Corruption and Elections: An Empirical Study for a
Cross-Section of Countries
Stefan Krause and Fabio Méndez
∗
June 2007
Abstract
In this paper, we study whether voters are more likely to “vote out” a corrupt
incumbent than to re-elect him. Specifically, we examine whether they retract their
support from political candidates who they think are corrupt by looking at changes
in an index of corruption perceptions between the current and the last elections. Our
results suggest that corruption in public office is effectively punished by voters. Furthermore, our findings support the idea that both the political system and the democratic
experience are important determinants of the voters’ reaction and control of corruption: while voters in countries with parliamentary systems or with relatively low levels
of democracy react negatively to an increase in corruption, no perceptible effect of this
kind was found in countries with mature democracies; and the evidence is inconclusive
in the case of countries with presidential systems.
JEL classification: E32, P16
Keywords: Corruption; Elections; Voting Function
∗
Department of Economics, Emory University, and Department of Economics, University of Arkansas.
Please send comments to skrause@emory.edu or fmendez@walton.uark.edu.
1
Introduction
As established in both the economics and political science literatures, the question of
whether politicians are held accountable for their policies plays a key role in controlling
corruption and preventing bad governance (see, for example, Ferejohn (1986), Wittman
(1989), Fackler and Lin (1995), Persson et al. (1997), and Lederman et al. (2005)). In
general, greater political accountability is regarded as beneficial, for it helps to narrow the
gap between the citizens’ objectives and the politicians’ choices.
In modern democracies, citizens may punish corrupt politicians by voting them out of
office, and they may reward honest behavior by re-electing an incumbent. Yet, as discussed
by Rundquist, Strom and Peters (1977), and by Peters and Welch (1980), the reaction of
voters to corruption in public office is complex and there are several reasons why voters could
choose to re-elect a corrupt politician.1
- Voters may not be well informed about the incumbent’s behavior and may not hold
her responsible.
- Voters may be willing to endure a corrupt public official in exchange for periods of
prosperity and general economic wellbeing.
- Voters may see corruption as a personal flaw and while they would not support a
corrupt candidate, they would still be willing to vote for that candidate’s party.
- When corruption is used to benefit certain ethnic or social groups, voters from these
groups might reward corrupt politicians and help her avoid a general loss of votes.
In this paper, we study whether voters are more likely to “vote out” a corrupt politician
than to re-elect him. More specifically, we examine whether voters retract their support
from political incumbents who they think are corrupt. We do so by studying the empirical
relationship between changes in the electoral votes received by incumbents and changes in
the perceived levels of corruption in a sample of 28 countries during the period of 1995-2006.
1
A useful link to public information and press releases regarding corruption in electoral process is the website for Transparency International (http://www.transparency.org/), which contains a substantial number
of articles and news stories about corrupt politicians seeking and obtaining re-election.
1
Similar studies analyze the effects of corruption charges on electoral results using data
for a single country only. Peters and Welch (1980), for example, study the electoral impact
of corruption charges on candidates in contest for the U.S. House of representatives. They
estimate that candidates accused of corruption suffer a loss of 6-11% of the expected vote.
Similarly, Chang and Golden (2004) find that charges of corruption significantly decrease
the re-election probability for Italian congressmen. In turn, Fackler and Lin (1995) collect
news stories and report a negative correlation between the amount of information about
corruption and the electoral support for the party in control of the presidency in the USA.
Ferraz and Finan (2005) report similar results for the case of Brazil.
In contrast with all these other papers, we use a cross-sectional sample of European, North
and South American countries, plus Japan. One noticeable advantage of using a cross section
of countries is that we are able to split our sample according to the system of representation
chosen by each country (presidential or parliamentary), and by their democratic experiences
in the post-war years. Splitting the sample in such ways allows us to explore the arguments
of Ferejohn (1986), Treisman (2000), and Lederman et al. (2005); who have suggested
that both, the type of political system and the exposure to democratic institutions, play a
determinant role in the electorate’s control of corruption.
Lederman et al. (2005) report that corruption is lower in countries with parliamentary
systems and where democracy has gone uninterrupted for a number of years. Treisman (2000)
also reports a negative correlation between long exposure to democracy and corruption. Both
of these studies interpret their results as evidence that the political structure and the electoral
system help determine the incentives for those in office (to be honest), and for the citizens in
general (to police and punish corrupt behavior). Noticeably, however, they do not investigate
whether their interpretation is consistent with the evidence from electoral results.2
Throughout the paper we use aggregate indices of corruption perceptions instead of
accounts of corruption charges. Thus, our measure of corruption does not reflect the actual
2
Also, see Ferejohn (1986) for a theoretical model where the voters’ control on office holders depends on
the political regime assumed.
2
behavior of the incumbent, but the voters’ perception of his behavior. The particular index
of corruption perceptions used in this study is the Corruption Perceptions Index (CPI) from
Transparency International, which we shall describe in more detail on Section 2.
