UNCOVERING THE DETERMINANTS OF CORRUPTION

MICHAEL JETTER AND CHRISTOPHER F. PARMETER

Abstract. Identifying the real causes of corruption has proven difficult because of lim-

ited data availability, lack of a unifying theoretical framework, and endogeneity concerns.

Combining data for a comprehensive list of 36 potential determinants of corruption across

123 countries (including 87 percent of the world population), we use Instrumental Variable

Bayesian Model Averaging to account for model uncertainty and endogeneity. In addition

to income levels, the extent of primary schooling emerges as a powerful predictor of lower

corruption levels. Rule of law and FDI matter particularly in developing countries. Finally,

we find some evidence for trade freedom, political rights, government size, and religious frac-

tionalization to contain corruption. These findings offer feasible avenues for policymakers,

as corruption does not seem to be driven by deeply rooted cultural attributes.

1. Introduction

Corruption is estimated to cost us at least five percent of global GDP, equivalent to US$2.6

trillion per year (World Economic Forum, OECD, 2013). However, we are still unable to pin

down the exact causes of corrupt behavior. This uncertainty is reflected in broad – rather

than specific – mission statements of international organizations, usually calling for trans-

parency, public condemnation of, and stricter laws against corruption (e.g., Transparency

International). In general, recent experimental studies have revealed corruption to depend

on culture, economic development, and institutions (Fisman and Miguel, 2007; Barr and

Serra, 2010; Brosig-Koch et al., 2011). The logical continuation of this line of research then

asks which cultural, economic, and institutional characteristics are driving corruption or the

Key words and phrases. Corruption, Bayesian Model Averaging, Primary Education, Instrumental VariableBayesian Model Averaging.Michael Jetter, Corresponding Author, Business School, University of Western Australia, Perth, Australia;Phone: +61.457.871.496, email: mjetter7@gmail.com; web: www.michaeljetter.com. Christopher F.Parmeter, Department of Economics, University of Miami; e-mail: cparmeter@bus.miami.edu.

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absence thereof. A detailed and comprehensive answer to this question should prove helpful

for policymakers and NGOs. In particular, it is important to understand whether corruption

is influenced by factors that can be changed within a society, or whether deeply rooted and

potentially fixed characteristics are at work.

Unfortunately, the literature has struggled to develop a shortlist of robust corruption de-

terminants and we have found it difficult to overcome three major empirical problems. First,

repeated country-level data on corruption has only become available since the late 1990s.

To make matters worse, single data points can suffer from measurement error as nobody

willingly reports corrupt acts. Second, no comprehensive theoretical framework exists that

is capable of uniting cultural, institutional, and economic factors in explaining corruption.

Consequently, the empirical literature has produced an openendedness of potential deter-

minants, an artifact summarized in Table 1. The number of factors potentially related to

corruption ranges from 7 to 28 and, much more worrisome, the variables found to be sta-

tistically relevant differ substantially. One of the few consistent findings notes that richer

countries tend to be less corrupt. And this finding highlights the third and final major

obstacle impeding our understanding of corruption: reverse causality. Not only are richer

countries usually less corrupt, but corruption may in turn decrease income levels, as famously

outlined by Mauro (1995). In summary, limited data availability, a long list of potential de-

terminants, and endogeneity concerns plague our understanding of why some countries are

more corrupt than others.

We propose a systematic solution to these three problems by applying the recently de-

veloped Instrumental Variable Bayesian Model Averaging (IVBMA) technique to a sample

of 123 countries, covering 87 percent of the world population. The main strength of BMA

methods lies precisely in finding robust predictors of an outcome variable within a large set

of potential regressors. In a closely related literature, the use of BMA has allowed economists

to make substantial contributions to explaining country-level differences in economic growth

2

Table 1. Recent empirical studies on potential determinants of corruption.

Paper Dependent # of Method Significantvariable1 variables determinants

Iwasaki and Suzuki(2012)

COC 16 OLS(panel)

democratization, duration of socialism,federal system, GDP/capita, marke-tization, rule of law, transformationpolicy

Fan et al. (2009) WBES 23 OLS decentralization, GDP/capita

Dreher et al. (2009) TI, ICRG 10 OLS,2SLS,3SLS

government effectiveness, latitude, le-gal origin, years of democracy

Serra (2006) TI, Graft 16 (28) OLS(EBA2)

colony, GDP/cap, % protestants, polit-ical instability, years of democracy

Arikan (2004) TI 10 OLS GNP/capita, imports, press freedom,school enrollment

Gatti (2004) ICRG 13 OLS(panel)

democracy, GDP/capita, tariffs

Knack and Azfar(2003)

TI, Graft,CPIA

7 OLS British colony, GDP/capita, stabledemocracy

Brunetti and Weder(2003)

ICRG 11 OLS(panel),2SLS

bureaucracy, Latin America, press free-dom, rule of law

Paldam (2002) TI 10 OLS culture, GDP/capita, inflation

Fisman and Gatti(2002)

ICRG,WCR,GEI, BI,TI, GCS

8 OLS,2SLS

decentralization index, GDP/cap, gov-ernment size

Swamy et al. (2001) Graft, TI 22 OLS British colony, GDP/capita, % womenin labor force, women’s influence index

Dollar et al. (2001) ICRG 11 OLS(panel)

GDP/capita, GDP/capita squared, %women in parliament

Treisman (2000) BI, TI 23 OLS British colony, federal states,GDP/cap, % protestants, tradeopenness, years of democracy

La Porta et al. (1999) PRS 10 OLS GDP/cap, latitude

Notes : 1Description: BI = Business International; CPIA = Country Policy and Institutional Assessment

(World Bank); COC = Control of Corruption Index; GCS = Global Competitiveness Survey; GEI =

German Exporter Index; Graft = Graft index from Kaufmann et al. (1999); ICRG = International Country

Risk Guide; PRS = Political Risk Services; TI = Transparency International (using the CPI = Corruption

Perceptions Index); WBES = World Business Environment Survey; WCR = World Competitiveness

Report. 2Extreme Bounds Analysis (Leamer, 1983, Leamer, 1985).

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(Doppelhofer et al., 2004; Masanjala and Papageorgiou, 2008; Durlauf et al., 2012; Moral-

Benito, 2012). Much like the corruption literature, the search for robust explanations of

income levels has been plagued by an openendedness of potential determinants (Brock and

Durlauf, 2001; Durlauf et al., 2005).

Beyond model uncertainty, this paper addresses the latent endogeneity problems of po-

tential corruption determinants when analyzing globally representative country-level data.

First, we average all variables over ten years, alleviating concerns about measurement er-

ror. Second, we employ IVBMA (Karl and Lenkoski, 2012; Koop et al., 2012), using values

lagged by one decade to instrument for those variables that have been shown to be most

vulnerable to endogeneity concerns. Here as well, the growth literature has pioneered the

consequent acknowledgement of endogeneity concerns (Horvath, 2013; Eicher and Kuenzel,

2014; Eicher, 2016) and the use of lagged values as instruments (Temple, 1999; Schularick

and Steger, 2010; Mirestean and Tsangarides, 2016). Recently, the use of lagged values of en-

dogenous variables as instruments has also been employed in cross-country analyses related

to corruption; Bhattacharyya and Hodler, 2010, instrument democracy with its lagged value

to estimate its effect on corruption and Arezki and Bruckner, 2011 employ lagged corruption

values as an instrument for corruption today to isolate corruption’s effect on oil production.

Our results provide a detailed picture of how countries can fight corruption, breaking down

broad categories, such as culture or institutions, into specific characteristics, while controlling

for a comprehensive list of potentially interfering factors. First, we find decisive evidence

that higher income lowers corruption. Second, a longer duration of primary education is

strongly indicative of lower corruption levels. This result is surprising since few papers have

found educational characteristics to be related to corruption (notable exceptions coming

from Arikan, 2004, Gatti, 2004, and Glaeser and Saks, 2006) and even fewer studies have

focused on education as a driving force behind the fight against corruption. As noted by

Glaeser and Saks (2006, p.1056), basic education can facilitate learning about politics and

understanding the implications of corruption. This may in turn raise the ability and interest

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to monitor public officials. Thus, promoting education may not only fuel economic growth

(Mankiw et al., 1992; Turner et al., 2007), but also contain corruption. Interestingly, policy

implications from a pure BMA analysis differ from IVBMA findings, highlighting the rule

of law and female participation in parliament, as opposed to income levels and education

standards. This further underlines the severity of endogeneity concerns in revealing the true

causes of corruption.

We then analyze a subsample of developing countries (broadly defined as non-OECD mem-

bers), since corruption is seen as particularly troubling in poorer nations. The corresponding

results confirm the importance of income levels, but also emphasize a strong presence of the

rule of law as an indispensable ingredient in fighting corruption. Thus, legal accountability

does matter, but mainly for countries in the process of development – potentially because

such institutional structures can be mostly absent in those countries. This finding corrobo-

rates recent insights about the role of institutions in explaining economic development (e.g.,

Acemoglu et al., 2005). Finally, foreign direct investment levels are highly predictive of cor-

ruption in developing nations, even though the sign of this relationship remains ambiguous,

potentially indicating a nonlinearity.

