Content uploaded by Toke S. Aidt
Author content
All content in this area was uploaded by Toke S. Aidt on May 12, 2015
Content may be subject to copyright.
CESifo DICE Report 2/2011
15
Forum
THE CAUSES OF CORRUPTION
T
OKE S. AIDT*
Corruption: what and where
Corruption is a persistent feature of human soci-
eties over time and space. The sale of parliamentary
seats in the “rotten boroughs” of Great Britain be-
fore the Great Reform Act of 1832 and “machine
politics” in the expanding immigrant cities in the
United States at the turn of the 19th century are
just two historical examples. Contemporaneous ex-
amples also abound, and not only from developing
countries, such as Nigeria, India and the Philippines,
and transition economies, such as Russia. The re-
cent “expenses scandal” that engulfed the House of
Commons in the United Kingdom serves as a time-
ly reminder that corruption regularly shows its face
also in the developed world.
While corruption is usually recognised when con-
fronted with it, it has proved more difficult to find
and agree on a precise and encompassing defini-
tion. Most economists, however, are satisfied with
some version of the follow-
in
g definition: “corrup
tion
is an act in which the pow-
er of public office is used
for personal gain in a man-
ner that contr
avenes the
rules of the game” (Jai
n
2001) or co
r
ruption is “the
sale by government officials
of government property for
personal gain” (Shleifer and
Vishn
y 1993). The latter is
also the definition that the World Bank
employs.
Examples of corruption thus defined include taking
bribes in return for issuing licenses; accepting a kick-
back for granting a defense contract; not enforcing
rules and regulations in exchange for a payment and
so on.
The Figure provides a snap-shot of the world dis-
tribution of corruption in 2010 based on the cor-
ruption perception index published yearly by
Transparency International. Each bar represents
one of the 178 countries surveyed in 2010. The
index ranges from 0 to 10, with a score of 10 indi-
cating absence of corruption and a score of 0 indi-
cating widespread corruption. To the left with high
index values, we find the Scandinavian countries
and Singapore, in the middle of the distribution we
find countries like Thailand and Greece, while the
most corrupt countries located to the far right in-
clude Iraq,Afghanistan and Somalia.A major chal-
lenge for economists and other social scientists
interested in the causes of corruption and in offer-
ing advice to policy makers is to explain not only
why corruption varies over time, as suggested by
the two historical examples given above, but also to
explain the distribution of corruption across the
world illustrated in the Figure.
* Faculty of Economics and Jesus
College, University of Cambridge,
UK. E-mail: tsa23@econ.cam.ac.
uk. This report was written while
visiting the Institute for Quanti-
tative Social Science at Harvard
University. The hospitality of the
Institute is gratefully appreciated.
I am also grateful to Jakob d
e Haan
for helpful advise.
0
2
4
6
8
10
Source: Transparency International (http://www.transparency.org).
W
ORLD
D
ISTRIBUTION OF
(P
ERCEIVED
)
C
ORRUPTION
2010
Corruption perceptions index
Countries
Notes: The corruption perceptions index scores countries on an inverse scale from 10 (very clean) to 0 (highly
corrupt). Each bar represents one of the 278 countries surveyed and ordered from low to high corruption.
Figure
A conceptual framework
To structure thinking about a complex issue such as
corruption, it is useful to have a conceptual frame-
work in mind. At the risk of over-simplifying a vast
theoretical literature on the causes of corruption, sur-
v
eyed superbly in, e.g., Tanzi (1998), Rose-Ackerman
(1999) or Jain (2001), at least three conditions are
necessary for corruption in the public sector to arise
and persist:
1. Discretionary power: the relevant public officials
(bureaucrats, politicians, etc.) must possess the
authority to design or administer regulations and
policies in a discretionary manner.
2. Economic rents: the discretionary power must
allow extraction of (existing) rents or creations of
rents that can be extracted.
3. Weak institutions: the incentives embodied in po-
litical, administrative and legal institutions must
be such that public officials are left with an incen-
tive to exploit their discretionary power to extract
or create rents.
