieving precisely the same
policy goals. As a result, the array of organizational interests, goals, and tactics grows in tandem
with the population of groups in the political environment (Hojnacki and Kimball 1999; Lowery
and Gray 2004b).
Greater group density does not automatically translate into greater group competition.
Even if they frequently oppose one another in policy battles, interest groups do not compete on
every issue. Groups lobbying on a common set of issues occasionally form coalitions of support
to signal unity of opinion to lawmakers (Heaney and Lorenz 2013; Hojnacki 1997; Mahoney and
Baumgartner 2015). However, coalitions are not usually stable. They may form to lobby on
one policy proposal and disband as soon as the next comes along. In systems where a greater
number of groups coexist, organizers of group coalitions face higher hurdles to collective action.
In order to signal consensus, they must bear higher costs to bring collaborating groups on
board, making coalition formation less likely.
Increased information availability, particularly when coming from sources with competing
organizational goals and perspectives, is likely to produce inconsistent information about the
possible implications of policy change. Lawmakers will hear rival narratives about the benefits
or damages that would result from a given proposal. The result is increased uncertainty about
the social or economic implications of a policy—how a proposed policy will affect constituents
or stakeholders. When uncertainty is raised, coordinated action among lawmakers to move a
proposal through the legislative process is less likely.
While much previous work has examined the influence of interest groups in politics, relatively
few studies have focused specifically on how interest groups influence outcomes in the legislative
4
process, either through bill advancement (Grossmann and Pyle 2013; Lewis 2013) or policy
change over an extended period of time (Baumgartner et al. 2009; Burstein 2014). All of these
studies examine how the efforts of individual groups or the coalitions of groups actively lobbying
on a given policy proposal affect the outcome. In three of the four studies, the authors find
that groups are most successful when attempting to stop legislation that they oppose.
Our study contributes to this literature by examining how the density of group populations
affects policy outcomes. Previous findings show that legislative productivity as a whole, as
measured by the ratio of bill enactments to total bill introductions, decreases as the size of
state interest communities increases (Gray and Lowery 1995). We go a step further by observing
the relationship between interest group populations and the likelihood of any particular policy
proposal to advance through the legislative process. We do not observe sides taken by individual
groups on each of our bills because such data on lobbying activity simply do not exist in most
states.1 Though we do not observe individual lobbying activity on each of our bills, we can
observe how the presence of organized interests in the political environment influences policy
decisions.
To summarize our argument, lawmakers turn to interest groups for both technical and
political information related to policy proposals. When more interest groups are organized
in a given political environment, lawmakers are potentially exposed to greater amounts of
conflicting information about the repercussions of any proposed policy change. Competing
views increase uncertainty among lawmakers, who decline to act collectively to move a proposal
forward through the legislative process. The consequence is policy stasis.
Evidence from State Legislatures
To gather supporting evidence, we observe how interest group density affects the progress of
bills through the legislative process. For data, we turn to policy proposals introduced in state
1
Observing sides taken by individual groups on a set of bills would require the type of intensive, qualitative
research conducted by Baumgartner et al. (2009) in Washington, D.C., but in all fifty state capitals—a prohibitively costly research design. Data do exist for the disclosure of spending or lobbying activity by specific
groups in some, but not all, states (see Lewis 2013 for an example of lobbying disclosure data collected by the
state of Wisconsin.)
5
legislatures. States are useful to study because their interest systems are similar enough to
merit comparison, but vary in size (Lowery and Gray 2009).
Our unit of analysis is the bill. Following a number of recent studies that rely on random
samples of bills or issues as a source of data (Baumgartner et al. 2009; Burstein 2014; Dusso
2010; Lewis 2013), we sampled 250 bills from a population of all bills introduced in the regular
sessions of state legislatures in 2007. Bill texts and histories were gathered from the public
webpages of 48 state legislatures.2 In order to include observations from every state while
reflecting the variation in the volume of policymaking activity across states, we used a stratified
random sample of bills. We drew at least two bills introduced in each state legislature (n=96),
but drew randomly from all state bills for the remainder of the sample (n=154).
Following Burstein (2014), we only sought bills that proposed substantive policy changes.
