The Effects of Corruption on the Efficiency of Government Health Expenditure

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The Effects of Corruption on the Efficiency of Government Health Expenditure Abhishek Kumar Department of Economics, Bates College, Lewiston, ME 04240

Transcript of The Effects of Corruption on the Efficiency of Government Health Expenditure

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The Effects of Corruption on the

Efficiency of Government Health

Expenditure

Abhishek Kumar

Department of Economics, Bates College, Lewiston, ME 04240

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The Effects of Corruption on the Efficiencyof Government Health Expenditure

A Senior Thesis

Presented to the Department of Economics

Bates College

in partial fulfillment of the requirements for the

Degree of Bachelor of Arts

by

Abhishek Kumar

Lewiston, Maine

April 17, 2009

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Contents

Acknowledgments iii

Introduction iv

Chapter 1. Forms of Corruption 1

1. Bribery (“Speed Money”) 1

2. Extortion 4

3. Embezzlement 6

4. Nepotism 10

Chapter 2. Theoretical Framework and Literature Review 12

1. Corruption and Efficiency 12

2. Corruption and Government Spending 14

Chapter 3. Scope of Study 17

Chapter 4. Methodolgy 19

1. Description of Data 19

2. Methodology 21

3. Descriptive Statistics 23

Chapter 5. Empirical Analysis 26

1. Visual Analysis and Correlation 26

2. Regression Analysis 28

Chapter 6. Conclusion 31

Appendix A 35

Bibliography 37

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Acknowledgments

To begin,

I would like to thank my loving family,

Mother, Father and Brother –

All for giving me the spirit to successfully complete this study.

Continuing my acknowledgments –

Professor Michael Murray, my advisor,

Who,

Through both his remarkable breadth of knowledge,

And his lucid explanations,

Gave me the tools and guidance I needed for this thesis.

The ones who saw me through it all:

My wonderful and slightly insane friends,

Who spent countless,

Countless,

Hours working beside me into the late hours of the night.

And most importantly,

I would like to acknowledge the memory of my elder cousin,

Whom I never had the opportunity to meet –

Whose life was claimed by medical malpractice.

You were the inspiration behind my work.

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Introduction

Corruption has existed through the ages: as long as we have a group of governing

individuals, there will be corruption to contend with. Various cultures have had to

deal with the problem of a corruptible public office, spanning from Biblical times to

modern day [1]. In feudal and medieval times, the governing body was essentially

chosen through corrupt methods such as nepotism, where rulers would pick relatives

to claim their throne after death. In modern times, however, many nations follow

democratic rule where citizens choose their government, but then the minority suffers

and opts corrupt methods to meet their goals. Corruption, as defined and accepted by

scholars throughout time, has been “the misuse of public power for private gain” [2].

While the general connotation that comes with that definition is negative, scholars

have shown both the beneficial and the detrimental side of corruption. Political

economist such as Samuel Huntington and Nathaniel Leff suggest that corruption may

be necessary in certain, overly-centralized economies to spur growth and development.

Opponents of corruption such as the economist Paulo Mauro have published numerous

articles denouncing the beneficial effects of corruption and finding robust evidence for

the negative impacts of dishonest bureaucratic practices. The pervasive arguments

for and against corruption have inspired the study conducted in this paper.

This literature begins with an overview of various forms of corruption, primarily

in the context of economic theory. It then moves on to using those definitions to con-

struct a theoretical framework for the empirical study, explaining both the proponent

and opponent arguments for corruption. With the conjectural skeleton constructed,

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INTRODUCTION v

the paper then uses infant mortality rates (per 1,000 live biths), an index for corrup-

tion and per capita government health expenditure to see which side of the corruption

debate has more merit. This task is carried out by answering two questions. Does

corruption increase infant mortality rates? Additionally, does corruption distort gov-

ernment health expenditure by decreasing its efficiency? By answering these two

questions, we hope to gain a deeper understanding of the two sides of the argument,

and hopefully, have results statistically significant enough to choose one argument

over the other.

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CHAPTER 1

Forms of Corruption

1. Bribery (“Speed Money”)

Often in very rigid bureaucracies, much of the work, especially in the form of

passing files from one official to the other in the hierarchy of command, does not

get done unless the officer in charge has some sort of “incentive” to do so. This

“incentive” generally takes the form of a bribe that is paid to the officer in control

of signing and passing along a file to his or her superior. This practice of soliciting

bribes to carry on bureaucratic work has been becoming more common in granting

licenses, permits and patents. However, the distinction must be made that in this

interaction between the government official and the bribe payer, it is the government

official that is corrupt. Generally, the bribe giver has no intention or inclination to

commit an unlawful act, but wants to speed up the process of the movement of the

filesthus, the term “speed money”.

1.1. The Model. Let us consider a simple model provided by Bose (2003) [3]

for this type of corruption, where a bureaucratic clerk (or agent) delays the process of

passing on an official file, say, an application. There may be complete or incomplete

applications submitted to the bureaucrat. Complete applications must be forwarded

within the first time period, whereas incomplete applications must be delayed until

all the missing information is providedthey will be delayed into the next time period.

Some assumptions of the model that come to light are that there is no concrete way

of determining the reason why the agent has delayed the application. He could have

delayed the application because it was either incomplete or because he wanted to

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1. BRIBERY (“SPEED MONEY”) 2

solicit a bribe. However, if he passes an application that is incomplete, we can clearly

say that some form of corruption has taken place–his client most likely has supplied

him with “speed money”. This model has two key abilities: one, it is able to quantify

the optimal bribe-price that a corrupt bureaucrat may ask for, and secondly, it can

ascertain the amount of social welfare loss.

To begin, each applicant has a cost associated with their time, some people place

very little value on their time and some place a very high value. So, if someone’s

application is delayed by a certain time, depending on the value they have placed on

that amount of time, they will suffer a cost equivalent to that value. For example, let

us look at an applicant who wants to get his application for a bar license approved

within a month because he has planned his next month’s expenses contingent upon

having his business up and running. If his application were to be delayed past that

month, the value he would place on his time would be equivalent to the possible

earnings from the bar if it were open. As such, the cost he would suffer for having his

application delayed, say, another month would be equivalent to the value he placed

on a thirty day delay.

In the model, this cost is represented by a distribution over an interveral [0, c̄]

according to the distribution function G(c). The interval goes from 0 to c̄ because it

captures all the different costs applicants have placed on time lost; the costs range

from 0 to the highest value of c̄. So, the applicant will be willing to pay a bribe that

matches his cost for the time his application is delayed.

