the rent-seeking activity in public sector: from predictability perspective

22 Pages·2007·0.54 MB·English

Applied Econometrics and International Development Vol.7-1 (2007)

PREDICTING CORRUPT PRACTICES IN THE PUBLIC SECTOR FOR 23
OECD COUNTRIES
KOYUNCU, Cuneyt*
BHATTACHARYYA, Gautam
Abstract:
This paper adopts the MIMIC approach to estimate the relative development of
corruption in 23 OECD countries for the period 1975-1993. The results indicate that
Japan, Portugal, Spain, Greece, South Korea, and Turkey face with relatively high level
of corruption while Netherlands, Norway, Ireland, Denmark, and Belgium experience
relatively low level of corruption over time. For the other countries, corruption falls in
either moderate category or fluctuates between two different categories. By using panel
regression, we also found a statistically significant negative impact of the estimated
corruption index on the investment level but not on the economic growth.
JEL Classification Codes: C33, C82.
Keywords: Corruption, bribery, government, growth, investment.

1. Introduction

Corruption described as misuse of position and power held by public officials for
personal gains is not the issue of only modern time but ancient time as well. It goes back
to almost 2500 years ago when corruption was first mentioned in an ancient Indian
manuscript (i.e. The Arthashastra written by Kautilya), maybe one of the oldest treatises
on economics. The manuscript draws attention to the forty different ways of corrupt
behaviors conducted by the government officials and urges the administration to impede
corruption. Therefore, it has been in human being’s daily economic and politic life in the
past, it exists now, and it seems to exist in the future at some extent.
The diversity of countries experiencing corrupt scandals in recent years underline the
fact that it is also the matter of developed countries as well as less-developed and
developing countries. However, the extent and frequency of corrupt act in an economy
may differ from country to country based upon several factors such as; country’s
economic well-being, degree of competitiveness in the market, degree of government
involvement in the economy, and quality and efficiency of public institutions. While
corruption is a fact of all economies to varying degrees, it is particularly main concern to
less-developed and developing economies since it may lower private investments and in
turn economic growth and development, induce misallocation of productive resources,
discourage foreign investments, reduce the resources available for infrastructure, public
services and anti-poverty programs, and undermine political stability and democracy.1
Briefly, corrupt activities may lead to harmful results in an economy, although they, as
contended in Leff (1964), Huntington (1968), Lui (1985), and Lien (1990), can be
efficiency improving by circumventing cumbersome regulations and facilitating business
transactions.

*Cuneyt Koyuncu: Dumlupinar University, The Faculty of Economics and Administrative
Sciences, Department of Economics, Kutahya, Turkey, e-mail: [email protected] and
Gautam Bhattacharyya: The Department of Economics, University of Kansas, Lawrence, Kansas
66045, USA.
Applied Econometrics and International Development Vol.7-1 (2007)

Since the issue may have such serious and undesirable impacts on an economy, it has
been one of the most controversial topics in the literature especially in the last decade.
Nonetheless, as the secrecy and illegitimacy of corruption make it almost impossible to
directly measure its level, most of the empirical studies widely utilize various survey-
based corruption indexes.2 On the other hand, these indexes are criticized from several
aspects. First, since they are generated from survey results, they may contain subjective
judgments of survey participants. Also, in some indexes, there are large variations in the
incidence of corruption among countries that have quite same development levels. For
instance, the Transparency International corruption index ranks Chile, the Czech
Republic, Malaysia, Poland, and South Africa as less corrupted countries than some
industrialized countries such as Greece and Italy.
In this case remaining question is if there is any methodology to generate a relatively
more objective and consistent corruption index. Therefore, in this paper we will attempt
to generate a corruption index of 23 OECD countries by utilizing MIMIC (multiple
indicators, multiple causes) approach for the period 1975-1993.3a,b To our best
knowledge, this will be the first study constructing a time series index of corruption
across countries by using observable time series data.
The results indicate that, among 23 OECD countries over time, Japan, Portugal, Spain,
Greece, South Korea, and Turkey face with relatively high level of corruption while
Netherlands, Norway, Ireland, Denmark, and Belgium experience relatively low level of
corruption. The behavior of the corrupt activities for the other countries falls in either
moderate category or its behavior is changing between low and moderate levels or
between moderate and high levels for the period 1975-93. In general, almost over the
entire period, Netherlands, Norway, and Ireland are the countries experiencing the lowest
level of corruption whereas corruption attains its highest level for particularly Spain,
Greece, South Korea, and Turkey in the sample. We also conducted some robustness
checks for the estimated corruption index. We found a strong correlation (i.e. 0.685 and
significant at 1% significance level) between the estimated index and ICRG index. We
also identified a statistically significant negative impact of the estimated corruption index
on the investment in a panel regression but not on the economic growth.
The paper proceeds as follows: section 2 explains the model; section 3 discusses
possible determinants and indicators of the corruption; we obtain and discuss the
estimation results in section 4; section 5 implements some robustness checks for the
results; and finally the last section concludes.

