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K Street on Main? How Political Institutions Cultivate a Professional Lobbying Elite


Abstract and Figures

This study explores the consequences of legislative turnover for the hiring of lobbyists and influence of interest groups. We argue that lobbyists develop durable relationships with lawmakers in assemblies with low turnover. Such relationships allow lobbyists to attract clients. We use a new, state-level measure of multi-client lobbying to show that legislative turnover and multi-client lobbying are inversely related: decreases in turnover are correlated with more multi-client lobbying. In a second set of analyses, we find that legislative term limits are associated with less multi-client lobbying. Since multi-client lobbying poses risks to the representation of individual interests and magnifies the effects of resource differences between interests, our results suggest that turnover may help more diverse interests to achieve political influence.
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K Street on Main
Legislative Turnover and
Multi-client Lobbying
James M. Strickland& Jesse M. Crosson
August 26, 2021
This study explores the consequences of legislative turnover for the hiring of lob-
byists and influence of interest groups. We argue that lobbyists develop durable re-
lationships with lawmakers in assemblies with low turnover. Such relationships allow
lobbyists to attract clients. We use a new, state-level measure of multi-client lobby-
ing to show that legislative turnover and multi-client lobbying are inversely related:
decreases in turnover are correlated with more multi-client lobbying. In a second set
of analyses, we find that legislative term limits are associated with less multi-client
lobbying. Since multi-client lobbying poses risks to the representation of individual
interests and magnifies the effects of resource differences between interests, our results
suggest that turnover may help more diverse interests to achieve political influence.
Assistant Professor, School of Politics and Global Studies, Arizona State University
Assistant Professor, Department of Political Science, Trinity University
Scholars, policymakers, and pundits have long debated the advantages and disadvantages
of legislative turnover. On one hand, legislator turnover lies at the heart of a functioning
representative democracy: an assembly’s responsiveness to changes in constituent opinion
requires at least some amount of incumbent replacement with new representatives. On
the other hand, high levels of turnover imply a loss of accrued policy expertise, institutional
memory, and general political knowledge. Indeed, as Madison aptly summarizes in Federalist
Papers 52 and 53, legislators “should have an immediate dependence on, and an intimate
sympathy with the people” (Madison 1789, 327). Yet, “no man [sic] can be a competent
legislator who does not add to an upright intention and a sound judgment a certain degree
of knowledge of the subjects on which he is to legislate” (332).
These concerns have been extended to modern scholarship on American legislatures,
where research has explored the negative consequences of legislator entrenchment at the
federal level, arguing that representatives evade serious electoral challenges by generating
opportunities for constituency service and developing a “personal vote” (e.g., Fiorina 1977;
Arnold 1979). That is, instead of campaigning on the sorts of policy positions that allow
voters to remove out-of-step representatives, legislators use casework and pork to generate
a positive impression of their offices’ work, insulating them from serious electoral challenges
(Cain, Ferejohn, and Fiorina 1987; Johannes 1984; Parker 1980). In response to such con-
cerns, some reformers suggested legislative term limits as a means for removing entrenched
legislators. Such limits were adopted in more than one fifth of state legislatures. After
the implementation of term limits, however, studies uncovered a wide variety of drawbacks.
These included a lack of policy making effort by legislators (Titiunik and Feher 2017), power
imbalances between legislators and executives (Kousser 2005), greater polarization (Olson
and Rogowski 2020), and a lack of democratic responsiveness (Lax and Phillips 2013).
Despite the centrality of turnover and related reforms to debates over representation,
electoral politics, and institutional design, comparatively little research has examined how
legislative turnover affects actors located outside of legislatures. A classic study exam-
ining the effects of term limits on the balance of power between legislators and governors
notwithstanding (e.g., Kousser 2005), studies of turnover generally focus on repercussions for
accountability, representation, intra-institutional advancement, and legislative effectiveness.
In this study, we contribute to the ongoing debate over the vices and virtues of legislative
turnover (and term limits) by examining its impact on one set of important outside actors:
lobbyists. We investigate the extent to which legislative turnover has hastened or hampered
the development of state-level “K Streets” full of professional, multi-client lobbyists.
We propose that turnover alters the structure of a state’s lobbying community: more
specifically, that turnover discourages the centralization of lobby contracts (clients) into the
hands of well-networked lobbyists. Turnover affects lobbyists’ abilities to attract clients by
disrupting established relationships between legislators and lobbyists. When legislators serve
in office for short periods of time, lobbyists have reduced abilities to build relationships and
attract clients by advertising access. With low turnover, however, lobbyists with relationships
attract clients more ably, enabling such lobbyists to act as de facto gatekeepers to their
legislator allies. To test our claims, we examine an original data set of lobby contracts
and legislative turnover across all American states and 28 years. We find support for our
expectations. In a supplementary set of analyses, we find that one institutional means for
encouraging turnover, term limits, is itself associated with less multi-client lobbying.
These findings contribute to existing scholarship by complicating conclusions about the
effects of legislative turnover on lobbyist influence. Previous work has typically focused on
how turnover (induced by term limits) empowers lobbyists via informational asymmetries
(e.g., Moncrief and Thompson 2001; Powell 2012). But while information may help a lobbyist
gain access, we point to lobbyists’ relationship-building as a key factor in the granting of
access (cf., Hall and Wayman 1990). Although legislators in high-turnover assemblies may be
less knowledgeable, such turnover, whether induced by term limits or otherwise, disrupts the
relationships that some lobbyists leverage to influence legislative processes. Given historical
decline in turnover in the states, our findings partly explain the rise of local “K Streets.”
Multi-client Lobbying and Representation
Multi-client lobbying occurs when an individual lobbyist is authorized to represent two
or more distinct interests during a single legislative session. Over the past thirty years,
multi-client lobbyists have become more prevalent at both the federal (Drutman 2015) and
state (Strickland 2020a) levels. These agents seem to have benefited especially from the
political mobilization of corporations and private entities. Lobbying on behalf of multiple
clients is quite common for members of lobby firms (teams of lobbyists who coordinate their
lobbying efforts and share revenue), but individual lobbyists may also represent scores of
clients. Most lobbyists with multiple clients likely work on a contract or retainer basis for
each client (Drutman 2015, 164 - 66).
The popularity of multi-client lobbyists raises at least two concerns over the represen-
tation of organized interests. First, at the individual level, multi-client lobbying may affect
the true representation of clients’ interests. Multi-client lobbyists behave as common agents
shared among multiple principals (Bernheim and Whinston 1986). Since clients do not coor-
dinate with each other and cannot observe their lobbyists working, multi-client lobbying may
give rise to special common-agency problems. Examples include lobbyists charging multiple
clients redundantly for the same hours of labor (which gives lobbyists incentives to attract
clients with overlapping interests) or clients attempting to outbid each other for services.
