Does Oil Cause Corruption? Test of an Institutional Explanation

Nurjamal Omurkanova

ECPR Joint Sessions of WorkshopsSt. Gallen, SwitzerlandApril 12-17, 2011

Draft paper. Comments are welcome, but please do not quote or circulate without author’s permission.

Abstract

Poverty is one of the biggest problems the world faces today. Thereby, Kaufman

(2007) claims that corruption causes poverty. In a similar vein, Barack Obama at the

United Nations Summit described corruption as "a single greatest barrier to

prosperity" and "a profound violation of human rights". Further, Aslaksen (2007)

argues that oil augments corruption. Following this logic, the oil-producing countries

should be more corrupt and poorer as well. However, there are oil-producing

countries in the world that are economically highly developed and have very small

corruption rates. Then, what can explain this variation?

I hypothesise that whether oil leads to more corruption is conditional on the quality of

institutions. That is, the weaker the quality of institutions is, the stronger the

corrupting effect of oil would be. For the empirical tests (in 130 countries over 20

years) I will use index of institutional quality based on rules like accountability,

transparency and rule of law. Thereby, power distribution, freedom of press and

judicial independence will be used as proxies for these institutions. Consequently, as

it is argued that institutions are endogenous both to oil and to corruption I will apply

instrumental variable tests to control for simultaneity.

1. Problem and Research Question

Poverty is one of the biggest challenges the world faces today. There are

millions of people who live on less than US$ 1.25 a day1. Fighting poverty is listed

first among seven Millennium Development Goals (MDGs) set by 189 world leaders

in New York in 2000. It is argued that corruption is the main reason why there is

insufficient progress in achieving the MDGs2. Barack Obama (2010)3 describes

corruption as the "single greatest barrier to prosperity" and "a profound violation of

human rights". In a similar vein, Kaufman (2007) argues that the causal arrow runs

from corruption to poverty. Besides, Aslaksen (2007) demonstrates that oil increases

corruption. Combining these two arguments one would assume that oil-producing

countries should be on average more corrupt and they should be on average poorer

as well. However, that is not always the case. There are oil-producing countries in

the world, which are economically highly developed and have very small corruption

rates.

For instance, Canada accounts for the world's 2nd largest proved oil reserves4

and is the world's 6th largest oil producer5. Further, G8 member Canada is highly

industrialized and economically developed. According to the CIA 2010 World

Factbook, Canada is the 26th richest country in the world with a GDP per capita of US

$38.100. However, Canada ranks 6th in 2010 Corruption Perception Index (CPI)6. In

contrast, the Russian Federation which is the world's largest oil producer7 and a G8

member state as well, with a GDP per capita of US $15.100, is one of the most

corrupt countries in the world (a score of 2.1 in the 2010 TI CPI).

Based on these two examples one might assume that the level of economic

development is responsible for high corruption rates in oil-producing countries.

However, the United Arab Emirates possesses the world's 5th largest oil reserves

and it is the world's 10th largest oil producer, and it is even richer than Canada (US

1 World Development Indicators, World Bank.2 The Anti-Corruption Catalyst: Realising the MDGs by 2015. Transparency International, September 2010.3 The statement was made at the United Nations Summit in New York, 20-22 September 2010. 4 Thus, 13 percent of the world's oil is under Canada. But the majority of these reserves are difficult to process. These reserves are in the form of oil sand, which should be specially treated to convert it into crude oil before it can be refined into usable products. CIA World Factbook5 International Energy Agency, 2010 Key World Energy Statistics, www.iea.org 6 Transparency International 2010 Corruption Perception Index (CPI). The 2010 CPI measures the degree to which public sector corruption is perceived to exist in 178 countries around the world. It scores countries on a scale from 10 (very clean) to 0 (highly corrupt).7 International Energy Agency, 2010 Key World Energy Statistics

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$40.000 GDP per capita). Nevertheless, the United Arab Emirates' corruption score

is 6.3, which is worse than Canada’s. What factors then can explain the variation in

corruption rates among the oil-producing countries?

So far, it is claimed that corruption is one form of rent seeking (Murphy,

Shleifer and Vishny 1993)8. Oil and gas resources are supposed to enable even

higher rents (Ades and Di Tella 1999). As a result, natural resources lower economic

performance because they foster rent seeking (Sala-i-Martin and Subramanian

2003). Murshed (2004) examines the link between resource abundance and

economic growth and, therefore, highlights the importance of institutions. He points

out that oil and minerals retard democratic and institutional development, which in

turn restrains economic growth. Thus, the growth effect of oil depends on the

institutional quality. For instance, during the resource booms politicians can choose

different macroeconomic policies. Those policies determine the growth effect of

natural resources. Most importantly, the choice of policies is contingent on the quality

of institutions (Robinson, Torvik and Verdier 2006). Hence, this study further

analyses the link between oil and corruption to explain differences in corruption levels

among the oil-producing countries. This study specifically aims to find out whether

there is a conditional relationship between oil and corruption dependent on the

quality of institutions.

To answer the above-mentioned questions, I will conduct a cross-national

time-series analysis of the link between oil and corruption. It will be a global analysis

with a special focus on the post-communist countries. In doing so, this study, on the

one side, tests the existing theories and, on the other side, aims to provide a new

explanation of the nature of the relationship between oil and the spread of corruption

with the help of new empirical analyses.

In the following I will review the state of the art and specify the gap in the

literature. Subsequently, I will elucidate my theoretical argument and state my

hypotheses. I will further introduce the research design, describe the data and justify

the methodology I use.

