P olitical instability, corruption and policy formation: the case of

Transcription

Journal of Public Economics 87 (2003) 1383–1405
www.elsevier.com / locate / econbase
Political instability, corruption and policy formation: the
case of environmental policy
Per G. Fredriksson a , *, Jakob Svensson b
a
Department of Economics, Southern Methodist University, PO Box 750496, Dallas, TX 75275 0496, USA
b
Institute for International Economic Studies, Stockholm University, 10691 Stockholm, Sweden
Received 27 January 2000; received in revised form 7 February 2002; accepted 19 February 2002
Abstract
This paper develops a theory of environmental policy formation, taking into consideration
the degree of corruptibility and political turbulence. The predictions that emerge are that the
interaction between the two variables is important. Political instability has a negative effect
on the stringency of environmental regulations if the level of corruption is low, but a
positive effect when the degree of corruption is high. Corruption reduces the stringency of
environmental regulations, but the effect disappears as political instability increases. The
empirical findings are fully consistent with the predictions of the model.
 2002 Elsevier B.V. All rights reserved.
Keywords: Bribery; Lobbying; Uncertainty; Environmental regulations
JEL classification: D72; D78; H20; Q28
1. Introduction
It is often argued that political instability and corruption induce socially
sub-optimal governmental polices, with potentially large adverse effects on social
welfare. Empirical evidence on the effects of political instability and corruption on
investment and growth also suggest that this is the case (on political instability and
*Corresponding author.
E-mail address: pfredrik@mail.smu.edu (P.G. Fredriksson).
0047-2727 / 02 / $ – see front matter  2002 Elsevier B.V. All rights reserved.
doi:10.1016/S0047-2727(02)00036-1
1384
P.G. Fredriksson, J. Svensson / Journal of Public Economics 87 (2003) 1383–1405
growth, see Alesina and Perotti, 1996; Alesina et al., 1996; Perotti, 1996;
Svensson, 1998; on corruption and growth, see Mauro, 1995; Wei, 2000; and
Fisman and Svensson, 2000). Up to now, however, the two literatures have
developed in a parallel fashion. The interaction between corruption and political
instability, and in particular their joint effects on policy formation, has not been
addressed.1 In this paper we make an attempt to reduce this gap in the literature.
We chose to study the determination of environmental policy. We believe there
are several advantages to using environmental policy as an example in a study of
the effects of political instability and corruption on policy formation.2 First,
environmental policy making is likely to be representative of many other forms of
government decision-making. Special interests influence environmental policy at
the expense of the electorate’s interests, similar to other policies. Thus, our results
are likely not to be exclusive for environmental policy-making, but have more
general applicability.
Second, a compatible index of environmental policies facing agricultural sector
producers was produced for the 1992 United Nations Conference on Environment
and Development (UNCED, 1992). This makes it possible to test the model’s
implications. Finally, in many countries, and in most developing countries in
particular, environmental policy has a relatively low priority in the political debate.
Thus, in data sets such as ours, with a large share of observations from developing
countries, the feedback from distorted environmental policy to political instability
and the probability of remaining in power is likely to be (almost) negligible. This
presumption makes it straightforward in the empirical work to identify the causal
relationships between corruption, political instability, and policy making.
Our theory builds on the model of trade policy determination by Grossman and
1
There is by now a fairly large literature on the relationship between political instability and
macroeconomic policy formation. One strand of this literature emphasizes executive instability and how
this may induce the incumbent to choose more myopic polices (see e.g. Tabellini and Alesina, 1990;
Persson and Svensson, 1989; Cukierman et al., 1992; and Svensson, 1998). The second strand
emphasizes distributional conflicts and sociopolitical instability, and how this may result in commonpool type problems (see e.g. Grossman, 1991, 1994; Grossman and Kim, 1996; Benhabib and
Rustichini, 1996). Bohn and Deacon (2000) study the effect of ownership risk on resource extraction
and deforestation. The literature on corruption and policy formation includes Shleifer and Vishny
(1994) who analyze the effects of corporatization and privatization when firms can pay bribes; Ades
and Di Tella (1997, 1999) who study the relationship between corruption, industrial policy, and
competition; Svensson (2000) who analyzes the relationship between corruption, public spending and
foreign aid; and Tanzi and Davoodi (1997) who empirically study the effect of corruption on
government public finance. For a case study of corruption and political crisis, see Perdomo (1995).
2
There is a growing literature on environmental policy making, focusing primarily on the effects of
democracy and civil liberties. Congleton (1992) finds that democracies were more likely to participate
in the Montreal Protocol, and Murdoch and Sandler (1997) report that this type of countries undertook
greater CFC emission reductions. Barrett and Graddy (2000) find that an increase in civil and political
´
freedoms improve some environmental quality measures. Lopez
and Mitra (2000) investigate
(theoretically) the effect of corruption on the relationship between income and pollution levels.
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Helpman (1994), extended to environmental policy making by Fredriksson (1997)
and Aidt (1998).3 In the present paper, a producer lobby attempts to influence the
incumbent government’s environmental policy by offering prospective bribes. The
incumbent weighs bribes (‘political contributions’) and aggregate social welfare in
an exogenously determined fashion.4 We argue that this weighing factor serves as a
useful measure of the level of corruption in the political system. Bribes are
explicitly given in order to influence government policy and not elections (see also
Schulze and Ursprung, 2001).5
We study the incentives of an incumbent government and a single producer
lobby group in a three-stage model. In the first stage, the lobby group offers the
incumbent government a bribe schedule. The size of the promised bribe depends
on the attractiveness of the policy chosen by the government in the second stage.
