Corruption in Campaign Finance

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Electoral Institutions and

Corruption in Campaign FinanceMargit Tavits

[email protected]

Joshua D. [email protected]

April 2012

Abstract

Recent research has suggested, but left untested, an argument according to whichas personal vote-seeking incentives increase and campaigns become more expensiveto run, politicians will seek illegal campaign funds with higher probability. In thispaper, we test this mechanism specically. We demonstrate that, while increasingillegal campaign contributions clearly results in increased perceptions of corruptionmore generally, electoral institutions are unable to explain either (a) the incidence of illegal campaign contributions or (b) the resulting level of perceived corruption. In aneffort to explain the incidence of illegal campaign contributions, we advance a theoryof clarity in campaign nance and demonstrate that although citizens are more likelyto perceive corruption in institutional settings with greater clarity, clarity is in itself not sufficient to curb corruption .1

1 Prepared for The Effects of District Magnitude Conference in Lisbon, Portugal, May 29-30, 2012. Pleasedo not cite without the authors permission.


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1 Introduction

What effect do electoral institutions have on political corruption? As the main linkage

between voters and their representatives, elections serve as a potentially powerful check on

the behaviors and actions of elected officials (Fearon , 1999; Powell, 2004; Przeworski, Stokes

and Manin , 1999). To the extent that voters can successfully identify which parties and

politicians are responsible for certain policy outcomes, then we expect that self-interested

politicians should cater to the interests of these voters ( Anderson, 2000; Powell and Whitten ,

1993; Tavits , 2007). As with policy outcomes, so with corruption. Simply holding elections

at all has been found to signicantly reduce the level of corruption in a polity ( Diamond and

Plattner , 1993; Doig and Theobald , 2000) and an ever growing literature on variation in levels

of corruption within democratic polities has begun to explore the impact that different types

of electoral institutions have on corruption ( Kunicova and Rose-Ackerman , 2005; Persson and

Tabellini , 2003; Tavits , 2007). As electoral institutions provide voters with greater clarity

regarding who is responsible for corrupt practices, and, in turn, incentivize politicians to

behave better, then we would expect to observe less corruption in these situations.

Scholars have had a difficult time, however, in pinning down the specic constellation of

electoral institutions under which we should observe the lowest levels of corruption. Some

have argued that the electoral competition that emerges from permissive electoral rules

should decrease corrupt practices. To this end, Persson, Tabellini and Trebbi (2001) and

Persson and Tabellini (2003) argue that larger voting districts where highly proportionate

outcomes result in fewer safe seats for incumbents induce greater competition for votes

and thus incentivize politicians to check their behavior while in office. Higher district magni-

tude, however, might also obscure who is at fault for corrupt practices by virtue of creating

large coalitional governments and weakening the oppositions ability to speak as a unied

voice in parliament ( Davis, Camp and Coleman , 2004; Kunicova and Rose-Ackerman , 2005;

Potter and Tavits , 2011). Similarly, while closed list proportional representation systems free

individual politicians from having to raise massive campaign war chests, they also when

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compared to open lists centralize a partys decision-making power in the hands of a small

coterie of party leaders who might be difficult for the voters to identify and remove from

office (Chang and Golden , 2006; Kunicova and Rose-Ackerman , 2005; Lederman, Loayza and

Soares, 2005).A recent and inuential article by Chang and Golden (2006) attempts to tackle these

tradeoffs. By drawing on the idea that politicians in different types of electoral environments

face differing incentives to cultivate their personal reputations rather than the reputations of

their parties, Chang and Golden (2006) argue that not all campaigns are equally expensive

to conduct. In particular, they argue, in proportional representation (PR) systems with

closed lists, politicians face decreasing campaign expenses as district magnitude increases.

By contrast, in PR systems with open lists, politicians face increasing costs of campaigning

as district magnitude increases. These differential costs hinge critically on the need of an

individual candidate to distinguish himself either from his copartisans or from candidates

belonging other political parties ( Carey and Shugart , 1995; Shugart, Valdini and Suominen ,

2005). As the electoral benet of cultivating ones own personal image declines, then so

should the expense of campaigning; conversely, a greater need to differentiate oneself from the

eld of candidates results in more expensive campaigns. As this nancial burden increases,

politicians who wouldnt otherwise be drawn toward illicit campaign contributions are now

more willing to engage in such practices. Because the same set of incentives exists for

all political candidates within the same country, these temptations to indulge in corrupt

campaigning aggregate across politicians to the system level, where voters are bound to

perceive higher levels of country-level corruption.