8 Svensson (2005) defines corruption mainly as bribes. The author argues that corruption and rent-seeking are not the same, although often interchangeable. According to Svensson, "rent-seeking is the socially costly pursuit of rents, often created by governmental interventions in the economy, while bribes are technically a transfer."

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2. Definition of the main concepts

2.1. Oil and Gas Resources

Natural resources are categorized into renewable and non-renewable

resources. Both oil and natural gas belong to the non-renewable energy resources.

In the wake of the energy supply shortages and the search for alternative energy

resources, oil (“black gold”) and gas (“blue fuel”) are in a very high demand. It is

therefore obvious why energy security is among the top priorities in the national

security programs of the nation-states worldwide.

The literature differentiates between point and diffuse natural resources.

Diffuse resources are natural resources that are geographically extended (e.g.

agricultural lands and forests) and therefore not easy to control. Point resources, in

contrast, are known to be spatially concentrated and can possibly be controlled by

certain groups with relative ease. More importantly, point resources are those

primarily associated with a resource curse (Mehlum, Moene and Torvik 2006, Leite

and Weidmann 2002, Sala-i-Martin and Subramanian 2003, Isham et al. 2003). Both

oil and natural gas relate to the point resources (Wick 2008).

According to the Reference Scenario of International Energy Agency (IEA)9, oil

and gas prices will rise continuously. For instance, the average IEA crude oil import

price in 2008 was US $3 per barrel. The price is assumed to reach US $87 per bbl. in

2015, US $100 per bbl. by 2020 and US $115 per bbl. by 2030 (in year-2008 dollars).

It means that the oil-producing countries will get even larger revenues from oil and

gas production. In this connection a critical question to ask: will these revenues

contribute to economic and social development or will they strengthen corruption?

2.2. Corruption

Corruption is defined as "a misuse of public office for private gain" (Treisman

2000; Sandholtz et al. 2000). In a similar vein, Kaufmann (1997, 117) defines

corruption as a theft of public resources. Further, corruption is neither a characteristic

of a social system or an institution, nor a trait of an individual, but rather an illegal

exchange (Oxford dictionary of politics 2009, 123; Sandholtz et al. 2000). Svensson

9 World Energy Outlook 2009 Fact Sheet: Energy Price Assumptions. Price volatility will continue, but the days of cheap energy are over. http://www.worldenergyoutlook.org/docs/weo2009/fact_sheets_WEO_2009.pdf

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(2005, 20) argues that corruption is an outcome of a country’s institutions, or put

differently, corruption is a response to the rules prevalent in a society.

Rose-Ackermann (2007) distinguishes between low-level (also labelled as

bureaucratic or petty corruption) and high-level (also labelled as political, systematic,

administrative or 'grand') corruption. Low-level corruption occurs when officials or

private individuals use basic laws and regulations for personal benefit. Systematic

corruption is, by contrast, performed by high level state officials and politicians and

thus implicates the whole governmental structure from top to bottom. This latter form

of corruption has also been categorized as 'state capture'. 'State capture' occurs as

purchase of laws and regulations by senior state officials who take advantage of their

office to formulate and adopt laws and regulations in order to privilege specific client

groups (Heywood 2009, 363-364).

2.3. Institutions

Institutions are “the rules of the game in a society” (North 1999, 3). In this line,

Bueno de Mesquita et al. (2003) indicate that institutions determine the way the

political system works. Thus, institutions influence political selection and political

survival. Institutions serve as restraints that force politicians to make certain

decisions.

Another important feature to be emphasized is the structure of the game. It is

critical whose choices are affected by the rules (North 2003). Skocpol (1973, 16)

argues that "the significant question to ask is [...] who controls the political

mechanism and how they are organized."

Alongside with the rules of the game, there are also players in the game.

Groups of individuals brought together for a common purpose build organizations.

Organizations can vary from political to educational bodies. However, in the matter of

cause-and-effect chain, institutions affect both what organizations come into

existence and the way they evolve (North 1999, 4-5).

3. Resource Curse Debate

Resource curse debate is an object of a large theoretical and empirical

literature which has developed rapidly during the last two decades. Per se

abundance in natural resources is generally perceived to be a blessing since natural

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resources are a highly potential source of economic enrichment. It is therefore

paradoxical why “more of a good thing could be bad” (Wick and Bulte 2009, 140). Put

simply, it is puzzling why richness in natural resources is associated with a curse.

The following chapter introduces three main pillars of the resource curse debate. In

particular, it presents results of empirical studies on linkage between resource

abundance and (1) persistence of autocratic political regimes, (2) slow economic

growth and (3) a higher probability of civil war onset.

3.1. Resource Abundance and Political Regimes

Oil income is both an exogenous and endogenous factor. On the one hand,

the income generated through the production of oil is defined by the quantity and

quality of petroleum available, i.e. by the country's geological endowment. On the

other hand, oil income depends on the investments into the oil industry. Following

this logic, the geological endowment is an exogenous variable, although investments

are endogenous to the country's economic development and quality of institutions.

Thus, "countries that are wealthier, more open to foreign investment, and provide

better legal protection for investors, are likely to invest more into their petroleum

industries," (Ross 2009, 4).

In this connection Ross (2001, 2009) claims that cases of coexistence of high

oil incomes and democracy are spuriously correlated due to the omitted variables,

namely rule of law, sound property rights and higher education levels. The omitted

variables are assumed to affect both the attraction of investments into the oil and gas

industry and the strengthening of democracy. Nevertheless, Ross precludes any third

variables "that would foster both more oil income and less democracy" (Ross 2001,

14). However, it might be exactly because of those omitted variables that in some

countries oil rents are associated with autocratic regimes, economic decline and

corruption. For example, a weak rule of law, non-transparent and non-accountable

institutions can easily enable an increase in oil production while using these

revenues to suppress democratization further.

