Embed Size (px)
Citation preview
Voter Information and Campaign Finance: How News
Coverage Affects Contributions∗
J. Baxter Oliphant† Bryn Rosenfeld‡ Lizette M. Taguchi§
July 16, 2014
Abstract
Using a plausibly exogenous measure of press coverage, we provide the first rigorous
empirical test of a classic result in formal models of electoral competition: that cam-
paign contributions increase with the share of uninformed voters. We show based on
an analysis of U.S. campaign finance data from 1990-2006 that House members who
represent low-information districts raise more money from political action committees
(PACs) than incumbents who serve higher-information districts. However, we also
demonstrate that voter information affects PACs’ contributions differently, depending
on the nature of their agenda. Groups with “narrower” (corporate) agendas give more
when voter information is low, while donations by more broadly representative groups
(e.g. organized labor) are unrelated to a district’s information environment.
∗We wish to thank Kosuke Imai, Tali Mendelberg, Michael Donnelly, and participants of the 2011 Soci-ety for Political Methodology Annual Meeting and the 2012 Midwest Political Science Association AnnualMeeting for valuable feedback on previous versions of this paper as well as David Stromberg and Jim Snyderfor providing their data. We are indebted to the Center for Responsive Politics for their campaign financedata.†Ph.D. Student, Department of Politics, Princeton University, Princeton NJ 08544. Email:
[email protected]‡Ph.D. Student, Department of Politics, Princeton University, Princeton NJ 08544. Email:
[email protected]§Ph.D. Student, Woodrow Wilson School, Princeton University, Princeton NJ 08544. Email: lt-
1
Introduction
Though two decades have passed since Baron (1994) observed that substantial campaign
expenditures and considerable voter ignorance are among the most salient characteristics of
U.S. elections, his observation still rings true. Campaign expenditures have continued to
grow, while the majority of voters remain uninformed about and uninterested in congres-
sional politics. This paper investigates the empirical relationship between interest groups’
campaign contributions and the electorate as mediated by press coverage of Congress. Our
central question is the following: “What is the effect of voter information1 on interest group
donations?”
The link between voter information and campaign finance matters for normative debates
about accountability, transparency, and representation in congressional elections. Does less
information about congressional politics mean more special interest money in elections? If
interest group donations are used to inform the electorate, do some types of interest groups
donate more than others to fill informational gaps? Does an inverse relationship between
voter information and campaign donations by corporate interests help to explain poorer
quality representation? While an exhaustive answer to all of these questions is beyond the
scope of this paper, we shed light on many of these connections using a novel measure of
voter information and comprehensive data on campaign contributions from 1990-2006.
Despite its important normative implications, there has been little empirical research to
date on the relationship between voter information and campaign contributions. A major
stumbling block is the inherent endogeneity of voter information to campaign contribu-
tions. Consider how voters gain information about congressional politics. Most voters learn
about their House member both from objective news sources and campaign advertisements.
1Throughout, we use the terms press coverage, information environment, and voter information inter-changeably to refer to our independent variable of interest. Evidence supporting the interchangeability ofthese terms for the measure we use can be found in Snyder & Stromberg (2010). The operational measureis described below.
2
Clearly, campaign advertisements are related to campaign contributions, and, hence, cam-
paign contributions are likely to be correlated with voter information. Survey-measured,
individual-level data on voters’ knowledge of congressional politics suffer from similar endo-
geneity problems.
Thus, to obtain an unbiased estimate of the effect of voter information on campaign
contributions it is necessary to separate the reciprocal effect in which campaign contributions
are used to inform and persuade voters. Building on the work of Snyder & Stromberg
(2010) and others, we employ a measure of voter information that is plausibly exogenous to
campaign contributions and captures the degree to which media markets and congressional
districts are overlapping, with press coverage of Congress increasing in the extent of overlap.
We describe this measure together with our strategy for identifying the causal effect of voter
information on campaign contributions at length below.
Relevant Literature
Much of the existing campaign finance literature focuses on the link between campaign
contributions and candidate success (Chappell 1982, Ashford 1986, Currinder 2009) or on the
relationship between donations and the legislative behavior of House members once in office
(Wright 1990, Ansolabehere, de Figueiredo & Snyder 2003). In addition, a number of scholars
address the pros and cons of campaign finance reform (Goidel, Gross & Shields 1999, Coleman
& Manna 2000) or analyze the voter welfare effects of various campaign finance proposals
(Coate 2001, Coate 2004, Ashworth 2006). More closely connected to our own research,
the political economy literature offers two related ways of thinking about the relationship
between interest-group contributions and voter information.
First, and most fundamentally, politicians solicit interest group contributions in order to
influence voters (Baron 1994, Morton & Myerson 1992). Contributions are used to persuade
3
uninformed and less informed voters, whose votes can be purchased, in essence, through
political advertising.2 It follows that the larger the share of uninformed voters in the total
voter population, the more productive are an interest group’s funds in buying votes for
favored candidates, and, therefore, the larger are its campaign donations all other things
equal (Baron 1994, Grossman & Helpman 1996).
In this tradition, influential models of electoral competition, such as Baron’s (1994), imply
an inverse relationship between voter information and campaign contributions. Again, the
negative relationship between voter information and campaign contributions arises in Baron’s
model, because candidates in districts with many uninformed voters have an incentive to
attract contributions from interest groups and use these donations to inform and persuade.
Although the basic prediction of a negative relationship between voter information and
campaign contributions has long been posited in the theoretical literature, empirical tests
are lacking. Our first contribution is thus to test whether this relationship holds empirically,
using more than a decade and a half of data on contributions to members of the U.S. House
of Representatives and a plausibly exogenous measure of voter information.