To test their argument, then, Chang and Golden (2006) interact district magnitude withlist type as their main explanatory variable and employ a country-level corruption percep-

tions index as their outcome variable. However, they leave untested the causal mechanism

relating electoral institutions to country-level corruption namely, they do not test whether

country-level corruption is specically being affected by institutions impact on politicians

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proclivity to engage in the illicit solicitation of campaign funds. In this paper, we aim to do

just that. First, we outline in greater detail the ways in which electoral institutions shape

personal vote-seeking incentives and connect these incentives to the expense of campaigns

and, by extension, the need to make resort to illicit campaign contributions. We employa cross-national measure of the frequency of illegal contributions to political parties as our

main explanatory variable for perceptions of system-level political corruption. Second af-

ter having found empirical support for this causal mechanism we back up one link in the

theoretical chain and explore the extent to which district magnitude and list type combine

to tell us something about the incidence of illegal contributions to political parties. Across

multiple empirical specications, we nd, contrary to the argument advanced by Chang and

Golden (2006), that these electoral institutional variables have no effect on campaign ille-

gality. Third, left without an explanation for the variation we observe in campaign-level

corruption, we develop a theory of clarity in campaign nance that explains this variation in

terms of the transparency of the fundraising process. We nd that citizens are more likely

to recognize corrupt fundraising practices when rules exist that regulate campaign nance

and provide for the disclosure of funding sources. Fourth and nally, we conclude with a

discussion that situates this paper in the broader framework of our on-going research.

2 Campaign Illegality and Corruption Perceptions

Money is fundamental in electoral politics. Whether a country provides for some level of

public subsidization of political parties or whether it caps political parties campaign ex-

penditures, the ability of politicians to successfully raise campaign funds matters to at least

some extent in the vast majority of electoral democracies ( Scarrow, 2007). The comparative

literature to this end speaks volumes. Across a broad range of electoral institutional settings,

comparative scholars have repeatedly unearthed evidence that raising and spending money

during election campaigns improves parties and candidates odds at reelection. Although

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this literature began in the American context ( Gerber , 1998; Green and Krasno , 1988; Ja-

cobson, 1980), the same results have turned up in institutional settings as diverse as Ireland

(Benoit and Marsh , 2003), Japan ( Cox and Thies , 2000), Belgium (Hooghe, Maddens and

Noppe, 2006), South Korea ( Shin et al. , 2005), France ( Palda and Palda , 1998), the UnitedKingdom (Johnston and Pattie , 1995), and Brazil ( Samuels, 2001). What is not constant

across these various countries, however, is the relative importance of campaign funds in win-

ning an election. Indeed, Chang and Golden (2006) argue that various combinations of list

type and district magnitude and the personal vote-seeking incentives they induce can

tell us a great deal about the relative importance of campaign funds and, by extension, the

lengths to which desperate politicians are willing to go in order to get them.

Following the well-developed literature on personal vote-seeking behaviors developed by

Cain, Ferejohn and Fiorina (1987), Carey and Shugart (1995) and Samuels (1999), the

authors argue that increased intraparty competition should drive up the value for politicians

of cultivating their own personal reputations rather than the more general reputation of the

party to which they belong. List type and district magnitude interact in important ways to

help determine the trade-off between these two types of reputations. In closed-list systems

with low district magnitude where a vote for a party list is, effectively, a vote for its

one or perhaps two candidates cultivating a personal vote is nearly akin to cultivating a

party vote. Here, the value of personal vote-seeking is relatively high. As district magnitude

increases in closed-list systems, however and as more and more candidates get placements

on increasingly lengthy lists each party is now no longer identiable with just one or two

politicians. Because voters cannot cast a vote below the level of the party, politicians should

work to cultivate their own distinct reputations less often than in closed-list systems with lowdistrict magnitude. The relationship is opposite under open-lists systems. Namely, as district

magnitude increases and the overall number of competitors (copartisan and otherwise) in

a district increases the fact that voters can vote for specic individuals induces these

candidates to work diligently to cultivate their personal reputations vis- a-vis the reputations

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of their parties.