Instead, Ross explains a strong relationship between oil income and

authoritarian rule by the means of the rentier effect. According to him, a rentier effect

is the combination of low taxes and high government spending (Ross 2009, 16). The

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rentier effect consists of spending10, taxation and a group formation effect. At the

same time and in contrast to his earlier argumentation, Ross (2001, 337) argues that

the rentier effect takes place in countries where the governments have an arbitrary

power to spend public money (oil and gas revenues). In other words, because of the

unlimited power, leaders in authoritarian regimes will opt for immediate gains from

the oil industry, i.e. for more oil income. Moreover, with the help of oil income they

will suppress any political opposition in order to stay in power.

To summarize, I will assume that poor institutions enable both more oil income

and less democracy. Accordingly, the repression effect happens by reason of

authoritarian governments that easily manage to employ public assets to suppress

any springs of constructive opposition. For instance, Bellin (2004) and Jensen and

Wantchekon (2004) address political survival and robustness of the authoritarian

regimes in the Middle Eastern and North African states. Inter alia, Bellin highlights

the presence of strong coercive apparatuses11 and repression traditions in such

countries. To maintain the massive coercive apparatuses ruling elites in these

countries enjoy exceptional access to the rents which are largely recovered by oil

and gas resources. Therefore, Bellin emphasizes the importance of "impartial and

effective state institutions" (Bellin 2004, 152) since he expects them to help

democratic transition and consolidation in the Middle East and North Africa.

Similarly, Treisman (2010) shows that oil does not doom Russia to

authoritarianism. Even so, oil does have a minor and indirect impact on the political

regime in the country. Accordingly, booms in oil prices in certain periods bring the

country to economic growth. In the periods of high popularity, a president can make

significant changes to the system and determine the path of a country's development.

Thus, decisive is not the amount of oil rents, but the quality of institutions

constraining the likelihood of undemocratic changes.

10 The rentier effect consists of a spending, the taxation and a group formation effect. The spending effect takes place when government use revenues from oil and gas industries in order to buy political support. The taxation effect occurs when the governments in oil rich countries lower taxes and as a result enjoy weakening of a public demand for democratic accountability. The group formation effect comes about when ruling elites in authoritarian oil-producing countries inhibit formation of independent groups which could somehow threaten theirs political survival. Moreover, the governments can also finance quasi-independent organizations to serve as a facade for freedom of opinion (Ross 2009, 20-21). 11 The region's states still remain the world leaders in the share of GDP (4.9% in 2007) spending on the defence issues (Bellin 2004, 147; The Military Balance 2009 Executive Summary of the International Institute for Strategic Studies).

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3.2. Resource Abundance and Economic Growth

3.2.1. Institutional Quality and Economic Growth

The significance of institutions is commonly accepted, yet there are different

approaches in defining whether institutions have a causal effect on a country’s

development or are a mere result of it. It is puzzling whether institutional quality

contributes to the growth, or whether growth causes institutional improvements. The

empirical evidence on this issue is controversial. For example, Acemoglu, Robinson,

and Johnson (2005) highlight the importance of institutions which shed light on

different patterns of economic development throughout the world. In other words, the

quality of institutions determines economic outcomes. Similarly, Olson (1996, 18)

illustrates that a great deal of variance in nations' wealth can be explained by the

differences in the quality of their institutions and economic policies. In contrast to this,

Boix and Stokes (2003) argue that the causal arrow runs from economic growth to

high quality institutions. Glaeser et al. (2004) propose another argument. They claim

that human capital serves both economic development and institutional

improvements. Thus, for instance, policies securing property rights could be

implemented regardless of the type of political regime in a country. Even dictators

can adapt policies that lead to economic growth and minimize poverty. Consequently,

institutional quality rises as a country becomes richer (Glaeser et al. 2004, 298).

Kaufmann and Kraay (2002) highlight two kinds of developments. First, they

argue that better governance increases per capita income. Second, they show that

this causal chain does not run both ways, in the sense that higher per capita income

does not necessarily result in better governance. The authors show that there can be

even a negative effect of economic growth on the quality of governance. This applies

especially to countries with bad distributional institutions. More precisely, the authors

consider ‘state capture’ to occur where elites benefit from misgovernance (Kaufmann

and Kraay 2002, 223-224).

Furthermore, Knack and Keefer (1998) show the importance of good

institutions in convergence between poor and advanced economies. They illustrate

that despite investments in human and physical capital poor countries cannot catch

up with wealthy economies due to the insufficient institutional quality. Consequently,

poor countries grow much more slowly and "the poor are getting poorer" (Knack and

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Keefer 1998, 591). Thus, good quality institutions cause economic growth. The

authors control for reverse causality by running four different robustness tests. They

do not find any empirical support for the obverse hypothesis that economic growth

leads to institutional improvements.

3.2.2. Institutional Quality and Economic Growth in Resource Rich Countries

Natural resource abundance is supposed to retard economic growth (Sachs

and Warner 1995, 1997 and Isham et al. 2002). In this context, there is a well-known

‘Dutch disease’ argument in the literature on a resource curse. Oil industry produces

large revenues. It draws capital and labour away from the manufacturing sector. As a

result, manufacturing sector becomes less competitive. Large oil profits increase

national income and attract more foreign investments into the primary sector. Inflation

and over-valued real exchange rates follow currency devaluation. Thus, ‘Dutch

disease’ predicts a resource curse, i.e. a negative impact of resource abundance on

a country’s development. However, ‘Dutch disease’ argument cannot explain diverse

resource abundance effects across countries. There also exist resource rich

countries which experience economic growth.