In the model, we assume that there is no government authority regulating the

bureaucrat’s activities. He can do as he wishes and charge whatever he wishes, and

because of which, we consider him a non-discriminating monopolist. However, the

bureaucrat does not benefit completely from being able to charge whatever he wishes:

he has no method of assessing the different costs each applicant places on their time.

So, if he charges a bribe that is, say, US 250, but the applicant only places a cost

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1. BRIBERY (“SPEED MONEY”) 3

of US 50 on the time the bureaucrat wants to delay the application, the agent will

receive zero dollars as a bribe. The applicant is not going to pay a bribe of US 250

if he only values his time at US 50–he will be fine with a delay in his application

processing.

Since the agent has no way of discerning the cost that applicants place on their

lost time, he will behave as any non-discriminating monopolist would: he will ask

for a bribe that maximizes his revenue. Let us examine more closely the distribution

function G(c) to understand the dynamics of the bribe. The variable c in the model

is the amount that the clerk will charge as a bribe, and the distribution of c is from

0 up until the value the clerk would charge as a bribe. As such, G(c) represents the

proportion of applicants with costs less than c.

Given this framework, we can state that the amount of applicants willing to pay

the bribe the clerk charges will be given by 1 − G(c) because 1 represents the total

amount of applicants and G(c) represents the porportion of applicants who value

their time less than the bribe the agent is charging. Since he is charging c as his

bribe, the bureaucrat’s revenue will be c times the total amount of applicants willing

to pay that bribe, c[1−G(c)]. To find the optimum bribe price we take the derivative

of the agent’s total revenue function with respect to c and solve for the resulting c∗:

(1.1)d

dc([1 − G(c)]) = 1 − G(c∗) − c∗G′(c∗).

We now solve for c∗, obtaining

1 − G(c∗) − c∗G′(c∗) = 0(1.2)

c∗G′(c∗) = 1 − G(c∗)(1.3)

c∗ =1 − G(c∗)

G′(c∗).(1.4)

Thus, c∗ solved for above is the optimum bribe price in this case.

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2. EXTORTION 4

The optimum bribe price, c∗, is the amount the bureaucrat will charge anyone

who wants to avoid the delay. If he does not receive c∗ as a bribe, the agent will

not pass the application. As such, the amount of applicants who value their time

between 0 and c∗ will not have their application delayed. However, there will also

be applicants that place a cost on their time that is higher than the optimum bribe

price–the total range of costs are in fact given by the variable c, which ranges from

0 to some value c̄.

Putting together the pieces discussed above will allow us to harness another aspect

of the model: its ability to ascertain the amount of welfare loss “suffered by members

of society that is not compensated by corresponding gains made by others” [3]. The

applicants who value their time less than the optimum bribe are the ones who suffer

the welfare loss in terms of having their application delayed unnecessarily. Mathe-

matically, the integral of the variable c, which encapsulates all the costs placed on

time by the applicants, with respect to G(c), the amount of people who value their

time less than c∗, will give us the welfare loss. We must take the integral from 0 to

the optimum bribe price of c∗. Thus, the integral,

(1.5) L(c∗) =

∫ c∗

0

(c)dG(c),

represents the welfare loss suffered by the applicants who have their application de-

layed because they do not pay the bribe; they place costs less than the optimal bribe

price on their time.

2. Extortion

The topic of extortion goes hand-in-hand with bribery as complements. As dis-

cussed in the previous section, bribery is the unlawful giving of money in order to

further ones own interests. Extortion essentially looks at the same situation, but

more from the eyes of the bribe taker. Extortion is the unlawful extraction of money

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2. EXTORTION 5

or property through intimidation [4]. Intimidation may come in the form of threats

to harm a person (or his family or friends) or his property, threats to reveal embar-

rassing information or threats to accuse him of a crimethe latter two generally fall

under the idea of “blackmail”.

Often times in the case of extortion, the bribe payers consider themselves as the

extortion victims. As seen in the case of bribery, the bribe payers do not want to

pay a bribe to ensure that their application is cleared; they are forced to, or extorted

into, paying a bribe to have their application signed. However, the debate between

which act of corruption, bribery or extortion, came first is often redundant. The

argument neglects the underlying fact that a corrupt act cannot occur without both

parties’ participation. The act of the bureaucrat receiving the bribe in our model

in the previous section would not have occurred if the official had not asked for the

bribe and the client had not agreed to pay the bribe: extortion and bribe paying are

not mutually exclusive.

The prime economic reason for extortion is related to the wage of the extorter.

Government officials get paid a certain salary, S, and they have costs in their own

lives, which we can call C. If these costs exceed their salary, that is C > S, they will

need some other form of income to compensate for those costs. As such, they adopt

a venal attitude and begin to extort bribes in order to make ends meet.

Surveys taken by Koshechkina and Grodeland in post-communist Ukraine provide

excellent examples of extortion occurring due to low wages [5]. It was found that of

all the people interviewed, 46 percent of them had experiences with extortion, either

being extorted or having extorted. Interestingly enough, of all the people surveyed, 13

percent mentioned that the issue was insufficient pay. A man named Khartsyzsk was

interviewed and he stated that he was extorted by a government official for bribes,

and in his interview, he stated that officials were not paid the amount they earned.

Officials work a certain amount and put in a certain amount of labor, which entitles

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3. EMBEZZLEMENT 6

them to some amount of earnings, but the government has their salary set much

below that earning amount. He then goes on to state that because the government

does not pay the officials as much as they earn, “people stoop to bribes”.

Continuing with the example of post-communist Ukraine, an excellent observation

of extortion is seen with police officials. Around 1989, police did not have a legitimate

salary; they had micro-salaries. These salaries were evidently not enough to make

ends meet. As such, there were numerous cases of police officials arbitrarily asking

citizens for bribes. Examples include police officials framing citizens to solicit bribes

or simply threatening to physically abuse them if they did not pay a bribe. Of course

this type of behavior went unchecked, as the legal framework in post-communist

Ukraine was weak.

Extortion is a terrible problem afflicting the economy. Repeated extortion creates

strong barriers to entry in an economy. Extortion creates transaction price inflation,

which makes it difficult for many individuals to invest in an economy [6]. If an

investment is worth price P , but the official in charge of approving application for that

investment extorts an additional price ǫ from the investor, the investment becomes

more costly at the price of

(1.6) P + ǫ = Pb,

where Pb is the price of the investment with the extorted bribe factored in. If this

new price of the investment is higher than the amount that the investor is willing or

able to pay, P ∗, that is Pb > P ∗, then he will not invest.