How do candidates for political office distinguish themselves from one another and build

their personal reputations? In the legislative arena, of course, empirical work has demon-

strated that parliamentarians break party ranks on legislative votes with greater frequency(Crisp et al. , 2004; Haspel, Remington and Smith , 1998). Studies of personal vote-seeking

incentives have also demonstrated that pork-barrel allocations to a sitting incumbents con-

stituency can positively impact her personal reputation ( Ames, 1995; Chang , 2005). It is

not immediately clear how either behavior, however, would contribute to aggregate-level in-

dicators of corruption more generally. While poor party discipline might adversely affect the

efficient passage of legislation or hamper the governing coalition formation process ( Carey ,

2003; Giannetti and Benoit , 2009; Morgenstern , 2004) and while pork-barrel politicking might

result in representational ties between voters and their elected officials predicated more on

clientelism than on party programs ( Kitschelt , 2000; Kitschelt and Wilkinson , 2007), neither

type of activity should by itself contribute to levels of corruption, per se. Rather, Chang

and Golden (2006) argue that personal vote-seeking incentives should make a difference in

citizens perceptions of corruption at the level of the campaign; more specically, they argue

that: in settings where incentives for the personal vote rise, candidates for public office need

larger baskets of individual campaign funds. They need money to advertise their individual

candidacy (p. 119).

In an effort to distinguish themselves not only from their copartisans but also from com-

peting candidates from other parties, individual politicians must engage in a number of

activities such as buying advertising time on television, printing and distributing campaign

yers and posters, etc. As Chang and Golden (2006) note, all of these campaigning activitiesmay tempt candidates to seek illegal campaign contributions, especially in contexts in which

the abilities of individual candidates to raise campaign funds may be legally circumscribed

in various ways (p. 120). Implicit in their argument is the assumption that more expensive

campaigns generally translate into more corrupt campaigns. We think this logic requires

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some qualication (after all, there are a number of other institutional and normative fac-

tors that shape politicians proclivities to engage in corrupt behaviors), but we grant these

authors basic premise that at least the temptation to make resort to corrupt practices is

perhaps signicantly exacerbated in the context of expensive campaigns. As Sajo (1998) hasargued, we observe cross-nationally that campaign nance laws are increasingly subject to

restrictive rules and the demand for more campaign spending is growing. Hence a turn to

illegality becomes almost inevitable (p. 44).

2.1 Testing Personal Vote-Seeking Incentives

To test the hypothesis that increased frequency in illegal contributions to political partiesshould, in turn, increase aggregate-level perceptions of corruption, we rst turn to the widely

utilized data set circulated by Treisman (2007) and collect data on 109 countries that satisfy a

rather liberal denition of minimal democracy: if the country was listed as free by Freedom

House in 2002 and 2003, it was automatically included in the data set; if it was listed as not

free in those same years, it was automatically excluded; if it was listed as partially free in

either year, we then turn to the countrys Polity IV scores and following Brancati (2008)

and others include it if it scored above a 5 on this metric. We are most interested in the

years 2002 and 2003, because these are the years covered by the data set we employ for our

explanatory variable, illegal contributions to political campaigns. This variable comes from

a survey question administered in the World Banks Global Competitiveness Report (GCR)

editions 02/03 and 03/04 which asks more than 7,300 business leaders across a number of

countries about the frequency of illegal donations to political parties. The index reports an

average gure of all respondents and ranges from 1 (very common) to 7 (never occurs).

The report only covers a subset of the 109 democracies collected from the Treisman data

set. The outcome variable of interest perceived corruption has been operationalized as

the two-year average during this time period of Transparency Internationals Corruption

Perceptions Index (CPI) which is an averaged reporting of survey respondents answers to

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questions about the extent of overall corruption in their country. This index ranges from

0 (highly corrupt) to 10 (not at all corrupt) and is one of the standard measurements of

corruption perceptions utilized in studies of this type.