With multi-client lobbying, there is also more potential for traditional principal-agent prob-
lems. While even single-client lobbyists may not lobby as contracted, multi-client lobbyists
may shirk for some clients while providing services for others (see Lowery and Marchetti
2012). Such selective shirking may occur especially if a lobbyist represents clients with con-
flicting interests. As an example, Goldstein and Bearman (1996) found that, in the American
states, there were 220 lobbyists registered to represent both tobacco and healthcare interests
simultaneously. Interestingly, all tobacco lobbyists were multi-client advocates (including
one who represented 42 clients). Previous scholarship has underscored other kinds of con-
flicts of interest (e.g., Yanamadala et al. 2012), and have argued that multi-client lobbying
invites deliberate dissembling since “in a single conversation with a legislator, a lobbyist may
deal with different issues, and on behalf of different clients” (Rosenthal 1993, 39).
Second, at an aggregated level, multi-client lobbying magnifies inequities in represen-
tation between monetarily rich and poor interests (as in Gerber 1999). Contract lobbyists,
especially those representing large numbers of clients, charge fees that exceed those of single-
client lobbyists (Strickland 2020a). The very reason behind these exorbitant fees—the univer-
sal, cross-issue appeal of lobbyists’ personal connections to powerful politicians—constitutes
a problematic disadvantage for poorer interests. Indeed, as previous research has found,
multi-client lobbyists do not necessarily amass their large, diverse clienteles because of their
superior policy knowledge. Rather, such lobbyists in both the Congress and state legislatures
advertise their relationships and access in order to attract clients (Drutman 2015, 158; Gray
and Lowery 1996a), which are of universal appeal to many types of interests, and which allow
for high fees (LaPira and Thomas 2017) Accordingly, business firms hire multi-client lobby-
ists more often than do resource-poor groups, reflecting other resource advantages enjoyed
by these interests (Strickland 2020a; Schlozman, Verba, and Brady 2012; Crosson, Furnas,
and Lorenz 2020). According to a long-time observer of state legislatures, “[i]n lobbying,
reputations grow over time, and certain lobbyists become known for their client lists, con-
tacts, and clout... as in other domains of life, the rich get richer and the poor have trouble
breaking in” (Rosenthal 1993, 27).
Turnover, Access, and Multi-client Lobbying
Given the importance of multi-client lobbying to representation, we investigate why some
institutional contexts appear to allow for more multi-client lobbying—and why such lobby-
ing has (as we show) grown over time. We propose an explanation for why turnover—and
institutions designed to encourage it—impedes the emergence of multi-client lobbying. In
Congress, scholars have pointed to political access as a key resource for lobbyists. How-
ever, turnover among lawmakers affects access negatively. Turnover makes it more difficult
for lobbyists to form long-lasting, profitable relationships with legislators. As incumbent
lawmakers retire or transition out of office, lobbyists must build new relationships and famil-
iarity with incoming lawmakers. For example, Meyer and Levine (2018) document lobbyists’
scramble to build new relationships with the more than 100 freshmen members entering
Congress following the 2018 mid-term elections. Even highly connected lobbyists sometimes
lose many clients in the wake of turnover, as did Tony Podesta (see Mullins and Bykowicz
2018). In the states, veteran lobbyists in term-limited states express frustration over having
to maintain relationships with and educate new lawmakers continuously. These challenges
are compounded by the fact that freshmen legislators are often hesitant to meet with lob-
byists because they view them as corrupt (Mooney 2007, 126 - 27; see Gross 2018 for an
Turnover among legislators and staffers has been shown to affect revolving-door lobbyists
in particular. Former members of Congress and staffers are among Washington’s best-paid
and most popular lobbyists because of their relationships (LaPira and Thomas 2017). In
fact, departures of former colleagues from government decreases the value of those personal
connections: when senators depart the Senate, their former staffers lose an average of more
than $180,000 in lobby contract revenue (Blanes i Vidal, Fons-Rosen, and Draca 2012).
Similarly, McCrain (2018) finds that former staffers make higher (lower) revenues as lobbyists
whenever they know more (fewer) current staffers, even after holding constant other factors
such as years of work experience on the Hill. At the state level, Strickland (2020b) finds that
proportionally fewer legislators become lobbyists in states with higher legislative turnover.
We therefore expect turnover to be a negative predictor of how much access-oriented
multi-client lobbying occurs within a political system. With turnover, new lawmakers re-
place former incumbents. With fewer relationships, or with relationships of shorter value,
lobbyists lose some ability to claim that they enjoy exclusive relationships with particu-
lar incumbents. In turn, turnover reduces lobbyists’ abilities to attract clients who seek
agents with relationships and access. Legislative turnover therefore disrupts the structure
of a state’s lobbying community by undermining the value of relationships and reducing the
incidence of multi-client lobbying.1
While our narrative helps to explain why clients hire multi-client lobbyists more often
under conditions of declining legislative turnover, we hasten to add that some clients may
remain loyal to their lobbyists in the short run, even under conditions of growing turnover.
Clients cannot observe the lobbying efforts of their representatives directly (Drutman 2015),
so reductions in political connections may not result in immediate reductions in multi-client
lobbyists for all clients. Instead, some may prefer to maintain their familiar lobbyist rela-
tions instead of seeking out and training new lobbyists. Nevertheless, we still expect to find
that changes (both positive and negative) in legislative turnover are correlated with changes
in multi-client lobbying. Gray and Lowery (1996b) found that interest groups frequently
mobilize (lobby) and demobilize (year after year) such that communities of organized inter-
ests are constantly changing. Moreover, organized interests have tended to grow in number
historically such that there are usually many new entrants into the advocacy community.
While there are always some entrenched lobbyists and interests, newly-mobilized interests
must choose between established, multi-client lobbyists and single-client (likely in-house)
lobbyists. Given that (as we show below) multi-client lobbying has tended to increase across
most states, we expect the relationship between multi-client lobbying and turnover to reflect
1We do not contend that multi-client lobbyists advertise only relationships. In order to attract clients,
lobbyists may develop expertise regarding the details of public policy or even procedural knowledge of how
legislatures operate (LaPira and Thomas 2014). We do argue, however, that (among all assets that lobbyists
may acquire) only personal relationships lose value in response to legislative turnover and, as a result,
multi-client lobbying decreases on balance in response to turnover. Under conditions of increasing turnover,
legislators have less expertise and knowledge, on average, and informational assets increase in value (Carey
1996; Carey, Niemi, and Powell 2000; Mooney 2007). If lobbyists achieve influence by sharing information
with lawmakers (as in Hall and Deardorff 2006), and also attract multiple clients due to their expertise and
knowledge, then we would expect multi-client lobbying to be correlated positively with turnover. While our
access-based narrative does not preclude the possibility that at least some lobbyists attract multiple clients
because of their informational assets, we nevertheless expect to see a negative correlation since personal access
is still required for lobbyists to convey their expertise or knowledge to lawmakers. Without the ability to gain
access or speak with those in power, lobbyists cannot share information (including valuable information) with
lawmakers. Other research has found that personal relationships are more universally valuable across issue
areas than policy knowledge (Bertrand et al. 2014). Relationships form over years of repeated interactions
and are strengthened during elections when lobbyists give campaign donations (Rosenthal 1993, 94-99).
how well multi-client lobbyists have come to dominate interest representation in legislatures
with different levels of turnover. Such historical changes in turnover thus help to explain the
rise of local K Streets. The presence of durable or sticky lobbyist-client pairings biases our
findings away from the hypothesized correlation.2
Measuring Multi-client Lobbying
To test our expectations, we develop a state-level measure of multi-client lobbying that
focuses on the extent to which lobby contracts (clients) are concentrated among lobbyists.