The lobby must also consider the exogenous probability that the policy is indeed
implemented. In the second stage, the government selects its optimal environmental policy and collects from the lobby group the bribe associated with its policy
choice.
The third stage is the policy implementation stage, when the environmental
policy decided upon in the second stage is implemented. With some exogenous
probability the incumbent faces a political crisis or challenge which leads to a loss
of power at the beginning of this period. This could take the form of, e.g. a coup
d’etat or a vote of no confidence. Note that if the incumbent government fails to
remain in power throughout all three stages, the lobby group’s bribe has yielded no
return, unless the new government implements the policy promised by its
predecessor without being committed to it.
The predictions that emerge are that the effect of corruption [political instability] on the stringency of environmental policy is conditional on the degree of
political instability [corruption]. Corruption reduces the stringency of environmental regulations, but the effect is reduced as the degree of political instability
increases. The incentive to offer a bribe is reduced when its expected return falls.
An increase in political instability has two opposing partial effects. First, bribery
becomes less attractive for the producer lobby because the likelihood that the
government remains in office throughout the policy implementation stage is
3
The modeling approach is a common agency model developed by Bernheim and Whinston (1986a).
Coate and Morris (1999), who build on Grossman and Helpman (1994), treat the political gift
offered by a firm as a bribe.
5
Rose-Ackerman (1975) points out that in practice corruption influences regulatory outcomes at
many stages and levels of the political process. Apart from bribing politicians during the initial policy
formulation, or exerting political pressure on officials to change existing rules or laws, policy makers
may also be induced to deviate from previously determined levels of monitoring, control, or
implementation of existing rules or regulations. Our theory focuses on the fine-tuning of environmental
policy, determined by formal political decisions subject to political pressure from a special interest
group. As basis for the empirical work in the later sections of the paper, the model could, however,
more generally be interpreted as incorporating all aspects of environmental policy outcomes.
4
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reduced, and thus the bribe becomes less likely to pay off. This effect of instability
is particularly pronounced when the level of corruption is high. However, this
effect is counterbalanced by the fact that the government now sees bribes as
relatively more attractive. The government is now less likely to be in office during
the policy implementation stage, and thus the probability is lower that it will
derive utility from its own policy choices. This effect of instability is strongest
when the degree of corruption is low. The net impact of political instability on
environmental policy thus depends on the level of corruption.
We test our predictions on cross-country data for 63 developed and developing
countries, and the results are consistent with the model’s predictions. On average,
corruption is significantly negatively correlated with the stringency of environmental regulations, but the marginal effect is reduced the higher the degree of political
instability. Moreover, the marginal effect of political instability is conditional on
the degree of corruption. For countries with high levels of corruption, the marginal
effect of increased instability on the stringency of environmental regulations is
significantly positive, while for countries with low levels of corruption the reverse
relationship holds.
The paper is organized as follows. Section 2 sets up the model, and Section 3
analyses the effects of corruption and political instability. Section 4 presents our
empirical work. Section 5 concludes.
2. The model
A small open economy has a ‘clean’ sector which produces a numeraire good z,
and a polluting (agricultural) sector which produces a good x. The economy has
consumers and agricultural firms. The population is normalized to 1. The
consumers derive disutility from pollution associated with the local agricultural
production. A representative consumer has utility given by: 6
U 5 c z 1 u(c x) 2 Xu
(1)
where c z and c x are consumption of the numeraire good z and good x, with world
and domestic prices equal to 1 and p * , respectively.7 u(c x) is a strictly concave and
differentiable sub-utility function. Production of x by each of the n $ 1 identical
firms is given by x i , where nx i 5 X. u is per-unit damage, which depends on the
amount h i spent by the firm on pollution control per unit of output, where uh , 0,
and uhh . 0. Thus, Xu represents aggregate emissions. The negative externality is
regulated by the incumbent government which employs a pollution tax t[T,
6
7
Corner solutions may result with quasi-linear preferences. We assume interior solutions, however.
The world market price p * is exogenously given as the country is a price taker.
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T [R, on per unit of damage from agricultural production. The determination of
the pollution tax is discussed further below.
Good z is produced with constant marginal cost equal to one. The cost of
producing good x is given by v(x i ,h i ), where we assume vx . 0, vh . 0, vxx . 0,
vhh . 0, and that vhx . 0, but ‘small’. Given the pollution tax, the profit function of
each firm is given by:
pi (t) 5 p * x i 2 v(xi ,hi ) 2 tu (h i ) x i
(2)
which yields the first-order conditions:
≠p
]i 5 p * 2 vx 2 tu 5 0
≠x i
(3.1)
≠p
]i 5 2 vh 2 tuh x i 5 0
≠h i
(3.2)
Eq. (3.1) simply states that firm i will produce up to the point where the price is
equal to the net-of-tax marginal cost. Eq. (3.2) compares the marginal cost of
reducing pollution (by increasing pollution control costs) with the marginal gain
(i.e. lower pollution taxes). Eqs. (3.1) and (3.2) implicitly define the equilibrium
values of x i and h i as functions of t: x(t) and h(t). Applying the implicit function
theorem to (3.1) and (3.2) yields ≠x i / ≠t , 0 and ≠h i / ≠t . 0; i.e. an increase in the
pollution tax reduces output and increases pollution control expenditures.
