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 30 years, multi-client lobbyists have become more prevalent at both the federal (Drutman, Reference Drutman2015) and state (Strickland, Reference Strickland2020a) 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, Reference Drutman2015, 164–6).

The popularity of multi-client lobbyists raises at least two concerns over the representation 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, Reference Bernheim and Whinston1986). Since clients do not coordinate with each other and cannot observe their lobbyists working, multi-client lobbying may give rise to 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 problems. 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, Reference Lowery and Marchetti2012). Such selective shirking may occur especially if a lobbyist represents clients with conflicting interests. As an example, Goldstein and Bearman (Reference Goldstein and Bearman1996) 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 conflicts of interest (e.g., Yanamadala et al., Reference Yanamadala, Bragg, Roberto and Brownell2012), and have suggested 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, Reference Rosenthal1993, 39).

Second, at an aggregated level, multi-client lobbying magnifies inequities in representation 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, Reference Strickland2020a). The very reason behind these exorbitant fees—the universal, 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 (Gray and Lowery, Reference Gray and Lowery1996; Drutman, Reference Drutman2015, 158), which are of universal appeal to many types of interests, and which allow for high fees (LaPira and Thomas, Reference LaPira and Thomas III2017). Consequently, business firms hire multi-client lobbyists more often than do resource-poor groups, reflecting other resource advantages enjoyed by these interests (Schlozman et al., Reference Schlozman, Verba and Brady2012; Strickland, Reference Strickland2020a; Crosson et al., Reference Crosson, Furnas and Lorenz2020). 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, contacts, and clout... as in other domains of life, the rich get richer and the poor have trouble breaking in” (Rosenthal, Reference Rosenthal1993, 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 lobbying 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. However, 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 familiarity with incoming lawmakers. For example, Meyer and Levine. (Reference Meyer and Levine2018) 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, Reference Mullins and Bykowicz2018). In the states, veteran lobbyists in term-limited states express frustration over having to maintain relationships with and educate new lawmakers continuously (Mooney, Reference Mooney2007, 126–7). These challenges are compounded by the fact that freshmen legislators are often hesitant to meet with lobbyists because they view them as corrupt (see Gross, Reference Gross2018 for an example).

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, Reference LaPira and Thomas III2017). In fact, departures of former colleagues from government decrease 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 (Vidal et al., Reference Blanes i Vidal, Draca and Fons-Rosen2012). Similarly, McCrain (Reference McCrain2018) 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 (Reference Strickland2020b) finds that proportionally fewer legislators become lobbyists in states with higher legislative turnover.

Building on these studies, we expect turnover to be a negative predictor of how much access-oriented multi-client lobbying occurs within a political system. With turnover, new lawmakers replace former incumbents. With fewer relationships, or with relationships of shorter value, lobbyists lose some ability to claim that they enjoy exclusive relationships with particular 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.

To be clear, these expectations rely upon the assumption that multi-client lobbyists rely more heavily on relationships, on average, than do their single-client counterparts. We believe this to be a reasonable assumption, based on previous research, and expect to find an inverse relationship between legislative turnover and multi-client lobbying. Certainly, it is possible that lobbyists may advertise skills or expertise other than relationships with incumbent officials, in order to attract clients. Indeed, they may develop expertise regarding the details of public policy, or even procedural knowledge of how legislatures operate (de Figueiredo and Richter, Reference de Figueiredo and Richter2014; LaPira and Thomas, Reference LaPira and Thomas III2014). Still, compared to other assets that lobbyists may acquire, personal relationships are more universally valuable to clients across issue areas—particularly compared to policy knowledge (Bertrand et al., Reference Bertrand, Bombardini and Trebbi2014). After all, lobbyists do not need to be personally acquainted with incumbents in order to develop policy expertise or procedural knowledge, but they do need access to convey such expertise.Footnote ¹ Unsurprisingly, then, as we outline above, much of the research on the revolving door emphasizes how access (as opposed to information) enables legislators-turned-lobbyists to amass especially large clienteles. And, when those relationships are lost or sour, such lobbyists lose significant revenue. As a result, we are confident in the assumption that multi-client lobbyists rely upon relationships and access-granting to a greater degree than other advocates, on average.

Even still, it is worth noting that possible hiring of lobbyists for their informational skills (rather than relational assets) likely simply biases our findings away from the hypothesized correlation. That is, because only personal relationships lose value in response to legislative turnover, a violation of our assumption could push the relationship between turnover and multi-client lobbying in the opposite direction.

Carey (Reference Carey1996), Carey et al. (Reference Carey, Niemi and Powell2000, 83), Moncrief and Thompson (Reference Moncrief and Thompson2001), Sarbaugh-Thompson et al. (Reference Sarbaugh-Thompson, Thompson, Elder, Strate and Ellig2004), and Powell (Reference Powell2012) all argue or find empirically that members of high-turnover assemblies have less policy expertise or procedural knowledge, on average, than members of low-turnover assemblies. As a result, Mooney (Reference Mooney2007) argues that lobbyists are more valuable sources of information in legislatures with high turnover or term limits. Hence, if information-starved legislators grant more access to lobbyists who have more expertise, and if lobbyists attract clients primarily because of their expertise (and not relationships), then we would expect multi-client lobbying to be correlated positively with turnover. As our empirical results will indicate, however, our evidence is not consistent with such a pattern.