We begin by compiling lists of lobbyist-client pairings described by Strickland (2019). These
lists indicate which lobbyists are authorized or registered to represent which clients in a
given state and year. Using the lists, we generate networks in which nodes represent clients
and connections (also known as “edges”) represent their shared lobbyists. In such networks,
larger numbers of connections generate higher density scores and signify more multi-client
As an illustration, Figure 1 shows the first twenty lobbyist-client pairings from the list of
registered lobbyists for the state of Alaska in 2000. The list shows the presence of three multi-
client lobbyists (e.g., Pat Clasby, Mitchell D. Gravo, Joe L. Hayes).3Using this abbreviated
2Our account diverges somewhat from previous accounts of turnover and lobbying, particularly those
based on the information lobbyists provide (see Hall and Deardorff 2006). According to prior accounts,
lobbyists provide valuable information to their legislator allies. Since term-limited legislators have reduced
policy expertise, they are presumed to rely more upon information sources like staffers, bureaucrats, and
lobbyists for information (Carey 1996; Carey, Niemi, and Powell 2000; Mooney 2007). Hence, with term
limits, lobbyists and those other actors are presumed to have more influence. Carey, Niemi, and Powell (2000,
83), Moncrief and Thompson (2001), Sarbaugh-Thompson et al. (2004), and Powell (2012) have each found
some evidence supporting these claims. While we do not doubt the value of information to legislators, we
instead link turnover and lobbying based on access being a precursor to informational subsidies. Popular and
scholarly advocates for term limits rely on access-based arguments. Both Ralph Nader and Tom Steyer, for
instance, relied on the access-driven perspective of lobbyists in their campaigns. Some scholarly accounts have
lent partial credence to this account, claiming that term limits decrease the probability of legislator “capture”
(Struble and Jahre 1991; Daniel and Lott 1997; Gordon and Unmack 2003). Capell (1996) argues that term
limits decrease predictability for groups by increasing turnover among legislative leadership, thereby making
it more difficult for groups to strategize and achieve influence. We do not dispute any claims for or against
information- and access-driven accounts of turnover and lobbying. Rather, one may bridge the accounts
by suggesting that turnover disrupts specific relationships but increases the information-based influence of
lobbyists generally.
3For all analyses, we treat lobbyist-client relations as non-directional.
Figure 1: Alaska Registered Lobbyists and Clients, 2000 (excerpt)
Lobbyist Client
Mark S. Hickey 3M Alaska Branch
Jan MacClarence Abused Women’s Aid in Crisis, Inc.
Sam Kito, Jr. Alaska Community Colleges’ Federation of Teachers
Reed R. Stoops Aetna
John L. George Aflac
Pat Clasby AgeNet
Lisa M. Parker Agrium
Jan Bouch Alaska Action Trust
Kimberly S. Rose Alaska Air Carriers Association
Kim Hutchinson Alaska Airlines
J. M. Walsh Alaska Association Independent Insurance Agents
Pat Clasby Alaska Association of Homes for Children
Joe L. Hayes Alaska Association of Realtors, Inc.
Mitchell D. Gravo Alaska Association Private Career Educators
Joe L. Hayes Alaska Auto Dealers Association
Thyes J. Shaub Alaska Bankers Association
Mark M. Higgins Alaska Bingo Supply, Inc.
Randy Virgin Alaska Center for the Environment
Mitchell D. Gravo Alaska Chiropractic Society
Caren Robinson Alaska Civil Liberties Union
list of pairings, we generate the network graph depicted in Figure 2. In the network, there
are twenty nodes (squares) that each represent one of the client organizations that registered
to lobby. Nodes are connected to each other by edges (lines) only if they shared at least one
common lobbyist. In the small network, three pairs of clients are connected to each other
because they hired a common lobbyist with another organization.
To measure our dependent variable of interest, overall state-year multi-client lobbying, we
use networks of registered lobbyist-client pairings as follows. First, we uploaded cleaned lists
of lobbyist-client pairings into Ras edgelists. These lists were then converted to networks
in which clients (nodes) are connected via shared lobbyists (ties), like the network depicted
in Figure 2.4The density score for each network is captured by how many ties there are in
the network, compared to the total number of all possible ties (Wasserman and Faust 1994,
4Rcode used to execute this transformation is included in the online appendix.
101-103). In any given network, there might be gtotal nodes (clients). Given a network of
gtotal nodes, the greatest possible number of edges within such a network is given by:
The corresponding density (∆) of the graph equals the proportion of existing edges (E) to
the maximum possible number of edges:
∆ = E
In words, density is the number of edges divided by the total possible edges that would
appear when all clients are represented by one massive multi-client lobbyist. For example,
there are twenty unique clients in the network in Figure 2. If all of them were represented
by one lobbyist, then there would be exactly 190 edges in the network. There are actually
3 edges in the network, so the corresponding density score is 3/190 = 0.016. Compare the
network in Figure 2 with the one presented in Figure 3, which is constructed using the first
twenty lobbyist-client pairings registered in Alaska for 2020. From the network in Figure
3, there are eighteen unique clients. If all of them were to be represented by one lobbyist,
then there would be 153 edges. The eleven edges in the network produce a density score of
approximately 0.072. This indicates that there is more multi-client lobbying (as measured by
edges) in the second network than in the first, relative to the maximum potential amount.5
Our method of measuring the prevalence of multi-client lobbying constitutes a notable
improvement over existing measures. Our method assigns a specific score to every network
5Within the networks we produce, clients may be connected to each other by more than one edge. This
occurs whenever a set of clients are all represented by two or more lobbyists (who may be members of firms).
Under such circumstances, our density measure has no upper bound since an infinite number of multi-client
lobbyists could presumably represent all of the clients simultaneously. In reality, however, our measures for
density rarely exceed 0.2. For network-level density statistics to capture the amount of multi-client lobbying
within a network of registered clients accurately, one other adjustment is made: loops or self-connecting
edges are not factored into a network’s density measure. Such loops appear within the main diagonal of the
adjacency matrices of our one-mode networks (i.e. networks with one kind of node) whenever interest groups
hire any number of lobbyists (whether multi- or single-client) to represent them (i.e., the main diagonal
provides the total number of lobbyists hired by each individual client).
