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The two sides of state capacity: how much is necessary to manipulate an election?

Jessica Fortin GESIS – Leibniz Institut für Sozialwissenschaften

Postfach 12 21 55 D – 68072 Mannheim

Germany +49.621.1246-514

jessica.fortin@gesis.org

Work in progress; please do not quote without permission

Abstract While effective state capacity can reasonably be considered a necessary condition for democratization, strong states do not automatically produce democratic regimes, nor do they guarantee their survival. Far from being sufficient conditions for democracy, strong or capable states are also thought to be indispensable for the maintenance of stable autocratic rule (Way, 2005). Indeed, most literature linking levels of corruption or state capacity and democracy have found J-shaped, or curvilinear relationships between democracy and administrative performance. Not only does the establishment of nondemocratic rule require an important level of elite organization and state capacity, central control and elite cohesion are also required to eliminate uncertainty during elections, to ensure compliance and prevent opposition activity across a wide territory. The present paper proposes to focus on this aspect and put to the test the hypothesis that a certain amount of state capacity is needed to engage in effective electoral malpractice, using various indicators of state capacity and the data from several sources such as Electoral Malpractice and Electoral Manipulation in New and Semi-Democracies dataset (Birch 2010), the Quality of Elections Data (QED) (Kelley, 2010, Kelley, 2012, Kelley and Kolev, 2010), The National Elections across Democracy and Autocracy (NELDA) (Hyde and Marionov, 2011).

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1. INTRODUCTION

The connection between a strong state and the establishment of democratic institutions

is becoming a well established finding (Bunce, 2000, Fortin, 2012, Fukuyama, 2004, Hashim,

2005, Hendley, 1997, Holmes, 1997, Huber et al., 1999, 1999, p. 2, Linz, 1996, O'Donnell,

1993, Przeworski, 1995, Roberts and Sherlock, 1999, Sharlet, 1998, Suleiman, 1999, Tilly,

2007, Wang, 1999). While effective state capacity can reasonably be considered a necessary

condition for democratization, strong states do not automatically produce democratic regimes,

nor do they guarantee their survival. Far from being sufficient conditions for democracy,

strong or capable states are also thought to be indispensable for the maintenance of stable

autocratic rule (Way, 2005). The establishment of nondemocratic rule also requires an

important level of elite organization and state capacity to eliminate uncertainty during

elections, to ensure compliance and prevent opposition activity across a wide territory. But

does electoral fraud really hinge on a strong state? What kind of state infrastructures must be

at the disposal of leaders to use tools related to election fraud to win elections?

The present paper proposes to put to the test the hypothesis that a certain amount of

state capacity is needed to engage in effective electoral malpractice, using various indicators

of infrastructural and coercive state capacity as well as figures from the Quality of Elections

Data (QED) (Kelley, 2010, Kelley, 2012, Kelley and Kolev, 2010), The National Elections

across Democracy and Autocracy (NELDA) (Hyde and Marionov, 2011), and the Electoral

Malpractice Project (Birch, 2009). The hypotheses developed and tested in this paper propose

two opposing mechanisms through which state capacity influences the quality of elections:

through infrastructural state capacity and coercive state capacity. Seemingly these two aspects

of state capacity operate according to opposing logics and affect different dimensions of the

electoral process. The paper focuses on the manipulation of the electoral process in general,

but also when disaggregated in different categories such as unfair media coverage, incomplete

or manipulated voter lists, falsification in ballot tabulation, vote-buying, as well as voter and

candidate intimidation.

Using 26 post-communist countries over the period 1989-2009, the main empirical

finding is that electoral fraud is more likely in countries where infrastructural state capacity is

weak; however the analyses also suggest that there is not a single answer to the question of

what causes electoral malpractice. Structures underlying electoral malpractice vary by the

type of electoral fraud under consideration; the effects of the independent variables

considered in this paper vary substantially from voter and opposition intimidation, vote-

buying, electoral list manipulation and ballot tempering. The remainder of the paper proceeds

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as follows. The first part outlines the theoretical underpinning of the paper and the hypotheses

developed. The second section presents the methodology employed and details the variables

and measurements. The third part presents the empirical findings. I conclude with a

discussion of the results and implications for future research.

2. ELECTORAL MISCONDUCT IN POSTCOMMUNIST COUNTRIES

While the majority of countries in the world cannot be considered liberal democracies,

almost all hold regular elections to fill the positions of heads of government and state. As

reported by Bunce and Wolchik (2011), by 2002, only eight countries out of 190 did not hold

elections. Elections have become a standard feature of both democratic and authoritarian rule.

What sets countries apart is the quality of the electoral contests they hold: how inclusive (who

is allowed to vote?), pluralistic (who is allowed to run?), competitive (who it allowed to

win?), and open (presence or absence of repression) electoral contests are determines how

democratic access to power is (Schedler, 2006). Given unequal advancements in democracy

in former communist countries, there is considerable variation in the quality of elections

taking place. While elections in East Central Europe have been mostly free and fair from the

beginning of the 1990s, competition has remained severely constrained in most former Soviet

republics. In the countries of the Commonwealth of Independent States (CIS) in particular,

power holders have made use of a wide range of illegitimate tools to attempt to hold on to

power since 1991; from the more obvious ballot stuffing and vote-buying, to the more subtle

opposition intimidation tactics and unfair media coverage.

Electoral malpractice, or fraud, defined as “clandestine efforts to shape election

results” (Lehoucq, 2003) is widespread among post-communist countries. A large body of

evidence accumulated by observation missions such as the OSCE indicate that current

incumbents use unfair techniques to increase their vote shares on a regular basis. In all

national legislative and presidential elections that took place in 28 former communist

countries between 1989 and 2004, only 26 percent of contests were free of political and

administrative problems or malpractice in the pre-election period, with regard to the legal

framework, or the integrity of the process on the election day itself according to the data base

Quality of Election Data (QED) (Kelley, 2010) (figures presented in table 1). Alternatively,

close to 50 percent of national elections held between 1989 and 2004 presented moderate to

major problems. The figures presented in Table 1 show just how prevalent electoral

malpractice is among postcommunist countries.

[TABLE 1 ABOUT HERE]

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Despite the frequency of incidents linked to fraud across elections, trends in electoral

manipulation do not seem to display a time dependency. Figure 1 presents the percentage of

election fraud between 1989 and 2004. The vertical axis measures the percentage of elections

in a given year that were judged ambiguous or unacceptable by the U.S. State Department

(reported in the QED data); this number varies from a single election in 1989 to 18 elections

in 2000. Figure 1 does not exhibit a clear development towards more, or less episodes of

electoral manipulation over time, when all categories of problems cited above are taken

together. When considered alone, the longevity of regimes does not affect the likelihood of

electoral malpractice in a particular direction. Under such conditions, it appears that the

incentives ruling elites face to shape the results of elections through extra-legal means do not

change as state institutions become more stable, and (authoritarian) power holders become

more established and secure.