For instance, Brunnschweiler (2009) demonstrates that in the post-communist

area there is no proof of the resource curse argument with regard to the linkage

between resource abundance and economic growth. The author argues that previous

studies (e.g. Sachs and Warner 1997 and 2001), which associate resource

abundance with bad institutions and slow growth use improper indicators (namely,

resource dependence) for resource wealth.

Further, Mehlum, Moene and Torvik (2006) show that the relationship between

resource abundance and economic growth depends on the institutional quality. The

authors argue that among resource abundant countries there are both growth losers

and growth winners. On one side there are countries like Botswana, Australia, and

Canada which have high economic growth rates. On the other side, there are

countries like Nigeria, Venezuela, Angola and Saudi Arabia, which are rich in

resources but nevertheless experience slow economic development. Hence,

Mehlum, Moene and Torvik (2006) differentiate between grabber friendly and

producer friendly institutions. The authors argue that the grabber friendly institutions

exist inter alia due to a weak rule of law, malfunctioning bureaucracy and corruption.

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Thus, by grabber friendly institutions, i.e. with institutions, which facilitate rent

seeking, resource abundance forces scarce entrepreneurial resources out of

production and into unproductive activities. In contrast, countries with producer

friendly institutions and rich natural resources attract investors into production and

hence speed up economic growth. "Institutions may be decisive for how natural

resources affect economic growth even if resource abundance has no effect on

institutions," (Mehlum, Moene and Torvik 2006, 3). Put simply, the existence of

strong institutions decreases the risk that richness in natural resources turns out to

be a curse (Kolstad 2009).

A number of further studies (Easterly and Levine 2003, Murshed 2004, Bulte,

Damania and Deacon 2005, Kapstein et al. 2008) also emphasize the importance of

institutional quality. Accordingly, a negative effect of natural resource abundance

occurs only via poorly functioning institutions. "Bad policies would be like a kind of

high fever from a bacterial infection. Packing the patient in ice would bring down the

fever but does not cure the infection." Thus, "correcting the policies without correcting

the institutions will bring little long-run benefit," (Easterly and Levine 2003, 37).

3.3. Resource Abundance and Civil War

Alongside with a negative effect of natural resource abundance on economic

growth and democratization process, the literature on resource curse also highlights

a link between resource wealth and civil wars. There exist two main arguments on

the causes of civil war onset in resource rich countries. The first one, the grievance

argument (Deininger 2003), points out that high inequality, ethnic fractionalization

and suppression of political freedoms lead to civil war onset. The second one, the

greed argument (Collier, Hoeffler and Söderbom 2004), suggests that rebels (or rebel

leaders) first of all want to enrich themselves. Accordingly, this is the main motive

why rebels are interested in civil war onset or its continuation (Wick and Bulte 2009,

141; Rosser 2006, 17). In the latter context, it is argued that natural resources can

well finance the duration of conflicts (De Soysa and Neumayer 2007). Further, the

literature points out that the linkage between resource abundance and civil war onset

depends on the type of natural resources. For instance, the point natural resources in

contrast to the diffuse resources are associated with both civil war onset and duration

of violent conflicts (Ross 2006, Wick and Bulte 2006). Last but not least, there are

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also studies which show that the causal arrow runs from conflicts to high resource

dependence and not the other way around, i.e. from the resource abundance to civil

war onset (Brunnschweiler and Bulte 2008).

Besides the main three pillars of the resource curse debate there is also a new

emerging one on the link between resource abundance and corruption increase.

Therefore, in the following I will present an overview of the state of the art on

corruption and its association with oil.

4. State of the Art

4.1. Systematic Research on Corruption

According to a general definition of corruption (Oxford dictionary of politics

2009, 123), “corruption […] is rather an illegal exchange”. In this regard, a need-to-

be-asked question is: what enables this “illegal exchange”? The following chapter will

therefore concentrate on the results of the most important studies on the causes and

effects of corruption.

To start with, institutional theories on corruption can be organized into two

groups. The first group deals with the role of economic and structural policies 12, the

second one emphasizes the role of institutions. The first group of institutional theories

underlines the importance of economic indicators, e.g. GDP per capita and human

capital, for the institutional improvements. Put simply, these theories regard

economic growth and education as the main explanatory parameters for corruption.

Thereby, they do not distinguish between corruption and institutional quality, since

they consider corruption to be an integral part of institutional quality. In contrast, the

second group of institutional theories analyses the role of institutions as the main

determinants of corruption. According to these theories, institutions are a set of rules

that put constraints on players (e.g. politicians, private companies and different

interest groups). Those rules define how well markets and political competition work.

Thus, the quality of institutions determines the extent to which corruption could

spread. To accentuate, corruption and institutional quality are not identical, but two

different concepts (Svensson 2005, 24-28).

12 In a similar vein, Sachs and Warner (1997) highlighted significance of "appropriate growth-oriented policies" to avoid a resource curse in resource-abundant countries (p.28).

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Next, a debate on a causal mechanism between corruption and poverty

belongs to a classic chicken-and-egg problem (Fisman and Miguel 2008, 15). For

instance, according to Treisman (2002), poverty causes corruption. He argues that

post-communist countries are on average more corrupt. Following this we would

expect the poorest of the post-communist countries to be the most corrupt ones.

However, it is not always a case. Besides, Kaufmann (1997, 118; 2007) states the

opposite, namely that corruption causes poverty.