The paper is structured as follows. Section 2 provides an overview of the existing lit-

erature and the variables included in our analysis. Section 3 highlights problems plaguing

our understanding of corruption determinants. Section 4 is dedicated to the methodolog-

ical foundations of our study, whereas section 5 describes our findings. Finally, section 6

concludes.

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2. Corruption and its Potential Determinants

The goal of evaluating the determinants of corruption has mostly been approached from

an empirical perspective, with few exceptions providing theoretical foundations.1 Contrary

to papers surrounding determinants of economic growth, the corruption literature has not

produced a consistent, dominant theoretical framework, such as a Solow model. One reason

may be the numerous forms and shapes in which corruption occurs, making it difficult to pin

down one consistent, comparable outcome variable, as opposed to analyzing income levels,

for example.

Nevertheless, the proposed corruption determinants can be sorted into four broad cate-

gories, much like the growth literature: institutional, economic, cultural, and geographical

factors. Although some variables may well form part of more than one group, our sorting

is motivated by the degree to which policymakers can influence the respective factor. Most

institutional and economic variables can be changed over time (albeit sometimes slowly),

whereas cultural and especially geographical components are mostly fixed.

2.1. Data Structure and Time frame. For all variables, with the exception of the in-

strumental variables, we estimate the average value from yearly observations between 2001

and 2010 on the country level. Overall, our sample includes data for 123 nations, covering

87 percent of the world population and almost doubling the sample size of the latest compre-

hensive analysis of corruption determinants by Serra (2006), who incorporates 62 countries.

Regarding the dependent variable, data availability today allows us to average corruption

over ten years, as opposed to three years (Serra, 2006, ; 1997 – 1999), further addressing

potential issues arising from measurement error and business cycles. In total, our sample is

drawn from 11 different sources, which are listed and described in Tables A1 and A2 of the

Appendix. It is important to note that our study is facing a fundamental trade-off between

1Notable exceptions coming from Shleifer and Vishny (1993) or Acemoglu and Verdier (2000). However,even these seminal contributions to our understanding of corruption prove difficult to test on an empirical,macroeconomic level, as they highlight internal monitoring within the public sector – something that isdifficult to measure on a comparable cross-country basis.

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the number of independent variables and data availability. The following analysis incor-

porates 36 potential corruption determinants, although we conducted numerous alternative

estimations, testing for the importance of a total of 52 potential determinants. However,

those 16 variables that are omitted from our main results never come close to statistical im-

portance in these alternative estimations, yet their inclusion would severely limit our sample

size. Throughout the following description of variables we mention which variables have also

been tested. Finally, even data on corruption scores and the remaining 36 variables are not

always available for the entire time frame from 2001 to 2010. However, we are confident that

our sample is representative of the available data today and comparable between countries.

In this context, Table A3 provides details on the availability of all included variables.

2.2. Measuring Corruption. Over time, researchers have used several sources for cor-

ruption measurements. Although few papers have used regional data (such as Glaeser and

Saks, 2006, for the United States) or micro-level data (e.g., Mocan, 2008), most analyses use

information on the country level. The most common sources used to measure corruption

on the country level are the International Country Risk Guide (ICRG) and the Corruption

Perceptions Index (CPI), published by Transparency International (TI). In general, correla-

tions between different measurements of corruption tend to be high (usually beyond 90 or

95 percent) and potential ideological biases have been shown to be small and quantitatively

unimportant (Kaufmann et al., 2004; Kaufmann et al., 2007). The ICRG is created by risk

analysts primarily for investment decisions, whereas the CPI represents an aggregate indi-

cator, based on survey data and private risk assessments. In this context, Knack and Azfar

(2000) and Serra (2006) provide deeper discussions, concluding that aggregate indices, like

the CPI, should be preferable over individual risk analyses, which can be driven by private

interests. Thus, we follow the majority of the literature by using the CPI, which ranges

from 0 to 10 with higher numbers indicating less corruption. In other words, the CPI mea-

sures absence of corruption. We draw additional comfort from the fact that the most recent

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Figure 1. 1 (red): Lowest quartile of available corruption scores. 2 (lightred): Second quartile. 3 (light blue): Third quartile. 4 (blue):Fourth quartile, least corrupt countries.

studies have used the CPI, as indicated by Table 1.

Figure 1 maps all available countries with their average CPI scores from 2001 to 2010. For

illustration purposes, we group them into four quartiles, where red indicates highly corrupt

and blue displays lower corruption levels. Notice that we do observe some regional clustering

of corrupt nations in Central Africa and Asia. The least corrupt economies are generally

found in Europe and North America, with few geographical outliers (e.g., Botswana, Chile,

or Japan). Table 2 displays all 123 sample countries sorted by continents and their average

CPI between 2001 and 2010. For these countries, the CPI, all independent and instrumental

variables are available. Note the wide variety of our sample with 28 OECD countries, but

also including a number of developing nations. Section 5.3 examines developing countries in

more detail.

2.3. Institutional Determinants. Moving to the corruption determinants proposed by the

corresponding literature, we start with institutional components. As corruption is usually

defined as “the misuse of public office for private gain” (Treisman, 2000), several features of

the public sector are likely to be closely related to corrupt behavior.

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Table 2. All 123 sample countries by average CPI from 2001 – 2010.

Country CPI Country CPI Country CPI

Africa, N = 38 Asia cont. Europe cont.Chad 1.73 Lao PDR 2.32 Estonia 6.20Guinea 1.84 Pakistan 2.34 Portugal 6.30Equatorial Guinea 1.88 Russian Federation 2.41 Spain 6.73Congo, Dem. Rep. 1.94 Kazakhstan 2.47 France 6.98Angola 1.96 Nepal 2.50 Ireland 7.54Guinea-Bissau 2.03 Philippines 2.52 Austria 8.13Cambodia 2.05 Iran, Islamic Rep. 2.54 United Kingdom 8.29Kenya 2.07 Vietnam 2.59 Norway 8.63Burundi 2.12 Syrian Arab Republic 2.84 Netherlands 8.83Congo, Rep. 2.13 Mongolia 2.89 Switzerland 8.87Cote d’Ivoire 2.16 India 3.08 Sweden 9.22Cameroon 2.17 Sri Lanka 3.29 Denmark 9.43Uganda 2.44 Thailand 3.44 Finland 9.47Libya 2.45 China 3.45Togo 2.52 Turkey 3.79 North America, N = 6Niger 2.54 Saudi Arabia 3.81 Honduras 2.51Gambia, The 2.60 Kuwait 4.58 Dominican Republic 3.06Mauritania 2.66 Korea, Rep. 4.92 Panama 3.40Mozambique 2.71 Malaysia 4.93 Mexico 3.48Madagascar 2.83 Jordan 4.98 Trinidad and Tobago 4.02Benin 2.87 Bahrain 5.48 Canada 8.70Algeria 2.89 Oman 5.64Mali 2.90 Qatar 6.24 Oceania, N = 3Malawi 2.94 Israel 6.48 Fiji 4.00Djibouti 2.98 Japan 7.33 Australia 8.69Gabon 3.04 New Zealand 9.46Swaziland 3.15 Europe, N = 31Senegal 3.16 Azerbaijan 2.10 South America, N = 15Egypt, Arab Rep. 3.18 Ukraine 2.42 Paraguay 2.11Burkina Faso 3.28 Moldova 2.79 Venezuela, RB 2.24Lesotho 3.32 Albania 2.81 Ecuador 2.27Morocco 3.37 Armenia 2.89 Bolivia 2.53Ghana 3.66 Belarus 2.89 Nicaragua 2.55Namibia 4.62 Georgia 2.94 Guyana 2.58Tunisia 4.66 Romania 3.22 Guatemala 2.76South Africa 4.69 Croatia 3.84 Argentina 2.86Mauritius 4.78 Bulgaria 3.90 Jamaica 3.46Botswana 5.82 Poland 4.14 Peru 3.64

Greece 4.23 Brazil 3.72Asia, N = 30 Latvia 4.24 Colombia 3.76Bangladesh 1.70 Slovak Republic 4.28 Costa Rica 4.72Tajikistan 2.04 Lithuania 4.78 Uruguay 6.14Kyrgyz Republic 2.08 Italy 4.89 Chile 7.22Uzbekistan 2.14 Hungary 5.02Indonesia 2.28 Slovenia 6.19

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First, we include size and effectiveness of a country’s government. Fisman and Gatti (2002)

and Billger and Goel (2009) find bigger governments, measured as government expenditure

divided by GDP, to exhibit lower corruption levels. Dreher et al. (2009) propose government

effectiveness to reduce corruption. Further, Gatti (2004) finds democratic countries to be less

corrupt, motivating us to include the Polity IV index, ranging from −10 (total autocracy)

to +10 (total democracy). Accordingly, we add more defined characteristics of the political

system, namely political rights, the extent of the rule of law (Brunetti and Weder, 2003;

Iwasaki and Suzuki, 2012), and property rights. Recently, Pandey (2010) has shown that

the history of political institutions and political power can influence corruption levels. In a

closely related context, the seminal works by Daron Acemoglu, Simon Johnson, and James

Robinson (e.g., Acemoglu et al., 2001; Acemoglu et al., 2005) suggest that institutional

aspects are closely associated with the abuse of public power.