Much, if not all, theoretical work on the causes of
corruption can be linked back to these three basic
conditions. Two contrasting views about how these
conditions interact, however, stand out. The first view
is the “institutional view”. It emphasises the role of in-
stitutions
(broadly defined to include political, bu-
reaucratic, juridical and economic institutions) and
presumes a causal order running from weak institu-
tions to corruption to poor economic outcomes. To
give an example, the absence of democratic account-
ability may foster political corruption which, in turn,
encourages rent extraction and excessive regulation
of economic activity. Corruption is, in other words,
seen as the symptom that something else, more fun-
damental, is wrong. Of course, feedbacks from eco-
nomic conditions to corruption are usually also ac-
knowledged as being important because they affect
the presence or absence of economic rents, and it is
also possible over longer time spans to imagine that
corruption could be the cause of weak institutions.
Yet, the organising and in many cases very powerful
analytic principle is to look for weaknesses in the
institutional framework to find the root causes of
corruption.
The second view is the “social interactions view”. It
emphasises that corruption is the outcome of self-
reinforcing social processes and downplays the
causal role of institutions.The basic idea can be illus-
trated with an example along the lines of Andvig and
Moene (1990). Corrupt colleagues are less likely to
report that you are corrupt than honest colleagues
simply because corrupt colleagues do not want to
risk triggering an external inquiry. This makes the
probability of being discovered/reported a decreas-
ing function of how many others in the relevant so-
cial group take bribes. The incentive of any one indi-
vidual to accept a bribe is obviously stronger when
the likelihood of being discovered is lower. Com-
bining these plausible assumptions, two types of sta-
ble situations can emerge: one in which no one takes
bribes because anyone who does would be reported
and punished, and one in which everyone take bribes
because no one will ever be reported and caught. In
other words, societies with otherwise very similar in-
stitutions and economic conditions may end up with
very different levels of corruption. Shifts in institu-
tions and economic conditions may trigger a swing
from one equilibrium to the other, but it requires big
shifts. According to this view, the key to understand-
ing corruption is to understand how underlying so-
cial interactions work.
Cross-national studies on the causes of corruption
At the macroeconomic level, much of what we know
about the causes of corruption originates from cross-
national comparisons of survey-based corruption
indices. These indices can be divided, roughly speak-
ing, into two categories. The first category contains
indices based on corruption perceptions, i.e., subjec-
tive assessments by business consultants, by local
and international businessmen or by ordinary citi-
zens about how much corruption there is in a given
country. The three most popular indices of this type
are the Corruption Perception Index, published by
Transparency International (and shown in the Figu-
re), the Control of Corruption Index, published by
the World Bank, and the Corruption Index published
by the International Country Risk Guide. These
meas
ures of corruption have many weaknesses (see,
e.g., the extensive discussion in Lambsdorff 2005);
one of the major ones being that there may be a sig-
nificant gap between perceptions and facts. The sec-
ond category includes indices of experienced corrup-
tion. As the name suggests, these derive from self-
reported experiences with corruption. The World
Bank’s World Business Environment Survey, for
example, asks managers in many countries to re-
spond to the following statement: “It is common for
firms in my line of business to have to pay some ir-
regular ‘additional’ payment to get things done”.
CESifo DICE Report 2/2011
16
Forum
CESifo DICE Report 2/2011
17
Forum
Clearly, these indices are also problematic because
of reporting biases, etc., but other than data for a few
countries on criminal convictions or legal cases re-
lated to corruption (e.g., Italy and the US, see Goel
and Nelson 2011), these are the macro data avail-
able, and they have, despite their weaknesses, given
a significant boost to research into the causes of cor-
ruption during the past decade. In fact, 35 (and still
counting) published, empirical studies have investi-
gated the relationship between these indices and at
least 75 potential causes of corruption.
The potential causes can usefully be divided into
four groups: (1) economic and demographic factors;
(2) political institutions; (3) judicial and bureaucrat-
ic factors; and (4) geographical and cultural factors.