We excluded all bills from the sample that made technical changes to existing legislation (i.e.
correcting or updating section numbering, clarifying language) or that appropriated funds.3
We also excluded resolutions, which are usually symbolic measures and not substantive policy
changes, and bills considered during special sessions. We drew a sample of legislation tackling
a very wide range of topics. Our bills addressed nationally salient issues, such as a Nevada bill
requiring voters to show photo identification at the polls, to incredibly arcane issues, such as
a Rhode Island bill naming a roundabout. The vast majority of bills dealt with mundane yet
important state policies: for example, criminal penalties, school safety, state employee benefits,
and highway construction regulations.
The dependent variable we used to measure how far bills advanced through the legislative
process is Bill Progress, a count of the number of stages of the legislative process through which
the bill advanced. The variable is ordinal and coded 1 if the bill died in committee, 2 if the
bill died in the originating chamber after being reported from committee, 3 if the bill died in
the opposite chamber, 4 if the bill was vetoed by the governor, and 5 if the bill ultimately
became law. Of these bills, the majority (156) died in committee. For the remaining bills, 15
2
Massachusetts did not make bill texts available for 2007. Kansas did not make bill histories available for
2007.
3
We estimate that a small percentage (< 10%) of all introduced bills fit these categories. Appropriations
bills were randomly drawn and discarded in fewer than 20 instances. Technical bills were even more infrequently
drawn, and came only from more professionalized legislatures like Illinois and New York.
6
were reported from committee without receiving a floor vote, 20 passed only in their originating
chamber, six were approved by the legislature as a whole but vetoed by the governor, and 53
became law. Descriptive statistics for all subsequent variables are presented in Table A1 in the
appendix.
Our principal independent variable is Density. The variable is a count of all interest groups
registered to lobby in the state where the bill was sponsored. Data from 2007 were previously
collected from the National Institute on Money in State Politics (NIMSP). Lowery, Gray, and
Cluverius (2013) provide more extensive descriptions of the data collection process.
We included control variables that capture variation across state political contexts that could
aid or hinder bill progress. First, we control for the demands made on legislators’ time and
attention. We include the variable Legislative Professionalism. More professional legislatures
meet in longer sessions, consider a higher number of bills, and have greater resources available
to them. They also tend to encourage the formation of more densely populated interest group
systems (Kattelman 2014). Controlling for this variable is crucial to distinguish our argument
that interest group density depresses the likelihood of policy change from an alternative theory
that professional legislatures, which happen to attract greater interest group attention, are less
likely to pass policy changes due to the high volume of proposals they must consider year to
year. Data for this variable come from a measure of legislative professionalism calculated by
Bowen and Greene (2014).
We also control for a number of institutional variables demonstrated in the legislative politics
literature as important in determining the fate of bills. First, we control for Cosponsors, a count
of the number of cosponsors a bill has. More cosponsors should predict greater bill success, since
it signals to non-sponsor legislators that the bill has a greater base of support (Browne 1985).
Second, we include a measure of party control of state governments. Bills will be more likely to
pass when the same party controls both chambers of the legislature and the governor’s office.
We use Unified Government, a dummy variable for which values of one indicate unified party
control. Third, we control for the party of each bill’s sponsror. Majority Party Sponsor is a
dummy variable with values of one indicating that the bill sponsor was a member of the majority
party in his or her chamber. We do not expect bills sponsored by members of the minority to
7
advance as far as bills sponsored by majority members (see Cox and McCubbins 2005). Finally
we control for the ideological orientation of each originating chamber, under the expectation that
liberal governments are more willing to pass changes to policy than conservative governments.
Government Ideology is measured as the ideal point of the median member of the chamber
where each bill originated, using data from Shor and McCarty (2011).
Finally, we included fixed effects for interest sectors. Previous studies have leveraged variation across sectors like business and health within a single interest community (usually Washington D.C.) to draw inferences about the relationships between government activity, mobilization,
and group spending (e.g. Hansen and Drope 2005; Mitchell, Hansen, and Jepsen 1997). However, groups do not mobilize in response to government activity to the same extent across
sectors. Factors such as economies of scale play a role in the resources available to groups
and, as a consequence, the amount of lobbying activity groups can engage in (Lowery and Gray
2009; Lowery, Gray, and Fellowes 2005). While comparing sectors within systems could produce
faulty inferences, assuming away differences across sectors while comparing systems could also
produce questionable conclusions. Therefore we include fixed effects for the 11 interest sectors
defined by NIMSP.