Further, countries in which the press enjoys more freedom have been found to be less

corrupt (Brunetti and Weder, 2003; Arikan, 2004; Freille et al., 2007), which leads us to

include the freedom of the press index, provided by Freedom House. A similar logic applies

to countries with less regulation on business activities (discussed by Tanzi, 1998), such as the

freedom to trade internationally.2 In this context, Treisman (2000) notes that corrupt officials

have an incentive to create barriers to trade, only to be able to collect bribes afterwards.

In addition to time-varying institutional factors we also include historical characteristics.

Treisman (2000), Serra (2006), and Dreher et al. (2009) find that it is not only the current

level of democracy, but also a long-lasting history of a democratic regime form, which al-

leviates corruption. Thus, we add a country’s consecutive years of democracy before 2001

to the list of potential determinants, as measured by the consecutive years of a positive

Polity IV index. We further include dummies for countries with a common law system and

2We also included four distinct tariff rates (simple and weighted mean in percent, all products; weightedmeans of primary and manufactured products in percent), but the posterior inclusion probability neversuggests importance with PIPs below 0.15. As tariff rates are not available for all sample countries theseresults are omitted, but available upon request.

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a federal structure. Treisman (2000), Fan et al. (2009), and Iwasaki and Suzuki (2012) have

proposed that countries with a federal structure are more prone to corruption, whereas Fis-

man and Gatti (2002) and Arikan (2004) suggest decentralization to be associated with less

corruption.

Our final category of institutional variables considers schooling characteristics. Previously,

educational factors have produced contradicting results in explaining corruption: Arikan

(2004) finds education to be related to less corruption, whereas Mocan (2008) finds that

more educated people are more likely to be asked for a bribe. Arguments for the former

finding include that with education comes a better understanding of how society works in

ethical terms or simply that one does not need to be corrupt to generate income. Similarly,

education may be necessary to understand and monitor public processes. On the other hand,

more educated people could be subject to more bribery requests, given they are usually

wealthier. We use three distinct measures for education in our analysis: the duration of

both primary and secondary education, as well as secondary enrollment rates. In additional

estimations, we also included primary and tertiary enrollment rates, but this information is

not available for a number of countries and years. However, both of these variables never

returned statistically relevant results (available upon request).

2.4. Economic Determinants. The majority of papers on corruption determinants find

economic characteristics to be associated with corruption levels, such as GDP per capita

(see Table 1). The most persistent finding throughout the literature lies in the observation

that richer countries are usually less corrupt. In addition to income levels, the degree of

international trade appears to be closely related to corruption. Arikan (2004) finds imports

as a fraction of GDP to decrease corruption, whereas Treisman (2000) underlines total trade

as a share of GDP in this context. Further, foreign direct investment has been suggested

to reduce corruption (Gatti, 2004; Larraın and Tavares, 2004). We readily incorporate all

three measurements into our analysis. Our final macroeconomic variable highlights the role

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of natural resources in the world economy. It has been suggested that an abundance of

natural resources fosters corruption levels, creating opportunities for rent-seeking behavior

(Da Cunha Leite and Weidmann, 2001; Bhattacharyya and Hodler, 2010).

In additional estimations we also included foreign aid inflows, the black market exchange

rate, unemployment rates, Gini coefficients, a measurement for oil reserves, OPEC member-

ship, and the inflation rate.3 However, none of these variables ever comes close to playing a

role statistically, but we lose a substantial number of observations for each of these. Thus,

we omit them from the main analysis, but results are available upon request.

2.5. Cultural Determinants. Beyond institutional and economic factors, the literature

has produced substantial evidence for various cultural characteristics influencing corruption

levels (Paldam, 2002; Fisman and Miguel, 2007). First, we follow Treisman (2000) and

Glaeser and Saks (2006) by incorporating total population size and the urbanization rate.

Recently, Billger and Goel (2009) find urbanization to lower corruption levels, whereas Mocan

(2008) finds bigger populations to exhibit more corruption.

In terms of the ethnic and spiritual composition of the country, we include the share of

protestants in society, as well as ethnic, language, and religious fractionalization. Treisman

(2000), Serra (2006), and Gokcekus and Knorich (2006) find that a higher fraction of protes-

tants is associated with lower corruption levels, everything else equal. Generally, numerous

papers include fractionalization indices along the lines of ethnicity, language, and religion,

finding a link to corruption (Glaeser and Saks, 2006; Dincer, 2008).4

Further, several papers suggest gender differences in corrupt behavior (Swamy et al., 2001;

Dollar et al., 2001; Mocan, 2008). Specifically, Swamy et al. (2001) find corruption to be less

3See Iwasaki and Suzuki (2012) for the role of development aid in corruption; Swamy et al. (2001), Brunettiand Weder (2003) for the black market exchange rate; Mocan (2008) for unemployment; Paldam (2002) andGlaeser and Saks (2006) for inequality; Larraın and Tavares (2004), Gatti (2004), and Arezki and Bruckner(2011) for oil; Brunetti and Weder (2003) for OECD membership; Paldam (2002) for inflation.4We also tested for the importance of ethnic and religious polarization, but neither of these variables werefound to have a statistical impact on corruption. As we would lose observations by including these variables,we omit them from our main analysis. These results are available upon request.

12

prevalent in countries where women form a larger share in parliaments. Consequently, we add

this variable to the list of potential corruption determinants. Regarding cultural heritage,

British colonies have been found to exhibit less corruption (Treisman, 2000; Knack and

Azfar, 2003; Gokcekus and Knorich, 2006). In addition, some papers test for an individual

effect in French, Portuguese, or Spanish colonies (e.g., Treisman, 2000), although the derived

coefficients are usually not statistically relevant. Nevertheless, we readily incorporate colonial

heritage into our analysis in the form of dummies for British, Dutch, French, Portuguese,

and Spanish origin.

2.6. Geographical Determinants. Our final group of regressors considers invariant geo-

graphical conditions. For instance, Brunetti and Weder (2003) find Latin American countries

to be significantly more corrupt than the rest of the world. Thus, we include dummies for

African, Asian, European, North American, and South American countries.5 With these

potential determinants in mind, we now move to describing the econometric issues that have

made it difficult to isolate robust corruption determinants.

3. Model Uncertainty, Endogeneity, and Reverse Causality

The task of assessing corruption determinants traditionally suffers from three major econo-

metric problems: limited data availability, model uncertainty, and endogeneity.

3.1. The Econometric Hurdles. First, reliable information on the degree of corruption

is more difficult to gather than, say, GDP data, since nobody willingly reveals corrupt

actions. In fact, annual data at the country level has only become available since the late

1990s.6 Thus, analyses of corruption determinants traditionally rely on fewer observations

than other macroeconomic studies, e.g., on economic growth or government size. In addition,

5Dreher et al. (2009) and La Porta et al. (1999) find that a country’s latitude might affect its degree ofcorruption, but this variable never proved to be relevant throughout our analysis and is therefore omitted inthe main analysis (results available upon request).6The International Country Risk Guide, derived from private risk assessments, provides an exception andhas become available in the mid 1980s, albeit for a much smaller number of countries.

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since neither the briber, nor the bribee gladly reports her actions, a researcher is likely to

face measurement error. This is especially true when relying on surveys and private risk

assessments. To alleviate these concerns, averaging corruption scores over several years can

provide more reliable values. In fact, taking averages over five or ten years has become

important for other macroeconomic variables that are not nearly as prone to measurement

error, such as government size (Ram, 2009) or economic growth (Acemoglu et al., 2008). Of

course, averaging over time further diminishes the number of observations.

Second, Table 1 indicates a fundamental uncertainty as to which variables should be used

in a generic corruption regression. Although this problem exists in other macroeconomic

domains, such as the growth literature, many of these areas have found ways to produce

sets of control variables, either by repeated empirical evidence across numerous studies or

sometimes by using Bayesian Model Averaging (BMA). Most prominently, Brock and Durlauf

(2001) use model averaging to study the determinants of economic growth and subsequent

work has carried out similar exercises, such as Durlauf et al. (2008), Durlauf et al. (2012),

and Mirestean and Tsangarides (2016).

Third, as with many other macroeconomic variables, the literature has accumulated evi-

dence for reverse causality concerns between corruption and several of its potential determi-

nants. In general, corruption has been shown to specifically affect institutional and economic

variables. For instance, Mauro (1995) and Mo (2001) highlight the impact of corruption on

economic variables, whereas Mauro (1998), Guriev (2004), and Lambsdorff (2005) point out

the effect of corruption on institutional characteristics.

3.2. Dealing with Endogeneity. The IVBMA methodology is precisely designed to ad-

dress small sample sizes, model uncertainty, and endogeneity concerns in finding robust

determinants of the dependent variable (Karl and Lenkoski, 2012; Koop et al., 2012). Fol-

lowing the extant empirical literature, we consider the following institutional and economic

14

variables as endogenous: income levels, government size, government effectiveness, the polit-

ical regime form, political rights, press freedom, property rights, rule of law, trade freedom,

trade and imports (both as percentage of GDP), and foreign direct investment. We refer

to the respective papers for why these factors should be considered as endogenous. (See

Treisman, 2000, for an intuitive discussion on endogeneity in this context. Dutt and Traca,

2010, show that corruption can influence trade flows. Smarzynska and Wei, 2000, Alesina

and Weder, 2002, and Egger and Winner, 2006, discuss how corruption can influence the

flow of foreign direct investment. We do not instrument for the share of natural resources in

GDP because of limited data availability.)