Here, it is also worth noting that, under conditions of changing turnover, some clients may remain loyal to their lobbyists in the short run. Clients cannot observe the lobbying efforts of their representatives directly (Drutman, Reference Drutman2015), 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 lobbyists instead of seeking out and training new ones. Here again, such a phenomenon would bias against our finding the hypothesized inverse relationship. Nevertheless, we still expect to find that changes in legislative turnover are correlated inversely with changes in multi-client lobbying, particularly given the growth of interest populations over time.

Using a networks-based measurement method, we show that multi-client lobbying has increased in most states over time, in tandem with decreases in turnover. In other words, lobbyists with multiple clients increasingly came to dominate interest representation in the states—changes which have consistently covaried with changes in legislative turnover. We believe these findings help to explain the historic rise of state-level K Streets.

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 (Reference Strickland2019). 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 lobbying.

As an illustration, Figure 1 shows the first 20 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).Footnote ² Using this abbreviated list of pairings, we generate the network graph depicted in Figure 2. In the network, there are 20 nodes (squares) that each represents one of the client organizations that are registered to lobby. Nodes are connected to each other by edges (lines) only if the corresponding clients shared at least one lobbyist. In the small network, three pairs of clients are connected to each other because they hired a common lobbyist with another organization.

Figure 1.

Alaska registered lobbyists and clients, 2000 (excerpt).

Figure 2.

Partial network of Alaska lobby clients, 2000.

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 R as 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.Footnote ³ The 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., Reference Wasserman and Faust1994, 101–3, ). In any given network, there might be g total nodes (clients). Given a network of g total nodes, the greatest possible number of edges within such a network is given by:

$${{g}\choose{2}} = g ( g-1) /2$$

The corresponding density (Δ) of the graph equals the proportion of existing edges (E) to the maximum possible number of edges:

$$\Delta = {E\over g ( g-1) /2} = {2E\over g ( g-1) }$$

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 20 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 three 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 20 lobbyist–client pairings registered in Alaska for 2020. From the network in Figure 3, there are 18 unique clients. If all of them were to be represented by one lobbyist, then there would be 153 edges. The 11 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.Footnote ⁴

Figure 3.

Partial network of Alaska lobby clients, 2020.

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, where 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.

Our method of measuring the prevalence of multi-client lobbying constitutes a notable improvement over an existing method. Our method assigns a specific score to every network (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 (Reference Strickland2019) 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.Footnote ⁵

Data

Our network density measures come from lists of registered lobbyist–client pairings produced 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 published 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 (Reference Wilson1989). 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., Reference Lowery, Gray, Cluverius and Harden2012; Gray et al., Reference Gray, Cluverius, Harden, Shor and Lowery2015).

In the states, multi-client lobbying is notably more prevalent today than several decades 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).Footnote ⁶ 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.Footnote ⁷

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.

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 >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 et al., Reference Primo, Jacobsmeier and Milyo2007). Second, as prefaced above, we generate models with both state and year fixed effects:

(1)$$\mathit{Density}_{it} = \beta \mathit{Turnover}_{it} + {\phi}{\bf X}_{it} + \alpha_i + \tau_t + \epsilon_{it}$$

In this specification, α is a state fixed effect and τ is 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, Reference Mummolo and Peterson2018). 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, X_it, 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 meaningful way. Our dependent variable (lobby network density) has been multiplied by 100 for easier reporting of coefficients.

Table 1.

Legislative turnover and multi-client lobbying

Note: *p < 0.05; **p < 0.01; ***p < 0.001 on two-tailed tests.

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 network 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 Turnover _it variable with an indicator for whether term limits were in effect within a state and year:

(2)$$\mathit{Density}_{it} = \beta \mathit{Term\_Limits}_{it} + \phi{\bf X}_{it} + \alpha_i + \tau_t + \epsilon_{it}$$

While this specification has some advantages, term limits went into effect in states at different points in time. Goodman-Bacon (Reference Goodman-Bacon2018) 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 X_it 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.

Table 2.

Term limits and multi-client lobbying

*p < 0.05; **p < 0.01; ***p < 0.001.

In the second set of models, we adopt the aggregation approach suggested by Bertrand et al. (Reference Bertrand, Bombardini and Trebbi2014). 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 $\widehat {Y_{it}}$ and resulting residuals $R_{it} = Y_{it} - \widehat {Y_{it}}$. The residuals from only the treated units are then binned into just two panels: one pre-treatment (p = 0), and one post-treatment (p = 1). R _ip are then regressed onto the treatment variable:

(3)$$R_{ip} = \beta \mathit{Term\_Limits}_{ip} + \epsilon_{ip}$$

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.