Corruption and government spending : The role of decentralization

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Department of Economics University of Oslo

Corruption and government spending

Master thesis for the Master of Philosophy in Economics degree

February 2

^nd

, 2009

The role of decentralization

Kristine Korneliussen

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Preface

First of all, I am deeply grateful to my supervisor, Jon H. Fiva, for sharing his ideas with me and always taking time to answer my questions, and whose involvement and enthusiasm I believe to be unique.

This thesis was written with the support of a much appreciated student stipend from

”Centre of Equality, Social Organization, and Performance” (ESOP) - a Norwegian Centre of Excellence funded by the Research Council of Norway.

I also wish to thank all my friends and family for bearing with me these years, and my fellow students for making everyday life enjoyable - even through the most unpleasant periods.

I owe a special thank to Nina and Daniel for providing me with useful tips and tricks with L^ATEX, and to Sofie for proofreading at the last minute.

All remaining errors are of course mine.

Oslo, February 2nd 2009 Kristine Korneliussen

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Abstract

This thesis points to a possible weakness of the empirical literature on corruption and government spending. That corruption affects the composition of government spending, and in particular that it affects education and health spending adversely, seems to be empirically well established. However, there exist additional literature closely related to corruption and government spending, treating

(i) a relationship between corruption and decentralization, and (ii) a relationship between decentralization and government spending.

These relationships are not accounted for in the literature on corruption and government spending. If corruption and decentralization are correlated, and in addition decentralization affects government spending, then omitting decentralization might cause biased results.

In order to test for possible omitted variable bias, a simple cross-country analysis is performed. Three versions of the classical linear regression model are specified, and estimated in Stata/MP 10 using ordinary least squares estimation. In the first version, the relationship between corruption and government spending, excluding decentralization, is investigated. In the next version, the model is extended to include decentralization as explanatory variable, while in the third version, an interaction term between corruption and decentralization is included as well. These models are then estimated using different measures of government spending on education and health as dependent variables.

The results support the findings that corruption adversely affects government spending on health and education. These findings seem to be very robust, also when decentralization is included as an explanatory variable. Nevertheless, there are indications that there might be an interaction effect between corruption and decentralization.

Specifically, government spending on health seems to be affected more negatively by corruption in decentralized countries. There is a need to investigate this further. In addition, an explanation of this interaction effect remains to establish. Meanwhile, an intuitive guess is that corruption can take different forms at different government levels, and that these different forms of corruption may have different effects on health spending.

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List of Tables

1 Summary statistics . . . 15

2 Regression results, education spending. . . 25

3 Regression results, health spending. . . 26

4 Regression results, education spending. . . 33

5 Regression results, health spending. . . 35

6 Regression results, education spending: Alternative decentralization measures. . . 43

7 Regression results, health spending: Alternative decentralization measures. . . 44

List of Figures

1 Possible directions of causality between corruption, decentralization and government spending. . . 2

2 A scatterplot between the two corruption measures used. . . 16

3 A scatterplot between CORR1 and EDSHARE1 . . . 24

4 Scatterplots between CORR1 and EDSHARE1/EDSHARE2 for each income group . . . 29

5 A scatterplot between CORR1 and HSHARE1 . . . 30

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1 Introduction

The study of the causes and consequences ofcorruption has been given much attention in the later years. The reason for this interest probably relies on the view that corruption is an undesirable bad. The World Bank has focused intensively on corruption in later years, which has attracted the attention of many researchers to this topic. Since the World Bank’s president emphasized the importance of eliminating corruption in the 1996 Annual Meetings speech, this has become one of their main concerns (The World Bank, 1997b).

Even though some researchers have argued that corruption can have positive effects¹, research dealing with the consequences of corruption today mainly focus on what the negative effects of corruption are, through which channels they work, and how severe the damages are. One example, well established in the literature, is that growth seems to be adversely affected by corruption (Mauro, 1995, Li et al., 2000, Mo, 2001). Through which channels this effect works still remains a topic for investigation.² Channels suggested and investigated in the literature are, among others: lower foreign direct investments, a more unequal income distribution, lower productivity, distortion of public investments, and size and composition of government spending.

This thesis aims at empirically examining the effect of corruption on certain parts of government expenditure, namely education and health expenditures. My analysis is motivated by the empirical findings of Mauro (1998) and Delavallade (2006) among others, that government spending on education, and perhaps also on health, seems to be negatively affected by corruption. A problem with their findings is that their analyses do not take into account theory that predicts

(i) a relationship between corruption and decentralization, and

(ii) a relationship between decentralization and government expenditure.

Figure 1 gives a graphical presentation of some of the relationships suggested in the literature, that are relevant for the study of corruption and government spending. The arrows symbolize possible directions of causality.

1 Kaufmann and Wei (2000) give a short literature review of, and discusses, what they call the

”efficient grease” hypothesis.

2Some authors have found the relationship between corruption and growth to be non-linear, i.e.

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Figure 1: Possible directions of causality between corruption, decentralization and government spending.

To my knowledge, there exist no empirical studies encompassing corruption, decentralization and government spending in one study. From the viewpoint of the corruption and government spending literature, ignoring a possible link between decentralization and corruption may cause results to be biased if decentralization also affects government spending.³ In other words, the results might suffer from omitted variable bias.

To investigate this possibility further, I have decided to base my analysis on that of Mauro (1998). His analysis is both simple and widely cited, and serves as a bench- mark for comparison. As a step in examining the effect of corruption on government expenditure, I replicate parts of his analysis with newer data and compare my findings to his. Next, I extend the analysis to include decentralization and an interaction term between corruption and decentralization as explanatory variables, to see whether the results change. The findings that corruption affects education and health expenditure adversely are strongly supported, and these findings seem to be very robust. Still, I do find weak evidence that the results might suffer from omitted variable bias. Including a measure of decentralization reduces the coefficient on corruption somewhat in some of the specifications, but it is still statistically significant. There seems to be an interaction effect between corruption and decentralization that needs to be accounted for

to adversely effect growth. See for instance M´endez and Sep´ulveda (2006).

