Votes or Money? Theory and Evidence from the US Congress

Abstract

This paper provides a survey on studies that analyze the macroeconomic effects of intellectual property rights (IPR). The first part of this paper introduces different patent policy instruments and reviews their effects on R&D and economic growth. This part also discusses the distortionary effects and distributional consequences of IPR protection as well as empirical evidence on the effects of patent rights. Then, the second part considers the international aspects of IPR protection. In summary, this paper draws the following conclusions from the literature. Firstly, different patent policy instruments have different effects on R&D and growth. Secondly, there is empirical evidence supporting a positive relationship between IPR protection and innovation, but the evidence is stronger for developed countries than for developing countries. Thirdly, the optimal level of IPR protection should tradeoff the social benefits of enhanced innovation against the social costs of multiple distortions and income inequality. Finally, in an open economy, achieving the globally optimal level of protection requires an international coordination (rather than the harmonization) of IPR protection.

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NBER WORKING PAPER SERIES
VOTES OR MONEY? THEORY AND EVIDENCE FROM THE US CONGRESS.
Matilde Bombardini
Francesco Trebbi
Working Paper 13672
http://www.nber.org/papers/w13672
NATIONAL BUREAU OF ECONOMIC RESEARCH
1050 Massachusetts Avenue
Cambridge, MA 02138
December 2007
The views expressed herein are those of the author(s) and do not necessarily reflect the views of the
National Bureau of Economic Research.
© 2007 by Matilde Bombardini and Francesco Trebbi. All rights reserved. Short sections of text, not
to exceed two paragraphs, may be quoted without explicit permission provided that full credit, including
© notice, is given to the source.

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Votes or Money? Theory and Evidence from the US Congress.
Matilde Bombardini and Francesco Trebbi
NBER Working Paper No. 13672
December 2007
JEL No. D72,H7,P48
ABSTRACT
This paper investigates the relationship between the size of interest groups in terms of voter representation
and the interest group's campaign contributions to politicians. We uncover a robust hump-shaped relationship
between the voting share of an interest group and its contributions to a legislator. This pattern is rationalized
in a simultaneous bilateral bargaining model where the larger size of an interest group affects the amount
of surplus to be split with the politician (thereby increasing contributions), but is also correlated with
the strength of direct voter support the group can offer instead of monetary funds (thereby decreasing
contributions). The model yields simple structural equations that we estimate at the district level employing
data on individual and PAC donations and local employment by sector. This procedure yields estimates
of electoral uncertainty and politicians effectiveness as perceived by the interest groups. Our approach
also implicitly delivers a novel method for estimating the impact of campaign spending on election
outcomes: we find that an additional vote costs a politician between 100 and 400 dollars depending
on the district.
Matilde Bombardini
Department of Economics
University of British Columbia
997 - 1873 East Mall
Vancouver, BC V6T 1Z1
Canada
matildeb@interchange.ubc.ca
Francesco Trebbi
Graduate School of Business
University of Chicago
5807 South Woodlawn Avenue
Chicago, IL 60637
and NBER
ftrebbi@chicagogsb.edu

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1Introduction
The role played by special interest groups in shaping policy-making is hard to ignore. One simple
reason is the considerable size of the amounts the special interest groups (SIGs) inject into the
political system. During the 1999-2000 election cycle the first 50 donor industries disbursed to
incumbents of the 106th Congress a cumulated $368,438,170, about the size of the GDP of a (not
so) small developing economy. In the 2005-2006 election cycle the first 50 donor industries disbursed
the 109th Congress $444,505,353. Much research effort has gone towards understanding the way in
which special interest groups (SIGs) affect the political process and policy formulation, if and how
SIGs buy influence. In particular, within this literature one specific path has been to investigate the
importance of campaign contributions by SIGs to politicians who value such donations as inputs
that increase their probability of electoral success.
