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Everyone was doing it: Culture, Rationalisation and Corruption Patrick Schneider Supervised by Dr Gautam Bose School of Economics, The University of New South Wales 24 October 2011 Submitted in partial fulfilment of the requirements of the degree of Bachelor of Economics with Honours

Transcript of Culture, Rationalisation and Corruption - UNSW Business … · Everyone was doing it: Culture,...

Everyone was doing it:

Culture, Rationalisationand Corruption

Patrick Schneider

Supervised by Dr Gautam Bose

School of Economics,

The University of New South Wales

24 October 2011

Submitted in partial fulfilment of the requirements of the degree

of

Bachelor of Economics with Honours

I hereby declare that this submission is my own work and to the best of

my knowledge it contains no materials previously published or written

by another person, or substantial proportions of material which have

been accepted for the award of any other degree or diploma at UNSW or

any other educational institution, except where due acknowledgement is

made in the thesis. Any contribution made to the research by others, with

whom I have worked at UNSW or elsewhere, is explicitly acknowledged

in the thesis.

I also declare that the intellectual content of this thesis is the product

of my own work, except to the extent that assistance from others in the

project’s design and conception or in style, presentation and linguistic

expression is acknowledged.

Patrick Schneider

Acknowledgments

I am incredibly grateful to Gautam Bose, my supervisor, for his openness, reassurance

and guidance at every stage throughout the year. I am also extremely grateful to Zhanar

Akhmetova and Alberto Motta who helped me find direction in my argument in the em-

pirical and theoretical sections respectively.

Two of my friends proof-read the thesis and provided helpful feedback throughout the

year as well. They are Susie Kaye and Patrick Hurley and I extend my thanks to them.

ii

Contents

Abstract 1

1 Introduction 2

2 Corruption 4

2.1 The Nature and Impact of Corruption . . . . . . . . . . . . . . . . . . . . 4

2.2 The Causes of Corruption . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2.2.1 Theoretical models of corruption . . . . . . . . . . . . . . . . . . . 5

2.2.2 Empirical models of corruption . . . . . . . . . . . . . . . . . . . . 7

3 Corruption and Social Psychology 10

3.1 Corruption as an Immoral Act . . . . . . . . . . . . . . . . . . . . . . . . . 10

3.2 Rational Choice and Morality . . . . . . . . . . . . . . . . . . . . . . . . . 11

3.3 Social Influence and Personal Morality . . . . . . . . . . . . . . . . . . . . 13

4 Theoretical Model 16

4.1 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

4.2 Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

4.2.1 General form . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

4.2.2 Closed form . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

4.2.3 Extensions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

5 Data 29

5.1 Corruption Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

5.1.1 Country level perception indices . . . . . . . . . . . . . . . . . . . . 29

5.1.2 Country level experience indices . . . . . . . . . . . . . . . . . . . . 31

5.1.3 Department level experience indices . . . . . . . . . . . . . . . . . . 32

5.2 Other Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

5.2.1 Variables of interest . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

5.2.2 History controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

5.2.3 Instruments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

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CONTENTS Patrick Schneider

6 Empirical Specifications 37

6.1 Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

6.2 Tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

6.2.1 Within country comparison—sign test . . . . . . . . . . . . . . . . 39

6.2.2 Cross country comparison—regressions . . . . . . . . . . . . . . . . 41

7 Results 44

7.1 Department Level Variation in Corruption . . . . . . . . . . . . . . . . . . 44

7.1.1 Systematic department e↵ects . . . . . . . . . . . . . . . . . . . . . 44

7.1.2 Idiosyncratic department e↵ects . . . . . . . . . . . . . . . . . . . . 46

7.2 Cross-Country Regression Estimation . . . . . . . . . . . . . . . . . . . . . 46

7.2.1 Baseline model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

7.2.2 Model 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

7.2.3 Model 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

7.3 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

8 Conclusion 56

A Appendix 58

A.1 Skewness and Kurtosis Test for Normality . . . . . . . . . . . . . . . . . . 58

A.2 Sign Test on Department Level Corruption Results . . . . . . . . . . . . . 59

A.3 Two Stage Least Squares Regressions . . . . . . . . . . . . . . . . . . . . . 61

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List of Figures

4.1 Equilibrium ⇤ with and without supervision . . . . . . . . . . . . . . . . . 26

5.1 Comparison of Perception Indices . . . . . . . . . . . . . . . . . . . . . . . 30

5.2 Comparison of Experience and Perception Indices . . . . . . . . . . . . . . 32

6.1 GCB 2010 and ES 2009—Department Kernel Densities . . . . . . . . . . . 40

7.1 GCB 2010 and ES 2009—Corruption Di↵erences for Idiosyncratic Rela-

tionships . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

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List of Tables

5.1 Descriptive statistics for corruption measures . . . . . . . . . . . . . . . . . 31

5.2 Correlation between country level corruption indices . . . . . . . . . . . . . 33

5.3 Department level GCB 2010 (No bribe) . . . . . . . . . . . . . . . . . . . . 34

5.4 Descriptive statistics for independent variables . . . . . . . . . . . . . . . . 35

6.1 Correlations between GCB departments . . . . . . . . . . . . . . . . . . . 38

7.1 Pairwise relationships (column � row) between departmental corruption

implied by sign test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

7.2 Country level regressions with logged dependent . . . . . . . . . . . . . . . 49

7.3 Department level regressions with logged dependent . . . . . . . . . . . . . 51

7.4 Model 2a results with di↵erent lags . . . . . . . . . . . . . . . . . . . . . . 53

A.1 GCB 2010 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

A.2 ES 2009 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

A.3 Two-tailed p-values – H1 : median of Yi �Xi 6= 0 . . . . . . . . . . . . . . 59

A.4 One-tailed p-values – H1 : median of Yi �Xi > 0 . . . . . . . . . . . . . . 59

A.5 One-tailed p-values – H1 : median of Yi �Xi < 0 . . . . . . . . . . . . . . 59

A.6 Two-tailed p-values – H1 : median of Yi �Xi 6= 0 . . . . . . . . . . . . . . 60

A.7 One-tailed p-values – H1 : median of Yi �Xi > 0 . . . . . . . . . . . . . . 60

A.8 One-tailed p-values – H1 : median of Yi �Xi < 0 . . . . . . . . . . . . . . 60

A.9 2SLS Regressions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

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Abstract

Microeconomic models of corruption tend to suggest that if bureaucrats face an opportu-

nity to be corrupt and the expected benefits are positive, they will take it. Drawing on the

criminology and social psychology literature, I propose a model of bureaucratic corrup-

tion that incorporates another important mechanism: cognitive dissonance that creates

the need to rationalise behaviour. Bureaucrats living in a society with a strong social

norm against corruption may or may not be able to rationalise, and therefore engage in,

corrupt behaviour. Their ability to do so depends on the organisational culture of the de-

partment they work in as well as the norms of society as a whole. The model has multiple

equilibria, which suggests that we should observe idiosyncrasies in the corruption levels

in government departments within countries, driven by these organisational cultures. We

should further observe that these cultures persist over time as new recruits are brought

into the fold by their veteran colleagues.

Analysis of newly available, disaggregated data on bribery in government depart-

ments finds patterns that support the implications of the theoretical model. This finding

is robust to controls for systematic department e↵ects and the department’s historical

corruption level, as well as for standard country level institutional and economic regres-

sors. The analysis elucidates some important correlates of corruption and also shows that

a department’s history is a strong predictor of its current corruption level. These findings

match the implications of the theoretical model.

1

Chapter 1

Introduction

We often talk about a ‘culture’ of corruption. We use the term to explain why four of

the last eight governors of Illinois have criminal convictions1, why you can expect to have

to pay a bribe for many government services in India (Bertrand, Djankov, Hanna, and

Mullainathan, 2007; Wade, 1982), and why development e↵orts in Africa are often so

unsuccessful (Moyo, 2009; Sardan, 1999). But although we lay the blame for corruption

at culture’s doorstep, our discussions rarely identify what, exactly, we mean.

Could it be that India, a country of a billion souls, has such a single unifying streak?

If so, why the constant protests and outpouring in the media against corruption2? The

identification of culture as something that only occurs at the national level is probably

not helpful. This thesis conceptualises culture as something that is developed by groups

of people. Thus although national cultures exist, others such as those that develop within

organisations are also of great importance to individuals.

There is a long literature within economics that models the decisions of government

o�cials3. These describe corruption as a result of the incentives o�cials face and have

been helpful in designing prevention programs4. This thesis extends this literature by

asking how the morals that o�cials hold and the cultures to which they are exposed can

a↵ect their decisions when they have the opportunity to be corrupt. It has been observed

that corruption does the most harm when it is an institutionalised part of government

processes, rather than the individual acts of a few bad apples. Hence, an understanding

of the links between corrupt agents and how these links serve to institutionalise corrupt

practices would add a beneficial foundation for anti-corruption e↵orts.

My thesis makes a theoretical contribution to the corruption literature by developing

a model that uses the methodology of past economic models, which tie corruption to

incentives, but is founded on a rich discussion of criminal behaviour and motivation from

the criminology and social psychology fields. The result is a model that explains individual

1Rod Blagojevich (2003-09), George H. Ryan (1999-2003), Dan Walker (1973-77) and Otto Kerner,Jr (1962-68).

2Witness the recent protest movement led by Anna Hazare, see http://www.economist.com/node/

21526904 accessed 17/10/20113Starting with Rose-Ackerman (1978).4See, for example, Klitgaard (1988).

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CHAPTER 1. INTRODUCTION Patrick Schneider

corruption in terms of o�cials’ personal morals, which are shaped by interactions with

their colleagues and broader society.

The theoretical analysis finds that an o�cial’s personal choice of morals will tend

to conform to those chosen by his colleagues. However, if his perception of what is the

moral norm is imperfectly based on the the norms of his colleagues and those of roader

society, there are multiple moral lines that he and his colleagues could coordinate on

in equilibrium. Thus, a corrupt culture develops in a government department, but the

cultures across departments need not be the same5. Furthermore, if the o�cial bases his

perceptions on his colleagues’ behaviour in the past, then these cultures will be highly

persistent. It is notable that the multiple coordinated equilibria and persistence of culture

are independent of incentives. Although the latter a↵ect decisions in the model as well,

they do not interfere with the main findings.

The second contribution made by this thesis is in testing the implications of the

model by using a cross-country dataset of people’s experiences of paying bribes to di↵erent

departments. This dataset has not been used in the empirical literature before. The use

of department level data allows me to extend the past literature by controlling for the

inherent corruptibility of a department’s work, as well as other known correlates. I employ

two approaches to testing the implications. The first approach analyses relationships

between departments within the same country using the sign test and finds that the

noisy relationships predicted by the theoretical model do indeed obtain. The second

approach analyses cross-country variation in departmental corruption using two models.

The first model controls for country and department e↵ects and finds that the noise

predicted by the theoretical model is observable. The second model adds a lag of the

department’s corruption level and finds it to be significant, which supports the prediction

that departmental corruption will be persistent.

The thesis is structured as follows. Chapter 2 provides an overview of the main

trends and findings of economic research into corruption, both from a theoretical and

empirical perspective. Chapter 3 draws on the social psychology literature to provide an

in-depth analysis of how moral considerations a↵ect decision making for individuals that

act within social environments. Chapter 4 then incorporates these factors into a formal

theoretical model of corruption which is able to explain the variation and persistence

of corruption in an organisation as phenomena that are independent of the associated

incentive structure. These implications are then tested in the empirical section. Chapters

5 and 6 outline the data and strategy, respectively, that are used in the empirical analysis,

and results are presented in Chapter 7. Chapter 8 concludes.

5This provides intuition for why so many Illinois governors, but none from Indiana, have criminalconvictions.

3

Chapter 2

Corruption

2.1 The Nature and Impact of Corruption

Corruption is defined in various ways. The broadest and most widely used definition is

that corruption is the use of public o�ce for private gain (Bardhan, 1997; Gupta, Davoodi,

and Alonso-Terme, 2002; Shleifer and Vishny, 1993; You and Khagram, 2005). Another,

clearer, definition is that corruption is the violation of the duty of public o�ce for private

gain (Bac, 1996; Frank and Schulze, 2000; Khan, 1996; Sardan, 1999). Although the aspect

of violation of duty is likely intended by most, it is necessary to include it in the definition

to distinguish between bad policy and bad implementation of policy1, although the former

can be caused by corruption. The acts themselves may take a variety of forms—bribery,

embezzlement, extortion, diversion of funds—and occur in a variety of spheres—political

or bureaucratic. This thesis’ focus is on acts of bribery at the bureaucratic level.

Various theories of the impact of corruption abound and can be generally classed as

‘grease’ or ‘sand’ theories (Bardhan, 1997). That is, proponents of either side argue that

corruption acts as grease in the wheels of government, speeding up ine�cient processes

and enhancing welfare, or sand, creating friction in the wheels of government, introducing

ine�ciency into processes and diminishing welfare.

The most commonly cited proponent of the grease theory is Le↵ (1964), who argues

that “. . . corruption provides the insurance that if the government decides to steam full-

speed ahead in the wrong direction, all will not be lost. . . ” (p.11). A mundane example of

this could be where some resource is allocated by a queue or some other ine�cient mech-

anism. Corruption of this system might introduce an auction style sell-o↵, sending the

resource to the person with the highest willingness to pay, increasing e�ciency. Although

it may be true that a particular corrupt act may serve some benefit somewhere, detailed

case studies and data analysis in more recent years have shown that such occurrences are

overwhelmingly exceptions to the norm (Klitgaard, 1988, pp. 30-38). Furthermore, if one

broadens the scope of analysis beyond the specific corrupt act, it is highly likely that the

costs it creates will outweigh any benefits (Rose-Ackerman, 1978, p. 95).

1For example, the tax farming systems used by the Roman and Ottoman Empires would be consideredcorrupt by the first definition but not by the second.

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CHAPTER 2. CORRUPTION Patrick Schneider

Theories and evidence for the sand theory are much more readily available. Cor-

ruption can negatively a↵ect the social and power structure of a society by entrenching

incumbent players (Shleifer and Vishny, 1993) and power figures (Khan, 1996) as well as

inducing or exacerbating inequality (Gupta et al., 2002; You and Khagram, 2005). This

can lead to brittle institutions that are unable to provide services (Wade, 1982), unwilling

to adapt (Johnston, 2005) or that undermine innovation (Murphy, Shleifer, and Vishny,

1993). It can waste resources by diverting them into rent-seeking (Krueger, 1974; Mur-

phy et al., 1993; Sardan, 1999) or covering up corrupt activity (Blackburn, Bose, and

Haque, 2006). And it can reduce investment by imposing an extra cost (Maitland, 2001;

Mauro, 1995) or by increasing uncertainty and thereby undermining incentives to invest

(Maitland, 2001; Mauro, 1995).

The work by economists on the impact of corruption is largely theoretical. Where

it is not, the empirical analysis tends to be of a more general nature; e.g. establishing

a causal relationship between corruption and investment. This is probably a result of

the data that are available—corruption indices tend to be problematic, there are very

few valid instruments and countrywide data on the impact factors discussed in theory

are hard to come by2. Furthermore, the main goal in researching corruption seems to be

to reduce the phenomenon. To this end, the assumption that corruption is a burden on

society (contemporarily taken to be “ . . . a well-proven fact. . . ” (Dusek, Ortmann, and

Lizal, 2005)) is enough to be a motivating force behind research into identifying its causes,

the focus of this thesis.

2.2 The Causes of Corruption

Various causal mechanisms for corruption have been suggested and studied in recent years.

The following outlines some of the key features of the empirical and theoretical work in

this area.

2.2.1 Theoretical models of corruption

There are various theoretical models of corruption that focus on di↵erent mechanisms

and e↵ects. At the core of most models is the familiar principle-agent framework where

the agent is a bureaucrat or politician and the principle a senior or the general public.