Aggregate pollution tax revenues equal:
t (t) 5 tu X(t)
(4)
where X(t) 5 nx(t). Tax revenues are assumed distributed equally to all individuals.
Let Y denote income of the representative consumer. Maximizing (1) subject to the
budget constraint Y 5 c z 1 p * c x yields consumption functions c x 5 d( p * ) 5 u 21
c
and c z 5 Y 2 p * d( p * ). The indirect utility function of a consumer can then be
expressed as V( p * ,t,Y) 5 Y 1 d ( p * ) 2 u X, where d ( p * ) 5 u[d( p * )] 2 p * d( p * ) is
the consumer surplus derived from consumption of good x. Note that there is no
consumer surplus from consumption of good z.
The profits obtained by the agricultural firms depend on the environmental
policy. The n firms are assumed able to organize into a lobby group that
coordinates a prospective bribe offer to the incumbent government. The consumers
are assumed to face sufficiently severe free-riding problems to be unable to
organize political action (see Olson, 1965). Thus, the environmental concerns are
not represented by a lobby group. The model defines a three-stage game between
the government and the lobby. Both players are risk neutral. The timing
assumptions are as follows:
Stage 1. In stage 1, the lobby group offers the incumbent government a bribe
schedule, denoted by LF . However, the lobby faces uncertainty on whether the
incumbent government will remain in power long enough for the lobby to reap a
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reward to its bribe (policy implementation occurs only in stage 3). With
probability 0 , g , 1 the government will be thrown out of office, and with a
probability (1 2 g ) it will remain. The lobby’s strategy consists of a continuous
function LF (t):T → R; i.e. it offers a specific bribe for selecting a policy t.
Stage 2. In the second stage, the incumbent government proceeds to set its
optimal environmental policy, given the lobby group’s strategy. The government
collects the associated bribe from the lobby. Bribes are used for the incumbent
politicians’ personal consumption during this stage.
Stage 3. In the third stage, the selected policy is implemented, given that the
incumbent government remains in power.8 Turnover could occur, e.g. because of a
vote of no confidence, or a coup attempt. In this case, the new government
announces that it will play the policy game again (in the next period). For
simplicity, the game between the lobby and the incumbent simply ends here in this
event.9 Since the challenger has not been able to draw bribes from the lobby within
the period, it will not be committed to past policy promises made by its
predecessor. However, we assume that with probability 0 , l , 1 the new
government chooses to implement the same environmental policy as its predecessor. It may, e.g., have other policy priorities apart from environmental policy
before the next election. With probability 1 2 l the new government sets a tax rate
t c (exogenously given) until a lobbying game starts between itself and the lobby.
When the pollution tax has been revealed, the firms set output and pollution
control levels.
The lobby must take the political instability (and the level of corruption) into
account in its formulation of its bribe schedule in the first stage. The indirect utility
(gross of the bribe) of the lobby group is given by the expected value of profits,
which equals:
EfVF (t)g ; [1 2 g (1 2 l)] np (t) 1 g (1 2 l) np (t c)
(5)
where E[.] is the expectations operator and np (t) is the lobbying firms’ aggregate
profits conditional on the tax t. In the scenario when the incumbent government is
removed, and the new government does not keep the predecessor’s policy, the
lobby’s exogenous utility equals np (t c).
The incumbent government is concerned with bribes and aggregate social
welfare. Bribes are used for personal consumption. Aggregate welfare is assumed
of concern to the incumbent government only if it stays in power, and we
8
Neither the lobby group nor the government is assumed to renege on their promises in the second or
third stages.
9
In order to keep the game relatively simple we abstract from possible strategic choices that the
lobby may make. For example, we do not model the lobby’s attempt to bribe the new government. This
may be an interesting topic for future research, however.
1389
normalize the welfare level to zero if the government is removed.10 If it remains in
power, welfare is given by:
V A (t) ; np (t) 1 d ( p * ) 1 t (t) 2 u (h(t)) X(t)
(6)
which is the sum of all profits, consumer surplus, pollution tax revenues, and
minus the consumers’ aggregate disutility from pollution. Note that we can find
the Pigouvian tax t 5 1 by taking the first-order condition of expression (6).11
The incumbent government’s objective function is given by:
E[I(t)] ; LF (t) 1 a(1 2 g ) V A (t)
(7)
which is a weighed sum of the firms’ bribe and the expected level of aggregate
social welfare. The weight a is the exogenously given weight on welfare relative
to bribes. In our view, a represents the degree of corruption since the bribe offer is
aimed at influencing government policy and not (explicitly) elections. This
formulation essentially captures the idea that the government trades off the bribe
(which it knows it will receive with probability one) with the expected value of
aggregate social welfare.
As discussed by e.g. Grossman and Helpman (1994) and Dixit et al. (1997), the
equilibrium in the well-known common agency model by Bernheim and Whinston
(1986a) maximizes the joint surplus of all parties. With a single lobby, the policy
outcome maximizes the sum of the lobby’s and the government’s joint welfare,
and the government receives a bribe just sufficient to give it the same utility level
as without a lobby.12 In our set-up, the characterization of the equilibrium pollution
tax, denoted by t * , is given by:
V Ft (t * ) 1 (1 2 g ) aV At (t * ) 5 0
(8)
Differentiation of (5) and (6) with respect to the pollution tax yields (using the
envelope theorem):
V Ft (t) 5 2 u (h(t)) X(t)[1 2 g (1 2 l)]
(9)
and
10
Once ousted from power, the incumbent can not seek re-election (see also Coate and Morris,
1999).