Second, another line of existing research views interest group contributions as signals of
a candidate’s competency. In models of electoral competition such as Prat’s (2002), interest
groups possess insider information about candidates’ quality. Based on this information,
they contribute to candidates in exchange for favorable policy positions. The level of a
candidate’s campaign spending (and not the content of political advertising, per se), thus,
serves as a costly signal to uninformed voters about a candidate’s competency.
One shortcoming of this literature, we argue, is its failure to theorize differences in the
strategic interaction between candidates and political action committees (PACs) which rep-
2Zaller (1992) makes a distinction between inattentive, uninformed public who are essentially unpersuad-able and the less informed, slightly attentive public who pay just enough attention to possibly be persuaded.The persuadable uninformed voters in these theories, empirically, are Zaller’s moderately informed, moder-ately attentive citizens.
4
resent more and less “encompassing” interests. By “encompassing,” we mean groups, such
as labor PACs and some types of membership PACs, whose interests are more closely aligned
with those of the median voter. We define “narrow” interest groups as those whose interests
are less closely aligned with those of the median voter—examples of these are corporate and
trade PACs. Most scholars seem to assume that all PACs fall into the latter category of
groups whose objectives differ from the median voter’s objective.3 We agree that campaign
contributions come from groups whose preferences are often at odds with the preferences of
the majority of voters, but we acknowledge that in this regard interest groups can be arrayed
along a continuum.
We begin with the assumption, shared by others in the literature such as Ashworth
(2006), that contributions are obtained in exchange for promises to pursue policies favorable
to the contributors, if the candidate is elected, and that voters understand this and form
expectations about the extent to which a candidate is beholden to special interests. Is it
realistic to assume that voters discriminate in this manner? As Ashworth points out, “The
fact that candidates who do not accept money from PACs make a point of emphasizing
that fact in their campaigns suggests that at least some voters will look more favorably on
them if they have not made deals with interest groups” (57). Given this, we expect strategic
divergence in campaign contributions by groups which represent more and less encompassing
interests. Our second contribution, then, is to demonstrate that while corporate PACs (with
less encompassing interests) contribute more in districts with a larger fraction of uninformed
voters, there is no evidence that labor PACs (with more encompassing interests) share this
tendency.
3For an explicit formulation of this idea, see Prat (2002, p. 999).
5
Accountability and Representation
Why should we care about the combination of voter ignorance and substantial campaign
contributions? Is there evidence that accountability and representation are diminished in
those districts where these factors coincide? Evidence suggests that PACs aim to influ-
ence the legislative process, with industries that stand to gain greater potential benefits
from government assistance contributing systematically more than others (Grier, Munger &
Roberts 1994, p. 911). Moreover, PACs purposively invest in representatives who are more
likely to offer a return (Gordon, Hafer & Landa 2007, p. 1057).
While there is a great deal of debate in the empirical literature about whether contribu-
tions affect congressional roll-call votes (see Ansolabehere, de Figueiedo, and Snyder 2003
for a recent review), contributions may nevertheless elicit favors in other kinds of legislative
activities. Hall & Wayman (1990), for example, find that contributions buy members’ time,
rather than their votes. According to these authors, the effects of moneyed interests ap-
pear not on the floor but in committee and are evinced by members’ patterns of legislative
involvement.4 Interest groups’ goal, Hall and Wayman write, “is not simply to purchase
support but to provide incentives for supporters to act as agents” (802). Representatives
whose voters have less information about their behavior are a more enticing target and have
a freer hand to act as agents for favored interests.
Previous research suggests that local media outlets are more likely than national out-
lets to provide information about members of Congress and that local coverage plays an
important role in shaping voters’ knowledge about their member of Congress and in in-
fluencing how congressional members vote (e.g. Hutchings 2003, Arnold 2006). It follows
4Like Hall & Wayman (1990), Esterling (2007) argues that contributions change how elected officialsapproach committee work. By examining the characteristics and campaign finances of representatives par-ticipating in hearings on Medicare, Esterling finds that representatives who are more analytical and technicalraise more “hard money” from PACs. Conversely, representatives who “focus on the symbolic aspects ofpolicies, aspects that are of interest to constituents, tend to receive fewer contributions” (106).
6
that representatives from low-information districts will see the costs of acting as agents for
interest groups as lower than members from high-information districts. Their activities on
behalf of constituents gain them less because they are less likely to be covered by the local
press. As such, representatives from low-information districts will be less motivated to en-
gage in constituent service activities and allocate more time to advancing interest groups’
agendas, which in turn furnish them with greater campaign contributions. Indeed, Sny-
der & Stromberg (2010) find that voter information affects politicians’ actions in Congress
as well as their policy choices. In particular, representatives in low-information districts
provide fewer constituent services; they deliver to their districts less federal money, make
fewer appearances before constituent-oriented committees, and are less likely to vote against
the party line than representatives in high-information districts. Our analysis shows that
these same underperforming representatives receive higher levels of interest group donations,
especially from corporate and trade PACs.
Theory
We contend that interest groups’ campaign contributions do not always serve as a pos-
itive signal to voters about a candidate’s quality. Instead, campaign contributions signal
what types of policies the candidate is likely to pursue in office. Put differently, campaign
contributions serve as an indicator of the extent to which interest groups are likely to exert
influence on a candidate’s legislative activities. These signals can be perceived positively
or negatively by voters. Whether a contribution is perceived positively or negatively by an
individual voter depends on the degree to which her preferences are consistent with those
of the interest group. Contributions by popular membership organizations are thus likely to
be perceived as positive signals.