[FIGURE 1 ABOUT HERE]

One of the reasons explaining the frequent use of electoral manipulation by

incumbents is that these tools are effective in ensuring victory (Donno and Roussias, 2012,

Simpser, 2008), despite the risks associated with democratizing uprisings following too

conspicuous attempts to temper with results (bearing in mind the large protests organized by

the opposition in Armenia, Azerbaijan, Belarus, Georgia, Ukraine, Kyrgyzstan, and more

recently Russia). In the countries included in this paper, all the elections that were judged

unacceptable by the U.S. State Department were unsurprisingly won by incumbents, as well

as the majority of elections deemed ambiguous (Table 2). Notwithstanding a few exceptions,

where power-holders were ousted in post-electoral protests, having recourse to electoral

manipulation, or electoral fraud is a strategy that clearly paid off for incumbents seeking to

hold on to power between 1989 and 2004 in post-communist countries.

[TABLE 2 ABOUT HERE]

The bulk of episodes of massive electoral fraud are found in the Caucasus, Central

Asia and Serbia pre-1998, which are the states also displaying the weakest levels of

infrastructural state capacity during the period under study (Fortin, 2010). Why is it, then, that

the majority of the most conspicuous electoral manipulation episodes recorded was in the

states with the weakest state infrastructure? Is political manipulation different from technical

incapacity?

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3. WHAT FACILITATES ELECTORAL FRAUD?

The literature addressing the causes of electoral fraud branches in two directions. A

first approach concentrates on the incentives candidates face to engage in corruption, treating

electoral fraud as the result of the decision of individual candidates rather than a top down

mechanism controlled by an incumbent or a ruling party. Mainly directed at established

democracies, this literature usually takes the quality of electoral institutions for granted. A

second branch of the literature addresses the phenomenon of electoral fraud from a top down

perspective, focusing on power holders’ incentives to engage in massive fraud to retain power

in competitive authoritarian regimes. While both branches deal with the incentives that make

agents more likely to bend or break the rules of the game to boost their votes, few studies

have focused on the conditions that make electoral manipulation possible. A few studies have

stressed the high potential costs of certain fraudulent activities such as vote-buying (Lehoucq,

2003, Lehoucq and Molina, 2003, Molina and Lehoucq, 1999, Schaffer, 2007, Schaffer and

Schedler, 2007, Wang and Kurzman, 2007) and the need for high cohesion within the state

apparatus and control over the state’s coercive means to manipulate an election (Way, 2003,

2005, Way and Levitsky, 2006), but have not proposed empirical verifications of these

hypotheses. The boundaries between intentional manipulation and technical (in)capacity

remain largely uncharted (Pastor, 1999).

The largest part of contributions focus on the institutional features which have built-in

incentives for candidates to engage in corruption in (established) democracies: some

structural elements such as electoral rules seem to facilitate, or constrain, certain types of

behaviour on the part of candidates and parties, that are associated with cultivating a personal

vote such as violence intimidation and vote-buying. Properties of electoral laws like district

size and electoral formula influence candidates’ choice of strategy to engage in illegal

behaviour or not: whether citizens vote for parties or candidates, and the level of intra party

competition being the defining features shaping these strategies (Carey and Shugart, 1995,

Hicken, 2007, Katz, 1986, Nyblade and Reed, 2008). However the findings are not

unanimous on the role of PR—and especially on the type of lists—in shaping incentives to

break the rules. A branch of research finds that closed-list PR systems are more prone to

corruption (Kunicova and Rose Ackerman, 2005, Persson and Tabellini, 2003), while other

contributors find that closed-list PR systems are less vulnerable to corruption than

majoritarian systems because they reduce the incentives to cultivate a personal vote (Birch,

2007b, Carey and Shugart, 1995, Chang, 2005, Chang and Golden, 2006).

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In emerging democracies the literature explaining electoral malpractice focuses on

other elements, such as the closeness of races, or inter-party competition, which in addition to

the factors mentioned above seem to shape the incentives for power holders to engage in

electoral fraud. In some of the scholarship on corrupt elections, electoral misconduct is seen

as a tool used to secure victory in close races (Argesinger, 1985, Cox and Kousser, 1981,

Lehoucq, 2003, Lehoucq and Molina, 2003, Mares and Zhu, 2011). While the idea that

electoral fraud is only used to secure numerical victory is intuitive, it has been challenged in

recent contributions. Authoritarian leaders also employ manipulation to increase their margins

of victory in asymmetrical races to discourage opposition parties from forming and running,

as well as voters to support them (Donno and Roussias, 2012, Simpser, 2008). For example as

outlined by Simsper (2008), in the 2000 presidential election in Georgia, rather than to be

content with an estimated 33 point advance over his adversary, Shevardnadze manipulated the

election to finally achieve 80% of all votes. Such large scale manipulation of results to

achieve “landslide” victories is not uncommon in former Soviet republics.

Despite the structure of incentives, do authorities in every country have the same tools

at their disposal to manipulate elections? While the contributions cited earlier in this section

illuminate the incentives which candidates face to engage in electoral manipulation, few

studies examine the infrastructures that make it possible for incumbents to have access to the

full menu of manipulation, and the existing contributions remain quite general about what

kind of infrastructure is necessary. A common assumption is that some top down control of

key state agencies and the control over sufficient means (coercive force, state finances) are

necessary for effective use of elections as instruments to confer legitimacy to authoritarian

rule. In other words, a certain level of state capacity is required to manipulate elections, but

what kind of capacity? Infrastructural, coercive, or both?

3.1 Types of state capacity: infrastructural vs. coercive

Michael Mann (1993), proposes a typology of state power as based on two

components. One is the infrastructural and administrative aspect, and the other is the despotic

power of the state. The former highlights the state’s capacity to achieve specific goals and

implement its policies. At the core of the infrastructural aspect of state power is the question

of the state’s authority over territory (Migdal et al., 1994). This understanding of state

capacity can be operationalized by examining the quality of public goods distribution in a

country, given the crucial role of public goods in establishing any state’s authority on

territories (Spruyt, 1994). By contrast, despotic power “derives from the range of actions that

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state elites can undertake without routine negotiation with civil society groups” (Mann, 1993,

p.59). The state’s ability to exert coercion hinges on this second type of power, where rulers

do not need the consent of people to rule. Hence states can be strong in two different ways

(Mann, 2008)

This paper proposes two mechanisms by which state capacity influences the quality of

elections: through infrastructural state capacity and coercive state capacity. On the one hand,

high infrastructural state capacity should serve as a constraining mechanism against most

categories of electoral fraud. If stronger rule of law curbs corruption by virtue of raising the

likelihood and costs of being caught (Ades and Di Tella, 1997, Damania et al., 2004), a

similar argument can be extended to the phenomenon of electoral fraud since both concepts

overlap. Electoral fraud is a form of corruption of the democratic process because it serves to

reduce the fairness of elections (Warren, 2004, Warren, 2006). The risk of reprisal shapes the

types of electoral fraud candidates decide to engage in: the existence of bodies supervising the

elections, the independence of these bodies, the likelihood of being detected and size of the

penalties.