The results of the paper regarding the effects other economic variables on the votes
received by the incumbent are consistent with those of previous studies in the literature:
higher output growth and lower inflation increase the incumbent’s support.3 In turn, with
regards to the effects of changes in the level of perceived corruption on the electoral support
for the incumbent, we find that an increase in corruption is positively associated with a loss
of votes in the general sample. Hence, our results suggest that corruption in public office (or
the perception thereof) is effectively punished by voters at the same time that good economic
conditions are rewarded.
Furthermore, our results support the idea that both the political system and the democratic experience are important determinants of the voters’ reaction and control of corruption. While our findings suggest that voters react negatively to higher levels of corruption
in countries with parliamentary political systems, in countries with presidential systems the
evidence is less conclusive. As for the democratic experience, we find that increased corruption is punished only in countries with relatively short exposure to democracy, with no
perceptible effect in mature democracies.
The remainder of the paper is organized as follows: Section 2 provides a detailed description of the data set used for this study. Section 3 presents the main econometric specifications, while Section 4 shows the estimation results. Finally, Section 5 concludes.
3
See Persson and Tabellini (1999), and Lewis-Beck and Stegmaier (2000) for a discussion of these issues.
3
2
Data description
The data used in the empirical analyses includes 28 countries and covers years between
1995 and 2007. For each electoral round, we collected data for the polling results, and the
political and economic conditions that characterized it. The set of countries in our sample
are mainly European and North and South American countries. Out of the total sample, 17
countries have a parliamentary system and 11 have a presidential system.
An effort was made to obtain data from as many countries and electoral periods as
possible; however, the sample size was limited by the availability of data, as is the case
for other empirical studies. Paldam (1991), for example, uses data from 17 high-income
democracies over the period 1948-1985. Similarly, Lewis-Beck and Mitchell (1990) use a
data set limited to 5 nations and 27 elections. Powell and Whitten (1993) use a cross section
of 19 industrialized nations for the period 1969-1988.
In our particular case, the sample is restricted by the availability of corruption perception
measures (the CPI index, for example, was first reported in 1995), and the data on political
parties and electoral results (since we require data for at least two complete electoral periods).
When matching the CPI data with information on political systems we are able to construct
a data set that includes 28 countries and a total of 93 elections. All sources and variable
definitions are summarized in a data appendix.4
Data on political parties and electoral results was obtained from three main sources: The
Dataset of Political Institutions (DPI) introduced by Beck et al. (2001) was the main source.
Two other complementary sources were used: the Political Dataset of the Americas (managed
by the Center for Latin American Studies at Georgetown University in collaboration with
the Organization of American States), and the database for European political parties and
elections.5
4
The complete data set can be downloaded at http://comp.uark.edu/~fmendez/data.
"Parties and Elections in Europe" includes a database about parliamentary elections in the European
countries since 1945 and additional information about the political parties and the acting political leaders.
The private website (http://www.parties-and-elections.de) was established by Wolfram Nordsieck in 1997.
5
4
Electoral systems were broadly classified as Presidential or Parliamentary following the
DPI’s classification. For presidential systems, the incumbent’s votes correspond to the votes
obtained by the party with which the incumbent president is associated. For parliamentary
systems, the incumbent’s votes correspond to the votes obtained by the party with the largest
presence within the government.
Notice that according to this definition, the incumbent party in a parliamentary system
may well not constitute the party with greatest amount of seats. In parliamentary systems,
whenever a number of small parties collude to form a coalition government, the party that
obtains more seats is not necessarily the one who is in charge of making policy (and is
therefore blamed for good or bad performance). Thus, defining the incumbent in this way
has the advantage of assigning responsibility to the actual policy maker.
For both systems we recorded the percentage of the votes obtained by the incumbent as
well as the percentage of votes obtained by that same party in the previous elections (when
it came to power). We also collected information on the percentage of parliamentary seats
the incumbent’s party controlled at the time of the elections, and information on ideological
identification to create a dummy variable taking the value of 0 for left oriented parties
and 1 for right or center oriented parties. In addition, we use a measure of the length of the
electoral cycle (in number of months between elections) and the degree of fractionalization of
the ruling government, as defined by the probability that two random draws from parliament
would produce legislators from different parties (Beck et al. (2001)).
To capture the exposure to democratic institutions, we classify countries as “mature”
or “new” democracies. In order to do so, we employ annual measures for institutionalized
democracy (DEMOC), available form the Polity IV dataset and developed by Marshall and
Jaggers (2002). The DEMOC indicator is based on an additive eleven-point scale (0-10).
We average these scores over the period 1949-2003 for each of the 28 countries in our sample
and identify “mature” democracies with countries for which the average value for DEMOC
is equal to 10, and include Japan, for which the value equals 9.45. Similarly, we identify
5
“new” democracies with countries for which the average value of DEMOC ranges between
3.11 (El Salvador) to 7.16 (Greece). There are 14 countries classified as “mature” and 14
countries classified as “new”.
In turn, for measuring changes in corruption perceptions we use the corruption perceptions index (CPI) from Transparency International. The CPI is “an index that draws on
multiple expert opinion surveys that poll perceptions on public sector corruption” and the
corruption of politicians (Transparency International, press release 2006). As of 2006, the
CPI has the greatest scope of countries of any perception index available (163 countries),
and has been used in multiple empirical studies (see, for example, Herzfeld and Weiss (2003),
and Persson et al. (2003)).
