Political Corruption and Voter Turnout:

Mobilization or Disaffection?

Elena Costas-Pérez

Universitat de Barcelona &

Institut d’Economia de Barcelona (IEB)

Work in progress. March 2014.

ABSTRACT: Corruption scandals may modify voter turnout, either by

mobilizing citizens to go to the polls to punish or support the malfeasant

politician or by demotivating individuals to vote as a consequence of

disaffection with the democratic process. We study whether these

effects depend on individual’s partisan leanings and/or the timing of

corruption scandals. Our database includes information on Spanish local

scandals from 1999 to 2007, and survey data on individuals’ turnout at

the 2007 local elections. We use a matched database to identify the

corruption-free pairs for our corruption affected municipalities’ sample.

Our results show that while neither past nor recent corruption scandals

have impact on turnout, repeated corruption cases boost abstentionism.

We also find that independent voters - those with no attachment to any

political party - are the only ones that withdraw from elections as a

consequence of corruption. Core supporters do not modify their

electoral participation after a scandal has broken out. Those who

support the incumbent do not even recognise that their party is corrupt,

while both independent voters and opposition core supporters report

higher levels of corruption perceptions once a scandal is revealed.

Keywords: electoral turnout, accountability, corruption

JEL Classification: P16, D72, D73

Contact address:

Faculty of Economics and Business

University of Barcelona

Av. Diagonal 690, torre4, planta 2ona

08034 Barcelona

Telf.: 00 34 93 403 90 12

e-mail: elenacostas@gmail.com

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1. Introduction

In democratic systems elections are an instrument through which citizens can punish

dishonest politicians by not voting for them, bringing down corrupt governments.

Studies on the effect of scandals on electoral outcomes do not find vast electoral

punishments of corruption, implying that in most cases malfeasant politicians are re-

elected1. These results have sometimes been interpreted as cultural acceptance of

corruption, a hypothesis that considers some societies more tolerant of scandals, as

voters do not react to them.

Nevertheless, it is important to remember that electoral outcomes are the result of who

votes and for whom. Once an election is called, individuals face the choice of a

participation decision. Some citizens may react to corruption by changing their decision

of whether to vote or abstain, rather than by withholding their electoral support for the

accused incumbent. In such cases, scandals would affect electoral outcomes in a broader

way than the incumbent’s vote share. To properly identify the overall impact of the

effect of corruption, voter turnout must be also considered.

Corruption scandals may modify individuals’ participation decision, either by

mobilizing people to go to the polls to punish or support the malfeasant politician or by

demotivating them to vote as a consequence of disaffection with the democratic process.

We refer to the former as a “mobilization effect” and to the latter as a “disaffection

effect” of corruption. Most of the studies focus on the aggregated impact of scandals on

voter turnout without distinguishing between the mobilization and disaffection effects

that corruption may have on individual electoral participation (Dominguez and McCann

and, 1998). Existing literature presents ad hoc explanations of turnout variations, which

do not respond to a planned strategy to identify those different effects. Hence, once the

analysis is done the impact of either the mobilization or the disaffection effects cannot

be differentiated. Moreover, aggregate level data used in some studies (i.e., Stockemer,

2013, or Stockemer et al., 2013) represents an additional obstacle to determining the

relative power of corruption’s mobilization and disaffection effects.

The aim of this paper is to analyse whether individuals modify their participation in

local elections after a corruption scandal affecting the incumbent is revealed. Using data

at the individual level, we are able to identify the mobilization and disaffection effects

that corruption may generate considering both the individual’s partisan leanings and the

timing of the scandals.

1Peters and Welch (1980); Welch and Hibbing (1997); Jiménez and Caínzos (2003); Ferraz and Finan

(2008); Rivero and Fernandez-Vazquez (2010); Chong, et al. (2012); Costas-Pérez, et al. (2012);

Fernández-Vázquez et al. (2013); and Winters and Weitz-Shapiro (2013).

2

On one hand, depending on partisan leanings, some citizens could be more or less likely

to be mobilized to vote as a result of corruption scandals. Individuals with strong

political leanings tend to go to the ballot to support their politicians. Also, higher

degrees of partisanship are a factor that may influence some individuals’ evaluation of

corruption scandals (Rundquist et al., 1977; Anduiza et al., 2012). In contrast,

individuals who occasionally vote do not exhibit high partisan attachments and are

likely to be more sensitive to scandals (Chong et al., 2012). Thus, the degree of

partisanship will play a crucial role in determining how citizens perceive accusations of

corruption of the incumbent, and how they translate their perceptions into voter

behaviour.

On the other hand, scandals occurring at different times may affect participation

decisions differently. Recent corruption cases may mobilize voters to the polls, while

past scandals may be forgotten (Fair 1978; Kramer 1971). Moreover, the timing of

corruption cases may also be a key issue shaping faith in the democratic system.

Repeated corruption cases might generate disaffection with the electoral system among

politically alienated citizens who eventually withdraw from participation in elections

(Kostadinova, 2009).

We consider local corruption scandals in Spain, which constitutes a good scenario for

use in testing the effects that corruption may have on electoral outcomes. The Spanish

case combines a recent wave of local scandals concentrates on the 2003 to 2007 term of

office, with a significant number of municipalities that have experienced repeated

corruption cases in two consecutive terms of office (1999 to 2003 and 2003 to 2007).

Our database on local corruption scandals allow us to verify if past, recent or repeated

corruption cases lead to different effects on voter turnout. Additionally to this database

we benefit from a survey run in several Spanish municipalities affected and non affected

by corruption scandals, recollecting individual information on, among others, voting

behaviour in the 2007 local elections, degree of partisanship, ideology, and corruption

perceptions2. The use of that specific survey data at the individual level permits us to

analyse the mobilization and disaffection effect of corruption on turnout depending on

the voter’s partisan leanings.

The matching strategy allows the identification of a valid control group for the

municipalities affected by corruption. A part from selecting the twin municipalities that

did not experience scandals in the period analysed, matching methods provides with

further advantages. First, the use of a matched sample improves the identification of the

2 We make use of both a matching sample and a survey designed by Solé-Ollé, A. and Sorribas-Navarro,

P. for the study “Does Exposure to Corruption Erode Trust in Government? Evidence from a Matched

Sample of Local Scandals in Spain” presented at the 7th Workshop on Political Economy (November

2013, Dresden).

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effect of corruption scandals on voter turnout, balancing the distribution of the

covariates of the two subsamples. Matching also increases the transparency of our

research design, as described in more detail in the section 3.2. We also report a series of

placebo tests that confirm the validity of our identification strategy.

Our results show that, on average, local corruption scandals make citizens 2.1% less

likely to vote. However, not all individuals modify in the same way their electoral

participation as a consequence of corruption. This study shows that partisan leanings

play a key role on shaping individuals’ response to corruption. We find that independent

voters are 3.6% less likely to vote if a corruption scandal occurs in their municipalities.

Core supporters, defined as those citizens who always vote for the same party, do not

seem to react to corruption scandals, irrespective of whether they support the incumbent

party or the opposition.

Regarding the timing of corruption scandals, we find that it is just when corruption is

repeated in different periods that citizens decide to abstain, disappearing that effect for

cases occurring either far away in time or in the years previous to the election analysed.

Analysing both individual’s partisan leanings and the timing of corruption, our results

show that corruption just has an effect on independent voters. We find that these

individuals are less likely to vote also in municipalities that have experienced past or

repeated corruption cases, accounting the later a 5.4% decrease in their likelihood of

voting. That implies that the disaffection effect of scandals predominates over the

mobilization factor.

Measuring the effect of scandals on voters’ corruption perceptions we can observe that

independent voters report higher levels of corruption perceptions after a scandal is

revealed. Core supporters of the incumbent involved in a corruption scandal do not

seem to realise that corruption occurs. On the contrary, core supporters of the opposition

parties increase their corruption perceptions if a scandal is revealed. We also find that in

the short term local scandals do affect corruption perceptions, but that does not modify

immediately individuals’ participation decision.

We make three main contributions to the existing literature. First, we benefit from a

research strategy specifically designed to distinguish between the mobilization and

disaffection effects of corruption on voter turnout. Second, this is the first paper to our

knowledge that empirically analyses how these two effects depend on partisan leanings.

We are able to identify the individuals who are potentially mobilized to vote, and those

who withdraw from elections as a consequence of corruption scandals. Third, we

contribute to the literature by testing the impact of corruption occurring at different

times on voter turnout, allowing us to untangle conclusions from earlier investigations

that did not differentiate voter’ responses based on the timing of scandals.

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Additionally, our study provides empirical evidence on the effect of actual corruption

cases. Most of the empirical papers on corruption’s determinants and consequences rely

on perceptions, which are easier to obtain but raise concerns on both their bias and their

accuracy with corruption itself. Moreover, we do not consider corruption at the

aggregate level, if not local corruption cases. Measuring corruption at the municipal

level provide us with a more extensive sample of cases. It also has an additional

advantage, since local cases makes easier for citizens to link each scandal with the

corresponding local politician, potentially increasing the accountability of municipal

elections.