Previous studies have also addressed the effect of different institutional

designs on corruption. According to this, the level of corruption depends on the

constitutional designs and electoral rules: presidential vs. parliamentary regimes,

federal vs. unitary systems, proportional vs. majoritarian representation rules

(Persson, Roland and Tabellini 1997; Persson, Tabellini and Trebbi 2003; Kunicova

and Rose-Ackerman 2005; Alt and Lassen 2008). For instance, Kunicova and Rose-

Ackerman (2005, 597) show that the closed party lists in conjunction with the

decentralized presidential systems are associated with higher corruption rates since

those rules make monitoring of the corrupt officials by the voters and opposition

parties more difficult. Alt and Lassen (2008) on the example of the American states

demonstrate the importance of a system of checks and balances and of having an

accountable judiciary. According to their findings, a mechanism of checks and

balances has a stronger effect in limiting corruption in a divided government, where

the executive and legislature branches controlled by different political parties. In a

case of a unified government, where the government cannot or does not always

control itself, an elected Supreme Court contributes to less corruption.

Similarly, Shleifer and Vishny (1993) highlight the importance of accounting

systems in preventing corruption. In accordance with their findings, political

competition enables stronger public control over the government's activities. On

contrary, regulatory discretion and monopolistic powers of bureaucrats facilitate

corruption strongly (Kaufmann 1997, Ades and Di Tella 1999). Therefore, the authors

stress both political and market competition as powerful instruments in fighting

corruption.

In their recent paper Kaufmann and Vicente (2011) introduce a three-

equilibrium-pattern argument: with no-corruption, legal and illegal corruption models.

The authors explain the strength of corruption based on the power of population

(proxied by equality), the cost of destruction from unrest (proxied by initial income)

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and the cost of overwhelming of elite-built institutional barriers (proxied by political

accountability). According to their findings, the level of political accountability is

decisive in moving from legal corruption model to no-corruption outcome. Hence, it is

obvious that the political accountability is detrimental in eradicating corruption.

4.2. Oil’s Effect on Corruption

Other authors detect a relationship between natural resources and corruption.

For example, Ades and Di Tella (1999) assume that higher oil and mineral rents lead

to higher corruption rates. However, the authors show that even if the effect of fuel

and minerals was positive, it was insignificant.

One can also draw a parallel between the effect of natural resource

abundance and foreign aid on education, growth, democracy and civil war. Under

the condition of bad institutions, oil and gas revenues and foreign aid funds can

easily be appropriated by corrupt politicians (Fisman and Miguel 2008, 13; Cabrales

and Hauk 2010).

In a similar vein, Bhattacharyya and Hodler (2009) demonstrate that resource

rents cause corruption if and only if there are democratic institutions of poor quality.

The authors associate good democratic institutions with the responsiveness of the

elected governments. The strong democratic institutions mean that governments

could stay in power only with a public support.

To summarize, on the one side, there are studies which use corruption as one

of the components of the index of institutional quality in explaining resource curse.

On the other side, there are studies which employ quality of institutions as main

determinants of corruption (e.g. Brunetti and Weder 2003, Kolstad and Wiig 2008,

Kaufmann and Vicente 2011). In doing so, the latter studies, however, have used

different institutions separately. I will identify the likely corrupting effect of oil on

institutional quality. Besides, I will create an index of institutional quality based on

three factors: political accountability, rule of law and transparency.

5. Theoretical Assumptions and Research Hypothesis

The literature argues that present corruption rates are predetermined by the

country's level of economic development in the last 300 years (Treisman 2006).

Accordingly, countries that were rich in 1700 (e.g. China and Netherlands) are

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supposed to have less corruption today. Thus, there should be a strong link between

economic development and corruption. However, China's wealth 300 years ago and,

moreover, its rapid economic development during the last three decades do not

predict its still high corruption indexes today.

Bueno de Mesquita and Downs (2005) show that economic growth can well

serve the interests of the autocratic leaders. In other words, economic growth has

been used for the political survival of the authoritarian regimes. The authors show

how in countries like China, Russia, Venezuela and Cuba autocratic leaders design

rules to pursue their interests. Thereby, the decisive thing was (and still continues to

be) that autocrats managed to keep opposition weak, i.e. under control by limiting

opponents' strategic coordination (Buena de Mesquita and Downs 2005, 80-82).

In a similar vein, a proverb says "fish rots from its head down." It means that

bad leadership leads to a bad organization or that a corrupt organization is the result

of a corrupt ruler or leader. Yet, it cannot be merely limited to the governor or ruling

elite. As Lord Acton13 argues, "power tends to corrupt; absolute power corrupts

absolutely. Great men are almost always bad men.” To summarize, unless there are

constraints to make the work of governments transparent, power will corrode

everyone. For this reason, the quality of a government cannot be judged by

considering only personal characteristics of leaders. The quality of institutions also

has to be evaluated.

Knack and Keefer (1998) show that weak institutions advance political

corruption by entrepreneurs. To survive on the markets, entrepreneurs are forced to

use their ties with governmental bodies, i.e. political rather than economic criteria. As

a result, corruption and nepotism come into play. Similarly, Lederman, Loayza and

Soares (2004, 11) argue that institutions are "ultimate determinants of corruption

because they shape the incentives facing governmental officials." In the authors’

opinion, political institutions influence corruption in two ways: by political

accountability and by the structure of provision of public goods. Accordingly,

democratic institutions like parliamentary systems and freedom of press bring

corruption down. More importantly, transparency and accountability applied

separately cannot help much in mitigating corruption. Only taken together they can

have a powerful effect (Lindstedt and Naurin 2005).