The typical study limits attention to a small number
of potential causes of particular interest and exam-
ines the reliability of the results by varying the set of
control variables (see, e.g., Treisman 2000, Paldam
2002 or Lambsdorff (2005) for examples of this ap-
proach). Unfortunately, it is not uncommon to dis-
cover that variables – such as press freedom, the size
of the public sector, economic
freedom or the level of democra-
cy – found to be significantly cor-
related with corruption in one
study are insignificant in another
which uses an alternative model
specification. Moreover, Treis
man
(2007) points out that “standard”
determinants of perceived corrup-
t
ion, such as GDP per capita and
measures of political and bureau-
cratic institutions, are largely un-
correlated with measures of ex-
perienced corruption. This is clear-
ly unsettling. One way out of the
forest, originally proposed by
Sala-i-Martin (1997) in a different
context, is to use so-called Sensi-
tivity Analysis to establish which
of the many potential determi-
nants or causes are robustly cor-
related with corruption. This in-
volves a systematic evaluation of
all possible linear (regression) mo-
dels with corruption as the depen-
dent variable and a fixed number
of potential determinants (typi-
cally 3 to 5) from the target list of
up to 75 as the explanatory vari-
ables. Sala-i-
Martin (1997)’s crite-
rion for robustness is that 95 percent of the cumulat-
ed density associated with the estimated coefficients
on the variable of interest (e.g., GDP per capita)
across all the models considered should be on one
side of zero. Sturm and de Haan (2005), however,
advocate going one step further and demand that a
robust variable should be significant at the 5 percent
level in at least 90 percent of all the regressions con-
sidered (test 1) and when there is more than one out-
come variable (as is the case with corruption where
one needs to study alternative indices) that this
should be true for the majority of outcome variables
considered (test 2). The result of such a Sensitivity
Analysis applied to the determinants of five widely
used corruption perception indices (including the
three mentioned above) are reported in de Haan
and Seldadyo (2005) and Seldadyo (2008). The Table
summarises the results by listing the 12 most robust
determinants of corruption and their correlation
with corruption. All 12 variables pass Sala-i-Martin
(1997)’s criterion for robustness for at least one cor-
ruption index, but we see that only six, in addition,
pass test 1 and that only two pass test 2. Based on
Table
The top-12 most robust determinants of cross-national corruption perceptions
Variable name Group
Sign of
correlation
Test 1 Test 2
Government effectiveness (3) (–) Yes Yes
Rule of law (3) (–) Yes Yes
GDP per capita (1) (–) Yes No
Regulatory quality (3) (–) Yes No
Political polarisation (2) (–) Yes No
Protestant fraction of the
population
(4) (–) Yes No
Presidentialism (2) (–) No No
Absolute latitude (4) (–) No No
Voice and accountability (2) (–) No No
Wage bill in % of GDP (3) (+) No No
Population size (1) (+) No No
Economic freedom (1) (–) No No
Notes: For the full set of variables included in the Sensitivity Analysis and for
information on definitions and sources, see Appendix 2 in Seldadyo (2008).
The 12 variables are the only ones that pass Sala-i-Martin’s criterion for
robustness for at least one of the five corruption perception indices studied.
“Group” refers to the four categories of causes of corruption listed in the
main text. “Sign of correlation” refers to the sign of the coefficient on the
relevant variable in 95% of the regressions. The outcome variable of these
regressions is one of the five corruption indices, ordered such that higher
values mean higher levels of corruption. Each regression includes up-to-three
potential determinants, lagged by a suitable number of years. “Test 1” re-
quires that the variable is significant at the 5% level in at least 90% of all
the regressions considered. “Test 2” requires that test 1 is passed for three of
the five corruption perception indices considered.
Source: Seldadyo (2008: chapter 3).
this, the two most robust determinants of corruption
are “government effectiveness” and “rule of law”,
from the World Bank’s Governance Matter Data-
base (Kaufmann et al. 2006). Both of these variables
correlate negatively with perceived corruption, how-
ever measured. “Government effectiveness” is a
com-
posite index related to the quality of public services,
the quality of the bureaucracy, the competency of
civil servants, and to the independence of the civil
service from political pressures, while “rule of law” is
an index related to the extent to which agents have
confidence in and abide the rules of society, the ef-
fectiveness and predictability of the judiciary and the
enforceability of contracts. A single variable from
each of the groups also passes test 1 (but not test 2).