Of course, collective action problems make it relatively more difficult for large groups to
7
organize, contributing to a situation in which the vast majority of interest groups do not
represent the preferences of a majority of voters in a district. In particular, the largest share
of interest-group contributions come from corporations and trade organizations, for which a
central priority is often to gain particularistic benefits, subsidies, or influence on regulatory
policy. These preferences tend to be at odds with those of a majority of voters (or are, at
least, viewed suspiciously by them). Hence campaign contributions from such interest groups
are more likely to be perceived as a negative signal.
Voters’ ability to hold politicians accountable depends on the level of information that
voters possess. In particular, we consider campaign contributions as a signal to voters about
divergence from their preferred legislative priorities. The extent of political news coverage
within a given district determines the probability that voters receive such signals regard-
ing interest-group contributions. Therefore, more news coverage implies smaller equilibrium
campaign contributions by “narrow” interest groups. In sum, candidates face a trade-off be-
tween campaign contributions which can be used to sway “persuadable” voters and electoral
support from “informed” voters. For groups whose interests diverge from those of voters,
the marginal benefit of contributions increases as the link between candidates and voters
becomes more tenuous.
These points lead us to two substantive hypotheses: 1) Total interest group campaign
contributions will be higher in low-information districts and 2) The effect of press coverage
depends on the type of interest group: whereas less encompassing interests, such as corpo-
rate and trade PACs, will contribute more in low-information districts (where a candidate’s
tradeoffs in terms of public perceptions are less acute), this relationship will not hold for
groups representing more encompassing interests, such as labor PACs and some membership
organizations.
8
Measures
The fundamental empirical problem of estimating the effects of the media on important
political outcomes like campaign contributions and quality of representation is that press
coverage is endogenous to these variables. Snyder & Stromberg (2010) offer the strategy to
address this problem by constructing a plausibly exogenous measure of a district’s newspaper
press coverage.5 Congruence, the main independent variable in our analysis, is defined for a
given congressional district as the market-weighted reader share of the newspapers in that
district:
Congruencedt =N∑
n=1
MarketSharendtReaderSharendt
where n denotes newspaper, d denotes district, and t denotes time. MarketShare6
captures newspaper n’s share of total newspaper sales in district d, while ReaderShare
reflects the share of newspaper n’s readers living in district d.7
This measure captures the degree to which newspapers and congressional districts are
overlapping. A low level of Congruence implies that a newspaper draws readers from multiple
congressional districts, and is therefore less likely to cover any one representative. The key
assumption is that the process which determines the structure of newspaper markets is
exogenous to the process that determines the boundaries of House districts.8 Note that
5The choice to focus exclusively on newspaper coverage arises from the absence of equivalent data aboutother media sources such as radio and television. Nonetheless, we believe this choice is justified. Mondak(1995), for example, finds that local newspapers are a key source of information about local politics, includingHouse elections. With their only major local newspaper on strike, Mondak found that the knowledge votersin Pittsburgh held about local House elections declined. In short, while other media outlets may providecoverage of national and international politics, local newspapers are often the only source to adequatelycover local Congressional campaigns.
6Constructing Congruence in terms of market shares avoids a definition that is dependent on the totalnewspaper readership in a given area, as total newspaper readership is endogenous to political knowledge aswell as to demographic characteristics like education and income.
7For an extended discussion of the definition and measurement of Congruence, see (Snyder & Stromberg2010).
8Substantively, this assumption implies that redistricting is not shaped by newspaper markets. If, for
9
instead of adopting the arbitrary simplification of voters into the binary categories informed
and uninformed, as have many formal models of political economy, we use Congruence as
a plausibly exogenous and, more realistically, continuous measure of voter information in a
given congressional district.
Data
To analyze how Congruence affects the strategic environment of campaigns and campaign
giving, we use data from The Center for Responsive Politics (CRP) on campaign contri-
butions over the period 1990-2006 and merge this data with Snyder & Stromberg’s (2010)
original dataset on press coverage and political accountability.9 The analysis presented in
this paper applies to incumbent candidates10 running for reelection in the same district as in
the previous election cycle.11 All contributions have been converted to constant year 2000
dollars using CPI figures collected from the Bureau of Labor Statistics.
The CRP data come from Federal Election Commission (FEC) campaign summary files
that aggregate campaign contributions for each candidate in all House races (primary and
general elections) for each election cycle. We focus exclusively on campaign donations from
interest-group PACs, and do not analyze donations by individuals or party committees. The
histogram in Figure 1 describes the distribution of total interest group donations in our
instance, politicians intentionally gerrymandered districts to avoid congruence with newspaper markets inorder to facilitate taking larger donations from interest groups, this assumption would be violated. However,we believe this scenario unlikely.
9We use CRP’s data instead of the FEC’s because it disaggregates trade and membership PACs, whichwe hypothesize to have agenda that are less and more encompassing, respectively. The data can be foundon the Center for Responsive Politics’s website: http://http://www.opensecrets.org/MyOS/bulk.php.
10Though we focus only on incumbents, note that donations made to incumbent candidates make up thevast majority of campaign contributions; challengers raise comparatively little. For instance, over the periodconsidered, incumbent’s raised, on average, approximately $435,000 from PACs, while challengers raised, onaverage, approximately $90,000.
11The data did not allow us to include incumbent candidates who ran for office in another district afterredistricting. These accounted for very few cases and are not problematic with respect to our hypotheses,because we expect political accountability to be stronger in cases where voters, donors, and representativeshave time to learn from their information environment.
10
dataset. Incumbents received a mean of about $435,000, and the median amount raised
from PACs is about $358,500.