2. The Model
There are several approaches in the literature used to generate data on unobservable (i.e.
latent) variable, which is corruption in our case. One of these approaches is MIMIC
(multiple indicators, multiple causes) model. A MIMIC model simultaneously considers
observable data on potential causal and indicator variables of an unobservable variable to
predict the values for that latent variable. The unknown coefficients are estimated in a set
of structural equations from which the latent variable cannot be measured directly. The
output of this model is a time series index for the latent variable. These time series
indices can be used to construct not only ordinal time series of corruption but also
cardinal ones as long as at least an exogenous level estimation of corruption for a sample
point is available to calibrate it. MIMIC model is mathematically formulated as follows:
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Koyuncu,C. and Bhattacharyya,G. Predicting Corrupt Practices of Public Sector in 23 Countries
y = λη + ε (1)
η = γ′x + ς. (2)
where η is the scalar latent variable (i.e., corruption in our case); y′ is a (1×p) vector of
indicators of η; x′ is a (1×q) vector of determinants (or causes) of η; λ and γ are (p×1)
and (q×1) vectors of parameters for the structural equations in equation (1) and equation
(2) respectively; and ε and ς are (p×1) vector and scalar random errors respectively. ς
and ε are assumed to be normally distributed and mutually uncorrelated, with Var(ς) = ψ
and Var-Cov(ε) = Θ.
A reduced-form equation is obtained by substituting the second into the first equation,
y = Πx + z, (3)
where Π = λγ′, z = λς + ε, and Var-Cov(z) = λψλ′ + Θ.
The regression matrix in equation (3) has rank one. In this setting, we can estimate only
relative magnitudes of the structural parameters. Hence, in order to remove the
indeterminacy in structural parameters, it is necessary to adopt a normalization. As is
usually done, we constrain one element of λ to a pre-assigned value. We use the LISREL
(linear interdependent structural relationship) software package to obtain maximum
likelihood estimates (MLE) of the elements of Π, and thus of λ and γ.
Once we have an estimate of the γ vector and set the error term ς to its mean value zero,
equation (2) enables us to predict ordinal values for η (i.e., corruption) at each sample
point. As mentioned before, if we have an exogenously determined value for η at one
sample point, by using this predetermined value, we can calibrate the within sample
ordinal predictions for η in order to get their level estimates.

3.A. Determinants of the Corruption
Currently, there exists rather extensive both theoretical and empirical literature on the
potential determinants of the corruption in public sector.4 We identify thirteen types of
potential determinants for corruption as follows:
(1) Openness (EXIMP): Trade restrictions may be one of the prime sources of
government-induced corruption. For example, if importing a certain good is subject to
quota, the necessary import licenses become very valuable and importing agents will
eagerly bribe officials who control their issuance. Less exposure to foreign trade can
reduce the competition in domestic market and this will in turn create some rents enjoyed
by domestic rent-seekers. Studies indicate that countries tend to have less corruption
when trade activities in these countries are relatively free of government inferences and
restrictions that officials can abuse.5 The ratio of sum of exports and imports to GDP is
utilized as a proxy for trade restrictions. Since more trade restriction (i.e. less openness)
potentially means more illicit activity, we hypothesize a negative sign for this proxy
variable.
(2) Discretionary Power (EMP): Existence of excessive regulations and authorizations
may nurture corrupt activities by letting government officials to use their discretionary
power. In many countries, especially in less developed and developing countries, the role
of the state is often carried out through the enforcement of many rules and regulations.
For instance, in these countries, opening a shop and keeping it open, borrowing money,
investing, owning a car, building a house, engaging in foreign trade, obtaining foreign
exchange, getting passport and others may require specific documents and authorizations.
The existence of these regulations and authorizations gives, in some extent, monopoly
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Applied Econometrics and International Development Vol.7-1 (2007)