To categorize our data, we coded each of our 250 bills as belonging to one of the 11 sectors
or categorized it to an “unknown” sector. Two coders were provided a one-sentence summary
of each bill and asked to assign it to the sector most likely to lobby on it. For example, an
Illinois bill requiring businesses to refund gift card balances under certain circumstances was
assigned to the “Retail and Business Services” sector. A Texas bill changing the salaries paid to
correctional officers was assigned to the “Government Organizations” sector. Initial intercoder
reliability was 0.69. The two coders discussed discrepancies in person and agreed upon a final
categorization for each bill.
Table 1 provides a count of bill topics by interest sector. The bills are distributed across all
sectors. The most frequently occurring bill types were government, health care, and finance.
The least frequently occurring bill types were those addressing issues of transportation and
communication. Those bills categorized as ‘unknown’ were particularistic bills for which it was
unclear that any broader community of organizations would have interest in lobbying on the
8
Table 1: Bills by Sector
Sector
Number of Bills
Percent Bills
78
30
23
21
16
16
15
15
14
12
7
3
31.2%
12.0%
9.2%
8.4%
6.4%
6.4%
6.0%
6.0%
2.4%
4.8%
1.2%
0.4%
Government Organizations
Health
Finance, Insurance, & Real Estate
Nonprofit
Retail & Business
Energy & Natural Resources
Manufacturing & Production
Education
Ideology & Single-Issue Groups
Transportation
Unknown
Communications
bill: for example, the Rhode Island roundabout bill mentioned above.
We deliberately chose not to include a measure of public opinion in our models. Following
(Burstein 2014), we selected a random sample of state bills to observe the influence of interest
group populations in routine policymaking situations, with the expectation of observing some
salient and some non-salient policies. To our surprise, random sampling yielded vanishingly
few bills addressing salient issues. Like Burstein (2014), we considered an issue salient if a poll
measured public opinion on it. We compared the topics of our bills to the 39 policy issues for
which Lax and Phillips (2012) obtained state-level public opinion estimates. By our count, 6 of
the 250 bills (2.4%) in our model could be considered as addressing salient issues. We consider
this a high estimate for salient issues; we counted a bill as salient even if it had only a tangential
relationship to an issue for which a poll was available. For the vast majority of the bills we
examined, public opinion estimates were simply not available. We suspect that most people
simply do not hold strong opinions on almost all of our bills due to the technical or mundane
subjects that they address: the licensure requirements for speech language pathologists, the
disposal of agricultural waste, and the sale of influenza vaccines to state health agencies.
9
Results
We expect that as interest group density increases, policy change proposals will be defeated
earlier in the legislative process. To test this expectation, we estimate the following model:
Bill Progress = β0 + β1 · Density + β2 · Legislative Professionalism + β3 · Cosponsors+
β4 · Unified Government + β5 · Majority Party Sponsor + β6 · Government Ideology+
β7 · Sector Fixed Effects + Because our dependent variable is ordinal, we estimate the model using ordered logistic
regression. Table 2 presents our results. We find tentative support for our expectation that
bills are less likely to progress when interest systems are more densely populated. The coefficient
estimate for the independent variable density is signed in the expected negative direction. The
estimate is statistically significant at the 0.1 level of confidence.
Turning to the other independent variables, the positive coefficient estimates for the legislative professionalism, majority party sponsor, and cosponsors variables suggest that bills
advance farther in more professional legislatures, when the bill sponsor is a member of the majority party, and as the number of initial cosponsors of the bill increases. However, none of the
coefficient estimates for these variables reach statistical significance at even the .1 level of confidence. Likewise, the negative coefficient estimate for the government ideology variable suggests
that more liberal governments are less likely to pass new policy changes, but this coefficient
estimate is also not statistically significant. In fact, the only other positive predictor of bill
progress is unified government, as evidenced by the positive statistically significant coefficient
estimate for the variable.