13 Lord Acton, Letter to Bishop Mandell Creighton, 1887.13

Further, there are also some studies which show a positive link between oil

and corruption. For example, Aslaksen (2007) introduces new measures of resource

dependence and uses panel data with fixed effects exploring the link between natural

resource abundance and corruption for up to 132 countries for the time period 1982-

2006. She demonstrates that more oil leads to an increase in corruption in

government. Leite and Weidmann (1999) also argue that abundance in natural

resources creates opportunities for rent-seeking activities and is strongly decisive in

determining corruption rates. However, at the same time, the authors stress the

importance of strong (or at least strengthened) institutions at the moment of natural

resource discoveries as a key to restrain the associated negative growth effects of

corruption. In the less developed countries quality of institutions have even greater

value as natural resource discoveries could have a much higher impact on corruption

if confronted with weaker institutions (Leite and Weidmann 1999, 31).

Similarly, Sala-i-Martin and Subramanian (2003) and Naim (2004) recommend

policies which are designed to improve the quality of public institutions. In particular,

Sala-i-Martin and Subramanian advocate more transparency and accountability while

making publicly all resource revenues and expenditures.

To summarize, institutions do matter in explaining variation of manifold effects

oil and gas rents could produce in a polity. Thus, the effect oil and gas revenues

could have on development indices depends on the quality of institutions in a given

country. Accordingly, I hypothesise that oil and gas revenues would contribute to

more corruption only in polities where poor quality institutions prevail.

H: Oil increases corruption if and only if the quality of institutions is poor.

6. Research Design

6.1. On aggregate indexes

Keefer (2009, 452)14 claims that aggregate indexes are difficult to interpret.

Although the coefficients could be significant, one cannot be sure about the factors

that cause a certain effect. Thus, to understand and better explain variation in

possibly corrupting effect of the oil revenues it is better not to restrain our research

14 For instance, Kaufmann and Vicente (2011) also emphasize the value of disaggregated data on corruption: firm-level instead of country-level data structures.

14

only to democracies and democratic institutions. Wright (2008) pinpoints the

importance of going beyond the accustomed dichotomy between democratic and

autocratic regimes. Whilst reinforcing the importance of political institutions in limiting

the power of autocratic leaders, Wright has demonstrated that there is a variation

between authoritarian regimes as well. Consequently, one has to assume that this

assumption also holds for differences within democracies. Therefore, it makes sense

to control for the conditional effect of oil on corruption cross-nationally and without

restricting our analyses to democracies and autocracies.

By the same token, according to Hallagan (2010), the variance in corruption

depends on the choice of institutions in dictatorships. Hallagan developed two

alternative models to show how "taxes" could be collected. The models display

different kinds of organizations, which in their term differently influence corruption in a

country. In his opinion, policy choice and choice of institutions determine conditions,

which can either facilitate or restrict corruption. For instance, even if a dictator

chooses the civil servants model to fight corruption and provide for transparency,

there are some crucial limitations for the success of this model. Because of the

dictator's "unlimited" power, i.e. absence of other political institutions that to constrain

such an arbitrary rule, the model of civil servants is to fail. Hence, organizations of

good quality can be easily paralysed unless they are protected by corresponding

institutions and do not depend on the leader's will.

To test the strength of disaggregated versus aggregated indexes in explaining

the observed variation in corruption scores among oil-producing countries, I will run

regression both on the individual parameters and on the (“aggregated”) index of

institutional quality. The index of institutional quality will be based on three

parameters, namely: accountability, rule of law and transparency. To address

collinearity and endogeneity concerns, I search for smaller and simpler proxies for

the institutional quality indicators.

6.2. Dependent Variable. Data on Corruption

There are three commonly used datasets available on corruption. The first

one, prepared by the Political Risk Services, is the corruption index of the

International Country Risk Guide (ICRG). There are two datasets that have different

starting points. The IRIS-3 dataset includes data for the period 1982-1997, and the

15

Researcher Dataset (ICRG T3B) accommodates data for the period 1984-2008

(Aslaksen 2007). The second one, published by Transparency International, is the

Corruption Perception Index. This index includes data for the time period from 1996

to 2009. The third dataset is on the Control of Corruption released by Kaufmann,

Kraay and Mastruzzi (2009). This measure reveals data for the time period from 1996

to 2008. Data for 1996-2000 was issued every second year, and starting from 2002 it

has being released on a yearly basis. All these three datasets, even if they employ

different data collection strategies, are highly correlated. The main difference

between these datasets is the years and countries they cover (Svensson 2005).

Notwithstanding small changes in the corruption index over time, the ICRG

dataset contains information on significant regime changes in some political systems

(e.g. in Latin America and Eastern Europe). This is to enable controlling for the

effects of the variables of interest, that is, the effects of political institutions on

corruption (Lederman, Loayza and Soares 2004).

To test my hypotheses I will use the corruption index developed by the

Political Risk Services. This index measures corruption within a political system and

takes into account actual or potential corruption in the form of excessive patronage,

nepotism, job reservations, 'favor-for-favors', secret party funding, and suspiciously

close ties between politics and business. The corruption index ranges from 0 to 6,

with higher values indicating less corruption. Lower scores indicate that "high

government officials are likely to demand special payments," and that "illegal

payments are generally expected throughout lower levels of government" in the form

of "bribes connected with import and export licenses, exchange controls, tax

assessment, police protection, or loans."