The rest of the paper is organized as follows. The next section provides the reasons that

the timing of corruption and partisan leanings could affect turnout once a scandal is

revealed, and outlines the main hypothesis to test. Section 3 defines the empirical

analysis followed and the construction of the database. Section 4 discusses the

estimation strategy. Section 5 presents the results and, lastly, section 6 concludes.

2. Corruption’s mobilization and disaffection effects

2.1. Previous literature

Neither theoretical nor empirical studies have arrived at unified conclusions on the

relationship between corruption and voter turnout3. An important shortcoming of

existing literature is the lack of an empirical strategy designed to identify the effects

once a scandal breaks out, i.e., an increase in electoral participation through a

mobilization of voters to the polls or a decrease of electoral participation as a result of

their disaffection with the democratic process. Kostadinova’s (2009) study on post-

communist transitional countries tries to identify both a direct –mobilization- and an

indirect –disaffection- effect of corruption on turnout. However, she considers

corruption perceptions, which may be correlated to voting decisions, casting doubt on

the model’s overall exogeneity.

It has been proven that good governance is related to citizens’ capacity to hold their

politicians accountable (Adsera, et al., 2003). Hence, if we understand elections as an

effective accountability tool, individuals who feel betrayed by a corrupt politician may

cast a ballot to bring him or her out of power. In such a case, corruption would act as a

mobilization factor. That mobilization effect would make some citizens, who would

otherwise abstain, go to the polls to punish the accused politician. After a scandal, party

3 In most cases empirical evidence confirms a negative relationship between corruption and turnout

(Domínguez and McCann, 1998; Kostadinova, 2009; Birch, 2010; Chong et al., 2012). In contrast, a

scholarly minority (Karahan et al., 2006; Escaleras et al., 2012) attributes to corruption a positive effect

on turnout. Finally, some studies do not find any relationship between corruption scandals and voter

turnout (Stockemer, 2013).

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members and sympathisers could also be called upon to mobilize to give their vote to

the accused politician, either by thinking the allegations are false or by giving

unconditional support. An alternative case, in the context of highly corrupt situations

with clientelistic networks, is that scandals could stimulate turnout if corrupt politicians

were to buy voters to retain their power (Karahan et al., 2006).

Conversely, corruption can also be a drain on voter turnout (Putnam, 1993; Warren,

2004; Chang and Chu, 2006). Corruption harms citizens’ trust in local and national

politicians, generating cynicism and voter apathy (Solé-Ollé and Sorribas-Navarro,

2013). That disaffection effect would make individuals less likely to vote for existing

corrupt political parties (Warren, 2004; Wagner et al., 2009). If corruption is repeated,

in the long run disaffected individuals may decide to disengage from the electoral

system (Chong et al., 2012). If voters consider that corruption is widespread, replacing

the corrupt incumbent for a new one will not fix the situation. In the most extreme

cases, widespread corruption might also cast doubt on the sustainability of the

democratic system (Kostadinova, 2009). Empirical evidence has confirmed this

negative relationship between corruption and turnout (Domínguez and McCann, 1998;

Andersen and Tverdova, 2003; Kostadinova, 2009; Stockemer, 2013; and Stockemer et

al., 2013).

2.2. Who and why will be affected by corruption?

As stated above, most existing studies are unable to differentiate among the relative

strengths of the mobilization and disaffection effects that actual corruption cases may

cause. We argue that the literature on corruption and voter turnout does not point

conclusively to voters’ reactions because two crucial factors are excluded from these

analyses: the role of individual partisan leanings and the timing of corruption.

First, we consider that corruption’s mobilization and disaffection effects will differ

depending on voters’ degree of partisan affinity with the incumbent involved. Similar to

our strategy, Chong et al. (2012) take a step forward over previous studies, proving with

experimental evidence for Mexico that exposing citizens to information on corruption

not only decreases voter turnout, but also negatively affects voters’ identification with

the corrupt incumbent's party. They find that providing information about high levels of

corruption has a bigger impact on challengers' votes than on incumbents'; however, their

data does not allow them to analyse if voters with a different degree of partisanship

respond differently to corruption.

Independent voters, those who occasionally vote or do not always vote for the same

party, tend to be more independent from partisan attachments, and they can be more

affected by shocks like occasional corruption (Rundquist et al., 1977; Feddersen and

Pesendorfer, 1996; Sobbrio and Navarra, 2010; Stockemer, 2013). Thus, the

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disaffection effect will be higher for those, so we would expect to observe independent

voters withdrawing from the elections if corruption scandals occur. Hence, the first

hypothesis regarding partisan leaning is:

H1.a: Independent voters, defined as those who do not always vote for the same party,

will be more likely to abstain if a corruption scandal is revealed.

On the contrary, core supporters have stronger partisan leanings and are less likely to

defect (Chong et al., 2012). If partisan leanings are strong, citizens may disregard

corruption as a determining factor in their decision and continue to vote for the party to

which they are ideologically aligned (Peters and Welch, 1980; and Anderson and

Tverdova, 2003). Ideology or party identification can also modify how voters evaluate

corruption, depending on the party to which the corrupt incumbent belongs. Supporting

this hypothesis, recent research of Anduiza et al. (2012) finds that individuals have a

partisan bias and are more tolerant to corruption if the politician involved belongs to

their own party. If that is true, we should find core supporters of corrupt incumbents

unaffected by corruption scandals when deciding to vote. Thus, the hypothesis to test is:

H1.b: Incumbent core supporters will not modify their electoral participation decision

after an incumbent’s corruption scandal is revealed.

Considering core supporters of opposition parties, we would expect that, even realising

that corruption exists, they will keep voting for their party options. It may be even

possible that, in the presence of corruption, some opposition core supporters who would

otherwise abstain would go to the polls to defeat the corrupt incumbent. Nevertheless,

people who identify themselves with a political party are more likely to vote (Norris,

2004), so it is improbable that we will find that mobilization effect. Regarding partisan

leanings, the last hypothesis is:

H1.c: Opposition core supporters will not modify their electoral participation decision

after an incumbent’s corruption scandal is revealed.

Second, we consider that scandals occurring at different points in time may modify the

influence of corruption’s mobilization or disaffection effect on individual participation

decision. There is evidence that voters tend to overweigh the information received

closer to elections, as recent events have greater influence on evaluations of

incumbents’ performance (Fair 1978; Kramer 1971). Another explanation for the fact

that later scandals could have greater effects on turnout is that voters are more attentive

to indicators of incumbents’ outcomes as an election approaches (Valentino and Sears

1998), being unable to easily recollect information on earlier performance (i.e., past

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corruption cases) (Huber et al. 2012). Also, old corruption cases might be difficult for

voters to remember.

Thus, while in the short term corruption can mobilize voters to bring down corrupt

governments, that relationship can be reversed if corruption continues over time. This

hypothesis is based on the fact that the persistence of corruption can differently affect

citizens’ trust in the political system. Corruption cases repeated over several years can

make citizens cast doubt on the democratic system’s capacity to keep politicians

accountable (Kostadinova, 2009). That distrust in the political system leads to

disaffection and alienation from politics, which may result in the decision to withdraw

from the electoral process, i.e., abstention. In that scenario, repeated corruption cases

will then generate the disaffection mechanism through which corruption scandals lessen

voter turnout.

Hence, the hypothesis regarding the effects of the timing of corruption cases on voters’

turnout is:

H2: Past corruption cases will not affect turnout, while recent scandals would either

have no effect or would mobilize people to vote. Repeated corruption in time will lessen

voter turnout as a result of the disaffection effect.

In conducting our study, we analyse these hypotheses, both independently and

considered together. We predict that independent voters - those more susceptible to

defection - will be more sensitive to repeated corruption scandals, which will further

harm their trust in the system. Regarding core supporters, it is difficult to speculate how

they will react to corruption occurring at different times, since we predict that they will

be less likely to modify their electoral participation as a consequence of corruption.

3. Data and Empirical Analysis

3.1. Data and typology of corruption scandals

In order to carry out our analysis, we use a novel database that includes information on

local corruption scandals in Spain and data on a survey of voting behaviour in Spanish

municipalities. We define a local corruption scandal as the “public allegation of

corruption brought to light by a newspaper”. Our data about corruption cases is based

on a report compiled by the Fundación Alternativas (2007). After a wave of local

corruption scandals starting in the first years of the 2000s, that Spanish think-tank hired

several journalists to gather all corruption-related stories published in national, regional

and local media between January 2000 and January 2007. However, the time period we

are interested in goes from the local elections in July 1999 to the ones in May 2007. For

that reason, we completed the Fundación Alternativas’s information with an internet-

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guided search4 on news on corruption scandals. We ended up with a total of 565

municipalities affected by corruption scandals during that period5. We also checked for

the non-partisan bias of our news, comparing our data with other corruption maps

compiled by media outlets from different political ideologies. The percentage of

corruption cases by political party was not statistically different in all databases,

verifying that our compilation of cases was not ideologically biased.