power to the officials who must inspect or authorize the activities. The officials may
refuse the authorization or issuance of required documents or may delay doing so for
months. Thus, officials can use their discretionary power to extract bribes from those
who need the authorizations or permits. The presence of these regulations requires
frequent contacts between individuals and government officials. We use the ratio of
government employment to total employment as a proxy for regulation and authorization
burden (or more concisely bureaucratic burden) on individuals. Any increase in the
number of public employees may make it more likely for an individual to encounter with
more bureaucratic burden. In this case, individuals may be more willing to bribe officials
to avoid contacting them. The above discussion is particularly true under the assumption
that public officials are endowed with arbitrarily used discretionary power without
monitoring or detection due to absence of well-established institutions, which is the case
for less-developed and developing countries. In that sense, we can consider the number
of public employees as the number of regulators in an economy. Nevertheless, this may
not be the case for industrialized countries for which institutions are well established and
detection of any illicit act is supported with tight scrutiny. Therefore, we may have a
positive or negative sign for this variable depending upon the characteristics of countries
in the sample.
(3) Bureaucratic Inefficiency (BIR): Bureaucratic inefficiency is computed as follows,
GN
BIR = it ,
it
G
it
where GN represents the size of the bureaucracy for the ith country at time t and it is
it
given by the share of government employment in total employment while G represents
it
the size of government for the ith country at time t and it is measured by the share of
government consumption in gross domestic product. The reasoning behind the BIR
measure is simple. If it takes 10% of employment to sustain a government size of 10%
then the government workers are as efficient as their private counterparts. However, if it
takes 20% of employment to sustain the same government size of 10% then we will say
that government workers are more inefficient. As a result, BIR values higher than one
represent more inefficient bureaucracy whereas BIR values lower than one represent
more efficient bureaucracy. If the bureaucracy gets more inefficient then it means that
transaction in government sector goes somewhat slow which may enable government
officials to extort some money from those who want to expedite their transactions. Thus,
we hypothesize a positive sign for BIR measure. Meanwhile one of the reasons for the
sluggishly progressing transactions in government sector might be the low wages, which
is often contended in the literature as one of the causes of corruption, in that sector.6
Government workers may deliberately slow down their job efforts in expectation of
collecting bribes as a way of compensation for their relatively low wages.
(4) Repudiation Risk of Government Contract (CONTREP): This measure assess the risk
of modification in a government contract in terms of repudiation, postponement, or
scaling down due to budget cutbacks, indigenization pressure, and a change in
government or in its economic and social priorities. It is scored from zero to ten and
lower score stands for higher risk. Given the uncertainty if the government will modify
the contract in above terms, a businessman will try to secure the deal by bribing the
officials to avoid any possible unpleasant adjustment on the contract. Of course, in this
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Koyuncu,C. and Bhattacharyya,G. Predicting Corrupt Practices of Public Sector in 23 Countries
case, higher risk (i.e. lower score) is correlated with higher illicit payments. Hence, we
expect to have a negative sign for CONTREP variable.
(5) Expropriation Risk (EXPRO): This measure indicates the risk of outright confiscation
or forced nationalization. It is also scored between zero and ten with lower score
indicating higher risk. In the absence of property right, businessmen may have no choice
other than to bribe in order to protect their investments and properties against any
possible seizure or nationalization. In deed, we expect to see more corrupt actions as long
as such potential risks exist. So, a negative sign is hypothesized for EXPRO variable.
(6) Rule of Law (RULELAW): This measure reflects the degree to which the people of a
country recognize the established institutions as a way to make and implement laws and
adjudicate disputes. It is scored from zero to six. Higher scores imply sound political
institutions, a strong court system, and provisions for an orderly succession of power
whereas lower scores mean a tradition of depending on physical force or illegal means to
settle claims. As It is clear from the implication of the RULELAW variable, the
existence of strong and unbiased court system will help to implement laws against those
who involved in illicit activities and in turn deter further corrupt practices. Also,
presence of strong and unbiased judiciary system assures the protection of property rights.
On the other hand, weak and biased justice system will urge people to pursue illegal
means to settle their claims and this will, of course, foster corrupt activities. Therefore,
we expect to have a negative correlation between corruption variable and RULELAW
variable.
(7) Political Rights (POLITIC): This measures the degree of political freedom in a
country. It is scaled from one to seven and one represents the highest political freedom
while seven stands for the lowest political freedom. Allowing the civil society to engage
more freely in political arena may lead to closer monitoring on civil servants and
politicians. Thus, people may vote corrupt politicians out of the office and bring honest
ones. We expect to see a positive sign for POLITIC variable.
(8) Civil Liberties (CIVIL): It indicates the degree of civil freedom in a country. It is