One aspect that has received little attention along this path of research is that, since the prob-
ability of being (re-)elected ultimately depends on the number of votes a politician can attract,
the legislator should take into account both the electoral strength of an interest group (i.e. the
share of voting population it represents) and its contributing possibilities when deciding whether
to support or not legislation in favor of such group.1On the one hand, SIGs that represent a large
number of voters in a district also benefit more from a given policy and therefore might contribute
more. On the other hand, such interest groups might be required to make fewer contributions if
they can pledge voter support.2The ability of employers to affect the electoral orientation of their
1The power of firms in terms of voter representation has been at the center of discussion following a recent move by
Wal-Mart: “In August, Wal-Mart distributed a letter to its employees in Iowa and three other states, highlighting what
it said were inaccuracies in criticism by Governor Tom Vilsack, as well as Senators Evan Bayh of Indiana and Joseph
Biden of Delaware and New Mexico’s Governor Bill Richardson. The letter encouraged employees to talk to ‘friends,
neighbours and family about the good that Wal-Mart does’. It also promised that the company would ‘keep you informed
about what these political candidates are saying about your company while on the campaign trail’. Wal-Mart has also
highlighted the significant number of its employees in both swing states. In Ohio its 50,000 workers represent roughly 1
per cent of voters in the 2004 presidential election, enough to be a factor in the current Senate battle between Sherrod
Brown - a Wal-Mart critic - and Mike DeWine, the Republican incumbent. Wal-Mart’s political action committee is
also one of the largest corporate donors to Mr DeWine’s campaign.” (Financial Times - September 30, 2006)
2The idea that politicians may accept lower contributions by firms that represent a large number of voters is
clearly expressed in the following interview to Representative Guy Vander Jagt (R-Michigan): “I have one Fortune
500 company in my district that was so fuddy-duddy that they would never ever, ever do anything to help me. If their
plane was going back to Michigan, they wouldn’t let me ride on it. And that was before we got all these rules in.
Nobody would do it now [accept a ride on a corporate jet], but back then, everyone would do it. When the Washington
Senators were still here, instead of [this company] getting me tickets, I’d scramble around and get them tickets. In
other words, I could not have been treated more shabbily in terms of anything they might do for me. And yet I always
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employees is illustrated by initiatives like the NAM (National Association of Manufacturers) Pros-
perity Project, which provides employers with the tools to inform workers of how their legislators
are voting on issues of concern to their sector.3
This turns out to be a quantitatively important mechanism at play in the data. The main
contribution of the paper is to show that the number of voters represented by interest groups is an
important variable in explaining the pattern of campaign contributions. The data indicate that an
inverted-U shape describes the relationship between the share of voters represented by an interest
group and the contributions to a legislator.
As a departure point, the paper exploits the variation in economic structure across US states
and congressional districts to investigate the relationship between the electoral strength of a given
interest group and the political contributions to a given politician. For each US House Representa-
tive and each Senator, we match PAC and individual contributions by each economic interest group
(e.g. tobacco, insurance, steel producers, textiles) to the number of employees in the corresponding
sector.4We find that, within each Congressional District and each State, an inverted-U describes
the relationship between campaign contributions and the number of employees in the sector repre-
sented by the corresponding interest group. At low employment levels (i.e. fewer voters), interest
group contributions to the politician increase with the number of employees in a sector. At higher
employment levels the interest group contributions decrease with the number of employees. Indeed,
the data show that the largest employers are practically never the largest contributors. This pattern
knocked myself out for them because they were the biggest employer in that county. Their health was essential to the
health of my constituents, the people who worked there.” - Speaking Freely by Martin Schram for the Center for
Responsive Politics (1995, First Edition)
The following quote by Senator Dennis DeConcini (D-Arizona) clarifies further the concept: “If I get a contribution
from, say, Allied-Signal, a big defense contractor, and they’ve raised money for me. And then they come in and say,
‘Senator, we need legislation that would extend some rule of contracting that’s good for us.’ They lay out the case.
My staff goes over it. I’m trying to help them. Why am I trying to help them? The cynic can say: ‘Well, it’s because
they gave you 5,000 bucks. And if you ran again, they’ll give you another 5,000 bucks.’ Or is it because they have
15,000 jobs in Arizona and this will help keep those jobs in Arizona? Now to me, the far greater motivation is those
jobs, because those are the people that are going to vote for me. But I can’t ignore the fact that they have given me
money... Now the ideal situation is if I was motivated only by the jobs and the merits and there was no money here -
that’s the way it ought to be - or if the money was so minimal that nobody would think it was a factor. If I could only
spend a half a million dollars in a Senate campaign and they could only give me $1,000, it would not be a factor.”
3See the project web site at http://www.bipac.net/page.asp?content=nam&g=nam&parent=p2demo.
In 2004 NAM reports “By Election Day, the program reached more than 19 million employees, delivered more than
40 million messages and helped 1.7 million employees with voter registration and early ballot information.”