The CPI is a composite index that draws on 12 different polls from 9 independent institutions and scores countries on a scale from zero to ten; with zero indicating high levels of
perceived corruption and ten indicating low levels of perceived corruption. For our purposes
in this paper, we are interested in measuring whether perceived corruption has increased
during the incumbent’s tenure or not. Thus, we use yearly CPI values to calculate the
change in CPI between the time the incumbent took office and the time the elections take
place. That is, we create the variable PCI (i.e., perceived corruption increase) which is
defined as P CI = −(CP It+e − CP It ), where t represents time (in years), and subscript e
represents the number of years between elections. A positive value for PCI corresponds to a
perceived increase in corruption, while a negative value corresponds to a perceived decrease
in corruption.
Finally, data for all the economic variables, namely GDP growth, inflation and unemployment, was obtained from the World Bank’s World Development Indicators Database (2006).
In the empirical section, we use the annual indices of each economic indicator averaged over
the duration of the electoral cycle in question.
6
3
Model Specification
For the econometric specification, we adopt a general voting function in which political
and economic variables enter simultaneously as determinants of voting behavior. As in most
of the economic voting literature (see Lewis-Beck and Stegmaier, 2000; Paldam, 1991; and
Powell and Whitten, 1993), the dependent variable is the gain (or loss) in the share of
votes received by the incumbent party with respect to the previous election. This change
is explained by a series of economic and political variables included in a set of explanatory
variables.
With regards to the economic indicators used as explanatory variables, we include a
measure of GDP growth and consumer price inflation. In some specifications we also control
for the behavior of unemployment. As explained before, all of these measures correspond to
the annual indices of each economic indicator, averaged over the duration of the electoral
cycle in question.
Previous studies have reported a connection between all of these economic variables and
both the popularity of the government and the votes obtained by the incumbent (see, for
example, Paldam (1991) or Powell and Whitten (1993)). In these studies, after controlling
for political variables that influence electoral results, an increase in the number of votes
received by the incumbent is associated with pre-electoral periods of elevated growth and
moderate inflation.6
In turn, the political variables used as independent variables include a measure of absolute government support, a measure of the length of the incumbent’s tenure (in years),
the ideological classification of the incumbent, the number of parliamentary seats controlled
by the incumbent, and the degree of fractionalization in the government. Below, we provide
some intuition for why these controls are important. As we discuss in Section 4, our main
6
See Lewis-Beck and Stegmaier (2000) for additional evidence in the case of US elections; Nordhaus,
Alesina and Schultze (1989) for evidence on inflation and unemployment performance; and Abrams and
Butkiewicz (1995) for an analysis of how state-level economic conditions have incidence on presidential
elections.
7
results are robust to the inclusion of these control variables.
As noticed by Powell and Whitten (1993), since different parties have different electoral
bases that remain stable over time, using the results of previous elections to control for
absolute government support is essential to the specification: “A loss of two percentage
points may mean something different for a government that won 40% last time as compared
with a government that won 60% last time.” We agree with this appreciation and include
this control in some of our specifications. Also as in Powell and Whitten (1993), we expect
its coefficient to be negative as it is easier to lose an absolute percentage of the votes from
a larger base.
We include a measure of the length of the incumbent’s tenure (in years) to capture the
depletion in government support that appears as the government ages and the initial popularity of the government vanishes. Such loss of popularity has been previously documented
by authors like Goodhart and Bhansali (1970) and Paldam (1986). Since corruption may
change as the end of the political tenure approaches, including the effects of the length in
tenure may be relevant for our estimation.
We also include a political ideology dummy and two variables that capture the cohesion
of the government. The dummy variable takes a value of 1 for right or center oriented parties
and a value of 0 for left oriented parties. Left and right leaning parties tend to have different
preferences and approaches to economic policy and voters might take these historical trends
into account when evaluating government performance.
We also control for government cohesion, which is measured by government fractionalization and the number of congressional seats controlled by the incumbent at the time of
the election. As explained by Lewis-Beck and Stegmaier (2000), the greater the perceived
cohesion of the incumbent’s government, the more likely voters are to charge the incumbent
with the responsibility of recent policies and, thus, of any perceived corruption.
Once our measure of the changes in corruption perceptions is included, the econometric
specification can then be summarized by the following equation:
8
→
−
→
−
V otesch = α + βP CI + δ E + γ P ,
(1)
where V otesch is the absolute change in the percentage of the popular vote captured by the
→
−
incumbent in any election with respect to the previous election; E is a vector of economic
→
−
variables (growth and inflation); P is a vector of political variables (used as controls); and
PCI, as before, represents the increase in perceived corruption.
4
Empirical analysis and results
Table 1 shows a descriptive summary of all variables of interest for each country, averaged
over each government period. Noticeably, the 28 countries in our sample exhibit sharp
differences in average perceived corruption (ranging between 3.65 for Turkey and a 8.86 for
the Netherlands), GDP growth (from 0.44% for El Salvador and 4.03% for Hungary) and
Consumer Price Inflation (-0.04% in the case of Japan and 69.18% for Turkey).