They are GDP per capita, the degree of political
polarisation, regulatory quality and the fraction of
Protestants in the population, which are all negative-
ly correlated with corruption.
While the Sensitivity Analysis methodology is help-
ful in finding robust correlations, it does not resolve
the much more thorny issue of causality. In fact, for
many of the variables that make it to top-12, we sus-
pect that the causality might run the other way. For
example, perhaps the government is effective
because corruption is low; or agents have trust in the
rule of law because corruption is low; and GDP per
capita is high because of low corruption, rather than
the other way around.
1
The corruption-development nexus
The question of causality is particularly pressing in
the case of the corruption-development nexus. Does
the strong and robust correlation between GDP per
capita and the various corruption indices tell us that
development reduces corruption or does it tell us
that corruption is an obstacle to development? Both
directions of causation are plausible. On the one
hand, corruption feeds on rents, and corrupt public
officials have an incentive to create and maintain
rent-creating but inefficient economic policies. The
likely consequence of such inefficiencies is a reduc-
tion in national income. Likewise, corruption often
works as a tax on factor accumulation and on invest-
ments in manufactured and human capital. This can
retard economic growth. On the other hand, high
levels of national income may be associated with a
greater willingness to combat corruption or, more
subtlety, high economic growth may provide incen-
tives for public officials to eliminate corruption-in-
duced economic inefficiencies in the present in order
to hang on to their office or job for the future where
even bigger rents can be extracted (Aidt and Dutta
2008). It is, of course, also possible that GDP per
capita and corruption are jointly determined and
caused by the same underlying institutional or cul-
tural factors.
Recent research has made some headway in sorting
out this puzzle. When applied at the macro level, the
theories of corruption based on social interaction ef-
fects suggest that corruption and economic develop-
ment may feed on each other and that we should,
therefore, not expect to observe simple linear and
unidirectional relationships between proxies for the
two. Corruption-development traps may lock some
countries into a “bad” equilibrium with high-corrup-
tion-low-growth, while others may converge to a
“good” equilibrium with low-corruption-high-growth
(B
lackburn et al. 2006). Aidt et al. (2008) and Aidt
(2009) model such interdependencies empirically
and find robust evidence that countries endogenous-
ly sort themselves into different corruption-growth
regimes depending on the initial quality of their
political institutions, and that, in the short to medium
run, corruption and growth feed on each other: high
growth tends to lower corruption which, in turn, en-
hances the growth potential. Moreover, using instru-
mental variables to isolate the link from corruption
to growth, it appears that conditional on converging
to the “bad” equilibrium, corruption has little effect
on growth, while the impact is large and negative for
countries converging to the “good” equilibrium.
Gundlach and Paldam (2009), on the other hand, in-
quire into the causal order over the very long run.
Their starting point is the widely accepted assump-
tion that all countries had more or less the same
(low) level of income per capita about 200 years ago
and that the actual cross-national income distribu-
tion today, therefore, effectively represents differ-
ences in long-run growth rates. The work by Dia-
mond (1997), moreover, suggests that deep pre-his-
torical factors, such as the number of domesticable
big mammals in pre-history, the number of domesti-
cable wild grasses in pre-history, climatic conditions
favourable for agriculture, relative East–West orien-
tation, etc., sowed the seeds that explain cross-na-
tional income patterns today. Since one can reason-
ably assume that such factors are exogenous to cur-
CESifo DICE Report 2/2011
18
Forum
1
Seldadyo (2008) uses lagged values of the potential determinants
in his Sensitivity Analysis to partially address this issue.
CESifo DICE Report 2/2011
19
Forum
rent corruption patterns, they can, in principle, be
used as instruments for national income in an esti-
mation of the income effect on corruption. Based on
this approach, Gundlach and Paldam (2009) con-
clude unambiguously that the long-run causality
runs from (high) income to (low) corruption or as
they put it, societies grow honest. Adding the two
pieces of evidence, it appears that in the short to
medium run, corruption and economic development
feed on each other in a self-reinforcing social pro-
cess,
2
while in the very long run, the process of devel-
opment dominates and is what causes corruption
levels to fall. The precise mechanism through which
this is supposed to happen remains, however, unclear,
but we may conjecture that it has to do with invest-
ments in stronger and more robust institutions.