Corruption arises when perverse incentives, combined with an ability for the agent to hide

his actions, make corrupt behaviour a dominant strategy. A simple illustrative example is

provided by Klitgaard (1988, pp. 69-74). In his model, agents are paid a wage to deliver

a service to the public. They have a choice of being corrupt (in which case they receive

an extra payment) or not. If they are corrupt, they exact negative externalities on their

principle. Their principle hence makes e↵orts to catch them, but is only successful with

some probability. When a corrupt agent is caught, they must pay a fine. In Klitgaard’s

2To be discussed more fully in Chapter 5.

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CHAPTER 2. CORRUPTION Patrick Schneider

model, agents also su↵er a moral cost if they choose to be corrupt, this is a fixed amount

that marginally reduces the incentive for corruption.

Another, more complicated, example of a principle-agent based model comes from

Bac (1996). He proposes a basic principal-agent framework but focuses on the role of su-

pervision. He notes that most of the agency literature on corruption takes the institutional

structure as given and focuses on organising the right agreement between principal and

agent to induce the desired behaviour. His article takes the opposite approach by fixing

incentives and varying the institutional structure to determine the comparative e↵ective-

ness of di↵erent models of supervision on limiting corruption. He finds that where there

are low variable costs to monitoring, it pays to have very horizontal supervision structures

(one supervisor to many agents). Where this applies with thresholds, the group of agents

need be split into groups and supervised in the same way. Where variable costs become

significant, however, there is no generally optimal structure and the explicit monitoring

costs need be identified.

Some models extend beyond the purely micro principle-agent framework in an at-

tempt to explain macro phenomena. Blackburn et al. (2006), for example, propose a

dynamic, general equilibrium model that incorporates a principle-agent framework to de-

velop a theoretical basis for the observed two-way causal relationship between corruption

and development. Their model incorporates a government, bureaucrats and households

whose decisions are interrelated. In the model, corruption acts as a drag on development

because resources are wasted trying to hide the transgressions. In the other direction,

development a↵ects corruption because it is linked to bureaucrats’ wages (the richer the

country, the better paid the bureaucrats) so in more developed countries, bureaucrats

have more to lose from corruption and there is hence less. There are two equilibria,

one with high corruption and low development, and one with low corruption and high

development.

Those models that analyse corruption at higher levels than the principle-agent re-

lationship tend to share this feature of multiple equilibria3. Another example is from

Murphy et al. (1993). This is a more abstract and simpler model than those consid-

ered above. In their setup, people simply have a choice of three occupations—market

production, subsistence production or rent-seeking (appropriating market production).

They show that where there are increasing returns to rent-seeking, multiple equilibria

will emerge—a stable point with everyone choosing market production over rent-seeking

or subsistence production, another stable point with high rent-seeking and people indif-

ferent between market and subsistence production and an unstable point where people

are indi↵erent between rent-seeking and market production.

Few models incorporate any moral aspect to the decision to become corrupt. Agents

are simply faced with a choice between alternatives that carry costs, benefits and risks

and choose to maximise their narrowly defined expected utility. Viewed in this light, the

3Multiple equilibria are present in some agent level analyses as well. See, for example, Nabin and Bose(2008).

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CHAPTER 2. CORRUPTION Patrick Schneider

decision process an agent goes through when choosing between being corrupt or not is

identical to his weighing a simple gamble. Some models do incorporate morality, usually

by giving agents exogenously defined, heterogenous personal corruptibility—for example,

Blackburn et al. (2006) and Bose and Gangopadhyay (2009) have both corruptible and

incorruptible agents—or adding a fixed ‘moral cost’ to choosing to be corrupt—for ex-

ample, Klitgaard (1988). These attempts recognise the unique nature of the decisions in

question; but the modifications are generally made without addressing the mechanisms

behind them and without focus on their implications.

One of the initial motivations for my research was that it seemed appropriate to place

agents within a moral structure that they have to take into account when making decisions.

Hence, my theoretical discussion in Chapters 3 to 4 proposes a model of corruption that

takes into account moral processes that are given a detailed theoretical foundation.

2.2.2 Empirical models of corruption

Since the seminal work by Mauro (1995) that used an index of perceived corruption in an

empirical analysis for the first time, there have been multiple studies using data to analyse

the causes of corruption. Most studies set out to test a single hypothesised relationship,

controlling for others, and find various interesting results, some examples of which follow:

� Ades and Di Tella (1999) argue that economies where there are higher rents will

have higher corruption because the gains from the activity are higher. It follows

that countries that are less open to trade should have higher corruption due to the

lower level of competition in the marketplace. They find that greater openness to

trade, measured by imports as a proportion of GDP, is a significant determinant of

perceived corruption.

� Fisman and Gatti (2002) argue that decentralisation of government power means of-

ficials will be more closely linked with the constituencies they serve, causing greater

accountability and therefore less corruption. They find that decentralisation, mea-

sured by subnational government expenditure over the total, is a significant deter-

minant of perceived corruption.

� Brunetti and Weder (2003) argue that a free press acts as a constraint against

corruption because o�cials can expect to be publicly outed if they are identified.

Therefore, countries with greater freedom of the press should experience lower levels

of corruption. They found that press freedom, measured by Freedom House’s index,

is indeed a significant determinant of perceived corruption.

� You and Khagram (2005) argue that inequality fractures society and that the larger

the gap between rich and poor, the more the rich seek extra-legal means like corrup-

tion to maintain their position. They find that inequality is a significant determinant

of perceived corruption.

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CHAPTER 2. CORRUPTION Patrick Schneider

� Aidt, Dutta, and Sena (2008) argue that economic growth should reduce corruption

because o�cials know there will be more to extract in next period, causing them to

wait until then to extort their share so as to not risk missing out. They find that

economic growth is a significant determinant of perceived corruption, though this is

dependent on the strength of accountability institutions.

Daniel Treisman published a comprehensive survey of this literature, appropriately

titled What Have We Learned About the Causes of Corruption From Ten Years of Cross-

National Empirical Research? (Treisman, 2007). In this paper, he summarises the various

relationships that have been proposed and found to have a significant relationship with

corruption levels. He then reproduces these studies to identify which relationships are

robust to di↵erent measures of corruption and the inclusion of various controls. He es-

tablishes that an overwhelming amount of the variation in perceptions indices can be

explained using a set of robust variables. In his most successful regression, he records an

R2 of 92% (Treisman, 2007, pp. 233-34, regression (7)). The explanatory variables for

corruption (here measured using the World Bank’s Control of Corruption Index 2005) that

are found to have significant relationships in this regression are the log of GDP per capita

in purchasing power parity terms, fuel exports as a proportion of merchandise exports,

the year a country opened to trade, the time it takes to open a firm, whether a country

is an old democracy, whether a country is a presidential democracy, the extent of press

freedom, the proportion of women in government, newspaper circulation and controls for

religion, colonial history and legal tradition.

All the empirical studies discussed so far used one type of corruption measure—

perception indices. These are created by organisations that conduct surveys and ask

respondents how corrupt they believe a country to be. Being based on opinion, there are

various issues with these data, as Treisman and others readily recognise4. An alternative

measure that has become available in more recent years is called an experience index.

These are created by asking survey respondents questions about their personal experiences

with defined acts of corruption, usually bribery.

In his survey article, Treisman does his analysis using both perceptions and experi-

ence indices. Although his models using the former have extremely high predictive power

and many significant relationships, his work using the same variables on the latter yield

very little. Predictive power remains high, albeit not as high, but significant relationships

are scarce, often weak and not robust to controls.

We can conceptualise perception and experience based indices of corruption as mea-

suring di↵erent things. Perception indices (insofar as they are reliable) measure a broad

range of activities whereas experience indices measure specific acts. Hence, experience in-

dices measure a subset of the activities measured by perceptions indices. It is thus to be

expected that some relationships identified in regressions with perceptions indices are not

be present with experience based ones. For example, the restrictions of a free press would

4To be discussed in greater depth in Chapter 5.

8

CHAPTER 2. CORRUPTION Patrick Schneider

assumedly constrain high level political actors much more than the low level bureaucrats

measured by experience indices. It is therefore unsurprising that this relationship is not

found to be robust to the change of dependent variable. Other di↵erences, however, can-

not be expected on these grounds. For example, why would the rents available in the

home market (measured using the year opened to trade) a↵ect broad corruption but not

low-level bribery?

One of the initial motivations for my research was this contrast between the findings

using perceptions and experience indices. Although they measure di↵erent things, their

core relation means we should expect more corroboration between the two sources than

Treisman finds. Furthermore, in many ways experience indices are superior data sources to

perceptions indices, making them worthy of further exploration in their own right. Hence,

my empirical analysis in Chapters 5 to 7 undertakes this exploration using Treisman’s

findings as a baseline and expanding on it in various ways informed by the theory in

Chapters 3 to 4 and allowed by the data.

There is an incongruity between the theoretical and empirical work on the causes of

corruption. As Treisman (2007) notes, whereas theories tend to be based on models of

individual behaviour within principle-agent frameworks, empirical modelling analyses re-

lationships at the country level. This disconnect is due to the nature of the available

data. Where attempts are made to ground empirical work in theory, mechanisms that

work at the micro level are extrapolated with “. . . sometimes tortuous logic to character-

istics of countries on which data are available.” (Treisman, 2007, p. 222). The result is

that “. . . variables are included in regressions with only rather flimsy notions of how they

might cause cross-national variation in corruption.” (ibid.).

One of the novel features of this thesis is that the theoretical model that is proposed

has implications at the organisation level which are then tested with data at the depart-

ment level. Although there is arguably still a gap here, it is not so vast as that between

individual and country.

9

Chapter 3

Corruption and Social Psychology

The theoretical discussion of corruption in this thesis extends previous models by focusing

on the moral implications that corrupt acts have for o�cials. As discussed in Chapter

2, past models have introduced moral considerations by including exogenous parameters.

The discussion in this thesis draws on work from other areas of economics and other

fields to develop a model that makes personal morality endogenous. This chapter holds

a general discussion of the sources the model will draw upon. It begins by presenting

why corruption should be thought of in moral terms at all. It then examines how moral

considerations might a↵ect individual decision making generally and how this process is

a↵ected by an individual’s social environment. These general ideas are incorporated into

a formal theoretical model in Chapter 4.

3.1 Corruption as an Immoral Act

There is always a moral significance to corrupt actions. By moral, I mean that there

exists a social norm that deems particular behaviours unacceptable. Hence, corruption is

morally significant because an o�cial who engages in such behaviours faces social sanctions

if caught, and/or personal feelings of guilt (Akerlof, 1976; Basu, 2000; Coleman, 1990;

Elster, 1989). That corruption should be considered immoral is not a ground breaking

statement. The word itself has a moral tone that “. . . designates that which destroys

wholesomeness.” (Klitgaard, 1988, p. 23). Evidence for the claim that corruption always

has moral significance is provided by Noonan (1984). In his legal history of corruption

over the last three thousand years, he concludes that “[b]ribery is universally shameful.”

(p. 702). No country, he contends, legalises bribery, no one speaks publicly about the

bribes he has paid or received and no one is honoured for being a briber or bribee (p.

702). According to Noonan, even if corruption has been practised to varying degrees by

o�cials in all ages, the act is and always will be an anathema to society (pp. 702-6).

In apparent contrast to Noonan’s conclusion is the common suggestion that par-

ticularly corrupt countries are that way because their cultures are amenable to corrupt

practices. Bardhan (1997), for example, identifies the common argument that “[w]hat

is regarded in one culture as corrupt may be considered a part of routine transaction

10

CHAPTER 3. CORRUPTION AND SOCIAL PSYCHOLOGY Patrick Schneider

in another.” (p. 1330). Such reasoning leads to the conclusion that attempts to re-

duce corruption in such countries will be futile or even unwelcomed by their citizens.

However, as Bardhan (1997) notes, arguments based on social norms such as these are

“. . . near-tautological. . . ” (p. 1331), in that they label the prevalent behaviour ‘culture’

and explain its prevalence by pointing to its status as ‘culture’.

The problem with such arguments is that they conflate what is done in a society with

what is deemed to be good in that society. Certainly, there are countries where corruption

appears to be much more embedded in government operations than in others—where the

interaction between expectations and decisions perpetuates a bad equilibrium that could

be termed a behavioural norm (Bardhan, 1997, pp. 1331-4). However, the entrenchment

of corrupt practices in a country does not appear to impact its moral status—the strength

of the social norm against it. Bardhan (1997), for example, notes that in most countries

where norms of gift-exchange or clan loyalty take precedence over public duty, “. . . public

opinion polls indicate that corruption is usually at the top of the list of problems cited by

respondents.” (p. 1330). Similarly, Sardan (1999) finds in his analysis of corruption in

Africa that it is “. . . as frequently denounced in words as it is practised in fact.” (p. 29).

Hence, although there are di↵ering incidences of corruption among countries, its status

as an immoral pursuit does appear to be universal. It is thus worthwhile to consider how

this fact may influence the decisions of those in positions that a↵ord corrupt opportunities

and, further, to question how the two—widespread corruption and widespread disapproval

of it—can exist simultaneously.

3.2 Rational Choice and Morality

How could the moral prohibition of corruption a↵ect o�cials’ decisions? As discussed in

Chapter 2, some models of corruption incorporate personal morality by adding an exoge-

nous moral cost or measure of personal corruptibility into the models. Using either of

these methods yields new results, but the exogeneity of the moral e↵ect limits their in-

trigue. Treatments of moral decision-making outside the corruption literature and outside

economics provide some alternative mechanisms that will prove useful here.

The key mechanism that I draw on relates to the psychological concept of cognitive

dissonance. Cognitive dissonance theory is founded on the observation that people find

it di�cult to maintain two contradictory ideas and that in order to reduce this di�culty,

people may alter their beliefs (rather than their behaviour) (Akerlof and Dickens, 1982, p.

308). The mechanism has been applied in economic models by Akerlof and Dickens (1982)

who use it to explain the ignorance of workers in hazardous industries of the dangers they

are exposed to, and by Rabin (1994) who uses it to explain why people who are faced with

higher moral costs to behaviour (such as those living in puritan societies) may actually

behave in a more deviant manner.

As Rabin’s model explicitly refers to the impact of moral beliefs on decision making,

11

CHAPTER 3. CORRUPTION AND SOCIAL PSYCHOLOGY Patrick Schneider

it is more appropriate to apply it to corruption than Akerlof and Dickens’. Rabin’s

mechanism may be interpreted in our context as follows. O�cials choose two variables—

their behaviour (i.e. the degree of corruption they indulge in) and their moral line (i.e. the

degree of corruption they consider morally permissible). If their behaviour is inconsistent

with their moral line (this will be constructed more formally next chapter), they experience

‘dissonance’ because their own assessment of their behaviour as immoral clashes with their

self-image as a good person. They attempt to reduce this dissonance by adjusting their

moral line. Such adjustment is costly. The optimal choice of behaviour and morals is

made where the marginal costs of adjustment and dissonance are equal to the marginal

benefit of the corrupt behaviour. The portrayal of beliefs as a choice variable is not an

ideal component of the model—beliefs are not something we can pick up and drop at

will—but it serves as a useful shortcut, given other restrictions of Rabin’s model.

This adjustment, or something similar, is observed in reality, although in more com-

plex ways than a mathematical model could capture. Rather than adjusting their moral

line to make behaviour acceptable, it is observed that people develop ‘rationalisations’

that cast the behaviour in a di↵erent, not immoral, light. The importance of rationalisa-

tion has its roots in criminology. Cressey, for example, analysed embezzlement in private

organisations by interviewing over 200 prisoners convicted of fraud1. He found that the

ability to rationalise the behaviour was one of the three necessary pre-conditions for a

decision to embezzle funds (Cressey, 1953, pp. 77-8). The other necessary preconditions

in Cressey’s model are the existence of a ‘non-shareable’ (shameful in the embezzler’s

eyes) financial problem such as gambling debts and the identification of their entrusted

position as a way to solve it. Examples of common rationalisations are embezzlers’ beliefs

that they were only borrowing the funds or that their behaviour was standard business

practice.

