corruption, growth and political regimes: cross country evidence

Vol. 22 (2006) 82–98

www.elsevier.com/locate/ejpe

Corruption, growth and political regimes: Cross

country evidence

Fabio Mendez a,*, Facundo Sepulveda b

aDepartment of Economics, Business Building Room 402, University of Arkansas, Fayetteville, AR, 72701, USAbEconomics Program, RSSS, The Australian National University, Coombs Building # 9, Coombs Close, Acton,

ACT 2601, Australia

Received 8 September 2003; received in revised form 13 December 2004; accepted 15 April 2005

Available online 21 July 2005

Abstract

This paper studies the effects of corruption on long-run growth incorporating measures of

political freedom as a key determinant of the relationship. Unlike previous empirical studies, we find

evidence of a non-monotonic relationship between corruption and growth after controlling for

several other economic variables and restricting the sample to those countries considered to be free.

Our results indicate that the growth-maximizing level of corruption is significantly greater than zero,

with corruption beneficial for economic growth at low levels of incidence and detrimental at high

levels of incidence.

D 2005 Elsevier B.V. All rights reserved.

JEL classification: D73; H10; 057Keywords: Corruption; Growth; Political regimes

1. Introduction

The effects of bureaucratic corruption on economic growth have been a topic of debate

over the last 40 years. On the one side, there is a view, exemplified by Myrdal (1989) and

Shleifer and Vishny (1993), that corruption is detrimental for investment and economic

0176-2680/$ -

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European Journal of Political Economy

see front matter D 2005 Elsevier B.V. All rights reserved.

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ress: [email protected] (F. Mendez).

F. Mendez, F. Sepulveda / European Journal of Political Economy 22 (2006) 82–98 83

growth. On the other side, Leff (1964), Lui (1985), and others have found it plausible for

corruption to be beneficial for economic growth at some levels. Up to this point, however,

the empirical evidence has supported the existence of a linear and negative correlation

between the level of corruption and the average rate of per-capita income growth (see

Mauro, 1995; Hall and Jones, 1999).1

Within this debate, a number of theoretical papers have shifted attention towards specific

elements that call into question the results of the typical empirical study. Elrich and Lui

(1999), for example, present a theoretical model in which the effects of corruption on growth

depend upon the political regime that oversees the economy. They consider two types of

political regimes: a bdemocraticQ one where bureaucrats compete over central power and an

bautocraticQ one in which a powerful and rational leadership is capable of imposing its will

on others. In their model, a relationship between corruption and growth is found in

democratic regimes only.

In a separate line of argument, other authors have suggested the possibility of a positive

output-maximizing level of corruption, thus challenging the notion of a linear relationship

between corruption and growth. Acemoglu and Verdier (1998) and Klitgaard (1988), for

example, use theoretical models to show that, if combating corruption is costly, then the level

of corruption that maximizes output might be greater than zero. Friedrich (1972), Nye (1989)

and Huntington (1968) have also suggested the existence of a positive growth-maximizing

level of corruption. Their argument, following Leff (1964) and Lui (1985), is that corruption

can be bbeneficialQ for growth at low levels of incidence by circumventing cumbersome

bureaucratic regulations.

The connection between the scope of government and the effects of corruption, however,

goes beyond the effects of bureaucracies. In particular, empirical studies by Tanzi and

Davoodi (1998), Mauro (1998) and Gupta et al. (2001) have shown that corruption alters the

composition of government expenditure towards less productive activities and, therefore,

that it is detrimental for growth.2 Thus, one cannot tell much about the impact of the

government sector on the relationship between corruption and growth before conducting

any empirical analysis.

This paper seeks to bridge the gap between the empirical evidence and the theoretical

literature by studying the type of nonlinearities discussed above. We focus on three

questions: First, do the effects of corruption on growth depend on the type of political regime

that rules the economy? Second, is there evidence of a positive growth-maximizing level of

corruption? And third, how are the effects of corruption on growth modified by the size of

the government?

In order to answer these questions, we follow Elrich and Lui (1999) and distinguish

between bfreeQ countries and bnot-freeQ countries according to the index of political rights

and civil liberties from Freedom House International. At the same time, we expand the

typical econometric specification by including a quadratic term for corruption that allows a

1 Egger and Winner (in press) however find empirical support for a positive relation between corruption and

foreign investment.2 On corruption and public expenditures more generally, see Abed and Gupta (2002) and the review by Hillman

(2004).

F. Mendez, F. Sepulveda / European Journal of Political Economy 22 (2006) 82–9884

test for a positive growth-maximizing level of corruption. We also include a measure of

government expenditures and study its interaction with corruption.