However, finding valid instrumental variables for potentially endogenous determinants has

proved elusive in the corruption literature, similar to studies on the determinants of economic

growth or government size.7 In general, prominent instruments at the macroeconomic level

usually include geographical characteristics or colonial origin. However, both of these variable

groups may independently be associated with corruption levels, making them poor candidates

in a comprehensive study of corruption determinants.

One avenue that has heretofore received little attention is the use of lagged values of the

endogenous variables – a common practice in the empirical growth literature (Temple, 1999;

Schularick and Steger, 2010; Mirestean and Tsangarides, 2016). Specifically, we use values

averaged from 1991 to 2000 for each endogenous variable to instrument for the respective

values taken in the years 2001 to 2010. For example, past values of income levels are

usually strong predictors of current income (i.e., “strong” instruments), but reverse causality

becomes less of a concern as future corruption levels are unlikely to affect past income levels

(i.e., instruments are “valid”).

Bazzi and Clemens (2013) provide an in-depth discussion of the strengths and weaknesses

associated with the use of lagged values as instruments in the specific context of assessing the

7One exception is provided by Larraın and Tavares (2004), who use geographical and cultural aspects asinstruments for foreign direct investment in explaining corruption. However, the exclusion restriction may beviolated, as cultural characteristics are likely to be directly (or through other channels) related to corruption.

15

determinants of economic growth. They note that accounting for all plausible connections

between lagged values of the endogenous variables and other relevant determinants of the

outcome variable would constitute too high of a standard for practical purposes; yet they

also urge caution in blindly using all available lags as instruments. While their discussion

is specific to the validity of instruments in cross-country growth studies, their insights are

germane to general econometric analysis using lagged instruments in the presence of numer-

ous potential determinants. Indeed, a number of recent country-level studies have employed

lagged values of endogenous regressors as instruments; in addition to the determinants of

economic growth, the underlying causes of democracy (Acemoglu et al., 2008), demographics

(Murtin, 2013), oil production (Arezki and Bruckner, 2011), and corruption (Bhattacharyya

and Hodler, 2010) have recently using lagged values as instruments for endogenous regressors.

4. Methodology

Our analysis closely follows the methodology proposed by Karl and Lenkoski (2012). Our

two-stage endogenous model for country i is

(1) CPIi = αXi + βWi + εi

and

(2) Xi = γZi + δWi + ρi

with Xi denoting a vector of those independent variables that may suffer from endogeneity

problems. Wi, on the other hand, denotes exogenous regressors, whereas Zi constitutes a

vector of instrumental variables. Finally, εi and ρi represent idiosyncratic error terms.

4.1. The General BMA Framework. A common solution to model uncertainty involves

averaging over all/many possible combinations of predictors. This was first suggested by

Leamer (1978) in his extreme bounds analysis (EBA) and Serra (2006) applies this technology

16

to sort through corruption determinants. However, it is widely agreed that EBA is too harsh

as it pertains to the impact a specific variable has on the outcome of interest (Sala-i Martin

et al., 1997; Brock and Durlauf, 2001). BMA is a feasible alternative to EBA, setting

priors on the inclusion of each potential covariate that enters the final, true model. Raftery

et al. (1997) show that, in the presence of predictor uncertainty, the performance of BMA is

superior to any single model selected using frequentist arguments. However, full BMA may

not be practical in some circumstances, as the number of possible variable combinations

increases quickly: for K predictors, the number of possible regression models to estimate

becomes 2K . This has led to the development of various algorithms based on the Markov

Chain Monte Carlo (MCMC) strategy.8

Let M = {M1,M2, . . . ,M2K} denote the model space where each model depends on a

vector of parameters θr (with r = 1, 2, . . . , 2K) characterized by a prior π(θr|Mr) and a

likelihood π(y|θr,Mr), where y is an N -dimensional vector such that y = β0ıN +Xrβr +ε, ıN

is an N×1 vector of ones, Xr is an N×Kr matrix of predictors and ε is a vector of stochastic

errors. Additionally, denote the posterior distribution as π(θr|y,Mr). The posterior density

of π(θr|y) is then given by

(3) π(θr|y) =2K∑r=1

π(θr|y,Mr)π(Mr|y).

The logic behind BMA is to obtain estimates for each model in M and then average over

the models using the posterior probabilities. This averaging requires the posterior model

probability, which is proportional to π(y|Mr)π(Mr),

(4) π(Mr|y) ∝ π(y|Mr)π(Mr),

8While other mechanisms exist to implement BMA for large K, such as Occam’s window (Madigan andRaftery, 1994) or Mode Oriented Stochastic Search (Lenkoski and Dobra, 2011; Eicher et al., 2012a), MCMCremains the main implementation device for applied researchers (Zeugner and Feldkircher, 2009; Ciccone andJarocinski, 2010; Feldkircher and Zeugner, 2012). Further, Eicher et al. (2011) note that their results remainconsistent across an MCMC algorithm and the leaps and bounds method to sample from the model space.

17

with marginal likelihood given by

(5) π(y|Mr) =

∫Rr

π(y|θr,Mr)π(θr|Mr)dθr

and π(Mr) representing the model probability. Two main implementation issues remain: the

integral in equation (4) is difficult to implement and the number of variable combinations

can be extremely large. These issues can be handled using a birth/death MCMC algorithm

– an adaptation from the mechanism originally developed by Madigan and York (1995).

4.2. Practical Issues. The birth/death MCMC algorithm consists of a mechanism for sam-

pling over a model space M, based on a Metropolis-Hastings algorithm (Metropolis et al.,

1953; Hastings, 1970). It simulates a chain of models, denoted by M (s) (for s = 1, 2, . . . , S),

where the algorithm draws candidate models from a particular distribution over the model

space and then accepts them with a certain probability. If a candidate model is not ac-

cepted, the chain remains in the current model (Koop et al., 2012). A candidate model M (c)

is drawn randomly from the set of models, including (i) the current model M (s−1), (ii) all

models which delete one predictor from M (s−1), and (iii) all models which add one predictor

to M (s−1). The acceptance probability then becomes

(6) α(M (s−1),M c

)= min

{π(y|M c)π(M c)

π(y|M (s−1))π(M (s−1)), 1

}.

Given the computational burden of BMA, the applied literature has favored use of the nat-

ural conjugate approach – the Normal linear model where ε ∼ N(0, τ−1IN). This leads to

π(βr|τ) ∼ N(0, τ−1 (grX

′rXr)

−1). In practice, this means that the prior density is centered

over the hypothesis that the explanatory variables do not have an effect on the dependent

variable, and that the covariance of the explanatory variables is proportional to the com-

parable data-based quantity. Additionally, we assume a non-informative prior for common

parameters to all models, denoted τ and β0. Specifically, π(τ) ∝ 1τ

and π(β0) ∝ 1. In this

context, using extensive simulation exercises, Fernandez et al. (2001) recommend selecting

18

gr = 1/K2 if n ≤ K2 or gr = n−1 if n > K2. Ley and Steel (2009), in contrast, propose a

Beta-Binomial prior on π(Mr), because the resulting prior model distribution is considerably

less tight and should thus reduce the risk of unintended consequences from imposing a par-

ticular prior model size. The latter method only requires one to choose the prior expected

model size. In particular, if the prior expected model size is equal to K/2, the model prior

is completely flat over model sizes.

With these considerations in mind, we estimate multiple Bayesian normal linear models

whose differences are given by the combination of predictors, and where we choose the best

models using a birth/death MCMC mechanism based on a Metropolis-Hastings algorithm.

Finally, we average these models, obtaining posterior parameters and predictive distributions.

Specifically, one model is randomly drawn and accepted if its marginal likelihood is superior

to the marginal likelihood of the current model – if not, the new model is randomly accepted

according to a probability that depends on the ratio of marginal likelihoods. The procedure

is performed many times and eventually we average all possible models weighted by the

number of times the specific model was selected. In the end, a variable becomes a candidate

to explain the dependent variable if it appears many times in models with high marginal

likelihoods.

4.3. The IVBMA Methodology. The crucial extension from a generic BMA framework

to the IVBMA methodology (Koop et al., 2012; Karl and Lenkoski, 2012) recognizes and

addresses potentially underlying endogeneity concerns. We follow the procedure developed

by Karl and Lenkoski (2012), who note that direct model comparisons are intractable, and

introduce the notion of a conditional Bayes factor (CBF). The CBF compares two models in

a nested hierarchical system, conditional on parameters not influenced by the models under

consideration. A benefit of the CBF for the models in both stages is that it is straightforward

to calculate and reduces to the normalizing constants of a multivariate normal distribution.

The empirical procedure of Karl and Lenkoski (2012) is similar in spirit to a two-stage

19

least squares estimator. In particular, it contains two sets of equations: in the first stage,

the endogenous variable is estimated as a function of a set of instrumental variables, in

addition to the exogenous variables from the principal regression of interest. Endogeneity is

considered in the covariance matrix of the stochastic perturbations, which connects the first

and second stages.