Cressey’s theory contrasts with the process in Rabin’s model in two ways. First,

rationalisations serve to reclassify behaviour so that the moral line does not apply to the

situation, whereas Rabin’s model explicitly moves this line. Second, it is key to Cressey’s

model that rationalisations are developed prior to the commission of the act, whereas

the act and the moral adjustment are simultaneous in Rabin’s model. In both cases,

the simplicity of Rabin’s approach is a feature of his goal—to use a simple mathematical

model to show how cognitive dissonance might a↵ect moral decision-making. Far from

being discounted by Cressey’s model, the simplicity of Rabin’s2 is given depth by it—we

can more clearly appreciate what it could mean to change a moral line.

If the ability to rationalise otherwise immoral behaviour is, indeed, a necessary

precondition to undertaking that behaviour, we should observe the same phenomenon

when analysing corruption. Recall that at the end of the previous section, we noted

that countries with high levels of perceived corruption appear, paradoxically, to share

1An overview of his work can be found in Wells (2008, pp. 13-21).2For example, morality and actions are conceptualised in terms of degrees along a continuum, rather

than having multiple potential natures.

12

CHAPTER 3. CORRUPTION AND SOCIAL PSYCHOLOGY Patrick Schneider

the belief that corruption is immoral. Studies find that people in these situations who

simultaneously hold this belief and engage in the behaviour exhibit this propensity to

rationalise. Bardhan (1997), for example, notes that “. . . there is a certain schizophrenia

in [the] voicing of concern [over corrupt behaviour]. . . ”(p. 1330)—corruption is often

something only other people are capable of. Similarly, Sardan (1999) finds that the various

layers of legitimacy that have been imposed on Africa over the years (from tribe to family

to religion to politics) provide o�cials with various ways of framing their behaviour that

escape its being deemed immoral.

Viewed in this light, an o�cial who faces a corrupt opportunity not only needs to

weigh the incentives of the opportunity (the benefit to him of undertaking the act and

the possibility of being caught), but must also be able to rationalise his behaviour so he

can maintain a positive self-image. Hence, whereas economic models find that corruption

will be more prevalent when the benefits are greater and supervision lower, this discussion

suggests that it will be more prevalent still when rationalisations are more easily developed

and maintained.

3.3 Social Influence and Personal Morality

The discussion so far has not addressed how rationalisations are arrived at. They are not

likely to be consciously chosen, otherwise they could not e↵ectively serve their purpose

of reducing cognitive dissonance. Hence, I will now discuss how the method and ratio-

nalisations for corrupt behaviour can be learned. The earliest theory to explain criminal

behaviour is Edwin Sutherland’s ‘di↵erential association’ hypothesis (Laub, 2006). Most

criminals, Sutherland argued, are a product of the relationships they have—through their

associations with others, individuals absorb the methods of and rationalisations for crime.

The theory has been criticised for its determinism (individuals have no agency in the the-

ory). Criminology has developed new, more nuanced explanations since, although the

e↵ect that peers can have on people’s behaviour remains important (Laub, 2006).

In his study of private sector embezzlement, Cressey finds that “. . . the attitudes

and values of persons other than [embezzlers] are of great significance in [embezzlement].”

(Cressey, 1953, p. 144). Specifically, he found that none of his subjects had developed

his own rationalisation for the behaviour, rather “. . . he necessarily must have come into

contact with a culture which defined those roles for him. . . ” (Cressey, 1953, p. 99).

Perhaps because the source of rationalisations was the observations by subjects about the

behaviour and opinions of others, it was common for them to believe that their behaviour

was standard practice (p. 110), and this belief itself served as a rationalisation for many.

Cressey’s theory builds on Sutherland’s model by positing that although methods and

rationalisations are learned from association, the actual act is committed for another

reason (in his case, a ‘non-shareable financial problem’).

The di↵erential association hypothesis and Cressey’s theory both describe how in-

13

CHAPTER 3. CORRUPTION AND SOCIAL PSYCHOLOGY Patrick Schneider

dividuals can come to be corrupted by their surroundings, but the acts are committed

by the criminals alone. As well as influencing the individual’s path to corruption, how-

ever, associations can develop into a corrupt subculture where the acts are committed

in concert with others. Ashforth and Anand (2003), for example, discuss the process by

which corrupt subcultures can develop and sustain themselves in private organisations.

They establish that corruption can become institutionalised in an organisation when the

behaviour becomes embedded in everyday processes, rationalisations are developed to

reclassify it as not morally salient and newcomers are incrementally socialised into it.

In their paper, Ashforth and Anand highlight how the forces already discussed in

relation to individuals are particularly potent when they become a part of the fabric of

an organisation. They discuss, for example, how corrupt practices might become a part

of a routine, with tasks divided up among di↵erent people so that no individual is fully

responsible (p. 12). This and other factors serve to habituate people into the work,

the result being that “. . . one becomes accustomed to the aversiveness and riskiness of

corruption, contributing to. . .mindlessness. . . and perhaps carelessness.” (Ashforth and

Anand, 2003, pp. 12-14). Hence, although people might resist at first, the repetition of

the act with others around them serves to make them more comfortable with it3. This

is similar to Cressey’s observation that embezzlers often believed that what they did

was standard business practice. Furthermore, the impact of rationalisations is amplified

in the environment described by Ashforth and Anand because there is feedback between

individuals’ own acceptance of rationalisations and that of the people around them, having

the e↵ect of a “. . .mutual echo [that] transforms them from self-serving fictions into

social facts.” (Ashforth and Anand, 2003, p. 24). Finally, whereas in Cressey’s model

rationalisations served to open the door to corrupt behaviour and another motivating

force was necessary to push people through, corrupt subcultures in organisations might

themselves breed these motivations. Corrupt groups, for example, may pressure new

members into incrementally adopting their behaviour, as described below:

“. . . newcomers are initially induced to engage in small acts that seem relatively

harmless and volitional as well as possibly visible, explicit and irrevocable.

These acts, though small, create some cognitive dissonance (I’m an ethical

person, so why did I do that?) that the newcomers can resolve by invoking the

rationalising ideologies that the subculture provides, thereby realigning their

attitudes with the acts (I guess it must not be so bad). The realigned attitudes

then facilitate an escalation of the behaviour and the process continues. Thus,

each step up the ladder of corruption enables later ones.” (Ashforth and

Anand, 2003, p. 29)

3A similar observation in more extreme circumstances is made in the analysis of Adolf Eichmann’swar crimes trial by Hannah Arendt (1977).

14

CHAPTER 3. CORRUPTION AND SOCIAL PSYCHOLOGY Patrick Schneider

That corruption is morally frowned upon makes the decision to engage in it significantly

di↵erent from other economic decisions. Not only do individuals need to have the op-

portunity and a positive expected payo↵, but they also need to be able to maintain a

positive self image. If they are unable to achieve this by rationalising corrupt behaviour,

they are unlikely to consider it an option, regardless of its rewards. Developing these

rationalisations and learning how to engage in the behaviour is much easier when an indi-

vidual’s associates also engage in the behaviour. In fact, when an individual is a part of an

organisation that is systematically corrupt they might find conformity to the behaviour

hard to resist. Based on the above discussion, we can see how it is likely that where

organisational cultures of corruption emerge, the behaviour is likely to persist over time

as its commission encourages its repetition. As Ashforth and Anand (2003) note, “. . . [i]n

a real sense, an organization is corrupt today because it was corrupt yesterday.” (p. 14).

If there is enough inertia of this type, the choices of individuals in corrupt organisations

could be completely unresponsive to changes in incentives.

15

Chapter 4

Theoretical Model

In this chapter, I incorporate the key insights discussed in the previous chapter into a

model in which the degree of corruptibility of an o�cial is determined by his own moral

stance as well as by peer e↵ects. The model is based on the class of ‘cognitive dissonance’

models pioneered by Akerlof and Dickens (1982). The particular form adopted here is

from Rabin (1994). In this model, Rabin uses the cognitive dissonance framework to

address decision making with moral concerns (he uses the example of animal rights). In

his formulation, the individual’s decision is sensitive to the moral position of his peers.

This framework for analysing decision making with moral and social constraints is ideally

suited to discuss corruption.

The central variable in the model is the o�cial’s own beliefs about the extent of

corruption that can be considered acceptable, which may be termed his ‘moral stance’.

A key feature of the model is that the o�cial’s moral stance is a↵ected by his perception

of the moral stances of others. Further, his perception of the moral stance of society at

large is disproportionately influenced by the attitudes of agents in his immediate vicinity,

specifically his workplace.

The model yields two principal propositions. First, within a given society with a

uniform structure of incentives and penalties, di↵erent organisations may display di↵erent

levels of corruption in equilibrium. Secondly, these di↵erent equilibrium corruption levels

will persist over time as individuals use past observations of their peers to form their

present beliefs.

This chapter proceeds with a careful description of the behaviour of an o�cial in

this model, emphasising the points of departure from Rabin’s framework. This leads

to the general model, which is then restated using explicit functional forms for the be-

havioural equations. This allows us to obtain closed form solutions for equilibria and

makes comparative static analysis transparent. In the last section the model is extended

to include considerations that are standard in the corruption literature—supervision and

heterogenous agents.

16

CHAPTER 4. THEORETICAL MODEL Patrick Schneider

4.1 Discussion

We imagine that the o�cials, the key actors in our model, work in departments with

given incentive structures and opportunities for corruption. Accepting opportunities for

corruption increases an o�cial’s income and each o�cial chooses the extent to which he

indulges in corrupt behaviour.

O�cials have personal morals that delineate acceptable from unacceptable behaviour

(for example, an o�cial might believe stealing money from a drug dealer is OK but taking

a bribe to let him escape indictment is not). If an o�cial behaves in a way that is

unacceptable by his own moral standards, there is a ‘cognitive dissonance’ between his

judgment that the behaviour is unacceptable and his belief that he is a good and nice

person. This dissonance gives him displeasure.

O�cials can also choose to modify their personal morals against which they judge

their own behaviour. Such adjustment might be in the form of the development of ratio-

nalisations (there is no victim, I deserve it, I do so much good that this is OK, etc.) that

justify a change in his standards of acceptable behaviour. Such adjustment of morals to

a more permissive stance is di�cult to achieve and sustain.

How di�cult moral adjustment is depends both on the magnitude of change and

the judgements and behaviour of the people with whom the o�cial interacts closely. It

is harder to make a bigger adjustment, but the adjustment towards a more permissive

personal attitude is easier, ceteris paribus, when the attitudes surrounding him are more

corrupt and permissive. This latter component is the ‘everyone was doing it’ e↵ect. The

model so far is identical to that proposed by Rabin (1994), with corruption substituted

for violation of animal rights.

My model deviates from Rabin’s in the specification of the o�cial’s perception of the

attitudes of the society around him. Each o�cial works in a particular department, and it

is reasonable to assume that he is exposed more to the attitudes and actions of colleagues

in his department than he is to those of agents in the society at large. The attitudinal

norm of the society may reasonably be represented as exogenous to the immediate choices

of an individual o�cial or a specific department. In the extreme case it may be an

explicit moral code that is insensitive to behaviour, as in the case of a religious doctrine

in a theocratic society—an absolute statement of what is right. The e↵ective moral code

within a department, on the other hand, would be significantly more sensitive to—indeed,

shaped by—the attitudes and choices of department o�cials.

In the present model the o�cial’s perception of the attitudes of society—of what is

acceptable or not acceptable—is determined by both the average attitude of society at

large as well as attitudes within the department. The intuition is that, since the o�cial

interacts more often and more closely with others in his department, the latter’s attitudes

may have a greater weight in determining his perception of the degree of corruption that

the society finds acceptable. If these perceptions then a↵ect his own choice of morals

17

CHAPTER 4. THEORETICAL MODEL Patrick Schneider

and subsequent behaviour, it may happen that two otherwise identical o�cials working

in departments that historically have di↵erent cultures regarding corruption might come

to behave in radically di↵erent ways.

In the next section we formalise the behaviour described above and specify the full

model. We then work with a closed-form version of the model to derive the results that

underlie the hypotheses tested in the empirical section of the thesis.

4.2 Model

4.2.1 General form

The economy consists of several departments, indexed by j, each of which employ a

number of o�cials indexed by i. All o�cials earn a common wage w. Each o�cial also

faces opportunities for corruption from which he can earn a maximum additional income

b. We assume that there is a clear ordering of opportunities from 0 to 1, where 0 is least

objectionable and 1 is most objectionable. Each o�cial chooses the extent to which he

indulges in corruption, �ij 2 [0, 1]. His income is then w + �ijb, from which he derives

utility

U(w + �ijb) where U 0 > 0 and U 00 < 0.

Each o�cial also has a moral line that delineates acceptable from unacceptable

behaviour, ij 2 [0, 1]. If an o�cial indulges in behaviour that is unacceptable by his

own moral standards (i.e. if �ij > ij), he experiences displeasurable cognitive dissonance

thus:

D(�ij � ij) where D0 > 0 and D00 > 0. (4.1)

In addition, the o�cial may also make a positive gain (cognitive ‘resonance’) if his be-

haviour exceeds his own moral standards.

O�cials can choose their moral standards, but it is costly to maintain a stance that

deviates from one that would arise naturally. Let the standard that arises naturally (to

be explained below) be �̂. If the o�cial wishes to maintain a stance 6= �̂, he incurs a

(psychological) cost

C( , �̂), where C > 0, C > 0, C�̂ < 0 and C �̂ < 0.

The beliefs of those around him matter to the o�cial. Specifically, we assume that

each o�cial wishes to act in accordance with the prevailing norm of his society, � 2 [0, 1].

But the beliefs of his society may not be observable to him, so he must estimate them.

His estimate is based on the average of the beliefs of those in his department, ✓j =P

i ij

Nj

and an explicit society-wide moral code that may be unrelated to behaviour, �. So the

18

CHAPTER 4. THEORETICAL MODEL Patrick Schneider

o�cials in department j perceive the moral stance of society to be

�̂j = �̂(�, ✓j). (4.2)

Thus the ‘natural stance’ of o�cial i in department j is �̂j, and he incurs cost C( ij, �̂j)

if he wishes to maintain a moral stance C( ij) that deviates from this.

Each o�cial’s utility is derived by combining the components discussed above as

follows:

Vij(�ij, ij) = U(w + �ijb)�D(�ij � ij)� C( ij, �̂j) (4.3)

Optimisation

Each o�cial solves his problem by optimising this objective function, with his perceptions

of the beliefs of those around him (�̂) taken as given. His optimal choice, as a function of

his perceptions and a vector a of parameters which include w and b, is defined by:

(�⇤ij(a, �̂j), ⇤ij(a, �̂j)) = arg max

�ij , ij

U(w + �ijb)�D(�ij � ij)� C( ij, �̂j) (4.4)

The optimal choice is found by setting first order conditions with respect to the choice

variables equal to zero. Hence �⇤ij and ⇤ij satisfy:

bU 0(w + �⇤ijb) = D0(�⇤ij � ⇤ij) (4.5)

D0(�⇤ij � ⇤ij) = C ( ⇤

ij, �̂j)

That is, the o�cial’s optimal choice of personal moral and corruption levels is found where

the marginal benefit of more corruption is equal to the marginal cost of the dissonance it

would cause, and where the marginal dissonance benefit of raising his moral line is equal

to the marginal cost of doing so.

Equilibrium

An equilibrium is a state where individual o�cials choose their actions optimally in re-

sponse to their perceptions, and in turn their perceptions are realised and perpetuated.

Note that, by (4.2) all o�cials within each department j, must have the same

perception �̂j, since �̂j is determined by the economy-wide variable � and the department-

level variable ✓j. Thus in equilibrium each o�cial must arrive at the same choice of moral

stance and action (�ij, ij). In turn this implies that the department’s average moral

stance ✓j must be equal to the common value of ij. Thus, for a given set of parameters

a an equilibrium is a pair (�⇤ij(a), ⇤ij(a)) that solves the optimisation problem (4.4) given

(w, b, �̂), and �̂ is determined by (4.2) with ✓j = ⇤ij.

