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European Journal of Political Economy
Vol. 19 (2003) 605 – 620
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The impact of economic freedom on corruption:
different patterns for rich and poor countries
P. Graeff a,*, G. Mehlkop b
b
a
Department of Sociology, University of Bonn, Adenauer Allee 98a, 53113 Bonn, Germany
Department of Macrosociology, Technical University of Dresden, Mommsenstr. 13, 01069 Dresden, Germany
Received 15 February 2002; received in revised form 28 July 2002; accepted 1 August 2002
Abstract
This paper investigates the impact of various components of economic freedom on corruption.
Some aspects of economic freedom appear to deter corruption while others do not. We identify a
stable pattern of aspects of economic freedom influencing corruption that differs depending on
whether countries are rich or poor. This implies that there is a strong relation between economic
freedom and corruption. This relation depends on a country’s level of development. Contrary to
expectations, we find that some types of regulation reduce corruption.
D 2003 Elsevier B.V. All rights reserved.
JEL classification: H10; H11; H50; K20; O5
Keywords: Corruption; Economic freedom; Economic development; Government
1. Introduction
Few papers examine the relation between economic freedom and corruption. The
empirical results of these studies are consistently the same: the more freedom, the lower
the level of corruption, implying that economic freedom is a deterrent to corruption
(Chafuen and Guzmán, 2000; Paldam, 2002).
Although economic freedom and corruption are phenomena for which the government
of a country is an important factor,1 from a theoretical point of view one would not
* Corresponding author. Tel.: +49-0228-73-84-26; fax: +49-0228-73-84-30.
E-mail address: [email protected] (P. Graeff).
1
Since data for the empirical analysis are only available for corruption amongst public officials, we narrow
the scope of the paper inasmuch as we shall refer to corruption concerning public officials only. This may hedge
the results of the analysis since corruption occurs outside the government too.
0176-2680/03/$ - see front matter D 2003 Elsevier B.V. All rights reserved.
doi:10.1016/S0176-2680(03)00015-6
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P. Graeff, G. Mehlkop / European Journal of Political Economy 19 (2003) 605–620
expect an empirical link between economic freedom and corruption to arise automatically. On the one hand, a government can impose restrictions on free trade via taxes or
licences, which creates the opportunity for civil servants to take bribes or to engage in
similar activities. To avoid those restrictions, some people will be willing to pay a bribe
to supply civil servants with what they demand. Corruption can also develop when
obstructions to economic freedom are imposed (Rose-Ackermann, 1999). On the other
hand, a free economy can also increase the pressure to use illegal methods in order to be
a step ahead of competitors. This can become more likely if business involves trading
with countries that are more prone to use corrupt means of trading. Furthermore, there are
different types of corruption depending on the norms and traditions in a society, the level
of development and many other factors. It is questionable as to whether economic
freedom always influences corruption in the same way, more or less independently of the
type of society.
Both corruption and economic freedom are multidimensional phenomena (Ades and
Di Tella, 1997). According to the empirical result that economic freedom reduces
corruption, it is important to examine which aspects of economic freedom influence
corruption. While corruption is usually measured as a certain type of illegal behaviour
(e.g. of public servants), the most frequently used indices of economic freedom (see
Gwartney et al., 2000; Messick, 1996; O’Driscoll et al., 2000) comprise various
components. The Fraser Index 2000, for example, consists of 23 components that are
classified into seven areas covering different aspects of economic freedom. To test the
presumed effect of economic freedom on corruption, one has to take a closer look at the
performance of these aspects.
This paper investigates the impact of economic freedom on corruption (on the basis of
the Fraser Index). To answer the question of whether economic freedom always affects
corruption in the same way, the effect is calculated for poor countries and for rich countries
separately.
The paper is organised as follows: in Section 2, we explain the indices of economic
freedom and corruption for the empirical study. In Section 3, we demonstrate that there is a
‘‘pattern’’ of aspects of economic freedom influencing corruption which is different for
poor and rich countries. In Section 4, we test the stability of these patterns. Finally, in
Section 5 we discuss the results.
2. Measurement of economic freedom and corruption
We analyse the impact of economic freedom on corruption by means of the Index of
Economic Freedom by Gwartney et al. (2000) and the Corruption Perception Index (CPI)
published by Transparency International Berlin.
