Corruption, economic development
and environmental policy
Author:
Lorenzo Pellegrini
Institute for Environmental Studies - IVM
The Netherlands
1
Corruption, Economic Development and Environmental Policy
Lorenzo PELLEGRINI1
Suncica VUJIC2
JEL codes: C31, E13, K00.
Keywords: Corruption; Development; Environmental Policy; Institutions.
1
Institute for Environmental Studies (IVM), Vrije Universiteit Amsterdam, De Boelelaan 1115, 1081 HV,
Amsterdam, The Netherlands. Tel: +31-20-4449555, Fax: +31-20-4449553, e-mail: [email protected].
2
Tinbergen Institute Amsterdam (TIA), Roetersstraat 31, 1018 WB, Amsterdam, The Netherlands. Tel: +31-20-
5513500, Fax: +31-20-5513555, e-mail: [email protected].
Corruption, Economic Development and Environmental Policy
2
1. INTRODUCTION
Institutions have recently become the centre of attention of much empirical analysis in
economics. Increasing evidence has given support to the hypotheses that institutions are persistent
over centuries and that they are fundamental determinants of the economic performance. In this
paper we put forward some evidences on the causal relationship of corruption on economic
development and we perform an empirical analysis checking two hypotheses: the first one is that
institutions, via economic development, can explain the stringency of environmental policy; the
second hypothesis is that, even controlling for actual income, institutions still have an important
effect on environmental policy. Our empirical analysis, performed through OLS and 2SLS
regressions with the use of instrumental variables, confirms both hypotheses.
Increasing empirical evidence is available to justify the interest that part of the economic
science has always shown with respect to institutions. Since long, theoretical arguments have
been put forward to suggest that “institutions matter” and that they are “the underlying
determinant of the long-run performance of economies” (North, 1990, p.107). Empirical literature
(e.g. Mauro, 1995 and Knack and Keefer 1995) has shown the presence of correlation between
measures of institutional quality and economic development. A new stream of literature, making
use of data on institutions, which became available relatively recently, found evidence of the
persistence of institutions and of their exogeneity with respect to economic development. i.e.
evidence has been produced supporting the hypothesis that the causal relationship between sound
(inappropriate) institutions and good (bad) economic performance runs from the former to the
latter and not vice versa. Acemoglu, Johnson and Robinson (2001) have shown that settlers’
mortality rates, used as an instrument for the quality of institutions at the time of early
colonization, can explain the institutional quality of former colonies at present days (after over
four centuries). Furthermore, they provided evidence supporting the hypothesis that institutional
quality can explain income levels. Easterly and Levine (2001), performed a similar analysis,
making use of a much wider set of explanatory variables (other than settlers’ mortality rates) for
the quality of institutions in colonial times and reached similar conclusions. Engerman and
Sokoloff (2002) demonstrated the effect of income inequality on institutions that were set at the
time colonisation started. They show the effect of inequality on the inclusiveness of the
Corruption, Economic Development and Environmental Policy
3
institutions that were shaped at that time, underline the persistence over centuries of these
institutions and the effects of their characteristics on present economic performance. Acemoglu,
Johnson and Robinson (2002) show that the reversal of fortune that took place in the 18th and 19th
century among European colonies, with the poorest colonies developing quickly and the richest
lagging behind, can be explained through the institutional setting of those countries in the 16th
century (rejecting the hypothesis that they were geographical factors to determine economic
development paths).
There is a rich literature also on the effects of institutions on policies’ formulation and
implementation: political economy (e.g. Kreuger 1993a, 1993b) ascribes socially sub-optimal
public policies not to a lack of cognitive capabilities but to the efforts of policy makers to capture
rents. Many authors have been explaining phenomena of under-provision of public goods through
models of self-interested policy-makers’ behaviour (e.g. Deacon, 1999 provided a model and a
detailed empirical analysis relating the level of government inclusiveness with the availability of
public goods). The case of environmental policy deserves to be further investigated being an
example of policies, involving many aspects of public goods or common pool resources’
problems, that clearly ask for sensible public intervention. The presence of institutional
deficiencies that can induce policy makers to follow their own interests rather then societal ones,
seems to be a major challenge for attaining sensible policies regarding public goods in general
and environmental quality in particular.