In other words, given a parameter set a ⌘ (w, b,�), an equilibrium is a pair

19

CHAPTER 4. THEORETICAL MODEL Patrick Schneider

(�⇤(a), ⇤(a)) that satisfies

(�⇤(a), ⇤(a)) = arg max�ij , ij

U(w + �ijb)�D(�ij � ij)� C( ij, �̂) (4.6)

�̂ = �̂(�, ⇤(a)) (4.7)

Note that this defines a partial equilibrium for the department. A general equilibrium for

the economy would potentially endogenise the society-wide standard �, which I have left

exogenous. This point is revisited below.

4.2.2 Closed form

In this following section, I use explicit functions and obtain closed form solutions for the

model. The money utility function is defined thus:

U(w + �ijb) = w + �ijb (4.8)

Here I have defined a linear money utility function. Such definition does not satisfy the

shape described earlier but is necessary for a unique solution1. Utility is increasing in the

base wage, corruption level and available bribes. The dissonance function is defined thus:

D(�ij � ij) = x(1 + �ij � ij)2 � x (4.9)

Where x is a parameter that scales the dissonance relative to the money utility function

(U). Note that � and take values in the interval [0, 1] hence � � has range [�1, 1].

On this interval D(.) satisfies the restrictions imposed in (4.1). This dissonance function

describes the psychological cost felt from behaving in a way that is at odds with personal

morals. With this function, o�cials who act exactly as their morals allow (�ij = ij) will

experience no dissonance; if they behave in a way that their morals prohibit (�ij > ij),

they experience dissonance that subtracts from utility; and if they are better behaved

than their morals allow (�ij < ij), this adds to utility.2 The moral adjustment cost

function is defined thus:

C( ij, �̂) = z

✓ ij

�̂

◆2

(4.10)

Where z is a parameter that scales the moral adjustment cost relative to the money utility

function (U). Again,the function satisfies the shape restrictions outlined earlier. This

cost function describes the social disutility felt from holding and maintaining permissive

morals, this cost is exacerbated if personal morals are at odds (in either direction) with

1Alternative forms such as log(w + �ijb) yield multiple optimal corruption levels for o�cials who haveset morals. My interest here is in the e↵ect of social interactions on choices, which is not a↵ected by thesimplification.

2These properties hold within the possible range of �1 �ij � ij 1.

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CHAPTER 4. THEORETICAL MODEL Patrick Schneider

what the o�cial perceives to be normal. That is, it is just as costly for an o�cial to

hold morals that are relatively higher than his perception of his peers’ as it is to hold

morals that are relatively lower—conformity is the easier path. Rabin (1994) uses a

similar functional form. He conceptualises this cost as something that is incurred at

dinner parties—people are required to make statements about their views; those whose

views are di↵erent from normal will experience discomfort, regardless of whether they are

stricter or more permissive than their peers.3

Finally, since agents are homogenous, the department’s average moral level is equal

to a representative agent’s decision, by definition ✓j =P

i ij

Nj= ij. Hence, in equilibrium

�̂ must satisfy �̂ = �̂(�, ij).

Optimisation

Using the defined functional form for the objective function, the first order conditions can

be found as in (4.5), and the optimal choice set for the o�cial derived:

b = 2x(1 + �⇤ij � ⇤ij) (4.11)

2x(1 + �⇤ij � ⇤ij) = 2z

⇤ij

�̂2(4.12)

Substituting 4.11 into 4.12 yields the optimal personal moral level in response to percep-

tions and parameters:

⇤ij =

b

2z�̂2 (4.13)

Substituting 4.13 into 4.11 yields the optimal corruption level in response to perceptions

and parameters:

�⇤ij =b

2z�̂2 +

b

2x� 1 (4.14)

Hence, for a given set of parameters (w, b, z, x) and perceptions �̂, there is a single optimal

choice set available to the o�cial:

⇣�⇤ij(a, �̂j),

⇤ij(a, �̂j)

⌘=

✓b

2z�̂2 +

b

2x� 1,

b

2z�̂2

◆(4.15)

This choice set has the expected properties that personal morality and corruption levels

are increasing with perceptions of society at large,@ ⇤ij@�̂

,@�⇤ij@�̂

> 0, decreasing with the

psychic costs of maintaining morals,@ ⇤ij@z

,@�⇤ij@z

< 0, and increasing with the magnitude

of the bribe opportunity,@ ⇤ij@b

,@�⇤ij@b

> 0. Also, personal corruption is decreasing in the

3The intricacies of conformity are discussed in Bernheim (1994), which proposes a model where peoplederive status from public perceptions of their personal predispositions (similar to our personal moral linehere).

21

CHAPTER 4. THEORETICAL MODEL Patrick Schneider

psychic cost of cognitive dissonance,@�⇤ij@x

< 0. It should be noted that for a fixed available

bribe, the optimal personal corruption level may be either above or below that which

is allowed by personal morals, depending on how strong the psychic cost of dissonance

is. If an o�cial is particularly susceptible (where b2x

< 1), he will benefit from setting a

high moral line but by being less corrupt than it permits (�⇤ij < ⇤ij). Conversely, o�cials

who are less susceptible will optimally behave in ways their own morals do not readily

condone.

Equilibrium

To determine equilibria, we need to explicitly specify the perceptions of the o�cials. I will

consider three cases. The first is where all o�cials perfectly observe the morals of those

within their department, but do not observe morals of agents outside, so that �̂ = ✓j. The

second is where o�cials observe some fixed moral level that is unrelated to people’s actual

choices, i.e., �̂ = � where � is exogenously determined. An example of this is a society

where ‘acceptable’ morals are specified by religious dogma. The third is where o�cials

observe some combination of these, such that �̂ = ↵� + (1 � ↵)✓j where ↵ is the weight

given to the exogenous moral line.4 By the homogeneity of o�cials it follows that, in

equilibrium, we will have ij equal across all o�cials i in department j. By the definition

of ✓j and the assumption of homogeneity, it follows that in all cases we will have ✓j = ij

in equilibrium.

Case 1. Substituting the perceptions in the first case into 4.13 yields the following interior

equilibrium5:

⇤ij =

2z

b(4.16)

Hence, when o�cials perfectly observe those around them, the average moral level in a

department will settle at this single point. Furthermore, as all departments are the same,

each department (and therefore society as a whole) will settle at this equilibrium moral

level. The corresponding equilibrium corruption level where o�cials perfectly perceive

their peers is

�⇤ij =2z

b+

b

2x� 1.

Case 2. Substituting the perceptions in the second case into 4.13 yields the following

equilibrium moral level:

⇤ij =

b

2z�2 (4.17)

4Note that the first two cases correspond to setting ↵ = 0 and ↵ = 1 respectively.5There is also a corner solution ( ⇤ij = 0) that satisfies the condition.

22

CHAPTER 4. THEORETICAL MODEL Patrick Schneider

Hence, when o�cials look solely to some exogenously set moral line (the ‘dogmatic line’),

there is a single equilibrium response as in the case with perfect observation. It is inter-

esting to note that the response to the dogmatic line does not necessarily conform to it.

In fact, the equilibrium moral level will only be equal to the dogmatic line in the case

where � = 2zb

(the equilibrium without the dogmatic line). If the dogmatic line is lower

than this level (less permissive), society will settle at a more permissive level than dogma

dictates. Conversely, if the dogmatic line is higher than this level (more permissive), then

society will settle at a more puritanical level. The corresponding equilibrium corruption

level where o�cials perceive only the dogmatic line is

�⇤ij =b

2z�̂2 +

b

2x� 1.

Case 3. Substituting the perceptions in the third case into 4.13 yields the following:

⇤ij =

b

2z(↵�+ (1� ↵) ⇤

ij)2 (4.18)

Solving for ij, using the quadratic formula, we find the possible equilibrium moral levels

are:

⇤ij =

zb� ↵�(1� ↵) ±

q�↵�(1� ↵)� z

b

�2 � (1� ↵)2 · ↵2�2

(1� ↵)2(4.19)

This yields real solutions if the discriminant is positive, which requires zb

> 2(1 � ↵)↵�.

Note that (1�↵)↵� < 14 since ↵ 2 [0, 1] and � 2 [0, 1]. Hence a su�cient condition for ⇤

to have two real roots is b < 2z. If this condition is satisfied and o�cials’ perception of

prevailing morality is a convex combination of their peers’ morality and that of the wider

society, there are two equilibria.

If the maximum bribe level b is too high or the cost of moral adjustment z is too low,

there is no equilibrium. If the parameter values are such that two equilibria exist, these

specify the morals to which o�cials conform at the department level. Clearly, however,

di↵erent departments can conform to di↵erent equilibria in the same society with the

same overarching code �.

I have not attempted to derive a general equilibrium for the entire society. However,

it can be easily seen that the model can be extended by specifying that � should be some

function of the ✓js across di↵erent departments j. However � is determined, the result

that is relevant to this thesis is that in equilibrium di↵erent departments can persist with

di↵erent levels of corruption, which is the possibility explored further in the empirical

analysis.

These results are a consequence of the chosen functional form—specifically the

square on the denominator of the cost function. They show that the cognitive dissonance

mechanism with incomplete perception of the morals of society can produce multiple equi-

23

CHAPTER 4. THEORETICAL MODEL Patrick Schneider

libria at the department level. With more complex functional forms the model will yield

a larger number of departmental equilibria that can coexist in the same economy. This

finding gives rise to the following hypothesis and implication:

Hypothesis 1. A government department’s organisational culture a↵ects its o�cials’

perceptions of what moral line is acceptable, which in turn a↵ects where they set their own

moral line and how they behave. When their perception of acceptable morality gives weight

to both their peers and an economy-wide standard, di↵erent departments may endogenously

settle at di↵erent equilibrium levels of corruption.

Implication 1A. Departments with identical incentive structures but di↵erent cultures

can exhibit di↵erent levels of corruption.

Furthermore, assuming o�cials’ perceptions of their peers are based on what they observed

of their colleagues in the past, we have a further hypothesis and implication:

Hypothesis 2. The feedback between the perceptions and decisions of o�cials within a

department will cause those perceptions to be realised in future periods. The department’s

culture can therefore endogenously persist over time.

Implication 2A. Departments with identical incentive structures but di↵erent cultures

can exhibit di↵erent levels of corruption persistently over time.

Testing implications 1A and 2A is the focus of the empirical sections.

4.2.3 Extensions

The model presented so far was simplified to elucidate how the cognitive dissonance

mechanism might work. In the following, I introduce extensions to account for supervision

and heterogenous agents that make the model more comparable to standard corruption

models.

Supervision

The first extension is to introduce supervision. We do this by imagining that some external

body conducts reviews of o�cials’ work, but is unable to observe everything and so only

catches corrupt behaviour with some probability, p, i.e. a corrupt o�cial will be caught

with probability p. If an o�cial is caught, we assume he is fined an amount equal to

his wages and any corrupt income. Hence, an o�cial’s expected money earnings are

(1� p)(x + �ijb). It is clear here that the e↵ect of introducing this probability of capture

reduces the marginal benefit of corruption. Consider, for example, the functional forms

used in the previous section. With this extension made to the objective function, the first

24

CHAPTER 4. THEORETICAL MODEL Patrick Schneider

order conditions in the o�cial’s optimisation problem are altered to:

(1� p)b = 2x(1 + �⇤ij � ⇤ij) (4.20)

2x(1 + �⇤ij � ⇤ij) = 2z

⇤ij

�̂2(4.21)

These yield the optimal choice set:

⇣�⇤ij(a, �̂j),

⇤ij(a, �̂j)

⌘=

✓(1� p)

b

2z�̂2 +

b

2x� 1

�, (1� p)

b

2z�̂2

◆(4.22)

Hence, with supervision, the o�cial’s optimal choice is scaled down by the likelihood he

will get to keep his proceeds. Similarly, the equilibria are scaled by this introduction but

their existence is una↵ected.

So far we have treated supervision as an exogenous parameter. It is perhaps more

realistic to assume that the extent of supervision will be related to society’s opinion of

corruption—hence p = p(�) where p(1) = 0, p0(�) < 0 and p(0) = 1. The optimising

o�cial will hence have to take this into account. Recall, however, that a key feature of this

discussion is that the o�cial’s perception of broader society is based on his observation

of his department colleagues and the dogmatic line. Hence, although in reality p = p(�),

the o�cial can only estimate the probability he will be caught—hence p̂ = p(�̂). Suppose

the particular shape of the supervision function is p(·) = 1��. Hence, we can restate the

o�cial’s first order conditions as follows:

�̂b = 2x(1 + �⇤ij � ⇤ij) (4.23)

2x(1 + �⇤ij � ⇤ij) = 2z

⇤ij

�̂2(4.24)

With these conditions, the o�cial still has a single best response to his perceptions:

⇣�⇤ij(a, �̂j),

⇤ij(a, �̂j)

⌘=

✓b

2z�̂3 +

b

2x� 1,

b

2z�̂3

◆(4.25)

The determination of equilibria is slightly altered although their existence still holds.

Figure 4.1 compares the equilibrium levels of ⇤ that arise with and without supervision

for particular values of the parameters (↵ = 0.3, b = 8, � = 0.25 and z = 1). The blue

curve tracks the marginal cost or moral adjustment function with explicit perceptions

(�̂ = ↵� + (1 � ↵) ij) as it varies with personal morals. The yellow and red curves

track the marginal benefit of corruption as it varies with personal morals for the cases

with and without supervision, respectively. The intersections of the latter curves with the

former show where the equilibrium levels of personal morals are. As the figure shows, the

introduction of supervision alters o�cials’ decisions. However, even when supervision is

endogenous, Implications 1A and 2A are not a↵ected.

25

CHAPTER 4. THEORETICAL MODEL Patrick Schneider

Figure 4.1: Equilibrium ⇤ with and without supervision

0.2 0.4 0.6 0.8 1.0y

2

4

6

8

Heterogenous agents

The second extension is to introduce heterogenous o�cials. We do this by imagining

di↵erent ‘types’ of o�cials. As discussed in Chapter 2, this has been done before in

models that include corruptible and incorruptible agents. Here I assume that the two

types di↵er in their susceptibility to the cost of adjusting their morals; i.e. high types6

(z = zH) experience more displeasure than low types (z = zL) for given perceptions

and personal morals. The result is that the two types respond di↵erently to the same

perceptions

⇤H =

b

2zH

�̂2 (4.26)

⇤L =

b

2zL

�̂2 (4.27)

Hence, faced with the same perceptions of what is acceptable, high types will choose a

less permissive personal moral stance than low types. From this we find that the two

responses to the same perceptions result in a relationship between the two such that:

⇤H =

zL

zH

⇤L (4.28)

If we now assume that these types occur in society in given proportions—say a fraction q 2[0, 1] are high types—and that departments all share the same mix of types, perceptions

in equilibrium are formed thus:

�̂ = ↵�+ (1� ↵)(q ⇤H + (1� q) ⇤

L) (4.29)

6As in high moral ground.

26

CHAPTER 4. THEORETICAL MODEL Patrick Schneider

Substituting 4.28 and 4.29 into 4.26 and 4.27 will thus yield the equilibrium moral choices

for high and low types respectively. For example, if we perform the operations for low

types:

�̂ = ↵�+ (1� ↵)

✓qzL

zH

⇤L + (1� q) ⇤

L

�̂ = ↵�+ (1� ↵)m ⇤L (4.30)

Where m =⇣q zL

zH+ (1� q)

⌘2 (0, 1). Substituting this into the expression for the o�-

cial’s optimal moral line (4.27), and solving using the quadratic formula yields the equi-

librium moral line for low types:

⇤⇤L =

zb� ↵�m(1� ↵) ±

q�↵�m(1� ↵)� z

b

�2 � (m(1� ↵) · ↵�)2

(m(1� ↵))2 (4.31)

Hence, as in the case where we assumed homogenous agents, there are two equilibria for

low types when their perceptions of acceptability depends on both their peers and the

dogmatic line. High types will also have two equilibrium moral lines. These will be equal

to the low types’ equilibria scaled down by zL

zH.