3The same is of course true for the decentralization and government spending literature in the case that corruption is excluded from the analyses.

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when conducting empirical analyses.

The thesis is organized as follows: Section 2 gives an overview of different definitions of corruption, decentralization, and government spending, and how they are usually measured. Section 3 gives an overview of the theoretical and empirical work this thesis rests on and points out where the literature is incomplete. In section 4, all variables used in the estimations and their sources are presented. Section 5 explains the econometric model and problems related to the estimation method. In section 6 the results from the regressions are presented and discussed, while section 7 concludes.

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2 Definitions and measurement

Due to different usages of the wordsdecentralization,corruption andgovernment spend- ing in the literature, there is a need for clarifying how they are defined in the literature, and what is meant by them in this thesis.

2.1 What do we mean by government spending?

It is not necessarily clear what is meant by government expenditure, or government spending. When we are interested in addressing the impact on government expenditure, we could either look for changes in the size of government expenditure and its components, or we could look for changes in the composition of government expenditure. The problem is that these concepts are sometimes used interchangeably. An observed increase in government spending on education for instance, does not reflect a change in the composition of government spending if all other components increase by the same proportion. Mauro (1998) for instance, is interested in addressing changes in the composition of government spending. As dependent variables, he uses government spending on health and education as share of GDP, which do not necessarily reflect these changes. I am mainly interested in how corruption affects the composition of government expenditure, and therefore I include two variables that reflect this better in my analysis: ”Government spending on health as share of total government expenditure”, and ”government spending on education as share of total government expenditure”.

These are described in further detail in section 4.

2.2 What do we mean by corruption?

The world bank has defined corruption as the abuse of public office for private gain (The World Bank, 1997a). The definition is ambiguous, and can be subject to a wide range of interpretations. Still, this definition, or some close variant, is widely used in the literature (Treisman, 2000, Arikan, 2004). There exist a number of alternative definitions of corruption in the literature, but I will not discuss these here.

Clearly, a broad and vague definition of corruption can lead to conflicting results, both theoretically and empirically, as different definitions are likely to capture different aspects of corruption. As an example, it is sometimes differentiated between grand, or political corruption, and petty corruption. As stated in the U4 Corruption Glossary

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(U4 Anti-Corruption Resource Centre, 2009):

High level or ”grand” corruption takes place at the policy formulation end of politics. It refers not so much to the amount of money involved as to the level at which it occurs - where policies and rules may be unjustly influenced.

The kinds of transactions that attract grand corruption are usually large in scale - and therefore involve more money than bureaucratic or ”petty”

corruption.

The different types of corruption could have different effects on for instance growth, and they could work through different channels. If so, this can to some extent explain the conflicting theoretical predictions.

Even if we had agreed on a working-definition of corruption, we could ask whether it is possible to measure corruption at all. Almost all measures of corruption that are available measure some form of perceived corruption. The issue of whether perceived corruption indices measure something close to corruption is discussed in detail in a working paper by Donchev and Ujhelyi (2008).⁴ They present evidence that some of the most widely used indices might be biased in systematic ways. According to them, some of the factors commonly found to ”reduce” corruption, for instance development and democratic institutions, systematically bias corruption perception downward from corruption experience. They also point out that perception indices seem to be affected by absolute levels of corruption, and not by relative levels. In other words, there is a bias toward rating large countries as more corrupt than small ones. There have been attempts to measureactual corruptionby surveying people about their experiences with corruption, but indices of perceived corruption are still the most frequently used. Even if perceived corruption is likely to be highly correlated with the true level of corruption, perceived corruption indices are likely to reflect measurement error. Changes in the score from one year to another does not necessarily reflect changes in the true level of corruption. Rather, it could reflect the inclusion of more sources or new information that improves accuracy, or it could reflect a change in people’s perception for some other reason than a change in the true level. Another issue is that there could be differences in the perceptibility of for instance grand and petty corruption, resulting in biased measures. These problems are hard to overcome, and therefore it is important

4Examples of other articles discussing problems related to corruption indices are Olken (2006),

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to bear in mind that what we actually study is the effect of perceived corruption on government spending.

2.3 What do we mean by decentralization?

That decentralization is used to mean quite different things is pointed out by Treisman (2002, page 2) among others:

...political economists have devoted increasing attention to the causes and consequences of centralization and decentralization. Yet scholarship is littered with so many different usages of these words that is often unclear just what they mean, if indeed they still mean anything at all.

In this working paper, he attempts to differentiate between different concepts of decentralization, such as decisionmaking decentralization, electoral decentralization and fiscal decentralization, to mention some. When looking at the causes of corruption and government spending we are mainly interested in fiscal decentralization, and in the proceeding of this thesis decentralization will be used to mean fiscal decentralization, unless otherwise stated. According to Treisman (2002), fiscal decentralization has to do with the way tax revenues and expenditures are distributed among different government tiers. Ideally, we would want a measure of decentralization to take into account to what extent sub-national governments have the right to decide over their revenue and expenditure shares. Moreover, we would perhaps want the measure to account for the number of government tiers. Two countries with the same number of sub-national governments, but with a different number of tiers, are not necessarily equally decentralized.

Two popular measures of degree of fiscal decentralization are ”sub-national revenues as share of total revenues” and ”sub-national expenditures as share of total expenditures” from the Government Finance Statistics (GFS) by International Monetary Fund (2001). One advantage of these is that they are relatively straightforward to measure, as they are purely budgetary measures. A disadvantage is that they may not measure the degree of fiscal decentralization, as we think of it, accurately. They neither account for the extent to which sub-national governments decide over their own revenues and expenditures, nor the number of government tiers. Also, they are only available for a relatively small number of countries, and only up until year 2000. Since 1990, they are available for less than 60 countries each year.

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”Vertical imbalance”, i.e. intergovernmental transfers as a share of sub-national expenditures (International Monetary Fund, 2001), is another frequently used measure of the degree of decentralization, which unfortunately suffers from the same weaknesses as the former two.