3. Embezzlement

One of the prime issues afflicting government efficiency is the embezzlement of

public fund–moneys that are supposed to be used as government spending, G, in

social programs, infrastructure development and et cetera. If a percentage of G is

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3. EMBEZZLEMENT 7

directed to bureaucrats pockets rather than its intended purpose, the efficiency of

the government with respect to spending decreases. At first glance, the most obvious

problem that comes to mind is that output will decrease as G decreases in

(1.7) Y = C + G + I + Xm,

but embezzlement is especially problematic because as the bureaucrat steals resources

that he is supposed to administer, he is essentially misappropriating public funds.

While the economy as a whole is affected due to the decrease in Y , it is exceedingly

difficult to officially notice this form of corruption because no private property is

stolen or exchanged. As such, individuals have no legal rights by which they may

seek compensation or have the funds reverted back to their original purpose.

To further elude authorities, there exists the issue that the true characteristics of

the good on which the public funds are to be spent is known only to the bureaucrat

in charge of handling that spending. Due to this asymmetric information, many

government officials find it convenient to embezzle public funds. If they put the

money in their pocket and provide a low-value substitute for the high-quality good

for which the money was originally intended, only they and a few close associates

would really know the difference between the two substitutes. Embezzlement of

public funds is not only detrimental to the bureaucrat’s job security, but also to the

society as a whole due to the loss of capital accumulation, which spurs growth and

development.

3.1. The Model. An effective model for embezzlement would be one that pro-

vides us with a value of the goods embezzled by a bureaucrat, which is also the amount

lost from government spending, G. Blackburn, Bose and Haque (2004) provide an

account of such a model [7]. For simplification, we assume that the bureaucrat (or

agent) in question places no value on risk–he is risk-neutral–and as such has no fear

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of punishment. The model is based off the idea that the bureaucrat in charge of

government spending on some project receives a certain amount of funds from the

government to make the said expenditures. However, because he is risk-neutral, he

chooses to embezzle a percentage of the funds he received for his private use.

The government gives him funds to spend on the good it wants the bureaucrat to

purchase. Now, if he were to blatantly take a fraction of the funds, he would not be

able to purchase the good he was asked to acquire and the authorities would catch

on fairly quickly. But he is clever and he uses the loophole that he is the only one

in charge of the said project and as such he is the only one who has full knowledge

of the good that is to be bought. He uses his unique knowledge to buy the good

his government has asked him to obtain, but at a much lower quality than what his

superiors had in mind. Because he is the only one that has knowledge of the exact

specifics of the product, the government will have a difficult time ascertaining whether

the good purchased meets their expectations of quality. Purchasing a product of lower

quality allows the bureaucrat to officially claim that he followed orders and purchased

the good he was asked to buy, but it allows him to save some money from what he

was given: he then puts this “saved” money in his pocket for his personal use.

The government gives asks the bureaucrat to purchase x amount of public goods

and gives him mθ per unit of the good to do so. The government expects the good to

be purchased at high quality, and a high quality good is expressed by the θ subscript

in mθ. If the agent was not corrupt, he would purchase the x amount of goods at mθ

and write that down in his account books. However, because he is corrupt, he decides

to deceive the government by purchasing the asked x amount of the good, but at a

lower-quality. Obtaining the good at a lower quality allows him to save money and

spend a lesser amount of mφ per unit, where the subscript φ denotes a low quality

good. So, while the cost of obtaining the good at high quality would have been xmθ,

the total cost of purchasing the lower quality good is only xmφ.

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3. EMBEZZLEMENT 9

To avoid prosecution the bureaucrat will claim that he spent xmθ, while truly

spending only xmφ. Such a scheme allows the bureaucrat to embezzle the difference

between the two amounts,

mE = xmθ − xmφ(1.8)

mE = (mθ − mφ)x.(1.9)

As such, the official has the opportunity to embezzle mE of the government funds for

his private use.

Thus far, we have modeled the gain the bureaucrat makes with his embezzlement

and the amount of money the government has lost (this value is equivalent to the

amount the official embezzled, mE), but have yet to model the total life-time gains

bureaucrat makes with his corrupt practices. Any government official receives a salary

for the work he does; this salary we will denote as wt. For the sake of simplicity, we

will assume that the bureaucrat works when he is young and consumes only when he

is old, allowing for no consumption between working and retirement. So, if he is not

consuming until he retires, he saves his entire income and his savings acrue interest

at the interest rate of r. As such, he will have a lifetime wealth of wL,

wL = wt + rwt(1.10)

wL = (1 + r)wt.(1.11)

Equation (1.11) represents the total lifetime wealth of a non-corrupt bureaucrat

that has no extra income besides the salary he had received and saved up at interest

rate r. A corrupt offical, however, has embezzled mE from the government, which

gets added onto his lifetime wealth of wL. Thus, a corrupt bureaucrat will have a

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4. NEPOTISM 10

total lifetime wealth of wL∗,

wL∗ = wL + mE(1.12)

wL∗ = (1 + r)wt + (mθ − mφ)x.(1.13)

4. Nepotism

Nepotism is the idea of according favors to relatives and friends in “the disburse-

ment of public resources, whether jobs, importing licenses or public housing, is usually

thought to contravene one of the basic principles of modern public administration:

the application of universalistic and objective criteria in decision making.” [1]. In the

direct context of political corruption, however, practicing nepotism is entails giving

positions within the government to relatives and friends. For instance, if the ruling

authority of a country (i.e. president, king, sheik and etc.) assign, say, their child to

be their successor instead of holding just elections for the position, it would constitute

nepotism.

There have been numerous examples of nepotism throughout history. The origin

of nepotism arises from the practices of this form of corruption in mediaeval and early

modern Europe. The institution of papacy, for instance, exhibited numerous exam-

ples of nepotism. The actions of Pope Sixtus IV essentially gave birth to the term,

who granted pardons for crimes and official positions to his nipoti (or nephews) [1].

This form of nepotism under Sixtus IV created much uncertainty and suspicion within

the administration and threatened to destroy the papacy. Moreover, it created ri-

valries within the papacy, where various figures attempted to replace existing nipoti

with their own. Nepotism then continued on through history-rulers such as the Czars

of Russia practiced nepotism, for example, by building their cabinets such that it was

primarily composed of relatives and the Czars’ successors generally tended to be their

sons. As such, nepotism has had a long history, continuing to this day.

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4. NEPOTISM 11

While nepotism is one of the longest lasting forms of corruption, it can be the

most dangerous as well. The issues of legality and the correlation with development

illustrate the damaging effects of nepotism [1]. The problem with legality and nepo-

tism is that under law the practice of nepotism is seldom illegal, even in modern

industrial countries. Generally, the powers that be are exempt from any sort of legal

action if they accord favors to relatives; only the public servants are subject to any

sort of censure. Furthermore, the detrimental effects of nepotism become evident

when it is observed that public offices practicing nepotism usually belong to the un-

derdeveloped nations (UDCs) of the world. For example, the developing country of

Nicaragua practiced nepotism for nearly a century with the Somoza political dynasty.