This result di↵ers from that with homogenous agents in that with homogeneous

agents, everyone in the department acted in the same way and based their actions on the

departmental average that was equal to the common behaviour. In the present case agents

observe two types of behaviour and act based on the weighted average of those. However,

this still yields multiple equilibria. The di↵erence is that in equilibrium, there are two

responses within a department to the same perceptions—high types are less corrupt than

the average while low types are more corrupt.

It is interesting to note that the multiple equilibria remain even if all departments

have the same mix of high and low types. We could easily see how two departments might

end up with di↵erent cultures if they are populated by entirely high and entirely low types,

respectively. The result found here, however, occurs even holding the mix constant across

departments. Hence, from the analysis in the previous sections, we could say that two

departments with identical incentive structures could exhibit very di↵erent patterns of

corrupt behaviour. We can now add the observation that even if these departments are

populated by o�cials with the same mix of predilections for immoral behaviour, they

might still exhibit di↵erent patterns—and if they did have a di↵erent mix, we would

expect the di↵erence in their patterns to be greater still.

The model shows that o�cials’ perceptions of what is morally acceptable a↵ects their

decisions such that the average behaviour in a department will settle on one of a number

of equilibria. Which equilibrium a department settles on is dependent on its history. In

contrast with previous models, di↵erent levels of corruption are not just an artefact of

27

CHAPTER 4. THEORETICAL MODEL Patrick Schneider

di↵erent incentive structures or supervision mechanisms; nor are they solely a function of

the personalities of the o�cials currently in the department. These results prompt us to

expect that observations of corruption in real world government departments will show

noisy variation (Implication 1A) and persistence over time (Implication 2A).

28

Chapter 5

Data

The remainder of this thesis is concerned with analysing the available data on corruption,

and testing the implications of the theoretical model. I will use the results found in

the comprehensive survey article by Treisman (2007) as the baseline for identifying the

relevant variables to use as controls and significant correlates. My contributions to this

literature are in using updated and department level data, and testing the implications

of a new model of corruption. I am unaware of any study that has used the department

level data before.

This chapter outlines the data that will be used in the analysis, with particular

attention given to the data on corruption. Chapter 6 outlines the empirical strategy that

will be used to test Implications 1A and 2A from the theory and Chapter 7 presents

results.

5.1 Corruption Data

Corruption is di�cult to measure. Because it is an illicit activity, there are no revealed

data available and statistical analysis must rely on stated survey data. These are avail-

able in two forms—perception indices (including Transparency International’s Corruption

Perception Index (CPI) and the World Bank’s Control of Corruption Index (CCI)) and

experience indices (Including Transparency International’s Global Corruption Barometer

(GCB) and the World Bank’s Enterprise Survey (ES)). Most of these indices are published

at the country level. The disaggregated GCB and ES results are, however, available at

the department level as well.

5.1.1 Country level perception indices

Perception indices are constructed using a standard methodology. An organisation with a

network of experts on di↵erent countries asks these experts to rate their country based on

their perception of that country’s level of corruption, or the extent to which questionable

payments are a regular part of doing business. The scores are averaged and transformed

and countries ranked based on the result.

29

CHAPTER 5. DATA Patrick Schneider

Recently, the indices that are used in empirical analysis, such as the CPI and CCI,

are not primary indices but rather aggregations of the results of multiple surveys that

have the structure outlined above. This enhances the number of observations per country

(a significant shortcoming of the earlier surveys) and has also broadened the scope of

interviewees somewhat beyond ‘experts’. The CPI score ranges from one to ten where

higher numbers denote the absence of corruption. The CCI score is a standardised score of

deviations from the average, ranging from approximately �2.5 to 2.5 with higher numbers

denoting the absence of corruption. Summary statistics are reported in Table 5.1.

Perception indices of corruption tend to show similar results. Figure 5.1 shows a

scatter plot of the CPI and CCI results. In this plot, lower numbers indicate more cor-

ruption, so the observations in the south-west corner are for the countries perceived to be

most corrupt and those in the north-east are those perceived to be least corrupt. The vis-

ible linear trend clearly shows the correlation between the two sources (also see Table 5.2).

Figure 5.1

-2-1

01

2C

CI 2

009

0 2 4 6 8 10CPI 2009

Comparison of Perception Indices

Perception indices are fraught with problems, outlined by Treisman (2007), You

and Khagram (2005) and Fisman and Gatti (2002). For example, the experts or others

surveyed may have no actual experience of corruption and so may rely on their recall of

stories they have heard, making them subject to the availability heuristic1. Furthermore,

1Where, when faced with uncertainty, “...people assess the frequency of a class or the probability

30

CHAPTER 5. DATA Patrick Schneider

Table 5.1: Descriptive statistics for corruption measures

Variable n Mean S.D.CPI10 178 4.01 2.09CCI09 204 �0.01 1.00GCB10 83 23.65 21.49

given that the indices are aggregations of others, and that these were ratings out of ten,

their magnitude is not directly interpretable—what is the di↵erence between a seven and

an eight?

These indices have served as the corruption data for most of the empirical analyses,

likely because they have been in existence and improving in quality for many years.

Another major advantage over experience based indices is that they tend to cover more

countries (in 2008, the CPI had 180 observations versus the GCB’s 65). However, the

coverage of experience based indices has been improving over recent years, and their last

release makes them a viable alternative.

5.1.2 Country level experience indices

Transparency International’s GCB presents survey results (summary stats are reported

in Table 5.1) about individual level experiences of bribery in di↵erent countries. There

are typically 1000 respondents per country and the total number of countries covered

has increased to 85 in the 2010 release (up from 65 in 2008). Respondents are asked if

they, or a member of their household, have had contact in the last twelve months with

any of nine government departments2. Those who report contact are asked a follow up

question of whether they, or a member of their household, have paid a bribe to that

department (Transparency International, 2010, pp. 38-39). Country scores are obtained

by reporting the proportion of respondents who reported paying a bribe to at least one

of these departments. Hence, country scores (GCBi) fall somewhere in the range:

max{ni1, ni2, . . . , nij}Ni

GCBi P

j nij

Ni

where nij denotes the number of people in country i who said they had bribed department

j and Ni is the total number who took the survey.

The results from the GCB country scores do not match those of the perception

indices nearly as well as the latter type do with each other. Consider Figure 5.2, which

plots the GCB country scores on top of Figure 5.1. As with the earlier figure, the bottom

south-west shows the most corrupt and the north-east the least corrupt countries. The

of an event by the ease with which instances or occurrences can be brought to mind...” (Tversky andKahneman, 1974, p. 1127)

2Education system, Judiciary/Judicial System, Medical Services, Police, Registry & Permit Services,Utilities, Tax Revenue, Land Services and Customs

31

CHAPTER 5. DATA Patrick Schneider

Figure 5.2

020

4060

8010

0G

CB

2010

-2-1

01

2C

CI 2

009

0 2 4 6 8 10CPI 2009

CCI 2009 GCB 2010

Comparison of Experience and Perception Indices

figure shows that countries with low levels of expert perceived corruption (6-10) actually

do have low levels of experienced corruption. However, in countries with high perceived

corruption (0-5), there is significant variation in experiences. This can also be seen in

Table 5.2 which shows the correlations between the di↵erent measures. Those between

perceptions indices are very high (� 95%) and significant, whereas those between percep-

tions and experience indices are much less pronounced ( 72%), although still significant.

5.1.3 Department level experience indices

The empirical work in corruption uses the country level data discussed above. One contri-

bution of this thesis is to further this work by analysing the department level data. These

data are not published in the primary report, but are made available in their raw form on

the Transparency International website3. I manually collected these disaggregated data

for every year they were available—2010, 2009, 2007 and 2006.

I have made two adjustments to the results. First, due to issues concerning the valid-

ity of the data collected from South Africa and Morocco that are raised by Transparency

International (Transparency International, 2010, p. 34), I have excluded observations re-

lating to these two countries. Second, because the bribery questions are conditional (if

3The 2010 release was downloaded from http://www.transparency.org/content/download/57678/

922770/Tabulations+by+country.zip accessed July 2011

32

CHAPTER 5. DATA Patrick Schneider

Table 5.2: Correlation between country level corruption indices

CPI10 CPI05 CCI09 CCI05 GCB10 GCB05CPI10 1n 178

CPI05 0.97 1n 156 160

CCI09 0.98 0.95 1n 177 160 204

CCI05 0.95 0.96 0.97 1n 176 159 199 199

GCB10 �0.66 �0.66 �0.68 �0.72 1n 81 80 83 83 83

GCB05 �0.66 �0.64 �0.67 �0.71 �0.81 1n 68 66 68 68 57 68

Note: All correlations are significant at the 1% level.

you had contact, did you bribe?), some observations are based on very few responses.

In South Korea, for example, only 24 respondents out of 1500 reported contact with the

judiciary or judicial services and of those, two reported paying a bribe. Hence, in order

to increase validity of the data, observations based on a small number of respondents

were coded as missing values. This biases against departments which have low levels of

contact—particularly customs. As such, the threshold for contact was set at 50 respon-

dents, which is low enough to exclude results based on very few observations but high

enough to allow variation in the customs figures (23 out of 85 observations are excluded).

Another source of disaggregated experience data is the World Bank’s Enterprise Sur-

vey (ES). This survey presents, among many other indicators, responses about firm level

experiences of bribery in di↵erent countries, with firms sampled from the manufacturing

and service sectors. The number of respondents per country in the 2009 survey is 485 on

average, but ranges from less than 100 to over 4000. Much like the structure of the GCB,

the survey asks if the respondents’ firm has had contact with the government over the last

year for a variety of purposes4. Those who report contact are asked a follow up question

of whether a gift or informal payment was expected or requested. The proportion who

answer positively are recorded and the results made publicly available.

As with any survey, the veracity of responses to the GCB and ES can be questioned.

Respondents had answered multiple questions including some relating to their opinions of

corruption and government action against it before answering the questions of interest to

us, which may have tainted their recall. Furthermore, given the generally illegal nature of

corruption, it is possible some respondents may not have wished to answer truthfully. In

4Those focused on in this analysis are—Operating Licence, Import Licence, Construction Permit,Electricity Connection, Phone Connection and Water Connection

33

CHAPTER 5. DATA Patrick Schneider

Table 5.3: Department level GCB 2010 (No bribe)

Department n Mean S.D.Education 83 0.86 0.15Judiciary 78 0.79 0.19Medical 83 0.84 0.14Police 82 0.74 0.24Registry 83 0.82 0.17Utilities 83 0.89 0.13Tax 81 0.86 0.16Land 77 0.82 0.18Customs 53 0.74 0.24

an attempt to overcome this constraint, Transparency International and the World Bank

assure anonymity and deliberately broaden the question to include the experiences of

members of respondents’ household, to allow for personal deniability. Johann Lambsdor↵,

who established Transparency International, suggests one further way to correct for this

is by using the proportion of respondents answering ‘no’ to the bribery question as the

interest variable instead of ‘yes’5. This strategy captures the ambiguous ‘don’t know’

responses, which he finds closely related to corruption levels. Treisman (2007) uses this

strategy and finds it does not significantly alter his results (p. 239) and I use it in the

tests in Chapter 7. Summary statistics for 2010 are reported in Table 5.3.

5.2 Other Data

The variables used to control for known correlates of corruption are based on the dataset

used in Treisman (2007) and include economic, political and institutional factors as well

as historical controls6. The particular variables were chosen from the multitude used in

Treisman’s paper for their jointly high predictive power of corruption (Treisman, 2007,

pp. 233-44, regression no. 7). Tresiman’s dataset is from 2005; I updated the values of

the key variables from the sources named by Treisman where possible. Where more recent

data was not available from Treisman’s source but an alternative was available, I opted

to use the more recent alternative. In some cases, this means the nature of the data is

slightly altered so my results are not directly comparable to Treisman’s.

5.2.1 Variables of interest

Summary statistics of the independent variables are reported in Table 5.4. The economic

variables are the log of GDP per capita in purchasing power parity adjusted terms from

2009 and fuel exports as a proportion of total merchandise exports from 2009. Both were

5A note by Lambsdor↵ on this strategy can be found at http://www.transparency.org/policy_

research/surveys_indices/gcb/2005 accessed 05/09/20116The structure of this section owes a debt of gratitude to Westling (2011)

34

CHAPTER 5. DATA Patrick Schneider

Table 5.4: Descriptive statistics for independent variables

Variable n Mean S.D.Log GDP per cap. PPP 163 8.80 1.25Fuel exports 124 16.22 27.12Year opened to trade 134 85.34 16.92Time required to open firm 85 3.59 0.88Democratic since 1950 213 0.28 n.a.Presidential democracy 213 0.36 n.a.Women in govt 169 14.72 10.78FH press freedom 195 47.08 24.45Newspaper circ. 2004 76 126.34 140.83

collected from the World Bank’s World Development Indicators series. The year opened

to trade is from Treisman’s original dataset7 and is coded with the two digit year when a

country opened, down to ‘50’ for 1950. The measure was created in 19958 and countries

that were not open as of that year were coded by Treisman as if they opened in 2000—

‘100’. The time required to open a firm is also from Treisman’s dataset9. Higher numbers

indicate more time interacting with bureaucratic processes is required in order to get a

business registered.

The political variables are the proportion of women in government and dummies

for being a democracy since 1950 and having a presidential democracy. The dummies

are both drawn from the 2010 Database of Political Institutions (Beck, Clarke, Gro↵,

Keefer, and Walsh, 2001), which reports on a range of political variables. The former is

constructed by interacting Beck et al’s measure of democracy with their measure of the

length of tenure for the current political system; those countries that have a value greater

than 59 (the 2010 release reports the 2009 values) are coded one and the others, zero.

The presidential democracy dummy is constructed as its name implies. The proportion of

women in government is drawn from the 2009 Democracy Cross-National Database release

3.0 constructed by Pippa Norris (DCND)10. This collects a wide variety of political and

economic variables from various sources for a large sample of countries. The variable used

here reports the percentage of women in government at the ministerial level in 200511.

The media institutions variables are press freedom and newspaper circulation. The

former is drawn from the index constructed by Freedom House (2009). Countries are rated

by Freedom House based on various factors relating to informal and formal restrictions

on the press and assigned a score where low numbers indicate greater freedom. The

newspaper circulation data are from the DCND. This figure reports the total average

7See http://www.sscnet.ucla.edu/polisci/faculty/treisman/Pages/publishedpapers.html

accessed December 20108By Sachs & Warner (1995), see p. 236 in Treisman (2007)9Created by Djankov et al. (2002), see p. 236 in Treisman (2007)

10See the data section at http://www.pippanorris.com/ accessed 28/05/201111Sourced by Norris from a 2007 UNDP report. See the DCND data codebook at http://www.

pippanorris.com/ accessed 28/05/2011

35

CHAPTER 5. DATA Patrick Schneider

daily newspaper circulation per 1000 inhabitants in 200412.

5.2.2 History controls

Following Treisman (2007), I also use controls for colonial history, legal tradition and

religion. The colonial history controls are dummies for whether a country was a colony of

various powers13 and were collected from the CIA World Factbook14. The legal tradition

controls are dummies for whether a country has a legal system based on various di↵er-

ent traditions15 and were collected from NYU’s Global Development Network Growth

Database16. The religion controls are dummies for whether a particular religion17 is pre-

dominant in a country (e.g. Saudi Arabia is coded ‘1’ for Muslim and ‘0’ for other

options) and were collected from the DCND. The religion control used here di↵ers from

Treisman’s, which is based on proportions subscribing to various religions. This depar-

ture was deemed appropriate as Treisman’s data on religion is from over three decades

ago. Note that these controls are used with France in mind as the base case—that is, a

non-colony with a French legal system and predominantly Catholic population.