Before examining the evidence of whether voters tend to punish a perceived increase in
corruption on behalf of the incumbent and whether differences in the political system and
the democracy levels translate into differences in the degree of reduction in support, it is
useful to verify if the countries in our sample comply with the empirical observations made
by Lederman et al. (2005) and Treisman (2000). These authors report that countries with
presidential systems and shorter exposure to democracy tend to exhibit a higher degree of
corruption.
Looking at Table 2, we can observe how the 17 countries with parliamentary systems
have an average corruption perceptions index of 6.87, nearly one point higher than for the
11 countries with presidential systems (5.88). Comparing mature democracies with new
democracies yields an even larger difference: an average score of 7.19 for the former versus
5.78 for the latter. In both cases, a simple test of differences in means suggests that the
respective averages from both sample splits are significantly different (to the 1% level in both
9
cases); consistent with the findings of those previous authors.
We now turn our attention to the main question in this analysis; namely, whether voters
tend to reduce their support for an incumbent when a perceived increase in corruption takes
place. In this regard, the results from estimating equation (1) using least squares estimation
for the entire sample are displayed in the first column of Table 3. For the total sample,
the results of this estimation reveal that an increase in perceived corruption is significantly
associated with a loss in voters’ support for the incumbent; and the coefficient is significant
at the 1% level.
As for the role of economic performance, consistent with previous findings, we also observe that a higher GDP growth rate is associated with an increase in voters’ support towards
the incumbent, while higher inflation is correlated with a punishment by voters in terms of
diminished support. Both coefficients are significant at the 1% level. Finally, the unemployment rate does not enter the regression with a significant coefficient.
Focusing our attention on the political conditions, we do not find significant evidence
suggesting they affect voters’ behavior, once we control for changes in perceived corruption
and our economic performance. In particular, we do not observe a reduction in the incumbent
party’s support base, as discussed by Powell and Whitten (1993); or a wear down effect, as
identified by Paldam (1991). Neither the votes obtained in the previous elections by the
incumbent, nor the length of their tenure, seem to have a significant outcome on the change
in voters’ support, albeit they do enter the regression with the expected negative coefficients.
A potential explanation for this discrepancy with the findings by Paldam (1991), and Powell
and Whitten (1993) is that, while their studies covers elections in industrialized countries,
our sample also takes into account elections in emerging markets and transition economies
(including 10 Latin American countries).
Our main results are robust to alternative specifications. In the second column of Table
3 we include only the political variables as controls; and, while we still fail to identify
evidence suggesting that other political conditions affect voters’ behavior, an increase in
10
perceived corruption remains negatively and significantly correlated with a change in the
votes received by the incumbent. This key finding is also robust when controlling only for
economic performance in the third column: the coefficients on perceived corruption increase,
GDP growth, and inflation remain virtually unchanged, and are still significant at the 1%
level; meanwhile, unemployment remains insignificant. Since this latter observation may be
the result of voters being more concerned with aggregate activity than employment, we focus
only on GDP growth and inflation as the economic control variables in the last column of
Table 3. Overall, we find that across all different specifications, the evidence robustly points
to an increase in perceived corruption resulting in a loss of votes by the incumbent.
In Table 4 we examine whether differences in the political system also amount to differences in the degree of voters’ punishment to increased corruption. Two specifications are
considered: the first includes only considers the principal economic variables (GDP growth
and inflation) as explanatory variables, while the second incorporates political variables as
controls. For countries with parliamentary systems, the results mirror the ones we obtained
when considering the entire sample: a perceived corruption increase, lower GDP growth, and
higher inflation are all significantly correlated with a loss of votes by the incumbent. These
results are robust to controlling for political variables, and for country-specific effects (results
displayed in column (1) of Table 6). As in the case of the entire sample, the political control
variables do not appear with significant coefficients; the only exception is the percentage of
votes the incumbent received in the previous election, which enters the regression with a
negative sign. Therefore, for parliamentary systems we do observe a reduction in support
due to a larger base, consistent with the findings of Powell and Whitten (1993).
For presidential systems, the evidence from the last column suggests that only an increase in perceived corruption is significantly associated with a loss of voters’ support for
the incumbent, and only at the 10% level. None of the economic variables enter the estimation with significant coefficients. However, we can infer from the overall goodness-of-fit
statistics that there is quite substantial amount of variability of voting behavior in coun-
11
tries with presidential systems that is not explained by our right-hand side variables. To
address this issue, we also include the political variables as controls, which did not generate
any significant change either in the coefficients of interest, or in the overall goodness-of-fit.
While none of the political controls enters the regression with a significant coefficient, their
inclusion does result in the coefficient on perceived corruption increase becoming no longer
significant. We also explored the possibility that extreme values for inflation in some countries with presidential systems may be responsible for the lack of precision in the estimates,
so we employed a transformed rate of inflation, given by T RI = π/(π + 1), again without
any noticeable change in the main results.7 Allowing once more for the possibility that
country-specific effects may be warranted, we performed a random-effects estimation, whose
results are reported in the second column of Table 6, only to find that the coefficient on the
increase in perceived corruption becomes even less significant. This leads us to note that the
evidence of a punishment by voters in presidential systems as a result of a higher degree of
corruption is inconclusive, and not robust to alternative specifications.