For our other variables, we follow as closely as possible the operationalizations employedby Chang and Golden (2006). Our measure of district magnitude and open list are both

based on variables that come from the Database of Political Institutions (DPI) described in

Beck et al. (2001). District magnitude equals the DPI variable mdmh in 2003, which is the

weighted average of the number of representatives elected by each constituency size. This

measure is convenient in that it averages district magnitudes across tiers in mixed-member

systems while also weighting the relative proportion of the two types of districts. Our open

list indicator is simply the reverse coding of the DPIs cl variable, which indicates whether

or not a list is closed when the electoral system is proportional representation. 2

We also include several control variables that have become commonplace in studies of

corruption perceptions. For these variables, we draw from both the DPI as well as the

database Democratic Electoral Systems Around the World, 1946-2000 (DESW) collected and

managed by Golder (2005).3 First, is whether or not a country is parliamentary . We draw

on the institution variable from Golder and recode it slightly to correspond with the values

presented in Chang and Golden (2006): this variable takes the value of 3 for parliamentary

systems, 2 for mixed systems, and 1 for presidential systems. We also control for the effective

number of legislative parties as drawn from Golder, who employs a corrective formula that

accounts for vote shares going to smaller parties that get reported as a disaggregated other

2 In some specications of our models, which we describe below, we also code as closed lists thoseelectoral systems that make use of plurality rules in single-member districts. In these cases, in terms of the

schema developed by Carey and Shugart (1995), we have no pooling of votes across parties, voters castingtheir single vote at the party level, and party leaders presenting a xed ballot which voters may not alter.This is a situation that Carey and Shugart (1995) describe as closed-list plurality. In our models, wecode these SMD cases with district magnitude of one and closed-list. These cases are excluded from modelsintended to replicate the ndings of Chang and Golden (2006), obviously, as these authors only test casesthat make use of proportional representation.

3 For variables drawn from Golder, we include values from 2000, which is the last year in his data set, butthe closest to the time period in which we are interested. This is less than ideal, but the DESW has somedistinct advantages over the DPI especially when it comes to institutional coding discrepancies.

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category in official election records. A number of other covariates have been found to have

a consistent effect on corruption perceptions. These are the percentage of the population

that identies as Protestant , which we take from Treisman (2007); the age of the democratic

regime , which we measure in years and take from the DPI; whether or not the country is a federal system and whether or not it is a former British colony , both as they are measured

by Treisman (2007); and two economic variables, both taken from the World Banks data

tables for the year 2003 a proxy of economic development (log of GDP per capita ) and a

proxy of a countrys openness to trade (the amount of imports of goods and services as a

percentage of the countys overall GDP).

In the empirical investigations that follow, we pursue the same strategy repeatedly. First,

we replicate the model specications employed by Chang and Golden (2006) using only,

from what we can gather, the countries that they include in their analysis. Second, we

employ the same model specication, but include the broader set of democracies for which

we collected data (including those plurality systems that we treat as closed lists with district

magnitudes of one). Third, we insert into these models our main explanatory variable of

interest before, fourth, deleting their electoral institutional variables altogether and relying

only on our explanation. Across many model specications, we return consistent evidence

that district magnitude, list type, and their interaction do not explain variation in country-

level corruption or campaign-level corruption. Interestingly, however, the causal mechanism

that drives their theoretical story holds up rather well: increased corruption at the level of

the campaign does indeed drive up perceptions of corruption at the country level. We rst

explore this phenomenon with a battery of OLS regression models presented in Table 1.

[Insert Table 1 About Here]

Our Model 1 replicates Chang and Goldens Model 2 in Table 1 of their article, where

they outline a purely electoral institutional model to explain variation in Transparency In-

ternationals CPI index. 4 Not only are none of the coefficients statistically signicant, but

4 Note that our N in this regression is 41 and theirs is 39. We simply included all of the countries they

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their signs are all opposite from what appears in their Model 3; furthermore, the explained

variance from this model is exceedingly small. Recall that Chang and Golden are employing

values from the 1996-1998 time period while we are chiey employing values (for the same

variables) from the 2002-2003 time period. At any rate, the relationship does not appear tobe robust to different time periods for the set of countries that they include in their analy-

sis. Our Model 2 is a replicated version of their Model 5, which is their main explanatory

regression with full inclusion of important control variables. 5 In our Model 3, we rely on

the same model specication, but now include the broader set of democracies for which we

have complete data. Note that the story remains the same: district magnitude, list type,

and their interaction continue to underperform.