When a state’s infrastructure is weak, power holders are freer to use the resources of

the state to buy electoral support. In the group of countries considered in this paper, those who

inherited weak state infrastructures after the collapse communism faced the most obstacles in

establishing functioning institutions of oversight: functioning agencies overseeing

privatization, courts staffed with competent personnel, law enforcement, and - most important

for the case at hand - electoral managements bodies (EMBs) (Fortin, 2012). High levels of

infrastructural state capacity should have positive impacts on the quality of EMBs.

Independent, impartial, and transparent regulatory and administrative electoral agencies are

crucial in providing credible institutional structures in which elections can take place (Birch,

2007a, Goodwin-Gill, 1994, López-Pintor, 2000, Pastor, 1999). EMBs also influence the

quality of campaign resource regulations and the potential for electoral bodies to monitor

candidates and enforce compliance with rules.

A growing literature links the establishment of independent, or multi-party nominated

electoral commissions with improved election quality in Africa, Latin America and former

communist countries (Birch, 2007a, Hartlyn, 1994, Mozaffar, 2002). Such effective EMBs

supervise and monitor the electoral process to diminish the risk of misuse of resources, ensure

fair media coverage, but also supervise the produce of up to date and accurate voter lists

which can be expensive. For example, in the early 2000s, the U.S. Agency for International

Development spent US$1.5 million to computerize Georgia’s voter list, which was rejected at

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the last minute by the Georgian Central Electoral Commission claiming it contained mistakes.

The Central Electoral Commission—loyal to Shevardnadze— then imposed a list that

excluded hundreds of thousands and contained convenient additions (Fairbanks, 2004). In

short, such institutions would serve to thwart decentralized activities performed by several

private and public agents like electoral lists manipulation and falsifying ballots on election-

day, for example. Higher infrastructural state capacity should therefore be linked with low

incidences of electoral fraud.

On the flip side, we can also hypothesize that coercive state capacity is required for

some aspects of electoral malpractice. A common line of argumentation posits that some of

the types of electoral fraud such as vote-buying and the use of violence and intimidation on a

large scale require substantial logistic and material costs (Lehoucq, 2003, Lehoucq and

Molina, 2003, Molina and Lehoucq, 1999, Schaffer, 2007, Schaffer and Schedler, 2007,

Wang and Kurzman, 2007). The establishment and maintenance of nondemocratic rule

requires an important level of elite organization and state capacity. Way, and later Levistky

and Way (2003, 2005, 2006) maintain that the creation of an authoritarian regime requires a

high degree of elite cohesion and some top-down control on the part of the government in

order to manipulate the electoral process:

“Central control and elite cohesion are also required to eliminate uncertainty during elections In the current international environment, manipulating elections involves being able to use the cover of laws and regulations to fragment, intimidate, and marginalize the opposition before it grows strong enough to mount a challenge on election day.” (Way, 2003, p.134).

Way and Levistky argue that the administration of coercion necessary to keep authoritarian

regimes going requires a certain form of cohesion within the state apparatus; among leaders,

police, military, and bureaucrats so that decisions—even coercive ones related to vote fraud—

can be enacted. Instances of voter intimidation and or obstruction, and candidate intimidation

and obstruction pertain directly to a state’s coercive branch. In general, it is the state’s

coercive arms, the police, security service, and military, which intervene to intimidate voters,

or obstruct campaigns efforts. Even the most uncompetitive electoral contest put on display

the coercive power of the state; “at one extreme are single-candidate plebiscites, which clearly

demonstrate the regime’s coercive power: that it can compel people to participate in a ritual

that everyone knows is “fake”” (Gandhi and Lust-Okar, 2009, p.406). In authoritarian

regimes, given that incumbents have control over state resources and means of coercion they

have a freer hand to use these tools.

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While most contributors agree that a certain amount of state capacity is needed to

engage in effective electoral malpractice, and that some infrastructural capacity is needed to

run elections, there exists to date no empirical verification about what type of state capacity is

needed, nor of the magnitude necessary. The remainder of the paper will focus on covering

this gap by proposing comparative empirical verification. I propose testing the following

hypotheses; weak infrastructural state capacity should be conducive to high levels of electoral

fraud, while high coercive power should also be linked to large levels of electoral

manipulation.

4. DATA AND METHODOLOGY

The hypotheses proposed above will be tested using a group of 26 postcommunist

countries from the first post-communist elections onwards.1 The countries considered are:

Albania, Armenia, Azerbaijan, Belarus, Bulgaria, Croatia, Czech Republic, Estonia, Georgia,

Hungary, Kazakhstan, Kyrgyzstan, Latvia, Lithuania, the Former Yugoslav Republic of

Macedonia (FYROM), Moldova, Mongolia, Poland, Romania, Russia, Slovak Republic,

Slovenia, Tajikistan, Turkmenistan, Ukraine, and Uzbekistan. Although interesting cases, I

was not always able to include Bosnia Herzegovina, Serbia and Montenegro in the present

study, due to very a very large amount of missing values on crucial variables. Taken together,

these 26 countries represent an ideal testing ground by offering a relatively controlled

environment concerning the development of their states and their constitutions (timing). Yet

we also observe large variations on both independent and dependent variables under study

which should enable us to verify the hypotheses using a full range of possible options.

The empirical verification will proceed in three steps. In a first step, I present bivariate

graphical evidence to illustrate the effects of infrastructural and coercive state capacity on the

general incidence of election fraud in the selected countries. In a second step, I use a series of

multivariate models to estimate the impact that infrastructural and coercive state capacity

might have on the general concept of electoral fraud. Third, to verify the robustness of these

findings, I disaggregate the general concept of electoral fraud in a set of more precise

categories to probe whether the effects of different types of state capacity operate in the same

way across a broad range of categories of electoral fraud.

4.1 Measuring the dependent variables: varieties of electoral malpractice

1 The unit of analysis considered, however is elections, not countries. This study takes into consideration both legislative (lower house) and presidential elections (when presidents are directly elected).