4It seems reasonable to proxy the number of voters an interest group represents with the number of employees in
the sector.
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is robust to a battery of specifications and controls and, to the best of our knowledge, has not been
explored before in the literature on political contributions. Furthermore, we believe this framework
highlights a channel of influence at work in a wider sample than the one we consider here. For
instance, several surveys of legislators indicate AARP as the most influential special interest group
in Washington. AARP gives $0 of political contributions by statute. These two facts cannot be
reconciled by standard models of lobbying, but they are rationalized in the framework we present,
given the large fraction of voters represented by the elderly.
From a theoretical standpoint, we interpret the evidence by modeling the interaction between
heterogenous interest groups in a district and a politician in a simultaneous bilateral bargaining
framework, which illustrates the effects of interest group size on the amount of campaign contri-
butions. Each interest group bargains with its representative over the latter’s support for a policy
favorable to the SIG and over the amount of contributions and voter support by the interest group.
The politician is interested in ensuring support because it faces electoral uncertainty and aims at
increasing the probability of winning by trading legislation support for (i) a guaranteed number of
votes by individuals members of the SIG’s and (ii) contributions that are then employed to affect
the decision of impressionable voters through advertising. The size of the interest group affects
the bargaining because: (i) a larger interest group benefits more from a given favorable policy and
must therefore give larger contributions, (ii) a larger interest group can ensure the legislator a wide
support in the sense of persuading the voters it represents to vote in favor of the politician and
therefore it might not be required to contribute as much, if it sufficiently increases the probability
of winning of the politician by just committing the support of its members.
The model delivers a structural relationship between votes and contributions, which we estimate,
thus obtaining a measure of the rate at which politicians transform contributions into votes, of the
degree of electoral uncertainty, and of the implicit ability of politicians to support legislation in
favor of interest groups. We employ our results to make four points.
First, according to our parameter estimates, each politician expects to be spending between
$100 and $400 in order to assure an additional vote through advertising and other forms of cam-
paigning. Levitt (1994) finds that campaign spending has a small impact on electoral outcomes5,
or equivalently, that to obtain on average one more vote politicians need to spend a large amount
of money. Interpreting Levitt’s estimates in this direction yields a cost of $130 − $390 per vote.
Our estimates, though the result of a different empirical approach, are of the same magnitude.
5The impact is also not significantly positive, but here we simply make use of his point estimates to illustrate the
comparison.
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Second, we relate our estimates of the cost of a vote to the density of population finding that
more urbanized districts have a higher cost of votes. This result is consistent with findings in
Stratmann (2004), who reports that some districts have a higher cost of media advertising. If we
think that cities like New York have both high density and high cost of media advertising then the
positive correlation we obtain can be rationalized.
Third, the estimates of ex ante electoral uncertainty are compared to measures of lopsidedness
using ex-post vote margins. We find that in districts where electoral races are closer (ex-post victory
margin is thin) our estimates indicate higher ex-ante uncertainty. Analogously, for races that are
considered more lopsided our estimates indicate lower ex-ante variance.
Fourth, by considering the electoral support offered by an interest group along with its contribu-
tions, we are able to recalculate the return to political ‘investment’, broadly defined and we assess
its magnitude. Ansolabehere, de Figueiredo and Snyder (2003) provide a comprehensive review of
the discussion surrounding the question of whether returns to political contributions are too high
(implying that contributions should be several orders of magnitude higher) or too low (implying
that we should observe very little contributions). The very nature of this question presupposes
that contributions are similar to an investment decision and that interest groups are buying favors
at some implicit price. The conclusion that Ansolabehere et al. reach is that if contributions
were truly an investment decision then we should observe higher levels of monetary support, as
their returns appear considerably higher than other types of investment. Therefore, they claim,
contributions must rather be a form of consumption. We argue that in order to calculate the re-
turn to contributions one needs to take into account that interest groups give votes (which can be
translated into money) and money.6The method we propose delivers considerably lower (and more
reasonable) returns.
Relation to previous literature
The literature on campaign financing is vast.7
The models that have been proposed in the
literature attribute to political contributions different motivations and consequences. We will focus
on those papers that are more relevant to this study here, with the full knowledge that this review
is far from complete.
Various theoretical models have identified reasons why contributions are given and how they
are used. According to these models contributions are given in order to (i) affect the policy choice
6So an extra dollar would earn return on a larger denominator and not the return found by simply dividing the
value of political favors by the amount of dollar contributions.