Figure 2: Partial Network of Alaska Lobby Clients, 2000
Figure 3: Partial Network of Alaska Lobby Clients, 2020
(list) of registered lobbyist-client pairings that is independent of the number of clients within
each network. Networks of varying sizes can all have roughly equal density measures given
equal incidences of multi-client lobbying. For example, networks of 50, 100, and 200 clients
each are assigned the density score of 0.0369 if they contain 45, 182, and 733 ties, respectively.
This approach is therefore an improvement over Strickland’s (2019) attempt to measure
multi-client lobbying. He predicted the number of unique lobbyist-client pairings within
each state while holding constant totals of lobbyists and clients. His approach does not
assign specific values to states. The approach is problematic to the extent that it relies on
large numbers of state observations and cannot be used to compare small numbers of states
to each other. By assigning specific values to networks of state lobbyists, we overcome both
these limitations.6
Our network density measures come from lists of registered lobbyist-client pairings pro-
duced within the American states dating from 1986 to 2013. These data match or exceed the
extensiveness of any previous data set on state-level lobbying in terms of both cross-sectional
and over-time variation. We draw these lists from three sources. First, we collected lists pub-
lished by secretaries of state and ethics agencies, available online or—most commonly—in
state libraries, archives, and document depositories. This meant traveling to various states
over several years. Second, we included lists published by Wilson (1990). Finally, we turned
to lists provided by the non-partisan National Institute on Money in State Politics. Lists
from the Institute were cleaned of duplicate lobbyist-client pairings. Institute lists have been
used elsewhere in research, on a smaller scale (e.g., Lowery et al. 2012; Gray et al. 2015).
In the states, multi-client lobbying is notably more prevalent today than several decades
6Despite these strengths, we note a shortcoming of using lists of registered lobbyists: the lists tell us little
about the actual nature of individual lobbyist-client pairings. For example, the lists do not report payments
or salaries, precluding us from measuring directly whether a client was represented by a firm or an in-house
advocate, or from measuring the value of lobbyists to clients directly. Nevertheless, we maintain that lists of
registered lobbyists are useful tools for measuring the incidence of multi-client lobbying since the lists clearly
indicate which lobbyists were authorized to represent which clients.
ago. In 1989 (N= 48), the average density score among states was 0.017 (σ= 0.014). In
contrast, the average state in 2011 (N= 47) exhibited a density score of 0.032 (σ= 0.030).7
To put this increase into perspective, in two networks each consisting of 100 different interest
groups, a network with a density score of 0.017 would have approximately 84 total ties linking
clients. A network with a score of 0.032 would have approximately 158 ties.8
Test 1: Legislative Turnover and Multi-client Lobbying
With two separate tests, we use density scores to test our expectations regarding turnover
and multi-client lobbying. In the first (“Test 1”), we regress network density directly on a
measure of legislative turnover, discussed at greater length below. In the second (“Test
2”), we adopt a difference-in-differences style approach to examine how one institutional
inducement of turnover—the implementation of legislative term limits—appears to influence
lobby network density. In both cases, we find support for our argument: greater turnover is
associated with less dense lobby networks or less multi-client lobbying.
Explanatory Variables
Before examining term limits in Test 2, we first examine how legislative turnover in gen-
eral covaries with lobby network density. In addition to detailing how we measure turnover,
we also control for several additional variables that may influence observed lobby network
density. Our measurements of these explanatory variables are detailed as follows.
Legislative Turnover. Turnover in a legislature occurs when new legislators replace in-
cumbent legislators who lost re¨election, were term-limited out of office, retired, or even died
while in office. We measure turnover, our primary independent variable of interest, using data
7These figures are calculated using only states in which lobbyists and clients have to re-register during
each legislative session. Observations are therefore excluded from Michigan and New Jersey.
8Additional descriptive information regarding lobby network density and legislative turnover can be found
in the online appendix.
compiled by Moncrief, Niemi, and Powell (2004).9Since house and senate assemblies often
exhibit different turnover rates, and because our lists of registered lobbyists include those
who targeted either representatives or senators (or both), we generated single state-level
turnover rates by weighting lower- and upper-chamber rates by their respective membership
sizes. If a state’s lower chamber contained twice as many members as the state’s upper
chamber, for example, then the lower chamber’s turnover rate was weighted twice as heavily
as the turnover rate for the upper chamber.10 This variable therefore captures the percentage
of new members in state assemblies. Turnover rates were typically available biennially, so bi-
ennial observations were repeated for years that occurred between elections. Since elections
typically occurred during even-numbered years, and because legislators typically were sworn
into office during the subsequent odd-numbered years, this variable is accordingly shifted to
reflect the percentage of new members for each session. Moreover, since repeating turnover
observations might artificially reduce the size of our standard errors, we also estimate models
in which only turnover observations from inaugural years are used. This effectively reduces
our sample size by one half, but does not alter our substantive results.11
Control Variables Given that we expect rates of multi-client lobbying to vary in response
to the value of personal relationships with legislators, we also expect to see a variety of other
correlations appear within our data set. We first control for each legislature’s membership
size. Tollison and McCormick (1981) argue that individual legislators have more proportional
influence over policy outcomes in small legislature than in large ones. This implies that
relationships with incumbents are more valuable in small assemblies, although this effect
9We thank the authors for kindly providing turnover measurements that span our entire data set.
10For Nebraska’s Unicameral, we employed the senate’s turnover rate, although regressions with one-party
dominance exclude observations from this state.
11We expect to find evidence for our expectations despite legislatures with higher turnover producing more
former legislators. Former members of Congress have been found to be among Washington’s most popular
lobbyists (LaPira and Thomas 2017). In state legislatures with high turnover, we should expect there to be
more former legislators looking for work, but with declining marginal increases (Strickland 2020b). Moreover,
Powell (2012, 193 - 98) finds that legislators who serve for shorter periods of time are more likely to perceive
themselves becoming lobbyists after serving in the legislature. If such patterns are present in the states, then
finding evidence for our expectations would be more difficult since former legislators who become lobbyists
usually represent multiple clients.
might also be due to members of smaller assemblies representing larger districts (see Powell
2012, 42).
We also control for each legislature’s staff resources. Kattelman (2015) found that more
interest groups are active in professionalized legislatures than in citizen or amateur legisla-
tures. This implies that additional staff persons in a legislature might allow for more access
for interest groups, thereby reducing their need for groups to hire (gatekeeping) lobbyists
who have exclusive relationships with legislators. Staff persons might also experience lower
turnover than legislators (particularly in term-limited assemblies), thereby further reducing
the need for lobbyists who personally know legislators. We include in our analyses Bowen
and Greene’s (2014) measure of staff spending across state legislatures. We expect that
states with higher staff expenditures will exhibit lower levels of lobby network density or
multi-client lobbying.