The CPI is the compilation of different survey data. It combines assessments of
previous years to reduce sharp variation in scoring which ‘‘[. . .] might be due to
high-level political scandals that affect perceptions, but do not reflect actual changing
levels of corruption’’ (Lambsdorff, 1999, p. 5). The CPI measures corruption from 0
(highest degree of corruption) to 10 (no corruption). A country is taken up in the
CPI if at least three surveys are available for the assessment of its level of
P. Graeff, G. Mehlkop / European Journal of Political Economy 19 (2003) 605–620
607
corruption.2 The measurement of perceived corruption includes, however, assessments
of the extent of illegal behaviour in a country in general.3
Over the last 3 years, especially in 1999, many new countries have been included in the
CPI. To extend the sample of countries, the average of the CPI data for every country over
1998 to 2000 has been used.
The core ingredients of the Economic Freedom Index are personal choice, protection of
private property and freedom of exchange. Individuals have economic freedom when their
legally acquired property is protected from invasion by others and when they are free to
use and exchange their property without violating the rights of other individuals (see
Gwartney et al., 1996).
This concept of economic freedom fits into the theoretical framework of methodological individualism which can be seen as a core feature of modern political economy
since Adam Smith ([1789] 1976). In this theoretical approach, the institutional framework
(economic freedom being a major part of it) within a society affects the incentives of
individuals, the productive effort and the effectiveness of resource allocation (De Haan and
Sturm, 2000; Jones, 1981; North and Thomas, 1973; Olson, 1996). Freedom of choice,
freedom to supply any kind of goods and resources, fair competition in markets (reflected
by Area I and II in the index), the availability of reliable money (Area III and IV), secure
property rights (Area V), and freedom to trade with others (Area VI) and the allocation of
capital by the market (Area VII) are indispensable elements of economic development and
progress.4 An institutional arrangement that is in conflict with economic freedom by
restraining trade, increasing transaction costs, undermining property rights and producing
uncertainty will reduce the incentives of rational individuals to engage in economic
activities. Thus, in an economically free society, the fundamental functions of government
are the protection of private property and the enforcement of contracts (De Haan and
Sturm, 2000, p. 217), and the provision of a few collective goods (like national defence)
that cannot be allocated by a private market (Buchanan, 1975). Anything less could violate
the economic freedom of individuals. The expansion of government’s size beyond these
core activities will have a negative impact on a country’s economy (see Gwartney et al.,
1998, pp. 168 –170) because of the excess burden of higher taxes and/or additional
borrowing, for instance. When government’s size expands, economic growth will decline.
According to Schumpeter (1942), growth is a discovery process driven by entrepreneurs.
While private markets reward innovation and progress but punish failure, adjustment to
change is slower in the public sector. An expanding government becomes more and more
2
The authors of the CPI made commendable efforts to maximize the reliability of the index by combining
various data sources. However, according to Lancaster and Montinola (2001, p. 10) ‘‘[. . .] researchers should bear
in mind that the index is not necessarily more accurate than any single poll. Determining which poll is most
accurate is not possible. Confidence in the CPI assumes each poll is subject to random error.’’
3
In regression models with corruption as dependent variable it might be problematic to use independent
variables that measure some kind of illegal behaviour, too. Area IV of the Economic Freedom Index (Gwartney et
al., 2000) includes the element IVb ‘‘Difference between the Official Exchange Rate and the Black Market Rate’’.
A black market exchange rate premium is both an obstacle to trade and an indicator of unsound money. It is also
an indicator for the extent of the shadow economy and illegal practices like corruption. To avoid the dependent
variable being confounded with the independent variables we excluded Area IVb from the regressions.
4
For a more detailed description of the Index of Economic Freedom and the areas, see Appendix B.
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P. Graeff, G. Mehlkop / European Journal of Political Economy 19 (2003) 605–620
Table 1
Correlations of the Economic Freedom areas and corruption (CPI)
CPI
CPI
Area I
Area II
Area III
Area IVa
Area V
Area VI
Area VII
Economic
Freedom
Area I
1.000 0.546**
–
0.000
86
85
1.000
–
85
Area II
Area III
0.417** 0.513**
0.000
0.000
85
85
0.040
0.154
0.716
0.158
85
85
1.000
0.309**
–
0.004
85
85
1.000
–
85
Area IVa
Area V
Area VI
Area VII
Economic
Freedom
0.364**
0.004
85
0.043
0.696
85
0.526**
0.000
85
0.287*
0.008
85
1.000
–
85
0.709**
0.000
88
0.509**
0.000
84
0.352**
0.001
84
0.562**
0.000
84
0.362**
0.001
84
1.000
–
85
0.521**
0.000
76
0.345**
0.002
76
0.294**
0.010
76
0.191
0.099
76
0.416**
0.000
76
0.430**
0.000
75
1.000
–
76
0.625**
0.000
85
0.195
0.073
85
0.714**
0.000
85
0.563**
0.000
85
0.628**
0.000
85
0.521**
0.000
84
0.478**
0.000
76
1.000
–
85
0.692**
0.000
85
0.169
0.123
85
0.752**
0.000
85
0.658**
0.000
85
0.713**
0.000
85
0.716**
0.000
84
0.577**
0.000
76
0.904**
0.000
85
1.000
–
85
First entry in every cell: correlation coefficient, second entry: level of significance, third entry: number of
observations; *p < 0.05; **p < 0.01, two-tailed test.
involved in the redistribution of income and regulatory activities which, in turn,
encourages individuals to seek income via government favours rather than through
production in exchange for income.