7A recent and detailed survey is provided by Stratmann (2005).
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of an incumbent government (Grossman and Helpman, 19948), (ii) to influence the platform of
political candidates (Grossman and Helpman, 19969), (iii) to increase the likelihood of election of a
candidate with a given (i.e. non-flexible) favorable position (Grossman and Helpman, 1996; Morton
and Myerson, 1992) or (iv) to buy access (Austen-Smith, 1995). Politicians find those contributions
useful because campaign spending can be used to inform voters of a candidate position (Austen-
Smith, 1987) or to convince them of the candidate’s quality (Coate, 2004).
Empirical studies of mechanisms (i) and (ii) have found some effect of contributions on voting
behavior on specific pieces of legislation10although others have not.11In this paper, we assume
contributions are valued by politicians and therefore affect legislators’ votes on certain bills. Effect
(iii) is hard to distinguish empirically from effect (ii), but many studies have nevertheless tried
to assess the impact of a given candidate spatial position on the contributions raised (Poole and
Romer, 1985; Poole, Romer and Rosenthal, 1987; McCarty and Poole,1998). Ansolabehere, Snyder,
and Tripathi (2002) and references therein discuss effect (iv)12.
Whether the politicians’ perception that contributions can indeed affect voters decisions is
justified has been the subject of very close empirical scrutiny. This literature has pursued the
goal of quantifying the impact of campaign spending on the share of votes obtained in the election
(Jacobson, 1978; Green and Krasno, 1988, Palda and Palda, 1998). The difficult task faced by
this literature has been to control for other variables that affect electoral outcomes and that might
therefore bias the estimate of the impact of spending. A few studies have addressed this issue
using different techniques and obtaining different results (Levitt, 1994; Milligan and Rekkas, 2006,
Erikson and Palfrey, 1998). This paper is not going to address the issue directly, but it offers an
implicit way of estimating the monetary value of a vote, which is just another way of expressing
how much money is needed to ‘influence’ an additional voter. Our methodology, using within-
district data, is not subject to bias coming from unobserved candidate characteristics because such
8Grossman and Helpman (1994) study the impact of political contribution on trade policy determination, but the
electoral process is not modeled and contributions are assumed to increase the utility of politicians.
9In Grossman and Helpman (1996) political candidates have a given position on some issues, but their platform on
other topics can be affected by contributions (valued as a tool to gather votes). Interest groups have two goals in giving
contributions: influencing the platform of candidates and affecting the probability of winning of those candidates that
are ex-ante aligned with them. In this paper, policy is taken to be exogenously given for the individual candidate,
who has the choice of supporting it or not. This is a realistic assumption when we analyze the case of an individual
politician during a given legislature.
10Baldwin and Magee (1998), Stratmann (2002)
11Ansolabehere et al. (2003) report a number of studies that have found mixed results in support of this hypothesis.
12The authors also have a relevant discussion of the relationship between size of the SIG’s membership and access
to politicians for AARP, Business Round Table, and other groups.
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characteristics are constant at the district level. Admittedly we cannot perform the same exercise
as the previous studies, but we can obtain an estimate for the implicit cost of a vote that is free of
individual candidate bias.
Another strand of research has focused on identifying the strategy of interest groups in terms of
choice of timing and recipients of contributions. Several papers have found committee assignments
and constituency characteristics to be important determinants of interest group donations, both
theoretically and empirically (Grier and Munger, 1991; Denzau and Munger, 1986; Stratmann,
1991). Generally, the view in these studies is that interest groups at the national level decide
where to allocate a given amount of money according, for instance, to whether the legislator’s
constituents’ interests are or not aligned with the interest group. The view that we take in this
paper is to consider an individual politician and abstract from the national interest group allocation
problem.
There is also relevant political science research on the role of groups as vote providers and
turnout, which include both theoretical and empirical contributions. For the former, see Morton
(1987, 1991), Schram (1990), Uhlaner (1989). For the latter, see Filer, Kenny, and Morton (1993)
and Nalebuff and Shachar (1999).
The study that comes closest to what we do in this paper is Stratmann (1992). Stratmann looks
at the relationship between farm PAC contributions in a given district and the fraction of farm
population in that congressional district13. He finds that farm PAC contributions are low for those
legislators whose district has a low fraction of rural population (approximately below the median
for the country) suggesting that, according to Stratmann, those politicians are ‘too costly’ to bring
to the farm cause because they do not have support for those policies from their constituency14.