6.3. Independent Variables

(1) Data on Oil and Gas Resources

The data on oil and gas resources is from the World Development Indicators

and the World Bank Adjusted Net Savings dataset. This dataset covers 149 countries

for the time period from 1970 to 2006. Additionally, I am going to use data from the

BP (Beyond Petroleum), Energy Information Administration (EIA), International

Energy Agency (IEA) and the World Factbook issued by Central Intelligence Agency

16

(CIA). These datasets provide data on global reserves, production and consumption,

stock and prices of oil and gas resources.

(2) Index of Institutional Quality

Mehlum, Moene and Torvik (2006) in their study on institutions and the

resource curse used the institutional quality index developed by Knack and Keefer

(1995). A number of further studies also employ index of institutional quality

(Robinson, Torvik and Verdier, 2006; Beck and Laeven, 2006; Cabrales and Hauk

2010). These indexes were created as an unweighted average of individual indexes

based on data from Political Risk Services or World Bank Governance Indicators

(e.g.: a rule of law, a bureaucratic quality index, a corruption in government index, a

risk of expropriation index and a government repudiation of contracts index 15). Thus,

these studies define corruption as one of the parameters of the institutional quality. In

contrast to these studies, I differentiate between the quality of institutions and

corruption. In my paper I aim to explain the likely corrupting effect of oil via

institutional factors. Therefore, I regress corruption on the institutional quality.

Further, to develop index of the institutional quality I employ rules like transparency,

accountability and rule of law.

(a) Transparency

According to Kaufmann and Bellver (2005) transparency leads to better socio-

economic performance and better human development indicators as well as to higher

competitiveness and lower corruption. Similarly, Kolstad and Wiig (2009) indicate that

transparency is a key in fighting corruption in resource-rich countries. Political elites

in these countries have an "exclusive" access to the resource rents and this is why

corruption is widespread there. When information is hardly accessible to the broad

audience, that is, only a few people have control over it, it is difficult to disclose

whether a bureaucrat is corrupt or not. In other words, in non-transparent systems

bad politicians or policies are less likely to be revealed and punished. Therefore,

transparency is decisive in limiting incentives of politicians to be involved in rent-

seeking activities. To summarize, corruption in oil-rich countries can be tackled in

different ways, whereas transparency reform is only one possibility, although a very 15 In the 2009 International Country Risk Guide dataset the last two indexes: a risk of expropriation index and a government repudiation of contracts index together with a payment delays index are included into the investment profile index.

17

important one. Hence, one should not lessen the significance of transparency, as

transparency can reduce corruption by helping make politicians more accountable to

the public.

To operationalize transparency I will use Freedom House data on Freedom of

Press. A free press is believed to be one of the most effective institutions in fighting

political corruption (Brunetti and Weder 2001, 1801). The data on freedom of press is

available for the time period from 1980 to 2009 for 196 countries. The index ranges

from 0 (best) to 100 (worst). The degree to which each country permits the free flow

of news and information determines the classification of its media as “free”, “partly

free”, or “not free”. Countries scoring 0 to 30 are regarded as having free media; 31

to 60, partly free media; and 61 to 100, not free media.

However, transparency on its own is not enough to reduce corruption (Kolstad

and Wiig 2009, Lindstedt and Naurin 2005). The authors claim, that effect of

transparency on corruption is conditional on education and accountability.

Accountability is of profound importance as there is a critical need for other agents

who can hold bad politicians accountable for the abuses of office.

(b) Accountability

The importance of political accountability has been also stressed by Robinson,

Torvik and Verdier (2006). The authors have highlighted the problem of predicting a

monotonic effect of resources on development. They argue that a good model should

explain why some resource rich countries prosper, while others grow slowly or even

experience economic decline. The authors pinpoint the importance of institutions and

especially the interaction between institutions and resources in explaining the above-

mentioned variances. The authors have developed a model of clientelism which

explains how politicians secure desirable election outcomes through inefficient

redistribution16 of resource rents. Thus, politicians seek political support by offering

jobs in the public sector. In other words, public patronage is actively employed. In

contrast, in countries with accountable institutions politicians are constrained in their

abilities to use clientelism in order to manipulate election outcomes. Consequently,

by such institutional constellations resource booms would lead to economic

development.

16 This model is similar to the spending effect argument made by Ross (2001, 2009). 18

Kaufmann, Kraay and Mastruzzi (2009) developed six aggregate and

individual indexes to measure the quality of governance. Thereby, they have defined

governance as "traditions and institutions by which authority is exercised" (2009, 5).

Their governance indicators particularly include voice and accountability, government

effectiveness, political stability and absence of violence, rule of law, regulatory

quality, and control for corruption. Kaufmann, Kraay and Mastruzzi (2009) define

voice and accountability as an ability of people to select their governments and the

existence of freedoms of expression, of association and of a free media. In the

International Country Risk Guide (2009) there is a similar indicator called democratic

accountability which measures the responsiveness of government to its people.

Thereby, the ICRG indicator is based on the type of governance which takes account

of an alternating democracy, dominated democracy, de-facto one-party state, de-jure

one-party state and autarchy. The ICRG indicator for accountability ranges from 0 to

6, with higher values indicating "better" accountability.

While taking into account endogeneity and collinearity concerns, I will use

Cingranelli & Richards’ electoral self-determination variable from their human rights

dataset as a proxy for the political accountability. This index ranges from 0 to 2, with

higher values being representative for a free and open political participation and a

citizens’ right to self-determination through free and fair elections in both law and

practice.

As I have mentioned it before, along with transparency and accountability I

use rule of law to account for institutional quality. In the following I introduce the

concept of the rule of law and describe data I employ as a proxy for this institution.