It is important to bear in mind that during the first Spanish democratic governments

(1979-1999) there were no exceedingly significant local corruption cases reflected in

Spanish media (Jiménez and Caínzos, 2006). The corruption cases we are studying are

related to land use regulations, one of the most important local corruption’s typologies

in Spain during the peak of the housing boom. Scandals involved local politicians

receiving bribes in return to changes over the land use plans already approved (i.e.,

reclassifying public land). Municipal governments in Spain are responsible for the land

use regulation, making easier to identify the effect of those scandals on electoral

outcomes. In that specific case, voters can clearly identify the incumbent as guilty for

the land-use related corruption.

The wave of corruption cases rose significantly in the late nineties, when Spanish media

started highlighting numerous corruption scandals and the judiciary also began the

investigation of some of them. The number of corruption cases shot up after 1999

(Costas-Pérez et al., 2012), peaking before the 2007 local elections. That distribution of

scandals makes the Spanish situation the optimal context to test our hypothesis that

corruption occurring at different points in time will have a different effect on citizens’

voting behaviour.

Our database accounts for 122 municipalities affected by corruption in the period from

June 1999 to May 2007. We have classified them among three different sub-categories

regarding corruption persistence that we will use throughout our analysis. First, 32

municipalities of our database experienced at least one corruption scandal in the term

1999-2003, but not corruption cases broke out afterwards. We have considered them as

‘past corruption cases’, since in the 2007 elections voters may have a faint memory

about these scandals. Second, 58 municipalities had at least one corruption scandal in

the term 2003-2007, but not corruption cases had broke out in the previous term. These

are classified as ‘recent corruption cases’ from the perspective of an individual facing

the 2007 local election. Finally, we have also considered those places that have

4 We used a paid digital information management service covering all national and many of the regional

newspapers, MyNews, until November 2009. Thus, we have an additional sample of corruption cases

occurring between the local elections of 2007 and November 2009 that we save to perform a placebo test

explained in section 4.6. 5 See Costas-Pérez et al. (2012) for more information on the construction of the corruption database.

9

experience repeated corruption cases, both in the 1999-2003 and the 2003-2007 terms.

32 municipalities are classified in the category of ’repeated corruption cases’.

Hence, our corruption database indicates whether there has been at least one corruption

scandal between June 1999 and May 2007 (the two terms of office analysed). Since our

objective is to measure the impact of corruption, we need a sample of individuals from

corruption-free municipalities to be compared with those from localities affected by

scandals. The fact of using a matched set of municipalities allows us to balance the

distribution of covariates between corruption-ridden and corruption-free municipalities,

to avoid biased estimations.

3.2. The matching strategy

In order to construct our analysis’ sample we use a matched database that identifies for

the selected municipalities affected by corruption a valid control group. Budget

limitations implied a restriction for the number of municipalities analysed in the survey.

Hence, a matching procedure was followed to select those corruption-free

municipalities to be compared with the corruption-ridden ones (our control and

treatment groups, respectively). Over the 565 municipalities that experienced at least

one corruption scandal between 1999 and 2007, a sample of 122 municipalities was

randomly select – stratified according to population size and term of corruption -. Thus,

old and new corruption cases are equally represented in the final database.

The municipalities that potentially belong to the control group are those with similar

characteristics to the corruption-ridden ones but where no scandals broke out. In order

to identify our control group a Propensity Score Matching approach was used6.

In order to construct the ‘propensity score’ a Logit model7 was estimated, using as a

dependent variable a dummy equal to one if a corruption scandal broke out in the

municipality, and zero otherwise. The Logit equation was estimated with data from all

the corruption-ridden and corruption-free municipalities. The municipal-level variables

used to implement the matching strategy were8: historical average levels of aggregate

turnout, population size, unemployment, ethnic diversity, income, school attendance,

divorce rate, and historical average of right voters9.

6 The full procedure and estimations results of Solé-Ollé and Sorribas-Navarro’s (2013) matching

strategy, as well as additional data checks, are available upon request. 7 Because of data limitations to carry out the matching we were limited to municipalities larger than 1.000

habitants. 8 Descriptive statistics of these data can be found in Table A.1. in the Appendix.

9 Two alternative estimations were also considered, including some additional covariates with low

explanatory power (i.e., percentage of elderly population, historical average of electoral margin of

victory, coalition governments, percentage of residents born in that municipality, newspapers per capita

or associations per capita), and also higher order terms of the main covariates and interactions amongst

them. Nevertheless, the balance did not improve in either case, so the first option was maintained.

10

With the Logit estimation the ‘propensity score’ was computed and control

municipalities were matched to the treatment ones based on having a similar value of

the ‘propensity score’. As the matching algorithm the ‘Nearest Neighbour with

replacement’10

was use. This method allows for more than one given control unit

matching more than one treatment unit, which increases the average quality of matching

and reduces the bias11

.

The matching strategy’s implementation allows to balance the covariates in the two

subsamples. We ended up with a control group of 97 municipalities that did not

experience any corruption scandal between 1999 and 2007. These, plus the 122 treated

municipalities where at least one corruption scandal broke out during the same period,

constitute the 219 municipalities included in our database12

.

To confirm that the sample of treatment and control municipalities used in this paper

was a good matching we conducted different tests. We first analysed the percentage

reduction in the standardised bias as the result of the matching procedure, finding a

considerable decrease that showed a statistically significant bias before the matching

(i.e., school attendance (66% drop) or divorce rate (99% drop)). Second, we performed

a comparison of means between treated and control units in the unmatched and matched

samples (see Rosenbaum and Rubin, 1985). Table A.2. in the Appendix shows the

means of each groups for all variables considered to perform our matching. The last

column of the table reports the test and p-values of the differences in means between the

treated and the control group. Once our sample is matched those difference are not

statistically significant anymore. Third, we re-calculated the propensity score on the

matched sample and compare the pseudo-R2 before and after matching

13.

We also performed a difference in means test for the individual level variables used in

our analysis, using the survey observations as the treatment and control groups. The

results of this test verify that interviewees from our treatment and control groups not

only live in very similar municipalities, but also share the same individual traits. Table

A.3. shows that, using our sample individual data, we arrive to the same conclusions

about our matching quality. For that reason we consider that matching at the individual

10

Other matching options were considered (e.g., ‘without replacement’). None of them worked that well

for some of the biggest municipalities, so they were not used. 11

The Nearest Neighbour matching may generate bad matches (i.e., the distance to the nearest neighbour

is too large). However, 95% of the matches had an absolute distance in the ‘propensity score’ lower than

0.01, so it can be considered that a considerably good matching was achieved. 12

Solé-Ollé and Sorribas-Navarro’s (2013) originally selected a sample of 160 treatment municipalities

and 130 controls, including scandals between 1999 and 2009. For the specific propose of this paper we

restricted the corruption cases to those occurring before the 2007 local elections, and that is the matched

sub-sample data that we use. All tests to verify that we have achieved a good matching have been done to

both samples. 13

They were 0.237 and 0.002, respectively. LR tests of joint significance of the regressors before and

after the matching have values of 1871.77 and 2.32, with p-values of 0.000 and 0.941.

11

level is not necessarily in our case since the citizens interviewed in the treatment and

control municipalities are already very similar.

After performing all these tests we can confirm the matching strategy successfully

created balance in baseline characteristics across our treatment and control groups, both

for the municipalities and the individuals analysed. An additional advantage of the

matching procedure is that it assures a complete transparency and predetermination of

our research design. Since the matching algorithm must be applied before the estimation

of the treatment, the decisions taken at this stage are no influenced by any information

on the estimation results (Ho et al., 2007).

3.3. Data on individual turnout and corruption perceptions

This paper makes use of a special survey designed to be conducted in the selected

matched municipalities14

. The survey was undertaken in November of 2009 and to

obtain an indicator of individual electoral turnout interviewees were asked if they voted

in the 2007 local elections or not15

.

As Table A.1 shows, our sample average turnout is a bit higher than the actual one16

.

Previous papers have also suffered from this "overreporting" bias, explained both by the

vote misreporting of actual non-voters among survey respondents and the

overrepresentation of actual voters (Traugott, 1989). However, several studies have

proved that the overreporting problem has no real effect on the actual implications of

the models’ estimations that try to understand the factors that may influence voting and

abstention (Hillygus, 2003). Also, recent research demonstrates that participation in

surveys does not increase vote likelihood (Mann, 2005). Thus, we are confident about

the implications drawn from our estimations’ results.

The survey also included a question on individuals’ corruption perceptions: ‘Which do

you consider is the degree of corruption in your local government?’. The interviewees

could answer one of the following five categories: 5 “very high corruption”, 4 “high”, 3

“medium”, 2 “low”, and 1 “none”17

.

Among other socioeconomic characteristics, the survey included questions regarding

political preferences (e.g., partisan attachment and ideology), and information on a

series of socio-economic controls (e.g., unemployed, type of job, marital status, etc.).

14

The questionnaire used by Solé-Ollé and Sorribas-Navarro’s (2013) is available upon request. 15

Individuals who did not vote in the 2007 local elections because they were too young or they were not

registered in that municipality are excluded from our analysis. 16

The actual voter turnout in the 2007 Spanish local elections for the municipalities analysed was 68.9%.