Total Contributions)
Num
ber
of R
epre
sent
ativ
es
0 500000 1500000 2500000
020
040
060
080
010
00
0 500000 1500000 2500000
0.0e
+00
5.0e
−07
1.0e
−06
1.5e
−06
Total Contributions
Den
sity
Figure 1: Total Interest Group ContributionsNote: These figures show the empirical distribution of total interest group contributions forall races in which incumbents ran for reelection between 1990 and 2006. The dashed verticalline marks the mean contribution of approximately $435,000.
Data on interest group contributions can be disaggregated into several categories of po-
litical action committees (PACs), including corporate, trade, labor, and membership.12 Ex-
amples of corporate PACs include those associated with Pfizer, UPS and Wal-Mart, while
examples of trade PACs include those of the American Medical Association and the National
12Gopoian (1984, p. 259) notes that “generic treatments of ‘labor’ and ‘corporate’ PACs mask the diverseorientations of individual PACs.” Though we acknowledge that such categorical treatments have drawbacks,we believe that these categories nonetheless capture essential distinctions among the agendas of various typesof PACs
11
Table 1: Amount Raised by Incumbents from Political Action Committees, 1990-2006
Total Corporate Labor Trade MembershipMean $434,841 $160,497 $88,260 $130,435 $17,057St. Dev. $338,027 $154,929 $94937.31 $103,399 $17,215N 2,914 2,896 2,699 2,896 2,804
Association of Homebuilders. Emily’s List and NARAL Pro-choice America support PACs
which fall into the membership category. The UAW and SEIU support labor PACs. Table 1
shows that contributions by corporate and trade groups together make up roughly two-thirds
of total interest group donations.13
Model Specifications
Following Snyder & Stromberg (2010), we exploit the panel features of our data to estimate
four different fixed-effects specifications of each of our models. These specifications allow us
to control for time-invariant but unobservable characteristics, better identifying the causal
effect of congruence. The models take the following general form:
Ddt = δCongruencedt + Xdtβ + αst + εdt
where d denotes district, t denotes time, s denotes state, Xdt is a matrix of control variables,
αst denotes state-by-year fixed effects, and Ddt denotes campaign donations. The dependent
variable in the first analysis is total interest group contributions. In the subsequent analysis,
we examine interest group contributions by type and sector.
State-by-year fixed effects control for time-invariant but unobserved characteristics of a
particular state in a given year. Similarly, our second specification employs district and year
fixed effects, thus focusing on variation in congruence within a particular district over time
13The FEC breaks PAC contributions into additional categories that are not reported here: Non-stock,Cooperative, and Non-connected. These are omitted because they are not a central part of the analysis.
12
and within a given year across all districts. A third specification controls for time-invariant
but unobservable characteristics of states and congressional election cycles (state and year
fixed effects), while a fourth does the same for districts and congressional election cycles in a
given state (district and state-by-year fixed effects). Of course, since our dependent variable
is measured for a given congressional race at the district level, we cannot use district-by-year
fixed effects, as there would be no source of variation. In sum, we use these several fixed
effects specifications to minimize threats to inference which might arise from unobserved
variables that are correlated both with Congruence and campaign donations, thus violating
the standard exogeneity assumption of OLS regression and biasing estimates of the causal
effect of Congruence.
Control Variables
All specifications include a comprehensive set of controls for the congressional race, repre-
sentative, and district. These controls are described in detail and descriptive statistics are
given in Table A.1 in the appendix. In brief, the controls fall into three categories: con-
gressional race controls, district controls, and candidate controls. The race controls include
the closeness of the election and a dummy for whether the incumbent is running unopposed.
The representative controls are age; whether the incumbent is a freshman; tenure in office;
whether the incumbent is a member of the majority party in congress; if the incumbent is
a party leader; if the incumbent serves on a powerful committee; and if the representative
is a chair or ranking member. At the district level we control for median income, urbanity,
population density, whether a city contains the majority of a district’s population, and the
total number of congressional districts in any city containing a majority of the population,
as well as the percent of the population that is elderly, employed by the military, employed
in agriculture, employed in blue-collar occupations, and foreign-born.
13
Results and Discussion
H1: Total interest group contributions will be higher in low-information districts
Table 2 reports the estimated coefficients and standard errors from linear regressions of total
interest group contributions on Congruence. The dependent variable in the table aggregates
contributions across all categories of interest groups. Thus, the first row of the table tests
the hypothesis that the strategic interaction between donors and candidates varies with the
amount of information available to voters, and, specifically, that interest group contributions
are likely to be higher in districts where voters are less well-informed.
Table 2: Predicted Effect of Congruence on Total Interest Group Contributions
Model 1 Model 2 Model 3 Model 4All PAC ContributionsCongruence -274,428*** -214,214* -252,733*** -299,707**
(72,674) (116,774) (67,622) (123,529)Observations 2,616 2,616 2,616 2,616R-squared 0.46 0.62 0.40 0.68Fixed Effects State × Year Year, District Year, State District,
State × YearControls × × × ×
Cluster robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1
Note: This table reports OLS regression coefficients predicting the effect of Congruence onPAC contributions to the campaigns of incumbents running for reelection. The coefficientsreported capture the effect of varying Congruence from zero to one,14 all other variablesbeing equal. The dependent variable is total interest group contributions. The coefficientsfor the control variables are not reported. Standard errors are clustered by representative.