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Despite the extra-legal and covert aspect of the phenomenon, there is an increasing

amount of scholarship on electoral malpractice which employs comparative empirical

verifications. Given the rising interest in such data, a number of projects offering detailed

information on electoral processes around the world are now available to researchers. For

instance, the Electoral Malpractice and Electoral Manipulation in New and Semi-Democracies

dataset (Birch, 2009), the Quality of Elections Data (QED) (Kelley, 2010, 2012, Kelley and

Kolev, 2010), the National Elections across Democracy and Autocracy (NELDA) (Hyde and

Marionov, 2011) provide a rich empirical catalogue detailing both the types of practices and

the occurrence of electoral fraud around the world which this paper will take advantage of.2

When we open the conceptual box of electoral fraud, we find a wide ranging type of

ways candidates and parties can rig the electoral process during the campaign, on election-

day, and after elections. Although there are a few competing conceptualization of the

phenomenon of electoral fraud (Birch, 2008, Calingaert, 2006, Elklit and Reynolds, 2005,

Schedler, 2002), this paper uses adapts the three categories proposed by Ziblatt (2009), more

or less faithfully. In the following analyses, I will make use of both general measurements of

electoral fraud (as provided by and a series of indicators for the different categories of the

larger concept.

A first category of electoral fraud pertains directly to coercion, where threats or

different methods are used to compel voters to vote for certain parties or candidates, but also

where power holders use intimidation tactics against the opposition to prevent them from

running. These will be measured by the following indicators: if there is evidence of voter

intimidation and obstruction (EMD), if there is evidence that the government harassed the

opposition (EMD), and weather opposition leaders prevented from running (EMD). A second

category is vote-buying, whether this is used for depressing votes or turnout. This will be

captured by an indicator evaluating the occurrence of vote-buying from EMD. The third

category regroups a few types of systematic procedural violations, that is, vote rigging,

polling station opening hours and location, registration roll tempering, but also the fairness of

the election coverage in the press. This last category of procedural violations being more

heterogeneous, I retained the following variables to tap into ins general meaning; the

improper use of campaign funds (QED), records of election day explicit cheating which is an

indicator that captures fraud related to the casting and tabulation of votes (QED), and last, I

include an indicator measuring the fairness of the media coverage during the campaign

(EMD).

2 Other data projects such as

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While this selection of indicators promises broad empirical coverage of different

varieties of electoral fraud, the items’ level of measurement limits the type of inference this

paper will be able to draw. As documented in other studies, electoral malpractice is likely a

continuous variable displaying more or less intensity, but available measurement does not

capture degrees (Donno and Roussias, 2012). In most cases the indicators are either ordinal,

or dichotomous, which confines the empirical verifications presented in this paper to

likelihood statements, and makes the evaluation of the substantive size of the effects of the

independent variables difficult to estimate.

4.2 Infrastructural State capacity

In this paper, the concept of state capacity will be measured via proxy of the quality of

the provision of public goods given their crucial role in establishing state authority on

territories (Spruyt, 1994). The approach to measurement hinges on a minimal definition of the

concept of state capacity in terms of infrastructural power, where infrastructural power is

defined as “the institutional capacity of a central state, despotic or not, to penetrate its

territories and logistically implement decisions” (Mann, 1993). This understanding of state

capacity is operationalized by examining the quality of provision of a class of collective

goods even a minimally-redistributive state should theoretically provide, as proposed by

recent work (Fortin, 2010). Rather than focusing on the measurement of absolute resources,

the measurement assumes that it is insufficient that a state should possess important human

and natural resources for the production of wealth: it must also be able to effectively make use

of those resources. This intentionally leaves aside the redistributive, despotic or coercive

power of states, since such aspects of power do not necessarily go hand in hand with

infrastructural power, although they sometimes do.

Some of the difficulty associated with this approach lies in finding not only

measurable and comparable instances of public-goods delivery, but also relevant data

documenting their existence and performance in the early years of transition, which is not

always simple. The index is based on the aggregation of five indicators: the ratio of tax

revenue to GDP in order to illustrate state taxing capacity, progress in infrastructure reform

(EBRD), levels of corruption (Heritage Foundation), the quality of property rights protection

(Heritage Foundation), and the ratio of contract intensive money (CIM), which are public

goods even a minimally redistributive state should be able to provide. To make the final

index, all indicators were standardized, and aggregated as a single measure. Factor and

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correlation analyses reveal that the association among the items is strong and that they

measure a similar latent construct.3

4.3 Coercive State Capacity

The concept of coercive state capacity will be measured by two quantitative indicators

taping into the phenomenon of tangible use of repression on civilians, and the latent potential

for a state to employ repression. According to Wintrobe (1998) one of the repressive tools

employed by dictators is to ban groups, association, and political parties opposed to the

government. The dictator must ensure that the population is monitored and sanctioned for

these measures to be effective: “The existence of a political police force and of extremely

severe sanctions for expressive and especially for organizing opposition to the government

(such as imprisonment, internment in mental hospitals, torture, and execution) is the hallmark

of dictatorships of all stripes” (Wintrobe, 1998, p.34). To tap into this concept of overt

repression, I will use the Physical Integrity Rights Index from the CIRI Human Rights Data

Project (Cingranelli and Richards, 2010). This measure is an additive index composed of

instances of politically motivated disappearances, extrajudicial killings by government

officials without due process of law, the politically motivated incarceration of people by

government officials, and the use of torture on political opponents, which are at the core of

the concept of coercive state capacity.

One drawback of this measurement is that it only captures the instances where states

actually make use of these tactics; it does not capture those in which the state actually

possesses the capability to do so, but where leaders chose not to for various reasons. Given

this negative aspect, I buttress the measurement of the concept of coercive power by using a

second indicator, a measure of size of military per 100 inhabitants, which is drawn from the

Correlates of War Project (Singer et al., 1972, Singer and Small, 1995).4 This

3 Despite being high in validity and reliability, the index of state capacity I employ as an independent variable in the following models suffers from an important weakness: missing data in the early years of transition. only 5% of cases are missing To mitigate the problem of missing data, I chose to impute a portion of the cases (for tax revenue and contract intensive money) using Amelia II (2001), the best option available to generate plausible and unbiased results (Allison, 2000). In the case of corruption levels, and the quality of private property protection, I chose a more conservative data interpolation technique where starting values are similar to the first year of actual data coverage by Heritage Foundation (in most cases around 1995). The final index of state capacity therefore contains both actual measurements and estimated measurements of individual indicators and should therefore be handled and interpreted with a measure of prudence in the very early years 4 As Albertus and Menaldo (2012) noted, the variable is right-hand skewed and they chose to correct the distribution through a natural logarithm power transformation. However in the present case, analyses show that a logarithm transformation is less efficient than leaving the variable in its initial shape. Only square and cubic

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operationalization of the concept is one of the most common in studies focusing on the onset

of civil wars (Hendrix, 2010). The use of this second indicator employed is justified on the

assumptions that at least “among autocracies, the military is a good proxy for internal

repression” (Albertus and Menaldo, 2012, p.153). The size of conventional military forces is

admittedly a crude measurement, since it does not capture other types of repressive and

specialized agencies used to control political opposition such as paramilitary units, security

forces, and secret police, which are considered paramount to size a state’s capacity not only to

maintain order, but also to repress opponents (Gurr, 1988). However I believe that using these

two indicators in combination allows me to cover a large part of the conceptual space of the

concept of coercive state capacity.5

4.4 Control variables, alternative hypotheses

Several additional control variables were considered for inclusion in the empirical

models in order to rule out the effect of omitted, or rival, hypotheses on the dependent

variables. I considered a range of variables we might expect to impact the likelihood of

electoral fraud which are detailed below.