Stratmann also finds that, conditional on the fraction being (approximately) above the median,
contributions first decrease in farm population (because politicians with larger farm constituencies
care more about farming and need to be compensated less for supporting farming-favorable policies),
but then increase. Stratmann explains that the latter effect is suggestive of the fact that politicians
with large farming constituencies are the ones with the highest productivity in pushing legislation
that is pro-farming and therefore PACs that try to maximize their return should invest more heavily
in them. Although this paper primarily focuses on the interaction between a politician and interest
13Rural fraction is used as a proxy for the fraction of population with some interest in policies favorable to
farming. This measure is also taken to proxy the position of the specific legislator about issues concerning farming,
independently of campaign contributions.
14An interest group interested in guaranteeing that the majority of legislators will support a given policy, will try
to influence the ‘least costly’ half of the legislature.
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groups in his electoral district, in Section 3 we discuss the relationship between our results and
Stratmann’s regressions.
The rest of the paper proceeds as follows. Section 2 introduces the data and presents the
reduced-form evidence. Section 3 presents a model of bargaining between the legislator and interest
groups and derives a structural relationship between votes and money. Section 4 presents the
estimation procedure and the structural estimation results. Section 5 concludes.
2Presentation of the data and reduced-form estimation
This section presents the data on the number of voters pertaining to each special interest group and
the amount of political contributions to each legislator by each interest group. The data come from
two sources. Data on local employment by sector are contained in the Country Business Patterns
database, an annual series15published by the U.S. Census Bureau, which provides U.S. county-level
employment16by 6-digit NAICS.17The county-level data is aggregated to the congressional district
level and the state level using the MABLE-Geocorr software.18
Campaign contributions data from the Federal Election Commission (FEC) files are collected
and aggregated by the Center for Responsive Politics (CRP). The CRP classifies Political Action
Committee (PAC) contributions and individual contributions according to the industry to which the
PAC or the individual donor is associated1920. We use the subset of groups identified by the CRP
15The series excludes data on self-employed individuals, employees of private households, railroad employees, agri-
cultural production employees, and most government employees.
16The Business Register database contains information about every known establishment in the United States. The
information on employment is summarized in CBP by establishment size bracket.
17In this paper we employ the 1989-90 and 1999-2000 issues.
18Supported by the Missouri Census Data Center. Whenever counties are split between two congressional districts,
we utilize the following methodology to allocate employment to the two districts. Consider county i, part of which
lies in congressional district d and part in d0. Define as POPidand POPid0 the population of county i in districts d
and d0respectively. The county-level employment in sector s, vsi is attributed to the two districts in the following
amounts: vsi
19FEC regulation requires the disclosure of the donor’s employer.
20Noticeably, the approach followed by the CRP may induce a form of measurement error in the data if voters’
POPid
POPid+POPid0and vsi
POPid0
POPid+POPid0.
contributions are unrelated to the economic SIGs inferred from employment data (for instance, because donors are
driven exclusively by ideological concerns). This would clearly induce attenutation bias in the data, moving against
the mechanism we present. As we report in what follows, attenuation bias does not seem to be a first-order issue,
given the quantitative strenght and precision of our estimates.
Finally, the absence of generalized information concerning affiliation to non-economic interest groups prevents
us from extending the analysis beyond economic SIGs. The effects we describe however directly generalize to any
instance in which groups have non-zero electoral mass.
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for which we have employment data and match the CRP interest groups to 6-digit NAICS sectors21
using the definitions reported by the U.S. Census Bureau. The 86 SIG’s and the corresponding
NAICS industries are listed in Table 1. For each SIG we have contributions to each member of the
Senate and the House of Representatives for the 101st (election cycle 1989-90) and 106th Congress
(election cycle 1999-2000). Data collected by the CRP have been extensively employed in the
politico-economic literature22.
As additional controls, data concerning electoral districts and elections are obtained from the
Office of Clerk of the House (for election results) and the Poole and Rosenthal’s voteview data
base23(for names, party affiliation, and characteristics of congressmen and senators). Finally, in
order to remove the largest outliers we winsorize contributions and number of workers at the 99th
percentile of the right-end tail of the pooled densities of each variable.
We now proceed to gauge the qualitative features of the data. Our starting point is to present
evidence of a non-monotonic, inverted-U pattern between contribution and SIG’s employment sizes.