Column (1) gives the baseline specification including all controls and state-by-year fixed
effects. Column (2) includes district and state fixed effects, while column (3) includes state
and year fixed effects. Column (4) introduces both district and state-by-year fixed effects
and thus reflects within-district variation in the level of interest-group contributions over
14
time. Across all specifications the coefficient on Congruence has the expected negative
sign, and, in all but the second model, it is significant at the 0.05 level. In Model 2, the
coefficient for Congruence is marginally insignificant at the 0.1 level. All standard errors
are clustered by representative. The use of robust standard errors allows observations for a
given representative to be correlated in some unknown way, inducing correlation in the error
terms across observations for a given member across several election cycles.
These results offer clear support for the hypothesis that interest group contributions are
likely to be higher in districts where voters are less well-informed, and suggests, in accordance
with our theoretical framework, that the strategic interaction between donors and candidates
varies with the amount of information available to voters.
Figure 2 gives a more intuitive picture of the substantive significance of these results.
Each point in the figure represents the estimated effect of a one standard deviation shift15
in Congruence on total contributions by interest groups. The estimates and both their
90 and 95 percent confidence intervals are reported in $10,000s. The effect of Congruence
on interest-group giving is substantial. A one standard deviation increase in Congruence
decreases the level of contributions from PACs in a given race by more than $60,000. This
is more than 13% of the average total interest-group contributions to incumbent candidates
over the period we study.
H2: The effect of voter information on campaign donations will depend on in-
terest group type
In this section, we report results from a second set of tests which distinguish between types of
interest groups. If Baron’s (1994) logic holds and all interest groups are rational actors seek-
ing to maximize the benefit of their campaign contributions by spending in low-information
15Obviously, this is a much more realistic increase in Congruence than the shift from 0 to 1 that isrepresented by the OLS coefficient.
15
●
●
●
●
−15 −10 −5 0 5
Model 4(District,
State x Year)
Model 3(Year, State)
Model 2(Year, District)
Model 1(State x Year)
Contributions in Constant Dollars ($10,000s)
Figure 2: Effect of Congruence on Total Interest Group ContributionsNote: This figure shows the estimated effect of a one standard deviation increase in Con-gruence (0.24) on the level of total PAC contributions using four different fixed effects spec-ifications. The bars represent 95% confidence intervals, while the hash marks denote 90%confidence intervals.
districts where the marginal cost of persuasion is lower, then we should see no differences in
strategy across types of interest groups. All interest groups should recognize the greater effi-
ciency of contributing in low-information districts and ceteris paribus donate more in those
races.
We hypothesize, however, that the strategic behavior of interest groups and candidates
depends on whether an interest group is “encompassing” or “narrow.” By “encompassing,”
we again mean interest groups, such as labor PACs, whose interests are more closely aligned
with those of the median voter. By “narrow,” we mean interest groups, such as corporate
PACs, whose interests are often at odds with those of the median voter. Hence, we expect
the strategic behavior of PACs and candidates to differ with regard to these broad classes of
interest groups. As voter information increases, we expect contributions by “narrow” interest
16
groups to decrease while contributions by “encompassing” interest groups remain unaffected.
In sum, we propose that PACs’ decisions to give and politicians’ choices to accept campaign
donations depend on how easily voters are able to hold them accountable—that is, on the
transparency of the campaign environment and the level of information available to voters.
In support of our hypothesis, as Table 3 shows, contributions by corporate and labor
PACs do not follow the same pattern. Representatives receive less money from corporate
PACs in high-information districts, whereas contributions by labor PACs are unaffected.
Further, consistent with our argument, the effect of Congruence on contributions by trade
PACs is similar to the effect of Congruence on corporate contributions. For membership
PACs, the coefficient on Congruence is small and unstable. Two of the four models are
statistically insignificant and the coefficient is never larger than $12,000, a fraction of the
estimated effect of Congruence on corporate contributions, for example. Thus, the effect of
Congruence on donations by membership PACs more closely resembles its effect on labor
PACs than its effect on corporate or trade PACs.
Figure 3 clearly makes the point that not all interest groups behave as previous theoret-
ical models have predicted. In fact, they do not all act strategically such as to maximize
the efficiency gains of donating to candidates in low-information districts. Though corpo-
rate contributions decrease by roughly $25,000 for each one standard deviation increase in
Congruence, the effect of Congruence on labor contributions is negligible. On average, labor
PACs donate no less in high-congruence districts than in low ones. In light of existing theory
on voter information and campaign finance, this result is surprising. Under the framework
we propose, it is not. As Snyder & Stromberg (2010) show, when candidates stand for re-
election in high information districts, they face stricter standards of accountability and do
more for their constituents. Both candidates and interest groups face a different strategic
environment in which donations from “narrow” interest groups may be less desirable.
17
●
●
●
●
−6 −5 −4 −3 −2 −1 0 1 2
Model 4(District,
State x Year)
Model 3(Year, State)
Model 2(Year, District)
Model 1(State x Year)
Contributions in Constant Dollars ($10,000s)
Corporate
●
●
●
●
−6 −5 −4 −3 −2 −1 0 1 2Contributions in Constant Dollars ($10,000s)
Labor
Figure 3: Contrasting Effects of Congruence on Contributions by Corporate andLabor PACsNote: These figures show the estimated effect of a one standard deviation increase in Congru-ence (0.24) on the level of corporate and labor PAC contributions using four different fixedeffects specifications. The bars represent 95% confidence intervals, while the hash marksdenote 90% confidence intervals.
Finally, Figure 4 summarizes our results by plotting the estimated marginal effect of
voter information on campaign contributions. It shows how total contributions from PACs
are predicted to change across the entire range of Congruence, while holding all controls at
their observed values. As voter information increases from low to high, the total raised from
PACs (top, solid line) falls by about $272,000. More modest changes in voter information
also have significant effects. A shift from one standard deviation above the mean to one
below it decreases predicted PAC contributions by $130,000.