Gwartney et al. (2000) convert the raw data of the components to a 0 to 10 scale.
Higher ratings are indicative of institutions and policies more consistent with economic
freedom. The Index of Economic Freedom adds up the area scores to a summary index
score. It can be expected that the correlation between the area scores and the summary
index score (and to the CPI accordingly) is positive. For Area I (Size of Government) this
is not true (see Table 1).5
Area I correlates negatively with the CPI and the other areas. Since this area is
indicative of the size of government, this correlation suggests that bigger governments are
exposed to less corruption (a high index score indicates more freedom, i.e. little govern5
Note that the number of observations differs somewhat, because there are some missing values within the
components or areas.
P. Graeff, G. Mehlkop / European Journal of Political Economy 19 (2003) 605–620
609
ment influence). This contrasts with the view of Becker and Becker (1997, p. 203) who
argue that in a small state, public officials should find less opportunity to use public
spending for private gain contrary to the general interest.
This negative correlation between size of government and the CPI makes the summary
index score of economic freedom inadequate for any further investigation of corruption. It
is wrong to evaluate the relationship between economic freedom and corruption with this
summary index score, because the weight of the suppression effect of the size of
government cannot be assigned to the impact of the summary index score on a dependent
variable. All other areas show high correlations in Table 1. The signs always point in the
expected direction. Therefore, it seems to be adequate to use the various area-indicators to
test the relationship between economic freedom and corruption.
3. Pattern of influence of economic freedom on corruption
Some theoretical approaches to corruption imply that the type of corruption varies
between societies because of their different histories and traditions (Scott, 1972).
Nepotism as a typical form of corruption may be likely to arise in societies where
particularistic obligations are pervasive. Those obligations are associated with pre-capitalistic, traditional societies (Lipset and Lenz, 2000). Tight family structures and mutual help
systems are marks of Eastern countries (Weede, 2000) implying a high level of corruption,
since tight networks make it difficult to resist pressure for favourable treatment (especially,
if a norm exists that grants unrestricted priority to relatives or friends).
With a few exceptions (such as Estonia), Transparency International rates most former
communist countries and most African countries as being highly corrupt. In the past, the
majority of these countries were characterised by family structures, hierarchical (religious) cultures and party particularism. Most of these countries are also poor (see
Appendix A). In recent centuries, the societies of most rich countries have moved towards
a market economy. They have abandoned many ideas of traditionalism, encouraged social
mobility and aligned decision processes to criteria such as rationality and effectiveness.
One of the very apparent indications of the changing society and economy during the
development process of rich countries is the declining importance of the family for
production processes (Ben-Porath, 1980). Probably, the rich countries have reduced the
likelihood of ‘‘traditional’’ types of corruption in this manner. Nevertheless, they have
also created new forms of corruption. The need to finance political campaigns is a source
of corruption that would never occur in poor, autocratic systems (Rose-Ackerman, 1999,
p. 142).
It seems almost impossible to assemble all the various factors of a society that foster or
impede corruption (Tanzi, 2000). But if a link between economic freedom and corruption
is assumed, the question arises as to whether improvements in economic freedom always
affects corruption in the same way regardless of whether the country is poor or rich.
We attempted to answer this question by regressing corruption on the seven areas of
economic freedom (see Table 2). Model 1 in Table 2 shows the results when the full
sample is used. When GDP per capita is also included as a control variable, the areas of
economic freedom have only a weak influence on corruption. Areas I and V reveal
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P. Graeff, G. Mehlkop / European Journal of Political Economy 19 (2003) 605–620
Table 2
Results of OLS- and Backward-Regression (dependent variable: mean of CPI 1998 – 2000)
OLS regression
Constant
Area I
Area II
Area III
Area IVa
Area V
Area VI
Area VII
GDP p.c.