But what of the causal mechanism described by Chang and Golden (2006), i.e the argu-

ment that increases in illegal campaign contributions result in increases in perceived country-

level corruption? In Models 4 and 5, we include our measure of illegal contributions to polit-

ical parties, rst in the presence of the electoral institutional variables and, second, in their

absence. Clearly this variable is a powerful predictor of corruption perceptions. As illegal

contributions become less frequent (in other words, as the GCR index increases toward its

max of 7), then the CPI index indicates less corruption (in other words, increases toward

its max of 10). Not only does including this variable buy us more explanatory variance,

it also more than makes up for eliminating the institutional variables altogether (note the

identical adjusted R 2 values between Models 4 and 5). In actuality, however, Chang and

Golden never argue that campaign contributions and electoral institutions should be entered

additively into models predicting variation in corruption perceptions. Rather, their theory

argues that institutions impact the frequency of illegal contributions which, in turn, impactperceptions of corruption at the aggregate level. In the next section, we back up one step in

the causal chain and take illegal campaign contributions as our dependent variable.

list in their appendix, of which there are 42 (one of our cases is dropped due to missingness).5 Here again, weve restricted ourselves to the countries included in the appendix of their article, but our

N comes to 38 while theirs is 32.

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3 Electoral Institutions and Campaign Illegality

If the theory articulated by Chang and Golden (2006) holds, we should expect that, as per-

sonal vote-seeking incentives increase in open list systems as district magnitude increases,

campaigns should become more expensive to run and, by extension, politicians should have

to rely more frequently on illegal campaign contributions. We nd weak support for this

argument in Model 1 of Table 2, where we include only those cases utilized in their study.

The coefficients in this model are correctly signed, but fail to achieve conventional levels of

statistical signicance. When we expand the data set to include a broader set of democracies

(Model 2), all of the coefficients actually reverse their signs and, again, fail to achieve sta-

tistical signicance. In Model 3, we drop the electoral institutional variables altogether and

actually manage to improve the amount of explained variance in their absence. The canonical

determinants of country-level corruption perceptions (i.e. Protestantism and democratic and

economic development) also appear to be statistically robust predictors of campaign-level

corruption as well.

Why do we nd null results when it comes to explaining variation in illegal campaign

contributions with electoral institutional variables? We believe that we can account for the

lack of statistically signicant results with two explanations. Recall, rstly, that our measure

of campaign illegality is very highly correlated with our measure of system-level corruption

perceptions (with r = 0 .9). It may well be the case that both of these indices are simply

tapping into the same latent idea namely, that if a country is corrupt, its corrupt all

the way down. Treisman (2007) notes that many of these cross-national indices actually

track very closely with one another regardless of whether citizens and experts are being

asked about the level of corruption in the bureaucracy, the party system, the country as

a whole, etc. In such corrupt environments, then, we think that politicians incentives to

cultivate their personal reputation probably fall far behind other incentives such as personal

enrichment or directly rewarding their political supporters.

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time again in scholarship on retrospective accountability and economic voting ( Anderson,

2000; Kiewiet, 2000; Powell, 2000; Powell and Whitten , 1993; Samuels, 2004). Przeworski,

Stokes and Manin (1999) argue, however, that this accountability relationship is not merely

conned to the economic realm and that voters if they can determine which individualsor parties are responsible for which decisions will aim to hold politicians accountable

across a much broader range of policy areas. A raft of recent scholarship has argued that

corruption is one such governmental outcome that voters might plausibly take into their

balloting decisions (Gingerich , 2009; Nyblade and Reed , 2008; Pereira, Melo and Figueiredo ,

2009; Tavits , 2007).6 Furthermore, these authors argue that certain constellations of political

institutions will either improve or lessen voters ability to identify and, subsequently, punish

incumbent politicians for corrupt behaviors. When institutions create clarity of responsibility

for the outputs of government, we would expect voters to make use of this information in

casting their votes ( Powell and Whitten , 1993; Tavits , 2007). Ceteris paribus, we assume

that voters would rather have clean representatives in elected office than corrupt ones;

to the extent that they can identify who the corrupt politicians are, we would expect these

incumbents to be thrown out of office.