We present evidence of this empirical regularity in Table 2. The table is divided in three sections,
corresponding to the House, the Senate, and the subgroup of Senators running for reelection at
the two sampling dates of November 2000 and 199024respectively. The dependent variables of
interest are contributions by each SIG s in district d, which corresponds more directly to Csd,
and, in alternative, the fraction of all contributions received by a politician from each SIG. The
independent variable, vsd, is the fraction25of total population of total employment in district d
represented by sector s. The four specifications that we estimate in Table 2 are:
(col. 1) Csd
= φ + δ1vsd+ µsd
(col. 2) Csd
= φ + δ1vsd+ κd+ ψs+ µsd
(col. 3) Csd
= φ + δ1vsd+ δ2v2
sd+ µsd
(col. 4) Csd
= φ + δ1vsd+ δ2v2
sd+ κd+ ψs+ µsd.
The first two specifications presented account for a linear relationship between the number of voters
represented by a given SIG and its contributions to a given legislator. A parametric (quadratic)
polynomial is the simple but flexible approximation that we employ in columns (3) and (4). In
21For the 2000 data. For the 1990 data we match the CRP groups to 4-digit SIC (1987 version) industries.
22Among the others see Ansolabehere, de Figueiredo, Snyder (2003) and de Figueiredo and Silverman (2004).
23Initially from Poole and Rosenthal (1997).
24The House is renewed every two years, while the Senate is staggered in electoral classes of size 1/3 every two
years. The term in office of representatives is therefore two years, relative to six years for Senators.
25We utilize shares of total population in order to account for possible differences in the size of the different polities.
This is not particularly important for the House, but it is relevant for the Senate.
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order to partial out unobserved sector-specific and politician-specific characteristics, we pool across
districts all the observations for each branch of Congress and include both sector and legislator
fixed effects. We include the fixed effects in columns (2) and (4).
The linear specifications indicate a positive correlation between contributions and size of the
lobby, δ1> 0, that is robust to the inclusion of fixed effects that is significant at standard confidence
levels. Such relationship holds for the House and the Senate in 1990 and 2000, indicating a consistent
pattern over time and across Congressional branches. As expected the relationship is stronger in
the subgroup of Senators up for reelection in November 2000.
More interestingly Table 2 shows that the pooled regression indicates a hump-shaped relation-
ship between votes and contributions: the parameters present a positive sign on the linear term and
negative on the quadratic (δ1> 0 and δ2< 0) and are always statistically significant, whether we
include the fixed effects or exclude them. In order to give quantitative intuition the table reports
also the point of maximum and the number of observations above the point of maximum of the
parabola implied by the estimated coefficients. For the House the peak is located between 1.4 and 3
percent of the overall district population. In a congressional district of size approximately 600,000
it corresponds to a SIG employing between 8,400 and 18,000 workers. This number is particularly
reasonable considering that the margin of victory in the 2000 House elections was on average about
80,000−90,000 voters, implying a pivotal group size around 40,000−45,000 voters. As we could
have expected, the number of observations above the point of maximum is not very large. Within
each district there are never too many relatively large voter groups (the distribution of industry
sizes is well approximated by a Pareto distribution)26. Furthermore, understanding the behavior
of the function over the rightmost portion of the size range is important. Large employers are
particularly interesting since they cover a substantial portion of the electorate.
For the Senate the peak of the inverted-U is located between 1.1 and 3.97 percent of the
State population. Senatorial races operate over substantially larger constituencies and the number
of lobbies large enough to exercise electoral pressure could differ from that for the House. This
notwithstanding, the data seem to support an hump-shaped relationship for Senatorial races as well,
especially for those Senators that had completed their fund-raising and were running for reelection
in 2000 and 1990 (part 3 of Table 2).
We now proceed in further detail conditioning along the two main dimensions of the data (by
district and by sector). Table 3(a) reports the results for the coefficients of interest after removing
26It is also mechanically impossible to have many relatively large sectors since their fractions of total employment
have to add up to one.
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the assumption of common behavior of the polynomial approximation across districts, while 3(b)
reports within-sector results. Within each district we estimate the equation:
Csd= κd+ δd,1vsd+ δd,2v2
sd+ µsd,(1)
and we consider the overall distributions of various test statistics (sign, 0.05 F-test, 0.05 and 0.10
t-tests). We find that δd,1> 0 and δd,2< 0 (i.e. the relationship between votes and contributions
exhibit an inverted-U shape) in almost all the districts27and such pattern is significant at least at
the 10 percent level generally in half the seats for all our samples28.