Beginning with Anastasio Somoza Garcia, he selected his successor as his eldest son,

Luis Somoza Debayle, and Debayle selected his successor as his second son, Anas-

tasio Somoza Debayle. While on the surface nepotism may seem not as harmful as

other forms of corruption, the lack of legal action against this corruption and the

correlation between it and development clearly exposes nepotism’s negative effects.

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CHAPTER 2

Theoretical Framework and Literature Review

1. Corruption and Efficiency

”In terms of economic growth, the only thing worse than a society with a rigid,

over-centralized, dishonest bureaucracy is one with a rigid, over-centralized, honest

bureaucracy.” – Samuel P. Huntington

The opening quote by Huntington (1968) [8] captures the mindset of most pro-

corruption activists. The claim they bring to the table is that often countries with

rampant corruption come with the caveat of having an overly rigid bureaucratic

system. Such governmental systems make for inefficient bureaucratic processes, as

red tape impedes the process of various applications for, say, licenses, permits and

et cetera. Nathanial H. Leff (1964) [9] brings to light the very valid point that in

many corrupt countries, governments have other priorities such as military spending

besides devoting time and effort to granting permits and licenses. Governments are

often indifferent to desires of entrepreneurs wanting to carry out economic activities,

or in the case of very leftist nations, governments may actually be hostile towards

the pursuits of private entrepreneurs. Similarly, bureaucracies with inefficient offices

and slow-moving file queues may also impede the positive economic intentions of

entrepreneurs. In such situations, corrupting government officials via graft or bribery

is often beneficial to efficiency within the economy. Hence, corruption is often seen a

much needed “grease” for squeaking the wheels of rigid administrations.

Pranab Bardhan (1997) [10] discusses through a different approach the notion that

corruption, especially in the form of bribery, promotes efficiency. Using the concept

of the Coasean bargaining process, Bardhan claims that bribery reaches allocative

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1. CORRUPTION AND EFFICIENCY 13

efficiency in the sense that only the lowest-cost firm is awarded permits and licenses.

As discussed in the previous chapter, the bureaucrat sets an optimal bribe price that

he will charge, and as such, there will be a number of applicants who will not pay

that amount and a number that will. Extended this idea further, if in the bribery

game private firms are competing for a license, the bureaucrat, wanting to maximize

his revenue, will only approve the application of the firm offering the highest bribe.

In most cases, the firm that is able to pay the highest bribe is the lowest-cost firm.

Hence, through this view, bribery would ensure that economic activity within the

country is generally carried out by the most efficient, lowest-cost firms.

Aside from the positive impression of corruption that it promotes efficiency, there

exists much literature on the negative effects corruption has on efficiency. Gunnar

Myrdal (1968) [11] found evidence in India that instead of bribes and “speed money”

improving efficiency, they actually create an inefficient bureaucratic system. Because

the government official in charge is looking to maximize his revenue, he will wait

and hold up the queue either until his optimal bribe price is met or until one of

the competitive applicants out-competes the other and offers the highest bribe price.

Hence, instead of speeding up the bureaucratic system, bribes often slow them down

given the monopolistic nature of the official.

In continuation with the counter arguments to bribery, the Coasean bargaining

process is also challenged by literature. While the license, permit or contract may

be awarded to the lowest-cost firm through the Coasean bargaining process, the lack

of legal enforcement of the contract creates further inefficiency. Boycko, Schleifer

and Vishny (1995) [12] bring to light a very important point: permits and licenses

granted or contracts established through corrupt means are not enforceable in courts.

In other words, if a government agent were to collect the bribe from an applicant,

there is no guarantee that he will provide the exact service or good in exchange, or

even provide it at all, since the applicant cannot go to court and say he paid a bribe

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2. CORRUPTION AND GOVERNMENT SPENDING 14

in order to complete the exchange. As such, even though the contract may be offered

to the lowest-cost firm through the Coasean bribery game, there is no guarantee that

the lowest-cost firm will actually see that contract officially implemented or license

granted.

2. Corruption and Government Spending

The theoretical framework forming linkages between corruption and government

spending goes hand-in-hand with our first chapter, especially the sections on bribery

and embezzlement. The crux of this section is the relationship between expected

government expenditure on a public good and the affect on the corresponding devel-

opment indicator. Schleifer and Vishny (1993) [13] propose a model, which discuss

the allocation of government expenditure under corruption, building on the simple

case of bribery as presented in the Bribery “Speed Money” section in Chapter 1.

The second approach takes into consideration the issue of embezzlement as discussed

in the Embezzlement section in Chapter 1.

In the simple model of bribery provided by Schleifer and Vishney (1993), the

bureaucrat sets an optimal bribe price at which he will approve an application or

provide good or service. If he does not receive that price, he will not follow through

with the request. Because the official is a non-discriminating monopolist, he is not

concerned with providing the much needed public good (i.e. books for education or

hospital beds) at the lowest cost; he is only looking to maximize his revenue. As

such, the price of the good sought after is increased due to the bribe. The bribe, in

other words, acts almost as a tax. Using simple demand and supply economics, we

can infer that an increase in price will drive output of that good down because some

of the consumers will inevitably be crowded out.

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2. CORRUPTION AND GOVERNMENT SPENDING 15

The bureaucrat will put down in his account books the amount he received from

the consumer minus the bribe amount. So, while we see the expected level of govern-

ment expenditure on that good, we see a much lower level of output than expected.

Low levels of necessity public goods such as hospital buildings or books for school

children will undoubtedly result in lower development indices for those sectors. For

example, Crockroft (1998) [14] finds that large irregular payments in terms of bribes

asked by school administrators for entrance and passing examination has a strong cor-

relation with low enrollment rates. Thus, corruption, especially via bribery, tends to

lower output of much needed public goods, thereby adversely affecting development

indicators.

Moving on to the second approach to the allocation of government expenditure

in relation to corruption, we discuss the idea of embezzlement. As shown earlier in

Chapter 1, in the case of embezzlement, the official in charge steals a portion of the

funds allocated to him by the government, but writes down in his accounts that he

used the complete amount for its intended purposes. In the process, he provides a

good of much lower quality. In such a situation, the output of that type of good is

not affected, however the overall quality being put into the sector for which the good

is intended declines. This situation poses a threat to the overall development of the

nation via two channels.

If, for example, good quality, durable medical equipment is replaced with cheaper,

fast-depreciating substitutes, the quality of healthcare in the country will be deeply

affected. In such a situation, mortality rates may rise, as the healthcare provisions

are not up to par and are unable to handle the medical demands of that country.