Finally, we perform our second sample split based on exposure to democratic institutions,
and report the results in Table 5. Interestingly enough, voters in countries that have had
stable democracies since the post-war do not seem to punish the incumbent party when
they perceive an increase in corruption, whereas voters in relatively “new” democracies do
significantly punish the governing party when they perceive an increase in corruption. These
results are robust to controlling for the inclusion of political variables, as shown on columns
(2) and (4) of Table 5; and to country-specific random effects, as shown in the last two
columns of Table 6.
As for the economic variables, GDP growth enters the regression with an expected positive
and significant coefficient only for the sub-sample of “new democracies”, while inflation
enters the regression with the expected negative and statistically significant sign for mature
democracies (for “new” democracies the coefficient is substantially smaller in absolute value
7
These results are available upon request from the authors.
12
and becomes non-significant once we control for country specific effects).
In short, our results suggest that while the empirical observation that corruption is lower
in countries with parliamentary systems can be explained by the greater control that voters
exert in those countries (and their dislike of corruption); the empirical observation that
corruption is lower in countries with older democratic traditions cannot be explained by the
voter’s control and punishment of corruption. Thus, the question of how does the length
and stability of a democratic system affects its level of corruption remains unanswered.
5
Conclusions and Implications
We evaluate the question of whether voters reduce their support for an incumbent,
whenever they perceive an increase an increase in corruption, by looking at a sample of 28
countries and 93 election periods. Our results suggest that a perceived rise in corruption in
public office is effectively punished by voters in the general election. For the entire sample,
this result is significant and robust to alternative specifications of the voting functions.
More specifically, our findings provide strong evidence that voters react negatively to
an increase in corruption in countries with parliamentary systems, compared to presidential
systems of government. A potential explanation for this result was discussed at the beginning
of this paper: in presidential systems, voters may perceive corruption as a personal flaw
associated with the current president and not necessarily with his or her party; while in
parliamentary systems, voters associate corruption with the entire party. An alternative
explanation could be that most of the countries in our sample with presidential system are
Latin American.
Finally, an increase in perceived corruption is punished more severely in countries with
relatively short exposure to democracy, and not significantly so in mature democracies. This
may be due to the fact that in relatively “newer” democracies, where voter turn-out during
elections is, on average, higher than in established democracies, the electorate becomes more
13
vigilant of the incumbent’s actions, creating a larger margin for punishment or reward at
the time of the elections.
6
Data Sources and Description
6.1
Appendix 1: The Electoral Results for Incumbents (ERI)
Data Base
The countries with presidential system included in the sample are: Argentina, Brazil,
Chile, Colombia, Costa Rica, El Salvador, Guatemala, Honduras, Mexico, Uruguay, and
USA. The countries with parliamentary system included in the sample are: Austria, Belgium, Canada, Denmark, Finland, Germany, Greece, Hungary, Japan, Netherlands, Norway,
Portugal, Spain, Sweden, Switzerland, Turkey, and the United Kingdom. Data is available
for download at: http://comp.uark.edu/~fmendez/data.
Sources of information
1. The World Bank’s Data Set for Political Institutions (DPI); Dataset of Political Institutions (DPI) introduced by Beck et al. (2001)
2. Political Dataset of the Americas (managed by the Center for Latin American Studies
at Georgetown University in collaboration with the Organization of American States)
3. The database for European political parties and elections. “Parties and Elections in
Europe" includes a database about parliamentary elections in the European countries
since 1945 and additional information about the political parties and the acting political
leaders. The private website (http://www.parties-and-elections.de) was established by
Wolfram Nordsieck in 1997.
4. The World Bank’s World Development Indicators (WDI).
14
Data on electoral dates (used to compute the length of the incumbent’s tenure), party
ideology, votes received by incumbent, incumbent’s seats in congress, fractionalization of
the governing coalition, were obtained mainly from the Database on Political Institutions in
Beck et al (2001), the Political Dataset of the Americas (managed by the Center for Latin
American Studies at Georgetown University in collaboration with the Organization of American States), and the database for European political parties and elections, and also partly
completed through direct inquiries to individual government sources. Data on GDP growth,
inflation and unemployment was obtained from the World Bank’s World Development Indicators (2004).
6.2
Appendix 2: Variable definition and methodology
Note: Each data point corresponds to a democratically held election.
cname: Country name.
id: Country identification number.
Syst:
Dummy variable. Syst =0 for countries with presidential systems. Syst=2 for
countries with parliamentary systems. Syst=1 for countries with mixed presidential
system. This classification matches the one provided in the DPI.
eyear: Year when general elections took place. For countries with presidential systems,
eyear contains those years when executive elections were held (starting from 1984).
For countries with parliamentary systems, eyear contains those years when legislative
elections were held (starting from 1984). These information was obtained from either
the DPI, the EDSA, the EPD, or a combination of them. The three sources of data
usually agree on the dates, but small corrections sometimes are required.
emonth: Month of the year when elections took place.