The results obtained show that the distinction made between free and not-free countries

is indeed important. In political regimes labeled bnot-freeQ, corruption does not affect

economic growth in the same fashion as in free countries. After controlling for several

other economic variables, we find evidence of a non-monotonic relationship between

corruption and growth for free countries only. This relationship is not significantly

modified by the size of the government.

These results are compatible with the findings of previous econometric studies that

estimate a linear model. The econometric specification used here, however, improves upon

the simple linear specification by increasing both the statistical fit and the robustness of

the model. Furthermore, the endogeneity problem that has usually been present in

previous cross-sectional studies is addressed by using a fixed-effects regression over 5-

year periods.

The following section provides a detailed description of the data. Section 3 discusses

the econometric work and the results obtained, and the last section of the paper elaborates

on the conclusions and the possibilities for additional research.

2. Description of the data

The empirical analysis uses data from a large sample of countries during the period

1960–2000. Values of annual population growth (POP), real income per capita (GDP),

annual GDP growth, secondary school enrollment rates (SED), the investment share of

GDP (Investment) and the share of government expenditures in GDP (Government) were

extracted from World Bank’s World Development indicators (2004).

In turn, data regarding the level of corruption was taken from three alternative

measures: the ICRG index, the IMD index and the CPI index. The ICRG index of

corruption comes from Political Risk Services Inc., a private firm that annually publishes

the International Country Risk Guide (ICRG). The ICRG contains a corruption index,

which is intended to assess the degree of corruption prevailing in a certain country and is

based on a survey among foreign investors.

The raw data of the corruption index in the ICRG ranges from 0 (most corrupt) to 6 (least

corrupt). In this paper, however, this index has been rescaled to range from 0 (most corrupt)

to 10 (least corrupt) in order to allow for an easier comparison with the other indices used.

The ICRG reports corruption for up to 130 countries between 1982 and 2001.

The IMD index of corruption is published by the Institute for Management

Development (IMD) in the World Competitiveness Yearbook and is based on a survey

among local managers in up to 50 countries. The index is based on a scale from 0 to 10

with lower values indicating that bimproper practices such as bribing and corruption

exist in public sphereQ (Herzfeld and Weiss, 2003). The index has been published since

1990.

The Corruption Perceptions index of corruption (CPI) is compiled by Transparency

International. This index is scaled from 0 (almost corrupt) to 10 (almost clean). The CPI

represents a bpoll of pollsQ and is compiled from up to 11 different surveys to businessmen,

Table 1

General descriptive statistics

Mean Std. Dev Max Min

ICRG 5.43 2.02 10 1.49

IMD 4.91 2.57 9.34 1.32

CPI 4.25 2.21 9.69 1.29

GDP growth 1.6 3.08 6.6 �6.08

POP 1.98 1.18 8.44 �0.36

SED 56.7 32.1 100 4.6

Investment 22.31 7.15 59.5 8.54

Government 16.88117 6.468877 43.09 5.97

Instability .8934012 .6320366 2.43 0


country experts, and local populations. The index reports perceived corruption for up to

100 countries within the period 1996–2003.

Since these corruption indexes are not available before 1980, for all of the cross-

sectional regressions we had to make the choice of using the average of these indexes for

the periods they are available as an approximation for the corruption level present during

the entire period (1960–2000). We believe that this is a reasonable assumption.

In order to classify countries as bfreeQ or bnot-freeQ we use the index of freedom from

Freedom House International. Since 1970 they have surveyed several countries, recorded

the state of several elements considered to be essential for freedom, and transformed these

elements into an index. This index is divided into two sub-indices: one of political rights

and another of civil liberties. Each sub-index ranges from 1 to 7, where a lower number

indicates a higher degree of freedom. To give an idea of what those numbers represent,

their report states that bas one moves down the scale below the category of 2, the level of

oppression increases, especially in the areas of censorship, political terror and prevention

of free associationQ.Freedom House classifies countries as bfreeQ if the sub-indices do not add more than

five, as bpartly freeQ if they add up between five and ten, and as bnot-freeQ if they add up to

11 or more. For the purposes of this study, countries will be categorized as free if the total

index is less than 7.5; however, the results remain mostly unaltered if a value in the range

of 5–9 is used to separate the categories.