More information on Spanish electoral outcomes can be found in: http://www.infoelectoral.mir.es/min/ 17

There were also two additional categories: 98 “do not know” and 99 “do not answer” that we do not use

these responses in our analysis.

12

3.4. Data on Individual’s Partisan Leanings

In order to test our hypothesis on the effects of partisan leaning on individual’s voter

turnout we classified interviewees depending on their degree of partisan attachment and

their self-assed ideology. To build our partisan leanings variables we first use the

following question asked in the survey: ‘In general, do you usually vote for the same

party in all municipal elections?’ Interviewees who always vote for the same party in

municipal elections were classified as ‘core supporters’, otherwise they were

considered as ‘independent voters’18

. As Table A.1 shows, ‘independent voters’

accounts for slightly more than half of our sample of individuals19

. Independent voters

include individuals who change their vote or also those who do not always vote.

In the survey, interviewees were asked to situate themselves on the left-right spectrum.

Specifically, they were positioned on a seven-point scale, where 1 represents ‘extreme

left’ and 7 represents ‘extreme right’20

. We have also elaborated a classification of the

Spanish political parties based on a combination of the ideological statements of each

party (where available), online rankings, as well as ad hoc decision rules to specify

party location. Our specification is necessarily arbitrary, but we consider it to account

for the complex reality of the particular national context21

.

In order to avoid mistakes in our classifications we have normalised both databases –

interviewees self reported ideology and party ideology – into a three-point scale, where

1 represents ‘left’, 2 ‘centre’ and 3 represents ‘right’. Then, we have combined the

information included in our two databases in order to match each individual with the

political party they share the same ideology in the left-right spectrum. With that

information we are able to define the ‘incumbent core supporters’ as those core

supporters who share the ideology –left, centre or right- of the government in office for

the previous term to the 2007 local elections (2003-2007). We classify the remaining

core supporters as ‘opposition core supporters’.

18

Hence, individuals who answered 98 “do not know” and 99 “do not answer” where considered as

‘independent voters’. Interviewees who answer in that question that they were too young to vote in the

2007 local elections were excluded from our analysis. 19

That value is in line with other countries, such as the 2013 Gallup Poll’s result that estimated that for

2013, on average, forty-two percent of Americans identified themselves as political independents. 20

Interviewees who did not select any ideology position as an answer were classified as neither from the

left nor from the right, but centre voters. 21

The classification in the left-right spectrum of the more than 200 Spanish political parties in office for

the period 2003-2007 is available by the authors upon request.

13

4. Estimation Strategy

Following Ho’ et al. (2007) suggestion, we use the same parametric analysis on the

matched sample as would have been used to analyse the original raw data. Derived for

the dichotomic behaviour of our dependent variable – turnout – we use a Logit model.

We must consider that, when the matching is not exact, the matching estimator will be

biased in finite samples (Abadie and Imbens, 2002). Hence, to reduce the biased term

that remains after matching, we perform an additional bias correction, adjusting for

covariates (Rubin, 1979, and Dehejia and Wahba, 1999). Specifically, we run a Logit

model with the matched sample and the covariates used in the estimation of the

propensity score (Ho et al., 2007).

Since a matching with replacement procedure was used to select the survey’s sample,

we must perform adjustments while implementing our analysis. We use weights in all

estimations to ensure that the parametric analysis reflects the actual observations (Ho et

al., 2007; Dehejia and Wahba, 1999). Thus, we weight control municipalities by the

number of times they are matched to a municipality affected by corruption.

In order to measure the effect of corruption scandals on individual’s voter turnout, we

estimate the following general specification:

Pr(Voteij=1) = α + β1Corruptionj + δX’ + εij (1)

where Voteij is a dummy variable equals to one if the individual i has voted in the

municipality j in the 2007 local elections; Corruptionj is a dummy variable equals to one

if at least a corruption scandal broke out in municipality j between June 1999 and May

2007 (two terms of office); X’ is a vector that includes the covariates used in the

propensity score estimation and additional individual-level information from the

survey22

; and εij is the error term.

The sign of β1 in Equation (1) will indicate which effect, either mobilization (positive

sign) or disaffection (negative sign), predominates if a corruption scandal is revealed.

In order to capture the effects of partisan leanings on individual’s turnout, and test our

hypothesise that some particular individuals are more or less likely to vote as a result of

corruption, we estimate alternative specifications of our model. We include in Equation

(1) interactions between corruption scandals and the variables indicating if the

individual is an ‘independent voter’ or an ‘incumbent’ or ‘opposition core supporter’.

22

We consider the following individual-level survey variables: income, education, gender, age, divorced,

unemployed, student, retired, and immigrant.

14

Pr(Voteij=1) = α + β1Corruptionj + β2Incumbent Corei + β3Opposition Corei +

β4Corruptionj x Incumbent Corei + β5Corruptionj x Opposition Corei + δX’ + εij (2)

In Equation (2) we include interaction terms between the dummy variable that indicates

if a corruption scandal has broke out in municipality j for the period analysed, and two

dummy variables that indicate whether the individual is an incumbent or an opposition’s

core supporter23

. Hence, for independent voters the effect of corruption scandals on their

participation decision will be identified by the estimation of the coefficient β1 (being

‘independent voter’ the base category of ‘incumbent core supporter’ and ‘opposition

core supporter’).

In the interaction model of Equation (2) the coefficients estimated for ‘incumbent core

supporter’ and ‘opposition core supporter’ (β2 and β3, respectively) are no longer

interpretable as the unqualified turnout difference between incumbent and opposition’s

core supporters with and without corruption scandals in their municipalities. Once

interactions are included in the model, these coefficients do not represent anymore a

meaningful partial effect.

The impact of corruption scandals for core supporter individuals will be represented by

the linear combination of the estimated coefficients of ‘corruption’ (β1) and the

‘incumbent core supporter’ or ‘opposition core supporter’ (β4 or β5, respectively). These

effects do not appear directly in the model, and the significance of their linear

combination must be tested after the estimation.

All corruption coefficients in Equations (1) and (2) estimate the overall effect of

scandals on turnout, regardless of the timing of the corruption cases. To capture the

different effects of corruption occurring in diverse points in time, we first estimate

Equation (1) defining few three different subsamples of corruption scandals: ‘past

corruption cases’ (municipalities that experienced scandals in the 1999-2003 term, but

not afterwards); ‘recent corruption cases’ (municipalities that experienced scandals in

the 2003-2007 term, but not before); and ’repeated corruption cases’ (municipalities

that experienced scandals in both 1999-2003 and 2003-2007 terms).

In order to estimate Equation (1) for each of the three different corruption subsamples

we have adjusted the matching data to just those municipalities affected by the specific

corruption type analysed, as well as their pertinent control pairs.

When matching techniques were applied to select the control municipalities in which to

run the survey, the three subsamples - past, recent, and repeated corruption cases –

were not separately considered. Taking the specific subsamples of the corruption-

affected municipalities analysed and then applying a new matching procedure to each of

23

A detailed description on how the partisan leanings variables are build can be found in section 3.4.

15

them would have required a two-stage matching procedure. Unfortunately, at that point

that procedure was not followed. However, in all estimations considering different

corruption timings the sample has been adjusted to the specific group of treated

municipalities, as well as their control twins assigned during the full matching

procedure. Thus, while we must be cautious in interpreting our subsample matched data

results, we achieve a good balance between the corresponding treatment and control

groups24

.

Regarding the effects of corruption scandals on voter turnout, we also consider the

combined case of different corruption timings and individual partisan leanings. That

interaction model follows Equation (2) specification, for each of the three subsamples of

corruption scandals: past, recent, and repeated corruption cases. The coefficient

interpretation will be the same as in Equation (2).

Finally, to account for the possibility that our results are driven by the fact that citizens

do not realise that corruption occurs, we analyse the effect of scandals on individuals’

corruption perceptions. In particular, we estimate the following specification:

Perceptionsij = α + β1Corruptionj + β2Incumbent Corei + β3Opposition Corei +

β4Corruptionj x Incumbent Corei + β5Corruptionj x Opposition Corei + δX’ + εij (3)

where Perceptionsij are the local corruption perceptions reported by the interviewees.

The same covariates used in the previous estimations are included. The interpretation of

the interaction effects will be the same as in Equation (2) and Table 3. However, to

account for the fact that our dependent variable is now categorical we estimate an

Ordered Logit model.

To deal with the multilevel structure of the dataset, with individuals belonging to

different municipalities, in all the estimations we cluster standard errors at the

municipality level. There are 219 municipalities in our general estimation.

5. Results

Our empirical study is structured in five different phases. First, we analyse how

individuals modify their participation in local elections if an incumbent’s scandal has

broke out in the two previous terms of office. Next, we compare the individual turnout

of citizens with different degree of partisan leanings facing corruption. Third, we

evaluate how the timing of corruption affects to that participation decision. Forth, we

combine different timings of corruption and individual’s partisan leanings. Finally, we

24

Full results are available by the authors upon request.