Decentralization is sometimes measured byfederalism, usually by classifying countries as either federal or non-federal. Treisman (2000) uses a dummy for federalism as measure of decentralization. Not only constitutional federal countries are classified as federal according to this measure, but also countries functioning as federal (admin- istrative federal countries). Neither this measure necessarily reflects the true level of decentralization, and since it is a dummy variable, it does not capture the degree of decentralization. However, this measure has been utilized in several empirical studies (Fisman and Gatti, 2002).

A discussion and empirical investigation of measurement problems related to measuring decentralization is carried out in Ebel and Yilmaz (2002). Comparing the GFS measures to other measures that (according to them) capture the degree of decentralization more accurately, they find considerable differences in the effect of different indicators on economic performance. Stegarescu (2005) follows up the work of Ebel and Yilmaz, and discusses problems with defining and measuring fiscal decentralization. He argues that the measures usually employed tend to overestimate the ”true extent” of fiscal decentralization, and he presents a new improved indicator of tax and revenue decentralization. Unfortunately, the indicator only covers 23 OECD countries, which makes it unsuitable for my analysis.

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3 Theoretical and empirical background

In this section, I sketch an overview of some of the existing literature relevant to the question of whether corruption affects government spending. Both theoretical and empirical literature is included, with an emphasis on cross-country studies as these are the most relevant to my empirical analysis

3.1 Corruption and the composition of government spending

3.1.1 Theory

To my knowledge, there exist few theoretical models of how corruption affects government spending, but several authors argue that there is a link between them. That corrupt government officials have the opportunity to affect expenditures was suggested by Shleifer and Vishny (1993). They do not present a formal model of any kind, but state the following (page 614):

Western observers often wonder about the preference for unnecessarily ad- vanced rather than ”appropriate” technology by Third World governments.

Overinvoicing provides the obvious explanation for this preference for ad- vanced technology. The rational managers and bureaucrats in poor countries want to import goods on which bribes are the easiest to take, not the goods that are most profitable for the state firms.

They further claim that corruption possibilities are abundant on infrastructure projects and defense, while they are much more limited on education and health.

Building on Shleifer and Vishny (1993), Mauro (1998) argues, without presenting a formal model, that corruption is likely to affect the composition of government expenditure. He mentions two main reasons for this. The first reason is the existence of rents in some sectors that motivates rent-seeking behavior. Especially in markets with a low degree of competition, such as the market for military equipment, large bribes will be available for government officials. The second reason is that corruption leads government officials to choose goods that have an exact value that is difficult to monitor; for instance specialized, high-technology goods. Based on this, Mauro (1998) argues that one should expect that larger bribes are available on defence or infrastructure equipment, than on education expenditures, such as salaries and textbooks. He admits that it is not totally obvious in which areas large bribes will be available, and

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in which not, but education is one area where it is reasonable to expect that bribes are difficult to collect. We should therefore expect to find that countries with more corruption spends relatively less on education than those with less corruption. He also suggests health expenditure as a candidate to be adversely affected by corruption.

3.1.2 Empirical studies

Mauro (1998) investigates empirically whether perceived corruption affects government expenditure, and finds evidence that it does. The only statistically significant finding is that education spending is adversely affected by corruption, but in addition he finds indications that health expenditure may also be adversely affected. These findings are supported by Gupta and Alonso-Terme (1998).⁵

In a paper by Goel and Nelson (1998) investigating the relationship between corruption and government size in a number of U.S. states, an effect from state government spendingtocorruption is established. This demonstrates the importance of questioning simultaneity problems when drawing causal conclusions.

A more recent study that supports Mauro’s (1998) findings has been performed by Delavallade (2006). She finds that public corruption distorts the structure of public spending by reducing social expenditure’s portion, i.e. education, health and social protection spending’s portion of total government spending.

All in all, the findings of Mauro (1998) that perceived corruption has a negative effect on government spending on education and health seem to be well established.

3.2 Decentralization and government spending

3.2.1 Theory

Theories dealing with decentralization and government spending build upon several strands of literature treating the consequences of decentralization, such as the effi- ciency of public goods provision. The different theories tend to emphasize different aspects of decentralization, and therefore differ in their predictions of the impact of decentralization. Some take a positive view and favor decentralization, others take a negative view.

5The sum of education and health spending as share of GDP is used as dependent variable, and the effect on education and health alone is not investigated.

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An early contribution to the theory on decentralization was the seminal paper by Tiebout (1956). In his original model, each community ends up providing the optimal level of services and taxes, because people differ in preferences and are mobile, i.e. they

”vote with their feet”. Models building on this framework have come to be known as Tiebout models, and take a positive view of decentralization.

Another strand of literature builds on a fiscal competition framework that encom- passes tax competition models, and yardstick competition models. The welfare consequences of fiscal competition are not straightforward, and the different models vary in their view on decentralization. Early models of tax competition have held a negative view of decentralization, arguing that increased decentralization leads to increased tax competition between local governments, which leads to inefficiently low taxes and sup- ply of public goods. This view was informally introduced by Oates (1972), and formally modeled by Wilson (1986) and Zodrow and Mieszkowski (1986). The positive effects of tax competition have also received attention in the literature. In Brennan and Buchanan (1980), the government is thought of as a revenue-maximizing Leviathan that needs to be restricted. Otherwise the public sector would be to large. Models building on this framework usually emphasize the disciplining effect decentralization can have on governments.

Further, there are models building on a rent-seeking framework, dating back to Tullock (1967) and Krueger (1974). Theories building on a rent-seeking framework take different views of decentralization, depending on what mechanisms they focus on. Some have pointed out that smaller interest groups may be better organized, and that decentralization therefore can lead to intensified rent-seeking activity (Becker, 1983). Tanzi (1995) also holds a negative view, arguing that the proximity to residents make local politicians/officials more vulnerable to rent-seeking activities. Others have pointed out that decentralization leads to less rent-seeking activity, since expected gains from engaging in it are reduced from intergovernmental competition (Sato, 2003).

Opponents of decentralization have also argued that there may be economies of scale in public services provision.