18
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Congruence
$0
$100
$200
$300
$400
$500
$600
Tota
l PA
C C
ontr
ibut
ions
(hun
dred
s of
thou
sand
s of
dol
lars
)
AllCorporateLabor
Figure 4: Predicted Contributions from Political Action Committees by Congru-enceNote: The figure shows predicted total contributions across the range of Congruence by allPACs (top, solid line), corporate PACs (middle, dashed line) and labor PACs (bottom, dot-ted line), based on the first column in Table 2 and Table 3, respectively. All other variablesare held at their observed values. Predicted contribution levels are shown with their 95%confidence intervals.
The dashed lines in Figure 4 show predicted contributions by corporate (dashed line
in the middle) and labor (dotted line on the bottom) PACs across the range of Congru-
ence. While giving by labor PACs is flat as voter information rises, donations by corporate
PACs have a significant negative slope. Around a Congruence of 0.75 differences in the
contribution strategies of corporate and labor PACs cease to be statistically meaningful. A
district’s information environment clearly has a different effect on giving by “narrow” inter-
ests compared to more “encompassing” groups. Our results suggest that corporate money
flows predominately into lower information districts, where corporate PACs outspend more
broadly representative groups, such as organized labor, by a wide margin. Fewer donations
by large corporate donors offers one plausible mechanism to explain why politicians do more
19
for their constituents in high-information districts (Snyder & Stromberg 2010).
Conclusion
Based on an analysis of Center for Responsive Politics data over the period 1990-2006, we
find that incumbent members of the House of Representatives who represent low-information
districts raise more money from PACs than incumbents who serve higher-information dis-
tricts. The data also suggest that PACs with “narrower” agendas are responsible for most of
this difference. Previous research by Snyder & Stromberg (2010) finds that representatives
with less informed constituencies have weaker relationships with their constituents across
a variety of dimensions. Our results suggest that ceteris paribus these same representa-
tives accept more money from interest groups—particularly corporate and trade PACs. As
such, our findings lend support to the idea that corporate contributions may be one possible
mechanism to explain these districts’ democratic deficit. These results add an important
new dimension to our understanding of how elected officials represent their constituents:
representatives from low-information districts tend to have stronger financial relationships
with certain interest groups compared to their peers.
Furthermore, these financial ties may have real implications for members’ legislative
behavior. Our evidence suggests that not all interest groups share the same contribution
strategies. Corporate and trade PACs, in particular, appear to give more to candidates from
low-information districts than they would if the representatives’ constituencies were better
informed. These findings suggest that the donations from PACs that are most likely to be
viewed skeptically by constituents are more common when there is less information available
to voters. These results also support the theory that PACs give more to representatives who
are able to dedicate more time and energy to their agenda.
That said, further analysis is needed to determine whether these representatives are
20
actually doing more for interest groups that give more (Hall & Wayman 1990) or are targeted
because interest groups hope they will serve their interests (Fox & Rothenberg 2011).16 We
suspect it is a mix of both, but more research is necessary to shed light on these post-election
outcomes. At this point, there is evidence that the financial relationship is different, which
suggests that the legislative relationship may be different.
Future studies would benefit from a more precise categorization and identification of
PACs and their agendas. CRP provides data that divides PAC contributions by economic
sector, but these divisions do not always mirror the types of agendas we wish to study.
Notwithstanding these limitations, preliminary evidence suggests that sectors with stronger
regulatory interests (finance, real estate, health care) do tend to give more in low-information
districts.17 Developing a better taxonomy of interest groups and further exploring the agen-
das that lead to more giving in low-information districts is a promising avenue for future
research.
The analysis in this paper deals primarily with the era before the Bipartisan Campaign
Reform Act (McCain-Feingold)18, the creation of “Super PACs”19, and the 2014 Supreme
Court ruling which struck down overall limits on individuals’ donations.20 While both the
McCain-Feingold legislation and the Citizen’s United decision left in place the restrictions
on PACs’ direct contributions to candidates, interest groups gained the ability to spend
16Fox & Rothenberg (2011) build a formal model that outlines possible complications from unlimitedindependent expenditures from Super PACs. Their non-contractual model highlights how PAC contributionsdo not result in quid pro quo relationships between representatives and PACs, but suggests, nonetheless,that incumbents do adjust their behavior to attract direct and indirect contributions from ideologically-allied interest groups because interest groups “match” their donations to ideologically sympathetic electedofficials.
17See Figure A.1 in the appendix for these results.18BCRA was passed in 2002 and became effective in 2004, which is the beginning of the final election cycle
in our dataset19The Supreme Court’s 2010 decision in Citizens United v. the Federal Election Commission created
Super PACs that can raise and spend unlimited amounts of money as long as they do not coordinate withcandidates and their campaigns.
20The 2014 decision in McCutcheon v. Federal Election Commission struck down overall limits for howmuch individuals can give to all candidates and party committees during an election cycle.
21
unlimited funds independently. Super PACs thus represent a new way of giving that our
data cannot address. How press coverage will affect spending by Super PACs is an important
topic for future investigation as the data cumulate.
Local information and news environments affect voters’ political knowledge, the quality
of congressional representation, the party loyalty of representatives, and, as we have shown,
the financial relationships of House members with interest groups. Recent research has
illustrated how information environments affect voters (see e.g. Prior 2007), and the findings
here compliment that work and expand our understanding of how representatives are affected.
For the foreseeable future, both money and uninformed voters are likely to remain a fixture
of congressional elections.
22
References
Ansolabehere, Stephen, John M. de Figueiredo & James M. Snyder. 2003. “Why is There so
Little Money in U.S. Politics?” The Journal of Economic Perspectives 17(1):105–130.