Adj. R2
N
Reset-Test
OLS regression
Backward regression
Model 1
Model 2a
Model 2b
Model 3a
Model 3b
all countries
poor countries
rich countries
poor countries
rich countries
3.104
0.352*
2.765
0.101
0.810
0.021
0.266
0.086
1.510
0.264*
2.308
0.026
0.157
0.274
1.957
0.0001**
4.909
0.750
68
significant
3.502
0.079
0.469
0.132
0.773
0.141
1.847
0.150*
2.509
0.204
2.001
0.206
1.182
0.280*
2.298
–
0.322
29
not significant
2.979
0.518**
3.113
0.088
0.542
0.028
0.214
0.018
0.181
0.661*
2.702
0.306s
1.296
0.514
1.751
–
0.743
39
not significant
2.682
–
–
0.599
0.542**
3.957
–
–
–
0.148**
3.100
–
–
–
0.560**
2.776
–
0.257**
3.114
–
0.710**
4.011
–
0.265
29
not significant
0.758
39
not significant
For Backward Regression only significant results are listed.
First entry in every cell except the last three rows: unstandardized regression coefficient, second entry: t value;
*p < 0.05; **p < 0.01, two-tailed test. A line reports that the variable is not significant at the 5% level.
significant coefficients, whereas the sign of Area I is counter-intuitive but was found in
earlier studies as well (e.g. Paldam, 2001).6
To separate poor and rich countries (models 2a, 2b, 3a and 3b in Table 2), we used the
OECD classifications into low-income countries, lower middle-income countries, upper
middle-income countries and high-income countries. We computed the mean of the GNP
per capita values that separated lower – middle income and upper – middle income countries
for the years 1990 to 1995. This value is almost equal to the median of the sample.
Macroeconomic data should always be treated with caution. Therefore, we checked all
models for outliers and used the Ramsey Reset Test of functional form. Since it is our aim
to discover those aspects that strongly influence economic freedom, we eliminated
insignificant variables using the backward elimination technique.
When—due to the theoretical arguments mentioned above—the sample is divided into
rich and poor countries, the only area that is significant for corruption in poor and rich
countries is Area VII (models 3a and 3b in Table 2). Otherwise, the significant variables are
6
The significant Reset Test in model 1 points to a problem. Since this model is not used later on we do not
go into further investigations on the problem. For a discussion of this problem refer to Paldam (1999, p. 13).
P. Graeff, G. Mehlkop / European Journal of Political Economy 19 (2003) 605–620
611
different for rich and poor countries. Only Area IVa is additionally significant in the poor
countries sub-sample. Corruption in rich countries is influenced by Areas I, V and VII.
If corruption is perceived as a consequence of economic freedom, in rich countries it
seems to be inversely associated to the size of government (Area I). Bigger government
results in lower levels of corruption. We shall refer to this unexpected result later on. Here,
we would like to suggest that this association does not reflect a simple effect of income.
The coefficient of Area I does not become insignificant or, at least, does not decrease
considerably when GDP per capita is inserted as a control variable in the equations (see
Section 4).
Area V measures the legal structure of a country. It stresses the aspects of the security
of private ownership rights, the risk of contract repudiation by the government and the
supportiveness of legal institutions for the principle of the rule of law. The possibility to
confiscate private property or to repudiate contracts gives public officials the opportunity
to abuse their public position for personal gain. Improving the legal structure leads to less
corruption. Therefore, the coefficient of this aspect of economic freedom shows a positive
sign. Furthermore, according to transaction cost theory, a weak legal structure (which is
used in an arbitrary manner) generates opportunities for corruption as well.
If market forces are used to allocate capital instead of political or governmental
considerations (Area VII), corruption decreases. This result is valid both for rich and for
poor countries.
For poor countries Area IVa is significant, too. It measures how easy it is to conduct
business in foreign currencies. Notice that the sign of the coefficient of Area IVa is
negative, which implies that the freedom to own foreign currencies leads to corruption. For
corrupt deals, this aspect of economic freedom reduces transaction costs, especially when
the participants of the illegal bargain belong to foreign countries.
The patterns influencing corruption seem to be different (except for the freedom of
exchange in capital and financial markets) for poor and rich countries. The remaining areas
appear to be unimportant in explaining corruption. Area II (The Structure of the Economy/
Use of Markets), Monetary Policy/Price Stability (Area III) and Freedom to Trade with
Foreign Countries (Area VI) are never significant, neither for poor nor for rich countries.