4.4.1. Socioeconomic factors. Socioeconomic factors are considered to affect the likelihood

of electoral fraud: behaviours like vote-buying were found to be more common in contexts

where poverty is prevalent (Anand, 2001, Brusco et al., 2002), even though the empirical

evidence is unclear. Hence the following analyses will include a control for the level of

development of each country measured by GDP per capita (World Bank, 2000 LCU). Another

socioeconomic control included is the level of urbanization, which also affects the candidate

strategies (Nielson and Shugart, 1999, Ramseyer and Rosenbluth, 1993), as well as the level

of inequality measured via GINI coefficients, to control for the effects of asymmetric

socioeconomic power on the incidence of electoral fraud (Ziblatt, 2009).

4.4.2. Institutions. A set of control variables is included to capture the effect of institutions on

the incentives to engage in electoral fraud. I include a dummy variable for the use of plurality

rules versus other types. Since legislative and presidential elections in countries with different

regime types have different importance for the exercise of power (for instance parliamentary

transformations could potentially slightly correct for the skewness. Since the correction would only be marginal, to ease interpretation, I will use the variable in its natural distribution. 5 Readers should not that CIRI’s Physical Integrity Rights Index is only weakly associated with the size of military forces per 100 inhabitants. Pearson’s r=-0.14 with 209 valid cases.

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elections in presidential regimes are less consequential than in a parliamentary regimes since

the executive is often not responsible to the assembly), I have computed a dummy variable to

capture if the prime position of power is at stake in the election. For presidential regimes,

presidential elections are coded 1, and for parliamentary regimes, legislative elections are

coded 1. This measures if power is really at stake in the election. I expect more fraudulent

activities when the stakes are highest.

4.4.3. Competition. Although we know that the form of competition, the closeness of races,

affects the incentives to engage in electoral fraud, I did not include a control many other

studies include for the following reason: “official election results are the combined outcome

of two unknown and unobservable variables-popular preferences and authoritarian

manipulation” (Schedler, 2006). A measure of the victor’s margin of victory would be an

endogenous predictor. In order to at least try to control for the competitiveness of the contest I

selected NELDA’s indicator of whether incumbents were confident of their victory prior to

the elections.6

4.4.4. Other controls. I include a control for the proportion of fuel exports to control for other

means regimes might have at their disposal to alter the electoral process since previous regime

has show that if the regime controls natural resources or other economic means, it should be

able to increase its spending (Morrison, 2009). Oil exports (measured as the percentage of oil

exports to total exports) will serve as a control variable for the incentives facing rulers to

engage in resources exploitation to extract rents and set up institutions that will facilitate this

purpose. The use of this variable is also drawn by Ross who argues that leaders in resource

rich countries make use of the rents to finance repression to stay in power (Ross, 2001). A

rough trend variable, the number of years elapsed since independence from communism, will

also serve to capture effects of maturation and regime development that could account for

some extent of variance in the dependent variables.

5. ANALYSES

What is the role of different types of state capacity on the quality of elections? Are

weaknesses in the key dimensions of state power leading to more or less electoral malpractice

in post-communist countries? Weaknesses in infrastructural state capacity indeed seem to be 6 Unfortunately this indicator contains few overlapping observations with other variables and could not be included in all models since it significantly reduced the already limited amount of cases.

15

related with higher levels of electoral fraud observed in elections. Figure 2 presents a bivariate

scatter plot between the level of infrastructural state capacity a variable combining the overall

quality of the elections (QED): the relationship between the two variables is non-trivial

(R2=0.53). The majority of cases of heavy tempering with electoral proceedings are located in

the upper right corner of the graph, in the states with lower levels of infrastructural state

capacity.

However, the relationship is not fully linear and presents some outliers, for instance

the Polish Sejm elections of 1989 and the 1997 presidential elections in Croatia where

massive electoral problems occurred in states with well functioning infrastructures. Although

they led to the installation of a non-communist prime minister and eventually of the demise of

the communist regime, Poland’s 1989 parliamentary elections were not competitive since

some blocks of seats were reserved in advance for certain preapproved groups and political

parties (Lewis, 1990). In Croatia, war-time leader Franjo Tuñman won both successive

presidential elections by large margins (56,7% in 1992 and 60,3% in 1997); Tuñman and his

party the Croatian Democratic Union (HDZ) ruled Croatia with a tight authoritarian grip until

his death in 1999. At the other extreme, we also notice that a weak state infrastructure should

not be automatically equated with electoral fraud. We find a large amount of cases in the

bottom left quadrant of Figure 2, which means that a strong state infrastructure is indeed no

sufficient condition to the holding of problem-free elections: Armenia’s very first presidential

elections held in 1991, for instance, are the only ones in the country’s history that were

largely perceived as free and fair by the international community (Fuller, 2008).

[FIGURE 2 ABOUT HERE]

[FIGURE 3 ABOUT HERE]

The link between coercive capacity and election quality reveals an inverse

relationship, as hypothesized in the previous section. Figure 3 presents a bivariate scatter plot

between of the proxy depicting the level of coercive state capacity and variable combining the

overall quality of the elections and the extents of problems identified (QED). Here as well, the

relationship between the two variables is sizeable (R2=0.34), and presents a large amount of

outliers including the Croatian presidential elections held in 1997. Although the climate of

coercion was among the lowest of the group of countries, the electoral process presented a

significant amount of problems. While most problem-free elections are located in the low

coercion environments, there is large variability in the level of physical integrity found in the

countries where elections were most problematic. This suggests either that the proxy selected

for the concept of coercive power does not cover the conceptual space of the concept, or that

16

crucial variables that have an impact on this relationship have been omitted. Further

verification of these findings will be established in the following section using multivariate

models.