11
The Pigouvian tax equals 1 since it is the marginal disutility of pollution.
12
Bernheim and Whinston (1986a) show the existence of a Nash equilibrium in a common agency
¨ ¨
game. Recent work on common agency with complete information includes Bergemann and Valimaki
(1998), and Prat and Rustichini (1998, 2000). The literature on common agency games with
asymmetric information includes Bernheim and Whinston (1986b), Martimont and Stole (1999, 2001),
and Grossman and Helpman (2001). Besley and Coate (2001) and Kirchsteiger and Prat (2001) discuss
inefficient equilibria in common agency games.
1390
S
≠X(t)
≠u (h(t)) ≠h(t)
V At (t) 5 (t 2 1) u (h(t))]] 1 X(t)]]] ]]
≠t
≠t
≠h(t)
D
(10)
Substituting expressions (9) and (10) into Eq. (8), and rearranging, we find an
explicit expression for the equilibrium characterization given by:
S
D
≠X(t)
≠u (h(t)) ≠h(t)
u (h(t)) X(t)[1 2 g (1 2 l)] 1a(1 2 g )(t * 2 1) u (h(t))]] 1 X(t)]]] ]]
≠t
≠t
≠h(t)
#%%%%%"!%%%%%$
#%%%%%%%%%%%%"!%%%%%%%%%%%%$
(2)
(1 )
50
(11)
Note that the equilibrium tax rate is strictly smaller than the Pigouvian tax. Since
the first term in (11) is negative, the last term must be positive. This requires
t * , 1. The intuition is the following. With no uncertainty and absent lobbying, the
government would chose t * 5 1. Corruption and instability, by reducing the
government’s relative weight on social welfare, both force the tax rate to be lower
than the socially optimal level.13
3. Corruption, political instability, and policy
In this section we analyze the effects of corruption and political instability on
environmental policy making. The aim is to derive testable hypotheses for our
empirical work carried out in the subsequent sections. We first investigate the
effect of corruption on environmental policy. Total differentiation of Eq. (11) with
respect to a yields:
S
D
≠X(t)
≠u (h(t)) ≠h(t)
(1 2 g )(1 2 t * ) u (h(t))]] 1 X(t)]]] ]]
≠t
≠t
dt *
≠h(t)
] 5 ]]]]]]]]]]]]]]] . 0
da
uDu
(12)
where uDu , 0 represents the second-order condition of the government’s maximization with respect to t, and can be derived from Eq. (11). uDu is required to be
negative for a maximum. Since from (11), t * , 1, Eq. (12) is positive. A reduction
in the level of corruption (an increase in a) implies that social welfare becomes
increasingly important, and therefore the equilibrium tax will approach the welfare
maximizing tax rate, i.e. the pollution tax increases. However, this effect is
adjusted by the probability that the incumbent remains in power long enough to
benefit from the welfare created by setting a tax rate t * , which occurs with
probability (1 2 g ). Note that when g approaches unity (high political instability),
13
Note also that in a small open economy quantity demanded is determined by a fixed world market
price. A change in the pollution tax affects the quantity produced and thus causes a proportional change
in imports. Demand does not depend on the pollution tax.
1391
Eq. (12) approaches zero. A change in corruption has close to no effect in this case
because welfare considerations are irrelevant to the government.
Next we turn to an investigation of the effect of political instability on
environmental policy. Differentiation of (11) with respect to g yields:
!%%%%%%%%%%$#%%%%%%%%%%"
(B )
≠X(t)
≠u (h(t)) ≠h(t) !%%%%$#%%%%"
a(t * 2 1) u (h(t))]] 1 X(t)]]] ]] 2 u (h(t)) X(t)(1 2 l)
≠t
≠t
dt *
≠h(t)
] 5 ]]]]]]]]]]]]]]]]]]
dg
uDu
(A)
S
D
(13)
which is indeterminate in sign. Term A in the numerator of expression (13)
represents welfare considerations. It is positive which implies that this effect
causes the tax to decrease when the political instability rises. The greater the
probability that the incumbent government is removed, the less weight is put on
social welfare. The intuition for the (negative) term B is that the lobby reduces its
lobbying for a lower tax when the turnover probability increases. This causes the
tax to increase.
Without further restrictions on the model’s parameters we cannot determine the
sign of expression (13). However, one fact can be established. For low values of a,
expression (13) is positive, but for high values of a, it is negative. Thus, when the
regime is highly corrupt, an increase in the political instability increases the
pollution tax. When the degree of corruption is low, an increase in the political
instability reduces the tax.14
4. Empirical work
4.1. Specification
The simple model laid out above yields two testable implications on the
relationship between policy formation, corruption and political instability, captured
in Eqs. (12) and (13). Our objective is to test these implications using crosscountry data on environmental policy.
The test can be formulated as follows:
t i 5 x 9i b x 1 b c c i 1 b p pi 1 b cp c i pi 1 ´i
(14)
where t i is the pollution tax in country i, x i is a vector of controls, c i is the level of
corruption in country i, pi is the degree of political instability and ´i is a zero
mean error term. b c , b p , b cp are coefficient scalars, and b x is a coefficient vector.
14
Note that our analysis applies also to emissions standard since tax revenue does not drive our
results.