15
peyr: Year when the previous general elections took place.
pemonth: Month of the year when previous elections took place.
cycle: Number of months spanning between elections. This number is presented in years,
so that a 4.25, for example, represents for years and 3 months.
growth: Average annual growth rate of GDP per-capita between the year in which the
incumbent took office and the year of the elections. This variable is constructed by
using the “GDP per capita growth (annual %)”, from the WDI.
infl: Average annual change in consumer prices between the year in which the incumbent
took office and the year of the elections. This variable is constructed by using the
“Inflation, consumer prices (annual %)”, from the WDI.
unempl: Average annual rate of unemployment between the year in which the incumbent
took office and the year of the elections. This variable is constructed by using the
“Unemployment, total (% of total labor force)”, from the WDI.
vinc: Percentage of valid votes received by the incumbent party. For countries with syst=0,
this number corresponds to the votes received by the party that holds the executive
office. For countries with syst=2, this number corresponds to the votes received by the
largest party in government.
vincp: Percentage of valid votes received by the incumbent party in the previous election
(that resulted in this party gaining control of the executive or, in the case of syst=2,
that resulted in this party becoming the largest party in government).
vchg: Decrease in voters support. vchg = vincp-vinc.
yp: Number of years that the incumbent party has been in power. For countries with
syst=0, this number corresponds to the number of years for which the incumbent
16
party has held the executive office. For countries with syst=2, this number corresponds to number of years for which the incumbent party has been the largest party
in government.
ideoinc: Dummy variable representing the political ideology of the incumbent party. For
left-wing parties we assign a value of 0, while for right-wing parties we assign a value
of 1.
seats: Number of government seats in parliament (at the time of the elections) divided by
total seats. This corresponds to the variable “majority” in the DPI data set for both
parliamentary and presidential systems.
govfrac: This variable corresponds to the government fractionalization measure of the DPI
dataset. By definition, this variable has a value of zero for governments in presidential
systems.
cpi: This is the Corruption Perception Index (CPI) corresponding to the electoral year;
or say, at the time the incumbent competes for office. The CPI was obtained directly
from Transparency International (2006).
pci: Increase in corruption perceptions measured by the change in the cpi index: PCI =
-(CPIt+e — CPIt).
17
References
[1] Abrams, B. A., and J. B. Butkiewicz (1995). “The Influence of State-Level Economic
Conditions on the 1992 Presidential Election”, Public Choice, 85, pp. 1-10.
[2] Beck, T., G. Clarke, A. Groff, P. Keefer, and P. Walsh (2001). “New Tools and New
Tests in Comparative Political Economy: The Database of Political Institutions”, World
Bank Economic Review, 15(1), pp. 165-76.
[3] Chang, E., and M. A. Golden (2004). “Does Corruption Pay? The Survival of Politicians
Charged with Malfeasance in the Postwar Italian Chamber”. Paper presented at the
Annual Meeting of the American Political Science Association, Hilton Chicago and the
Palmer House Hilton, Chicago, IL
[4] Fackler, T., and T.-M. Lin (1995). “Political Corruption and Presidential Elections,
1929-1992”, The Journal of Politics, 57(4), pp. 971-993.
[5] Ferejohn, J. (1986). “Incumbent Performance and Electoral Control”, Public Choice,
50(1-3), pp. 5-25.
[6] Ferraz, C., and F. Finan (2005). “Exposing Corrupt Politicians: The Effect of Brazil’s
Publicly Released Audits on Electoral Outcomes”, mimeo.
[7] Goodhart, C. A. E., and R. J. Bhansali (1970). “Political Economy”, Political Studies,
18, pp. 43-106.
[8] Herzfeld, T., and C. Weiss (2003). “Corruption and Legal (In) effectiveness: An Empirical Investigation”, European Journal of Political Economy, 19, 621-632.
[9] Lederman, D., N. Loayza, and R. R. Soares (2005). “Accountability and Corruption:
Political Institutions Matter”, Economics & Politics, 17(1), pp. 1-35.
18
[10] Lewis-Beck, M. S., and G. Mitchell (1990). “Transnational Models of Economic Voting:
Tests from a Western European Pool”. Revista del Instituto de Estudios Economicos, 4,
pp. 65-81.
[11] Lewis-Beck, M. S., and M. Stegmaier (2000). “Economic Determinants of Electoral
Outcomes”, Annual Review of Political Science, 3, pp. 183-219.
[12] Marshall , M. G., and K. Jaggers. (2002). Polity IV Dataset. [Computer file; version
p4v2002] College Park, MD: Center for International Development and Conflict Management, University of Maryland.
[13] Nordhaus, W. D., A. Alesina, and C. L. Schultze (1989). “Alternative Approaches to the
Political Business Cycle”, Brooking Papers on Economic Activity, 1989 (2), pp. 1-68.
[14] Paldam, M. (1986). “The Distribution of Electoral Results and the Two Explanations
of the Cost of Ruling”, European Journal of Political Economy, 2, pp. 5-24.
[15] Paldam, M. (1991). “How Robust Is the Vote Function? A Study of Seventeen Nations
over Four Decades”, Economics and Politics. The Calculus of Support. Edited by H.