From the theories above, it is not clear what effects we should expect decentralization to have on the size of government spending. The fiscal competition view predicts that the size of government should decrease, while the economies of scale view predicts an increase in size. When it comes to composition of government spending, it seems to be more consensus that social spending is predicted to decrease as a consequence of

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decentralization.

The empirical evidence is conflicting. Oates (1985) investigates his own hypothesis of the state as a Leviathan in a cross-section of countries, and in a cross-section of U.S.

states. He finds no evidence of an effect of decentralization on the size of government.

Using cross-country data to estimate the impact of decentralization on the size of government, Ehdaie et al. (1994, page 14) state the following: ”the simultaneous decentralization of the national government’s taxing and spending powers exerts a negative and significant influence on the overall size of public sector”and ”revenue sharing with taxing decisions concentrated in the hands of national government eliminates the negative influence of decentralization of the spending power”. In other words, decentralization of spending powers alone is not found to have an effect on government size, but followed by a decentralization of taxing power it is associated with a smaller public sector.

Anderson and van den Berg (1998) find no evidence of a relationship between fiscal decentralization and the relative size of the public sector to total economic activity in a cross-section of 45 countries. By including informal economic activity in the measure of total economic activity instead of only looking at GDP, they argue that the estimate of the relative government sector size gets more precise. As a result, the effect of decentralization disappears.

Using panel data, Jin and Zou (2002) find evidence that expenditure decentralization leads to smaller national governments, larger subnational governments, and larger aggregate governments. Revenue decentralization increases subnational governments by less than it reduces national governments, hence leads to smaller aggregate governments. Vertical imbalance tends to increase the sizes of subnational, national, and aggregate governments.

Utilizing Stegarescu’s (2005) new and improved decentralization measure, Fiva (2006) finds evidence that fiscal decentralization affects both the size and composition of government expenditures in a panel of 18 OECD countries. Moreover, he finds that tax revenue decentralization and expenditure decentralization have different effects: Tax revenue decentralization is associated with a smaller public sector, while expenditure decentralization is associated with a larger public sector. The reason for

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the differing effects seems to be that they work through different channels. The effect of tax revenue decentralization seems to go through lower spending on social secu- rity transfers, while the effect of expenditure decentralization works through increased government consumption.

3.3 Decentralization and corruption

3.3.1 Theory

The theory on decentralization and corruption rests on much the same literature as the theory on decentralization and government spending, and decentralization can be viewed as either positive or negative in this case as well.

Dincer et al. (2006) for instance, present a formal model of yardstick competition to explain a negative relationship between decentralization and corruption. They model corruption as a direct appropriation of tax revenue.

Arikan (2004) also presents a yardstick-type model. His model predicts that decentralization, in the form of an increase in the number of jurisdictions, decreases corruption. In this model, there is assumed a tax on mobile capital. A tax-increase in one jurisdiction therefore leads to a capital flight. When the number of jurisdictions increases, capital flight in response to tax increases will be even more pronounced. It therefore leads each jurisdiction to choose a lower tax rate. Tax income in this model is used for providing a public good, and corruption is modeled as the amount of tax income that is not spent on public good provision, in correspondence with Dincer et al.

(2006). A lower equilibrium tax rate therefore makes extraction of corrupt earnings more difficult.

Others have suggested that decentralization can increase corruption, because the local level is less likely to attract high-quality bureaucrats (Fisman and Gatti, 2002).

The empirical findings differ in their conclusions of the relationship between decentralization and corruption.

Huther and Shah (1999) look at fiscal decentralization, and report a negative (un- conditional) correlation between fiscal decentralization and corruption, but as Fisman and Gatti (2002) note, such an analysis suffers from severe omitted variable bias. Treis- man (2000) finds the exact opposite effect. He uses a dummy variable for federalism

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as measure of decentralization, and finds that federal countries have higher rates of corruption.

3.4 A gap in the existing literature

The above literature review has roughly presented theory and empirical studies in three separate research areas. In my opinion, these areas need to be integrated to a larger extent. From the viewpoint of the empirical studies of corruption and government spending, ignoring the literature on decentralization and corruption, and decentralization and government spending, can imply that the results suffer from omitted variable bias. This comes from the fact that if decentralization and corruption are correlated, and both decentralization and corruption have an effect on government spending, then omitting decentralization from the analysis will result in a biased estimate of corruption. The direction of the bias depends on the signs of the correlation between corruption and decentralization, and the correlation between decentralization and government spending. How corruption, decentralization and government spending are related is not obvious. Another possibility is that the effect of corruption on education and health spending differs between decentralized and centralized countries, i.e. there is an interaction effect between them. Omitting this from the analysis can also result in wrong inferences. This is where this thesis attempts to add to the literature, and in the proceeding the effect of including decentralization and an interaction term between corruption and decentralization as explanatory variables of education and health spending is investigated.

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4 Data

The sample of countries included in the analyses is entirely the result of data availability. Ideally, I would have included all countries, but unfortunately the data on the different variables was only available for a limited number of countries. The samples also vary between the different regressions. Still, I do believe that in all cases they cover a large set of different countries; some developing and some developed. Sum- mary statistics for all variables are listed in table 1, and the number of countries each variable is available for is listed there.

4.1 Dependent variables

I have included four different dependent variables in the regressions. The variable ED- SHARE1 measures government expenditure on education as a share of GDP, and was collected from UNESCO (2008). The variableHSHARE1 measures government expenditure on health as share of GDP. It was constructed combining ”Total expenditure on health as percentage of GDP” and ”General government expenditure on health as percentage of total expenditure on health”, both from WHO (2008).

I have also included two variables measuring education and health spending as share of total government expenditure, to see whether an effect on education and health spending as share of GDP could actually be due to an increase in the total budget, and not to a change in the composition of government expenditure. The two variables are EDSHARE2 (UNESCO, 2008) and HSHARE2 (WHO, 2008), respectively. All four dependent variables have been averaged over the period 1999 to 2006 for reasons that will be explained subsequently.

4.2 Key explanatory variables

The data on corruption is drawn from two different sources. The first source is the Corruption Perceptions Index (CPI) by Transparency International. This is an index of perceived corruption, ranging from 0 to 10; 0 being the most corrupt, and 10 the least corrupt. It is available for a sample of 196 countries. This is the main measure of corruption included in the analysis due to the availability of data.