Secondly, because he will be selling the public good at a lower price, he will be

increasing demand for that product. As such, the government will be pumping more

funds into that good, but receiving very little benefit. Gupta, Davoodi and Tiongson

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2. CORRUPTION AND GOVERNMENT SPENDING 16

(2008) [15] mention that the treasury will see very large revenue losses in the long-

run, and will be forced to curtail supply of that needed public good. Such a case will

once again adversely affect development indicators.

Despite the vast amount of literature outlining the negative effects of corruption

on government spending, there still exists one author, Nathanial Leff, who attempts

to present corruption in a positive light when considering government expenditure.

Leff (1964) approaches corruption and government spending through the behavioral

aspect of corruption-inflicted economies. Leff claims that a discussion of the effects of

corruption on government spending is only valid if the primary goal of the government

is economic development [9].

The previous section, Corruption and Efficiency, mentions that many of the

underdeveloped nations also tend to be very overly-centralized bureaucracies – it is

these over-centralized nations that tend to have a highly income elastic demand for

development. Such governments may instead choose to spend on lavish satisfaction

of high-ranked government officials, building up military defense or funding programs

to keep the population from revolting. Because it is generally the underdeveloped

nations that have high levels of corruption, there may be discrepancies in empirical

results showing low government expenditure on development projects. The low figures

may not be occurring due to corruption, but simply due to the propensity of the

government to not prioritize development as their primary goal.

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CHAPTER 3

Scope of Study

The previous chapter shows considerable theoretical framework built up to predict

the negative effects of corruption on the efficiency of government expenditure. It is,

however, the counter argument provided by Nathanial Leff that inspires this study.

The goal of this paper is to challenge the notion put forth by Nathanial Leff and

test the hypothesis that corruption adversely affects the efficiency of government

spending, particularly in health care expenditure. Using infant mortality rate (per

1000 live births) as our dependent variable, corruption as our independent variable

and per capita government spending on health care as our control, we hope to show

that corruption does in fact have a negative effect on the efficiency of health care

expenditures.

This study sets itself apart from others because much of the available literature

has studied the efficiency of government expenditures in primarily education, with

only very little work done on efficiency in the health sector. Before we begin, we

must provide credible evidence by citing past studies that corruption does in fact

negatively affect government expenditure. Paulo Mauro (1998) [16], for instance,

uses the corruption index provided in the International Country Risk Guide, which

ranges from 0 to 6 (with 0 being the cleanest and 6 the most corrupt) to test the

affects of corruption on government spending. He finds using ordinary least squares

(OLS) estimation that a one standard deviation increase in the corruption index (s.d.

= 1.45) leads to a 0.6 percent of GDP increase in spending on education. Similary,

the study finds that a one standard deviation increase in corruption also leads to

a statistically significant 0.58 percent increase in government spending on health.

17

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3. SCOPE OF STUDY 18

Given these results, we can proceed safely with our analysis knowing that corruption

does in fact have an inverse relationship with government spending, especially in

health care.

Moving on to literature that has discussed specifically effects of corruption on

the efficiency of health care expenditure, we look to Gupta, Davoodi and Tiongson

(2008) [15]. The study conducts a cross-sectional analysis of 128 countries using also

the International Country Risk Guide’s corruption index. The index, however, is

rescaled for the article: it ranges from 0 to 10, with 0 being the least corrupt and 10

the most corrupt. The paper finds to a 1 percent statistically significant level that

a unit increase in the corruption index is associated with a large 37 percent increase

in infant mortality rates (per 1,000 live births). As such, the large magnitude of the

change in mortality rates suggests that corruption has a terribly adverse affect on the

efficiency of health expenditure.

In this study, we would like to continue in the spirit of the previous literature

mentioned in this chapter. However, the analysis done in this paper will set itself

apart from others in the same area by taking the empirical study a step further.

We will not only attempt to decipher the relationship between corruption and the

efficiency of health expenditure, but also determine the effects of the interaction

between corruption and health expenditure on infant mortality rates. Moreover, we

will also try to understand the changes in the marginal effect on infant mortality

rates due to corruption with respect to changes in health expenditure. As such, this

paper will quantify how the health care efficiency changes directly with corruption

and public spending, as well as the added benefits or costs of changing health care

expenditure at different levels of corruption.

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CHAPTER 4

Methodolgy

1. Description of Data

The dependent variable we will be using is the Infant Mortality Rate (per 1,000 live

births). The two independent variables will be corruption, given by the Corruption

Perceptions Index (CPI), and Per Capita Government Health Expenditure. The three

variables make for a good choice because they allow us to understand not only the

direct effects of corruption and health expenditure on the health care system, but

they also allow us to observe the efficiency of the government in terms of expenditure

on health.

1.1. Corruption Perceptions Index. To find a relationship between corrup-

tion and government efficiency within the health center, we will use panel data from

2001 to 2005. To conduct such a study we need to have some measure of corrup-

tion. Such a measure is given by the Corruption Perceptions Index (CPI) provided

by Transparency International (TI) and the University of Passau.

The CPI is an index constructed based upon interview perceptions of corrution.

The interviews are conducted in the form of surveys of primarily business people

and risk analysts who are either sufficiently close to instances of corruption or have

considerable experience in the study of corruption. The two organizations, TI and

the University of Passau, claim that the individuals they interview are experienced

in the field such that they are able to adequately recognize corruption when they

see it or experience it [17]. The most recent CPI (for 2008) includes data gathered

19

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1. DESCRIPTION OF DATA 20

from the following sources: ADB (Asian Development Bank), AFDB (African De-

velopment Bank), BTI (Bertelsmann Transformation Index), CPIA (Country POlicy

and Institutional Assesment by the World Bank), EIU (Economist Intelligence Unit),

FH (Freedom House Nations in Transit), GI (Global Insight), IMD (International In-

stitute for Management Development), MIG(Merchent International Group), PERC

(Political and Economic Risk Consultancy, Hong Kong) and WEF (World Economic

Forum) [18].

The CPI ranges from 1 to 10, with 1 being the most corrupt and 10 being the

cleanest (or most honest). Additionally, the CPI also assigns rankings of corruption

to every country, with 1 being the least corrupt and the highest ranking being the

most corrupt. For instance, in the 2008 ranked countries from 1 to 180, where 180 was

the most corrupt country. Table 4.1 looks at the year 2005 in the 2008 Corruption

Perceptions Index and it shows Denmark ranked as the least corrupt at 1 and Somalia

ranked as the most corrupt at 180.

Table 4.1. CPI (compiled in 2008) by Country for 2005

Ranking Country CPI

1 Denmark 9.52 New Zealand 9.63 Sweden 9.2...

......