Concluding remarks
So what are the causes of corruption? The short
answer is that many factors play a role and in differ-
ent ways at different time horizons. Yet, for practical
purposes, applying the two general principles – insti-
tutional weaknesses and social interaction effects –
alluded to above will in most cases not miss the tar-
get too badly and, therefore, offer a workable guide
to those interested in understanding the causes of
corruption and in doing something about them.
References
Aidt, T. S. and J. Dutta (2008), “Policy Compromises: Corruption
and Regulation in a Democracy”, Economics and Politics 20(3),
335–60.
Aidt, T. S., J. Dutta and V. Sena (2008), “Governance Regimes,
Corruption and Growth:Theory and Evidence”, Journal of Compa-
rative Economics 36, 195–220.
Aidt, T. S. (2009), “Corruption, Institutions and Economic De-
velopment”, Oxford Review of Economic Policy 25, 271–91.
Andvig, J. C. and K. O. Moene (1990), “How Corruption may Cor-
rupt”, Journal of Economic Behaviour and Organization 13(1),
63–76.
Blackburn, K., N. Bose and E. M. Haque (2006), “The Incidence and
Persistence of Corruption in Economic Development”, Journal of
Economic Dynamics and Control 30, 2447–67.
de Haan, J. and H. Seldadyo (2005), “The Determinants of
Corruption: A Reinvestigation”, unpublished paper prepared for
the EPCS-2005 Conference Durham, England, 31 March–3 April
2005.
Diamond, J. (1997), Guns, Germs, and Steel: The Fates of Human
Societies, Norton, New York.
Goel, R. K. and M. A. Nelson (2011), “Measures of Corruption and
Determinants of US Corruption”, Economics of Governance,in
press.
Gundlach, E. and M. Paldam (2009),“The Transition of Corruption:
From Poverty to Honesty”, Economics Letters 103(3), 146–48.
Jain A. K. (2001), “Corruption: A Review”, Journal of Economic
Surveys 15(1), 71–121.
Kaufmann, D., A. Kraay and M. Mastruzzi (2006), “Measuring
Governance Using Cross-country Perceptions Data”, in S. Rose-
Ackerman, ed., International Handbook on the Economics of Cor-
ruption, Edward Elgar, Cheltenham, UK.
Lambsdorff, J. G. (2005), Consequences and Causes of Corruption:
What do We Know from a Cross-Section of Countries?, University
of Passau, Passau.
Paldam, M. (2002), “The Cross-country Pattern of Corruption:
Economics, Culture and the Seesaw Dynamics”, European Journal
of Political Economy 18(2), 215–40.
Rose-Ackerman, S. (1999), Corruption and Government, Causes,
Consequences and Reform, Cambridge University Press, Cam-
bridge, UK.
Sala-i-Martin, X. X. (1997), “I Just Ran Two Million Regressions”,
American Economic Review, Papers and Proceedings 87(2), 178–83.
Seldadyo, H. (2008), Corruption and Governance Around the
World: An Empirical Investigation, Ph.D. dissertation, Rijks-
universiteit Groningen, PPI Publishers, Enschede, The Netherlands.
http://dissertations.ub.rug.nl/FILES/faculties/feb/2008/h.s.gunar-
di/11_thesis.pdf
Shleifer, A. and R. W. Vishny (1993), “Corruption”, Quarterly
Journal of Economics 108, 599–618.
Sturm, J.-E. and J. de Haan (2005), “Determinants of Long Term
Growth: New Results Applying Robust Estimation and Extreme
Bounds Analysis”, Empirical Economics 30(3), 597–617.
Tanzi, V. (1998), “Corruption Around the World: Causes, Conse-
quences, Scope and Cures”, IMF Staff Papers 45 (4), 559–94.
Treisman, D. (2000), “The Causes of Corruption: A Cross-national
Study”, Journal of Public Economics 76, 399–57.
Treisman, D. (2007), “What Have we Learned About the Causes of
Corruption From Ten Years of Cross-national Empirical Re-
search?”, Annual Review of Political Science 10, 211–44.
2
Paldam (2002) refers to this as seesaw dynamics.