5.2.3 Instruments

The models I will describe in the next section which analyse the relationships between

the variables described above have severe endogeneity issues. The relationship between

GDP and corruption is particularly problematic. As discussed in Chapter 2, there are

various theoretical reasons why corruption would itself a↵ect a country’s income, and

empirical evidence supports this. It is hence necessary to account for reverse causa-

tion by instrumenting GDP. Again, I will follow Treisman’s strategy. He considers two

possibilities—1700 income per capita and 1820 income per capita, both sourced from

Maddison’s (2003) historical GDP dataset (see Treisman (2007, p. 226)). The first is a

valid instrument if “. . . one is willing to assume that a country’s per capita income in 1700

a↵ects current corruption. . . only via the e↵ect on subsequent economic development. . . ”

(Treisman, 2007, p. 226). Using 1700 income per capita reduces the available countries

to 22, so Treisman also uses 1820 income per capita, which o↵ers more observations, on

the same grounds as the other measure.

12Sourced by Norris from a 2008 UNESCO report. See the DCND data codebook at http://www.

pippanorris.com/ accessed 28/05/201113Britain, France, Spain/Portugal, USSR, Other or non-colony14See https://www.cia.gov/library/publications/the-world-factbook/index.html accessed

2/03/201115Socialist, German, Scandinavian, British or French16See http://dri.fas.nyu.edu/object/dri.resources.growthdatabase accessed 22/02/201117Protestant, Muslim, Catholic or Other

36

Chapter 6

Empirical Specifications

There are two goals in my empirical analysis. The primary goal is to test the implications

from the theory outlined in Chapter 4. The secondary goal is to explore the departmental

corruption data, which has not been used before in the empirical literature. The pursuit

of these two goals is, of course, complementary.

Pursuit of the secondary goal simply means I will replicate others’ work (with new

data) more fully than otherwise. The result of this is that, as well as testing the im-

plications of the model, I can compare the results to what has come before. In order

to achieve my primary goal, I employ two approaches to testing—one examines within

country variation between departments and one that uses regression analysis to examine

cross country variation. The latter approach involves two models—one that controls for

country and department e↵ects to isolate the noise predicted by Implication 1A and one

that introduces an extra control for departmental history to control for the persistence

predicted by Implication 2A.

Recall that Hypothesis 1 in the theory is that there will be idiosyncratic variation

in corruption across departments, caused by their cultures. Lacking any data on culture

at the department level, I cannot directly test this mechanism. I can, however, test

Implication 1A—that corruption levels are noisy at the department level.

Suppose the Hypothesis 1 were not true, and departmental corruption levels were

only a↵ected by country level factors. In this scenario, we should observe the same

proportion of people paying bribes to each department, or very small di↵erences resulting

from sampling errors. The correlations between the departments in the GCB are reported

in Table 6.1. Although high, they are unsurprisingly far from perfect and we can reject

this alternative now.

One explanation for this within country variation that still does not admit Hy-

pothesis 1 is that di↵erences between departments within countries are caused by some

department e↵ect. That is, some departments might be systematically more corrupt than

others due to the inherently ‘corruptible’ nature of the work that they undertake. For

example, it seems likely that the police, who have contact with criminal activity and

are bestowed with the legitimate use of violence, will generally be more corrupt than an

education department, who do not and are not.

37

CHAPTER 6. EMPIRICAL SPECIFICATIONS Patrick Schneider

Table 6.1: Correlations between GCB departments

Educ. Jud. Med. Pol. Reg. Util. Tax Land Cust.Education 1Judiciary 0.86 1Medical 0.83 0.76 1Police 0.81 0.88 0.74 1Registry 0.81 0.87 0.77 0.89 1Utilities 0.74 0.76 0.67 0.76 0.87 1Tax 0.80 0.83 0.71 0.78 0.87 0.91 1Land 0.81 0.84 0.78 0.83 0.91 0.86 0.90 1Customs 0.80 0.86 0.72 0.87 0.87 0.75 0.83 0.87 1

Hence, now suppose that Hypothesis 1 were still not true, but that departmental cor-

ruption levels were only a↵ected by country level and these, what I will term ‘systematic

department’, e↵ects. In this scenario, there should be very little noise left in departmental

corruption levels, once country and systematic department e↵ects are accounted for. We

can test this alternative hypothesis in two ways—one using the raw department data to

make within country comparisons and the other using it to make cross country compar-

isons, controlling for country and systematic department e↵ects. If these tests show little

noise, we can reject the hypothesis in favour of the alternative.

Another strategy is to focus on Hypothesis 2 from the theory—that the feedback

between decisions and perceptions causes departmental cultures to persist. Implication

2A suggests that the culture of a department today a↵ects its corruption level, and its

culture is born of the culture in the past. We would hence expect a lagged measure of

that department’s corruption to be a strong predictor of its current corruption. Hence,

the final test will be to include this lagged measure in the regressions of departmental

corruption levels. This test is the closest of the three to testing the actual mechanism

proposed in the model.

The following outlines the empirical strategy to be implemented in the next chapter.

To begin, I consider the distribution of the corruption data to be used, and its implications

for testing. I then consider the two approaches employed to test the hypotheses—the

first is the within country comparison and the second is the cross country regressions.

The latter approach involves two di↵erent models that test alternative implications from

the theory against a baseline model informed by the previous literature. It should be

noted that these tests can help us seek evidence supporting the theory, but they do not

necessarily reject alternative models with similar implications.

6.1 Distribution

The department level corruption indices are not normally distributed. Figure 6.1 plots

the kernel densities for the each department in the GCB 2010 and ES 2009 survey results.

38

CHAPTER 6. EMPIRICAL SPECIFICATIONS Patrick Schneider

Both plots show the distributions are strongly positively skewed with right tails of varying

fatness. A skewness and kurtosis test was performed on each series to test for normality.

This test compares the skew of a distribution and the sharpness of its peak and tails

with those expected in a normal distribution (D’Agostino, Belanger, and D’Agostino Jr,

1990). Assuming normality, the probability of observing the data is below 5% for each

series in the GCB except for the police. Similarly, the probability is below 5% for each

of the covered series from ES1. Hence, it is very unlikely these data come from a normal

distribution. This implies that it would be appropriate to use non-parametric tests to

analyse the raw data, as these do not rely on the restrictive normality assumption.

6.2 Tests

6.2.1 Within country comparison—sign test

A sign test can perform within country comparisons of departments without making the

restrictive normality assumption. The sign test is a non-parametric test that performs a

pairwise comparison of two data series and tests the null hypothesis that they are equally

likely to be larger than each other (that the relationship is noisy). Formally, the sign test

takes two series, X and Y , and tests the hypothesis that for a randomly selected pair of

observations (xi, yi), the di↵erence between the observations is equally likely to be positive

or negative. This amounts to calculating zi = yi � xi and examining the distribution of

zi to see if its median is zero (Mendenhall, Wackerly, and Schae↵er, 1990, pp. 674-677).

The sign test assumes that pairs of observations are randomly and independently selected.

The latter assumption is valid as each observation is for a separate country. The former

assumption could be challenged on the basis that there is selection bias in the countries

covered by the surveys. This challenge limits the external validity of any findings from

the test but is unavoidable.

This test is useful for our purposes because it can act as a test for the strength of

systematic department e↵ects on corruption. If we expected departmental corruption to

only be a↵ected by country level and systematic department e↵ects, then the di↵erence

between the level of corruption in two departments within any given country should have

a consistent sign. Consider the example used earlier, where we expect the police to be

more corrupt than the education department. If there were no e↵ects other than country

and systematic department e↵ects, then the di↵erence between police and education cor-

ruption should be positive for every country. If this were observed, the null hypothesis

of the sign test would be rejected in favour of the alternative that the probability of the

experienced police corruption being greater than education is more than 50%.

Performing the sign test on each pair of departments identifies the existence and

direction of dominant systematic relationships. Rejection of the null in a two-tailed test

1The skewness and kurtosis test results are reported in Appendix A.1

39

CHAPTER 6. EMPIRICAL SPECIFICATIONS Patrick Schneider

Figure 6.1

02

46

8D

ensi

ty

0 .2 .4 .6 .81 - % of respondents who did not pay bribe

EducationJudiciaryMedical ServicesPoliceRegistryUtilitiesTaxLandCustoms

kernel = epanechnikov, bandwidth = 0.0422

GCB 2010 - Department Kernel Densities0

.02

.04

.06

Dens

ity

0 20 40 60 80 100% of firms expected to give gifts

Operating LicenceImport LicenceConstruction PermitElectricity ConnectionPhone ConnectionWater Connection

kernel = epanechnikov, bandwidth = 6.5906

ES 2009 - Department Kernel Densities

40

CHAPTER 6. EMPIRICAL SPECIFICATIONS Patrick Schneider

identifies a relationship where a systematic e↵ect dominates, as discussed above; a one-

tailed test can then determine the direction of this relationship. Failure to reject the

null in a two-tailed test identifies either an equal or a noisy relationship. If, upon deeper

investigation of these relationships, we then observe variation in di↵erences (ruling out

the possibility that the departments are identical), we have a preliminary indication that

idiosyncratic e↵ects exist.

6.2.2 Cross country comparison—regressions

Using multiple linear regression allows for stronger tests of the hypothesis using the avail-

able data. As discussed in Chapter 5, I will use the regressions in Treisman (2007) as the

starting point. Treisman tests relationships between corruption and other factors using

OLS regressions of the following form:

corri = �0 + �1Ci + "i (6.1)

Where i indexes country and Ci is the vector of the independent variables and controls

discussed in Chapter 5.

Baseline model

The first step in this analysis is to rerun these regressions using the model in (6.1) to

establish what di↵erence, if any, there is between results found with perceptions and

experience indices. Treisman finds many significant relationships and very high predictive

power (R2 = 0.92 in model (7) pp. 233-234) using perceptions indices but very few

significant relationships when the experience index is used as the dependent (pp. 239-40).

I expect these results to be reproducible with the newer data. This provides a baseline

against which to compare the results of the models to come.

Model 1

The next step is to extend the baseline analysis by disaggregating the corruption expe-

rience data and using the new data as the dependent. The theoretical determinants of

department level corruption are as follows:

corrij = �0 + �1Ci + �Dj + �X⇤ij + uij (6.2)

Where j indexes department, Dj is a dummy variable for each department, controlling

for systematic department e↵ects (the base is the education department) and X⇤ij is the

unobserved idiosyncratic culture e↵ect. If it were not true that the last factor was a de-

terminant of departmental corruption then the following would be an unbiased estimator:

corrij = �0 + �1Ci + �Dj + uij (6.3)

41

CHAPTER 6. EMPIRICAL SPECIFICATIONS Patrick Schneider

Hence, in this step, it is assumed that the hypothesis is false. The dependent variable is

departmental corruption and the independents are the standard country level regressors

and controls as well as controls for systematic department e↵ects. Here, we are interested

in the level of noise remaining in the model after these factors are controlled for. A lower

adjusted R2 indicates more noise and therefore more room for the theory.

Using department level data introduces a new issue. As each country has up to

nine observations, it is unlikely that each observation is independent. This could lead

to violation of the model’s assumption that the data are independently and identically

distributed. If present, this violation biases standard errors. Given this risk, department

level standard errors are adjusted using cluster methods, similar to robust standard errors

but where the weight is unique to the common element (in this case, country) rather than

the observation.

Model 2a

The next step is to recognise that if the hypothesis is true, then not controlling for culture

e↵ects introduces omitted variable bias into the model. Being able to control for culture

e↵ects would thus be helpful for two reasons—it would remove this bias and also provide

a testable estimate of the relationship that the theoretical model proposes. There is no

direct data on the culture within the departments in question, but the data on corruption

in these departments from previous periods might provide a valid proxy (Wooldridge,

2009, pp. 306-12). The logic here is that according to the theory, current culture should

be closely linked to the past culture. If the past culture also causes past corruption (as is

proposed by the theory), then we can use past corruption as a proxy for present culture.

This proxy relationship is expressed formally as follows:

X⇤ij = ↵0 + ↵1corrij,lag + µij (6.4)

Substituting the proxy relationship in (6.4) into the model outlined in (6.2) and rearrang-

ing terms yields:

corrij = (�0 + �↵0) + �1Ci + �Dj + �↵1corrij,lag + vij (6.5)

Where there is a composite error term vij = uij + �µij. Hence, in this final step, the

strength of the relationship between the present and lagged levels of corruption (signif-

icance of the coe�cient on corrij,lag) gives an indication of the role that a department’s

history has to play in its present corruption level. This model is rerun using di↵erent lags

to assess the persistence of the relationship over di↵erent periods of time.

Model 2b

The last step utilises the time dimension of the department level corruption data to rerun

Model 2a with more observations. The department level corruption data are available for

42

CHAPTER 6. EMPIRICAL SPECIFICATIONS Patrick Schneider

the years 2010, 2009, 2007 and 2006. This structure means a pooled-cross section (2010

and 2007) can be analysed with a one year lag. The model estimated in this step is similar

to the model in 6.5, but is formally expressed as follows:

corrijt = (�0 + �↵0) + �1Cit + �2Ci + �Dj + �↵1corrijt�1 + ⇣ijt (6.6)

The di↵erence here is that some country level variables are allowed to vary over time and

we are limited to a lag of one year.

I considered using the time dimension of the data to run other standard time series

models but found that they weren’t appropriate. First di↵erencing, for example, is a

technique aimed at eliminating time persistent but unobservable e↵ects. Although this

is extremely useful in other circumstances, it is precisely these e↵ects we’re interested in

here, so it was not deemed appropriate to exploit the time dimension in ways other than

those already discussed.

Robustness checks

Tests for joint significance of categories of interest variables and functional form misspeci-

fication (RESET) are performed on each of the above regressions. All standard errors are

reported in robust form for country level regressions and clustered form for department

level regressions. All reported regression results use the log transformation of the cor-

ruption (dependent and lag) variables. The department level data are transformed using

the method recommended by Lambsdor↵2, where log corrij = �ln(1.01 � no bribeij); in

other cases, the simple natural log is used where log corri = ln(corri). The level versions

of the models were also run but were generally found to be inappropriate specifications.

2See http://www.transparency.org/policy_research/surveys_indices/gcb/2005 accessed05/09/2011

43

Chapter 7

Results

7.1 Department Level Variation in Corruption

There is evidence for both systematic and idiosyncratic department e↵ects on corruption.

This is confirmed by sign tests1 on the pairwise comparison of each variable in the GCB

and ES results. To see this, refer to Table 7.1, which shows the likely direction of system-

atic department e↵ects. The table was constructed by first conducting a two-tailed sign

test on each pair of departments at the 5% level of significance to see if there was any

indication of a systematic department e↵ect. If the null hypothesis was not rejected, the

cell was coded ‘0’ to indicate no dominant systematic department e↵ect. If the hypothesis

was rejected, a positive one-tailed test was conducted at the 5% level of significance to see

if there was any indication that the column department was systematically more corrupt

than the row department. If the null hypothesis was again rejected, the cell was coded ‘+’

to indicate a systematic department e↵ect where the column department is more corrupt

than the row department. If the null hypothesis was not rejected, the cell was coded ‘�’

to indicate the opposite.

Take, for example, the police department, whose relationships are found in the fourth

column of the GCB table. The series of ‘+’ symbols indicates that, at the 5% level of

significance, the police department is systematically more corrupt than the education,

judiciary, medical, registry, utilities, tax and land departments. The ‘0’ in the last row

indicates no evidence of a systematic relationship with the customs department which,

after further examination, might allow us to identify an idiosyncratic relationship.

7.1.1 Systematic department e↵ects

There is strong evidence that systematic department e↵ects impact the level of corruption

in various departments. This is shown by the wealth of significant relationships identified

by the sign test in both the GCB and ES. Looking at a department’s relative number of

the di↵erent relationships shows some very interesting patterns. From the GCB, a rough

hierarchy of systematic department e↵ects can be found by ranking departments by the

1The p-values of two- and one-tailed tests are reported in Appendix A.2

44

CHAPTER 7. RESULTS Patrick Schneider

number of positive, then neutral and then negative relationships—Police > Customs >

Judiciary > Registry = Land > Medical > Education > Tax > Utilities.