179 Myanmar 1.8180 Somalia 2.1

1.2. Government Health Expenditure Per Capita. The second component

we use to analyze government efficiency in the health sector is Per Capita Govern-

ment Expenditure on Health (PPP int. US Dollar). The statistics for this variable

are provided by the World Health Organization’s (WHO) National Health Accounts

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2. METHODOLOGY 21

(NHA). Health Expenditure per capita is calculated as

(4.1)Public Health Expenditures

Total Population,

giving us the amount the government spends on health care per person in the coun-

try. Government health expenditure consists of recurrent and capital spending from

government (both central and loca) budgets, external borrowings and grants (which

includes donations from international agencies and non-governmental organizations.

Additionally, this statistic includes social (or compulsory) health insurance funds [19].

1.3. Infant Mortality Rate. Lastly, the dependent variable of our model is

the Infant Mortality Rate (per 1,000 live births). The data for this variable are quite

credible, as they are an amalgamation of estimates from three well-known organiza-

tions: the World Health Organization (WHO), UNICEF and the World Bank. They

are based primarily on household surveys, census data and vital registration [20].

The calculation for these data are carried out by dividing the number of infants dy-

ing before reaching one year of age by the number of 1,000 live births in a given

year. The mortality rate can range from 100 percent to 0 percent, implying that 100

percent means all infants per 1,000 births has died and 0 percent suggesting that no

infant out of 1,000 births has died.

2. Methodology

The effectiveness of our variables depends heavily upon how they are set up in our

model. Our aim is to understand both the effects of corruption and health expenditure

on infant mortality, as well as the effects of corruption through health expenditure

on infant moratilty to capture government efficiency. We have three hypotheses for

our analysis:

(1) An decrease in corruption (an increase in the CPI) will cause infant mortality

rates to decline.

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2. METHODOLOGY 22

(2) An increase in per capita health expenditure will cause mortality rates to

decline.

(3) An increase in government efficiency will lead to lower mortality rates.

To test our hypotheses, we will use the basic model,

(4.2) MOR = β0 + β1(CPI) + β2(HEXP) + β3[(CPI)(HEXP)] + ǫ,

where MOR is the Infant Mortality Rate (per 1,000 live births), HEXP is the Per

Capita Government Health Expenditure and CPI is the Corruption Perceptions In-

dex.

The β1(CPI) and β2(HEXP) terms allow us to capture the direct effects of corrup-

tion and health expenditure on infant mortailty. According to our three hypothesis,

an increase in both CPI and HEXP should have a negative effect on MOR. The

β3[(CPI)(HEXP)] term, however, allows us to see how corruption works through

health expenditure. The β3[(CPI)(HEXP)] term captures government efficiency in

the sense that it helps us quantify the marginal effect on MOR from adding either

one unit of CPI or HEXP. If the coefficient of this term is estimated as a positive

value, then it means that marginal effect on MOR from adding either one unit of

CPI or HEXP is increased by the other. For example, if the term is positive, then

the marginal effect on infant mortality rates by adding, say, another unit of health

expenditure is increased as corruption decreases (or as CPI increases). As such, this

term will allow us to permeate through the fog of corruption and see whether health

expenditures are being efficiently put to use.

Given the model in Equation (4.2), we can translate our three hypotheses into

terms of a regression analysis by saying that we would like to reject the following null

hypotheses (We are unaware of the effects of the interaction term, and it is the effect

we are looking for in the thesis; thus, we will not create a null hypothesis for that

term):

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3. DESCRIPTIVE STATISTICS 23

(1) H0 : β0 = 0

(2) H1 : β1 ≥ 0

(3) H2 : β2 ≥ 0

Now, to understand the marginal effect of corruption on infant mortality rate

as a result of one unit change in health expenditure, we implement two new terms.

The added term CPI2 will allow us to determine what happens marginally to infant

mortality rate as the level of corruption changes. Then implementing another inter-

action term (CPI2)(HEXP ) will give allow us to see the marginal change in infant

mortality rates with respect to a change in health expenditure at different levels of

corruption. Adding these two terms to Equation (4.2), we get our second model:

MOR = β0 + β1(CPI) + β2(HEXP) + β3[(CPI)(HEXP)]

+β4(CPI2) + β5(CPI2)(HEXP) + ǫ.

To test our hypotheses, the data that we will be using will be in panel format.

We will take a total of 60 countries from the 180 countries assessed in the Corruption

Perceptions Index of 2008. The aim is to get a full range of CPI values, from low

(more corrupt) to high (very honest). In the spirit of that goal, we will take 20

countries that fit the more corrupt criterion and 20 of the ones that are the most

honest, and lastly, take 20 from the middle that have CPI values between 4 and

6. The panel data will be constructed such that each country is assesed from 2001

to 2005. In other words, the panel data will have a range of five years (from 2001

to 2005) with 60 cross-sections (for an excerpt of the data please see Table 6.1 in

Appendix A).

3. Descriptive Statistics

One of the prime issues that could create bias in our regression analysis is the

presence of multicollinearity between our two independent variables. To check for

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3. DESCRIPTIVE STATISTICS 24

multicollinearity before we begin our analysis, let us employ the variance-inflation

factor (VIF) test. To carry out the VIF test, we set one of our independent variables

as the dependent variable in a regression model:

(4.3) CPI = β0 + β1HEXP + ǫ.

We then regress this equation and find the value of R2 and substitute it into

(4.4) VIF(β̂1) =1

1 − R2

to calculate the VIF factor for β̂1 [21]. If the value of the VIF is greater than 5, then

we have to be concerned about multicollinearity.

Table 4.2 contains the VIF factor for β̂1, as well as basic descriptive statistics

for our two independendent variables. We see that our VIF value is a low number

of 1.15, leaving us free of multicollinearity issues and allowing us to proceed safely

with our regression analysis. Table 4.2 also shows that we have achieved our goal of

Table 4.2. Means, Standard Deviations, Sample Sizes and VIF

Variable Mean s.d. N Minimum Maximum VIF

CPI 6.35 2.49 151 2.4 9.9HEXP 1558.08 1018.46 151 166 5006 1.15

obtaining a large range of CPI values, specifically from very corrupt value of 2.4 to

an extremely honest number of 9.9.

With the threat of multicollinearity nullified, we may now move onto describing

our dependent variable, infant mortality rate per 1,000 live births (MOR). We present

the descriptive statistics of MOR in Table 4.3. The key statistics that jump out for

Table 4.3. Mean, Standard Deviation, and Sample Size for MOR

Variable Mean s.d. N Minimum Maximum

MOR 12.24 18.70 156 2.20 120.60

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3. DESCRIPTIVE STATISTICS 25

MOR are that there is a large difference between the minimum value (2.20) and the

maximum value (120.60) with a fairly large standard deviation of 18.70.