Summing up, we conclude that the impact of economic freedom on corruption depends
(with the exception of the freedom of exchange in capital and financial markets—Area
VII) on which areas of economic freedom are taken.7
4. Additional test for stability
To test the stability of our results, we also insert control variables that could be regarded
as determinants of corruption in a macroeconomic analysis (see Mauro, 1995; Treisman,
7
Since unexpected sign effects could occur (like in the case of Area I), we used a two-tailed tailed test and
the usual 5% criterion. The patterns would have differed slightly if variables at the 10% level had been used.
Usually, different measures of corruption reveal high inter-correlation (much higher than 0.90). Therefore, the
results and the patterns do not change if a different corruption index (Kaufman et al., 1999a, 1999b) is used
instead of the CPI. There is one exception: Area V becomes significant in the poor countries sub-sample when the
Kaufman – Kraay – Zoido – Lobatón index is used.
612
P. Graeff, G. Mehlkop / European Journal of Political Economy 19 (2003) 605–620
2000; Paldam, 2001). These determinants could be classified as having economical and
cultural or social impacts.
In poor countries with high levels of illiteracy, many people have little understanding of
governmental operations (Rose-Ackerman, 1999). To control for this, we include the
primary school enrolment rate. For such people, it is often also not clear as to what they
should expect from a legitimate government. In such a situation, corruption is more likely
because people suppose that they ought to present gifts as gratitude for favourable
decisions (Pasuk and Sungsidh, 1994, Mauss, 1966). This way, corruption is less a matter
of bargain, it is more a matter of culture and social exchange. Because countries classified
as being highly corrupt under-invest in education (Mauro, 1998) and neglect the creation
of human capital, we also include the secondary school enrolment rate. Often, poor
countries also have rapid population growth and therefore we take up average population
growth. For the state, this creates problems in providing social and health services and in
creating an adequate infrastructure. Investment in capital-intensive infrastructure or health
facilities often provides lucrative opportunities for corruption (Mauro, 1997). To proxy for
this, we include investment as share of GDP. Prevalent corruption should be (negatively)
linked to the level of the economic development of a country (reflected by GDP per capita)
(Treisman, 2000), its rate of economic growth of GDP and the level of democracy. Finally,
we considered the inequality of income distribution proxied by the Gini coefficient as a
possible determinant for corruption. High-income inequality may correspond to perceptions of unfair state operations and foster feelings of injustice which could make the
incidence of corruption more likely (Smelser, 1971). In order to test the stability of the
patterns for poor and rich countries, we inserted these control variables one after another
and ran backward regressions.
For the group of the poor countries, none of the variables in the equations that were
significant before (Area IVa and Area VII) change sign or qualitatively affect the value of
their coefficients when the control variables are added to the equations, with one
exception. Area II becomes significant and Area VII loses significance if primary school
enrolment rates are considered in the model.
The pattern of influencing variables of economic freedom on corruption seems to be
exceptionally stable for rich countries, too. Although population growth, growth of GDP
and primary school enrolment rate are significant in their influence on corruption, Areas I,
V and VII remain as the only significant variables of economic freedom. There is only one
exception: Area V becomes insignificant when secondary school enrolment rates are
added to the model as a control variable.8
8
It is obvious that the patterns may depend on the criterion to distinguish poor and rich countries. To test the
stability of the patterns we changed the composition of the sub-samples. In the World Development Reports of the
World Bank, the countries are ranked as rich and poor countries depending on their most recent per capita Gross
National Product. To test the validity of our results, we used the classification of values of 1990 (benchmark:
US$2450, see World Bank, 1992) and also the classification of 1995 (US$3100, see World Bank, 1997) instead of
the mean of the years from 1990 – 1995 that had been used in the previous calculations. We estimated the
equations with backward regression once again and used the same control variables taken up in previous
regressions. The sample size differs slightly but the patterns for rich and the poor countries remains stable. In
order to save space, the detailed results are not reported here, but are available on request.
P. Graeff, G. Mehlkop / European Journal of Political Economy 19 (2003) 605–620
613
Table 3
Extreme Bound Analysis for poor countries (dependent variable: mean of CPI 1998 – 2000)
Adj. R2
N
Additional variables
Robust/
fragile
3.261
0.231
35
population growth,
growth of GDP
robust
0.581
0.500
3.323
2.348
0.274
0.196
36
29
0.666
0.622
0.551
3.679
3.560
2.394
0.272
0.274
0.120
36
36
30
Variable
Coefficient
Area VIa
high:
0.606
base:
low:
high:
base:
low:
Area VII
Significance
secondary school
enrolment
population growth
robust
growth of GDP,
primary school
enrolment
The column ‘Coefficient’ refers to the standardized coefficient. In case the base regression had the highest
(lowest) coefficient the cells in the row ‘high’ (‘low’) are empty.