In the aggregate, then, we would expect self-interested politicians in high clarity environ-

ments to read the writing on the wall and improve their behavior so as to avoid punishment

at the ballot box. While empirical scholarship has tended to examine this relationship at

the national level ( Potter and Tavits , 2011), we have reason to expect that clarity in the

relationship between voters and politicians will reduce corruption at the level of the election

6 Note that the ndings of these studies suggest that voters are actually taking corruption into account intheir voting decisions and punish corrupt behavior. Indeed, surveys around the world indicate that people

perceive governmental corruption as a harmful and undesirable outcome. This is the case even in societieswhere corruption is endemic and people not only experience it on a regular basis but may even benet fromit in certain instances ( Persson, Rothstein and Teorell , 2010). For example, over 60 percent of Africans citizens of countries suffering from widespread corruption and clientelism regard corruption by publicofficials as wrong and punishable ( Afrobarometer , 2006). Furthermore, evidence from the U.S. contextalso suggests that voters punish incumbents who were charged with corruption: on average, a corruptionallegation cost an incumbent about 10 per cent of the vote and led to electoral defeat in 25 per cent of the cases ( Welch and Hibbing , 1997). Overall, we believe that it is safe to assume that corruption is anelectorally salient issue.

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This conclusion is in line with the expectations of scholars studying the effects of campaign

nance regulation and policy-makers advocating for such rules. Indeed, regulation of the

nancing of political parties is usually introduced with the specic goal of reducing public

perceptions of corruption and seen as a remedy for corrupt campaign strategies both in theU.S. and comparative context ( Hibbing and Theiss-Morse , 1995; Primo and Milyo , 2006;

Roper , 2002; Thompson , 1995; van Biezen and Kopecky, 2007; Weber , 1999). Many ethics

and campaign nance statutes in the U.S., for example, include a statement that these laws

enhance citizens beliefs in clean government ( Primo and Milyo , 2006; Rosenson, 2009). The

causal mechanism that we articulate here provides a theoretical rationale for these widely

shared expectations.

5 Testing Clarity in Campaign Finance

In order to construct our additive metric of clarity in campaign nance in as principled a

fashion as possible, we evaluated many of the questions that were asked by the researchers

who put together the Funding of Political Parties and Election Campaigns database at

the International Institute for Democracy and Electoral Assistance (IDEA) and evaluatedthe extent to which different combinations of these questions tapped into the latent (and

inherently unmeasurable) concept of clarity. In particular, we focused on the following three

dichotomous yes/no questions: (1) Is there a system of regulation for the nancing of political

parties? (2) Is there provision for disclosure of contributions to political parties? and (3)

Is there provision for public disclosure of expenditure by political parties? In an effort to

construct a measure of the internal consistency of these three questions, we turned to the eld

of psychometric testing, which has pioneered a number of measures of internal reliability. One

such metric for questions that can assume continuous values is the widely-utilized Crombachs

alpha ( Cortina , 1993) and, in the specic case of variables with dichotomous outcomes, its

derivative a metric that is known as the Kuder-Richardson Formula 20, or KR20 ( Kuder

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adopt certain institutional determinants of clarity in campaign nance. This is a salient

concern to raise, but the direction of the impact is unclear: do more corrupt countries adopt

institutions with greater clarity in an effort to clean up their act 7 (perhaps responding to

inducements offered by international monitoring organizations) ( van Biezen and Kopecky,2007) or, by contrast, do more corrupt countries adopt institutions with lesser clarity so

that they might continue to engage in their malfeasant activities? As it turns out, with

our particular subset of democracies, the endogeneity problem is empirically irrelevant. We

averaged the CPI country-level values for the three years leading up to our investigative

period of 2003 and found that they are not at all correlated with our additive measure of

clarity ( r = 0.005). Even still, because we know that previous levels of corruption inuence

current levels of corruption, we include this lagged average of corruption perceptions in the

models that follow. If clarity ends up exerting a statistically signicant impact on campaign-

level corruption while controlling for previous levels of corruption, then this will be a truly

robust nding .8

[Insert Table 1 About Here]

In fact, we nd just that across the different model specications we report in Table 3. As

before, the rst model includes only those cases utilized by Chang and Golden (2006). This

model has impressive explanatory power and it holds up well when we include our broader set

of democracies in Model 2. Dropping the electoral institutional values from the specication

of Model 3 does not curtail claritys substantial impact, nor does it reduce the amount of

variance we are able to explain. Curiously, however, the coefficient on clarity in campaign

nance is in the opposite direction that we would predict, i.e. as clarity increases, businessleaders perceive a higher incidence of illegal contributions to political parties. Depending on

which model we utilize and holding other variables constant moving across our observed

7 Witko (2007) argues this to be the case in the U.S. context.8 The results continue to hold when the lagged average of corruption perceptions is removed from the

analysis.