16

observe how scandals modify corruption perceptions to better understand the electoral

behaviour of the individuals analysed.

4.1. General Results

The results of the Logit estimation of Equation (1) using our matched sample are

presented in Table 125

.

[Table 1 about here]

We find that a corruption scandal’s reveal reduces the likelihood that an individual

would vote in the local elections. That negative and statistically significant effect on

voter turnout holds whenever we adjust for both the contextual-level variables and the

individual characteristics (columns 2 to 4). As explained above, the adjustment for

covariates in our model aims to reduce the potential bias that remains after matching.

However, the fact that our results are robust to the inclusion and exclusion of these

covariates indicates that such bias is not a relevant issue in our estimation.

Our negative estimation of the corruption coefficient indicates that, overall, the

disaffection effect of corruption prevails over the mobilization factor. However, the

interpretation of the logistic coefficients is not straightforward. To measure the

substantive effects of the significant factors and to better understand the consequences

of these findings we have computed a simulation of the impact of a corruption scandal

revealing. For that reason we have used our estimates to perform a series of first

difference calculations for an average voter26

. We focus on the estimated changes in the

probability of voting that results from the fact of having a corruption scandal in that

municipality or not -holding all the other variables constant-. Thus, predicted turnout

probabilities were simulated using the significant coefficients from the Logit estimation

of Equation (1). Predicted probabilities in Table 5 indicate that, on average, the

revelation of a corruption scandal decreases individuals’ probability of voting by 2.1%.

Given that turnout in the local elections of 2007 for the municipalities analysed is

68.9%, with a standard deviation of 0.10, our results reveal that corruption scandals

account for a non-vast – but significant - decrease in aggregate turnout levels.

Nevertheless, estimations in Table 1 consider how all citizens react to corruption

scandals. As previously argued, depending on partisan leanings, some particular

individuals could be mobilized to vote, while others could decide to withdraw from the

elections as a consequence of the scandals.

25

Complete results of all covariates have been omitted for reasons of space but are available upon

request. 26

These and all subsequent predicted coefficients reported were simulated using the CLARIFY program

in Stata (Tomz, et al. 2000).

17

4.2. Partisan Leanings

Table 2 shows the Logit estimation of Equation (2), as well as the linear combinations’

test of the different coefficients.

[Table 2 about here]

Estimations in Table 2 are controlled by the fact that the individual can be either an

independent voter or an incumbent or opposition core supporter, and also include

interaction terms between corruption and the individual’s partisan leanings. Following

the same strategy as Table 1, column (1) estimates the model without adjusting for

covariates, column (2) includes contextual-level variables, column (3) includes

individual-level variables, and column (4) accounts for both groups of controls. The

corruption coefficient is not statistically significant when we do not include covariates

in the specification. Once we have included them in the model, all estimations indicate a

negative and statistically significant effect of corruption. Adjusting for covariates

reduces the bias that remains after matching, and the estimation of the coefficient is

stable in all specifications.

Table 2 also reports the linear combination tests for the statistically significant

interactions between corruption and individuals’ degree of partisanship. With that

information we can verify the hypotheses stated in Section 2.1 regarding partisan

leanings. Our first hypothesis (H1.a) states that independent voters will be more likely

to abstain if a corruption scandal is revealed. Our results show that this is true once we

adjust for covariates, indicating a predicted decrease in the independent voters’

probability of voting by 3.6% (Table 5).

To consider the reaction of the core supporters we must observe in Table 2 the linear

combination test between the coefficients associated to ‘corruption’ and its interaction

with ‘incumbent’ or ‘opposition core supporter’ in Equation 2. In hypothesis H1.b we

considered those regular voters who share the same ideology than their local

government in office. We predicted that partisan leanings would make individuals more

tolerant to their own party corruption, do not modifying their participation decision as a

result of a scandal. Our results confirm that incumbent core supporters do not modify

their electoral participation decision if a corruption scandal is revealed. That would

support the idea that voters accept incumbent’s corruption if they belong to the same

party (Anduiza et al., 2012).

Finally, in order to verify our hypothesis H1.c, which takes into consideration the core

supporters of opposition parties, we analysed the linear combination of coefficients

associated to ‘corruption’ and its interaction with ‘opposition core supporter’ in

18

Equation (2). Our tests’ results verify that opposition core supporters do not change

their electoral participation.

It is important to bear in mind that the estimations in Tables 1 and 2 measure the overall

impact of any scandal that broke out between June 1999 and May 2007. For that reason

in the following sub-section the persistence of corruption scandals is taken into account.

4.3. Timing of corruption

To test our hypothesis that corruption occurring in different points in time may modify

individual’s participation decision we have considered three different subsamples

depending on the subcategories of all corruption cases: ‘past corruption cases’, ‘recent

corruption cases’ and ‘repeated corruption cases’. The Logit results of those

estimations are presented in Table 3.

[Table 3 about here]

In Table 3 estimations the matching sample has been adjusted to the municipalities

affected by the specific corruption cases analysed, as well as their pertinent control

pairs.

Panel A and B in Table 3 show the results of the estimation of Equation (1) considering

‘past corruption cases’ and ‘recent corruption cases’, respectively. None of them are

statistically significant, indicating that neither corruption occurring far away in time, nor

cases revealed in the years previous to the elections, modifies individuals’ electoral

participation decision. Citizens seem to forget about past scandals, and our results do

not support the idea that individuals give more importance to later scandals (Fair 1978;

Kramer 1971). A potential explanation is that citizens perceive information received

closer to elections as electoral noise and are unable to distinguish between actual

corruption cases and electoral strategies of opposition parties.

By contrast, Panel C shows that repeated corruption cases have an impact on voter

turnout, making individuals from those municipalities more likely to abstain in the local

elections.

After experienced corruption scandals in consecutive terms of office, citizens may call

into question the efficiency of the elections as an accountability tool to keep politicians

accountable. That will boost a disaffection effect with the democratic system, making

some individuals withdraw from the electoral process. Thus, repeated corruption in time

will lead the disaffection effect to predominate over the mobilization factor, decreasing

voter turnout.

Table 5 indicates that the predicted individual turnout decreases by 4.5% for those

individuals from municipalities that have experienced at least one corruption scandal in

19

both terms of office analysed (1999-2003 and 2003-2007). That drop is much higher

than the 2.1% decrease when we considered all corruption cases.

These results confirm our hypothesis regarding the timing of corruption (H2):

occasional corruption (either past or recent cases) has no effect on voter turnout, while

scandals repeated in time decrease the likelihood of an individual to vote.

Following our analysis of individual reactions to corruption and how scandals occurring

at different times modify voter turnout, we proceed to estimate the combined effect of

both factors.

4.4. Timing of corruption and Individual’s Partisan Leanings

Table 4 shows the Logit estimation of Equation (2) considering all corruption cases and

the three sub-categories of scandals: ‘past corruption cases’, ‘recent corruption cases’

and ‘repeated corruption cases’. It also reports the linear combinations’ test of the

different coefficients27

.

[Table 4 about here]

Column (1) in Table 4 measures the impact of all corruption cases (‘corruption’)

occurring between 1999 and 2007 on voter turnout, controlling by the fact that the

individual can be an independent voter or an incumbent or opposition core supporter.

Column (2) considers the same equation and category of corruption cases, also

including the interactions terms between corruption types and individuals’ partisan

leanings. Columns (3) and (4) follow the same specification for the subsamples ‘past

corruption cases’, (5) and (6) for ‘recent corruption cases’ and finally, (7) and (8) for

’repeated corruption cases’. Even with the introduction of partisan leanings, scandals

are significant for all corruption cases and repeated corruption (columns (1) and (7),

respectively).

Our results show that hypothesis H1.a - that independent voters will be more likely to

abstain if a scandal is revealed - holds when repeated corruption cases are taken into

account (column (8)). As Table 2 shows, HI.a is also true when we analyse all

corruption cases (column (2)). Past and recent corruption cases do not affect the

participation decision of independent voters. Just considering independent voters, Table

5 shows that the overall effect of corruption decreases the probability of voting by

3.6%. Repeated corruption cases will account for a 5.4% decrease in the independent

voters’ probability of voting.

27

Tables 4 and 5 show the estimation adjusted for both the contextual-level variables and the individual

characteristics. Alternative estimations not adjusting for those covariates lead to the same results, but they

have been omitted for reasons of space. Complete results are available upon request.

20

Hence, we can affirm that, except for past and recent scandals, corruption scandals do

have an effect on independent voters, who will withdraw from the elections as a

consequence of a disaffection effect.

Hypothesis H1.b expected that higher partisanship’ degrees would make individuals

more tolerant to their own party corruption. Hence, we expected incumbent core

supporters to do not modify their participation in the elections as a consequence of

corruption. As seen in Table 2, incumbent core supporters do not change their electoral

participation decision if a scandal is revealed; this holds true even when the different

subcategories of corruption are considered.

If we analyse the behaviour of the opposition core supporters we can see, from the

linear combination tests showed in Table 4, that they neither modify their electoral

participation for none of the corruption categories analysed. Our results verify that

opposition core supporters will not increase their turnout if a scandal is revealed but it

will remain the same.