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Fig. 1. The marginal effect of corruption on environmental policy conditional on degree of political
instability [Eq. (12)] (Model simulation).
Eq. (14) allows the effect of corruption (political instability) on the pollution tax
rate to be conditional on the degree of political instability (corruption). Formally,
the model yields two predictions:
dt
dt
Prediction 1. ] , 0 and p→`
lim ] 5 0
dc
dc
dt
dt
].0
Prediction 2. c→o
lim ] , 0 and clim
→` dp
dp
The predictions are illustrated in Figs. 1 and 2.
Fig. 2. The marginal effect of political instability on environmental policy conditional on level of
corruption [Eq. (13)] (Model simulation).
1393
4.2. Data
To test the model’s predictions, data on stringency of environmental policy,
corruption, and political instability are required. All these variables are difficult to
measure, and in this section we spend some time to describe the variables (proxies)
we employ. Our measure of the stringency of environmental regulations is an
index for the agricultural sector constructed by Dasgupta et al. (1995) (ENVLPOLICY). This index is compiled from individual country reports written for
use at the 1992 United Nations Conference on Environment and Development
(UNCED, 1992). These country reports are based on survey questions and were
prepared under well-defined guidelines from the UNCED. The reports provide
specific information about the state of the environmental regulatory framework,
focusing on existing environmental policies, legislation, control mechanisms, and
enforcement.15 The information gathered thus makes it feasible to produce an
index of the final environmental regulations facing the producers in the agricultural sector. Using the information gathered, a quantitative index was developed by
Dasgupta et al. (1995) for a set of 31 countries. The answers on each of 25
questions were assigned a score from 0 to 2. Each question posed was answered
with regards to water pollution, air pollution, land use, and biodiversity (i.e. a total
of 100 questions). The resulting scores were added to yield an index with a
maximum of 200. The data set was extended by Eliste and Fredriksson (2002) to
63 countries using an identical methodology as Dasgupta et al. One advantage of
the index is the relatively narrow sectoral focus; i.e. that it measures the stringency
of environmental regulations in the agricultural sector alone.
As a proxy for corruption, we use an index of public corruption drawn from a
private international investment risk service (Political Risk Services), the ICRG
index (see Knack and Keefer, 1995). The ICRG index has been used extensively in
the cross-country corruption literature. We reverse the scale of the index so that 0
indicates least corrupt and 6 most corrupt and denote the re-scaled variable by
CORRUPTION. In the model, the weight a measures to what degree the
government is ‘captured’ by special interests. The ICRG corruption index squares
well with this definition. It is based on the opinions of experts and measures the
extent to which ‘high government official are likely to demand special payments’
and to which ‘illegal payments are generally expected throughout the government’.
Political instability, or more precisely the perceived probability of a government
crises, is not (directly) observable. There are different approaches in the literature
to overcome this problem. Svensson (1998) and others estimate the probability
with a probit model using actual regime shifts as dependent variable. However,
such an approach may be problematic for two reasons. Regime shifts do not
15
The ENVLPOLICY index can be decomposed into sub-indices. Dasgupta et al. (1995) report that
the correlations between the sub-indices range from 0.86 to 0.98.
1394
necessarily capture the within-incumbency uncertainty of remaining in office,
since many regime shifts occur as a result of fixed elections. In addition, since the
estimated political instability measure will be a generated regressor, the estimates
of the standard errors may be biased. To avoid these problems we use instead a
measure of governmental crises, as defined by Banks (1994). Banks define
governmental crises as any rapidly developing situation that threatens to bring the
downfall of the present regime. This variable nicely captures the within-period
uncertainty we want to measure. We take an average over a 10-year period prior to
the policy formation measure (1981–1990), so as to minimize measurement
problems. The variable is denoted by INSTABILITY.
The recent empirical literature on environmental policy provides guidance
concerning other explanatory variables to include in regression (14) (see, e.g.
Eliste and Fredriksson, 2002). In the base specification we include two sets of
controls (x), one set capturing demand factors and one set capturing structural
features that may influence the degree of environmental quality through other
channels than those highlighted in this paper. Assuming that environmental quality
is a normal good, the demand for environmental policies should increase with
income. Thus, we expect the logarithm of per capita income (LGDPPC) to be
positively correlated with ENVLPOLICY. Next, the farm lobby’s pressure for less
stringent regulations should depend on the marginal cost of environmental
regulations to the producers. The total marginal effect on profits depends on the
size of the agricultural sector. The larger is the output level of the agricultural
sector the more influential should the farm lobby be in the political equilibrium. In
a survey of the empirical interest group literature, Potters and Sloof (1996) find
that the greater a lobby’s stake, the greater its success. However, Olson’s (1965)
theory implies that the larger the sector, the greater the free-riding problems,
working against the above effect. We use as a measure of lobbying strength
employment in agricultural as a share of total employment. The expected effect of
the share of employment in agriculture (ALABOR) on ENVLPOLICY is ambiguous.
The second set of controls aims at capturing structural differences between
countries that might influence environmental quality (see Congleton, 1992; and
Murdoch and Sandler, 1997). We include a dummy variable for industrial
countries, proxied by membership in the OECD (DEVELOPED), and a dummy for
democracies, created using data from Freedom House (DEMOCRACY).
We have cross-country data for 63 countries for 1990. Data sources are reported
in Table A.1, and descriptive statistics in Table A.2.