Norpoth, M. S. Lewis-Beck, and J. D. Lafay. The University of Michigan Press, Ann
Arbor.
[16] Persson, T., and G. Tabellini (1999). “Political Economics and Macroeconomic Policy”,
Handbook of Macroeconomics, Volume 1C, edited by J. B. Taylor and M. Woodford,
1999, pp. 1397-1482.
[17] Persson, T., G. Tabellini, and F. Trebbi (2003). “Electoral Rules and Corruption”,
Journal of the European Economic Association, 1(4), pp. 958-989.
[18] Persson, T., G. Roland, and G. Tabellini (1997). “Separation of Powers and Political
Accountability”, Quarterly Journal of Economics, 112, pp. 1163-1202.
19
[19] Peters, J. G., and S. Welch (1980). “The Effects of Charges of Corruption on Voting
Behavior in Congressional Elections”, The American Political Science Review, 74(3),
pp. 697-708.
[20] Powell Jr., G, B., and G. D. Whitten (1993). “A Cross-National Analysis of Economic
Voting: Taking Account of the Political Context”, American Journal of Political Science, 37(2), pp. 391-414.
[21] Rundquist, B. S., G. S. Strom, and J. G. Peters (1977). “Corrupt Politicians and their
Electoral Support: Some Experimental Observations”, The American Political Science
Review, 71(3), pp. 954-963.
[22] Treisman, D. (2000). “The Causes of Corruption: A Cross-National Study”, Journal of
Public Economics, 76, pp. 399-457.
[23] Wittman, D. (1989). “Why Democracies Produce Efficient Results”, Journal of Political
Economy, 97(6), pp. 1395-424.
20
Table 1: Summary Statistics (Averages per government period for each country)
Argentina
President
3.69
Votes for
Incumbent
(%)
44.00
Austria
Parliament
10.00
32.23
7.72
1.80
Belgium
Parliament
10.00
15.25
6.64
2.04
1.70
10.50
1.00
58.66
78.26
Brazil
President*
4.67
46.43
5.82
0.95
13.06
5.33
0.25
60.51
0.00
4
Canada
Parliament
10.00
38.35
8.42
2.61
1.83
7.67
0.25
56.38
0.00
4
Country
System
Dem.
Index
Corruption
Perception
Index
5.07
GDP
Growth
(%)
1.04
Consumer
Inflation
(%)
0.76
10.00
1.00
62.12
0.00
2
1.85
14.00
0.40
60.38
45.04
5
3
Years in
power
Party
Ideology
Incumbent
Seats (%)
Fractionalization (%)
# of
Elections
Chile
President
4.42
49.97
8.21
3.54
4.87
13.00
1.00
55.00
0.00
3
Colombia
President
6.58
57.63
6.90
2.31
6.11
4.00
0.50
10.00
0.00
2
Costa Rica
President
10.00
31.90
6.64
2.45
11.08
6.00
0.67
40.35
0.00
3
Denmark
Parliament
10.00
32.67
8.13
1.70
2.14
4.67
0.50
44.57
36.84
4
El Salvador
President
3.11
54.84
4.35
0.44
2.10
15.00
1.00
57.14
0.00
2
4
Finland
Parliament
10.00
24.40
7.80
3.15
1.28
5.33
0.50
66.88
67.25
Germany
Parliament
10.00
38.12
6.20
1.28
1.47
9.00
0.50
51.01
32.69
4
Greece
Parliament
7.16
42.43
6.72
3.43
3.87
9.00
0.00
53.15
0.00
3
Guatemala
President
2.87
33.51
5.65
0.17
6.78
4.00
0.50
55.75
0.00
2
Honduras
President
2.98
48.81
5.73
0.64
9.80
6.00
1.00
50.00
0.00
3
4
Hungary
Parliament
2.58
36.93
6.15
4.03
12.71
4.00
0.00
59.58
34.25
Japan
Parliament
9.45
34.01
6.32
1.12
-0.04
6.67
0.75
54.12
16.25
4
Mexico
President
1.55
41.03
6.22
1.39
13.46
15.50
0.33
42.18
0.00
3
Netherlands
Parliament
10.00
25.37
8.86
1.81
2.26
5.33
0.50
59.33
61.16
4
Norway
Parliament
10.00
22.23
6.96
2.50
2.01
5.33
0.50
34.54
35.86
4
Portugal
Parliament
4.98
39.78
7.20
1.65
3.09
5.33
0.50
50.07
6.98
4
3
Spain
Parliament
4.65
41.38
5.44
2.69
2.85
6.00
0.67
52.00
12.47
Sweden
Parliament
10.00
38.90
7.52
2.54
1.08
8.00
0.25
51.81
29.17
4
Switzerland
Parliament
10.00
22.53
6.95
0.87
0.75
6.00
0.33
82.36
74.17
3
3
Turkey
Parliament
7.13
14.08
3.65
1.55
69.18
3.50
0.33
50.81
56.13
United Kingdom
Parliament
10.00
39.95
6.10
2.42
2.54
6.00
0.33
63.19
0.00
3
United States
President
10.00
49.06
6.39
2.25
2.35
6.00
0.33
50.52
0.00
3
Uruguay
President
6.31
26.57
3.75
0.20
15.85
7.50
0.67
91.12
0.00
3
Grand Average/Total
-
35.33
6.70
1.97
6.50
7.37
0.49
54.81
24.36
93
* Mixed system with presidential elections.