Due to the importance of institutional soundness for economic development and for the quality
of public policies, it appears that the increasing stringency of environmental policy related to
increasing income could be due to different reasons. On the one hand, it is possible that income
affects environmental policies because citizens will ask for environmental quality improvements,
as their income increases, and governments are responsive. On the other hand it is possible that,
income paribus, governments will respond in different ways, depending on their institutional
characteristics, to the claims of their citizens.
In the next section we present the data used in the paper. In section 3 the theoretical hypothesis
are stated, tested and then checked for robustness. Section 4 concludes and indicates scope for
future research.
Corruption, Economic Development and Environmental Policy
4
2. THE DATA
New scope for the test of the interaction among institutions, economic development and public
policies came from the empirical literature since, eventually, quantitative data on institutions
became available3. These data are obtained from sources that are used by companies to evaluate
investment opportunities in foreign countries and cover aspects of the economic milieu that are
considered important by the economic agents that will make use of them: risk of expropriation,
definition of property rights, contract enforceability, infrastructure quality, working of markets,
bureaucratic efficiency, political and institutional stability, repudiation of contracts by
government and so forth. Moreover, data on institutional settings on different countries started to
be available as a consequence of the general availability of more data and of the widespread use
of statistical data and indices by international institutions and by policy advisors. The data utilised
in the paper belong to the latter category: they are data on corruption, available since 1995,
gathered by an international institute concerned with corruption: Transparency International. In
the original version, to a lower value of the index, that ranges from 1 to 10, corresponded a higher
level of corruption. In the present study, the original values have been subtracted to 10, so an
“increase in the corruption index” will have the intuitive meanin g “increase in corruption”.
The data on environmental policy stringency we used are based on the reports that were selfcompiled by individual countries prior to the UN Earth Summit that took place in Rio in 1992.
These data reflect several aspects of agricultural environmental policy: from policy formulation to
its implementation to general awareness in the public of environmental issues. Their base year is
1990. The reports were completed by the governments, representatives of the business sector and
of Non Governmental Organizations of the countries concerned. The presence of NGOs in the
process should warrant objectiveness to the survey and avoid complacency typical of
governmental self-reporting. Dasgupta, Mody, Roy and Wheeler (1995) first developed an index
of environmental policy stringency (for the agriculture sector) based on the questionnaires
collected by the UN Environmental Program. Their country sample included 31 countries
randomly chosen among the ones that participated in the conference. Fredriksson and Svensson
(2002) compiled, using the same methodology, the index for another 31 countries (also randomly
selected); now with a sample of 62 countries it is possible to perform cross-country analysis with
3
The Freedom House indexes of political freedoms and civil liberties (also known with the name of their creator
Raymond Gastil) and the indicators from Business Environmental Risk Intelligence were the first to appear in the
early ‘70s.
Corruption, Economic Development and Environmental Policy
5
some confidence in the results. The index ranges from 1 to 250, with a lower value implying a
less stringent policy.
The data on income used here are the most commonly used in empirical literature: the Summer
and Heston database, specifically the Penn World Table 6.1 (income levels are adjusted taking
into consideration Purchasing Power Parity). The variables used as instruments for corruption, in
the robustness checks, are an ethnolinguistic fractionalization index and dummy variables that
indicate the legal origin of the countries (French, German, British or Scandinavian). These
variables were available from the World Bank data base.
A thoroughly description of the data and of their sources is provided in the appendix.
3.
THEORETICAL HYPOTHESES AND THEIR EMPIRICAL TESTING
A common suggestion, found in the theoretical literature and in recent empirical analysis, is
that institutions are one of the main determinants of economic performance (see the discussion in
the introduction). An intuitive expectation is that, if the environment is a normal good4, and if
public policies tend to reflect the preferences of citizens, at higher levels of income environmental
policy should be more stringent then at lower levels. The hypotheses we will test concern the way
corruption affects environmental policy. We expect sound institutions to increase the supply of
environmental quality, and thus the stringency of environmental policies, through their effect on
income. Then, we test whether this is the whole effect of corruption on environmental policy.
That is, we check if, after controlling for income, institutions are still a significant variable in
explaining environmental policy stringency or not.
It must be noted that the subject of our enquiry is closely related to the Environmental
Kutznets Curve (EKC), but we are not testing the curve itself. Indeed, the fact that increasing
demand for environmental quality (reflected into more stringent policy) would actually produce
improvements in environmental quality requires further assumptions (apart from the one that
governments’ policies r eflect citizens’ preferences) regarding the effectiveness of the policy. At
the same time, the studies that try to decompose the EKC usually assume competing underlying
forces that produce the inverted-U shape for certain pollutants (Panayotou, 1997). On one side are
scale effects that induce polluting emissions to increase with the size of economic activity. On the
4
A normal good is a good for which an increase in income causes an increase in demand. i.e. it is a good whose
demand elasticity on income is positive.