The police and customs appear to be systematically more corrupt than most other

departments, supporting the examples in Chapter 6 and conforming to intuition. At the

other end of the scale, the tax and utilities departments appear to be systematically less

corrupt than most other departments. That the tax department should be systematically

less corrupt than others is surprising. Although governments certainly have a strong

incentive to keep this department clean, anecdotal and case study evidence (for example,

see Klitgaard (1988, pp. 13-51)) leads us to expect tax departments to be fairly corrupt.

One explanation for this result, then, could be that the nature of the tax department’s

work is such that bribes will be paid willingly (e.g. to push through inflated deductions)

rather than extorted by o�cials. Such collusive arrangements are much less likely to be

admitted by survey respondents. This sheds light on one of the limitations of the data—

it is only likely to reflect acts of bribery that respondents feel either angry about or not

personally culpable for. The survey only represents itself as a measure of bribery, but it

is likely that the scope of the activity is even greater than the results suggest.

The same process, but using the ES data now, shows that the construction permit

process appears to be systematically more corrupt than all other departments and the

phone connection process less so than most others. The rough hierarchy of systematic de-

partment e↵ects implied by the ES is—Construction Permits > Electricity Connections >

Water Connections > Operating Licences > Import Licences > Phone Connections.

Table 7.1: Pairwise relationships (column� row) between departmentalcorruption implied by sign test

GCB 2010 Educ. Jud. Med. Pol. Reg. Util. Tax Land Cust.Education + + + + � 0 + +Judiciary � � + 0 � � 0 0Medical � + + 0 � � 0 +Police � � � � � � � 0Registry � 0 0 + � � 0 +Utilities + + + + + 0 + +Tax 0 + + + + 0 + +Land � 0 0 + 0 � � +Customs � 0 � 0 � � � �

ES 2009 Op. Lic. Imp. Lic. Cons. Per. Elec. Con. Pho. Con. Wat. Con.Op. Lic. 0 + 0 0 0Imp. Lic. 0 + + 0 0Cons. Per. � � � � �Elec. Con. 0 � + � 0Pho. Con. 0 0 + + +Wat. Con. 0 0 + 0 �

45

CHAPTER 7. RESULTS Patrick Schneider

7.1.2 Idiosyncratic department e↵ects

There is also strong evidence for idiosyncratic department e↵ects. Each cell coded ‘0’

in Table 7.1 indicates that there is no evidence for a dominant systematic relationship

between the two departments. This means that the two departments are either system-

atically the same or that the relationship is noisy.

In order to determine which alternative is the case, we can simply look at the

distribution of di↵erences. Figure 7.1 maps box plots for each of these relationships.

These show that the relationships do, in fact, exhibit noisy di↵erences. Take, for example,

the plot for the police and customs departments in the GCB. The spread of the whiskers

either side of the zero line shows that in many countries, the police are more corrupt than

the customs department (e.g. Argentina, Bosnia, Latvia, Pakistan, Russian Fed. and

others) and that the opposite is true for many others (e.g. Afghanistan, Kosovo, Lebanon,

FYR Macedonia, USA and others). This idiosyncrasy is evident for each combination of

departments where a dominant systematic e↵ect was not identified. In fact, it is observable

even in combinations where there is a dominant systematic e↵ect (e.g. in Vietnam in

2010, 16% less people reported paying a bribe to the judicial system than the education

department), although not to the same extent.

Analysis of the department level data finds evidence in support of Implication 1A—that

variation in corruption has an idiosyncratic component. This variation is separate from

systematic department e↵ects caused by the corruptible nature of a department’s work.

It is also separate from the scope of the department’s work, as both indices measure

the incidence of bribery conditional on the respondent’s having had contact with the

department. We thus fail to reject Hypothesis 1 from the theory—that departmental

subcultures can sustain di↵erent corruption levels in departments.

7.2 Cross-Country Regression Estimation

In this remainder of this chapter, I present the results from the cross-country regression

models outlined in Chapter 6 with a focus on the GCB department data. My starting

point was to run each of the models in the following section with the full set of Treisman’s

independent variables, as outlined in Chapter 5. However, I found various issues with

these models that a↵ected their interpretation and gave me cause to make modifications.

First, there were very few observations available for the newspaper circulation variable.

Its inclusion in the models tended to render them incapable of producing any meaningful

results as sample sizes were so reduced. It was removed from the set of regressors to

enhance sample size and also on the reasoning that it and the FH Press Freedom mea-

sure should capture similar e↵ects2. Second, although Treisman’s ‘democracy since 1950’

2They have a correlation coe�cient of -0.56, which is significant at standard levels.

46

CHAPTER 7. RESULTS Patrick Schneider

Figure 7.1

-.4-.2

0.2

.4Pe

rcen

t diff

eren

ce

GCB 2010 - Idiosyncratic Corruption Differences

Education - Tax Judiciary - RegistryJudiciary - Land Judiciary - CustomsMedical - Registry Medical - LandPolice - Customs Registry - LandUtilities - Tax

-60

-40

-20

020

40Pe

rcen

tage

poi

nt d

iffer

ence

ES 2009 - Idiosyncratic Corruption Differences

Operating lic. - Import lic. Operating lic. - Electricity con.Operating lic. - Phone con. Operating lic. - Water con.Import lic. - Phone con. Import lic. - Water con.Electricity con. - Water con.

47

CHAPTER 7. RESULTS Patrick Schneider

variable was found to be significant, the models that included it all su↵ered from func-

tional form misspecification. Replacing this variable with a control for the square of the

length that democracy has presided in years reduces this issue. I proceed now through the

Baseline Model, Model 1 and Model 2 with the leaner and modified set of independent

variables.

7.2.1 Baseline model

The first step in the cross-country analysis is to establish the Baseline Model, which is

closely based on one of the models from Treisman (2007). The results reported in Ta-

ble 7.2 show the relationships between the log transformation of country level corruption

measures and our independent variables. Although not an exact reproduction of Treis-

man’s model (logs are used rather than levels and changes to variables have been made

as discussed), the findings are similar.

The estimations that have a perception index as the dependent variable (1 & 2) have

high predictive power (adjusted R2 is 81%) and find various precisely estimated relation-

ships. Higher development, less prominence of fuel exports and shorter waiting times

to open a firm are all significantly correlated with lower levels of perceived corruption.

Greater press freedom and opening to trade earlier are also correlated with lower per-

ceived corruption, although these relationships are not robust to the inclusion of controls

for legal tradition, colonial history and religion (hereafter referred to as ‘the controls’).

In contrast, the estimations that have an experience index as the dependent variable (3

& 4) have lower predictive power (adjusted R2 is 68% when the controls are not included

and 65% when they are) and few precisely estimated relationships. Higher development,

more women in governmental positions and the squared length of democracy are all found

to be correlated with less experience of bribery, although the latter two relationships are

not robust to the inclusion of the controls. The relationships discussed above and the

di↵erences between the results found with perception and experience indices are similar

to those identified by Treisman, as expected.

7.2.2 Model 1

From this point on, the analysis departs from the previous literature by using the de-

partment level data on experiences of bribery as the dependent variable. Estimations

5 & 6, reported in Table 7.3, are a reproduction of the Baseline Model, but use this

new dependent variable. The relationships are comparable to those found in the country

level equivalents (3 & 4) but the predictive power is much lower. This is as expected—

if we found that experiences of department level bribery were well explained solely by

country level factors, that would imply no room for systematic department e↵ects, which

were found in abundance earlier in this chapter, let alone the idiosyncratic culture e↵ects

predicted by Hypothesis 1 in the theory.

48

CHAPTER 7. RESULTS Patrick Schneider

Table 7.2: Country level regressions with logged dependent

1 2 3 4log CPI10 log CPI10 log GCB10 log GCB10

ln GDP per 0.1421⇤⇤⇤ 0.1713⇤⇤⇤ �0.5582⇤⇤⇤ �0.5856⇤⇤⇤cap. PPP (0.0373) (0.0507) (0.1074) (0.2073)

Fuel exports �0.0038⇤⇤⇤ �0.0035⇤⇤⇤ 0.0046 0.0047(0.0011) (0.0012) (0.0034) (0.0040)

Year open �0.0047⇤ �0.0019 0.0034 �0.0036to trade (0.0025) (0.0028) (0.0064) (0.0111)

Time required to �0.0772⇤⇤ �0.1097⇤⇤ 0.0345 0.1255open firm (0.0291) (0.0497) (0.1416) (0.2080)

Presidential �0.0470 0.0111 0.0227 0.1978democracy (0.0666) (0.0708) (0.2773) (0.3269)

FH Press �0.0039⇤ �0.0023 �0.0037 �0.0047freedom (0.0021) (0.0025) (0.0172) (0.0105)

Women in 0.0026 0.0024 �0.0194⇤⇤ �0.0120government (0.0019) (0.0024) (0.0076) (0.0105)

Years democracy 0.0000 0.0000 �0.0001⇤ �0.0001squared (0.0000) (0.0000) (0.0001) (0.0001)

Constant 0.9364⇤ 0.5320 7.9348⇤⇤⇤ 8.0175⇤⇤(0.4764) (0.6429) (1.3861) (2.9449)

History controls no yes no yes

Adjusted R

2 0.81 0.81 0.68 0.64n 72 72 52 52

Notes: Dependent variable measures absence of corruption in 1–2 and presence of corruption in 3–4.White robust adjusted standard errors are reported. Significance of coe�cients is shown by ⇤ wherep < 0.10, ⇤⇤ where p < 0.05 and ⇤⇤⇤ where p < 0.01.

49

CHAPTER 7. RESULTS Patrick Schneider

The next step is to examine the results for Model 1, which are reported in Table 7.3

as estimations 7 & 8. These repeat the baseline specification at the department level, but

include dummies to control for systematic department e↵ects. The department dummies

are highly individually and jointly significant3 and their inclusion improves the predictive

power of the model by six percentage points, both with and without the controls. This

confirms the finding earlier in the chapter that there are strong systematic factors that

a↵ect department level corruption. Although Model 1 has good predictive power4, it

remains less powerful than the Baseline Model using the country level experience index

and there is still significant unexplained variation in reported experiences of bribery.

In this step, we assumed that the hypothesis was not true. That is, we assumed that

country and systematic department e↵ects fully explained department level experiences

of corruption. In Model 1, we have controlled for these e↵ects and found that there is still

a significant amount of noise left in the dependent variable. Model 1 thus leaves room

for other explanations of the level of corruption in departments. The hypothesis that the

idiosyncratic organisational cultures of government departments a↵ects their corruption

levels is one such explanation that is consistent with these results.

7.2.3 Model 2

Implication 2A from the theory states that corruption levels will be persistent over time. If

the decisions of o�cials within a department are based on their perceptions of those around

them and their past experiences, their decisions will settle in a coordinated equilibrium

and the same modes of behaviour will persist over time. In this model, we introduce a lag

of the dependent variable to proxy for the department’s culture, but also to capture this

persistence e↵ect. As outlined in Chapter 6, the coe�cient to this lag tells us the joint

impact of past corruption on present culture and present culture on present corruption.

The results for Model 2a are reported in Table 7.3. Estimations 9 & 10 repeat the

estimations in Model 1 but include the lagged dependent as an explanatory variable. The

new term is very precisely estimated and its impact is highly economically significant—in

estimation 9, a 10% increase in experiences of bribery in the previous period is predicted

to be associated with a 4.1% increase in the present period. The inclusion of this new

term increases the predictive power of the model by 12 percentage points, both with and

without the controls. With the inclusion of this new term, however, previously significant

relationships are no longer found to be so. With such a short lag, this is possibly caused

by the persistence in value of the other variables. Alternatively, there could be too few

observations to have enough variation to establish other relationships.

3F (8, 52) = 9.05 and p = 0.000 for estimation 7 and F (8, 52) = 9.19 and p = 0.000 for estimation 8

4Adjusted R

2 is 53% in estimation 7 and 58% in estimation 8.

50

CH

AP

TE

R7.

RE

SULT

SPatrick

Schneider

Table 7.3: Department level regressions with logged dependent

Baseline Model 1 Model 2a Model 2b5 6 7 8 9 10 11 12

log GCBD10 log GCBD10 log GCBD10 log GCBD10 log GCBD10 log GCBD10 log GCBD10&07 log GCBD10&07log GDP per 0.4469⇤⇤⇤ 0.3495⇤⇤ 0.4482⇤⇤⇤ 0.3526⇤⇤ 0.1838 �0.0457 0.1529⇤ �0.0874cap. PPP (0.0970) (0.1475) (0.0977) (0.1496) (0.1164) (0.1504) (0.0870) (0.1246)

Fuel exports �0.0037 �0.0031 �0.0037 �0.0031 �0.0004 0.0016 �0.0015 0.0016(0.0023) (0.0031) (0.0023) (0.0031) (0.0024) (0.0032) (0.0023) (0.0026)

Year open �0.0026 �0.0036 �0.0022 �0.0033 �0.0035 �0.0113 �0.0084⇤ �0.0179⇤⇤⇤to trade (0.0046) (0.0073) (0.0047) (0.0075) (0.0054) (0.0079) (0.0047) (0.0067)

Time required to 0.0948 0.0988 0.0921 0.1026 0.1170 0.1016 0.1939⇤⇤ 0.2563⇤⇤open firm (0.0710) (0.1119) (0.0707) (0.1142) (0.0902) (0.1127) (0.0820) (0.1146)

Presidential 0.0741 �0.2177 0.0768 �0.2149 0.0912 �0.3078 0.3143⇤ �0.0019democracy (0.2106) (0.2336) (0.2108) (0.2351) (0.2176) (0.2638) (0.1686) (0.2331)

FH Press �0.0017 0.0015 �0.0015 �0.0018 �0.0023 0.0061 �0.0068 0.0019freedom (0.0050) (0.0059) (0.0050) (0.0060) (0.0049) (0.0072) (0.0043) (0.0061)

Women in 0.0123⇤⇤ 0.0005 0.0128⇤⇤ 0.0008 0.0035 �0.0088 0.0036 �0.0085government (0.0059) (0.0056) (0.0059) (0.0058) (0.0081) (0.0076) (0.0064) (0.0072)

Years democracy 0.0001 0.0001 0.0001 0.0001⇤ 0.0001⇤⇤ 0.0001⇤⇤ 0.0001⇤⇤ 0.0001⇤⇤squared (0.0000) (0.0000) (0.0001) (0.0000) (0.0000) (0.0001) (0.0001) (0.0001)

log GCBDt�1 0.4124⇤⇤⇤ 0.4335⇤⇤⇤ 0.4512⇤⇤⇤ 0.4761⇤⇤⇤(0.1051) (0.0928) (0.0908) (0.0810)

Constant �2.2531⇤ �1.0679 �2.0694⇤ �0.9080 �0.6478 2.2801 �0.3237 2.6064(1.1744) (2.0141) (1.1816) (2.0541) (1.2112) (2.0209) (0.9706) (1.7085)

Department controls no no yes yes yes yes yes yesHistory controls no yes no yes no yes no yes

Adjusted R

2 0.47 0.52 0.53 0.58 0.65 0.70 0.68 0.72n 446 446 446 446 324 324 511 511

Notes: Dependent variable measures absence of corruption. Cluster adjusted standard errors are reported. Significance of coe�cients is shown by ⇤ where p < 0.10, ⇤⇤where p < 0.05 and ⇤⇤⇤ where p < 0.01.