The political rights sub-index was also used to create a variable intended to

approximate the degree of political instability within a country (Instability); this variable

was constructed by taking the standard deviation of the political rights index for the period

in question. Although this might not be a perfect measure of political instability, one would

Table 2

Correlation matrix of corruption indexes

IMD CPI ICRG

IMD 1

CPI 0.9638 1

ICRG 0.8239 0.9125 1


certainly expect that the countries that have a more volatile score in political rights are the

ones that are less stable.

Other studies such as Alesina et al. (1996) have used the probability of the opposition

taking over, or the number of changes in power over a certain period in time, as a measure

of political instability. These measures may not be adequate for the case of free countries

since free countries are likely to be governed by democracies and stable democracies in

turn are likely to have frequent changes in power. The alternative proposed here does not

suffer from this inconvenience.3

Appendix A shows the source and the definition of all data variables used in this study.

Additionally, Tables 1 and 2 offer a summary of descriptive statistics for these variables.

Table 2, in specific, shows the correlation matrix for the country averages of the alternative

corruption indices. As shown there, all measures of corruption are highly correlated with

each other.

3. Empirical analysis and results

The typical empirical studies of corruption and growth, like Mauro (1995), Knack and

Keefer (1995), Li et al. (2000) and Rock and Bonnett (2004), generate cross-sectional

regressions for which the average rate of economic growth is the dependent variable and a

standard list of regressors are used as independent variables. This standard list includes the

initial level of income per-capita in 1960, the rate of population growth, the secondary

school enrollment ratio, and in some cases the ratio of investment to GDP. The investment

ratio is often excluded from the estimations as some authors consider it to be a likely

source of endogeneity.

We shall use this typical econometric framework (to which we will refer as brestrictedQfrom now on) as the base of our econometric estimations; but as a robustness check in

some regressions we also include additional variables that are frequently found in growth

accounting exercises. These additional variables include a measure of government

consumption, our measure of political instability, and three dummy variables that identify

Latin-American, African and Scandinavian countries.

Table 3 presents the results obtained in the OLS estimation of the restricted model.

Columns (1)–(3) show the results of the estimations for which the ICRG index is used to

measure corruption; whereas columns (4)–(6) and (7)–(9) show the results of the

estimations for which corruption is measured by the IMD and the CPI indexes,

respectively. As shown in Table 3, the results of the estimation do not seem to depend

on the corruption index chosen.

Besides using alternative corruption indexes, the only difference across columns in

Table 3 is the number of explanatory variables. For the simpler specifications (columns

(1), (4) and (7)), the coefficient on corruption is found to be significantly different from

3 As a robustness check, two other alternative measures of political instability were used: the annual turnover of

government’s key decision makers (taken from the database of political institutions described in Beck et al., 2001)

and the political instability indicator from the World Bank’s governance data (Kaufmann et al., 2003). All results

in the paper remained unaltered when these measures were used.

Table 3

Dependent variable: per capita GDP growth (1960–2000)

ICRG IMD CPI

(1) (2) (3) (4) (5) (6) (7) (8) (9)

Corruption 0.002 0.0016 0.0018 0.003 0.0016 0.0013 0.004 0.003 0.003

(2.06) (1.59) (1.88) (2.6) (1.71) (1.35) (3.62) (3.50) (3.29)

POP 0.0007 �0.0007 0.0007 �0.01 �0.007 �0.007 0.0005 �0.0019 �0.0002

(0.28) (�0.34) (0.30) (�2.85) (�2.60) (�2.46) (0.20) (� 0.86) (�0.09)

SED 0.0003 0.0001 0.0007 �0.0002 �0.0001 �0.0002 0.0002 �0.00001 0.00002

(3.79) (1.50) (0.8) (�1.61) (�1.23) (�1.67) (1.85) (�0.12) (�0.21)

GDP (1960) �2.0E�6 �1.2E�6 �1.4E�6 �1.7E�6 �1.2E�6 �1.5E�6 �2.0E�6 �1.7E�6 �1.7E�6

(�3.67) (�2.57) (�2.88) (�3.47) (�3.18) (�3.67) (�4.25) (�3.73) (�3.65)

Investment 0.002 0.0017 0.002 0.0018 0.002 0.0017

(6.36) (5.54) (5.10) (3.75) (6.25) (5.07)

Government 0.00006 0.0001 �0.00004

(0.17) (0.36) (�0.12)

Instability �0.002 �0.0039 �0.0009

(�0.72) (�1.27) (�0.36)

LA �0.008 �0.009 �0.0071

(�1.94) (�1.75) (�1.76)

AF �0.013 �0.004 �0.01

(�3.19) (�0.65) (�2.65)

SC �0.003 �0.003 �0.005

(�0.57) (�0.73) (�0.84)

Constant �0.011 �0.034 �0.022 �0.058 �0.0006 0.017 �0.005 �0.028 �0.018

(�1.06) (�3.58) (�2.06) (4.07) (�0.04) (1.0) (�0.57) (�3.30) (�1.83)

N 85 84 84 40 40 40 77 77 77

Adjusted R2 0.31 0.53 0.57 0.24 0.55 0.61 0.34 0.57 0.59

T-statistics are in parentheses.