Additionally, one-party dominance may suppress multi-client lobbying by reducing the
number of lobby firms with partisan ties in a state. Lobby firms often develop ties with
members of single parties, and represent clients whose interests are more closely aligned
with the ideologies of those parties (cf., Furnas, Heaney, and LaPira 2019). In states with
more partisan competition, at least two partisan camps of lobby firms might exist in order to
facilitate access to legislators in different parties. When legislatures are dominated by single
parties, however, there might be fewer lobby firms. We include a folded 6-year Ranney
(1976) index in our models with the expectation that this measure (which captures the level
of one-party domination) is negatively associated with multi-client lobbying.
We control for whether a state has direct democracy. Boehmke (2002) finds that interest
populations are about 17 percent greater in states with direct democracy than in states
without it, and that most of the additional groups consist of citizens’ interests. It is possible
that citizens’ interest groups behave differently from other interests, particularly in their
propensity to hire multi-client lobbyists (see Strickland 2020a). We include a dichotomous
indicator in our models for direct-democracy states.
We also control for whether a state’s legislature did not convene during a year when
lobbyists where nevertheless required to register. Biennial sessions used to be much more
common among the states prior to the 1960s, and only four legislatures currently meet
once every two years (see Squire 2012). In some of these states, including North Dakota
and Texas, lobbyists are required to register every year, even when the legislature does not
convene. Hence, density measures are likely affected by the biennial absence of legislators.
Finally, we control for a variety of lobby regulations that might affect which lobbyists
register. Newmark (2005) proposes a measure of lobby laws that consists of three compo-
nents: lobbyist registration criteria, activities that lobbyists are prohibited from engaging
in, and reporting requirements for lobbyists. Strickland (2019) presents evidence that reg-
istration criteria increase numbers of multi-client lobbyists, but that prohibitions and more
reporting requirements have counteracting effects. We include his measures of these laws
in our models, including the interactive effects between them. We also control for whether
states allow lobbyists to register as members of firms. California, New Jersey, New York,
and Pennsylvania allow lobbyists or clients to register as members of firms, which artificially
magnifies the number of lobbyist-client pairings or ties in their networks. We include a
dummy indicator for these states. We also include a dummy indicator for whether lobbyist
or client registrations do not expire from one year to the next. Michigan’s lobbyist law,
adopted in the early 1980s, does not require lobbyists to re-register during each session. In
New Jersey, the Election Law Enforcement Commission does not maintain termination dates
for individual clients, thereby allowing the names of clients to accumulate under the names
of individual lobbyists over time. In both states, density measures are likely inflated due to
the over-time accumulation of clients under lobbyist names. Finally, we control for whether
a state’s lobby law required employees of lobbyists to register even if they did not lobby
lawmakers. Only Arizona has such a requirement, and the law may lead to artificially high
density measures in that state.
Design and Specification
In our first test, we estimate straightforward, linear regressions of lobby network density
and legislative turnover at the state-year and state-session levels. Our dependent variable of
interest is lobby network density, a quotient that can assume values between zero and infinity
(although observations greater than 0.2 are rare). In our first set of models (Models 1 and 2),
we leverage both within- and across-state variation to test our hypotheses, while the second
set leverages only within-state variation—holding unobserved cross-state variation constant.
Since observations are repeated for multiple years within states, state-specific confounders
other than control variables may affect our observations. We address this issue in two
ways. First, to address heteroskedasticity by state, we estimate models with standard errors
clustered by state but without state and year fixed effects (Primo, Jacobsmeier, and Milyo
2007). Second, as prefaced above, we generate models with both state and year fixed effects:
Densityit =βTurnoverit +φXit +αi+τt+it (1)
In this specification, αis a state fixed effect and τis a fixed effect for year. State-level
effects capture state-level means in density. Year-level effects capture national trends. As
a result, the coefficient estimates that these models produce are based only on within-state
changes in density that occur over time and in response to turnover changes (Mummolo and
Peterson 2018). Nevertheless, as we underscore below, our empirical tests provide strong
and consistent support for the hypothesized relationships in the data. In all models, we
introduce a vector of the control variables, Xit, described above.
Models 1 and 3 of Table 1 were estimated using our full set of turnover observations,
while Models 2 and 4 were estimated using observations only from inaugural years (usually
odd-numbered years) in which new legislators were installed. Since Nebraska’s unicameral
legislature is officially non-partisan, observations from that state are missing from all the
models. Nevertheless, including those observations does not alter our results in any mean-
ingful way. Our dependent variable (lobby network density) has been multiplied by 100 for
easier reporting of coefficients.
The results presented in Table 1 provide support for our expectations regarding turnover.
Across all model specifications, legislative turnover is a discernible, negative predictor of
lobby network density. Beyond statistical significance, this relationship represents a notable
substantive association. According to Model 4, if a legislature experienced an increase in
turnover by one standard deviation (i.e., around 10.24 percent more members leaving) from
one session to the next, then the corresponding state’s lobby density score would decrease by
approximately 0.0046 (or 12.45 percent of one standard deviation in density). For perspec-
tive, in a state with 100 clients and a density score of 0.05 or 248 ties, such an increase in
turnover would reduce the number of ties to roughly 225, or (in other words) result in about
23 contracts with multi-client lobbyists being dissolved. A larger increase in turnover from
one session to the next, from 10 to 40 percent or about three standard deviations, would
decrease density by about 0.01344 units, on average. Such an increase in turnover, which
is a realistic but extreme change given our data, would reduce the network’s number of ties
to roughly 181.12 These trends do not account for numbers of lobbyists, clients (nodes), or
pairings within each lobby network, but as we show in the online appendix, the association
between turnover and multi-client lobbying remains statistically and substantively significant
when controlling for those totals.
Other variables provide mixed or limited insight into rates of multi-client lobbying. In
models with clustered standard errors, there is weak evidence of more multi-client lobbying
in states with larger assemblies. This evidence is reversed when one estimates models with
state and year fixed effects. Since models with effects estimate coefficient sizes based only on
12We provide descriptive information regarding legislative turnover in the online appendix. The biggest
shifts in turnover within our data set include a shift from 61.7 to 21.7 percent in Alaska between 1993 and
1995, and a shift from 19.6 to 59.6 percent in Michigan between 2001 and 2003.