A reasonable insight that we obtain from this table is that heterogeneity across congressional
and senatorial races is quantitatively relevant. The fitted parabolas in column (1) change from
district to district considerably. For instance, albeit the estimated mean peak of the parabola for
the House in 2000 was 0.018, the standard deviation across district was almost as high (0.013).
In the section devoted to structural estimation we devote considerable attention to what specific
characteristics of the races may determine the pattern of contributions. The approach of Table
3(a) operates within politician by construction and does not allow accounting for unobserved SIG’s
characteristics that might be correlated with sector size and could be inducing certain levels of
contributions. In column (2) we control for the value added of the sector in 2000, as computed by
the Bureau of Economic Analysis, to obviate such design problem. The results of column (1) are
broadly confirmed. We can reject at 5 percent the joint hypothesis of δ1d= 0 and δ2d= 0 for more
than 2/3 of the districts. Column (3) of Table 3(a) repeats the analysis excluding from the sample
four particular sectors29exhibiting often a large employment level and a low level of contributions
and that might be suspected of driving the results (notice that from column 1 and 2 an average
between 5.3 and 6.2 SIG’s locate on the declining portion of the parabola). The results do not
change substantially once we exclude those four observations. In fact, the results suggest that
there is variation on which sectors belong to the declining portion of the parabola (their number
varies between 3.7 and 5.2).
Table 3(b) reports the results for the coefficients of interest after removing the assumption of
27Congressional districts for the House and States for the Senate.
28We perform three types of tests on the subset of districts that present point estimates δd,1 > 0 and δd,2 < 0.
First we test whether we can reject the null hypothesis that jointly δd,1= δd,2= 0 at the 5% confidence level. Second
we test whether we can reject the null hypothesis that separately δd,1 = 0 and δd,2 = 0 at the 5% confidence level.
Finally we repeat this second test at the 10% confidence level.
29The SIG excluded are Retail Sales, Hospitals and Nursing Homes, Food and Beverage, Restaurants and Drinking
Establishments.
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common behavior of the polynomial approximation across sectors. Within each sector we estimate:
Csd= hs+ δs,1vsd+ δs,2v2
sd+ µsd,
and report tests on the signs of δs,1and δs,2similarly to the case of district-level regressions. We
find that for about two thirds of the sectors δs,1< 0 and δs,2> 0. In about half the sectors such
pattern is significant at the 10 percent level30in the House, while the results for the Senate are less
conclusive, mostly due to the fact that we are not distinguishing between Senate seats that are up
for vote and those that are not.
An intuitive check for the nonmonotonicity documented in the previous tables is that by and
large the largest employers should not be the largest contributors both within districts and within
industries. It turns out they are not. Table 4 presents evidence of this finding. In the first panel
of Table 4 we first report the number of districts in which the largest employer in that district is
the top contributor and we find that this is the case for less than 2% of the districts31. The second
line in the same panel reports the number of districts in which the top 5 percent (ventile) of sectors
is the largest contributor. This condition is realized in less than 4% of districts. A monotonic
increasing relationship between money and votes can hardly be reconciled with these figures. The
second panel of Table 4 repeats the same calculation considering the distribution of contributions
across districts for a given sector. We find that the largest employer in a sector is also the largest
contributor in generally less than 7% of sectors. This fraction increases when considering the case
of the top ventile of districts within each sector: the top 5 percent employment group is the top
contributor in 18% to 37% of the cases. In the majority of instances sectors do not pay the largest
contributions where their employment is the largest. We report the same type of evidence in Figure
1 where the employment size of the largest contributor is plotted against the employment size of
the largest employer within a district (Figure 1a, for 106th and 101st House) and within a sector
(Figure 1b). If contributions were increasing in employment size then all observations should lie
along the 45◦line, but we observe that the large majority of the observations lie strictly above such
line. The graphs provide a snapshot of the size dispersion of the largest contributors as well.
As additional checks we report two important robustness extensions in Appendix Tables A1
and A2. Tables 2 and 3 do not allow for any role of electoral challengers. We consider this in
Table A1. Large incumbency advantages are a robust feature of US Congressional elections and
challengers usually garner relatively small amounts of resources for elections. Nonetheless, some
30We perform an F-test with a null hypothesis of δs,1 = 0 and δs,2 = 0 on the subset of sectors where we find
coefficient point estimates that point to a hump.