One possible explanation for the generally weak findings may be the sample size. The 250
bills observed in our data reflect a very small fraction of the roughly 110,000 bills considered by
state legislatures in 2007. Though the coefficient estimates for all variables are generally signed
in line with expectations, large standard errors do not allow us to reject null hypotheses at the
standard .05 level of confidence. More data may be needed to clarify these tentative results.
10
Table 2: Density and Bill Progress
Dependent variable:
Bill Progress
Density
-0.40∗
(0.24)
Legislative Professionalism
0.09
(0.12)
Majority Party Sponsor
0.47
(0.31)
Cosponsors
0.02
(0.01)
Unified Government
0.87∗∗∗
(0.34)
Government Ideology
-0.12
(0.26)
Sector Fixed Effects
Yes
Observations
BIC
250
631.67
Note: ∗ p<0.1; ∗∗ p<0.05; ∗∗∗ p<0.01. Coefficient estimates obtained using ordered logistic
regression. Significance tests are two-tailed.
Committee Passage and Policy Change
While the model above shows how group density affects how far bills advance through the
legislative process, it does not distinguish the importance of interest group density at different
stages of the legislative process. However, scholars have remarked that lobbying is more influential in earlier stages of the process, such as agenda setting and committee hearings, than
in final floor votes (Austen-Smith 1993; Hall and Wayman 1990; Hojnacki and Kimball 1998).
It could be the case that the effect of density is manifested primarily in the committee stage,
rather than in final floor votes. However, Gray and Lowery (1995) find that interest community
11
Table 3: Density, Committee Passage, and Bill Enactment
Dependent variable:
Passed Committee
Became Law
Density
-0.32
(0.25)
-0.43
(0.30)
Legislative Professionalism
0.04
(0.12)
0.14
(0.15)
Majority Party Sponsor
0.37
(0.32)
0.73∗
(0.42)
Cosponsors
0.03∗
(0.02)
-0.01
(0.03)
Unified Government
0.68∗∗
(0.35)
1.10∗∗∗
(0.42)
Government Ideology
-0.06
(0.26)
-0.30
(0.32)
Yes
Yes
Constant
-0.69
(1.30)
-0.59
(0.86)
Observations
BIC
250
408.59
250
315.61
Sector fixed effects
Note: ∗ p<0.1; ∗∗ p<0.05; ∗∗∗ p<0.01. Coefficient estimates obtained using logistic regression.
Significance tests are two-tailed.
size affects both the number of bills introduced and the total number of bills enacted by the
legislature.
To test the robustness of the results above, we estimate the effects of density and our controls
on bill passage through committee and bill passage into law. Because our two corresponding
variables (Passed Committee and Became Law ) are binary, we estimate models using logistic
regression. Like above, evidence in support of our expectations will be found in a negative
coefficient estimate for the density variable.
12
1
0
0
.2
.2
Pr(Enacted)
.4
.6
Pr(Passed Committee)
.4
.6
.8
.8
1
Figure 1: Predicted Probability of Committee Passage and Bill Enactment by Density
0
.5
1
1.5
2
2.5
Number of Registered Groups (in thousands)
3
3.5
0
(a) Committee Passage
.5
1
1.5
2
2.5
Number of Registered Group (in thousands)
3
3.5
(b) Enactment
We present the analyses in Table 3. First, we examine the effect of density on a bill passing
committee in the first column of the table. As expected, the coefficient estimate for density is
negative, but not statistically significant at the .05 level of confidence. This finding indicates
that as density increases, the likelihood of a bill passing committee decreases. Among the control
variables, only the coefficient estimates for the cosponsors and unified government variables
achieve statistical significance. These results indicate that as the number of bill cosponsors
increases or when one party controls both legislative chambers and the governor’s office, the
likelihood of a bill passing committee also increases, in line with expectations.
Panel A in Figure 1 shows the predicted probability of a bill passing from committee across
observed values of interest group density. The Y-axis indicates the probability of passage
while the X-axis indicates the number of interest groups registered to lobby in the state in
thousands of groups. As the number of groups approaches zero, the predicted probability of a
bill passing committee is about 0.4. However, as the number of groups approaches the maximum
observed value of 3335, the predicted probability falls to about 0.2. Though we cannot reject
the null hypothesis that group density has no effect, the results tentatively suggest a negative
relationship between group density and committee passage.