4.3. Results
As a benchmark, in Table 1, we report the results with only the controls as
explanatory variables. Regression 1 shows that the demand for environmental
quality is increasing in income. The share of employment in agriculture
1395
Table 1
Environmental policy regressions—Basic findings
Equation
LGDPPC
ALABOR
(1)
(2)
(3)
(4)
(5)
27.78***
(3.45)
0.334
(0.206)
29.59***
(3.13)
0.403*
(0.205)
16.09***
(5.59)
0.016
(0.215)
41.02***
(10.67)
210.49
(6.78)
13.38***
(4.37)
20.021
(0.197)
38.64***
(9.67)
213.58*
(7.25)
25.05**
(2.13)
24.77
(8.47)
13.20***
(4.75)
20.041
(0.182)
39.55***
(8.66)
213.28*
(6.95)
27.59***
(2.26)
254.38**
(21.56)
24.52**
(10.74)
18.28
17.06
14.28
13.33
12.82
0.80
63
0.83
62
0.88
62
0.89
61
0.90
61
DEVELOPED
DEMOCRACY
CORRUPTION
INSTABILITY
CORRUPTION 3
INSTABILITY
S.E. regression
2
Adjusted R
Observations
Notes: OLS regressions. Dependent variable is ENVLPOLICY. Each regression includes a constant,
not reported. Standard errors in parenthesis are adjusted for heteroskedasticity (White, 1980).
***,**,* Denotes significance at the 1, 5, and 10 percent level, respectively.
(ALABOR) enters positively, although marginally insignificant at the 10-percent
level, suggesting that the more numerous the farmers (relatively seen), the greater
their free riding problems and the less successful their lobbying.
There is one apparent outlier in the sample—Iceland. Dropping Iceland from the
sample reduces the standard errors of the regression by almost 10 percent.16 As
evident from regression 2, ALABOR now enters significantly positive.
In regression 3, we add the second set of controls. Even after controlling for the
demand for environmental quality, there appears to be structural differences
between developed and developing countries which our first set of controls do not
fully capture. Specifically, controlling for income, industrial countries have
significantly stricter environmental policies. The democracy dummy enters with a
negative sign, but the coefficient is insignificantly different from zero.
In regression 4, we add CORRUPTION and INSTABILITY. CORRUPTION
enters negatively and significant but with a relatively small coefficient, while the
effect of political instability is not significantly different from zero. These initial
findings are consistent with the model. In the model, a reduction in the level of
16
We leave Iceland out of the sample since it dramatically increases the standard errors on several
variables. Results with Iceland included are available upon request.
1396
corruption implies that the degree of policy deviation from the social welfare
maximizing level is reduced, given the level of tax revenues. However, the size of
this effect should depend on the degree of political instability. The unconditional
effect of instability on stringency is ambiguous in the model.
Adding the interaction term CORRUPTION 3 INSTABILITY yields regression
5. As predicted by the model, the interaction term enters positively and significantly, suggesting that the effects of political instability and corruption on
policy formation are interdependent.
The marginal effect of CORRUPTION on ENVLPOLICY is depicted in Fig. 3.
Note that for high degrees of political instability, we cannot reject the hypothesis
that the marginal effect is zero. Fig. 4 plots the relationship between the marginal
effect of INSTABILITY on ENVLPOLICY, conditional on CORRUPTION. The
marginal effect is negative for low levels of corruption but significantly positive
for high levels of corruption, consistent with the model’s prediction.
Figs. 5 and 6 illustrate the total effect of corruption and political instability on
environmental policy. Fig. 5 shows that the marginal effect of corruption is
negative for both countries with high instability (1 standard deviation above the
mean of INSTABILITY) and countries with low instability (1 standard deviation
below the mean of INSTABILITY). Interestingly, the estimated policy level for the
most corrupt countries in the sample is higher for those with a high degree of
instability than for those with a low degree of political instability. Thus the data
suggest that very corrupt but fairly politically stable countries perform worst with
respect to environmental policy. In our data set, which combines developing and
developed countries, these are typically highly autocratic regimes with little or no
political opposition. The data (and model) suggest that bribery in these types of
political environments yields a higher payoff.
Fig. 3. Marginal effect of corruption conditional on degree of political instability. Note: The dotted
lines indicate the 95 percent confidence interval. Based on regression 5 in Table 1.
1397
Fig. 4. Marginal effect of political instability conditional on level of corruption. Note: The dotted
horizontal lines indicate the 95 percent confidence interval. Based on regression 5 in Table 1.
For most countries; that is, for almost the whole data range on instability (Fig.
6), more corrupt countries have less stringent environmental regulations than less
corrupt ones. However, the sign of the marginal effect is conditional on the level
of corruption, and in highly unstable countries the estimated stringency of
environmental policy is about the same.
Summarizing the preliminary evidence, the interaction term neatly separates the
effects of corruption and political instability on environmental policy formation.
Corruption is significantly negatively correlated with the stringency of environmental policies, but the effect is reduced the higher the degree of political
instability. Conversely, political instability is negatively correlated with the
Fig. 5. Relationship between environmental policy and corruption for countries with high and low
political instability. Note: Based on regression 5 in Table 1. All other controls evaluated at the mean.
1398
Fig. 6. Relationship between environmental policy and political instability for countries with high and
low corruption. Note: Based on regression 5 in Table 1. All other controls evaluated at the mean.
stringency of environmental policies, conditional on low level of corruption. For
countries with high levels of corruption, the reverse relationship holds: the
marginal effect on stringency of increased instability is positive.