In the next step, we use Extreme Bound Analysis (EBA) to test the strength of the
pattern after inserting more than one control variable into one equation (see Leamer,
1983, 1985; Levine and Renelt, 1992; De Haan and Siermann, 1998). The idea of the
EBA is to select a number of control variables that have been shown to have an impact
on corruption and to insert any possible linear combination of up to three control
variables additionally to the variable(s) of interest into the equation. The relationship
between the areas of economic freedom and corruption is labelled as being robust if,
independent of the choice of control variables, the standardised coefficient of the
variable of interest remains statistically significant and does not change. According to
Sala-i-Martin (1997), the test applied in the EBA is too strong—any coefficient changes
if enough regressions with different variables are run. For this reason, we decided to
take only four control variables into account: growth of population, growth of GDP,
primary school enrolment rates and secondary school enrolment rates. These are the
Table 4
Extreme bound analysis for rich countries (dependent variable: mean of CPI 1998 – 2000)
Adj. R2
N
Additional variables
Robust/
fragile
3.953
0.764
31
growth of GDP,
primary school enrolment
robust
0.330
3.957
0.758
39
0.370
3.050
0.846
30
population growth,
secondary school enrolment
fragile
base:
low:
high:
0.343
0.200
0.643
2.776
1.506
4.482
0.758
0.761
0.783
39
32
31
base:
low:
0.482
4.011
0.758
39
Variable
Coefficient
Area I
high:
0.410
Area V
base:
low:
high:
Area VII
See Table 3.
Significance
primary school enrolment
primary school enrolment,
secondary school enrolment
robust
614
P. Graeff, G. Mehlkop / European Journal of Political Economy 19 (2003) 605–620
control variables with the biggest impact on corruption in our sample—neither the Gini
coefficient, GDP per capita, the investment rate nor the level of democracy has a
significant effect at all.9 The results of the EBA are reported in Tables 3 and 4.
Freedom to Use Alternative Currencies (for poor countries), Size of Government
(for rich countries) and Freedom of Exchange in Capital and Financial Markets (for
rich and poor countries) have been identified as predictors of corruption. These
variables ‘‘survive’’ even the strong Extreme Bound Analysis (see Tables 3 and 4).
There is a relationship between the property rights and corruption in rich countries—but this relationship is not robust. If the school enrolment rates are inserted
into the equation in addition, the effect of the property rights is not far removed
from zero.
5. Discussion
From a broader point of view, this paper deals with the impact of state regulation
on the incidence of corruption.10 By and large, the Index of Economic Freedom
measures forms of regulations in order to describe economic freedom as the absence
of regulation. In practice, such regulation means little in many (mainly poor)
countries because the state lacks enforcement capacity. Economic freedom may arise
from the ineffectiveness of the state to monitor compliance with rules (Bratton,
1989). Putting the meaning of freedom this way, corruption could work as an
informal buffer which mediates particularistic and universalistic interests (Smelser,
1971).
Up to now, there is no theory that is able to explain the relation between
economic freedom and corruption (Chafuen and Guzmán, 2000). Nevertheless, it is
obvious that in modern economies many restrictions of economic freedom provide
opportunities for corruption. The authority of public officials to grant import or
export permits (Shleifer and Vishny, 1993) or to impose import and export duties or
to induce taxes are examples. We found the restrictions of capital and financial
markets (Area VII) to have the biggest impact on corruption.
Especially when political leaders or senior public officials are involved in
corruption (Scott, 1972), the absence of financial restraints plays a major role.
Economic freedom deals with a country’s link to the broader world and this link
could be beneficial or obstructive for illegal actors. If money could be hidden abroad
or is easily taken across borders, it is much easier to launder money from corrupt
9
Only those variables which have proved to have an influence on the areas were used in our EBA. Since
there is no criterion to preselect variables for an EBA by this we limited the calculation effort (Sala-I-Martin,
1997).
10
With this point of view we claim state regulation being causal for the incidence of corruption. But state
regulation might result from corruption deals, too. There are only a small number of public officials with the
power and the opportunity to create prescriptions or regulations at their corruption partners’ behest. The vast
majority of corrupt public officials use prescriptions or regulations that already exist and construe those in favour
of their partners in corruption. Therefore, it is more appropriate to investigate the influence of state regulation on
corruption than the opposite direction of causality.
P. Graeff, G. Mehlkop / European Journal of Political Economy 19 (2003) 605–620
615
deals or other criminal acts. According to our findings (Area IVa), this occurs mainly
in poor countries.