5.1 Multivariate models

In the following analyses, I estimate a number of empirical models using varying

specifications to test the robustness of the state capacity hypotheses. I first present a series of

models testing the hypotheses on general measurements of electoral fraud, and then present

models verifying the hypotheses on subtypes of electoral malpractice: coercion, vote-buying

and procedural irregularities.

Table 3 reports the findings for a pooled analysis that covers the period between 1989

and 2004, for 26 post-communist countries. The following analyses draw on ordered logit

models for ordinal dependent variables with clustered robust standard errors, given that

QED’s “extent of problems” variable contains 4 ordinal categories.7 In Model 1 of Table 3, I

present a baseline model only including infrastructural state capacity and the two variables

measuring coercive state capacity. Model 1 substantiates the patterns displayed in Figures 2

and 3 concerning the effects of infrastructural versus coercive state capacity on the likelihood

of electoral fraud. The parameter estimate — presented in odds ratios in Table 3 — for

infrastructural state capacity is negatively linked with high levels in electoral manipulation.

The direction of the relationship is as expected between both indicators of coercive state

capacity and the dependent variable, although coercive state capacity measured in terms of

military personnel does not achieve statistical significance. Of importance, the effects

uncovered seem considerable in the case of infrastructural state capacity. For a one unit

increase in levels of infrastructural state capacity, the odds of high levels of problems versus

the combined lower categories decreases by about 90 percent, holding the other variables

constant.

[TABLE 3 ABOUT HERE]

The addition of several control variables in Model 2 does not significantly alter the

form of these relationships; here however, the coefficient for coercive state capacity measured

in terms of military personnel reaches an acceptable level of statistical significance. All three 7 The small sample and spread out categories make the estimates fragile. A series of Brant tests (Long and Freese, 2006) indicated that the parallel lines assumptions underlying ordered logistic regressions were violated. For verification, the following analyses were also performed using generalized ordered logit models for ordinal dependent variables (Williams, 2006). The results are sensibly the same. Additional testing reveals that the cut points separating the values of the dependent variables are statistically significantly different from each other in the three models contained in Table 3.

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indicators of state capacity present parameter estimates in the expected direction. For every

unit increase in infrastructural state capacity, the odds of high levels of electoral fraud versus

the combined lower categories are 0.068 times lower: the effect of this variable is

considerable. As well, every unit decrease in CIRI’s physical integrity rights index increases

the probability of high levels of problems in elections,8 while increases in military personnel

per capital is linked to high levels of problems in elections. As expected, coercive state

capacity and infrastructural state capacity function in opposing logics in the group of

countries under study.

Socioeconomic controls also mostly perform in the expected direction. Although

strongly linked with infrastructural state capacity, GDP per capita and the percent of urban

population present similar relationships as found in existing literature. Increases in levels of

development and urbanization are linked to lower likelihood of electoral malpractice. Yet, the

sign of the coefficient for the level if inequality in each case is puzzling; it seems, all else

being held constant, that inequality is negatively linked with the likelihood of important

election fraud. This counterintuitive finding is difficult to account for substantively. Given the

recent communist past of our cases, it could be that levels of inequality were more similar in

many countries during early transition years than were Länder in Imperial Germany over a

century ago, where this theory was originally tested (Ziblatt, 2009).

The two institutional control variables considered exert some explanatory power on

the dependent variable, which mirrors existing findings. The dummy variable measuring

usage of plurality electoral rules is a dominant influence. Majoritarian electoral contests

increase of the odds of electoral malpractice by a factor of 4. While the effect of this control

dwarfs other variables, it is unclear if its magnitude is driven by the composition of this

sample containing an abnormally large amount of presidential regimes, and thus presidential

elections (presidential elections employing majoritarian electoral rules), or if some intrinsic

features of electoral rules indeed facilitate power abuse.9 The second variable, whether an

election is held for the position holding most power in a country, is positively linked to the

likelihood of electoral fraud. This result suggests that incumbents invest more resources to rig

races in which the potential of losing is more consequential.

8 The physical integrity rights index ranges from 0 (no government respect) to 8 (full government respect). 9 Because of the strong correlation between electoral rules and regime type, I was not able to include a regime type control variable to these analyses. However, ne could hypothesize that the effects of regime types are encapsulated in both electoral rules and election for the most powerful position. As well, a set of dummy variables to control for election type (presidential, legislative, fused) either turnout out insignificant or was dropped due to collinearity.

18

In line with a number of previous studies, the coefficient for fuel export in percent

GDP is significant in the hypothesized direction. Despite this confirmatory result, predicted

probabilities (not shown) indicate that the effect of oil, when translated into resources for

political leaders, likely only operates at very high values of the variable and with a large

degree of uncertainty. Considered over its average range, the effect of this variable is

therefore likely trivial. Although modelled crudely in the form of a linear effect measured in

the number of years since the fall of each country’s communist regime, the effects of time

displays a positive coefficient with the extent of problems observed in postcommunist

elections, which is contrary to some of what is posited in the literature. For instance, Lindberg

finds that “repeated electoral activities create incentives for political actors fostering the

expansion and deepening of democratic values. In addition to improving the democratic

qualities of political regimes, a sequence of elections tends to expand and solidify de facto

civil liberties in society” (Lindberg, 2006). It seems that in the group of countries at hand, the

more elections are carried out, the higher the odds of electoral fraud. Although the effect of

this variable is not large, there could be a reverse process of authoritarian learning at work in

certain countries, where incumbents become more capable to make use of the repertoire of

electoral manipulations.

The addition of a control variable measuring the competitiveness of elections in Model

3 gives us some clues about the importance of interparty competition on the likelihood of

electoral fraud. In our cases, where incumbents or ruling parties were confident of their

victory before the elections, the odds of high levels of electoral fraud versus the combined

lower categories were 2.6 times higher (however, given the small number of cases in this

model, I would advise cautiousness in interpreting this coefficient).

To illustrate the substantive effects of different types of state capacity on the

probability of fraudulent elections, I simulated the effect of infrastructural and coercive state

capacity on the probability of observed fraudulent elections using Model 2 (holding all

control variables at their mean value). The results are presented in Table 4. Infrastructural

state capacity clearly has the largest marginal effects on the dependent variable. Moving from

the minimum to the maximum value of state capacity the probability of observing a problem-

free election increases by 0.52, while the probability of observing a highly problematic

election decreases by a factor of 0.44. The changes in predicted probabilities for the two

indicators of coercive state capacity are smaller for these categories of the dependent variable,

but still substantively influential.