scored from one to seven and one represents the highest civil liberty while seven stands
for the lowest civil liberty. Civil freedom and political freedom are complements and two
inevitable main characteristics of the democracy. The right to freedom of opinion and
expression is indispensable condition for free media. By allowing people to express their
ideas freely and transmit them to others by the media will raise the public awareness on
wrong doings in public sector and put pressure on corrupt officials. Civil liberties are
also important in a society for it will allow people to establish non-governmental
organizations (NGOs) to monitor closely each step taken by corrupt officials and
politicians. Since lower score indicates higher civil freedom, a positive association
between CIVIL variable and corruption is anticipated.
(9) Religion (PROTES): There are some studies that find correlation between corruption
and religion.7 These studies consider the Catholicism, Eastern Orthodoxy and Islam as
more hierarchical religions, which rarely teach their followers to challenge the office-
holders relative to theological cultures shaped by more egalitarian or individualistic
religions, such as Protestantism. Therefore, hierarchical religions can be detrimental to
civic engagement, which is one of the important factors helping monitor and curb
corruption. The variable is measured as percentage of population professing protestant
faith in a country. In this case, we hypothesize a negative sign for the PROTES variable.
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(10) Ethno-linguistic Fractionalization (ETHNO): Ethno-linguistic fractionalization
index (ELF) is measured as follows,
2
I ⎛n ⎞
ELF = 1 - ∑⎜ i ⎟ , I = 1, . . . , I,
⎝ N ⎠
i=1
where n is the number of people in the ith ethnic group while N stands for total
i
population. Also, I is the number of ethno-linguistic groups in a country. ELF gives the
likelihood that two randomly picked persons from a country will not be coming from the
same ethno-linguistic group as of 1960. Hence, the higher ELF scores indicate a more
ethno-linguistic fractionalization in a country. Shleifer and Vishny (1993) contend that
ethnically more heterogeneous countries are more likely to experience more corruption
since ethno-linguistic division will lead to a less organized type of corruption. They
believe that less organized type of corruption will be more deleterious than well-
organized (or collusive) type of corruption, which is experienced in homogenous
societies. Thus, we expect to see more corruption in more ethnically divided countries;
therefore, a positive sign is hypothesized for ETHNO variable.
(11) Democracy (DEMOC): This is a dummy variable and it takes 1 if the country
experiences uninterrupted democracy in all 46 years between 1950 and 1995 otherwise it
takes 0. A country is considered as democratic if: (i) the chief executive is elected, (ii)
the legislature (at least its lower house) is elected, (iii) more than one party runs for the
elections, and (iv) during the last three elections of a chief executive there has been at
least one turnover of power between parties. We will expect to have a higher likelihood
for revealing corrupt officials in more democratic and open societies because in a
democratic system through the free elections people may take down the corrupt officials
or competitors of the office will have an incentive to discover and publicly reveal the
incumbent’s misuses of the office.8 Because more democratic and open societies might
experience lower level of corruption, we expect to have a negative sign for the DEMOC
variable.
(12) Federalism (FEDE): This is also a dummy variable. It takes one if administrative
structure of a country is federal, otherwise zero. A state is defined as federal state under
which: (i) at least two levels of government rule the same land and people, (ii) each level
has at least one area of action in which it is autonomous, and (iii) autonomy of each
government is, at some extent, guaranteed. There are two different arguments on the
causality of federal system to illicit activities in the literature.9 Some scholars argue that
centralized system is an aggravating factor for corruption and that a federal state structure
leads to more honest and efficient government by providing for competition between sub-
jurisdictions or between levels of governments in the provision of public goods and
services, which may be the source of kickbacks. By contrast some researchers state that
corruption may be more widespread at the local level than central level since local
authorities usually have more discretionary power than central ones and local officials are
likely to face more pressing demands from local interest groups in matters such as
taxation. The linkage between corruption and federalism is not obvious; hence, FEDE
variable can take any sign.
(13) Political Stability (LEADER): This variable gives the average number of government
leaders per year in period 1980-1993 and is computed by dividing number of government
leaders in that period by the length of period. Geddes (1997) argues that when a new
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Koyuncu,C. and Bhattacharyya,G. Predicting Corrupt Practices of Public Sector in 23 Countries
party comes to power it will have greater incentives to reform the illicit practices of
former ruler. However, we might argue that frequent turnovers in ruling power (or earlier
changes in ruling power than it is scheduled) may be the indication of how rampant
corruption is in a society in the sense that people can be terminating the ruling duration of
the corrupted politicians earlier than its regular schedule by voting them out.10 Being not
a good example as a top official perhaps encourage and exacerbate corrupt practices at
lower level officials. As a result of above discussion, we can say that more political
instability can lower or intensify corruption. Briefly, the relation between political
stability and corruption is dubious; therefore, LEADER variable may take any sign.