The second source is The International Country Risk Guide (ICRG) by The PRS Group, Inc. This is a private firm that offers an index of perceived corruption in a

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Table1:Summarystatistics VariableDescriptionMeanStd.Dev.Min.Max.N Dependentvariables EDSHARE1GovernmenteducationspendingasshareofGDP4.6732.1930.25715.975172 HSHARE1GovernmenthealthspendingasshareofGDP3.7792.4270.27516.331193 EDSHARE2Governmenteducationspendingasshareof15.1234.963.232.78154 totalgovernmentspending HSHARE2Governmenthealthspendingasshareoftotal10.5814.2551.26325.487193 governmentspending Keyexplanatoryvariables CORR1Perceivedcorruptionindex,range0-104.1152.0981.359.75196 CORR2Perceivedcorruptionindex,range0-104.4271.850.67710141 FED1Federaldummy0.0930.29101215 FED2Federaldummy0.3290.4730173 SUBEXPSub-nationalshareofexpenditures24.81814.2362.3658.5662 SUBREVSub-nationalshareofrevenues19.313.920.8561.9561 CORR1*FED1Interactionterm=CORR1×FED10.5391.77508.813189 Controlvariables SHARESCHOOLAGEPopulationshareaged5-1421.0575.889.70031.17193 GDPCAPGDPpercapitain20000.6190.9380.0094.636179 OPENTotaltrade(imports+exports)asshareofGDP90.58351.8132.02377.68187 LOWINCDummyforlowincomecountry0.2340.42501209 MIDINCDummyformiddleincomecountry0.4550.49901209 HIGHINCDummyforhighincomecountry0.3110.46401209 AFRICADummyforAfrica0.2540.43601189 ASIADummyforAsia0.2040.40401191

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0246810CORR2

0 2 4 6 8 10

CORR1

Figure 2: A scatterplot between the two corruption measures used.

cross-series of countries for the years 1984 to 2008. This measure of corruption is the same as Mauro (1998) uses. The index ranges countries on a scale from 0 to 6, where 0 corresponds to most corrupt and 6 to least corrupt. It is available for a sample of 141 countries, and is included in order to test for sensitivity to choice of measurement in the analyses.

In order to enhance comparability of the two measures, I have transformed the ICRG index from a 0 to 6 scale and into a 0 to 10 scale by multiplying it with 10/6.⁶ Both measures are averaged over a period of eight years (1999-2006).

The CPI measure is labeled CORR1, while the ICRG measure is labeled CORR2.

The two measures are highly correlated (correlation coefficient 0.8582), but an inspection of the scatter plot between the two in Figure 2 reveals that there is some variation.

If the two measures were perfectly correlated, they should fall along the displayed 45 degree line, which they do not. Countries with high levels of perceived corruption (low scores), tend to be rated more favorably on the CORR1 index relative to CORR2. In the empirical analysis, I run regressions with each of the two measures separately, to see whether the difference between them affects the results.

There is no consensus on how to best measure the extent of decentralization. I

6This has also been done by others, for instance Tanzi and Davoodi (1997).

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have included four different measures to see how sensitive the results are to choice of measurement.

FED1 is a dummy variable for whether a country is classified as federal⁷ or not (1 if federal, 0 if else). This is the variable I have chosen as the main decentralization variable. It is taken from Treisman’s (2002) Decentralization dataset available online, and is available for a sample of 215 countries. Again, the large sample size is the reason for choosing it as main variable. It not only classifies constitutional federations as federal,but also takes into account the degree to which countries function as ad- ministrative federal countries. Still, only 20 of the 215 countries (9%) are classified as federal.

FED2 (Inman, 2008) is also a dummy for whether a country is classified as federal or not. It is only available for a very small sample of countries, but I have nevertheless chosen to include it in order to compare the results from regressions with FED1 to those with FED2. Also, 24 of the 73 countries (33%) covered are classified as federal, so despite its small sample, more countries are classified as federal than forFED1. The correlation between FED1 and FED2 is 0.869 (sample of 70 countries).

SUBEXP and SUBREV measure the sub-national share of expenditures and the sub-national share of revenues, respectively (International Monetary Fund, 2001). These are both purely budgetary measures, and hence they are subject to the problem that they do not necessarily reflect sub-national governments’ true power to tax and spend.⁸ Nevertheless, they have one big advantage over FED1 and FED2 in that they capture different degrees of decentralization to a larger extent. Also, they are frequently used in the literature, and including them in the analysis makes it possible to compare the results to previous studies. ForFED2,SUBEXP andSUBREV, a problem is that they are only available for a relatively small sample. This raises the concern that the sample may suffer from sample selection bias.

4.3 Control variables

In addition to the key explanatory variables described above, I have included a set of control variables that are likely to affect government spending on education and health,

7Federal countries are those classified as federal by Daniel.J.Elazar in ”Federal systems of the world a handbook of Federal confederal and autonomy arrangements”.

8Several problems related to using these as measures for degree of fiscal decentralization are dis-

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in order to avoid omitted variable bias.

Following Mauro (1998), real GDP per capita is included as a control variable.

GDPCAP is real GDP per capita in year 2000 (The International Monetary Fund, 2008). SHARESCHOOLAGE measures ”Percentage aged 5-14” in year 2000, i.e. the population aged 5 to 14 as the share of total population in 2000, and is taken from UNdata (United Nations Statistics Division, 2008). Mauro (1998) uses the share of population aged 5-20, which probably would have been a more appropriate measure, but this data was not available at the time of my analysis.⁹ LOWINC, MIDINC and HIGHINC are dummies for country income group (low income, middle income and high income) taken from World Bank (2007). I have also included a measure of degree of openness, OPEN (Heston et al., 2006). It measures total trade (imports + exports) as share of GDP in 2000. Two regional dummies, AFRICA and ASIA (Treisman, 2002), have also been included.