The CBF selects the best models in each stage in an iterative way, using a Gibbs sampler.

The second stage then uses the predicted values of the first stage regression, together with

the remaining exogenous variables in the initial regression framework to estimate the second

stage equation. These second stage estimates are then averaged to produce the final IVBMA

estimates. Note that this approach is more appropriate than two separate BMA analyses,

where the instrumental regression is estimated using BMA and a second BMA analysis is

conducted in the second stage using the fitted regressors from the first stage. This is also

computationally more efficient. In the two stage approach all models returned from the first

stage would then be used to generate predicted values for the second stage and a further

BMA analysis is run. IVBMA models the full system jointly, reducing computing time

dramatically. In Karl and Lenkoski (2012)’s application, the difference is over 90 fold.

5. Empirical Findings

5.1. BMA Results. Table 3 shows the corresponding results from our BMA analysis, sort-

ing all variables by their posterior inclusion probability (PIP) into the true model. In this

context, the conventional literature on classifications for BMA proposes five categories, fol-

lowing Kass and Raftery (1995) and Eicher et al. (2012b): PIP > 0.99 provides decisive

evidence, 0.95 < PIP < 0.99 strong evidence, 0.75 < PIP < 0.95 positive evidence, and

0.50 < PIP < 0.75 suggests weak evidence. In addition, the conditional posterior sign

(CPS) indicates whether the proposed effect on the CPI is negative (CPS tends to zero) or

positive (CPS tends to one).

The BMA findings produce decisive evidence for the rule of law to be associated with

20

Table 3. BMA results for dependent variable CPI (total sample of 123 coun-tries). This table shows first stage inclusion probabilities (PIP), theposterior mean (Mean), the posterior standard deviation (PostSD), as well as the conditional posterior sign (CPS) for each vari-able included.

Variable PIP Post Mean Post SD CPS

Rule of law 1.000 1.514 0.276 1.000Female parliament seats 0.973 0.026 0.008 1.000Urbanization rate 0.917 0.942 0.389 1.000Property rights 0.897 0.020 0.009 1.000Absence of political rights 0.847 0.175 0.128 1.000Polity IV 0.388 0.023 0.034 0.965Share of protestants 0.255 0.247 0.476 1.000Asia 0.217 -0.067 0.148 0.000Government effectiveness 0.194 0.108 0.254 1.000Duration of primary education 0.153 0.018 0.049 1.000Population size 0.145 -0.013 0.036 0.000Religious fractionalization 0.135 0.056 0.165 1.000Trade openness 0.106 0.000 0.001 0.003Years of democracy 0.085 0.001 0.002 0.995Imports as % of GDP 0.082 0.000 0.002 0.028Dutch origin 0.079 -0.026 0.107 0.000Absence of press freedom 0.072 0.000 0.003 0.228Language fractionalization 0.068 0.019 0.087 1.000Ethnic fractionalization 0.061 0.019 0.094 0.995Portuguese origin 0.051 0.012 0.070 1.000GDP per capita 0.043 0.003 0.024 0.796Government size 0.043 0.000 0.003 1.000South America & Caribbean 0.042 0.007 0.054 0.956Trade freedom 0.041 0.000 0.002 0.968Europe 0.040 -0.004 0.042 0.163Duration of secondary education 0.039 -0.001 0.016 0.162Africa 0.038 0.000 0.043 0.600British origin 0.035 -0.004 0.033 0.029Enrollment rate secondary education 0.034 0.000 0.001 0.300Federal system 0.034 -0.003 0.034 0.051Spanish origin 0.031 0.002 0.036 0.727Natural resource rents 0.030 0.000 0.001 0.500Common law 0.030 -0.001 0.027 0.384Foreign direct investment 0.029 0.000 0.003 0.816French origin 0.028 -0.001 0.022 0.348North America 0.026 0.000 0.038 0.600

21

corruption. The suggested relationship is positive (CPS equal to one), implying that a

stronger prevalence of the rule of law leads to higher CPI scores, i.e., less corruption. Further,

the BMA analysis proposes strong evidence for the claim that a higher percentage of women

in parliament corresponds to less corruption. Beyond that, the findings indicate positive

evidence for urbanization, property rights, and political rights to be positively associated

with the absence of corruption. Notice also the absence of GDP per capita in this list.

How do these findings compare to the existing literature on corruption determinants?

Regarding the role of law and order, Brunetti and Weder (2003), Dreher et al. (2009),

and Iwasaki and Suzuki (2012) also find an important relationship to corruption on the

country level. In a related literature, Acemoglu et al. (2005) show that the enforcement of

property rights and the rule of law have been crucial in determining income levels today.9 Our

BMA findings conclude that the enforcement of law and order is also crucial in explaining

corruption levels, another vital characteristic in the development of a country. Note that

other comprehensive studies on corruption determinants, such as Treisman (2000) or Serra

(2006), do not explicitly incorporate the rule of law. Further in line with the argument

provided by Acemoglu et al. (2005), property rights and political rights also appear closely

related to corruption levels in our BMA results.

Regarding female representation in parliament, our BMA findings confirm conclusions

from Dollar et al. (2001) and Swamy et al. (2001). It appears as if having a larger fraction

of women as political decision-makers can indeed decrease corruption levels. This notion is

also related to recent findings on women exhibiting higher ethical standards (Grove et al.,

2011). Here again, major comprehensive studies at the time (Treisman, 2000; Serra, 2006)

have not incorporated measurements for female participation in politics.

Four of the five meaningful correlates of corruption are related to the institutional frame-

work of a country. These findings relate well to conclusions by Fisman and Miguel (2007)

9Specifically, page 397 of Acemoglu et al. (2005) and Acemoglu et al. (2001) describe the importance ofproperty rights and how these are closely linked to the rule of law.

22

who find that (the enforcement of) regulations can drastically reduce corruption. Similarly,

Barr and Serra (2010) find that foreign students behave consistent to the corruption level

in their country of origin in a laboratory experiment, but this correlation disappears once

students have spent significant time in a much less corrupt country, such as the United King-

dom. Thus, institutional rules and regulations may be fundamental in describing corruption.

Nevertheless, these findings from our BMA analysis could be influenced by reverse causality.

Thus, we now move to the IVBMA framework, intended to alleviate such concerns.

5.2. IVBMA Results. Table 4 retains all sample countries and shows the second stage

of our IVBMA estimation, using lagged values of the potentially endogenous variables as

instruments. We immediately notice that several of our IVBMA estimates differ from our

basic BMA results, potentially implying endogeneity bias hampering the interpretative power

of a generic BMA framework here. Now, GDP per capita and years of primary education

emerge as crucial factors with PIPs of one, suggesting decisive evidence for their importance.

In addition, Table 4 displays the range of the estimated coefficient, which allows us to deduce

the likely sign of the relationship between the determinant of interest and corruption. As

expected, income levels and the length of primary schooling are positively associated with

the absence of corruption.

Further, we find positive evidence for trade freedom (PIP of 0.88), the rule of law (PIP

of 0.84), and a federal system (PIP of 0.79) in determining corruption. Surprisingly, female

participation in parliament no longer plays a role in determining corruption (PIP of 0.48).

The same applies to the urbanization rate and the institutional characteristics highlighted

in our BMA findings regarding property rights and political rights (PIPs of 0.20 and 0.63).

In general, comparing Table 4 to Table 3 reveals meaningful differences. Most notably,

income levels and primary education are taking the lead, replacing the rule of law and

female participation in parliament as the top predictors of corruption. The fact that richer

countries emerge as less corrupt has been well-established as the most persistent finding in

23

Table 4. Main IVBMA results for dependent variable CPI (total sample of123 countries). This table shows second stage inclusion probabilities(Prob), the posterior mean (Mean), as well as 2.5%, 50% and97.5% posterior quantiles (Lower, Median, Upper, respectively)for each variable included.