4.4. Robustness test
We ran several robustness tests on the results reported in Table 1. In regression
1 in Table 2, we exploit a shorter time period in constructing our political
instability measure. INSTABILITY1 is the average number of governmental crises
over a 5-year period prior to 1990. All results continue to hold. All political
variables are now significant at the 1-percent level.
Another possible objection to the results reported in Table 1 are that they are
influenced by reverse causation, or measurement problems in the explanatory
variables. These potential problems are likely to be of less importance for the
political instability proxy (INSTABILITY), since the 10-year averaging of political
crises will moderate extreme values in particular years and minimize any
simultaneous correlation between the error term and p in Eq. (14). The main
problem thus refers to the corruption variable. We deal with the potential
simultaneity (and measurement) problem by instrumenting for corruption using
dummy variables indicating legal origin of a country as instruments.17 There are
good reasons to expect that legal origin performs well as instrument for corruption.
La Porta et al. (1999) suggest that legal origin have an important effect on
17
We also instrument for the interaction term using INSTABILITY LEGAL ORIGIN as additional
instruments.
1399
Table 2
Environmental policy regressions—Robustness test
Equation
LGDPPC
AGLAND
DEVELOPED
DEMOCRACY
CORRUPTION
(1)
(2)
(3)
(4)
(5)
(6)
13.21***
(3.76)
20.036
(0.178)
36.96***
(7.74)
212.67*
(6.88)
28.19***
(2.22)
8.90**
(4.32)
20.203
(0.198)
35.78***
(7.57)
217.94*
(9.21)
218.11***
(6.16)
2120.3**
(47.78)
59.47**
(23.36)
14.08***
(5.04)
0.075
(0.194)
38.80***
(11.17)
213.14*
(7.64)
13.44***
(3.96)
20.033
(0.180)
40.16***
(8.51)
213.91*
(7.09)
27.45***
(2.23)
246.76**
(20.92)
24.88**
(10.27)
13.50***
(3.55)
20.033
(0.176)
36.23***
(7.72)
214.02**
(6.85)
29.44***
(2.48)
249.85**
(23.31)
22.52**
(10.53)
13.27***
(4.01)
20.036
(0.182)
41.41***
(8.98)
213.93*
(7.13)
27.29***
(2.29)
243.12*
(21.64)
24.08**
(9.90)
INSTABILITY
CORRUPTION3
INSTABILITY
INSTABILITY1
CORRUPTION3
INSTABILITY1
CORRUPTION( EIU)
231.69***
(10.13)
242.69***
(15.13)
19.56***
(6.55)
229.22**
(11.68)
62.52*
(32.82)
CORRUPTION( EIU)
3 INSTABILITY1
CORRUPTION3
INSTABILITY 3 DEVELOPED
28.61
(7.07)
S.E. regression
Over-indentifying
restriction test
12.48
Adjusted R2
Observations
0.91
61
15.75
10.2
[0.12]
60
13.64
12.85
12.51
12.84
0.89
59
0.90
60
0.91
59
0.90
61
Notes: OLS regressions (columns 1, 3–5), and 2SLS (column 2). Dependent variable is ENVLPOLICY. Each regression includes a constant, not reported. Standard errors in parenthesis are adjusted for
heteroskedasticity (White, 1980). ***, **, * Denotes significance at the 1, 5, 10 percent levels,
respectively. Over-identifying restriction test is a test of the over-identifying restrictions, asymptotically
distributed as x 2 under the null of instrument validity, with p values reported in brackets.
property rights which in turn would affect the level of corruption. The first stage
regressions also indicates that the instruments are good. The F-statistics (reported
in Table 2) on the excluded instrument in the first stage regressions are highly
significant, and the partial R 2 values (obtained by regressing the endogenous
variables on the instruments, once the common variables have been netted out) are
high. The IV results are reported in regression 2. All results remain intact, and the
test for over-identifying restrictions (Hausman, 1983) indicates that we cannot
1400
reject the null hypothesis of the validity of the exclusion restrictions. That is, we
find no evidence that the legal origin dummies belong in the environmental policy
regression. Regression 3 provides additional support for the empirical findings. In
regression 3 we exploit another corruption index that has been commonly used in
the economics literature (see for instance Ades and Di Tella, 1999); The
Economist Intelligence Unit index [CORRUPTION( EIU) ].18 We obtain results
very similar to those in Table 1.
Until now we have taken an extremely conservative approach with respect to
outliers: only one observation (Iceland), which might be the result of faulty
reporting (measurement errors), has been dropped. However, some fairly serious
outliers remain in the sample. In particular, there is one country with a value of
INSTABILITY more than 6 standard deviations above the mean. There are also two
countries coded as having maximal corruption (6). This is roughly two and half
standard deviations above the mean for the corruption variable. While there is no
theoretical justification for deleting these observations, it would be of considerable
concern if our results were completely driven by them. To examine this possibility,
we dropped the aforementioned observations and re-ran regression 5 in Table 1.
The results, reported in regressions 4 (dropping the country with extreme
instability score) and regression 5 (dropping the countries with extreme corruption
score), indicate that the findings are not driven by these outliers.