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zero and the estimated coefficient ranges from 0.002 to 0.004. These results coincide very

closely with the other empirical studies that use this restricted specification. Mauro (1995),

for example, estimated the corruption coefficient to be 0.002.

As the list of explanatory variables expands, the magnitude of the coefficient on

corruption remains roughly the same but, in some cases, the coefficient of corruption

becomes statistically insignificant. Other authors including Mauro (1995), Keefer and

Knack (1997), and Li et al. (2000), have also reported similar concerns regarding the

robustness of their results. In their case, the coefficient on corruption becomes

insignificant after controlling for other important determinants of growth.

It is immediately apparent, however, that the statistical model of Table 3 is not ideal to

test the hypotheses that the effects of corruption on growth are non-linear or that they

depend on the degree of political freedom. Thus, in order to address these questions we

separate the sample between free and not-free countries and we estimate an alternative

bunrestrictedQ model that includes a quadratic term for corruption. Tables 4 and 5 show the

results of the estimation of the unrestricted model for the free and not-free subsamples

respectively. The results of using all three of the alternative corruption indexes are

presented in both tables.

As shown in Table 4, in the case of free countries the results of the estimations suggest

the existence of a positive growth-maximizing level of corruption. For these countries,

the coefficients on corruption and corruption squared are always significant at the 1%

level and the coefficient estimates are robust to the inclusion of all other independent

variables. Moreover, these results do not vary as the specific corruption index used

changes.

The growth maximizing level of corruption implied by the estimations can be easily

calculated. Using the ICRG index, this level is found to be 8.6, 7.2, and 8 for columns (1)–

(3) respectively. Using the IMD index, the level is found to be 6.8, 7.2 and 6.2 for columns

(4)–(6) respectively. And finally, using the CPI index, the level is found to be 8.1, 8.1, and

7.1 for columns (7)–(9), respectively.

Given that a higher corruption index denotes a lower incidence of corruption and that

all indexes vary from 0 to 10, these results imply that economic growth reaches its

maximum level for small but positive amounts of corruption. Noticeably, the estimated

growth-maximizing level of corruption is well beyond the minimum levels of corruption in

our sample. Countries with corruption levels lower than these estimated growth-

maximizing levels (for all three indexes) include: Australia, Austria, Canada, Denmark,

Finland, Germany, Iceland, Ireland, Israel, Luxemburg, Netherlands, New Zealand,

Norway, Sweden, Switzerland, UK and USA.

The results presented in Table 4 contrast with those in Table 5, in which the not-free

countries subsample is studied. Columns (1)–(3) in Table 5 show the results of the

estimations for which the ICRG index is used to measure corruption; whereas columns

(4)–(5) and (6)–(8) show the results of the estimations for which the IMD and the CPI

indexes were used, respectively. For the case of the IMD index, the regressions could not

be estimated when the sample size fell below 10 countries.

As shown in Table 5, the results of the estimations do not allow us to establish any

significant correlation between corruption and economic growth for the subsample of not-

free countries. The size of the estimated coefficients of corruption and corruption squared,

Table 4


Free countries

ICRG IMD CPI

(1) (2) (3) (4) (5) (6) (7) (8) (9)

Corruption 0.019 0.016 0.016 0.015 0.013 0.015 0.018 0.013 0.012

(3.22) (3.27) (3.41) (4.44) (4.40) (3.83) (4.11) (3.60) (2.89)

Corruption2 �0.0011 �0.0011 �0.0010 �0.0011 �0.0009 �0.0012 �0.0011 �0.0008 �0.0008

(�2.61) (�2.83) (�2.80) (�4.01) (�4.00) (�3.69) (�3.21) (�2.91) (�2.46)

POP �0.0009 0.0005 �0.0012 �0.008 �0.007 �0.005 �0.0012 �0.0014 �0.0011

(�0.30) (0.23) (�0.53) (�3.00) (�2.96) (�1.99) (�0.42) (�0.57) (�0.44)