From Table 4’ results we find that neither incumbent’s core supporters, nor opposition’s

ones, do modify their voting predisposition as a consequence of corruption scandals.

We cannot identify then any effect of corruption on the core supporters’ level of

electoral participation. That could be indicating that the mobilization and the

disaffection effects are cancelling each other out for individuals with a strong

attachment to a political party. To verify that, and also to detect if core supporters do not

see their party malfeasance as corruption, or if they do so but still decided to keep

voting for them, we estimate the effect of corruption scandals on individuals’ corruption

perceptions.

4.5. Corruption Perceptions

In Table 6 we can see the Ordered Logit estimation of Equation (3), as well as the linear

combinations’ test of the different interaction coefficients28

.

[Table 6 about here]

Our results show that, except for past corruption cases, scandals always increase

independent voters’ perceptions of corruption, even controlling for individuals’ partisan

leanings. However, after including in our model interaction effects between scandals

and ideology, results are not homogeneous among all groups of individuals.

28

The number of observations in Table 6 drop from previous results. That is explained for the fact that

several interviewees did not answer the question about corruption perceptions. Since the effect of

scandals on individuals’ perceptions is not the principle objective of our paper we decided not to scarify

all those observations in our main results. We have also performed the thresholds tests of all the

specification of our Ordered Logit, indicating that it is no possible to reduce the number of cuts delimiting

a category.

21

Incumbent’s core supporters do not report in any of the corruption types analysed higher

levels of corruption perceptions. Those citizens do not seem to realise that their party is

involved in a scandal. They could be depreciating the importance of scandals attributed

to the incumbent they support, do not giving credit to the corruption accusations. Or,

even considering the potential existence of corruption, incumbent core supporters could

be more tolerant in judging their own party scandals (Anduiza et al., 2012).

Considering all corruption cases (‘corruption’), both independent voters and

opposition's core supporters increase their corruption perceptions if a scandal breaks

out. We have already proved that opposition's core supporters do not modify they

participation decision as a consequence of these scandals, but they report higher levels

of corruption perception indeed. That supports our suspicion that if corruption is

present, core supporters of opposition parties will keep voting for their party options in

order to bring down the corrupt incumbent. We have not observed a mobilization effect

for these individuals, something unlikely since core supporters by definition tend to

vote. However, we can affirm that the disaffection effect do not play a relevant role for

opposition core supporters.

For ‘past corruption cases’ none of the individuals’ typologies modify their corruption

perceptions. That could be explained for the fact that some voters could have forgotten

about old cases. Independent individuals are less likely to vote in municipalities with

past corruption cases, however they do not report higher levels of corruption

perceptions. Our suspicion about that result is that voters in municipalities which have

already had corruption scandals in the past modified their perceptions by then, not

reporting afterwards higher corruption awareness.

For ‘recent corruption cases’, both independent voters and opposition's core supporters

increase their corruption perceptions after a scandal is revealed. We did not find any

effect on turnout for none of these groups. Hence, in the short term local scandals do

affect corruption perceptions of independent voters and opposition's core supporters, but

that does not modify immediately individuals’ electoral participation decision.

Finally, ‘repeated corruption cases’ do also increase corruption perceptions for both

independent voters and opposition's core supporters. However, independent individuals

will be the only ones that will modify their voter turnout as a consequence of repeated

corruption scandals.

As in the previous estimations, considering that that we have a non-linear model with

multiple interaction terms, the simulation-based approach is the most practical

alternative to estimating the actual impact of corruption scandals on individuals’

perceptions (Tomz et al., 2000). Table 7 reports the simulated parameters of our

significant coefficients from the ordered Logit estimation of Equation (3).

22

[Table 7 about here]

We have realised the simulations for the model including all contextual and individual

covariates, as well as the whole set of interactions (columns (2), (4), (6) and (8) in Table

6). We see that when a corruption scandal breaks out independent voters are 3.2% more

likely to perceive their municipality as having “very high corruption”. That value

increases to 4% for recent corruption cases. However, as we saw in Table 4 that does

not imply that independent voters withdraw from the elections if a recent corruption

case has been revealed in their municipality. A similar effect occurs for opposition core

supporters, which are 3% more likely to report “very high corruption” perceptions if a

corruption scandal breaks out. For repeated corruption cases, they are 3.9% less likely to

consider their municipality as “none” corrupt, while that value for independent voters is

3.5%. However, as we saw in Table 6, it is important to remark that modifications of

perceptions if a scandal breaks out depend to a large extent on the individual’s baseline

corruption perceptions. Hence, if independent voters already have a very bad opinion on

their local politicians’ integrity, they will be less likely to report a higher increase of

their corruption perceptions once a scandal is revealed. In any case, information from

Tables 4-7 indicates that corruption perceptions are not immediately translated into

electoral actions for all individuals’ types.

4.6. Placebo tests

The ‘conditional independence’ or ‘unconfoundedness’ assumption lies on the

presumption that the treatment satisfies some sort of exogeneity (Rosenbaum and

Rubin, 1983; Imbens and Wooldridge, 2008). As explained above, that will require

turnout to be independent from corruption scandals. If that is the case, systematic

differences in individual turnout between treated and control municipalities, with the

same individual and contextual traits, will be attributable to the treatment -corruption

scandals. We will check for the exogeneity of our treatment by testing that our results

are not driven by either spurious correlation or some omitted trend that affects both

corruption cases and electoral turnout. Hence, we will conduct two placebo tests to

verify that levels of voter turnout are not explained by future corruption

Our first placebo test considers our general sample of observations -aggregated at the

municipal level-, and verifies that corruption occurring after 1999 does not explain

levels of turnout in that year:

Turnout99= -0.005 Corruption>99 + 0.932Turnout87-95 (4)

(0.007) (0.038)***

where Turnout99 is the local electoral turnout in 1999 for the municipalities in our

23

sample, Corruption>99 is a dummy variable equal to one if at least one corruption

scandal broke out after 1999, and Turnout87-95 is the average level of electoral turnout

between 1987 and 1995. Standard errors are in brackets and ***

indicates a 1% level of

significance. Thus, corruption scandals had no significant effect on previous levels of

turnout, confirming the assumption that there is not an omitted variables bias.

Since the original sample accounted for 160 treatment municipalities, including those

that experienced corruption after the local elections of 2007, we have 38 additional

treatment municipalities that have been not used in our analysis. Those municipalities

had at least a corruption scandal between May 2007 and November 2009, when the

survey was carried out, but not anytime before. Thus, we can also perform a placebo test

with the corruption cases revealed after the 2007 local elections. That placebo test

follows the specification:

Turnout07= 0.008 Corruption07-09 + 0.828Turnout87-95 (5)

(0.012) (0.044)***

where Turnout07 is the local electoral turnout in 2007 for the municipalities in our

sample, Corruption07-09 is a dummy variable equal to one if at least one corruption

scandal broke out after 2007, and Turnout87-95 is the average level of electoral turnout

between 1987 and 1995, which was used to conduct our matching. Standard errors are

in brackets and ***

indicates a 1% level of significance. Hence, our second placebo test

confirms previous results, verifying the ‘conditional independence assumption’ holds.

5. Conclusions

This paper studies the effects of local corruption scandals on voter turnout. We find that

corruption generates disaffection with the electoral system among politically alienated

citizens. As a result, some individuals will abstain, withdrawing themselves from the

electoral process, and helping corrupt incumbents to keep in office.

With the use of data on Spanish local corruption cases from 1999 to 2007, and survey

information on individual’s turnout, we are able to use a balanced matched sample of

corruption-ridden and corruption-free municipalities. Our general results support the

hypothesis that the disaffection effect of scandals predominates over the mobilization

factor. On average, the fact that a corruption scandal has been revealed, decreases in

2.1% individuals’ probability of voting. That result is similar to Chong’s et al. (2012)

study, which finds that exposing Mexican citizens to information on corruption

decreases voter turnout by 3%.

24

However, the main paper’s contribution is that we are able to identify both individual’s

partisan leanings and the timing of the scandals as key factors on determining how

potential voters react to corruption.

First, our results show that scandals just have an effect on turnout for those citizens

without strong political attachments. Independent voters are 3.6% less likely to vote if a

corruption scandal is revealed. On the contrary, core supporters do not react to

corruption scandals, irrespective of whether they support the incumbent party or the

opposition.

Second, we find that, while neither past nor recent corruption scandals have an impact

on individuals’ voting participation decision, corruption cases repeated in time lead to

higher abstention. For those municipalities that have experienced at least one case in

both terms of office analysed (1999-2003 and 2003-2007), the individuals’ probability

of voting decreases in 4.2%, twice as much as when we consider all corruption scandals.

Hence, the persistence of the scandals leads the disaffection effect to predominate over

the mobilization factor, decreasing voter turnout.