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CHAPTER 5

Empirical Analysis

Begining our empirical analysis, we will attempt to gain a visual relation between

our dendent variable, MOR, and the two explanatory variables, CPI and HEXP. Our

hypotheses were that as CPI increases, MOR should decrease, and similarly, HEXP

and MOR should have an inverse relationship. We will visually test these hypotheses

by constructing scatter plots for both MOR versus CPI and MOR versus HEXP.

1. Visual Analysis and Correlation

Let us first look at the scatter plot of MOR versus CPI given in Figure 5.1, which

also contains the best-fit (regression) line. While there seem to be many outliers at

0

20

40

60

80

100

120

140

2 3 4 5 6 7 8 9 10

CPI

MO

R

Figure 5.1. CPI vs. Infant Mortality Rate (per 1,000 live births).

the lower values of CPI, a relation between MOR and CPI can be drawn. As the

regression line shows, an increase in honesty within the country, or an increase in CPI,

26

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1. VISUAL ANALYSIS AND CORRELATION 27

causes infant mortality rates (MOR) to decline as hypothesized. Moreover, looking at

the dot plot given in Figure 5.1 in Appendix A, we can observe that infant mortality

rates remain low in countries where corruption is low, but they rise significantly when

corruption begins to rise, approximately after CPI values descend below about 4.

Now, let us attempt to visualize an answer to our hypothesis that an increase

in per capita government health expenditure decreases mortality rates. Figure 5.2

displays the scatter plot for government health expenditure per capita (HEXP) versus

infant mortality rate (MOR). Akin to the Figure 5.1, we see many outliers once again

0

20

40

60

80

100

120

140

0 1,000 2,000 3,000 4,000 5,000 6,000

HEXP

MO

R

Figure 5.2. Government Health Expenditure per Capita vs. InfantMortality Rate (per 1,000 live births).

in the lower values of the CPI. This pattern seems to suggest that corruption has

a varied range of effects on infant mortality rate, but the regression line seems to

suggest otherwise. The trend the best-fit line depicts is that CPI and MOR rate have

an inverse relationship such that as CPI increases, MOR declines. Hence, this trend

implies that as corruption decreases, infant mortality rates also tend to decrease.

As a preliminary test of our hypotheses (2) and (3), and as a confirmation of

our extrapolation of the scatter plots, we look at the correlation between MOR and

CPI and the correlation between MOR and HEXP. Looking first at Table 5.1, we see

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2. REGRESSION ANALYSIS 28

that our intuition is confirmed because CPI and MOR have a negative correlation

of -0.52, suggesting that as CPI increases, MOR decreases. Similarly, Table 5.2

Table 5.1. Correlation between MOR and CPI

CPI MOR

CPI 1.00 -0.52MOR -0.52 1.00

shows a negative correlation between HEXP and MOR, implying that as HEXP

increases, MOR decreases. So far, our hypotheses are being confirmed–a decrease

Table 5.2. Correlation between MOR and HEXP

HEXP MOR

HEXP 1.00 -0.39MOR -0.39 1.00

in corruption has a negative effect on infant mortality rates and an increase in per

capita government health expenditures also decreases mortality rates.

2. Regression Analysis

While we have somewhat of a confirmation of our hypotheses (2) and (3), we still

lack numerical proof that our visual analyses are correct. Moreover, we still have to

reject our other two null hypotheses: H0 : β0 = 0 and H3 : β3 ≤ 0. To reject all

the null hypotheses that we set forth in Chapter 3, we must test our model given in

Equation (4.2). For our best estimate, we will run an ordilary least squares (OLS)

regression to estimate Equation (4.2) to get the fitted line,

(5.1) ˆMOR = β̂0 + β̂1(CPI) + β̂2(HEXP) + β̂3[(CPI)(HEXP)] + e,

where β̂1 represents the amount by which MOR changes with each unit change in

CPI, β̂2 quantifies the amount by which MOR changes with each unit change in

HEXP, and lastly, β̂3 captures the amount by which MOR changes with each unit

change in the interaction variable, [(CPI)(HEXP)].

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2. REGRESSION ANALYSIS 29

Looking at the regression results presented in Table 5.3, the first thing we see

is that all our results are statistically significant, as for all variables ρ < 0.05. The

Table 5.3. Regression Results for Model 1

Variable Coefficient Std. Error t-Statistic ρ

C 50.652 6.182 8.193 0.0000CPI -5.8180 1.1295 -5.1511 0.0000

HEXP -0.0157 0.0051 -3.0729 0.0025(CPI)(HEXP) 0.0020 0.0007 2.7233 0.0072

R2 0.3098Adjusted R2 0.2957No. of Obs. 151

second null hypothesis stated that H1 : β1 ≥ 0, and our significant coefficient of

-5.8180 allows us to reject that null hypothesis. The third null hypothesis H2 :

β2 ≥ 0 is also rejected as the coefficient, -0.0157, is bellow zero. Rejecting these

null hypotheses allows us to confirm the hypotheses we laid out in Chapter 3. The

results confirm that both CPI and per capita government health expenditure have

an inverse relationship with infant mortality rate. The lesser corrupt countries have

lower mortality rates, whereas the more corrupt countries have a higher mortality

rate. Similarly, an increase in per capita government health expenditure decreases

infant mortalitiy rates. Lastly, the positive value for β3 says that the marginal effect

of higher health expenditure is increased as corruption decreases.

Model 1 captured the marginal effect of a change in health expenditure with

respect to a change in corruption, but now Model 2 will be implemented to understand

the changing marginal effects of health expenditure on infant mortality. Table 5.4

presents the regression results for Model 2. The regression results of Model 2 return

all coefficents as significant. The least significant coefficient of them all is β̂5, which

significant only at the 10 percent level.

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2. REGRESSION ANALYSIS 30

Table 5.4. Regression Results for Model 2

Variable Coefficient Std. Error t-Statistic ρ

C 116.776 19.215 6.077 0.0000CPI -32.921 7.416 -4.440 0.0000

HEXP -0.033 0.0152 -2.171 0.0315CPI×HEXP 0.010 0.005 2.089 0.0385

CPI2 2.251 0.610 3.690 0.0003CPI2×HEXP -0.00073 0.00038 -1.911 0.0580

R2 0.372Adjusted R2 0.351No. of Obs. 151

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CHAPTER 6

Conclusion

The aim of this paper was two-fold: (a) to confirm the hypothesis decreasing

corruption and increasing health expenditure have an inverse relationship with infant

mortality rates, and (b) to understand how corruption affects the efficiency of health

expenditure spending. We have found statistically significant results that allow us to

tackle both of our goals.