[TABLE 4 ABOUT HERE]

19

Given that the dependent variable employed in Models 1 to 3 is general and highly

aggregated, we can further refine the findings by decomposing the phenomenon of electoral

manipulation empirically to examine which aspects of sate capacity are at work in specific

types of fraudulent activities. Table 4 presents a series of seven logistic regression models

using different dependent variables depicting subtypes of electoral fraud; voter and candidate

intimidation, whether contestants were banned, the occurrence of vote-buying, blatant

election day cheating, problematic voter lists, and unfair media coverage.10 Although the

analyses are limited by the small number of valid observations in some of the dependent

variables, the disaggregated categories of electoral fraud reveal some interesting patterns that

allow the observation of some interesting variations.

[TABLE 5 ABOUT HERE]

All seven models presented in Table 4 display negative parameter estimates between

levels of infrastructural state capacity and the different forms of electoral fraud.11 When all

other variables are held constant, low levels of infrastructural state capacity increase the

likelihood of flawed elections of all types. The two indicators measuring the level of coercive

state capacity do not achieve the same unambiguous effects across our dependent variables.

CIRI’s Physical Integrity Rights Index has no statistically significant impact on coercive

aspects of electoral malpractice; that is, on the likelihood of voter intimidation, candidate

intimidation, or the banning of candidates. This means that the use of torture, extrajudicial

killing, political imprisonment and disappearance does not increase the likelihood of voter

and opposition harassment when other factors are taken into account. The only significant

effect of this indicator in the theorized direction is in the model estimating the likelihood of

unfair media coverage. As well, the second proxy employed to tap into the concept of

coercive state capacity achieved limited impact except in two instances. In the models

estimating the likelihood of voter intimidation and instances of election-day cheating, the size

of the coefficient for the size of military personnel per capita is statistically significant in the

hypothesized direction.

10 All original dependent variables from EMD and QED were measured in ordinal scales. when ordered logistic regressions were performed, the small amount of cases made it even impossible to perform Brant tests (Long and Freese, 2006), and yielded insignificant thresholds (cut points used to differentiate the adjacent levels of the response variable). 11 GDP per capita was dropped in some models due to high collinearity with infrastructural state capacity which made both variables loose statistical significance when included together. In the models where GDP per capita was omitted, readers must be aware that the variable only achieves significance once infrastructural state capacity was pulled from the analyses. Both variables therefore share a large variance that makes it difficult to estimate their independent effects in very small samples.

20

Vote-buying appears to be a different class of phenomenon. Not only is the measure of

fit (pseudo-R2) much lower for this model than for the others, but many predictors experience

a sign reversal. In the case of vote-buying, CIRI’s Physical Integrity Rights Index is positively

associated with the likelihood of vote-buying, so is higher urbanization. This distinctive

picture becomes clearer when an additional control for the presence of a ceiling on political

parties expenditures is added to this model (International Institute for Democracy and

Electoral Assistance (IDEA), 2003).12 Not only does the parameter estimate for ceilings on

expenditures become dominant in the model, it reverses the signs of level of urbanization and

inequality in a direction that fits existing contributions. Even if there is a possibility that these

results are statistical artefacts of this small sample and the measurements employed, dynamics

of voter-buying seem to tap into a different logics, one where uncontrolled party expenditures

and patron-client networks are some of the crucial factors facilitating this phenomenon, and

where coercion does not play a leading role (Wang and Kurzman, 2007), although coercion

sometimes accompanies vote-buying (for example Mexico, see Shelley, 1999).

6. CONCLUSIONS

This paper has examined the effect of infrastructural and coercive state capacity on

electoral fraud in postcommunist countries. The main empirical finding is that electoral fraud

is more likely when infrastructural state capacity is weak. Infrastructural capacity probably

acts as a proxy to evaluate how imperfections in voting infrastructure shape the ability of

elites to interfere electorally. This hypothesis allows some insight with a view to separate

intentional political manipulation from individuals from technical incapacity, two set of

factors that can lead to low quality elections (Pastor, 1999). This paper also addresses the

literature on robustness of a state’s coercive apparatus and the prospects for democratization.

Although it could be the case that a state’s coercive apparatus functions as an intervening

variable sustaining autocracy as proposed by Albertus and Menaldo (2012), the infrastructural

capacity seems to trump the coercive side of the state at some point, at least in the case of

electoral fraud.

However the analyses also suggest that there is not a single answer to the question of

what causes electoral malpractice. Structures underlying electoral malpractice vary by the

type of electoral fraud under consideration: voter and candidate intimidation, fraudulent

12 IDEA’s data coverage is limited and excludes 11 out of our 26 countries. These results should be interpreted with a measure of prudence. As well, it should be noted that the presence of ceilings on political parties’ expenditures does not achieve statistical significance in any other model presented in Table 4.

21

tabulation of votes, unfair media coverage of campaigns, and vote buying seem to engage

different sets of facilitating structures. The findings in this paper bring more nuances to some

the existing literature that deals with electoral fraud as a single type of general phenomenon.

The results show that different variables seem to explain different varieties of electoral fraud

better than others. The large differences across models open the path for new theorization, and

more careful empirical testing. While this paper does not address what motivates agents to

engage in cheating to increase vote shares, but only the structures, which should serve to hold

at bay, or facilitate the use of different types of electoral fraud, the present findings could

serve to revisit some of the theories underling incentive structures.

Empirically, we seem to be a long way from finding definitive answers. This paper’s

main empirical limitation is that none of the analyses allow looking into the magnitude of the

phenomenon of electoral fraud. For now, most data projects offer limited measurement of

whether a type of phenomenon occurs or not, or crude ordinal classification from which we

can only draw limited inferences. This is probably due to the difficulties in observation. If we

knew by how much votes were inflated, we could establish the magnitude of ballot fraud.

Some, however, doubt that we will be able to find reliable measurement for phenomena like

fraud and intimidation (Przeworski et al., 2000). It also seems that there is yet little empirical

basis for categorizing different varieties of electoral fraud, and at times, little agreement

between data sources which raises some issues about how much we know about electoral

fraud and the possibility generalization.

22

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TABLE 1 EXTENT OF PROBLEMS IN ELECTIONS 1989-2004 28 POST-COMMUNIST COUNTRIES

EXTENT OF PROBLEMS Count Percent

No problems 44 25.73 Minor problems only 40 23.39 Moderate problems 54 31.58 Major problems 33 19.30

Total 171 100 Source: QED (Kelley, 2010) for 28 postcommunist countries from 1989 to 2004

TABLE 2 TYPES OF WINNER BY OVERALL ELECTION QUALITY DID INCUMBENTS WIN THE ELECTIONS?