3.B. Indicators of the Corruption
After identifying the potential determinants of corruption as above, we distinguish two
types of possible indicators for corruption as follows:
(1) Tax revenue (TAXREV, INCTAX): When corruption contaminates the tax and customs
officials, we may observe reductions in government revenue since some of the payments
made by taxpayers can go directly to the pockets of tax inspectors or custom officials as a
result of collusive behavior between taxpayer and collector rather than into the
government treasury.11 Corrupt practices exercised by tax and customs officials in return
for kickbacks may include: (i) providing tax exempt certificates to persons who are not
qualified for them, (ii) deleting or removing a taxpayers record from the official records,
(iii) creation of multiple false taxpayer identifications to expedite tax fraud, (iv) writing
off tax debt without any justifications, (v) letting goods pass across a national border
without collecting any duty payments, (vi) understating tax or duty liability through
acceptance of documents undervaluating or misclassifying goods in the processing of a
custom entry, (vii) facilitating false tax and duty refund claims by providing certification
of export of goods that have been consumed in the domestic market or that have not been
produced at all, and so on. We use two variables, namely TAXREV and INCTAX, to
capture relation between corruption and tax revenue. TAXREV variable is given by the
share of total tax revenues in total gross domestic product while INCTAX variable is the
ratio of total income tax revenue to total tax revenue. We expect to see a negative
correlation between corruption and TAXREV and INCTAX variables.
(2) Poverty (LGDPPC): Corruption is likely to increase poverty because it deteriorates
the income earning potential and economic well-being of the poor trough several
channels.
First, corrupt administrations tend to divert funds from the projects which will indirectly
improve and enhance the quality and economic well-being of human capital to the big
projects (so-called ‘white elephant’ projects) which are mainly chosen because they lend
themselves to extract more bribes not because they provide direct or indirect benefits to
human capital. For instance, empirical and anecdotal discussions indicate that
government authorities tend to decrease expenditure for education and health since public
expenditure for education and health does not lend themselves to corrupt practices. On
the other hand, they inclined to invest in big and technically sophisticated projects such as
military projects that may not have any contribution to economic well-being of human
capital.12
Second, reduction in tax revenues as a result of illicit practices in customs and tax
administrations will reduce the funds allocated for fighting poverty. As a result of above
arguments, we can say that poverty becomes widespread in countries with rampant
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corruption. We utilize natural logarithm of per capita gross domestic product (LGDPPC)
as a proxy variable for poverty. LGDPPC variable also partially capture the effect of
corruption on growth since higher per capita gross domestic product is positively
correlated with growth. Therefore, the sign that LGDPPC variable will take will indicate
the impact of corruption on growth as well as on poverty. A negative sign is
hypothesized for LGDPPC variable.