Arnold, R. Douglas. 2006. Congress, the Press, and Political Accountability. Princeton
University Press.
Ashford, Kathryn L. 1986. “The Role of Corporations in the 1980 U.S. Congressional Elec-
tions.” Sociological Inquiry 56(4):409–431.
Ashworth, Scott. 2006. “Campaign Finance and Voter Welfare with Entrenched Incum-
bents.” The American Political Science Review 100(1):55–68.
Baron, David P. 1994. “Electoral Competition with Informed and Uniformed Voters.” The
American Political Science Review 88(1):pp. 33–47.
Chappell, Henry W., Jr. 1982. “Campaign Contributions and Congressional Voting: A
Simultaneous Probit-Tobit Model.” The Review of Economics and Statistics 64(1):pp.
77–83.
Coate, Stephen. 2001. “Political Competition with Campaign Contributions and Informa-
tive Advertising.” National Bureau of Economic Research Working Paper 8693. See
http://www.nber.org/papers/w8693.
Coate, Stephen. 2004. “Pareto-Improving Campaign Finance Policy.” The American Eco-
nomic Review 94(3):628–655.
Coleman, John J. & Paul F. Manna. 2000. “Congressional Campaign Spending and the
Quality of Democracy.” The Journal of Politics 62(3):757–789.
23
Currinder, Marian. 2009. Money in the House: campaign funds and congressional party
politics. Boulder, CO: Westview Press.
Esterling, Kevin M. 2007. “Buying Expertise: Campaign Contributions and Attention to
Policy Analysis in Congressional Committees.” The American Political Science Review
101(1):93–109.
Fox, Justin & Lawrence Rothenberg. 2011. “Influence without Bribes: A Noncontracting
Model of Campaign Giving and Policymaking.” Political Analysis 19(3):325–341.
Goidel, Robert K., Donald A. Gross & Todd G. Shields. 1999. Money Matters: Consequences
of Campaign Finance Reform in House Elections. Lanham, MD: Rowman & Littlefield
Publishers.
Gopoian, J. David. 1984. “What Makes PACs Tick? An Analysis of the Allocation Patterns
of Economic Interest Groups.” American Journal of Political Science 28(2):259–281.
Gordon, Sanford C., Catherine Hafer & Dimitri Landa. 2007. “Consumption or Investment?
On Motivations for Political Giving.” Journal of Politics 69(4):1057–1072.
Grier, Kevin B., Michael C. Munger & Brian E. Roberts. 1994. “The Determinants of In-
dustry Political Activity, 1978-1986.” The American Political Science Review 88(4):911–
926.
Grossman, Gene M. & Elhanan Helpman. 1996. “Electoral Competition and Special Interest
Politics.” The Review of Economic Studies 63(2):265–286.
Hall, Richard L. & Frank W. Wayman. 1990. “Buying Time: Moneyed Interests and the
Mobilization of Bias in Congressional Committees.” The American Political Science
Review 84(3):797–820.
24
Hutchings, Vincent L. 2003. Public Opinion and Democratic Accountability: How Citizens
Learn about Politics. Princeton University Press.
Mondak, Jeffery J. 1995. “Newspapers and Political Awareness.” American Journal of Po-
litical Science 39(2):513–527.
Morton, Rebecca B. & Roger B. Myerson. 1992. “Campaign Spending with Impressionable
Voters.” Discussion Papers 1023, Northwestern University, Center for Mathematical
Studies in Economics and Management Science.
Prat, Andrea. 2002. “Campaign Advertising and Voter Welfare.” The Review of Economic
Studies 69(4):999–1017.
Prior, Markus. 2007. Post-Broadcast Democracy: How Media Choice Increases Inequality in
Political Involvement and Polarizes Elections. New York: Cambridge University Press.
Snyder, James M. & David Stromberg. 2010. “Press Coverage and Political Accountability.”
Journal of Political Economy 118(2):355–408.
Wright, John R. 1990. “Contributions, Lobbying, and Committee Voting in the U.S. House
of Representatives.” The American Political Science Review 84(2):pp. 417–438.
Zaller, John. 1992. The Nature and Origins of Mass Opinion. New York: Cambridge
University Press.
25
Appendix
●
●
●
●
−3 −2 −1 0 1
Model 4(District,
State x Year)
Model 3(Year, State)
Model 2(Year, District)
Model 1(State x Year)
Contributions in Constant Dollars ($10,000s)
Finance, Insurance, & Real Estate
●
●
●
●
−3 −2 −1 0 1Contributions in Constant Dollars ($10,000s)
Health Care
Figure A.1: Effect of Congruence on Finance, Insurance, Real Estate, and HealthCare PACsNote: A sector-level analysis shows that the inverse relationship between interest groupcontributions and information is particularly strong for finance, insurance, real estate, andhealth PACs. The left panel of this figure shows the estimated effect of a one standarddeviation increase in Congruence (0.24) on the level of contributions by finance, insurance,and real estate PACs, while the right panel shows the same for health care PACs. Fourdifferent fixed effects specifications are shown with similar results. The bars represent 95%confidence intervals, while the hash marks denote 90% confidence intervals.