5
Corruption, Economic Development and Environmental Policy
6
other side there are composition and income effects. The composition effect refers to possible
shifts of the specialization of the economy at different sectors of the economy. Thus, an economy
that is decreasing the size of its industrial sectors in favour of services could experience an
improvement of some environmental indicators. Additionally, there is the pure income effect that
would take up the evolution of citizens’ preferences and the availability of resources for
abatement. This last effect should be unambiguously positive in environmental terms, and is one
of the main issues of our study. Specifically, we will test the existence and magnitude of such a
channel and the possibility that an institutional variable could affect independently the way
environmental policy evolves.
We test the above-mentioned hypotheses regressing a system of two equations. The first is:
ln(Y90i )=α 0 +α1Ci +α 2 Zi +ε i ,
(1)
where ln(Y 9 0 i ) is the natural logarithm of income in the year 1990, is our index of corruption
and Zi is the vector of control variables. The second is:
eps i =β 0 +β1ln(Y90i )+β 2 Zi +ε i ,
(2)
where eps is the Environmental Protection Stringency Index.
The results of the first regression are reported in Table 1. In order to test the hypothesis that
institutional quality, summarized by our corruption index, is a fundamental determinant of income
level we regress equation (1) without any variable in the Zi vector. The corruption index alone, as
a dependent variable, produces the considerable result of an adjusted R2 of 64%. The coefficient is
statistically and economically significant and, overall, the result is remarkable. It must be noted
that the value of the coefficient of corruption denotes the importance of institutions in general and
not only of corruption. Indeed, the widespread of corrupted practices among civil servants and
politicians discloses cumbersome bureaucratic rules, bureaucrats that are not well prepared or do
not have proper incentives, a judiciary system that is ineffective; i.e. the existence of corruption
reveals the existence of several institutional problems. Throughout the paper, mentioning the
effects of corruption we will partially refer also to the other institutional debacles that are
correlated with corruption. Furthermore, comparing the results of regression (1) and (2) Table 1, it
is apparent that there could be a mis-specification problem: indeed, omitting the relevant variable
Corruption, Economic Development and Environmental Policy
7
from the model (that is income in 1980), the coefficient on the corruption index is likely to be
biased and the standard error overestimated. At the same time, the R2 in regression (1) is already
very high, and magnitude and significance of the coefficient of corruption so large that we
considered it worth mentioning this result though it must be interpreted cautiously (the same
considerations apply to the first two regressions reported in Table 2).
Moreover, running such a simple OLS regression does not say much about the causal
relationship between institutions and income. Indeed, it can be argued that rich countries can
afford good institutions and that it is welfare that creates sound institutions and not the other way
around. Theoretical literature emphasizing the importance of institutions in the stream of neo and
old institutional economics has been supporting the hypothesis that it is the institutional milieu
that shapes the economic performance of economies. Recent empirical analyses, mentioned in the
introduction, also support this hypothesis that is auxiliary to the other two hypotheses that are
explored empirically in the paper. Furthermore, in order to check for the validity of our
conclusions we run the regression (4) using the natural logarithm of income per capita in the year
1980 (lnY1980) as a control variable (the variable Zi) and using instrumental variables for
corruption.
Taking into consideration, as a control variable, a measure of income in the year 1980 tells us
if there is some explanatory power left in the corruption variable with the respect of income in
1990, for a given level of income in 1980. As expected statistical and economic significance of
the corruption index are reduced, but the variable is still significant at a 5% level and the new
coefficient is still negative, implying that corruption maintain its negative effect on economic
performance.
Another way to check for the way causality runs, in the relationship between economic
development and corruption, is to use as instrumental variables for corruption some variables that
cannot be influenced by economic development. Mauro (1995) was the first to use instrumental
variables to approximate corruption, and used the ethnolinguistic fractionalisation index (ethno)
developed by Taylor and Hudson (1972). The reasoning behind using this index is that ethnic
groups are supposed to have internal solidarity that induces their members to use their power to
favour the members of their group; eventually this sort of behaviour would cause corruption.