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values on the additive scale of clarity would result in a shift down the GCR metric (i.e.

toward more illegal contributions) of roughly 1 point. Substantively speaking, this is the

amount of space that separates the GCR scores of, say, Denmark from Tunisia or, say,

France from Thailand. Although the variables impact operates in the opposite directionfrom what we would hypothesize, clearly we are registering an important trend here. In our

concluding section, we discuss this counterintuitive nding and outline how we intend to

explore it further in our future research.

6 Conclusion and Future Research

We began this article by asking about the link between electoral institutions and aggregate-

level measures of political corruption. One very important article in this eld of study has

suggested a theoretical story that begins with the incentives institutions create for individual

politicians and ends with predictions about perceived corruption at the level of the country

(Chang and Golden , 2006). The specic causal argument links an increase in the value of

personal-vote seeking to an increase in the expense of campaigning and a greater need to

provide pork-barrel projects for ones constituents; regardless of the broader set of norms andinstitutional checks in society, when politicians depend more on money to win elections, they

become more desperate in their solicitations. Other things being equal, this should result in

a higher incidence of illegal campaign contributions to parties and, in the aggregate, higher

levels of perceived corruption in the country more generally.

Our test of this causal chain is not perfect, but we have tried to do justice to the argu-

ment itself, which we nd a priori plausible and compelling. Empirically, we nd support for

the untested causal mechanism that Chang and Golden (2006) describe: namely, that as the

incidence of illegal donations to parties increases, so too does corruption in the aggregate.

Backing up one step earlier in the causal chain, however, yields disappointing results: it ap-

pears that the variation we observe in illegal donations to political parties has very little to

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do with the personal vote-seeking incentives generated by the interaction of district magni-

tude and list type. Rather, it seems that the more traditional determinants of country-level

corruption (i.e. Protestant norms and economic and democratic development) are also the

more powerful predictors of campaign-level corruption.Left casting about for an explanation, we turn to the literatures on economic voting and

clarity of responsibility to develop a theory of clarity in campaign nance. We believe that

this is a productive rst step in the literature on campaign nance reform, which is a eld

of research that has been described by some of its greatest practitioners as theoretically

underdeveloped ( Nassmacher , 1993; Scarrow, 2007). Our argument is that, should campaign

nance be regulated and made more transparent, voters ought to use this information to

inform their perceptions of politicians corruptness. To the extent that voters then internalize

this information with their vote choice and, subsequently, punish corrupt politicians at the

ballot box, then we should observe less corruption at the level of campaigns in the presence

of regulations that offer voters more clarity.

Our empirical analysis indicates that this story is partially accurate. Increased clarity

results in an increased perception of the frequency of illegal campaign contributions. One

way to interpret this is that clarity in campaign nance is likely to serve its intended purpose

of informing the public about any illegal campaign activities. Indeed, a handful of empirical

studies about the effects of campaign nance regulation in the U.S. context reach a similar

conclusion: stronger campaign nance laws have been associated with increased perceptions

of corruption (Rosenson, 2009) and public cynicism (Gross and Goidel , 2003). This is ar-

gued to result from the increase in available information about the misconduct of political

parties and candidates in settings where regulations have been adopted. For example, Primoand Milyo (2006) demonstrate modest improvements in peoples sense of efficacy from party

funding disclosure laws and argue that such laws provide citizens with the necessary infor-

mation to be better citizens (p. 35). Under conditions of greater clarity in campaign nance

it is likely that the media will report on more violations, there is more talk about public

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officials amassing illegal funds (Rosenson, 2009), and perceptions of campaign corruption

become more widespread as a result ( Montinola and Jackman , 2002).

What is curious, however, is that increased perceptions of corruption do not seem to

incentivize politicians to subsequently change their behavior. Or, at least, any such changeis not reected in reduced levels of campaign corruption. To some extent, our analysis is

hamstrung by a lack of longitudinal data; we can imagine a scenario where it takes multiple

iterations of voters punishing incumbent politicians before they get the message and begin

to change the way they go about funding their campaigns. We are also limited to using

perceptions rather than actual instances of corruption. It is possible that actual corruption

has deceased as a result of increased clarity of campaign nancing. The fewer instances of

corruption may simply be getting a lot more coverage because these instances are easier to

detect. Getting accurate data on actual campaign corruption across countries and/or over

time is a challenge in its own right. Furthermore, such data, even if obtained, are likely

to reect the bias in reporting mentioned above and, therefore, unlikely to give us accurate

answers.