SED 0.0001 0.0001 0.0001 �0.0002 �0.0002 �0.0002 �0.0001 �0.0001 �0.00009

(0.97) (0.98) (0.23) (�2.23) (�2.35) (�2.60) (�0.87) (�1.06) (�0.93)

GDP (1960) �1.1E�6 �7.8E�6 �8.5E�6 �1.3E�6 �1.2E�6 �1.2E�6 �1.2E�6 �1.0E�6 �7.5E�6

(�2.18) (�1.70) (�2.06) (�4.04) (�4.33) (�4.18) (�2.53) (�2.59) (�1.90)

Investment 0.0017 0.006 0.0011 0.0009 0.0017 0.0009

(3.93) (1.43) (3.02) (2.17) (4.30) (2.09)

Government �0.0003 �0.0001 �0.0001

(�1.08) (�0.51) (�0.62)

Instability 0.0003 �0.002 0.002

(0.11) (�0.70) (0.79)

LA �0.011 �0.007 �0.009

(�2.57) (�1.95) (�2.17)

AF 0.032 . 0.024

(3.35) (2.56)

SC 0.0038 0.008 0.003

(0.70) (1.72) (0.73)

Constant �0.044 �0.07 �0.036 0.029 0.029 0.011 �0.018 �0.043 �0.020

(�1.97) (�3.62) (�1.66) (2.44) (2.44) (0.8) (�1.16) (�3.05) (�1.19)

N 45 45 45 30 30 30 43 43 43

Adjusted R2 0.31 0.50 0.64 0.56 0.67 0.70 0.38 0.58 0.65


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89

Table 5


Not-free countries

ICRG IMD CPI

(1) (2) (3) (4) (5) (7) (8) (9)

Corruption 0.012 0.010 0.018 �0.028 �0.003 �0.002 0.0030 0.009

(1.44) (1.46) (2.89) (�1.91) (�0.18) (�0.42) (0.52) (1.54)

Corruption2 �0.0011 �0.0010 �0.0018 0.0033 0.0007 0.0012 0.0005 �0.0001

(�1.29) (�1.30) (�2.73) (2.42) (0.41) (2.13) (0.91) (�0.21)

POP �0.0007 �0.002 �0.0018 0.0035 0.0004 0.0011 �0.0006 0.0037

(�0.14) (�0.61) (�0.38) (0.41) (0.06) (0.30) (�0.18) (0.79)

SED 0.0007 0.0003 0.0002 0.0010 0.0006 0.0007 0.0004 0.0004

(3.84) (1.71) (1.17) (1.77) (1.21) (4.69) (2.14) (2.25)

GDP (1960) �7.0E�6 �7.4E�6 1.3E�7 �2.3E�4 �1.9E�4 �2.0E�4 �2.0E�4 �2.0E�4

(�1.13) (�1.40) (0.03) (�2.64) (�2.36) (�4.72) (�4.02) (�3.36)

Investment 0.002 0.0011 0.002 0.0013 0.0010

(3.98) (2.35) (1.87) (2.49) (1.98)

Government 0.00018 �0.0010

(0.25) (�1.34)

Instability �0.004 �0.005

(�1.04) (�1.22)

LA �0.018 �0.004

(�2.39) (�0.67)

AF �0.023 �0.010

(�4.25) (�1.82)

Constant �0.032 �0.04 �0.03 �0.037 �0.05 �0.003 �0.025 �0.018

(�1.54) (�2.59) (�1.71) (�0.98) (�0.93) (�0.26) (�1.55) (�0.95)

N 40 39 39 10 10 37 34 34

Adjusted R2 0.38 0.55 0.70 0.43 0.65 0.56 0.68 0.74


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the sign of these coefficients, and the t-statistic values associated with them are all very

sensitive to the number of explanatory variables in the regression and to the specific

corruption index used.

In contrast to the results obtained for the free countries subsample, any relation between

corruption and growth that is found in Table 5 can be shown to vary with small changes to the

econometric specification and the coefficients of corruption are often not significantly

different than zero. Without freedom, the effects of corruption do not work in the same way

as they do with freedom. We return to study this issue in the next sub-section, where we

conduct fixed-effects regressions.

3.1. Addressing endogeneity

The results presented in the previous section are susceptible to twomajor criticisms. First,

it is possible that corruption and growth respond simultaneously to an omitted factor. Such

factor could be a cultural disposition towards leisure or morality, the legal framework, the

historical evolution of the nation in question, etc. Second, onemay think that the incidence of

corruption is directly affected by the rate of economic growth; as for example, it could be the

case that rich, fast-growing countries have more resources to combat and control corruption.