As mentioned above, some of the variables have been averaged over the period 1999-2006. The reason for averaging is to eliminate some of the variation in the data that is due to measurement error, and also to avoid capturing changes in education and health spending caused merely by business cycle fluctuations. As discussed in section 2.2, perceived corruption can fluctuate from one year to another for reasons other than actual changes in corruption. An example is that increased disclosure of corruption can affect people’s perception of corruption negatively, even though it results from the increased effort for detecting corruption and not from an actual increase.

9The unavailability of this variable is also noticed by Delavallade (2006), who instead uses the ratio of under-15 population as control variable.

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5 Econometric specification

I specify three versions of the Classical Linear Regression (CLR) model. These are estimated in Stata/MP 10 using Ordinary Least Squares (OLS) regression.

5.1 A model of the effect of corruption on government expen- diture

To investigate the relationship between corruption and government spending, I start by specifying the following model:

Yi =α+β×Corruptioni+γ×Xi+εi (1) Subscript i refers to countryi, α is a constant term, corruption is the explanatory variable with coefficient - or marginal effect -β₁,Xis a vector of control variables with coefficient vector γ₁, and ε is the error term. Parameters with a ”hat” refer to OLS estimates. The control variables included are: GDP per capita in year 2000, population share aged 5-14, degree of openness, income level dummies and regional dummies for Africa and Asia, as described in section 4.

The included set of control variables differs somewhat from Mauro’s (1998). He includes four control variables in the education spending regressions, and none in the regressions on health spending. The control variables he uses are: Real GDP per capita, consumption expenditure as share of GDP, population share aged 5-20 and an index on political stability. For two of his control variables, I was not able to obtain data; population share 5-20 and political stability. Population share aged 5-14 is included instead of the first, while the latter is left out. Instead of using consumption expenditure as share of GDP as a control variable, I chose to use education spending as share of total government spending as dependent variable in some of the specifications. Per capita GDP was included to control for stage of economic development¹⁰, in accordance with Mauro (1998). I have included a measure of degree of openness, as several empirical studies have found openness to be a determinant of both size and composition of government spending (Rodrik, 1998, Balle and Vaidya, 2002, Avelino et al., 2005). In addition, dummies for income group and region are included to see

10This has to do with the concept, known asWagner’s law, that government spending as share of GDP tends to grow as a country becomes richer

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whether there are different effects for developed and developing countries¹¹, and to rule out regional-specific effects. If the effects of being in a specific income group, or being African or Asian, are captured through the other variables included, then the estimated coefficients of these dummies will be zero. If they turn out to be different from zero, then there are some characteristics with these countries that we have not identified and that are excluded from the model.

Under a certain set of assumptions, OLS estimation yields best linear unbiased (BLUE) and consistent estimates. Unbiasedness means that the expected value of the estimator equals the true value, i.e. E( ˆβ1) = β1, while consistency means that plim ˆβ₁ =β₁, i.e. ˆβ₁ converges in probability to the true value of β₁ as the sample size increases (Greene, 2003, pages 886 and 899).

The CLR assumptions (Greene, 2003, page 42):

(A1) Linearity

(A2) Full rank, i.e. no multicollinearity (A3) Exogenous independent variables:

E(εi |Corruptioni,Xi) = 0, i= 1, . . . , n (A4) Homoscedasticity and nonautocorrelation:

E(ε_iε_j |Corruption_i,j,X_i,j) =







σ² forj =i, 0 for j 6=i, (A5) Exogenously generated data

(A6) Normally distributed error terms

5.2 Threats to the CLR model

Stock and Watson (2007, page 327) list five primary threats to the internal validity of a multiple regression study:

11Countries in the middle-income and lower-income groups are those we usually refer to asdeveloping countries.

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1. Omitted variables

2. Functional form misspecification 3. Measurement error in the regressors 4. Sample selection

5. Simultaneous causality

If one of these is present, assumption (A3) is violated (E(ε_i | Corruption_i,X_i) 6= 0), which in general means that the OLS estimator is biased and inconsistent.

Omitted variable bias occurs when a variable is omitted from the analysis, and the following two conditions are true (Stock and Watson, 2007, page 187):

1. the omitted variable is correlated with an included regressor, and 2. the omitted variable is a determinant of the dependent varaiable.

The solution to this problem is to include the variable in the regression - the problem is of course that mostly we do notknow that a certain explanatory variable is excluded from the regression, or if we know, we are not able to measure it.

Functional form misspecification means that the functional form of the estimated regression function differs from that of the true regression function. For instance, it could be that the true model is nonlinear which would imply that our OLS estimates are biased. The problem of misspecification can often be detected by an inspection of scatter plots between the dependent and independent variables. Figures 3 and 5 in section 6 does, in my opinion, not reflect any clear nonlinear relationship between the corruption measure and the dependent variables.

Measurement error could of course be present, and if so, we would not get valid results even if our model was correctly specified. One concern in this this study is how much measurement error the perceived corruption indices entail, as discussed in section 2.2.

Sample selection bias could also be present. At the most, my sample consists of a little more than 160 countries, which means that approximately 40-50 countries are excluded from the analysis due to data availability. Comparing my sample to Mauro’s (1998), which includes at the most 103 countries, I consider the risk of sample selection

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Simultaneous causality, or simultaneity bias, occurs when the dependent and independent variables are parts of a simultaneous equation system. In this case causality does not only run from the regressor to the dependent variable, but also from the dependent variable to the regressor.

There exist methods that try to overcome these threats to the multiple regression model. Two frequently used estimation methods are instrumental variables (IV) estimation and panel data regression.

IV estimation can be used to overcome problems of omitted variables, measurement errors and simultaneous causality. The problem is finding valid instruments, i.e.

instruments that are both relevant and exogenous¹². Relevant means the instrument is highly correlated with the variable to be instrumented, and exogenous means the instrument only affects the dependent variable through the instrumented variable.