Variable Prob Mean Lower Med Upper

GDP per capita 1.000 0.185 0.008 0.149 0.661Duration of primary education 1.000 0.155 -0.080 0.165 0.297Trade freedom 0.879 0.017 0.000 0.016 0.036Rule of law 0.842 1.206 0.000 1.544 1.778Federal system 0.789 -0.010 -0.300 0.000 0.247Language fractionalization 0.716 0.135 -0.269 0.055 0.633Absence of political rights 0.626 -0.058 -0.659 0.000 0.121French origin 0.605 0.131 -0.077 0.055 0.528Urbanization rate 0.574 0.411 -0.063 0.293 1.407Dutch origin 0.558 -0.258 -0.830 -0.067 0.095Female parliament seats 0.479 0.012 0.000 0.000 0.034Government size 0.442 0.004 -0.029 0.000 0.037Polity IV 0.353 -0.025 -0.171 0.000 0.012Europe 0.326 -0.024 -0.270 0.000 0.162South America & Caribbean 0.300 0.008 -0.522 0.000 0.360North America 0.300 -0.089 -0.617 0.000 0.032Ethnic fractionalization 0.279 0.061 -0.091 0.000 0.495Share of protestants 0.279 0.145 0.000 0.000 0.984Foreign direct investment 0.274 -0.011 -0.066 0.000 0.000Government effectiveness 0.263 0.214 0.000 0.000 1.145Religious fractionalization 0.237 0.078 0.000 0.000 0.550Asia 0.226 -0.030 -0.282 0.000 0.020Property rights 0.179 0.010 0.000 0.000 0.089Common law 0.168 -0.016 -0.223 0.000 0.026Africa 0.037 -0.008 -0.094 0.000 0.000Population size 0.026 0.000 0.000 0.000 0.000British origin 0.005 -0.001 0.000 0.000 0.000Absence of press freedom 0.000 0.000 0.000 0.000 0.000Trade openness 0.000 0.000 0.000 0.000 0.000Imports as % of GDP 0.000 0.000 0.000 0.000 0.000Years of democracy 0.000 0.000 0.000 0.000 0.000Spanish origin 0.000 0.000 0.000 0.000 0.000Portuguese origin 0.000 0.000 0.000 0.000 0.000Duration of secondary education 0.000 0.000 0.000 0.000 0.000Enrollment rate secondary education 0.000 0.000 0.000 0.000 0.000Natural resource rents 0.000 0.000 0.000 0.000 0.000

24

the corresponding literature. This result confirms recent conclusions highlighting a strong

causal influence of income on corruption, as opposed vice versa (Gundlach and Paldam,

2009). After all, economic growth may indeed be a powerful medicine against corruption.

The forceful emergence of basic educational standards, on the other hand, comes as a

surprise, considering the corruption literature. Although a list of empirical studies on cor-

ruption determinants controls for some measurement of schooling, few find a statistically

meaningful relationship to corruption. In fact, virtually no paper has specifically proposed

education as a driving factor in explaining corrupt behavior. One exception is provided by

Truex (2011), who has found educational outcomes to be a meaningful predictor of indi-

vidual attitudes towards corruption in Nepal. On the country level, those papers that do

control for education usually incorporate average educational attainment as the sole variable

capturing education. In this context, using a BMA framework provides a firm advantage,

as incorporating additional variables is not punished by the model (for example by allowing

for less degrees of freedom) and we are able to incorporate several distinct measurements

of different educational aspects: primary, secondary, and tertiary schooling and enrollment

rates.10 Our findings strongly suggest the extent of primary schooling as a powerful predictor

of the level of corruption.

Overall, institutional variables lose ground in the IVBMA framework, compared to the

results from a basic BMA estimation. For instance, the rule of law still appears relevant with

a PIP of 0.84, yet is not as dominant as suggested by our BMA results (PIP of one). Thus,

law and order does seem to matter in determining corruption, but not as much as income

levels and basic education. Further, we now observe regulations on free trade as a mean-

ingful determinant of corruption, rather than rights over property and political involvement.

These results are in line with findings by Paldam (2002) and Iwasaki and Suzuki (2012).

Most papers usually incorporate the extent of trade (e.g., imports as a fraction of GDP), yet

10Tertiary schooling never proves to be statistically relevant in alternative estimations and has been ex-cluded from the main analysis because of limited data availability (missing for 34 sample countries). Thecorresponding results are available upon request.

25

our results suggest that it is not the realization of trade, but rather the regulatory frame-

work surrounding trade that is related to corruption. Notice that this institutional aspect

differs greatly from other, previously noted institutional characteristics, such as political

rights. This distinction may prove important for designing powerful measurements against

corruption.

To illustrate our findings, Table 5 displays the relevant data for the ten most and ten

least corrupt countries in our sample. First, it becomes very clear that the least corrupt

nations are also among the richest, whereas the most corrupt economies are poor, with the

exception of Equatorial Guinea posting income levels right around the sample mean. In

terms of primary enrollment rates, especially Angola and Tajikistan exhibit shorter primary

education (4.8 and four years on average), whereas Australia and New Zealand are standing

out with a longer than average primary school duration of seven years. Differences between

the least and most corrupt nations then become evident when looking at regulations on

trade freedom: the top ten economies all show more than one standard deviation above the

sample mean, whereas the bottom ten show below average values, with the exception of

Angola. This strong distinction can also be observed for the prevalence of the rule of law,

as nine of the top ten countries display values over two standard deviations above the mean.

Eight of the ten most corrupt nations, however, score more than one standard deviation

below the sample mean.

From these general IVBMA results, we now consider a subsample that excludes the OECD

member nations, thereby focusing on developing countries (although developing is broadly

defined in this context).

5.3. Focusing on Developing Countries. Corruption has traditionally been a more per-

sistent problem in developing countries with many economies being plagued by rampant,

persistent corruption. Thus, one natural extension of our main IVBMA analysis comes from

focusing on those nations specifically. In related research areas, Masanjala and Papageorgiou

26

Table 5. The 10 least and 10 most corrupt countries among all 123 samplecountries.

Country CPI GDP/capita Duration of Trade Rule Federalin 2000 US$ primary freedom of law system

education

10 Least Corrupt Countries

Finland 9.47 26,394 6 83 1.91New Zealand 9.46 14,770 6 81 1.86Denmark 9.43 31,087 6 83 1.93Sweden 9.22 30,980 6 82 1.88Switzerland 8.87 36,320 6 85 1.90 yesNetherlands 8.83 25,614 6 83 1.74Canada 8.70 25,167 6 84 1.78 yesAustralia 8.69 23,978 7 80 1.77 yesNorway 8.63 40,009 7 85 1.94United Kingdom 8.29 27,868 6 83 1.68

Mean 8.96 28,218 6.2 82 1.84

10 Most Corrupt Countries

Bangladesh 1.70 456 5 36 -0.80Chad 1.73 261 6 55 -1.27Guinea 1.84 488 6 51 -1.29Equatorial Guinea 1.88 6,710 5.4 55 -1.25Congo, Dem. Rep. 1.94 93 6 62 -1.79Angola 1.96 1,014 4.8 71 -1.32Guinea-Bissau 2.03 157 6 60 -1.28Tajikistan 2.04 211 4 73 -1.07Cambodia 2.05 441 6 59 -1.15Kenya 2.07 434 6 65 -0.96

Mean 1.92 1,026 5.52 59 -1.22

Sample Mean 4.00 6,710 5.6 69 -0.10 0.12Sample Std. Dev. 2.05 9,718 0.9 11 0.95 0.33Sample Median 3.17 2,040 6 70 -0.37 0

27

(2008) find substantial heterogeneity across countries in determining income levels. Similarly,

Cervellati et al. (2014) find the relationship between income and democracy to differ sub-

stantially when splitting a global sample into subgroups of nations. Thus, the determinants

of major macroeconomic variables can vary across countries with different backgrounds. In

our context of corruption determinants, we now apply the same IVBMA methodology to

those countries that were not members of the OECD as of 2010, producing a sample of 95

countries, equivalent to 72.5 percent of the world population.

Table 6 displays the corresponding findings. Most notably, income levels remain crucial,

confirming our main finding from analyzing the global sample in Table 4. Beyond that,

the rule of law reemerges as a strong indicator of less corruption, returning a PIP of one,

as opposed to 0.84 in the general IVBMA analysis. Thus, legal accountability matters

specifically for developing nations. Another interpretation of this finding may be that once a

certain threshold of law and order is reached, there are little gains with respect to corruption

after that. If the rule of law is not very developed, on the other hand, strengthening its

power can carry valuable benefits in the fight against corruption.

Further, foreign direct investment emerges as a meaningful predictor of corruption levels

in developing nations with a PIP of 0.97. These results confirm previous conclusions from

Larraın and Tavares (2004). Note that the lower bound of the derived coefficient returns a

negative sign (-0.122), whereas the upper bound is positive, displaying virtually the same

magnitude (0.123). Thus, the sign of the relationship between FDI and corruption remains

ambiguous, but a close connection is implied.

Below the threshold level of PIP = 0.95, we find positive evidence for the importance

of political rights, government size, and religious fractionalization in explaining corruption

in non-OECD nations. The ranges of the estimated coefficients (lower and upper bounds

in Table 6) suggest that these three variables lower corruption. Overall, institutional char-

acteristics emerge with more force in this subsample, as opposed to the global sample. It

is interesting to see that bigger governments contain corruption, as previously suggested by

28

Table 6. IVBMA results for dependent variable CPI, using non-OECD coun-tries (95 countries). This table shows second stage inclusion prob-abilities (Prob), the posterior mean (Mean), as well as 2.5%, 50%and 97.5% posterior quantiles (Lower, Median, Upper, respec-tively) for each variable included.