(c) Rule of Law

According to Bedner (2010, 50-51), rule of law has two main functions. The

first function is “to curb arbitrary and inequitable use of state power”, whereby “the

core idea is that the sovereign is bound by law”. The second function is “to protect

citizens’ property and lives from infringements or assaults by fellow citizens”. This

protection is to be regulated by the state. Thus, the rule of law as its name already

says, is an institution. This institution devises certain rules and constraints of the

game. For instance, "in the U.S. constitution the idea of rule of law is more frequently

expressed as government of laws, not of men" (Safire 2008, 634). "A rule of law is

contrasted with arbitrary power, as happens in a police state, or the personal whim of

19

a dictator. The essence is that 'judges' should decide only according to the rule laid

down, not according to their own sense of justice or personal preference" (Robertson

2002, 432). Thus, rule of law is "the assertion that a nation's leaders must abide by a

written constitutions or unwritten common law" (Safire 2008, 634).

Sachs and Warner (1997) have shown that the rule of law index and index for

bureaucratic quality of the ICRG dataset are highly correlated (0.98). However, they

have opted for the rule of law index as a "general index for the efficiency of legal and

government institutions" (p. 21). This index consists of two sub-components, each

comprising zero to three points. The Law sub-component is an assessment of the

strength and impartiality of the legal system, while the Order sub-component is an

assessment of popular observance of the law. Kaufmann, Kraay and Mastruzzi

(2009) have also designed a rule of law index. Their index measures the extent to

which agents have confidence in and abide by the rules of society, in particular the

quality of contract enforcement, the police, and the courts, as well as the likelihood of

crime and violence. Although the World Governance Indicators’ index of rule of law is

quite comprehensive, the problem with its use for my empirical analysis is the short

time span that covers time period from 1996-2008. Therefore, as a proxy for the rule

of law I use US State Department’s political terror scale. This variable varies from 1

to 5, with higher values meaning more political violence and terror. The data is

available for the time period from 1976 to 2008 for 186 countries.

6.4. Control Variables

Previous studies have identified a number of socio-economic and political

factors that control for corruption. In particular, Treisman (2006) provides a good

summary of different control variables. Economic development, education and

democracy score are the most commonly used control variables.

Based on my theoretical argument and empirical model I will use economic

development (GDP per capita) and years of schooling to control for the effect of oil

and gas on corruption increase.

7. Methodology

Aslaksen (2009) argues that cross-country analyses, used in earlier studies on

resource abundance and corruption, have a number of shortcomings. These studies

20

did not pay attention to the heterogeneity problem. Aslaksen shows that there are

different intercepts for different countries. Fixed effects are to allow analysis of the

within-country variation in oil rent and corruption rates as well as controlling for an

unobserved cross-country heterogeneity.

Therefore, she suggests using panel data with both time series and cross-

sectional variation (Aslaksen 2009, 14-15). Measure of corruption released by the

ICRG datasets is an ordinal variable and cannot be treated as a continuous variable.

Thus, as the ICRG measure of corruption is a discrete variable, the distance between

different units is not the same (Wooldridge 2009). Therefore, a linear regression

model cannot be used for the analysis of such a data, as it will often give misleading

results (Long 2005).

7.1. Model

If a relationship between two or more variables depends on the value of one or

more other variables then one has to deal with a conditional hypothesis (Brambor,

Clark and Golder 2005). To test conditional hypotheses we can apply multiplicative

interaction models. In these models conditional variables modify the effect of the

independent variable (X) on the dependent variable (Y). Therefore, to test my

hypotheses I will use an interaction term as well.

Corruptionit = αit + β1Oilit + β2Instit + β3Oil×Instit + β4Controlsit + eit

7.2. On the Endogeneity Problem

In single equation regression models there is a unidirectional relationship

between the dependent and independent variables. It means that the causality, if

any, runs from the independent (Xs) to dependent (Y) variable. However, there are

also cases when this type of link cannot be observed. Hence, it is quite possible that

the causal arrows run not only from Xs to Y, but Y can also influence Xs. In such a

case, a bilateral relationship between Y and Xs takes place. Consequently, one has

to use more than one regression equation in order to avoid a problem of biased

results. This type of regression model is known as a simultaneous equation

regression model. In other words, dependent and independent variables are

interdependent or causing each other simultaneously. Such jointly dependent 21

variables are known in statistics as endogenous variables (Gujarati and Porter 2010,

pp. 345-349). To sum up, an endogenous variable is "a variable that is an inherent

part of the system being studied and that is determined within the system. That is, an

endogenous variable is caused by other variables in a causal system," (Vogt 1993,

pp. 81-85).

Ross (2001, 2009) has asserted that institutions are endogenous to natural

resources. Mehlum, Moene and Torvik (2006, 16) have found that politicians could

be interested in weaker institutions in order to have more or less unlimited access to

resource rents, and for the same reason they could favour less democracy.

Nevertheless, Sachs and Warner (1997, 2001) have demonstrated that correlation

between resource abundance and institutions is weak and insignificant.

Given this controversy on the causal link between natural resource

abundance, institutional quality and corruption I will have to control for a bilateral

relationships between these variables with the help of special statistical techniques

and tests. In particular, I will use instrumental variable tests.

For instance, Dunning (2009, 8) shows that Acemoglu, Johnson, and

Robinson (2005) used colonial settler mortality rates as an instrumental variable for

current institutions whilst explaining the impact of institutional frameworks on the

economic performance. Thereby, Acemoglu, Johnson and Robinson emphasize that

the disease environments during the colonial years did not affect current economic

performance in former colonies directly. This effect takes place only through their

impact on current institutions.

22

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