4. Estimation Results
We explain sources for the data in the Appendix. Moreover, all of the variables used in
the estimation are standard normalized with mean 0 and variance 1. Also, for the reason
mentioned earlier, before estimating models in equation (1) and (2), one of the parameters
of indicators are normalized to –1 or 1 depending on our prior expectation for this
particular indicator.The results of simultaneously estimating the models in the first and
second equation are shown in Table1 of the Appendix. In the table, coefficients are given
in boldface; standard deviations are in parentheses; and t-statistics are in italics. Table 1
also reports goodness of fit measures. Small values of chi-square statistic indicate a good
fit of the model. Also, small values of Akaike’s Information Criterion (AIC), Expected
Cross-Validation Index (ECVI), and Root Mean Square Residual (RMR) favor the model,
while large values of Goodness of Fit Index (GFI), Adjusted Goodness of Fit Index
(AGFI), and Parsimony Goodness of Fit Index (PGFI) reflect a good fit for the model.13

The model in the Table 1 has three indicator and thirteen causal variables. In this model,
we normalize the parameter between corruption and tax revenue (TAVREV) to negative
one.14 At the 5% significance level, among the coefficients of potential determinants of
corruption, only coefficients for EXIMP, COTREP, EXPRO, POLITIC, CIVIL,
DEMOC, and FEDE are statistically significant and take the expected signs. Alike the
results in Ades and Di Tella (1997,1999), we found that the degree of openness to foreign
trade negatively affects the level of corruption in a country. This finding implies that
economic competition as measured by the degree of a country’s openness to foreign trade
lowers corruption by reducing the rents that can possibly be exploited by the government
officials. We could not get statistically significant coefficient estimation for discretionary
power variable that takes a negative sign. We also can’t identify any statistically
significant impact of bureaucratic inefficiency on corruption even though the coefficient
of the related variable takes the expected positive sign. The results, as expected, conclude
a statistically significant negative contribution of the variable for repudiation risk of
government contracts on corrupt activities. This means that a higher risk for a
modification in a government contract may lead to higher bribes because businessmen
might want to protect themselves against any possible changes in the contract by bribing
the officials. We may also expect to observe higher corrupt practices in these countries in
which the property right is not protected by laws. In our model, a significant negative
relation between the expropriation risk variable and corruption supports this expectation.
This finding indicates that countries with high risk of forced nationalization and
government seizure may experience more illicit activities since businessmen and
entrepreneurs may tend to evade such possibilities by giving kickbacks to government
authorities. The coefficient of rule of law variable takes a negative sign, as expected, and
it is statistically significant at 10% level. It can be interpreted as countries in which
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Koyuncu,C. and Bhattacharyya,G. Predicting Corrupt Practices of Public Sector in 23 Countries
people depend more on strong and unbiased judiciary system rather than illegal means to
settle their claims and in which the laws are fully implemented by strong court system
against illicit practices may have lower level corruption. According to our results, two
factors, namely politic and civil liberties, which are indicators of democratic and open
society, play an important role in the reduction of corrupt activities in an economy. Both
coefficients of political and civil freedom variables take the expected sign and imply a
statistically significant positive correlation between these variables and corruption. In
other words, we expect to observe a higher risk of revealing corrupt officers in a society
endowed with more civil and political rights and freedom since in such a society people
will be able to monitor and publicize any wrong doing in government administrations
through free media and non-governmental organizations (NGO’s). Unlike the finding of
Treisman (2000), we found a positive association between the percentage of Protestant
population in a society and corruption but it is not a statistically significant relation. As it
is stated in the article of Shleifer and Vishny (1993) and shown in the empirical finding of
Mauro (1995), our finding also supports the hypothesis contending that countries
populated by various ethnic divisions are more likely to find a less organized (and
therefore potentially more harmful) type of corruption rather than a more organized (and
therefore potentially less deleterious) type of corruption. Ethnic fractionalization variable
is statistically significant at 10% level and it positively contributes to corruption. Longer
exposure to democracy may negatively contribute to illicit practices in an economy since
democratic systems allow greater civic engagement in administrative arena through free
elections and this in turn gives opportunity to people to vote corrupt politicians out of the
office and replace them with honest ones. Our finding on the coefficient of democracy
validates the previous argument and it has statistically significant negative impact on the
level of corruption. As contended in Wilson (1989), Manor (1990), Prud’homme (1995),
and Treisman (2000), we found a positive statistically significant association between
federalism and corruption. This can be so because local officials usually hold more
discretionary power relative to their central counterparts. The coefficient of political
stability variable is statistically significant at 10% level and takes a positive sign.
Interpretation of this positive sign is that politically instable countries may experience
more corrupt practices because absence of a good and honest leader example may
encourage illicit activities at the low rank officials. The sign of the coefficient for income
tax revenue variable supports the hypothesis that corruption can reduce income tax
revenues but it not statistically significant to endorse that hypothesis. On the other hand,
we found a strong statistically significant negative relation between corruption and per
capita GDP, which is proxy variable for the poverty. What this tells us is that corruption
deteriorates poverty in a country by reducing growth, misallocating funds and resources,
reducing spending for education and health, and so on.
Corruption index. We use the coefficients in equation (2), which are statistically
significant at 5% significance level, to generate our corruption index of 23 OECD
countries for the period 1975-1993. After setting the error term to its mean value zero in
equation (2), we generated corruption index by using the statistically significant
coefficient estimates of causal variables in the model in Table 1. This yields the
equation:
Corruption Index (CI) = - 0.09 × EXIMP - 0.08 × CONTREP – 0.09 × EXPRO + 0.18 ×
POLITIC + 0.08 × CIVIL – 0.20 × DEMOC + 0.10 × FEDE.
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Table 2.a presents the value of the Index and the rank from the lowest level of corruption
to the highest one, for years 1975 and 1993. The resulting index values for corruption in
the 23 OECD countries during the period 1975-1992 are reported in Table 2 of the
Appendix. The numbers in the table are in terms of standardized normal values.
Countries in Table 2 are ranked from the lowest level of corruption to the highest one.