Variable Prob Mean Lower Med Upper

GDP per capita 1.000 0.219 0.040 0.231 0.383Rule of law 1.000 1.029 0.692 1.011 1.410Foreign direct investment 0.968 -0.023 -0.122 -0.049 0.123Absence of political rights 0.916 0.118 -0.046 0.097 0.338Government size 0.826 0.034 0.000 0.035 0.067Religios fractionalization 0.800 0.342 -0.003 0.356 0.802Urbanization rate 0.726 0.278 -0.186 0.167 1.075North America 0.611 -0.114 -0.585 0.000 0.182Ethnic fractionalization 0.611 0.172 -0.207 0.036 0.743Female parliament seats 0.547 0.002 -0.006 0.000 0.016Europe 0.537 -0.084 -0.442 0.000 0.149Federal system 0.421 0.027 -0.180 0.000 0.315Trade openness 0.411 -0.001 -0.009 0.000 0.001Dutch origin 0.395 -0.102 -0.480 0.000 0.000Natural resource rents 0.374 -0.004 -0.020 0.000 0.000Polity IV 0.368 0.013 0.000 0.000 0.065Common law 0.342 -0.029 -0.256 0.000 0.110Spanish origin 0.295 0.141 0.000 0.000 0.720Share of protestants 0.295 0.101 -0.356 0.000 0.944Duration of primary education 0.263 0.059 0.000 0.000 0.290Absence of press freedom 0.189 -0.001 -0.010 0.000 0.005Government effectiveness 0.105 0.043 0.000 0.000 0.571British origin 0.079 -0.014 -0.218 0.000 0.000South America & Caribbean 0.011 0.004 0.000 0.000 0.000Asia 0.005 0.000 0.000 0.000 0.000Property rights 0.000 0.000 0.000 0.000 0.000Trade freedom 0.000 0.000 0.000 0.000 0.000Imports as % of GDP 0.000 0.000 0.000 0.000 0.000Years of democracy 0.000 0.000 0.000 0.000 0.000Portuguese origin 0.000 0.000 0.000 0.000 0.000French origin 0.000 0.000 0.000 0.000 0.000Africa 0.000 0.000 0.000 0.000 0.000Duration of secondary education 0.000 0.000 0.000 0.000 0.000Enrollment rate secondary education 0.000 0.000 0.000 0.000 0.000Population size 0.000 0.000 0.000 0.000 0.000Language fractionalization 0.000 0.000 0.000 0.000 0.000

29

Fisman and Gatti (2002) and Billger and Goel (2009). Given that larger governments are

usually associated with lower growth (Bergh and Henrekson, 2011), the connection between

government size, income levels, and corruption may provide interesting avenues for further

research. It is also surprising that religious fractionalization is suggested to lower corruption.

As corrupt activities require a certain degree of trust between the briber and the bribee, it

is possible that different religious (and therefore cultural) backgrounds impede collusion.

However, these hypotheses remain speculative at this point, indicating promising avenues

for future research.

6. Conclusions

The previous empirical literature has made important contributions in describing the his-

torical origins of corruption, highlighting cultural heritage, institutions, and development.

Drawing on these findings, we focus on isolating the exact sources of corruption while ad-

dressing model uncertainty and endogeneity concerns. We construct a comprehensive sample

of 36 (originally 52) potential corruption determinants from 11 different data sources for 123

countries over the time frame from 2001 to 2010. To better isolate causality, we use a recently

developed IVBMA strategy and instrument potentially endogenous corruption determinants

(institutional and economic variables) with their lagged values from 1991 to 2000.

Our results deliver decisive evidence for income levels and the extent of primary education

as the main determinants of corruption, independent of cultural characteristics. Confirm-

ing findings from Glaeser and Saks (2006) who study corruption in the US, this result is

encouraging for political and societal efforts to promote broad, extensive coverage of ba-

sic education. Beyond the conventionally expected gains from raising education standards,

such as higher growth rates via human capital formation (Mankiw et al., 1992), containing

corruption may also be a consequence. In fact, the Inter-American Development Bank has

recently acknowledged education as a potential medicine against corruption (IDB, 2015) and

30

providing universal primary education for all humans is one of the United Nations Millen-

nium Development Goals. An educated population is likely more aware of corrupt activities

and more able to monitor public officials, everything else equal (Glaeser and Saks, 2006).

Finally, we also find some evidence for trade freedom and the rule of law to be associated

with the absence of corruption.

We then consider a subsample of 95 non-OECD countries, addressing the fact that cor-

ruption is most rampant in developing economies. Income levels remain crucial and, in

addition, foreign direct investment receives some support as a meaningful determinant of

corruption. Further, the rule of law emerges as a forceful predictor of diminished corruption

scores. Thus, institutions do matter, but particularly in developing countries and specifically

the presence of law and order. In a broader context, this result is in line with Acemoglu

et al. (2001) who provide strong evidence for the importance of the expropriation risk (a

concept closely aligned with the rule of law) in determining long-term development. Specific

to corruption, Brunetti and Weder (2003) and Iwasaki and Suzuki (2012) have found that

the presence of law and order matters. Finally, we find evidence (albeit weaker) for political

rights, government size, and religious fractionalization to limit corruption.

Overall, this study intends to provide a clearer picture of how corruption is determined

and particularly how societies can fight corruption. This study suggests distinct economic

and institutional characteristics as the main drivers of corruption, whereas deeply rooted

historical, cultural, and geographical aspects remain secondary. These findings should give

hope to and offer precise avenues for policymakers determined to combat corruption.

31

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36

Appendix

Table

A1.

Data

sources

and

calculation

s

Varia

ble

Sourc

eC

alc

ula

tion

CP

IT

ransp

aren

cyIn

ternatio

nal

Com

posite

index

,m

easu

ring

the

deg

reeof

perceiv

edco

rruptio

non

asca

lefro

m0

to10,

where

hig

her

valu

esin

dica

teless

corru

ptio

n

Institu

tionalFactors

Gov

ernm

ent

sizeW

orld

Bank

Gen

eral

gov

ernm

ent

final

consu

mptio

nex

pen

ditu

re(%

of

GD

P)

Gov

ernm

ent

effectiv

e-ness

World

wid

eG

overn

ance

Indica

tors

(1996-

2011,

Kaufm

ann

etal.,

2010)

Estim

ate

of

gov

ernance

effectiv

eness

(ranges

from

approx

imately

-2.5

(wea

k)

to2.5

(strong)

gov

ernance

perfo

rmance)

Polity

IVP

olity

IV(M

arsh

all

and

Jaggers,

2002)

Varia

ble

polity

2m

easu

ring

level

of

dem

ocra

cy,ra

ngin

gfro

m-1

0(to

tally

auto

cratic)

to+

10

(tota

ldem

ocra

cy)

Absen

ceof

politica

lrig

hts

Quality

of

Gov

ernm

ent

(Teo

rellet

al.,

2011)

Mea

sures

wheth

erp

eople

can

particip

ate

freelyin

the

politica

lpro

cess,w

ithva

lues

rangin

gfro

m1

(most

free)to

7(lea

stfree).

Pro

perty

rights

Quality

of

Gov

ernm

ent

(Teo

rellet

al.,

2011)

Rangin

gfro

m0

to100,

with

100

represen

ting

the

maxim

um

deg

reeof

pro

perty

rights

pro

tection.

Rule

of

lawQ

uality

of

Gov

ernm

ent

(Teo

rellet

al.,

2011)

Rule

of

lawestim

ate

Yea

rsof

dem

ocra

cyP

olity

IV(M

arsh

all

and

Jaggers,

2002)

Consecu

tive

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rsw

ithpolity

2>

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efore

2001

Com

mon

lawT

reisman

(2000)

Dum

my

for

English

com

mon

lawsy

stem

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eral

system

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mof

Fed

eratio

ns

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my

for

federa

lstru

cture

Absen

ceofpress

freedom

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om

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refro

m0-1

00

(under

30:

free;31-6

0:

partly

free;61-1

00:

not

free)

Tra

de

freedom

Index

of

Eco

nom

icF

reedom

0–

100

(the

hig

her

the

more

freedom

)

Eco

nomic

Factors

GD

Pp

erca

pita

World

Bank

Ln(G

DP

per

capita

in2000

US$)

Imp

orts

as

%of

GD

PW

orld

Bank

Imp

orts

of

goods

and

services

as

%of

GD

P

Tra

de

op

enness

World

Bank

Exp

orts

plu

sim

ports

as

%of

GD

P

Foreig

ndirect

invest-

men

tW

orld

Bank

Foreig

ndirect

investm

ent

in%

of

GD

P

Natu

ral

resource

rents

World

Bank

Tota

lnatu

ral

resources

rents

in%

of

GD

P

Cultu

ralFactors

Popula

tion

sizeW

orld

Bank

Ln(to

tal

popula

tion)

Urb

aniza

tion

rate

World

Bank

Share

or

urb

an

popula

tion

inso

ciety

Share

of

pro

testants

Intern

atio

nal

Relig

ious

Freed

om

Data

(Grim

and

Fin

ke,

2006)

Share

of

pro

testants

inso

ciety

Eth

nic

fractio

naliza

tion

Alesin

aet

al.

(2003)

Eth

nic

fractio

naliza

tion

Language

fractio

naliza

-tio

nA

lesina

etal.

(2003)

Language

fractio

naliza

tion

Relig

ious

fractio

naliza

-tio

nA

lesina

etal.