In either case, corruption would be correlated with the error term in the OLS regression and

the estimates would be biased.

Past studies have used instrumental variable techniques in an attempt to correct this

potential bias. The main instrument in the literature has been the Ethno linguistic

Fractionalization (ELF) index. This variable, however, has been shown to be directly (and

indirectly) correlated with economic growth (Easterly and Levine, 1997) and thus, it

cannot be considered as a valid instrument in our regressions.

Alternatively, in this study we attempt to control for endogeneity by conducting a fixed-

effects regression where the variables are averaged over three 5-year periods: 1984–1989,

1990–1995 and 1996–2000. The use of 5-year averages reduces short run fluctuations and

allows us to concentrate on the relationships of interest for this study.4

A fixed-effects regression will effectively control for endogeneity due to time invariant

effects, such as the state and quality of socio-political institutions; but it will not address

endogeneity due to the possible interactions between higher growth rates and greater

resources to combat corruption, or other time varying effects. Levin and Satarov (2000) and

Paldam (2002) have presented evidence for the existence of both types of endogeneities.

Unfortunately, only the ICRG index of corruption can be used for this exercise, as it is the

only one with data covering all these years. The ICRG index, however, has been shown to be

highly correlated with the other two indexes, and, in light of the previous results in this study,

we would not expect the results to be sensitive to changes in the corruption index used (at

least for the case of free countries).

Table 6 shows the results of these fixed-effects regressions for the case of free

countries. The econometric specifications of columns (1)–(3) include both a linear and a

4 Many other authors have also worked with 5-year averages for similar purposes. See, for example, Deininger

and Squire (1996), Li et al. (2000), and Paldam (2002).

Table 6

Dependent variable: per capita GDP growth

Fixed-effects regressions—free countries

(1) (2) (3) (4) (5) (6)

Corruption 0.010 0.014 0.013 �0.0005 �0.0006 0.0002

(2.06) (2.54) (2.45) (�0.32) (�0.33) (0.14)

Corruption2 �0.0010 �0.0012 �0.0011

(�2.33) (�2.80) (�2.54)

POP 0.009 0.008 0.006 0.007 0.006 0.005

(1.43) (1.48) (1.23) (1.22) (1.14) (0.92)

SED 0.0002 0.0002 0.0003 0.0002 0.0003 0.0003

(1.31) (1.67) (1.96) (1.56) (1.84) (2.09)

Initial GDP �2.8E�6 �2.6E�6 �2.5E�6 �2.2E�6 �1.9E�6 �1.8E�6

(�3.06) (�3.24) (�3.15) (�2.45) (�2.40) (�2.42)

Investment 0.002 0.0018 0.002 0.002

(3.81) (2.89) (4.30) (3.29)

Government �0.0027 �0.003

(�3.31) (�3.62)

Instability 0.003 0.003

(0.70) (0.70)



quadratic term of corruption; while columns (4)–(6) mimic the restricted specification and

do not include the quadratic term. Besides this difference, only the number of explanatory

variables changes from one column to the next.

The results confirm the previous findings regarding the existence of a positive growth-

maximizing level of corruption for free countries. As shown in columns (1) to (3) of Table

6, the estimated coefficients of corruption and corruption squared are roughly the same as

those estimated before and shown in Table 4. These coefficients are always significant at

the 1% level and they remain unaltered as the list of explanatory variables expands to

include our measures of investment, government consumption and political instability.

Interestingly, the estimated growth maximizing level of corruption now becomes lower. It

amounts to 5, 5.8 and 5.9 in columns (1) to (3), respectively.

In columns (4) to (6) of Table 6, where the restricted specification was used and the

linear term of corruption was tested alone, the estimated coefficient of corruption was

never significantly different than zero. The coefficients for all other variables, however,

are not affected by the change in the econometric specification.

The regressions shown in Table 6 are reproduced in Table 7 for the not-free

subsample of countries. As before, the results of the estimations do not allow us to

establish a stable or significant correlation between corruption and economic growth. In

column (1), for example, the results show a non-monotonic relationship between

corruption and growth, but as the number of explanatory variables increases, such a

conclusion is no longer supported. In some cases, like those of Columns (2), (3) and (5),

the results suggest a positive correlation between corruption and growth, and in other

cases like columns (4) and (6), the coefficient of corruption was found to be statistically

no different than zero (but with different signs).