Fredriksson and Svensson (2002) use the legal origin of the country as an instrument for
corruption: they claim that the legal system of the country affects the way property rights are set
and this in turn affects corruption. At the same time, both ethnolinguistic fractionalisation and
Corruption, Economic Development and Environmental Policy
8
legal origins are long lasting institutions, unlikely to be affected by income, and this makes good
instruments out of them6. Using these instruments separately in the first stage regression creates
low adjusted R2s, so we preferred to use them both. We estimate this equation:
Ci = γ 0 +γ 1ethnoi +γ 2 legorigi +ε i ,
(3)
where ethno is the index of ethnolinguistic fractionalisation (equal to the probability that two
randomly selected persons in the populace of a country belong to different ethnic groups) and
legorig is a vector of dummy variables that take value 1 if the country has German, Scandinavian,
British or French legal origin. The adjusted R2 is 25%7.
Using the predicted value of Corruption into equation (1), we obtain the results summarised
into regressions (3) and (4), Table 1. The results of the second stage regression (3), indicate that
even using instrumental variables that are less likely to be affected by income level, as
instruments for corruption, corruption retain a considerable explanatory power with the respect to
economic development, indeed the adjusted R2 is 36% and the coefficient is, again, significant (as
it was in regression (1)). A regression with just one dependent variable with such a high R2 is a
noteworthy result.
6
Acemoglu, Johnson and Robinson (2001) criticise the use of ethnolinguistic fractionalisation as a instrument
because, according to them, economic development in Europe is the underlying cause of the relative linguistic
homogeneity of European countries.
7
The adjusted R2 of corruption regressed only on legal origins and on ethnolinguistic fractionalisation are,
respectively, 19 and 21%.
Corruption, Economic Development and Environmental Policy
9
Table 1. Regressions as in equation (1)
Independent
variables
(1)
(2)
LnY1990
lnY1990
(3)
lnY1990
(4)
lnY1990
10.340***
1.30***
10.692***
1.289***
(0.180)
(0.484)
(0.353)
(0.402)
·
Corruption
–0.365***
-0.085***
(1.445)
(0.084)
(0.022)
Constant
Corruption
–0.289***
(2,684)
(0.034)
–0.041**
(0.017)
lnY1980
0.891***
0.912***
(0.9642)
(0.047)
(0.038)
Adjusted R2
0.64
0.96
0.36
0.97
Number of cases
41
41
33
33
OLS (regressions 1, 2) and 2SLS (regressions 3, 4) estimation with lnY1990 per capita in the year 1990 as
dependent variable. Superscripts *, **, *** correspond to a 10, 5, 1% of significance respectively. Standard
deviations are in parenthesis under the independent variables (for Corruption it is based on the 41 countries
sample), standard errors are in parenthesis under the coefficients.
To check the hypothesis regarding the effect of institutions on environmental policy we
estimate equation (2), the results are summarised in Table 2. Running regression (5) we check the
relationship between income and environmental policy; we find that higher levels of income
induce environmental policy to be more stringent. The coefficient on lnY1990 is positive and
significant and the adjusted R2 is 75%. Income is a fundamental determinant of the environmental
policy stringency level. This result conforms to what we expect from standard economic theory,
assuming environmental quality to be a normal good. In regression 6, we run a 2SLS where the
first stage regression is regression (3) and we use the estimated lnY1990 into equation (2). This
regression gives us an estimate of how much of the environmental protection stringency index can
be explained by corruption through its effect on income. The coefficient is again positive and
significant and the R2 is very high: 74%. The part of income that is determined by institutions is a
fundamental variable for environmental policy stringency setting.
The last question we want to address is whether corruption has an influence on environmental
policy stringency after controlling for income. In regression (7), we see that this is the case: after
Corruption, Economic Development and Environmental Policy
10
controlling for income, the coefficient of corruption is still highly significant statistically and is
considerable in magnitude. To estimate the remaining effect (apart from the indirect effect
through income) of corruption we can see that multiplying the standard deviation of corruption for
the coefficient we obtain: 2.684X7.157=19.209. An increase (decrease) of one standard deviation
of the corruption index would increase (decrease) the environmental policy stringency index by
19 points (the standard deviation of the eps is 41.13) after controlling for income. In regression
(8), we check this result using the corruption index determined with the instrumental variables.
The values identified in this regression confirm our previous finding.