Mauro (1998) runs a two-stage least squares (2SLS) regression to test for simultaneity bias. He wants to test whether corruption alters the composition of government spending, or if composition of government spending causes corruption. The instruments he uses are: an index of ethnolinguistic fractionalization, a dummy for whether the country ever was a colony, a dummy for whether the country achieved indepen- dence after 1945, a ”black market premium” variable and an ”oil” dummy. To me, it is not obvious that these instruments should be considered valid instruments for corruption. On the contrary, these are all factors that are likely to affect either size or composition of government spending in one way or the other; and not necessarily only through corruption. This would indicate endogenous instruments. Since I was not able to obtain data on all instruments, I chose not to replicate his instrumental variables estimations.

Another possibility for controlling for certain types of omitted variables is to use a form of panel data estimation, such as the fixed effects (FE) regression model. By using panel data regression, we can get rid of country-specific characteristics that affect composition of government spending, but that we for some reason do not observe. The main problem with using panel data estimation in this case, is that there is little variation in the data over years, and data on some of the variables are not available for longer time periods.

Based on the discussion above, I have chosen to stick to estimation by OLS, but

12The two criteria for good instruments (Stock and Watson, 2007, page 423).

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bearing in mind possible weaknesses of this method and the implications this may have for the conclusions.

5.3 Extending the model

As already mentioned in section 3.4, I suspect that Mauro’s (1998) analysis suffers from an omitted variable bias, stemming from the exclusion of decentralization as explanatory variable. If this is the case, ˆβ1 in equation 1 will be asymptotically biased and inconsistent (Kennedy, 2003, page 51). I therefore extend the model in equation 1 to include decentralization as an explanatory variable.

Y_i =α+β₁×Corruption_i+

β₂×Decentralization_i+γ₂×X_i+ε_i (2) As mentioned in section 3.4, it could be that the effect of either decentralization or corruption depends on the other, i.e. there is an interaction effect. To test for this, the model is extended to include an interaction term between corruption and decentralization, CORR1*FED1, which was constructed by multiplying CORR1 and FED1.

Yi =α+β1×Corruptioni +β2×Decentralizationi +

β₃ ×(Corruption_i×Decentralization_i) +γ×X_i+ε_i (3) The marginal effects of a change in either corruption and decentralization are:

∂Y_i

∂Corruption =β₁+β₃×Decentralization, and (4)

∂Yi

∂Decentralization =β₂+β₃×Corruption. (5) From equation 4 we see that when the decentralization variable is a dummy variable (as with FED1),β₁ is the marginal effect of an increase in corruption for a non-federal country, while β1+β3 is the marginal effect for a country classified as federal.

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6 Results

6.1 Education spending

Figure 3 displays a scatter plot and the linear prediction between CORR1 and (ED- SHARE1). The correlation between them is approximately 0.32.

From table 2 it is evident that the impact ofCORR1 onEDSHARE1 is significant and positive for all specifications. A high score on the perceived corruption index (low levels of corruption) is associated with higher spending on education. For the specifications with EDSHARE1, i.e. government spending on education as share of GDP, as dependent variable, ˆβ₁ is significant at the 1% level. ˆβ₁ should be interpreted as the predicted change in EDSHARE1 of a one unit increase in CORR1, holding all other variables constant. In specification (2), ˆβ₁ ≈ 0.65, meaning that the predicted change in education spending as share of GDP of increasing the corruption score by one unit, is 0.65 percentage points. To illustrate this, consider Bangladesh which ranks lowest (number 196) on the CORR1 index with a score of 1.35. By improving their corruption score from 1.35 to 2.35, EDSHARE1 is predicted to increase from 2.4 to 3 percent of GDP. This would move them to rank number 136. In order to reach the mean of EDSHARE1 (4.67), they would have to improve their CORR1 score by 3.5 points.

051015EDSHARE1

2 4 6 8 10

CORR1

Figure 3: A scatterplot between CORR1 and EDSHARE1

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Table2:Regressionresults,educationspending. (1)(2)(3)(4)(5)(6)(7) EDSHARE1EDSHARE1EDSHARE1EDSHARE1EDSHARE2EDSHARE2EDSHARE2 CORR10.2780.6490.6490.6530.8100.8060.839 (4.63)***(4.68)***(4.67)***(4.57)***(2.23)**(2.22)**(2.28)** FED1-0.1160.03421.1962.732 (-0.39)(0.07)(0.84)(0.72) CORR1*FED1-0.0284-0.280 (-0.31)(-0.55) SHARESCHOOLAGE0.09900.09800.09790.4650.4760.476 (2.28)**(2.25)**(2.24)**(3.35)***(3.34)***(3.33)*** GDPCAP-0.437-0.425-0.423-0.338-0.457-0.441 (-1.64)(-1.55)(-1.53)(-0.48)(-0.66)(-0.63) OPEN0.003390.003280.003260.0007550.001790.00182 (0.89)(0.84)(0.84)(0.08)(0.19)(0.19) LOWINC-0.740-0.725-0.7331.5891.4041.324 (-0.70)(-0.68)(-0.68)(0.52)(0.45)(0.42) MIDINC0.5330.5440.5371.3581.2501.193 (0.76)(0.77)(0.76)(0.67)(0.60)(0.57) AFRICA0.06780.06320.0705-1.619-1.588-1.526 (0.09)(0.09)(0.10)(-0.88)(-0.86)(-0.82) ASIA-0.606-0.600-0.598-1.558-1.634-1.621 (-1.51)(-1.50)(-1.49)(-1.03)(-1.08)(-1.06) N161144144144130130130 adj.R20.0930.2330.2280.2220.1400.1380.133 tstatisticsinparentheses *p<0.10,**p<0.05,***p<0.01 Constanttermincluded,butnotreported.CoefficientonGDPCAPwasmultipliedby10000.