26
Table 3: Predicted Effect of Congruence on Interest Group Contributions by Type
Model 1 Model 2 Model 3 Model 4CorporateCongruence -124,930*** -54,856 -115,739*** -101,494*
(36,184) (54,316) (33,630) (56,846)Observations 2,598 2,598 2,598 2,598R-squared 0.46 0.64 0.40 0.69TradeCongruence -81,716*** -69,910* -72,359*** -98,010**
(25,155) (35,999) (23,235) (38,887)Observations 2,598 2,598 2,598 2,598R-squared 0.45 0.64 0.39 0.70LaborCongruence -2,667 -15,309 -5,604 3,887
(14,574) (25,204) (13,832) (25,566)Observations 2,424 2,424 2,424 2,424R-squared 0.67 0.77 0.63 0.81MembershipCongruence -11,187*** -4,567 -10,802*** -4,123
(3,665) (6,949) (3,509) (6,927)Observations 2,509 2,509 2,509 2,509R-squared 0.39 0.55 0.31 0.62Fixed Effects State × Year Year, District Year, State District,
State × YearControls × × × ×
Cluster robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1
Note: This table reports OLS regression coefficients predicting the effect of Congruence onPAC contributions to the campaigns of incumbents running for reelection. The coefficientsreported capture the effect of varying Congruence from zero to one, all other variablesbeing equal. The dependent variable is contributions by a particular category of PAC. Thecoefficients for the control variables are not reported. The standard errors are clustered byrepresentative.
27
Tab
leA
.1:
Contr
ol
Vari
able
s
Sta
nd
ard
Vari
ab
leN
am
eD
esc
rip
tion
Mean
Devia
tion
clos
eT
he
clos
enes
sof
anel
ecti
on,
mea
sure
dby
the
neg
ativ
eab
solu
tediff
eren
ceb
etw
een
the
Dem
ocr
atic
shar
eof
the
vote
and
0.5.
-0.2
150.
155
open
seat
Indic
ator
for
anop
ense
at0.
106
0.30
9unco
nte
sted
Indic
ator
for
anunco
nte
sted
race
0.17
90.
383
age
Age
,m
easu
red
ona
conti
nuou
ssc
ale
53.1
1710
.079
fres
hm
anIn
dic
ator
for
fres
hm
anst
atus
0.15
40.
361
tenure
Ten
ure
inoffi
ce,
mea
sure
don
aco
nti
nuou
ssc
ale
4.45
34.
090
majp
arty
Indic
ator
for
mem
ber
ofth
em
ajo
rity
par
tyin
Con
gres
s0.
547
0.49
8par
tyle
ader
Indic
ator
for
whet
her
the
repre
senta
tive
isa
par
tyle
ader
0.01
60.
126
pow
erco
mIn
dic
ator
for
whet
her
the
repre
senta
tive
serv
eson
ap
ower
ful
com
mit
tee
(Appro
pri
atio
ns,
Budge
t,R
ule
s,an
dW
ays
and
Mea
ns)
0.28
80.
453
chai
rIn
dic
ator
for
whet
her
the
repre
senta
tive
isa
chai
r0.
047
0.21
4ra
nkm
emIn
dic
ator
for
whet
her
the
repre
senta
tive
isa
rankin
gm
emb
er0.
046
0.20
9dw
nom
1D
Wnom
inat
esc
ore
mea
suri
ng
are
pre
senta
tive
’sid
eolo
gy0.
029
0.44
5p
cturb
anP
erce
nt
urb
anin
the
dis
tric
t0.
688
0.28
6urb
2In
dic
ator
for
the
seco
nd
quin
tile
ofurb
anit
y0.
116
0.32
1urb
3In
dic
ator
for
the
thir
dquin
tile
ofurb
anit
y0.
177
0.38
2urb
4In
dic
ator
for
the
fourt
hquin
tile
ofurb
anit
y0.
189
0.39
2urb
5In
dic
ator
for
the
fift
hquin
tile
ofurb
anit
y0.
445
0.49
7ld
ensi
tyL
ogof
dis
tric
t’s
pop
ula
tion
den
sity
5.88
51.
982
ld2
Indic
ator
for
the
seco
nd
quin
tile
oflo
gged
pop
ula
tion
den
sity
0.17
30.
378
ld3
Indic
ator
for
the
thir
dquin
tile
oflo
gged
pop
ula
tion
den
sity
0.45
50.
498
ld4
Indic
ator
for
the
fourt
hquin
tile
oflo
gged
pop
ula
tion
den
sity
0.28
50.
451
ld5
Indic
ator
for
the
fift
hquin
tile
oflo
gged
pop
ula
tion
den
sity
0.07
90.
270
cdp
erci
14dum
my
vari
able
sfo
rth
eto
tal
num
ber
ofco
ngr
essi
onal
dis
tric
tsin
any
city
conta
inin
ga
majo
rity
ofth
ep
opula
tion
2.41
44.
263
CP
Ilm
edin
com
eL
ogof
the
dis
tric
t’s
med
ian
inco
me
inye
ar20
00co
nst
ant
dol
lars
-0.7
590.
374
pct
old
Per
cent
ofth
edis
tric
t’s
pop
ula
tion
aged
65or
older
0.13
40.
040
pct
milit
ary
Per
cent
ofth
edis
tric
t’s
pop
ula
tion
emplo
yed
by
the
milit
ary
0.00
60.
014
pct
farm
erP
erce
nt
ofth
edis
tric
t’s
pop
ula
tion
emplo
yed
inag
ricu
lture
0.01
10.
012
pct
fore
ign
Per
cent
ofth
edis
tric
t’s
pop
ula
tion
that
are
fore
ign-b
orn
0.08
40.
099
pct
blu
ecol
Per
cent
ofth
edis
tric
t’s
pop
ula
tion
emplo
yed
inblu
e-co
llar
occ
upat
ions
0.07
00.
023
pct
vete
rans
Per
cent
ofth
edis
tric
t’s
pop
ula
tion
that
are
vete
rans
1.14
31.
388
28