Next we examine the effect of density on a bill enactment in the second column of Table
3. The negative coefficient estimate for the density variable indicates that an increase in the
number of lobbying groups decreases the likelihood of a bill ultimately being enacted. Two
13
other control variables exert statisticially significant effects. As in the previous model, the
likelihood of bill enactment also increases under unified government. And the results show that
bills sponsored by members of the majority party are more likely to pass into law.
Panel B in Figure 1 displays the predicted probability of bill enactment across observed
values of interest group density. The probability of bill enactment is generally lower than the
probability of a bill passing committee, controlling for all independent variables. As the number
of registered groups approaches zero, the probability of a bill becoming law is about 0.2. The
probability shrinks to 0.05 as the number of groups reaches the maximum observed value.
Discussion
Our analysis of the data provides tentative support for our expectations that the increased
density of interest groups lobbying on a given issue inhibits the progress a bill will make through
the legislative process. The results show that proposed policy changes tend to be defeated earlier
in the legislative process in states with densely populated interest communities than in states
with sparse interest group activity, controlling for several other variables. Further analysis
showed that the relationship between density and progress is negative at both the committee
stage and the bill enactment stage.
The findings we present build upon research demonstrating that interest groups are quite
effective at preserving the status quo (Baumgartner et al. 2009; Burstein 2014; Lewis 2013). Our
study contributes to this literature by observing that characteristics of broader group systems
play a role in determining policy outcomes, complementing literature examining the actions
and spending habits of individual groups (Grossmann and Pyle 2013; Lewis 2013). Our study
also makes a contribution by expanding the scope of study to multiple political environments.
Our research design leverages variation in group densities within issue areas across 48 states,
rather than restricting our scope to one lobbying context.
A limitation of our study that will require attention in future research is that we are unable to observe the exact mechanism by which larger group populations reduce the chance of
successful policy change. It could be that the increased lobbying activity that comes with a
14
larger number of groups leads to information overload and increased uncertainty among lawmakers. This mechanism would complement Lewis’ (2013) finding that more groups directly
lobbying on a particular bill is associated with a lower chance of bill passage. However, it
could also be that a larger number of groups leads to greater “negative power” of the lobbying
community over policy outcomes (Bachrach and Baratz 1962; Lowery 2013). A research design
bringing lobbying disclosure data to bear in multiple legislative contexts could be a good step
towards distinguishing between these two mechanisms. Our study is also constrained by time.
Observations of policy change over multiple years, as in studies by Baumgartner et al. (2009)
and Burstein (2014), could give a more complete picture of the long-term influence that group
populations wield.
Our study provides early evidence showing that policy change is less likely as more groups
enter the fray. Lobbyists are not often successful in extracting preferred policy changes from
legislatures. However, groups can influence policy successfully through bids to preserve the
status quo. Our findings thus have potentially worrisome implications for policymaking. Given
that interest group populations (particularly economic groups) continue to grow over time
(Lowery, Gray, and Cluverius 2015; Lowery, Gray, and Fellowes 2005), larger populations could
lead to paralysis in policymaking as the status quo becomes more unpalatable for lawmakers to
change. The continued proliferation of organizations, which provide much-needed information
to policymakers, may serve only to obfuscate policy solutions. A result could be elected officials
being unable or unwilling to adapt policy to fit contemporary needs.
15
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18
Appendix
Table A1: Summary Statistics
Variable Name
Description
Mean
Std. Dev.
Passed Committee
1 = Passed, 0 = Died
0.38
–
Became Law
1 = Passed, 0 = Died
0.21
–
Bill Progress
Count of stages passed (0 to 5)
2.14
1.64
Issue Area Density
Lobbying groups per issue area in state (in hundreds)
1.32
1.37
Legislative Professionalism
Bowen & Greene’s measure
1.07
2.51
Unified Government
1 = unified party control, 0 = split control
0.44
–
Cosponsors
Count of bill cosponsors
3.95
9.50
Majority Party Sponsor
1 = bill sponsor in majority, 0 = else
0.70
–
Gatekeeping
1 = committee gatekeeping power, 0 = else
0.90
–
19