Table 2. Regressions as in equation (2)
Independent
(5)
(6)
(7)
(8)
variables
eps
eps
eps
eps
–209.762***
–288.659***
–32.210
–134.918***
(30.849)
(38.916)
(50.086)
(47.274)
Constant
lnY1990
37.073***
21.019***
32.685***
(0.963)
(3.401)
(4.815)
(4.348)
·
lnY
1990
45.820***
(0.775)
(4.299)
Corruption
–7.157***
(2,684)
(1.727)
·
Corruption
–8.561***
(1.445)
(2.581)
Adjusted R2
0.75
0.74
0.82
0.83
Number of cases
41
41
41
33
OLS (regressions 5, 7) and 2SLS (regressions 6, 8) estimation with the Environmental Protection Stringency
Index (eps) as dependent variable. Superscripts *, **, *** correspond to a 10, 5, 1% of significance respectively.
Standard deviations are in parenthesis under the independent variables (for lnY1990 it is based on the 41 countries
sample), standard errors are in parenthesis under the coefficients.
Summing up, in the paper we checked empirically a number of hypothesis concerning the
relationship between institutions, economic development and environmental policy. Our findings
confirm the hypothesis that institutions are fundamental determinants of the level of economic
development of economies and that, in turn, economic development is a primary determinant of
environmental policy stringency. Furthermore, we checked what the effects of institutions are on
Corruption, Economic Development and Environmental Policy
11
environmental policy stringency before and after taking into account their effect on income. The
total effect of institutions on environmental policy is substantial. Moreover, the direct effect of the
institutional variable, after controlling for income, is still considerable and underlines the
relevance of institutional qualities, not only for economic development, but for welfare enhancing
policies in general.
4.
CONCLUSIONS
In this paper we tested some hypothesis about the importance of institutions for economic
development and for environmental policy. We found that institutions are relevant determinants
of the income level of countries. Additionally, through their effect on income, we found that
institutions are important factors in shaping environmental policies. These results confirm the
intuitive hypothesis that, if environmental quality demand is increasing in income and sound
institutions foster economic development, institutional quality will produce stricter environmental
policies. Moreover, we found that institutions have also an additional effect (that does not depend
on the influence of institutions on income) on environmental policy. That is, given the same
income level, countries with better institutions will still have a more stringent environmental
policy.
The way institutions can affect environmental policy are numerous and estimating the
existence of a remaining effect, apart from the income channel, can be considered as the first step
into analysing which institutional settings, and for what reasons, can give way to more rigorous
environmental policies. At the same time, considering the positive effect that sound institutions
have on income levels, there seems to be scope for conjugating economic development and
environmental protection.
Though institutions have shown a tendency to persist over time, becoming conscious of their
importance, individual countries and international institutions could focus their attention not only
on policy soundness but also on the quality of the overall institutional environment. The
possibility of challenging the inertia of institutions must be verified empirically now that a
consensus about the importance of institution’s strength is emerging. At the same time, realising
that institutions do not just affect the economic performance of countries, but also the way
welfare-enhancing policies are set (such as environmental ones) suggests further arguments in
favour of strategies apt to improve institutional quality.
Corruption, Economic Development and Environmental Policy
12
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APPENDIX 1: REGRESSIONS’ VARIABLES SOURC ES AND DESCRIPTION
The data collected and elaborated in the paper are from the following database:
•
Penn
World
Table
6.1
by
Alan
Heston
&
Robert
Summers
(http://pwt.econ.upenn.edu/; http://webhost.bridgew.edu/baten). Income measures.
•
The World Bank Database available in the internet site of the world bank
(http://www.worldbank.org/research/growth/pdfiles/request2.xls). Data on legal origins
and ethnolinguistic fractionalisation.
•
Corruption
Perceptions
Index
by
Transparency
International
(http://www.transparency.org/) and Center for Globalization and Europeanization of
the Economy, Georg-August-University of Goettingen (http://www.gwdg.de/~uwvw/).
Data on corruption.
•
Fredriksson and Svensson database provided directly by the authors. Data on
environmental policy stringency in the agriculture sector.
Y 9 0 ; Y 8 0 is income per capita in the years 1990 and 1980, respectively.
C is an average of the value of the corruption perception index for the period 1980-1985
(Corruption Perceptions Index).
eps is the index of environmental policy stringency for the agriculture sector.
ethno is the index of ethnolinguistic fractionalization.
legorig is a dummy variables that indicate the legal origin of the countries (French, German,
British or Scandinavian).