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Table3:Regressionresults,healthspending. (1)(2)(3)(4)(5)(6)(7) HSHARE1HSHARE1HSHARE1HSHARE1HSHARE2HSHARE2HSHARE2 CORR10.5890.4980.4980.4750.6610.6610.550 (10.07)***(4.72)***(4.71)***(4.40)***(2.49)**(2.47)**(2.01)** FED10.0291-0.826-0.312-4.392 (0.09)(-1.56)(-0.40)(-2.81)*** CORR1*FED10.1730.825 (1.69)*(3.40)*** GDPCAP0.3380.3350.3211.0431.0801.014 (1.49)(1.47)(1.38)(2.01)**(2.06)**(1.89)* OPEN-0.00102-0.000993-0.000825-0.00905-0.00935-0.00855 (-0.29)(-0.28)(-0.24)(-1.74)*(-1.78)*(-1.68)* LOWINC0.08620.08390.1211.3571.3821.560 (0.15)(0.15)(0.21)(0.93)(0.94)(1.07) MIDINC0.4170.4150.4461.4121.4321.579 (0.89)(0.88)(0.94)(1.43)(1.44)(1.59) AFRICA-0.290-0.289-0.314-0.954-0.972-1.092 (-1.23)(-1.22)(-1.32)(-1.01)(-1.02)(-1.15) ASIA-0.982-0.982-0.997-2.255-2.249-2.320 (-3.03)***(-3.02)***(-3.04)***(-2.48)**(-2.48)**(-2.58)** N177163163163163163163 adj.R20.4090.4840.4800.4820.2430.2380.261 tstatisticsinparentheses *p<0.10,**p<0.05,***p<0.01 Constanttermincluded,butnotreported.CoefficientonGDPCAPwasmultipliedby10000.

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As I have argued earlier, looking at the components of government spending as share of GDP is not necessarily a good way of assessing changes in the composition of government spending. Changes in one component could reflect a change in total spending as well. EDSHARE2, government spending on education as share of total government spending, is therefore included as dependent variable in columns (5)-(7) in table 2. We see that the impact of corruption, ˆβ1 is significant at the 5% level in all three cases.¹³ This indicates that the change in education spending in fact does reflect a change in the composition of government spending. In other words, the results support the findings of Mauro (1998) and Delavallade (2006) that government spending on education as share of GDP is lower for countries with higher levels of corruption.

Neither the inclusion ofFED1 norCORR1*FED1 change the result that corruption has an impact on education spending. The estimates of ˆβ1 are almost unaffected and remains highly statistically significant, and neither ˆβ₂ nor ˆβ₃ is significant in any of the specifications. In other words, the hypothesis that specifications (2) and (5) suffer from omitted variable bias is not supported.

For the control variables, SHARESCHOOLAGE, i.e. the share of population aged 5-14, has a positive and significant effect on both EDSHARE1 and EDSHARE2, as we should expect it to have. For EDSHARE1 it is significant at the 5% level, and for EDSHARE2 it is significant at the 1% level. None of the other control variables are significant in any of the specifications, but we can still look at their signs to see if they display the expected relationships. The estimate of OPEN is positive in all six specifications. This corresponds to the findings in the literature, that openness is positively correlated with the size of government spending, and specifically with education spending as share of GDP (Rodrik, 1998, Avelino et al., 2005).

Figure 4 displays scatter plots of corruption and government spending on education as share of GDP for each income group. These plots display a clear pattern between the income groups: We see that the spread in corruption rating is larger within the

13The same regressions with CORR2 as explanatory variable are presented in table 6 in the Ap- pendix. With EDSHARE1 as explanatory variable, the results look almost the same, but for the specifications with EDSHARE2 as dependent variable, the coefficient on corruption is now insignifi- cant. In other words, the results seem to be sensitive to choice of measurement in this case, but due to the reduced sample size we cannot rule out the possibility that this is caused by sample selection or less statistical power.

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high income group, smaller for the middle income group and smallest for the low income group. In addition it seems that, disregarding a few extreme values, there is a lower spread in education spending in the low income group. We also see that the linear prediction line has a positive slope in all three groups, indicating that there is a positive relationship between absence of corruption and education spending within each income group. LOWINC displays negative signs for EDSHARE1, meaning that low income countries’ governments tend to spend less on education as share of GDP than high income countries (HIGHINC). For EDSHARE2 as dependent variable the estimates of LOWINC are positive, so compared to high income countries, low income countries seem to spend a larger share of total government spending on education. MIDINC is positive in all specifications, so it seems that middle income countries spend more on education than high income countries, both as share of GDP and as share of total government spending. But, as already mentioned, none of the estimated coefficients are significantly different from zero.

The estimates of AFRICA displays positive signs for the specifications with ED- SHARE1 as dependent variable¹⁴, and negative signs for those with EDSHARE2 as dependent variable. If this reflected the true relationship it would mean that African countries spend a larger share of GDP than the rest of the world (Asia excluded) on education, but a smaller share of total government spending. ASIA is negative in all six specifications, meaning Asian countries spend less on education, both as share of GDP and as share of total government spending, compared to rest of the world.

6.2 Health spending

Figure 5 displays a scatter plot and the linear prediction betweenCORR1 andHSHARE1.

The correlation between them is approximately 0.64, and from the figure there seems to be a clear linear pattern.

From the regression results reported in table 3, it is evident that CORR1 has a positive impact on both measures of health spending, significant at the 1% level for HSHARE1 and at the 5% level for HSHARE2. Again, the interpretation of ˆβ₁ is the expected change in either HSHARE1 orHSHARE2 of a one unit increase in CORR1, holding all other variables constant. This can be exemplified by looking at Bangladesh - the country with the lowest (worst) corruption score in the sample. Bangladesh

14But with extremely low t-values (≤0.10).

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0 2 4 6 8 1012

1416 HSHARE1

0246810 CORR1

Middle income countries

0 2 4 6 8

16 14 12 10

EDSHARE1

246810 CORR1

High income countries

0 2 4 6 8

16 14 12 10

HSHARE1

246810 CORR1

High income countries

0 2 4 6 8 10 12 14 16

EDSHARE1

0246810 CORR1

Low income countries

0 2 4 6 8 10 12 14 16

HSHARE1

0246810 CORR1

Low income countries

0 2 4 6 8 10 12 14

16 EDSHARE1

0246810 CORR1

Middle income countries Figure4:ScatterplotsbetweenCORR1andEDSHARE1/EDSHARE2foreachincomegroup

Corruption and government spending : The role of decentralization

Department of Economics University of Oslo