Perhaps a more straightforward explanation for this negative coefficient, however, is this:

illegal campaign contributions buy politicians more votes than the number of votes they lose

by virtue of appearing corrupt to voters. In other words, the advantages of breaking the

rules may far outweigh the costs. We believe this tradeoff is the locus of additional work

on campaign nance as it relates to political corruption. Specically due to the inherent

difficulties in collecting observational data of the sort that we would need to test this tradeoff

we are working on a laboratory experiment whereby voters and politicians interact

in an environment where corrupt practices are either easy or difficult to detect. We areinterested in the tradeoff in votes and money from the politicians perspective as well as the

tradeoff in policy outcomes and clean government from the voters perspective. We expect

that in some mock institutional scenarios, politicians will value illegal contributions more

than the small number of votes they might lose by virtue of being perceived as corrupt.

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Additionally, we expect that voters will make their balloting decisions based on a mix of

ideological proximity to the candidates as well as their impressions of the candidates levels

of corruption. In the end, we hope that this will be a productive contribution to the edging

comparative literature on campaign nance regulations as well as the much better-developedliterature on the determinants of political corruption.

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Table 1. OLS Model Predicting 2002-2003 Average Transparency International CPI Value.

D.V. Model 1 Model 2 Model 3 Model 4 Model 5

District 0.005 0.007 0.005 0.006

Magnitude (0.014) (0.008) (1.204) (0.004)

Open-List 3.460 0.816 0.468 0.312(1.880) (0.960) (0.406) (0.342)

DM x 0.185 0.059 0.023 0.019Open-List (0.143) (0.067) (0.036) (0.030)

Illegal 0.879 0.886Contributions (0.161) (0.161)

Parliamentary

0.267 0

.026 0

.004 0

.041System (0.333) (0.165) (0.137) (0.135)

Eff. Number 0.057 0.073 0.075 0.011of Parties (0.137) (0.091) (0.075) (0.056)

Protestant 0.016 0.020 0.005 0.005Population (0.007) (0.005) (0.005) (0.005)

Age of 0.012 0.027 0.009 0.012Democracy (0.016) 0.008 (0.008) (0.007)

Federal 0.432 0.087 0.025 0.004(0.522) (0.327) (0.270) (0.271)

Log GDP 3.427 2.248 1.652 1.552per capita (0.891) (0.356) (0.315) (0.307)

British 0.779 0.656 0.591 0.513Colony (0.744) (0.384) (0.323) (0.290)

Imports 0.002 0.001 0.002 0.000(0.012) (0.007) (0.006) (0.006)

Intercept 5.134 8.012 5.084 5.043 4.583(0.532) (2.730) (1.204) 1.008 (0.921)

N 41 38 68 66 66Adj. R 2 0.02 0.83 0.86 0.91 0.91

Signicance indicated by * p 0.05, ** p 0.01, and *** p 0.001. Models 1 and 2 include only thosecases in Chang and Golden (2006). Models 3-5 include all democracies with SMD systems coded DM =1and Open-List =0.

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Table 3.OLS Model Predicting 2002-2004 Average World Bank Incidence of Illegal Campaign Donationsto Political Parties.

D.V. Model 1 Model 2 Model 3

District 0.001 0.004Magnitude (0.003) (0.002)

Open-List 0.997 0.191(0.386) (0.230)

DM x 0.062 0.002Open-List (0.029) (0.021)

Clarity in 0.242 0.142 0.159Campaign Finance (0.081) (0.061) (0.062)

Protestant 0.003 0.006 0.006Population (0.004) (0.003) (0.003)

Age of 0.009 0.010 0.011Democracy (0.006) (0.004) (0.004)

Log GDP 0.411 0.235 0.117per capita (0.360) (0.262) (0.260)

Past Corruption 0.543 0.452 0.412(2000-2002) (0.090) (0.076) (0.075)

Intercept 2.385 1.836 1.655(1.049) (0.725) (0.729)

N 37 59 59Adj. R 2 0.91 0.88 0.88

Signicance indicated by * p 0.05, ** p 0.01, and *** p 0.001. Model 1 includes only those cases inChang and Golden (2007). Models 2 and 3 include all democracies with SMD systems coded DM =1 andOpen-List =0.

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