In the pursuit of observing a decrease in mortality rates with a decrease in corrup-

tion and an increase in health expenditure, the regression results of Model 2 in Table

5.3 answer our questions. We find that a one unit change in CPI, a one unit decrease

in corruption, decreases mortality rates by 5.81 units. Moreover, we find that a unit

change in per capita government health expenditure lowers infant mortality rate by

0.016 units. As such, these results confirm our hypothesis that corruption and health

expenditure are negatively correlated with infant mortality rates.

In the spirit of understanding how corruption affects government efficiency, we

ran two regressions, results of which appear in Tables 5.3 and 5.4. Looking at the

regression results in Table 5.3 for Model 1, we see that β̂3 has a positive value of 0.002.

This suggests that the marginal effect of an extra unit of per capita health expenditure

on infant mortality rates is increased by 0.002 units as corruption decreases by one

unit. This result implies that at higher levels of corruption health expenditure is quite

inefficient because it takes a decrease in corruption to increase the marginal effects of

increasing health expenditure. Such a result may be found because in more corrupt

countries, dishonesty in the form of bribery and embezzlement distort government

spending. As discussed in Chapters 1 and 2, moneys are diverted from their intended

31

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6. CONCLUSION 32

destination (i.e. extra hospital beds, better medical equipment and better medical

infrastructure) to the pockets of corrupt bureaucrats.

The regression results of Model 2, on the other hand, tell a similar but a more

in-depth story. Right away, we still see the same negative correlation between a

decrease in corruption and an increase in health expenditure with infant mortality

rates. Moreover, the positive coefficient of CPI × HEXP still exists, implying the

same results as discussed in the above paragraph. From the new terms we added,

CPI2 seems have a positive coefficient. This is interesting because CPI2 turns our

model into a quadratic function, and combined with the negative coefficient of CPI,

it suggests that the beneficial effects of decreasing corruption are marginally dimin-

ishing. In other words, decreasing corruption is a very strong factor in highly corrupt

countries in the quest for decreasing infant mortality rates, but decreasing corruption

beyond a certain point (in much lesser corrupt countries) has very little effect if none

at all.

The last term, however, in Model 2 gives us a true insight into the efficiency of

health care expenditure with respect to corruption. To gain a complete understanding

of what β̂5 is telling us, let us take its derivative with respect to HEXP :

(6.1)d( ˆMOR)

d(HEXP )= −0.033 + 0.01(CPI) − 0.00073(CPI)2.

The plot of this derivative is given in Figure 6.1 with values of CPI ranging from

our minimum and highest CPI value. The plot offers a great visual look at what

is happening in our regression. The marginal effect of increasing per capita health

expenditure on infant mortality rates is initially increasing with diminishing marginal

returns as corruption decreases. However, the marginal effect eventually levels off,

and then, the marginal effect actually begins to decrease – that is, we begin to see

negative effects on infant mortality rate by increasing health expenditure by one unit.

Before we begin to explain this phenomenon, let us calculate the exact value where

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6. CONCLUSION 33

4 6 8 10CPI

-0.015

-0.010

-0.005

dMOR

dHEXP

Figure 6.1. Plot of d(MOR)/d(HEXP ) versus CPI.

the marginal returns are zero and begin declining. We do this by taking another

derivative – the derivative of Equation (6.1) with respect to CPI – setting it equal

to zero and solving for CPI:

d

dCPI(−0.033 + 0.01CPI − 0.00073CPI2) = 0.01 − 0.00146CPI(6.2)

CPI = 6.85.(6.3)

Our calculation tells us that the increasing diminishing marginal returns level off at

a CPI value of about 6.85. After this value, the marginal effects become negative.

An explanation of this leveling off and negative change in marginal effects goes

hand-in-hand with the explanation of why β̂4 has a positive value. The positive value

of β̂4 suggests that there are initially increasing diminishing returns, but the negative

value of β̂5 hints that at some value of CPI, these marginal returns become negative.

A statistical explanation for that could be cleaning up corruption only increasing

health care expenditure when the countries are considerably corrupt - it is only in

highly corrupt countries where we see a strong negative correlation between the CPI

and HEXP in Figures 5.1 and 5.2. The data begin to lose correlation at higher values

of the CPI, and such we see erratic results. After all, in the regression results of

Model 2 in Table 5.4, it was β̂5 that was the least statistically significant.

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6. CONCLUSION 34

A theoretical, more real-world approach to an answer would be that corruption

truly only poses a problem and affects health expenditure in the more corrupt coun-

tries. As corruption decreases, the correlation between corruption and health expen-

diture decreases because there are many other factors that affect the composition of

government expenditure; we rarely see bribery or embezzlement as being the prime

cause of a distortion in government expenditure in more advanced economies. While

the results showing negative marginal returns after a CPI value of 6.85 are statisti-

cally significant, practically they may not be very trust-worthy. There are most likely

many other factors at work that may be causing mortality rates to decline. Moreover,

in developed nations, mortality rates are so low that a small change for the negative

does not constitute as problematic. As such, the leveling off and negative marginal

returns are observed perhaps due to lack of statistical strength at higher values of

CPI or the lack of explanatory power corruption has in explaining infant mortality

rates via health expenditure in lesser corrupt countries.

In conclusion, our study has been very worthwhile. We have confirmed with

statistical significance that both a decrease in corruption and an increase in health

expenditure are correlated negatively with infant mortality rates. Moreover, we have

gained a deeper understanding of government expenditure efficiency, particularly in

health care. We see that corruption does, in fact, distort health expenditure, thereby

lowering the efficiency of public spending on health care. However, such an effect

can only be explained in countries where corruption is rampant and not in countries

where corruption is poorly correlated with health expenditure and infant mortality

rates.

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Appendix A

Table 6.1. Excerpt of Full Data Set Used (using 2008 updated figures)

Country Year CPI Mortality rate, infant Govt. Health Expenditure(per 1,000 live births) (per capita)

Denmark 2005 9.5 4.10 2650Denmark 2004 9.5 4.40 2084Denmark 2003 9.5 4.40 2236Denmark 2002 9.5 4.40 2366Denmark 2001 9.5 4.90 2531

New Zealand 2005 9.6 5.34 1720New Zealand 2004 9.5 5.30 1415New Zealand 2003 9.5 5.20 1527New Zealand 2002 9.5 5.50 1547New Zealand 2001 9.4 5.35 1596

......

......

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35

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Page 44: The Effects of Corruption on the Efficiency of Government Health Expenditure

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