Yet another concern is that the assumption of common parameters across
countries may be too restrictive. The sample size of around 60 observations
prohibits us from implementing any full-fledged tests on pooling (and thus to draw
too strong conclusions from the results). In regression 6 we report one indication
that our restrictive assumption is a suitable first approximation, however. Regression 6 adds an additional interaction term to test if the conditional impact differs
between industrial and non-industrial countries.19 The data suggest that there is no
statistically significant difference, thus providing some support for our base
specification. Finally, we added additional controls to the base specification,
including two measures of the marginal damage of production.20 The results
18
The index was first published by the Business International Corporation in the early 1980s, a
private firm now incorporated into The Economist Intelligence Unit.
19
Regression 6 excludes two two-way interactions. Adding them do not change the results. As they
are insignificantly correlated with ENVLPOLICY and to minimize the loss of degrees of freedom, we
leave them out.
20
The two variables are the share of agricultural land to the total land area, measuring the extent to
which marginal lands prone to erosion are used in production, and fertilizer use per hectare, which
measures the potential damage from runoffs from fields into waterways. The marginal environmental
damage may play a role in the determination of environmental policy if the more severe is
environmental damage on the margin, the greater the political pressure from the environmental interests
for environmental regulations.
1401
remained intact. To conclude, the findings reported in Table 1 appear to be robust
to a number of potential statistical problems.
5. Conclusion
The literature on corruption and political instability has previously investigated
the separate effects of these variables on policy making, ignoring their joint
impacts. This paper developed a theory of environmental policy formation, taking
into consideration the degrees of corruptibility and political turbulence. The
predictions that emerge are that the interaction between the two variables is
important.
The empirical findings are fully consistent with the predictions of the model. It
appears that we have identified an interaction between corruption and political
instability that may be important in the determination of other forms of economic
policy.21 Further research in this area appears fruitful.
Acknowledgements
We would like to thank Fredrik Andersson, Richard Damania, Greg Huffman,
Angeliki Kourelis, Essie Maasoumi, Daniel Millimet, Kamal Saggi, James Snyder,
¨ ¨ seminar participants at the 2000 Public Choice
Gordon Tullock, Juuso Valimaki,
Society Meetings in Charleston, the 2001 EAERE Meetings in Southampton, and
at The World Bank, for helpful comments and suggestions, and Roberta Gatti for
providing us with some of the data. We also thank two anonymous referees and in
particular the Co-Editor, Stephen Coate, for very constructive comments and
suggestions. Financial support from the Swedish International Cooperation Agency
(SIDA) is gratefully acknowledged. The authors blame only each other for any
remaining mistakes. The findings, interpretations, and conclusions expressed in
this paper are entirely those of the authors. They do not necessarily represent the
view of SIDA.
21
It should be noted that although the interaction between corruption and political instability appears
important for the determination of environmental (and economic) policy, it may be less crucial for the
allocation of shorter term projects and contracts where a bribe yields a payoff at a relatively closer
point in time. In this case, there is less uncertainty for the bribe giver. The authors thank James Snyder
for this observation.
1402
Appendix A
Table A.1. Variable definition and data sources
Variable
Definition and source
ENVLPOLICY
Index of stringency of environmental regulations. Source: Dasgupta et al.
(1995) and Eliste and Fredriksson (2002), based on UNCED (1992).
LGDPPC
The logarithm of gross domestic product per capita in 1987 US dollars.
Source: World Bank (1992).
ALABOR
Share of total employment in agriculture (%). Source: World Bank
(1997a,b).
DEVELOPED
Dummy variable for OECD countries.
DEMOCRACY
Democracy dummy taking the value 1 if the score on the Freedom House’s
ranking of political and civil rights is smaller or equal to 4. The Freedom
House index is on a scale from 1 to 7, where 1 is most free. Source: Gastil
(1982) and subsequent issues).
CORRUPTION
Corruption index (IRCG) on a scale from 0 to 6, where 6 is most corrupt.
Source: Knack and Keefer (1995).
INSTABILITY
Average number of governmental crises (any rapidly developing situation
that threatens to bring the downfall of the present regime), 1981–1990.
Source: Banks (1994).
INSTABILITY1
Average number of governmental crises (any rapidly developing situation
that threatens to bring the downfall of the present regime) 1986–1990.
Source: Banks (1994).
CORRUPTION( EIU)
Ranking (from 0–10) of countries according to ‘the degree to which business
transactions involve corruption or questionable payments’. Mid-1990s. The
original source is The Economist Intelligence Unit. The index has been
normalized to [0,1] by Kaufmann et al. (1999), where 1 is most corrupt.
Source: Kaufmann et al. (1999).
LEGAL ORIGIN
Origin of a country’s legal system. Source: La Porta et al. (1999), obtained
from Roberta Gatti.
Table A.2. Descriptive statistics
ENVLPOLICY
LGDPPC
ALABOR
INSTABILITY
CORRUPTION
DEMOCRACY
Mean
Median
Max.
Min.
S.D.
Obs.
111.9
7.71
31.72
0.130
2.18
0.51
91.0
7.55
18.75
0.100
2.00
1.00
186.0
10.23
94.12
1.100
6.00
1.00
43.0
4.38
2.17
0
0
0
41.3
1.63
28.10
0.177
1.49
0.50
63
63
63
63
62
63
1403
Table A.3. Correlation matrix
ENVLPOLICY
LGDPPC
ALABOR
DEVELOPED
INSTABILITY
CORRUPTION
DEMOCRACY
1
0.890
20.727
0.883
0.157
20.748
0.703
1
20.882
0.832
0.190
20.716
0.794
1
20.632
20.186
0.550
20.756
1
0.182
20.703
0.744
1
20.084
0.251
1
20.671
1
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