Therefore, it might be somewhat problematic to state that all aspects of economic
freedom are deterrents to corruption. When lack of economic freedom increases the
transaction costs of illegal bargains, corruption could become less likely. Unfortunately,
increasing the transaction costs for illegal actors is usually an obstacle for legal actors as
well.
When the incidence of corruption is considered for poor and rich countries separately,
general differences could be detected which are approximated by the variables used to test
the stability of our results. In poor countries, many people have low levels of education.
There is often a huge gap between the rich and the poor. The growth of GDP is small or
negative while the growth of the population is large.
In rich countries, the paradox emerges that countries with larger governments
usually have lower corruption. We doubt that Size of Government is a useful element
in the Index of Economic Freedom being linked to corruption. Government size is
regarded as being an indicator of fiscal regulation (Tanzi, 2000) but it also includes
many aspects which could hardly be perceived as being regulatory (e.g. loans of public
officials). Furthermore, the concept of government size needs defining in order to
separate the amount of public spending which is useful for an economy to prosper
from the amount of public spending which does harm to the economy, something
which Gwartney et al. (2000) seem to have forgotten in constructing their index.
Different authors suggest different separation values (Tanzi and Schuhknecht, 2001;
Gwartney et al., 1998).
If one is to explain corruption through economic freedom, it seems more appropriate to
focus on how public servants work. As Tanzi (1998, p. 10) points out: ‘‘Rather, the way
the state operates and carries out its function is far more important than the size of public
sector activity.’’ It is influenced by many factors. A crucial determinant might be the
incentive to engage in illegal activities or the ease in conducting illegal business. For poor
countries, the ease of winding up affairs severely affects the likelihood of corruption (Area
IVa). This is not true for rich countries where corruption is more affected by the legal
structure in a country (Area V) and, therefore, by the way the state supports the rule of
law.
The empirical findings lead to two suggestions: first, that there are regulations which
decrease corruption by increasing the transaction costs of corruption. Second, that big
governments in rich countries are not equivalent to a high level of corruption. Therefore,
one has not only to investigate the relationship between the size of government and
corruption but also the legal and institutional capacity of monitoring and sanctioning
illegal bargaining.
Acknowledgements
We thank Erich Weede, Jakob De Haan, Ekkart Zimmermann, the referees and the
participants of the SOM workshop on economic freedom in Groningen, November 2001,
for their comments and suggestions.
616
P. Graeff, G. Mehlkop / European Journal of Political Economy 19 (2003) 605–620
Appendix A . CPI and GDP p.c. by country
Poor countries
Rich countries
Country
CPI
GDP p.c.
Country
CPI
GDP p.c.
Albania
Bulgaria
Cameroon
China
Colombia
Costa Rica
Ecuador
Egypt
El Salvador
Guatemala
Honduras
India
Indonesia
Jamaica
Jordan
Kenya
Lithuania
Malawi
Morocco
Namibia
Nicaragua
Nigeria
Pakistan
Paraguay
Peru
Philippines
Romania
Senegal
Tanzania
Thailand
Tunisia
Uganda
Ukraine
Zambia
Zimbabwe
2.30
3.23
1.63
3.33
2.77
5.37
2.43
3.10
3.87
3.15
1.75
2.87
1.80
3.80
4.57
2.20
3.95
4.10
4.17
5.33
3.05
1.57
2.45
1.75
4.47
3.23
3.07
3.40
2.10
3.13
5.07
2.37
2.30
3.47
3.77
708.48
1508.29
654.99
507.07
1975.25
2563.56
1541.45
996.13
1557.63
1428.54
703.81
338.79
960.09
1642.02
1482.42
337.80
2271.24
152.76
1318.59
2113.00
443.71
260.43
483.62
1823.65
2297.68
1053.18
1405.00
550.30
158.19
2525.59
1990.61
281.78
2302.43
420.18
675.53
Argentina
Australia
Austria
Belgium
Botswana
Brazil
Canada
Chile
Croatia
Czech Republic
Denmark
Estonia
Finland
France
Greece
Hungary
Ireland
Israel
Italy
Japan
Latvia
Malaysia
Mexico
Netherlands
New Zealand
Norway
Poland
Portugal
Russia
Slovak Republic
Slovenia
South Africa
South Korea
Spain
Sweden
Switzerland
Turkey
United Kingdom
United States
Uruguay
Venezuela
3.17
8.57
7.60
5.60
6.07
4.00
9.20
7.03
3.20
4.57
9.93
5.70
9.80
6.67
4.90
5.13
7.70
6.83
4.63