51

CHAPTER 7. RESULTS Patrick Schneider

The first of these issues is partly remedied by rerunning the estimations with di↵erent

lags. Results are reported in Table 7.4. The significance of the persistence relationship

is still observed when using 2007 and 2006 observations as lags, although its strength is

somewhat diminished—a 10% increase in experiences of bribery in 2006 is predicted to be

associated with a 3.6% increase in present day experiences. Using these other lags does

bring some of the other relationships back—most notably the log of GDP per capita and

time required to open a firm, although the latter is only precisely estimated when the

2006 lag is used. Unfortunately, the data only go back to 2006, so earlier lags are not

available.

The second of these issues is remedied by utilising the time dimension of the data

in the way outlined in Chapter 6 as Model 2b. The 2010 and 2007 results are pooled and

run in the same way as Model 2a (with a one year lag), results are reported in Table 7.3 as

11 & 12. This method increases the number of observations and improves the predictive

power of the model by a further two to three percentage points. With the greater sample

size, the coe�cient to the lag variable remains precisely estimated as are other factors

such as the year open to trade, time required to open a firm and the square of the length of

democracy. Hence, the persistence of experiences of bribery that is observed in Model 2 is

showing some relationship other than the persistence of the other independent variables.

Interestingly, the relationship found with time required to open a firm in estima-

tions 11 & 12 is in the opposite direction to that expected. Theory tells us that more

bureaucratic choke points (in the form, for example, of the process of registering a busi-

ness) create more opportunity for corruption and, hence, we expect more experiences of

corruption. In contrast to theory, the relationship predicted here is that the longer the

time required to register a business, the less the expected experienced corruption. This

relationship is robust to the inclusion of the controls (in fact the coe�cient grows in

magnitude). This finding contradicts the findings of Treisman and some of my own esti-

mations (1 & 2). One possible explanation for this could be that the corruption inducing

aspects of this variable’s e↵ect are being captured by the lag, and that what is left is the

e↵ect of a more thorough, but less corrupt, government.

In this step, we come closest to testing the actual mechanism in the model. In the-

ory, the present level of corruption is caused by present organisational culture, which is

related to past experiences. An implication of this is that we should observe persistence

in corruption levels within departments. The inclusion of lagged dependents finds strong

evidence of this, albeit over rather short periods of time. The strength of this relationship

is robust to the available lags, strengthened by pooling di↵erent (but adequately dis-

tant) time periods and cannot be explained solely by the persistence of other explanatory

variables.

52

CHAPTER 7. RESULTS Patrick Schneider

Table 7.4: Model 2a results with di↵erent lags

7 13 14log GCBD10 log GCBD10 log GCBD10

Lag year xxxx 2009 2007 2006ln GDP per 0.1838 0.2578⇤⇤ 0.2406⇤⇤⇤cap. PPP (0.1164) (0.1155) (0.0867)

Fuel exports �0.0004 0.0011 0.0017(0.0024) (0.0033) (0.0025)

Year open �0.0035 �0.088 �0.0069to trade (0.0054) (0.0060) (0.0060)

Time required to 0.1170 0.0077 0.1556⇤⇤open firm (0.0902) (0.0824) (0.0786)

Presidential 0.0912 0.0067 0.1826democracy (0.2176) (0.2311) (0.1911)

FH Press �0.0023 0.0043 �0.0036freedom (0.0049) (0.0053) (0.0051)

Women in 0.0035 0.0110 0.0093government (0.0081) (0.0088) (0.0069)

Years democracy 0.0001⇤⇤ �0.0000 0.0001squared (0.0000) (0.0000) (0.0000)

Dept corruption 0.4124⇤⇤⇤ 0.3618⇤⇤⇤ 0.3597⇤⇤⇤in year xxxx (0.1051) (0.0910) (0.1071)

Constant �0.6478 �0.5351 �0.9652(1.211) (1.3291) (1.1525)

Department controls yes yes yesHistory controls no no no

Adjusted R

2 0.65 0.67 0.67n 324 189 279

Notes: Dependent variable measures absence of corruption. Cluster adjusted standard errors are reported.Significance of coe�cients is shown by ⇤ where p < 0.10, ⇤⇤ where p < 0.05 and ⇤⇤⇤ where p < 0.01.

53

CHAPTER 7. RESULTS Patrick Schneider

7.3 Limitations

The empirical tests allow for the implications of the theory. These results should, however,

be carefully interpreted as they are limited in important ways. First, it should be noted

that none of the tests are a direct test of the theory itself. Rather, they test whether

alternatives to the theory are enough to explain observed variation—that is, do country

and systematic department e↵ects alone cause variation in experiences of bribery. Each

test finds that these factors do not fully explain the variation, but this is not to say

that the theory is therefore confirmed. There are various, in fact infinite (Friedman,

1966), alternative explanations for the observed phenomena. Hence, we can only say that

the results fail to rule out our hypothesis. Further research could isolate these di↵erent

explanations for departmental idiosyncrasies. For example, if we had data on the incentive

and supervision structures of the same departments across countries, we would be closer

to being able to isolate the culture e↵ect—although these two factors would arguably be

jointly determined.

By controlling for the persistence of experiences of bribery, Model 2 comes closest

to testing the actual theory; although it, too, does not rule out alternative explanations.

A second limitation of these tests, however, is that the lags used in Model 2 are short.

If corrupt organisational cultures persist over time, we would expect this to be true over

periods much longer than the available data allows us to test. Further research could seek

to find department level measures of incentives, corruption or organisational culture from

the more distant past in order to establish how persistent the behaviour actually is.

A third limitation is that the relationships between the dependent and independent

variables are likely to be highly endogenous. We expect, for example, that corruption

would be jointly determined with economic development and red tape (time to open

a business) and can see that other endogenous relationships are likely. This fact has no

e↵ect on the findings we’re interested in, but it does bias the coe�cients of the explanatory

variables so their interpretation is spurious. An attempt was made to correct for at least

some of this by instrumenting the log of GDP per capita with the log of GDP per capita

in 1820 per Treisman’s paper. Doing so substantially reduces the number of observations

available and gives spurious results that I will not discuss here, although they are reported

in Appendix A.3.

A fourth limitation is the data used to measure corruption itself. As has been dis-

cussed at length, the department level experience data collected by Transparency Inter-

national seek to measure the proportion of people required to pay a bribe to a department

given that they had contact with that department. Due to the illicit nature of bribery,

it is likely that survey respondents have not fully reported their behaviour. Steps were

taken where possible to take this into account (for example, by using ‘no’ responses as

the dependent variable). Even so, it is likely that insofar as the GCB measures anything,

it measures only those bribes that respondents paid resentfully (as suggested by tax de-

54

CHAPTER 7. RESULTS Patrick Schneider

partments’ surprising relative cleanliness). Hence, the relationships discussed in these

tests apply to a small part of the widespread phenomenon that is corruption. The use

and interpretation of these data is therefore limited. However, by using department level

data, which is closer to the predictions of most theoretical models and less processed after

the original survey, it is an improvement on past research and shows new avenues that

can be explored in the future.

55

Chapter 8

Conclusion

This thesis examines how the moral decisions of government o�cials can cumulatively

create organisational subcultures that foster corrupt behaviour. Analysts of criminology

and social psychology argue that rationalisations of and methods for morally deviant

behaviour must be learned from close associations. When these associations occur in the

context of an organisation, patterns of corrupt behaviour can come to be a part of that

organisation with the e↵ect that they become normalised and widespread.

Whereas most economic corruption models focus on structural incentives, my model

takes a di↵erent approach by examining organisational culture. Using a modified version

of the cognitive dissonance model by Rabin (1994), I examine o�cials’ decisions when they

must contend with their own personal morals and also their perceptions of what is normal.

I find that idiosyncratic di↵erences between the average corruption levels of di↵erent

departments can exist, independently of incentives, when o�cials base their perception

of what is a normal moral attitude on a mix of a fixed social norm and the beliefs of

their colleagues. I further find that departmental corruption levels will be persistent as

the feedback between o�cials’ perceptions and decisions create an organisational culture

that propagates the behaviour.

The implications of the theoretical model are supported by tests using department

level data that measures experiences of bribery. Specifically, it was found that even

after controlling for standard country level correlates of corruption and for systematic

department e↵ects, there was significant variation in experienced corruption levels that

went unexplained. Furthermore, controls for lagged corruption levels were found to be

highly significant in all specifications, robust to various controls and persistent over various

periods of time.

The theory hypothesises that the noise in departmental corruption levels is caused

by these departments settling in di↵erent cultural equilibria—that is, a di↵erent ‘nor-

mal’ exists for o�cials in one department than in another. It further hypothesises that

the persistence in corruption is explained by the departments staying in their cultural

equilibrium—that is, what is considered ‘normal’ today is based on what was considered

‘normal’ yesterday.

The finding that moral concerns can result in idiosyncratic, persistent corrupt sub-

56

CHAPTER 8. CONCLUSION Patrick Schneider

cultures developing in government organisations has significant policy implications. It

is most often argued that the way to reduce corruption is to target the incentives to

pursue the behaviour. Such a recommendation undoubtedly follows from the nature of

the principle-agent models most often used to describe the phenomenon. If, however, an

organisation has a corrupt subculture, it may be that the behaviour will persist in the

face of changes to incentives. The theory thus recommends that any corruption reform

program include an attempt to disrupt this subculture; e.g. by discrediting prevalent

rationalisations. Indeed, insofar as the work of government is such that opportunities

for corruption are necessarily present1, targeting enabling subcultures in organisations

might provide opportunities for corruption reduction beyond what incentive schemes and

increasing supervision can o↵er.

1As Cressey similarly noted of opportunities for embezzlement in businesses (Cressey, 1953, Preface).

57

Appendix A

A.1 Skewness and Kurtosis Test for Normality

Table A.1: GCB 2010

Department Observations Joint �2 p-valueEducation 83 36.45 0.000Judiciary 78 14.47 0.001Medical 83 16.04 0.000Police 82 8.61 0.014Registry 83 20.75 0.000Utilities 83 30.30 0.000Tax 81 24.92 0.000Land 77 14.55 0.001Customs 53 7.01 0.030

Table A.2: ES 2009

Department Observations Joint �2 p-valueOperating Licence 43 19.41 0.0001Import Licence 44 6.84 0.0326Construction Permit 50 6.17 0.0458Electricity Connection 49 7.92 0.0190Phone Connection 47 27.23 0.0000Water Connection 42 11.71 0.0029

58

APPENDIX A. APPENDIX Patrick Schneider

A.2 Sign Test on Department Level Corruption Results

GCB 2010

Table A.3: Two-tailed p-values – H1 : median of Yi �Xi 6= 0

Educ. Jud. Med. Pol. Reg. Util. Tax Land Cust.Education –Judiciary 0.000 –Medical 0.008 0.009 –Police 0.000 0.009 0.000 –Registry 0.000 0.110 0.581 0.000 –Utilities 0.048 0.000 0.004 0.000 0.000 –Tax 0.182 0.000 0.005 0.000 0.000 0.057 –Land 0.000 0.165 0.908 0.001 1.000 0.000 0.000 –Customs 0.000 0.410 0.000 0.583 0.003 0.000 0.000 0.021 –

Table A.4: One-tailed p-values – H1 : median of Yi �Xi > 0

Educ. Jud. Med. Pol. Reg. Util. Tax Land Cust.Education –Judiciary 1.000 –Medical 0.998 0.04 –Police 1.000 0.998 1.000 –Registry 1.000 0.055 0.780 0.000 –Utilities 0.024 0.000 0.002 0.000 0.000 –Tax 0.091 0.000 0.002 0.000 0.000 0.984 –Land 1.000 0.083 0.636 0.001 0.500 1.000 1.000 –Customs 1.000 0.864 1.000 0.292 0.999 1.000 1.000 0.995 –

Table A.5: One-tailed p-values – H1 : median of Yi �Xi < 0

Educ. Jud. Med. Pol. Reg. Util. Tax Land Cust.Education –Judiciary 0.000 –Medical 0.004 0.998 –Police 0.000 0.004 0.000 –Registry 0.000 0.966 0.291 1.000 –Utilities 0.986 1.000 0.999 1.000 1.000 –Tax 0.940 1.000 0.999 1.000 1.000 0.028 –Land 0.000 0.947 0.454 1.000 0.590 0.000 0.000 –Customs 0.000 0.205 0.000 0.795 0.002 0.000 0.000 0.011 –

59

APPENDIX A. APPENDIX Patrick Schneider

ES 2009

Table A.6: Two-tailed p-values – H1 : median of Yi �Xi 6= 0

Op. Lic. Imp. Lic. Cons. Per. Elec. Con. Pho. Con. Wat. Con.Op. Lic. –Imp. Lic. 0.418 –Cons. Per. 0.000 0.000 –Elec. Con. 0.268 0.038 0.003 –Pho. Con. 0.200 0.635 0.000 0.000 –Wat. Con. 0.736 0.500 0.012 0.088 0.001 –

Table A.7: One-tailed p-values – H1 : median of Yi �Xi > 0

Op. Lic. Imp. Lic. Cons. Per. Elec. Con. Pho. Con. Wat. Con.Op. Lic. –Imp. Lic. 0.872 –Cons. Per. 0.000 0.000 –Elec. Con. 0.134 0.019 1.000 –Pho. Con. 0.946 0.785 1.000 1.000 –Wat. Con. 0.750 0.250 0.998 0.978 0.000 –

Table A.8: One-tailed p-values – H1 : median of Yi �Xi < 0

Op. Lic. Imp. Lic. Cons. Per. Elec. Con. Pho. Con. Wat. Con.Op. Lic. –Imp. Lic. 0.209 –Cons. Per. 1.000 1.000 –Elec. Con. 0.923 0.992 0.002 –Pho. Con. 0.100 0.318 0.000 0.000 –Wat. Con. 0.368 0.845 0.006 0.044 1.000 –

60

APPENDIX A. APPENDIX Patrick Schneider

A.3 Two Stage Least Squares Regressions

Table A.9: 2SLS Regressions

log CPI10 log GCB10 log GCBD10 log GCBD10 log GCBD10ln GDP per �0.1622 �0.2458 �0.0216 �0.0211 0.0907⇤

cap. PPP [0.2174] [0.7175] (0.0610) (0.0612) (0.0485)

Fuel exports �0.0007 0.0005 �0.0003 �0.0004 �0.0005[0.0030] [0.0101] (0.0009) (0.0008) (0.0009)

Year open �0.0090 0.0049 �0.0021 �0.0020 �0.0016to trade [0.0058] [0.0152] (0.0017) (0.0017) (0.0014)

Time required to �0.1001⇤⇤ 0.1351 �0.0063 �0.0062 0.0069open firm [0.0412] [0.1228] (0.0087) (0.0087) (0.0138)

Presidential �0.2430⇤ �0.2186 0.0259 0.0258 0.0841democracy [0.1343] [0.4181] (0.0575) (0.0573) (0.0517)

FH Press �0.0045 0.0022 0.0003 0.0004 0.0004freedom [0.0048] [0.0157] (0.0016) (0.0016) (0.0011)

Women in 0.0028 �0.0252⇤⇤ 0.0020 0.0021 0.0021government [0.0033] [0.0101] (0.0014) (0.0014) (0.0013)

Years democracy 0.0001⇤ �0.0001 0.0000 0.0000 �0.0000squared [0.0000] [0.0001] (0.0000) (0.0000) (0.0000)

log GCBDt�1 0.2143(0.2253)

Constant 4.2762⇤ 4.2183 1.2197⇤ 1.2364⇤ �0.1453(2.5933) (8.5771) (0.7063) (0.7047) (0.5615)

Department controls no no no yes yesHistory controls no no no no yes

R

2 0.68 0.63 0.19 0.23 0.56n 38 30 256 256 165

Note: ln GDP per cap. PPP is instrumented using 1820 GDP per capita in each estima-tion. Dependent variable measures absence of corruption in all estimations but that usingGCB10 as the dependent. Square brackets [] denote White robust standard errors; paren-theses () denote cluster adjusted standard errors. Significance of coe�cients is shown by⇤ where p < 0.10, ⇤⇤ where p < 0.05 and ⇤⇤⇤ where p < 0.01.

61

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