Taking into account both individual’s partisan leanings and the timing of corruption

together, we find that independent voters are less likely to vote also when repeated

corruption cases are considered. For that case, independents’ likelihood of voting is

reduced by a 5.4%. Past and recent corruption cases do not seem to affect any group of

individuals. Neither incumbent nor opposition core supporters change their electoral

participation decision if a scandal is revealed, irrespective of the timing of corruption

analysed. Thus, our results show that, with the exception of recent scandals, corruption

just have an effect on independent voters, who will withdraw from the elections as a

consequence of a disaffection effect.

Analysing individuals’ corruption perceptions, we observe that core supporters of the

incumbent do not seem to realise that corruption occurs. They do not report higher

levels of corruption perception when a corruption scandal breaks out, supporting

Anduiza’s et al. (2012) findings that citizens are more tolerant to an incumbent’s

malfeasance if they share the same ideology. Core supporters of opposition parties and

independents increase their perception of corruption if a scandal is revealed; however,

the former do not modify their electoral participation. We surmise they continue to vote

for the candidate of the party with which they have a closer ideological affinity to defeat

the party involved in a corruption scandal

Voter turnout is one of the most important indicators of a political system’s democratic

health. We find that local corruption scandals affect voter turnout by demotivating

citizens to vote. These results allow us to reinterpret some conclusions reached by

previous studies, which attribute to cultural explanations the absence of notable

25

electoral punishment of corruption. Considering the non-trivial effect we find on voter

turnout attributable to scandals, we confirm that some individuals react to corruption by

withdrawing from elections. It is difficult to speculate what would happen if

independent voters did not withdraw from the elections as a consequence of corruption.

In any case, since drops in turnout tend to have a greater effect on minority parties29

, it

is highly probable that the actual votes of demotivated citizens would make it much

more difficult for corrupt politicians to stay in power. Considering both the incumbent’s

vote loss and the drop in voter turnout, the actual impact of corruption on electoral

outcomes may still be much higher than pointed out in existing literature.

29

Hansford and Gomez, (2010).

26

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Table 1: Effects of corruption scandals on voter turnout: all corruption cases.

Variables (1) (2) (3) (4)

Corruption -0.146 -0.165 -0.164 -0.164

(0.088)* (0.071)

** (0.087)

* (0.074)

**

Contextual-level variables NO YES NO YES

Individual-level variables NO NO YES YES

Observations 8,014 8,014 8,014 8,014

Notes: (1) Dependent variable: whether the individual has voted (=1) or not (=0). (2) Standard

errors clustered at the municipal level in parentheses; ***

: p<0.01. **

: p<0.05. *: p<0.1. (3)

Estimation method: Maximum Likelihood; (4) Treated observations weighted as 1, and control

observations weighted by the number of times they are matched to a treatment observation.

Table 2: Effects of corruption scandals and individual's partisan leanings on

voter turnout. Logit results.

Variables (1) (2) (3) (4)

Corruption -0.189 -0.213 -0.237 -0.244

(0.145) (0.123)* (0.140)

* (0.133)

*

Corruption x Inc. core sup. -0.035 0.002 0.043 0.088

(0.230) (0.218) (0.236) (0.226)

Corruption x Opp. core sup. 0.140 0.154 0.218 0.223

(0.265) (0.270) (0.272) (0.279)

Incumbent core sup. -0.257 -0.224 -0.221 -0.194

Test: β1+β4 ≠ 0 [0.249] [0.294] [0.392] [0.390]

Opposition core sup. -0.034 -0.049 -0.059 -0.019

Test: β1+β5 ≠ 0 [0.826] [0.778] [0.756] [0.917]

Contextual-level variables NO YES NO YES

Individual-level variables NO NO YES YES

Observations 8,014 8,014 8,014 8,014

Notes: (1) Dependent variable: whether the individual has voted (=1) or not (=0). (2)

Standard errors clustered at the municipal level in parentheses; ***

: p<0.01. **

: p<0.05. *:

p<0.1. (3) Estimation method: Maximum Likelihood; (4) Treated observations weighted

as 1, and control observations weighted by the number of times they are matched to a

treatment observation; (5) Test of linear combinations of coefficients β1+β4/5≠0,

measuring the interaction's total effect. p-values in brackets.

30

Table 3: Effects of corruption scandals on voter turnout: timing of corruption. Logit

results.

Panel A (1) (2) (3) (4)

Past corruption cases -0.143 -0.139 -0.151 -0.124

(0.162) (0.135) (0.151) (0.130)

Contextual-level variables NO YES NO YES

Individual-level variables NO NO YES YES

Observations 1,796 1,796 1,796 1,796

Panel B (1) (2) (3) (4)

Recent corruption cases 0.046 -0.000 0.065 0.033

(0.112) (0.112) (0.112) (0.112)

Contextual-level variables NO YES NO YES

Individual-level variables NO NO YES YES

Observations 3,129 3,129 3,129 3,129

Panel C (1) (2) (3) (4)

Repeated corruption cases -0.329 -0.276 -0.384 -0.324

(0.125) ***

(0.116) **

(0.140) ***

(0.118) ***

Contextual-level variables NO YES NO YES

Individual-level variables NO NO YES YES

Observations 3,089 3,089 3,089 3,089

Notes: (1) Dependent variable: whether the individual has voted (=1) or not (=0). (2) Standard errors

clustered at the municipal level in parentheses; ***

: p<0.01. **

: p<0.05. *: p<0.1. (3) Estimation

method: Maximum Likelihood; (4) Treated observations weighted as 1, and control observations

weighted by the number of times they are matched to a treatment observation.

Table 4: Effects of timing of corruption scandals and individual's partisan leanings on voter turnout.

Logit results.

Variables All corruption cases

Past corruption

cases

Recent corruption

cases

Repeated corruption

cases

(1) (2) (3) (4) (5) (6) (7) (8)

Corruption -0.180 -0.244 -0.166 -0.268 0.004 -0.036 -0.309 -0.368

(0.078)**

(0.133)* (0.137) (0.174) (0.119) (0.155) (0.127)

** (0.190)

*

Corruption x Inc.

core sup.

--.-- 0.088 --.-- 0.007 --.-- 0.203 --.-- -0.053

(0.226) (0.382) (0.306) (0.389)

Corruption x Opp.

core sup.

--.-- 0.223 --.-- 0.529 --.-- 0.043 --.-- 0.259

(0.279) (0.305)* (0.287) (0.382)

Incumbent core

sup. --.-- -0.156 --.-- -0.261 --.-- 0.167 --.-- -0.421

Test: β1+β4 ≠ 0 [0.440] [0.439] [0.552] [0.194]

Opposition core

sup. --.-- -0.021 --.-- 0.261 --.-- 0.007 --.-- -0.109

Test: β1+β5 ≠ 0 [0.912] [0.338] [0.976] [0.708]

Contextual-level

variables YES YES YES YES YES YES YES YES

Individual-level

variables YES YES YES YES YES YES YES YES

Observations 8,014 8,014 1,796 1,796 3,129 3,129 3,089 3,089

Notes: (1) Dependent variable: whether the individual has voted (=1) or not (=0). (2) Standard errors clustered at the

municipal level in parentheses; ***: p<0.01. **: p<0.05. *: p<0.1. (3) Estimation method: Maximum Likelihood; (4)

Treated observations weighted as 1, and control observations weighted by the number of times they are matched to a

treatment observation; (5) Test of linear combinations of coefficients β1+β4/5≠0, measuring the interaction's total

effect. p-values in brackets.

31

Table 5: Effects of corruption scandals on voter turnout. Percentage changes in the probability of voting.

Variables

All corruption

cases

Past corruption

cases

Recent corruption

cases

Repeated corruption

cases

(1) (2) (3) (4)

All citizens -0.021 -- -- -0.042

Independent

Voters -0.036 -- -- -0.054

Incumbent core

supporter -- -- -- --

Opposition core

supporter -- -- -- --

Notes: (1) Estimates were generated by running post-estimation simulations of the significant coefficients from Table 4

using the Clarify routine in Stata as described by Tomz et al. (2000), setting each explanatory variable at its mean value;

(2) Dependent variable: whether the individual has voted (=1) or not (=0); (3) Treated observations weighted as 1, and

control observations weighted by the number of times they are matched to a treatment observation; (4) All estimations

include contextual and individual level variables.

Table 6: Effects of corruption scandals on individual's corruption perceptions. Ordered Logit results.

All corruption cases

Past corruption

cases

Recent corruption

cases

Repeated corruption

cases

Variables (1) (2) (3) (4) (5) (6) (7) (8)

Corruption 0.327 0.358 0.194 0.261 0.356 0.417 0.391 0.360

(0.108)***

(0.117)***

(0.163) (0.197) (0.131)***

(0.161)***

(0.1710)**

(0.193)*

Corruption x Inc. core

sup.

--.-- -0.325 --.-- -0.262 --.-- -0.397 --.-- -0.183

(0.146)**

(0.277) (0.216)* (0.227)

Corruption x Opp.

core sup.