Table 1: Legislative Turnover and Multi-client Lobbying
Dependent variable:
Lobby Network Density x 100
Model 1 Model 2 Model 3 Model 4
Legislative Turnover -0.042-0.048-0.040∗∗∗ -0.045∗∗
(0.018) (0.021) (0.011) (0.017)
Legislature Size 0.002 0.003 -0.062∗∗ -0.069
(0.002) (0.002) (0.021) (0.032)
Staff Spending (in millions) -2.047∗∗ -2.050∗∗∗ -4.395∗∗∗ -4.293∗∗∗
(0.681) (0.593) (0.646) (0.866)
One-Party Dominance -1.431 -0.516 -2.032 -1.479
(1.611) (1.752) (1.245) (1.931)
Direct Democracy -0.304 -0.291 -0.240 -0.575
(0.385) (0.390) (1.068) (2.376)
Not in Session 2.002-1.0851.241-1.805
(0.957) (0.472) (0.526) (2.442)
Lobbyist Definitions 0.235 0.230 0.386 0.326
(0.331) (0.349) (0.205) (0.288)
Lobbyist Prohibitions 0.655 0.785 -0.0289 0.217
(0.391) (0.450) (0.467) (0.680)
Lobbyist Reporting 0.173 0.179 0.3990.455
(0.147) (0.150) (0.172) (0.240)
Definitions * Prohibitions -0.042 -0.039 0.094 0.096
(0.104) (0.121) (0.101) (0.149)
Definitions * Reporting -0.015 -0.015 -0.091-0.095
(0.061) (0.065) (0.045) (0.065)
Firms Register 13.542∗∗∗ 13.628∗∗∗ 8.410∗∗∗ 11.895∗∗∗
(3.429) (2.916) (0.928) (1.279)
Non-Expiring Registrations 9.471∗∗∗ 10.037∗∗∗ 19.420∗∗∗ 14.391∗∗∗
(2.524) (1.149) (1.483) (2.428)
Lobby Employees Register 1.0401.547∗∗ -0.370 0.303
(0.475) (0.488) (1.718) (3.302)
Constant 2.500∗∗ 2.356∗∗ 10.750∗∗∗ 11.014
(0.853) (0.803) (3.042) (4.767)
Fixed Effects? X X
Observations 684 352 684 352
No. of States 49 49 49 49
R20.611 0.656 0.787 0.792
Adjusted R2- - 0.756 0.738
Note: p<0.05; ∗∗p<0.01; ∗∗∗ p<0.001 on two-tailed tests.
within-state changes, we believe that these findings are artifacts of a lack of variation: our
sample includes only four legislature membership size changes, in three states. With regard
to staff spending, our results suggest that increases in spending are associated with decreases
in multi-client lobbying. One-party dominance and direct democracy status are not good
predictors of multi-client lobbying in any of the models we present. While session status is
a significant predictor of lobbying in some models, this result differs between models (since
Models 2 and 4 largely exclude observations from years during which legislatures did not
meet). Our results also show that lobby laws measured by Newmark (2005) have little effect
on multi-client lobbying but that idiosyncratic registration procedures (e.g., allowing firms
to register, having non-expiring registrations, or requiring lobbyist employees to register) do
matter, at least in terms of how lobbyist lists are structured. In general, legislative turnover
is the most consistent predictor.
Test 2: Term Limits and Multi-client Lobbying
In our turnover analyses, we found evidence that legislative turnover erodes the value of
relationships with incumbents and thereby reduces multi-client lobbying. In a second set of
tests, we examine whether the implementation of legislative term limits is also negatively
correlated with multi-client lobbying. We make use of three different estimation strategies
in order to demonstrate the robust negative relationship between term limits and lobby net-
work density apparent in our data.
First, we estimate models with two-way fixed effects (TWFE) and the same vector of
control variables and from Test 1, simply replacing our T urnoverit variable with an indicator
for whether term limits were in effect within a state and year:
Densityit =βTerm Limitsit +φXit +αi+τt+it (2)
While this specification has some advantages, term limits went into effect in states at
different points in time. Goodman-Bacon (2018) argues that, in models with fixed effects,
these differences in treatment year affect coefficient estimates. Thus, to test the robustness of
our findings, we next treat the adoption of term limits as a staggered difference-in-differences
design and rerun our models in two ways. In the first set of models (one nested, the other
with the Xit variables), we add linear time trends for each state instead of traditional fixed
effects. The results of these tests are found in Models 7 and 8 of Table 2.
In the second set of models, we adopt the aggregation approach suggested by Bertrand et
al. (2004). This approach attempts to control for baseline differences between treated and
control units, albeit quite aggressively. The approach first estimates the full model in (2),
absent the treatment variable of interest. Using this model, one then generates predicted c
and resulting residuals Rit =Yit c
Yit. The residuals from only the treated units are then
binned into just two panels: one pre-treatment (pi= 0), and one post-treatment (pi= 1).
Rip are then regressed onto the treatment variable:
Rip =βTerm Limits ip +ip (3)
This approach is thought to be particularly conservative in that it severely reduces statistical
power in several regards. Nevertheless, it provides an additional robustness check on the
results presented in Table 1.
Finally, before presenting results from these analyses, it is important to note that our
data exhibit pre-treatment trends consistent with the parallel trends assumption underlying
these models. We present a visualization of these trends and examine pre-treatment leads
in the online appendix.
We summarize the results of our term limits analyses in Table 2. Across all specifications,
we find support for the claim that the implementation of term limits is associated with drops
in lobby network density. While the statistical significance of these findings is consistent
among all specifications, the substantive significance of the result is also noteworthy. Holding
all other variables at their means or optimal values, term-limited states exhibit densities that
are approximately 0.0053 units lower than similar non-term-limited states, on average. These
differences are notably large in comparison to the average lobby network density of 0.00307,
even after averaging over all time periods.13
Figure 3 further underscores the magnitude of these results. In the graph, gray dots and
lines represent the actual observed densities in 1989 and 2011, for states that never adopted
term limits.14 As noted earlier, nearly all of the states in the data experienced a noticeable
growth in lobby network density over the 20-year period between these sets of observations.
In black, we depict a counterfactual for the non-term-limited states: the predicted density
in 2011 for each non-term-limited state (on the basis of the above regression with the small-
est effect), were each state to have introduced term limits between 1989 and 2011. As the
graph plainly depicts, the introduction of term limits could have significantly attenuated the
observed growth in lobby network density observed in non-term-limited states. For added
perspective on the magnitude of this relationship, we add box and whisker plots for all lobby
network densities (in both term-limited and non-term-limited states) next to the 1989 and
2011 scatter plots.
Taken together, these results suggest that term limits achieve one type of intended objec-
tive: the disruption of relationships between legislators and lobbyists. This does not prove
13These figures are generated from Model 2. The predicted densities are divided by 100 since our dependent
variable had been multiplied by 100 for the regression tables.
14We select 1989 and 2011 for the purposes of this illustration because they are among the most complete
panels in the data set and are entirely pre- and post-term-limit.