6.07
3.17
5.07
3.33
8.97
9.40
9.00
4.30
6.53
2.30
3.70
5.75
5.07
4.00
6.57
9.43
8.80
3.60
8.67
7.60
4.35
2.53
7896.74
19 265.02
28 377.67
26 624.29
3143.83
4250.04
19 125.25
3805.14
3799.64
4934.09
33 652.12
3564.72
24 507.55
26 095.07
10 764.81
4402.23
16 531.67
14 748.50
18 618.88
40 769.74
3064.13
3897.44
3286.30
25 274.87
16 009.16
32 206.49
2935.26
10 364.71
2819.53
3311.03
9219.72
3452.93
9258.03
14 067.01
25 904.73
44 266.71
2734.69
18 412.03
26 373.24
5489.84
3542.24
P. Graeff, G. Mehlkop / European Journal of Political Economy 19 (2003) 605–620
617
Appendix B . List of variables
Name
Variable
Source
CPI 98—2000
Mean of corruption perception index 1998 – 2000
Area I
Size of government: consumption, transfers,
and subsidies 1996/7
(A) General government consumption expenditures
as a percent of total consumption
(B) Transfers and subsidies as a percent of GDP
Structure of the economy and use of markets 1996/7
(A) Government enterprises and investment as a
share of the economy
(B) Price control: extent to which businesses are
free to set their own prices
(C) Top marginal tax rate
(D) The use of conscription to obtain
military personnel
Monetary policy and price stability 1996/7
(A) Average annual growth rate of money supply
during the last 5 years minus the growth rate of
real GDP during the last 10 years
(B) Standard deviation of the annual inflation
rate during the last 5 years
(C) Annual inflation rate during the most recent year
Freedom to use alternative currencies 1996/7
(A) Freedom of citizens to own foreign currency
bank accounts domestically and abroad
Note: We excluded Area IVb from
the regressions (see footnote 3).
Legal structure and property rights 1996/7
(A) Legal security of private ownership rights
(B) Viability of contracts
(C) Rule of law: legal institutions supportive
of the principles of rule of law and access to a
nondiscriminatory judiciary
International exchange: freedom to trade with
foreigners 1996/7
(A) Taxes on international trade
(i) Revenue from taxes on international
trade as a percent of exports plus imports
(ii) Mean tariff rate
(iii) Standard deviation of tariff rates
(B) Non-tariff regulatory trade barriers
(i) Percent of international trade covered by
non-tariff trade restraints
(ii) Actual size of trade sector compared
to the expected size
Transparency
International 2000
Gwartney et al.
(2000)
Area II
Area III
Area IVa
Area V
Area VI
Gwartney et al.
(2000)
Gwartney et al.
(2000)
Gwartney et al.
(2000)
Gwartney et al.
(2000)
Gwartney et al.
(2000)
(continued on next page)
618
P. Graeff, G. Mehlkop / European Journal of Political Economy 19 (2003) 605–620
Appendix B (continued)
Name
Variable
Source
Area VII
Freedom of exchange in capital and
financial markets 1996/7
(A) Ownership of banks: percent of deposits
held in privately owned banks
(B) Extension of credit: percent of credit
extended to private sector
(C) Interest rate controls and regulations that
lead to negative interest rates
(D) Restrictions on the freedom of citizens to
engage in capital transactions with foreigners
Economic freedom 1996/7, summary rating
Gwartney et al.
(2000)
Economic freedom
GDP p.c.
Arithmetic mean of gross domestic product per
capita 1990 – 1997 constant market prices 1995
Gini
Gini-coefficient of distribution of income
Duration of
Political System
Logarithm of the duration of a political
system before 1998. Since the duration
of two political systems was shorter than
1 year, to calculate the logarithm of the
values their score was fixed to 1.
Average annual growth of population
percent 1990 – 1997
Average annual growth rate of gross
domestic product percent 1990 – 1997
Gross domestic investment as percent of gross
domestic product, arithmetic mean of 1990 – 1995
Percentage of age group enrolled in
primary education 1990
Percentage of age group enrolled in
secondary education 1990
Level of democracy 1995
Population avg. ann.
growth 1990 – 97
Growth of GDP
1990 – 97
Investment 1990 – 95
Primary school
enrolment
Secondary school
enrolment
Level of democracy
Gwartney et al.
(2000)
World Development
Indicators 1997
CD-ROM
World Bank
(2000, pp. 282 – 3)
Polity 98 database
World Bank
(1998, pp. 194 – 5)
World Bank
(1998, pp. 210 – 1)
World Development
Reports (1990 – 1995)
World Bank
(1993, pp. 294 – 5)
World Bank
(1993, pp. 294 – 5
Polity 98 database
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