Table 2a. Index of corruption and Ranking of lowest corruption
Country 1975 1993
Index Rank Index Rank
Australia AUS 0.022 16 -0.325 15
Austria AUT -0.216 11 -0.532 12
Belgium BEL -0.467 8 -0.846 8
Canada CAN -0.222 10 -0.348 14
Denmark DNK -0.616 4 -0.877 4
Finland FIN 0.004 15 -0.795 9
France FRA -0.155 13 -0.594 10
Great Britain GBR -0.626 3 -0.546 11
Greece GRC 1.632 19 0.845 22
Iceland ISL -0.531 7 -0.870 5
Ireland IRL -0.559 5 -1.073 2
Italy ITA 0.059 17 -0.225 16
Japan JPN 0.923 18 0.761 20
Korea KOR 2.843 22 0.601 19
Netherlands NLD -0.943 1 -1.108 1
New Zealand NZL -0.546 6 -0.849 7
Norway NOR -0.729 2 -0.913 3
Portugal PRT 2.536 21 0.091 18
Spain ESP 3.340 23 0.764 21
Sweden SWE -0.158 12 -0.865 6
Switzerland CHE -0.380 9 -0.497 13
Turkey TUR 2.081 20 2.088 23
USA USA -0.066 14 -0.110 17
Note: See data for 1985-1992 in table 2b of the Appendix

For the sake of easing interpretation of the results, let us categorize corrupt activities in
three groups, respectively low, moderate, and high level of corruption. We can also
define that: highly corrupted countries are those of which corruption ranks fall into the
range of 17-23; moderately corrupted countries are those of which corruption ranks fall
into the range of 9-16; and lowly corrupted countries are those of which corruption ranks
fall into the range of 1-8. Among 23 OECD countries, Japan, Greece, Turkey, South
Korea, Spain, and Portugal fall into highly corrupted countries group over the estimation
period 1975-93. Except a couple of years, Turkey and South Korea hold the last two
seats of the corruption rank, which are the seats for the highest level of corruption, in the
sample. In these six countries, corruption slightly has an upward trend for Greece,
Turkey, and Japan; a downward trend for Spain and Portugal; and a mixed pattern for
South Korea.
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