Table 2: Term Limits and Multi-client Lobbying
Dependent variable:
Lobby Network Density x 100 Density Residuals
Model 5 Model 6 Model 7 Model 8 Model 9
Term Limits in Effect 1.408∗∗∗ 1.346∗∗∗ 0.592+0.6980.873
(0.395) (0.401) (0.318) (0.342) (0.348)
Legislature Size 0.057∗∗ 0.049
(0.021) (0.033)
Staff Spending (in millions) 0.004∗∗∗ 0.002
(0.001) (0.001)
One-Party Dominance 1.452 0.133
(1.249) (1.288)
Direct Democracy 0.042 0.126
(1.061) (0.370)
Not in Session 0.792 1.1961.5951.642
(0.539) (0.526) (0.725) (0.709)
Lobbyist Definitions 0.4090.512
(0.206) (0.464)
Lobbyist Prohibitions 0.083 0.210
(0.468) (0.188)
Lobbyist Reporting 0.3570.342
(0.172) (0.167)
Definitions Prohibitions 0.074 0.013
(0.102) (0.042)
Definitions Reporting 0.082 0.080
(0.045) (0.079)
Firms Register 5.258∗∗∗ 7.854∗∗∗ 7.551 7.944
(0.884) (0.939) (6.718) (6.702)
Non-Expiring Registrations 1.297 20.119∗∗∗ 319.622∗∗∗ 302.130∗∗∗
(0.941) (1.488) (51.536) (76.052)
Lobby Employees Register 21.734∗∗∗ 0.492 1815.9671680.775
(1.231) (1.795) (813.997) (833.163)
Constant 1.026 8.299∗∗ 65.50573.993 0.646
(0.807) (3.094) (28.645) (63.842) (0.299)
Model TWFE TWFE Lin. Trends Lin. Trends Residuals
Observations 694 684 685 675 165
R20.764 0.786 0.887 0.890 0.037
Adjusted R20.734 0.755 0.868 0.870 0.031
Note: +p<0.10; p<0.05; ∗∗p<0.01; ∗∗∗ p<0.001
Figure 4: Counterfactual Densities for Non-Term-Limited States
Observed lobby densities for non-term-limited states in 1989 and 2011 (gray), compared to projected densities,
were each state to have introduced term limits after 1989 (black). Densities are logged to aid in visualization.
Box and whisker plots refer to the entire distribution of densities in 1989 among both term-limited and
non-term-limited states.
that term limits were, on the whole, a positive democratic reform in the states. Rather, the
results point to a separate set of considerations in the assessment of term limits that mer-
its additional scholarly examination. Moreover, they provide additional evidence consistent
with our assertion that turnover alters the value of individual relationships and affects the
representation of organized interests.
Discussion and Conclusion
In this study, we found that increases in legislative turnover in the American states dis-
rupt lobby networks and serve as negative predictors of multi-client lobbying. These findings
are consistent with a growing literature pointing to the consequences of turnover for legis-
lators and staff persons. Strickland (2020b) finds that there are fewer former legislators
(proportional to all former legislators) registered to lobby in states with higher turnover.
Similarly, McCrain (2018) finds that former congressional staffers enjoy more access as lob-
byists, but that their access fades as their former colleagues leave Congress. We add to
this body of research by showing how higher turnover affects not just lobbyists with prior
government experience but alters entire lobby communities in states.
Our narrative presumes that interest groups are at least somewhat scrupulous when
choosing which lobbyists to hire, or that they have some knowledge regarding their advocates’
levels of access and influence. Recent accounts argue that clients suffer from informational
asymmetries when choosing which lobbyists to hire or how long to continue paying them (see
Drutman 2015, Strickland 2020b). Despite the informational advantages that lobbyists have
over clients, however, we nevertheless find evidence that changes in the value of political
relationships influences how contracts are distributed within lobby networks. Our results
are consistent with a re-sorting of clients among lobbyists in response to turnover, in spite
of the informational asymmetries that clients endure.
More broadly, our findings speak to long-standing debates about the representational ad-
vantages and disadvantages of turnover. Traditionally, such debates have centralized around
key institutional features, such as the frequency of elections and term length, since at least the
framing of the U.S. Constitution (Madison 1787). We believe that our results—the clustering
of clients in the hands of small numbers of lobbyists or firms, as a result of low turnover—
should also raise normative concerns over both the quality and equality of political influence
and representation. Lobbyists with multiple clients have more opportunities to shirk than
those with one client each. Such opportunities grant lobbyists more personal discretion over
which interests are represented faithfully and which ones are neglected. Moreover, if some
lobbyists have gatekeeping relationships and dominate the attention of legislators, then they
are able to extract exorbitant fees for their services. Scholars of interest representation have
long underscored that groups active in legislatures tend to reflect the wealthier and profes-
sional classes of American society (see Schlozman, Verba, and Brady 2012); but, it stands to
reason that the effects of resource differences are magnified in response to these expensive,
exclusive lobbyist-legislator relationships. This logic reflects similar concerns over revolving-
door lobbying. As previous scholarship has shown, former members of Congress tend to
attract many clients and are the best-paid lobbyists in Washington (see LaPira and Thomas
2017, 89). They enjoy exclusive relationships with former colleagues who grant them access
and influence (Makse 2017). These assets allow revolving-door advocates to collect high fees
for their labor: premiums that are more easily afforded by monetarily rich business or occu-
pational groups than by public interest groups (Baumgartner et al. 2009, 199; Berry 1977;
Strickland 2020a). Given historically low turnover in Congress, it should come as no surprise
that former members are among the best paid and most popular lobbyists in Washington.
Future studies may build upon our findings by exploring additional relationships between
legislative turnover and lobbyist value. While the value of lobbyists’ relationships and famil-
iarity with incumbents decreases with turnover, we measure this value indirectly by treating
the incidence of multi-client lobbying as a proxy for the gatekeeping ability of lobbyists. A
more direct measure for gatekeeping value requires examining the salaries that multi-client
lobbyists receive, similar to others’ measures of the salaries of revolving-door lobbyists (i.e.,
LaPira and Thompson 2017; Blanes i Vidal, Draca, and Fons-Rosen 2012). For now, though,
we have provided consistent evidence that increased turnover influences the representation
of organized interests, and we reserve additional research questions for future studies.
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... While the initial set of regression models with fixed effects show that term limits and lobby laws are correlated with the hiring of lobbyists by interest groups, the measure of overall lobbying does not reflect the popularity of multiclient advocates over singleclient ones. Interest groups in some states rely more often on multiclient lobbyists than on single-client ones (see Strickland and Crosson 2016). These differences may prove problematic for measuring the overall level of lobbying. ...
Across the United States over time, numbers of registered interest groups have continued to increase, but these populations mask the total amount of lobbying that is occurring within America’s statehouses. Among registered interests, average numbers of hired lobbyists have increased markedly since the late 1980s. This study both quantifies this increase and identifies a set of causal variables. Previous studies have proposed a variety of short-term, political and long-term, institutional factors that govern rates of lobbying. Using a new data set spanning multiple decades, I find that changes in lobbying can largely be ascribed to institutional variables, including the implementation of term limits and regulations on lobbying. Lobby regulations, one-party dominance, and legislative expenditures also appear to play a role in determining rates of multiclient lobbying. Direct democracy and state spending do not affect the hiring of lobbyists by registered interest groups.