--.-- 0.146 --.-- -0.045 --.-- 0.105 --.-- 0.237

(0.130) (0.231) (0.176) (0.182)

Incumbent core sup. --.-- 0.032 --.-- -0.001 --.-- 0.020 --.-- 0.177

Test: β1+β4 ≠ 0 [0.796] [0.997] [0.914] [0.384]

Opposition core sup. --.-- 0.505 --.-- 0.216 --.-- 0.521 --.-- 0.597

Test: β1+β5 ≠ 0 [0.002] [0.339] [0.004] [0.014]

Contextual-level

variables YES YES YES YES YES YES YES YES

Individual -level

variables YES YES YES YES YES YES YES YES

Observations 6,259 6,259 1,387 1,387 2,394 2,394 2,478 2,478

Notes: (1) Dependent variable: Perceptions of local political corruption: 5=Very High Corruption. 4=High. 3=Medium.

2=Low. 1=None. (2) Standard errors clustered at the municipal level in parentheses; ***: p<0.01. **: p<0.05. *: p<0.1. (3)

Estimation method: Maximum Likelihood; (4) Treated observations weighted as 1, and control observations weighted by the

number of times they are matched to a treatment observation; (5) Test of linear combinations of coefficients β1+β4/5≠0,

measuring the interaction's total effect. p-values in brackets.

32

Table 7: Effects of corruption scandals on individual's corruption perceptions.

Simulations.

Variables Perceptions of local political corruption category

None Low Medium High Very High

All Corruption Cases

Independent Voters -0,053 -0,035 0,007 0,049 0,032

Incumbent core supporter -- -- -- -- --

Opposition core supporter -0,056 -0,031 0,009 0,049 0,030

Past corruption cases

Independent Voters -- -- -- -- --

Incumbent core supporter -- -- -- -- --

Opposition core supporter -- -- -- -- --

Recent corruption cases

Independent Voters -0,070 -0,044 0,008 0,066 0,040

Incumbent core supporter -- -- -- -- --

Opposition core supporter -0,075 -0,038 0,010 0,066 0,037

Repeated corruption cases

Independent Voters -0,035 -0,047 0,003 0,048 0,031

Incumbent core supporter -- -- -- -- --

Opposition core supporter -0,039 -0,042 0,006 0,049 0,027

Notes: (1) Estimates were generated by running post-estimation simulations of the

significant coefficients from Table 6 using the Clarify routine in Stata as described by

Tomz et al. (2000), setting each explanatory variable at its mean value; (2) Dependent

variable: Perceptions of local political corruption: 5=Very High Corruption. 4=High.

3=Medium. 2=Low. 1=None; (3) Treated observations weighted as 1, and control

observations weighted by the number of times they are matched to a treatment

observation; (4) All estimations include contextual and individual level variables.

33

Appendix.

Table A.1: Definition of the variables and Summary Statistics

Variable Definition Mean St.Dev. Min Max

Individual-level variables

Turnout Dummy variable coded 1 if the individual voted in the

2007 local elections 0,775 0,418 0 1

Income Self-reported socio-economic classification (1-5): 1: Low;

2: Medium-low; 3: Medium; 4: Medium-High; 5: High 3,357 1,308 1 5

Schooling Highest level of education completed (1-5): 1: any studies;

2: primary; 3: secondary; 4: graduate 46,323 16,697 1 5

Age Age in years 0,499 0,500 18 99

Female Dummy variable coded 1 for females 0 1

Divorced Dummy variable coded 1 for people who are divorced or

separated 0,046 0,209 0 1

Unemployed Dummy variable coded 1 for people who are unemployed 0,142 0,349 0 1

Student Dummy variable coded 1 for students (do not work) 0,058 0,233 0 1

Retired Dummy variable coded 1 for people who are retired 0,208 0,406 0 1

Immigrant Dummy variable coded 1 for people who are not born in

Spain 0,035 0,183 0 1

Corruption

Perceptions

Self-reported perceptions of local political corruption (1-

5): 1: None; 2: Low; 3: Medium; 4: High; 5: Very High

Corruption

2,719 1,313 1 5

Ideology Self-reported ideology (1-3): 1: Left; 2: Centre; 3: Right 1,831 0,711 1 3

Independent Dummy variable coded 1 for people who do not always

vote for the same party 0,512 0,500 0 1

Incumbent core

Supporter

Dummy variable coded 1 for people who always vote for

the same party, and their ideology is the same as the

incumbent's

0,215 0,411 0 1

Opposition

core Supporter

Dummy variable coded 1 for people who always vote for

the same party, and their ideology is the same as the

opposition' party

0,272 0,445 0 1

Contextual-level variables

Corruption Dummy variable coded 1 for municipalities with at least

one corruption scandal in the period 1999-2007 0,482 0,500 0 1

Past

corruption

cases

Dummy variable coded 1 for municipalities with at least

one corruption scandal in the period 1999-2003, but were

not corruption has broke out afterwards

0,110 0,313 0 1

Recent

corruption

cases

Dummy variable coded 1 for municipalities with at least

one corruption scandal in the period 2003-2007, but were

not corruption has broke out before

0,188 0,390 0 1

Repeated

corruption

cases

Dummy variable coded 1 for municipalities with at

corruption scandal in both periods: 1999-2003 and 2007-

2009.

0,185 0,388 0 1

Voter turnout Average voter turnout at the 1987, 1991 and 1995 local

elections 0,687 0,084 0,508 0,922

Income p.c.

Average socio-economic condition. Arithmetic average of

the socio-economic condition according to their

employment status

0,951 0,118 0,610 1,200

Divorced Percentage of divorced and separated among all

population 0,029 0,012 0,002 0,074

Graduate

Percentage of population with third level studies (diploma,

degree and doctorate) among population 16 years and

older

0,128 0,069 0,016 0,434

Unemployment Percentage of unemployed among individuals aged 20-59 0,157 0,079 0,048 0,675

Ethnic diversity

1- Σk(Popk/Population)2 where Pop_contk is population

whose nationality is from continent k, and k refers to

Europe, Africa, America and others

0,058 0,058 0 0,279

Right voters Average historical vote share that the right wing parties

obtained in 1979, 1982, 1986 and 1989 local elections 0,226 0,093 0,037 0,483

log(Population) Log of the registered population 10,545 1,778 6,973 14,957 Notes: (1) Source of the individual-level variables: own-designed survey (see Box A.1). (2) the contextual-level variables: (i)

2001 Census of Population (National Institute of www.ine.es), for Income p.c., % Divorced, % Graduate, % Unemployed,

population by continent used to construct the Ethnic diversity index, and Population. (ii) Database on corruption scandals,

construct (see section 3 for more

details). (iii) Voting data from the Ministry of the Interior, used for the construction of the % Right voters and % Vote turnout variables.

34

Table A.2: Differences in means between Treated and Control groups.

Mean t-test

Treated Control [p-value]

Unmatched sample

% Vote turnout 0.541 0.654 4.25 [0.000]

Income p.c. 0.947 0.939 1.09 [0.282]

% Divorced 0.026 0.018 14.09 [0.000]

% Graduate 0.106 0.077 12.57 [0.000]

% Unemployment 0.147 0.143 0.88 [0.381]

Ethnic diversity 0.060 0.035 10.83 [0.002]

% Right voters 0.507 0.505 0.36 [0.724]

log(Population) 9.610 8.182 27.31 [0.003]

Matched sample

% Vote turnout 0.708 0.692 -1.28 [0.202]

Income p.c. 0.947 0.934 -0.79 [0.428]

% Divorced 0.026 0.026 0.03 [0.976]

% Graduate 0.114 0.104 -1.09 [0.278]

% Unemployment 0.151 0.166 1.23 [0.220]

Ethnic diversity 0.053 0.054 0.08 [0.932]

% Right voters 0.219 0.218 -0.12 [0.908]

log(Population) 9.788 9.678 -0.51 [0.610]

Observations 122 97

Note: (1) Treated group = municipalities where at least one corruption scandal

broke out during the period 1999-2007; Control group=municipalities where

no corruption scandal broke out during the same period.

Table A.3.: Differences in means (survey observations).

Mean t-test

Treated Control [p-value]

Turnout 0.833 0.846 -1.01 [0.220]

Income 2.702 2.721 0.02 [0.479]

Schooling 3.184 3.159 0.03 [0.700]

Age 47.968 47.243 0.72 [0.260]

Female 0.522 0.504 0.02 [0.225]

Divorced 0.044 0.039 0.00 [0.424]

Unemployed 0.130 0.140 -0.01 [0.327]

Students 0.045 0.052 -0.01 [0.222]

Retired 0.241 0.226 0.01 [0.283]

Immigrants 0.039 0.031 0.01 [0.168]

Ideology 1.841 1.817 0.02 [0.324]

Independent 0.510 0.520 -0.01 [0.463]

Incumbent core Supporter 0.224 0.203 1.28 [0.200]

Opposition core Supporter 0.265 0.275 -0.01 [0.507]

Interviewees per municipality 34.779 38.016 -1.18 [0.241]

Number of municipalities 122 97

Note: (1) Treated group = municipalities where at least one corruption scandal

broke out during the period 1999-2007; Control group=municipalities where

no corruption scandal broke out during the same period.