Overall election quality

Acceptable Ambiguous Unacceptable No 44 2 0 Yes 65 7 48

Total 109 9 48 Source: QED (Kelley, 2010) for 28 postcommunist countries from 1989 to 2004

FIGURE I: PERCENTAGE OF ELECTIONS JUDGED AMBIGUOUS OR UNACCEPTABLE 1989-2004 (QED)

Source: QED (Kelley, 2010) for 28 postcommunist countries from 1989 to 2004

Poland 1989

02

040

6080

100

% A

mb

iguo

us o

r U

nac

cept

able

Ele

ctio

ns

1989 1992 1995 1998 2001 2004Year

27

FIGURE 2: OVERALL ELECTION QUALITY AND EXTENT OF PROBLEMS IN RELATION TO INFRASTRUCTURAL STATE CAPACITY (QED)13

FIGURE 3: OVERALL ASSESSMENT OF THE ELECTION IN RELATION TO LEVEL COERCIVE STATE CAPACITY (QED)14

13 The variable combining overall election quality and extent of problems is from QED dataset, and ranges from 1 to 5. Graph 2 contains 148 valid observations. R2= 0.53. Vertical line is positioned at the average of infrastructural state capacity. 14 The variable combining overall election quality and extent of problems is from QED dataset, and ranges from 1 to 5. Graph 3 contains 153 valid observations. R2= 0.34. . Vertical line is positioned at the average of SIRI’s Physical integrity Index (-5.19)

Croatia 1997

Poland 1989

Georgia 1995

Gerogia 1992Mongolia 1990

Armenia 1991

01

23

45

Minimum MaximumLevel of State Capacity

Level of Problems Lowess

Croatia 1997

Mongolia 1990

Serbia 2003

Russia 1995

Georgia 1992

Russia 2004

Croatia 1997

Mongolia 1990

Serbia 2003

Russia 1995

Romania 2000

Georgia 1992

Russia 2004

01

23

45

Low coercion High Coersion

Level Physical Integrity (Proxy for State Coercive Capacity)

Extent of Problems Fitted values

28

TABLE 3. ORDERED LOGISTIC REGRESSION MODELING THE EXTENT OF PROBLEMS RECORDED IN ELECTIONS (QED) Model 1 Model 2 Model 3 OR/se OR/se b/se Extent of problems in Election

Infrastructural State Capacity 0.068*** 0.172*** 0.171** (0.03) (0.11) (0.13)

Coercive State Capacity I 0.645*** 0.768* 0.691** (Physical Integrity) (0.08) (0.11) (0.11)

Coercive State Capacity II 1.553 3.258* 2.130 (Military per 100 pers.) (0.63) (2.03) (1.54)

GDP per capita WB 0.999* 0.999** (0.00) (0.00)

Percent urban population 0.926*** 0.932** (0.02) (0.03)

Inequality (GINI index) 0.910** 0.879*** (0.03) (0.04)

Election for most powerful position 2.205* 2.962** 1=yes (0.91) (1.57)

Plurality electoral rules 4.111** 2.552 1=yes (2.84) (2.01)

Fuel exports (% GDP) 1.060*** 1.073*** (0.02) (0.03)

Time since first year of 1.164*** 1.176*** independence or transition (0.64) (0.07)

Was the ruling party confident of 2.634* victory before elections? (1.39)

Observations 149 134 106 McFadden’s R2 0.342 0.432 0.479 * p < 0.10, ** p < 0.05, *** p < 0.01

29

TABLE 4. THE IMPACT OF INFRASTRUCTURAL AND COERCIVE STATE CAPACITY ON ELECTORAL FRAUD Change in Predicted Probabilitiesa

Type of state capacity Ordered Logit

Estimatesb No problems Minor problems Moderate problems

High problems

Infrastructural State Capacity -1.77*** 0.52 0.34 -0.43 -0.44

(0.67)

Coercive State Capacity I -0.27** 0.12 0.33 -0.31 -0.14

(Physical Integrity) (0.14)

Coercive State Capacity II 1.19* -0.11 -0.31 0.31 0.12

(Military per 100 pers.) (0.61)

(N=134)

χ2 (df=10) = 63.40

McFadden’s R2 = 0.43 Source: Extent of Problems QED (Kelley, 2010) for 26 postcommunist countries from 1989 to 2004 a Change in the predicted probabilities of reaching each level for an increase from the minimum to the maximum value of each independent variable, while holding all other independent variables constant at their means. b The top entries are ordered logit coefficients. Standard errors are in parentheses. **p<.001; *p<.01 (two-tailed tests)

30

TABLE 5. LOGISTIC REGRESSIONS MODELING DIFFERENT VARIETIES OF ELECTORAL FRAUD Voter

intimidation Candidate

intimidation Contestants

banned Vote-buying Election day

cheating Low admi-nistrative capacity

Unfair media

coverage OR/se OR/se OR/se OR/se OR/se OR/se OR/se Infrastructural State Capacity 0.045*** 0.063*** 0.052*** 0.396 0.020*** 0.058*** 0.015* (0.04) (0.06) (0.05) (0.44) (0.02) (0.05) (0.04)

Coercive State Capacity I 1.137 1.265 0.909 2.435*** 0.983 1.081 0.494** (Physical Integrity) (0.21) (0.26) (0.28) (0.77) (0.16) (0.22) (0.15)

Coercive State Capacity II 7.587* 2.750 0.272 1.524 14.540*** 2.225 0.560 (Military per 100 pers.) (8.77) (2.99) (0.31) (1.62) (11.96) (2.32) (0.59)

GDP per capita 0.999* 1.000 1.000 (0.00) (0.00) (0.00)

Percent urban population 1.032 1.049 1.027 1.067* 1.006 0.991 0.912 (0.03) (0.03) (0.04) (0.04) (0.03) (0.03) (0.06)

Inequality (GINI index) 0.975 0.994 0.801*** 1.090 1.024 1.011 0.930 (0.06) (0.07) (0.06) (0.09) (0.05) (0.06) (0.07)

Election for most powerful position 0.283 0.807 1.209 0.633 1.486 0.486 2.805 1=yes (0.23) (0.61) (0.97) (0.43) (0.78) (0.30) (2.39)

Plurality electoral rules 0.384 2.473 1.991 0.936 4.155* 0.568 1.420 1=yes (0.40) (2.47) (2.20) (0.95) (3.54) (0.62) (1.63)

Fuel exports (% GDP) 1.022 1.018 1.043 1.006 0.970* 1.011 1.061 (0.03) (0.02) (0.03) (0.02) (0.02) (0.01) (0.04)

Time since first year of 1.195* 1.155 0.895 1.103 1.430*** 1.614*** 0.874 independence or transition (0.12) (0.12) (0.10) (0.13) (0.13) (0.20) (0.09)

Observations 73 71 74 70 133 134 76 McFadden’s R2 0.307 0.312 0.422 0.189 0.449 0.363 0.531

Exponentiated coefficients * p < 0.10, ** p < 0.05, *** p < 0.01