31All the data used in table 4 and the graphs are not winsorized in order to properly compute frequencies.
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special interests contribute to challengers, either exclusively or jointly with incumbents/favorites.
Table A1 shows that considering the contributions to incumbents net of challengers’ contributions
does not change the frequency and robustness of the non-monotonicity that we report in the data
in any significant way. As a second check we consider the case where SIGs may not deliver all their
votes to only one candidate. To address this we split the SIG’s votes proportionally to the splitting
of the SIG’s contributions across the two different candidates32. Table A2 shows the robustness of
the results along this dimension. This specific way of controlling for SIG’s vote-splitting does not
change the frequency and robustness of the non-monotonicity.
By and large the reduced-form evidence tends to support the idea of a non-monotonic relation-
ship between number of SIG’s voters and SIG’s contributions. This particular feature of the data is
novel to the best of our knowledge and surprisingly robust. In the next section we present a model
of the interaction between a legislator and several interest groups that rationalizes the results.
3 The model
This section presents a formal description of the game between politicians and SIGs. An initial
caveat is in order. The model is rigged toward empirical estimation and as such it is simplified
(radically) along several dimensions. Being critical of our own approach, we require cross-checking.
First, whenever restrictive assumptions are made, we show how the empirical results are affected
by relaxing them. Second, we check our structural estimates against information external to the
model: parameter estimates should appear unreasonable if the model’s assumptions are excessively
restrictive.
3.1Structure of the polity
Legislature and policy choice
Consider a jurisdiction where the population is divided into D equally sized electoral districts.
The parliament is formed by D legislators, each representing an electoral district d, d = 1,...,D.
The task of the legislature is to pass or reject a set of policies. In order to simplify matters we
disregard the agenda-setting stage and consider the decision of each legislator d to vote in favor
or against each of the exogenously proposed policies. We do not model the interaction among the
legislators and the determination of the national policy since we are interested in the district-level
32Given the absence of turnout and electoral data by SIGs, we are constrained by addressing the matter indirectly
in Table A2.
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interaction between the incumbent legislator and the set of local interest groups, in view of future
electoral competition between the incumbent and a challenger.
Special interest groups
The economy is divided into S sectors producing goods or services. For the purpose of this
model a sector s in electoral district d is a group of capital owners and workers, which share a
common interest in policies that favor the sector.33In each electoral district d the economy is
characterized by a different size distribution of sectors. The size of interest group s in electoral
district d is represented by the number of workers/voters in the sector: vsd(the set of all voters who
have some stakes in policies that favor the sector). We indicate with the vector y = (y1,y2,...,yS)
the set of policies proposed by the agenda setter. Policy ysmight be an industry-specific subsidy
or tariff, which increases the rent of interest group s. We assume that the benefit of the lobby
depends only on the aggregate income of the interest group. Ignoring for now the role played by
contributions, the income of the interest group depends on the benefit from policy ys. We allow the
benefit from ysto depend on the size of the interest group and the ability of the politician. Interest
group size matters because, for example, the benefit created by a subsidy given to an industry is
increasing in the size of the industry. By allowing the benefit to depend on the specific politician,
we want to capture the idea that more experienced legislators are more likely to be effective at
supporting a piece of legislation and increase the size of the benefit to the interest group they agree
to support. For simplicity let us assume that in the absence of policy ysthe rent of the interest
group is zero.34The expected utility of interest group s, denoted by Usdis therefore:
Usd= γd+ ρdvsd+ εsd
where ρdand γdare the legislator-specific parameters and εsdis a random component that
might depend on the specific ability of a politician to support a particular sector.
We assume that agents with a stake in sector s act as a unified special interest group vis-á-vis
the district legislator.35Since this paper focuses on the interaction between interest groups and
33Although we recognize that the interests of workers and capital owners might not always coincide, we here focus
on policies for which they are sufficiently aligned.
34Members of the interest group might have other sources of income, which do not depend on the policy imple-
mented. We disregard them here.
35We will be interchangeably use the expression (special) interest group and lobby, even though the word lobbyist
would more strictly identify individuals that act on behalf of interest groups and do not necessarily decide on the
amount of political contributions. Lobbyists are more likely to channel information to the legislators while interest
groups decide independently to make campaign contributions (through their PAC’s, for example, in the United
States).
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