Table 7


Fixed-effects regressions—not-free countries

(1) (2) (3) (4) (5) (6)

Corruption 0.045 0.007 0.011 0.002 �0.003 �0.0016

(3.47) (1.17) (1.60) (0.57) (�1.76) (�0.86)

Corruption2 �0.005 �0.0013 �0.0014

(�3.46) (�1.81) (�1.91)

POP 0.001 �0.0011 �0.008 �0.0017 �0.012 �0.008

(0.13) (�2.69) (�2.04) (�0.20) (�2.91) (�2.01)

SED �9.1E�5 �0.0003 �0.0003 �4.3E�5 �0.0003 �0.0003

(�0.16) (�1.24) (�1.27) (�0.07) (�1.15) (�1.18)

Initial GDP �1.1E�5 �6.5E�6 �6.1E�6 �7.4E�6 �5.1E�6 �4.7E�6

(�2.66) (�2.81) (�2.91) (�1.68) (�2.32) (�2.35)

Investment 0.002 0.002 0.002 0.002

(5.44) (4.95) (5.44) (5.00)


(�3.40) (�3.80)

Instability �0.012 0.0011

(�3.37) (�3.09)



Thus, by studying Tables 4 5 6 and 7, our analysis suggests that the distinction made

between free and not-free countries is in fact pertinent; that the links between corruption

and growth are different in free countries than what they are in not-free countries.

We may only speculate about the specific mechanisms that produce this result, as there

are several plausible explanations: 1. In a controlled economy, corruption receipts and

economic activities are both endogenous decisions of the policy maker and, thus, they do

not necessarily affect one another. 2. In a controlled economy the difference between

corrupt and legal acts is blurred and the quality of the corruption indexes available might

suffer in these cases. 3. The private agent’s incentive to bribe might be smaller in

controlled economies where returns to private capital are reduced, thus minimizing the link

between corruption and private investment. 4. The ratio of fund-grabbing corruption vs.

speed-money corruption is likely to be higher in less democratic economies and, therefore,

more likely to go unnoticed in the data.

In what follows, we continue our study of the relation between corruption and growth,

but we concentrate our analysis in the case of free countries only.

3.2. The role of the government

So far, in the case of free countries we have shown evidence supporting the existence of

a quadratic relationship between corruption and growth. Thus, holding everything else

constant, the rate of growth of an economy is greatest when there is a low but positive

level of corruption.

At least two potential explanations exist for these findings: On the one hand, as proposed

by Huntington (1968) and De Soto (1989), it is possible that corruption promotes investment

Table 8


The role of government

(1) (2)

Corruption 0.012 0.002

(2.18) (0.58)

Corruption2 �0.0012

(�2.52)

Gov*corruption 0.0001 �0.0001

(0.56) (�0.58)

POP 0.006 0.005

(1.12) (1.01)

SED 0.0003 0.0003

(2.00) (1.99)

Initial GDP �2.5E�6 �1.9E�6

(�3.15) (�2.47)

Investment 0.0018 0.002

(2.92) (3.10)


(�2.23) (�1.51)

Instability 0.002 0.002

(0.60) (0.47)



that is otherwise hindered by government procedures, bureaucratic red tape, and other

regulations. On the other hand, as pointed out by Klitgaard (1988) and Acemoglu and

Verdier (1998), it is possible that the resources necessary to combat bureaucratic corruption

become greater as the level of corruption decreases and thus, that a small but positive level of

corruption is optimal for the economy.5

Which one of these explanations is more accurate is a question of importance, as they

both have different implications regarding the role of the government sector: in the first case,

the greater the scope of the government, the greater the optimal amount of corruption. In the

second case, the amount of the government expenses can have both positive and negative

effects on the marginal cost of combating corruption and it would be difficult to say a priori

which one of those effects dominates the other.6

In order to study the role of the government, an interaction term between corruption and

the share of government expenditure was included as an explanatory variable and the fixed

effects estimations were recalculated. Table 8 shows the results. Column (1) uses the

unrestricted specification while column (2) studies the role of the government within the

restricted model where corruption enters the equation linearly.

6 An increase in public sector wages, for example, is likely to make it easier for governments to combat

corruption. An increase in public sector military expenses, in contrast, is more likely to make it harder (see Tanzi

and Davoodi, 1998).

5 In an alternative theoretical formulation of corruption and crime, Liew (1992) finds that at high levels of

corruption only a massive injection of resources can reduce corruption, whereas for low levels of corruption any

increase in resources will reduce it.


Authors such asMauro (1995, 1998) and Tanzi and Davoodi (1998), who have reported a

linear and negative correlation between corruption and growth, have also claimed that this

result is partly due to the interaction between corruption and government expenses. As they

explain, corruption alters the composition of government expenses towards less productive

activities, and thus, the greater the government expenses are, the greater the negative effects

of corruption are as well. Thus, a linear model of corruption is better suited to study these

arguments.