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For decades, critics of pluralism have argued that the American interest group system exhibits a significantly biased distribution of policy preferences. We evaluate this argument by measuring groups’ revealed preferences directly, developing a set of ideal point estimates, IGscores, for over 2,600 interest groups and 950 members of Congress on a common scale. We generate the scores by jointly scaling a large dataset of interest groups’ positions on congressional bills with roll-call votes on those same bills. Analyses of the scores uncover significant heterogeneity in the interest group system, with little conservative skew and notable inter-party differences in preference correspondence between legislators and ideologically similar groups. Conservative bias and homogeneity reappear, however, when weighting IGscores by groups’ PAC contributions and lobbying expenditures. These findings suggest that bias among interest groups depends on the extent to which activities like PAC contributions and lobbying influence policymakers’ perceptions about the preferences of organized interests.
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This article examines lobbying firms as intermediaries between organized interests and legislators in the United States. It states a partisan theory of legislative subsidy in which lobbying firms are institutions with relatively stable partisan identities. Firms generate greater revenues when their clients believe that firms’ partisan ties are valued highly by members of Congress. It hypothesizes that firms that have partisan ties to the majority party receive greater revenues than do firms that do not have such ties, as well as that partisan ties with the House majority party lead to greater financial returns than do partisan ties to the Senate majority party. These hypotheses are tested using data available under the Lobbying Disclosure Act from 2008 to 2016. Panel regression analysis indicates that firms receive financial benefits when they have partisan ties with the majority party in the House but not necessarily with the Senate majority party, while controlling for firm-level covariates (number of clients, diversity, and organizational characteristics). A difference-in-differences analysis establishes that Democratically aligned lobbying firms experienced financial losses when the Republican Party reclaimed the House in 2011, but there were no significant differences between Republican and Democratic firms when the Republicans reclaimed the Senate in 2015.
The canonical difference-in-differences (DD) estimator contains two time periods, ”pre” and ”post”, and two groups, ”treatment” and ”control”. Most DD applications, however, exploit variation across groups of units that receive treatment at different times. This paper shows that the two-way fixed effects estimator equals a weighted average of all possible two-group/two-period DD estimators in the data. A causal interpretation of two-way fixed effects DD estimates requires both a parallel trends assumption and treatment effects that are constant over time. I show how to decompose the difference between two specifications, and provide a new analysis of models that include time-varying controls.
Across the United States over time, numbers of registered interest groups have continued to increase, but these populations mask the total amount of lobbying that is occurring within America’s statehouses. Among registered interests, average numbers of hired lobbyists have increased markedly since the late 1980s. This study both quantifies this increase and identifies a set of causal variables. Previous studies have proposed a variety of short-term, political and long-term, institutional factors that govern rates of lobbying. Using a new data set spanning multiple decades, I find that changes in lobbying can largely be ascribed to institutional variables, including the implementation of term limits and regulations on lobbying. Lobby regulations, one-party dominance, and legislative expenditures also appear to play a role in determining rates of multiclient lobbying. Direct democracy and state spending do not affect the hiring of lobbyists by registered interest groups.
It has been predicted that term limits in state legislatures-soon to be in effect in eighteen states-will first affect the composition of the legislatures, next the behavior of legislators, and finally legislatures as institutions. The studies in Term Limits in State Legislatures demonstrate that term limits have had considerably less effect on state legislatures than proponents predicted. The term-limit movement-designed to limit the maximum time a legislator can serve in office-swept through the states like wildfire in the first half of the 1990s. By November 2000, state legislators will have been “term limited out” in eleven states. This book is based on a survey of nearly 3,000 legislators from all fifty states along with intensive interviews with twenty-two legislative leaders in four term-limited states. The data were collected as term limits were just beginning to take effect in order to capture anticipatory effects of the reform, which set in as soon as term limit laws were passed. In order to understand the effects of term limits on the broader electoral arena, the authors also examine data on advancement of legislators between houses of state legislatures and from the state legislatures to Congress. The results show that there are no systematic differences between term limit and non-term limit states in the composition of the legislature (e.g., professional backgrounds, demographics, ideology). Yet with respect to legislative behavior, term limits decrease the time legislators devote to securing pork and heighten the priority they place on the needs of the state and on the demands of conscience relative to district interests. At the same time, with respect to the legislature as an institution, term limits appear to be redistributing power away from majority party leaders and toward governors and possibly legislative staffers. This book will be of interest both to political scientists, policymakers, and activists involved in state politics.
Building on previous work on lobbying and relationships in Congress, I propose a theory of staff-to-staff connections as a human capital asset for Capitol Hill staff and revolving door lobbyists. Employing lobbying disclosure data matched to congressional staff employment histories, I?nd that the connections these lobbyists maintain to their former Hill coworkers primarily drive their higher relative value as lobbyists. Speci?cally, a 1 standard deviation increase in staff connections predicts an 18% increase in revenue attributed to the lobbyist during her?rst year. I also?nd that the indirect connections lobbyists maintain to legislators through knowing a staffer in a legislative of?ce are of potential greater value than a direct connection to a senator given a large enough number of connections. This article sheds additional light onto the political economy of the lobbying industry, making an important contribution to the literature on lobbying and the revolving door phenomenon. © 2018 by the Southern Political Science Association. All rights reserved.
Fixed effects estimators are frequently used to limit selection bias. For example, it is well known that with panel data, fixed effects models eliminate time-invariant confounding, estimating an independent variable’s effect using only within-unit variation. When researchers interpret the results of fixed effects models, they should therefore consider hypothetical changes in the independent variable (counterfactuals) that could plausibly occur within units to avoid overstating the substantive importance of the variable’s effect. In this article, we replicate several recent studies which used fixed effects estimators to show how descriptions of the substantive significance of results can be improved by precisely characterizing the variation being studied and presenting plausible counterfactuals. We provide a checklist for the interpretation of fixed effects regression results to help avoid these interpretative pitfalls.
Although legislative studies is thriving, it suffers from one glaring weakness: a lack of truly comparative, cross-institutional research. Instead, research focuses overwhelmingly on the U.S. Congress. This unfortunate fixation limits the way scholars approach the testing of many compelling theories of legislative organization and behavior, and it ignores the invaluable research possibilities that comparison with the 99 American state legislative chambers offers. State legislatures are easily compared to Congress: They arise out of the same political culture and history. Their members represent the same parties and face the same voters in the same elections using the same rules. And the functions and roles are the same, with each fully capable of initiating, debating, and passing legislation. None of the methodological problems found when comparing presidential system legislatures with parliamentary system legislatures arise when comparing Congress and the state legislatures. However, while there are great similarities, there are also important differences that provide scholars leverage for rigorously testing theories. The book compares and contrasts Congress and the state legislatures on histories, fundamental structures, institutional and organizational characteristics, and members. By highlighting the vast array of organizational schemes and behavioral patterns evidenced in state legislatures, the authors demonstrate that the potential for the study of American legislatures, as opposed to the separate efforts of Congressional and state legislative scholars, is too great to leave unexplored.