(2003)

Relig

ious

fractio

naliza

tion

Fem

ale

parlia

men

tsea

tsW

orld

Bank

Pro

portio

nof

seats

held

by

wom

enin

natio

nal

parlia

men

ts

British

,D

utch

,F

rench

,P

ortu

guese,

and

Spanish

orig

in

5dum

mies

for

form

erco

lonies

Dura

tion

of

prim

ary

ed-

uca

tion

World

Bank

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tion

of

prim

ary

educa

tion

inyea

rs

Dura

tion

of

secondary

educa

tion

World

Bank

Dura

tion

of

secondary

educa

tion

inyea

rs

Enro

llmen

tra

tesec-

ondary

educa

tion

World

Bank

Sch

ool

enro

llmen

tseco

ndary,

gro

ss%

Geo

gra

phica

lFactors

Africa

,A

sia,

Euro

pe,

North

Am

erica,

South

Am

erica&

Carib

bea

n

5co

ntin

enta

ldum

mies

for

Africa

,A

sia,

Euro

pe,

North

Am

erica,

South

Am

ericaand

Carib

bea

n

37

Table A2. Summary statistics of full sample (123 countries). All variablesrepresent averages from annual observations between 2001 and2010, unless indicated otherwise.

Variable Mean (Std. Dev.) Min. Max. N

CPI 3.978 2.055 1.7 9.470 123

Institutional FactorsGovernment size 15.357 5.471 3.25 36.789 123

Government effectiveness 2002 – 2010 -0.013 0.931 -1.657 2.223 123

Polity IV 3.995 6.239 -10 10 123

Absence of political rights 2001 – 2009 3.435 2.07 1 7 123

Property rights 45.793 21.346 10 91 123

Rule of law 2002 – 2009 -0.099 0.936 -1.787 1.936 123

Years of democracy in 2001 16.992 17.446 0 42 123

Common law system in 2001 0.187 0.391 0 1 123

Federal system in 2001 0.122 0.329 0 1 123

Freedom of the press 47.874 22.712 9.5 93.400 123

Freedom to trade 69.099 11.195 34.5 85.150 123

Primary education, duration (years) 5.58 0.905 3 8 123

Secondary education, duration (years) 6.398 0.878 4 9 123

School enrollment, secondary (% gross) 74.171 29.678 9.576 138.277 123

Economic Factors

GDP per capita 7.746 1.545 4.535 10.597 123

Imports as % of GDP 44.557 19.456 12.205 118.362 123

Trade as % of GDP 85.935 36.074 25.812 199.906 123

Foreign direct investment 4.165 3.051 0.039 14.801 123

Total natural resource rents (% of GDP) 11.372 16.984 0.009 68.86 123

Cultural Factors

Population size 16.238 1.496 13.333 20.986 123

Urbanization rate 0.559 0.221 0.097 0.986 123

Share of protestants in 2001 0.045 0.119 0 0.84 123

Ethnic fractionalization in 2001 0.453 0.253 0.002 0.930 123

Language fractionalization in 2001 0.386 0.285 0.002 0.923 123

Religious fractionalization in 2001 0.414 0.235 0.004 0.86 123

Women in national parliaments in % 15.414 9.170 0 45.49 123

British colony 0.195 0.398 0 1 123

Dutch colony 0.057 0.233 0 1 123

French colony 0.211 0.41 0 1 123

Portuguese colony 0.057 0.233 0 1 123

Spanish colony 0.163 0.371 0 1 123

Geographical Factors

Africa 0.309 0.464 0 1 123

Asia 0.244 0.431 0 1 123

Europe 0.252 0.436 0 1 123

North America 0.049 0.216 0 1 123

South America & Caribbean 0.122 0.329 0 1 123

Instrumental Variables

Government size (1991 – 2000) 16.221 6.731 4.571 42.195 123

Government effectiveness (1991 – 2000) -0.019 0.912 -1.873 2.053 123

Polity IV index (1991 – 2000) 3.025 6.358 -10 10 123

Absence of political rights (1991 – 2000) 3.593 2.013 1 7 123

Property rights (1991 – 2000) 53.271 21.136 10 90 123

Rule of law (1996, 1998, 2000) -0.057 0.940 -2.044 1.986 123

Absence of press freedom (1991 – 2000) 46.921 22.051 6 92.25 123

Trade freedom (1991 – 2000) 60.245 14.737 14 81.567 123

GDP per capita (1991 – 2000) 7.497 1.542 4.807 10.5 123

Imports as % of GDP 40.454 20.404 8.334 124.932 123

Trade openness (1991 – 2000) 75.847 36.854 17.874 223.726 123

Foreign direct investment (1991 – 2000) 2.984 4.345 -2.542 38.736 123

38

Table A3. Data availability main sample (123 countries).

Variable Availability Missing observations

CPI 2001 – 2010 1: 9 countries. 2: 16 countries. 3: 7 countries. 4:8 countries. 5: 3 countries. 6: Djibouti, Guineau-Bissau. 9: Fiji.

Institutional FactorsGovernment size 2001 – 2010 1: 7 countries. 2: 3 countries. 3: 5 countries.

4: Angola, Burkina Faso. 5: Benin, Niger. 8:Guinea-Bissau.

Government effec-tiveness

2002 – 2010 None.

Polity IV 2001 – 2010 NoneAbsence of politicalrights

2001 – 2009 5: Russia.

Property rights 2001 – 2010 5: Angola, Burundi. 8: Congo, Dem. Rep.Rule of law 2002 – 2009 4: Russia.

Years of democracy 2001 None.Common law 2001 None.Federal system 2001 None.Absence of pressfreedom

2001 – 2010 None

Trade freedom 2001 – 2010 5: Angola, Burundi. 8: Congo, Dem. Rep.

Economic FactorsGDP per capita 2001 – 2010 1: 3 countries.

Imports as % ofGDP

2001 – 2010 1: 5 countries. 2: Libya, Trinidad & Tobago. 3: 3countries. 4: Burkina Faso, Jamaica. 5: Guyana,Niger. 8: Guinea-Bissau.

Trade openness 2001 – 2010 1: 5 countries. 2: Libya, Trinidad & Tobago. 3: 3countries. 4: Burkina Faso, Jamaica. 5: Guyana,Niger. 8: Guinea-Bissau.

Foreign direct in-vestment

2001 – 2010 1: 6 countries. 2: Gambia.

Natural resourcerents

2001 – 2010 1: 9 countries.

Geographical FactorsAfrica, Asia, Eu-rope, North Amer-ica, South America& Caribbean

2001 None.

Notes : Column 2 shows the years for which the respective variable is generally available. Column 3 first

displays the number of missing observations followed by the number of countries that are missing the

respective number of observations. For instance, “1: 7 countries” indicates that 7 sample countries are

missing one annual observation of the respective variable. If the indicated time frame is 10 years (e.g., 2001

– 2010), this means that 7 countries only have 9 annual values available for that variable between 2001 and

2010.

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Table A3. Data availability main sample (123 countries) – Continued.

Variable Name Availability Missing observations

Cultural FactorsPopulation size 2001 – 2010 None.Urbanization rate 2001 – 2010 None.Share of protes-tants

2001 None.

Female parliamentseats

2001 – 2010 1: 19 countries. 2: 10 countries. 3: Congo,Dem. Rep., Saudi Arabia. 4: 3 countries. 5:Fiji, Qatar.

Ethnic fractional-ization

2001 None.

Language fraction-alization

2001 None.

Religious fraction-alization

2001 None.

British, Dutch,French, Por-tuguese, or Spanishorigin

2001 None.

Duration of pri-mary education

2001 – 2010 None.

Duration of sec-ondary education

2001 – 2010 None.

Enrollment ratesecondary educa-tion

2001 – 2010 1: 34 countries. 2: 13 countries. 3: 8 countries.4: 4 countries. 5: 9 countries. 6: Libya, Oman.7: 4 countries. 8: 4 countries.

Instrumental VariablesGovernment sizet−1 1991 – 2000 1: 3 countries. 2: Cambodia, Djibouti. 3: Qatar.

4: Estonia. 6: Angola. 9: Lao PDR.Governmenteffectivenesst−1

1996, 1998, 2000 None.

Polity IVt−1 1991 – 2000 2: Slovak Republic.Absence of politicalrightst−1

1991 – 2000 2: Russia, Slovak Republic.

Property rightst−1 1995 – 2000 1: 28 countries. 2: 6 countries. 3: 4 countries. 4:5 countries.

Rule of lawt−1 1996, 1998, 2000 1: Russia.Absence of pressfreedomt−1

1993 – 2000 None.

Trade freedomt−1 1995 – 2000 1: 28 countries. 2: 6 countries. 3: 4 countries. 4:5 countries.

lngdpt−1 1991 – 2000 2: Cambodia. 4: Estonia, Kuwait. 8: Libya. 9:Ireland, Qatar.

Foreign directinvestmentt−1

1991 – 2000 1: 13 countries. 2: 5 countries. 3: Algeria, Gam-bia. 4: 3 countries. 6: Georgia.

Trade opennesst−1 1991 – 2000 1: 3 countries. 2: Cambodia. 3: Jamaica, Qatar.4: Estonia.

Imports as % ofGDPt−1

1991 – 2000 1: 3 countries. 2: Cambodia. 3: Jamaica, Qatar.4: Estonia.

Notes : Column 2 shows the years for which the respective variable is generally available. Column 3 first

displays the number of missing observations followed by the number of countries that are missing the

respective number of observations. For instance, “1: 7 countries” indicates that 7 sample countries are

missing one annual observation of the respective variable. If the indicated time frame is 10 years (e.g., 2001

– 2010), this means that 7 countries only have 9 annual values available for that variable between 2001 and

2010.

40