The results shown in Table 8 regarding the role of the government are similar for both

specifications.7 In both columns, the estimated coefficient for the interaction term is

statistically insignificant. In column (1) the coefficient of the government expenses is

negative and significant at the 1% level. In column (2) the coefficient of the government

expenses is also negative but insignificant. Thus, although the level of government

expenses seems to have a negative effect on growth, it does not alter the growth

maximizing level of corruption.

These results are not sufficient to support either one of the arguments made by authors

like Huntington (1968) or Mauro (1998). In one argument, bigger governments will make

corruption more productive or beneficial as bigger governments provide additional

bureaucratic constraints. In the other argument, bigger governments make corruption more

detrimental as corruption reduces the allocation efficiency of the spending. In either case,

there is a direct link between the size of the government and the effects of corruption on

growth. We do not find such a direct link.

In contrast, the results in Table 8 are supportive of the ideas put forward byAcemoglu and

Verdier (1998) and Klitgaard (1988). In their works, it is the marginal cost of combating

corruption and not the size of the government sector that directly determines the growth-

maximizing level of corruption. The question regarding the links between the costs of

combating corruption and the amount of government expenses, however, goes beyond the

scope of this paper and is left for future research.

In sum, a definitive judgment about the exact role of the government cannot be made in

this study. Direct measures of the cost of combating corruption, of the cost that bureaucratic

regulations impose on investment and of the misallocation of government expenses due to

corruption would be needed to explore this issue further.

4. Conclusions and future research

The empirical evidence reported here supports the claim that the type of political regime

is an important determinant of the relation between corruption and economic growth. For the

case of free countries we find evidence of a non-linear relationship between corruption and

income growth. This relationship is not modified by the size of government expenditures.

In these countries, controlling for all other characteristics, the level of corruption that

maximizes the rate of growth is greater than zero. This finding remains unchanged under

7 When the interaction term of government and corruption is included at the same time than corruption and

corruption squared, some collinearity is created. The correlation coefficient between either corruption or

corruption squared and the interaction term falls below 0.45.


several specifications that include a variety of independent variables and fixed-effects

regressions.

The empirical literature that noticed a linear relationship between corruption and growth

failed to differentiate between free and not-free countries. Once this differentiation is made

the alternative specification proposed in this study is preferred to the traditional one in terms

of robustness and goodness of fit. Thus, the incorporation of the Freedom Index proves to

be a key element in the analysis and may be an important avenue of future research.

The fact that the effects of corruption are not independent of other political and

institutional elements is important by itself as it suggests that corruption might not be an

inherent evil of developing economies but the consequence of other government policies, or

socio-political circumstances; and thus, that public policies designed to eliminate corruption

alone might not be optimal for growth.

Conducting more empirical analysis to further clarify the role of government in a corrupt

economy would require a more detailed data set. In this respect, the world development

indicators have included since 2002 two variables that attempt to measure the costs of slow

bureaucracies directly. These variables include the number of days it takes to establish a legal

business, as well as the actual costs. Future studies could benefit from this data.

Acknowledgements

We wish to thank Rowena Pecchenino, Gerhard Glomm, Jeffrey Wooldridge and two

anonymous referees of this journal for their comments. We also thank Thomas Herzfeld

and Jungmin Lee for their assistance. All errors are ours.

Appendix A. Variable definitions

GDP growth Annual growth rate of GDP per-capita. World Development Indicators

(2004)

ICRG International country risk guide corruption index. Political Risk Services Inc.

IMD Institute for management development corruption index. World Competitiveness

Yearbook

CPI Corruption perceptions index of corruption. Transparency International.

POP Average annual population growth. World Development Indicators (2004)

SED Gross enrollment rate in secondary schooling. World Development Indicators

(2004)

GDP Yearly gross domestic product per-capita, constant 1995 USA dollars. World

Development Indicators (2004)

Investment Yearly gross fixed capital formation (% of GDP). World Development

Indicators (2004)

Government General government final consumption expenditures (% of GDP). World

Development Indicators (2004)

Instability Standard deviation of the political rights sub-index provided by Freedom House

International


LA Dummy variable taking the value of 1 for Latin American countries and 0 otherwise

AF Dummy variable taking the value of 1 for African countries and 0 otherwise

SC Dummy variable taking the value of 1 for Scandinavian countries and 0 otherwise

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