21
Table 2: Average Corruption Perception Index across Elections
Average Corruption
Perceptions Index
No. of countries
Parliamentary
System
Presidential
System
Mature
Democracies
New
Democracies
6.87
5.88
7.19
5.78
17
11
14
14
22
Table 3: Dependent variable: Change in votes of incumbent
(Entire Sample)
Economic and
Political
Political
variables only
Variables
Economic
variables only
GDP growth
and inflation
only
-3.306
(0.00)
-3.318
(0.00)
Perceived
Corruption Increase
-2.899
(0.00)
GDP growth
2.252
(0.00)
2.497
(0.00)
2.501
(0.00)
Inflation
-0.154
(0.00)
-0.142
(0.00)
-0.142
(0.00)
Unemployment
0.132
(0.65)
0.013
(0.96)
Votes incumbent
(previous election)
-0.006
(0.98)
0.017
(0.94)
Years in power
-0.060
(0.74)
-0.005
(-0.98)
Party Ideology
-0.580
(0.77)
-2.893
(0.16)
Incumbent seats
-0.067
(0.50)
-0.116
(0.27)
Fractionalization
0.058
(0.44)
0.064
(0.41)
R-squared
0.294
0.142
0.258
0.258
F-statistic
4.00
(0.00)
2.51
(0.03)
8.90
(0.00)
12.07
(0.00)
-3.105
(0.01)
Sample size for all specifications is 93 elections and 28 countries. p-values computed using
robust standard errors are in parentheses.
23
Table 4: Dependent variable: Change in votes of incumbent
(Pooled regression: Parliamentary vs. Presidential Systems)
Parliamentary System
Presidential System
All variables
Econ. Var.
All variables
Econ. Var.
Perceived
Corruption Increase
-2.909
(0.01)
-2.712
(0.00)
-5.080
(0.17)
-5.080
(0.09)
GDP growth
2.151
(0.00)
2.102
(0.00)
2.658
(0.35)
3.051
(0.18)
Inflation
-0.144
(0.00)
-0.115
(0.00)
-0.256
(0.56)
-0.413
(0.28)
Votes incumbent
(previous election)
-0.290
(0.08)
0.388
(0.61)
Years in power
0.100
(0.58)
-0.235
(0.62)
Party Ideology
-0.502
(0.78)
2.105
(0.82)
Incumbent seats
0.015
(0.19)
0.001
(0.99)
Fractionalization
-0.048
(0.46)
R-squared
0.434
0.375
0.179
0.132
F-statistic
4.32
(0.00)
11.27
(0.00)
1.01
(0.47)
1.77
(0.20)
No. of countries
17
17
11
11
No. of elections
63
63
30
30
p-values computed from robust standard errors are in parentheses.
24
Table 5: Dependent variable: Change in votes of incumbent
(Pooled regression: “Mature” vs. “New” Democracies)
Mature Democracies
New Democracies
All variables
Econ. Var.
All variables
Econ. Var.
Perceived
Corruption Increase
-0.519
(0.77)
-0.824
(0.57)
-2.894
(0.07)
-3.393
(0.01)
GDP growth
1.001
(0.33)
0.956
(0.24)
3.108
(0.00)
3.651
(0.00)
Inflation
-1.846
(0.08)
-1.961
(0.05)
-0.178
(0.07)
-0.103
(0.01)
Votes incumbent
(previous election)
-0.034
(0.87)
-0.242
(0.46)
Years in power
-0.067
(0.79)
-0.122
(0.78)
Party Ideology
-0.100
(0.97)
0.477
(0.89)
Incumbent seats
0.061
(0.53)
-0.175
(0.16)
Fractionalization
0.002
(0.97)
-0.023
(0.52)
R-squared
0.406
0.393
0.521
0.448
F-statistic
0.95
(0.50)
1.82
(0.16)
9.98
(0.00)
25.67
(0.00)
No. of countries
14
14
14
14
No. of elections
52
52
41
41
p-values computed from robust standard errors are in parentheses.
25
Table 6: Dependent variable: Change in votes of incumbent
(Random effects regression)
Parliamentary
System
Presidential
System
Mature
Democracies
New
Democracies
Perceived
Corruption Increase
-2.712
(-0.03)
-5.080
(0.24)
-0.824
(0.66)
-3.362
(0.09)
GDP growth
2.102
(0.00)
3.051
(0.23)
0.956
(0.34)
3.680
(0.00)
Inflation
-0.115
(0.03)
-0.413
(0.41)
-1.961
(0.00)
-0.103
(0.30)
R-squared
0.375
0.132
0.393
0.448
Wald chi-squared
25.15
(0.00)
2.29
(0.52)
21.98
(0.00)
16.49
(0.00)
No. of countries
17
11
14
14
No. of elections
63
30
52
41
p-values are in parentheses.
26