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POLICY REFORM AND NATURAL RESOURCE MANAGEMENT
WEEK 3: DAY 3
ECONOMIC GROWTH, POVERTY AND THEIR IMPACT ON THE
ENVIRONMENT
by Nico Heerink and Abay Mulatu, Wageningen Agricultural University
CONTENTS
1. INTRODUCTION
2. ECONOMIC GROWTH AND THE ENVIRONMENT
2.1. The “Environmental Kuznets Curve” (EKC)
2.2. Empirical evidence
2.3. Policy implications of the EKC
3. INCOME INEQUALITY AND THE ENVIRONMENT
3.1. Household incomes, income inequality and the environment
3.2. Income inequality and environmental policy
3.3. Empirical evidence
4. POVERTY AND THE ENVIRONMENT
4.1 Interactions between poverty and the environment
4.2 Some examples
EXERCISES
REFERENCES
LIST OF TABLES
Table 1. An overview of major empirical evidence on the relationship between
economic growth and environmental impact
Table 2. Regression results for EKC with and without income inequality
LIST OF FIGURES
Figure 1. Schematic representation of the forces leading to the downward-
sloping part of the Environmental Kuznets Curve
2
1. INTRODUCTION
From the foregoing days in this module it is clear that economic policy reform
measures aimed at restoring macroeconomic balances and promoting economic
growth can have important effects on the state of the natural environment. Important
elements of such packages like price liberalisation, exchange rate adjustments, trade
liberalisation, or public expenditure cuts have major consequences for the incentives
faced by economic actors in a country, and hence for their use of natural and other
resources. It is important to note, however, that also the outcomes of the policy
reforms in terms of income changes of different socio-economic groups may have an
important bearing on the environment. If economic growth is restored, does this mean
that the environmental damage will increase in proportion to the rate of economic
growth? And if some groups in the country suffer from, either temporarily or
prolonged, income losses, will these groups have to rely on the abuse of natural
resources for their survival? In assessing the impact of economic policy reforms on the
environment it is important to take these (medium to long term) income effects into
account in order to obtain a more balanced view.
The income-related effects of macroeconomic and sectoral policy reforms on the
environment are the topic of this chapter. In recent years, there has been much
discussion on the potential beneficial effects of economic growth on some aspects of
the environment after a certain threshold income level has been reached. This relation
with economic growth is referred to as the “Environmental Kuznets Curve” (EKC) in
the literature. The next section summarises this discussion, and its implications for
policy. A rather novel topic is the impact of changes in income inequality on the
environment. When the micro relation between household incomes and some aspects
of the environment resembles an EKC, then both the average income level and the
degree of income inequality matter for reducing the total environmental damage in a
country. This issue is discussed in more detail in section 3. Finally, a very important
aspect is the degree of poverty in a country. The poor usually have little choice but to
rely on the exploitation of natural resources for making a living. The interactions
between poverty and natural resources are the subject of section 4. The chapter ends
with an exercise in which some empirical tests can be made of the relation between
different indicators of environmental quality, average income, and income inequality.
2. ECONOMIC GROWTH AND THE ENVIRONMENT
2.1. The “Environmental Kuznets Curve” (EKC)
There is a conventional view that continued economic growth would bring about an
ever-increasing deterioration of the environment. Hence economic growth may not be
sustainable. This view rests on two basic notions. First, as greater economic activities
require material inputs and energy, the limited resource base would eventually be
exhausted. Second, the rates of accumulation and emissions resulting from increased
economic activity would exceed the regenerative capacity of the Earth’s atmosphere.
One could, however, come up with various arguments why such a scenario may not be
inevitable. As many would argue, the scale of economic activity is only one of several
3
factors that affect the state of the environment. Factors, such as the structure of goods
and services, the efficiency with which inputs are transformed to a unit of output, the
ability to substitute away from scarce resources, and the ability to reduce
environmental damage per unit of input or output, also play a significant role in
determining the environmental impact of a unit of economic activity. This idea is
commonly demonstrated by decomposing the effects of economic activity on the
natural environment into three mechanisms. The first is the scale effect, which is the
basis for the pessimistic view on economic growth. It captures the simple intuition
that with the nature of economic activity unchanged, an increase in output has to
proportionately increase the level of pollution. Two other effects may however, offset
this effect. The second is the composition effect, which refers to the possibility of a
decline in emission if the share of pollution-intensive activities in GDP decreases over
time. The third is the technique effect that refers to the possible changes in the
methods of production. If the output of pollution per unit of output declines (due to a
substitution by less polluting techniques of production) emission could again fall as
the scale of economic activity increases. The extent to which the various offsetting
factors come into play would depend on the incentive structure facing agents.
A relevant question in this respect is evidently: what are the specific grounds for
optimism, i.e., for the offsetting factors to materialise following economic growth?
The answers seem to revolve around historical facts, empirical evidence, and
theoretical and logical arguments. Let us consider this issue taking two broad
categories of environmental resources: marketable non-renewable resources and non-
marketable regenerative resources.
Marketable non-renewable resources. Contrary to the predictions of natural resources
exhaustion, as reported in particular in the early 1970s by Meadows et al. (1972),
many would argue that there is much scope for optimism if we consider historical
trends in resource availability, and allow for economic feedback mechanisms.
Economic feedback mechanisms depend on the smooth operation of the market.
Actual or perceived shortages of natural resources result in price increases, which in
turn may induce substitution by other less scarce resources, and development of
resource-saving technologies. The belief in the operation of the market forces might
give reason to expect that (at least for those environmental aspects for which there are
markets or the environment is an argument in the production function) some self-
correcting forces would set in to counter a monotonic degradation of the environment.
This results from a probable response to the changing level of the degradation or
changes in factor prices. One could also argue that the presently known fixed level of
natural resource reserves should not necessarily be taken as what is available on the
planet, and on which predictions of exhaustion are to be based. As Beckerman (1992)
noted, “...the ‘known’ reserves at any point in time are only the reserves that have
been worth finding. Exploration continues if the known reserves begin to look
inadequate in relation to expected levels of demand, particularly since this leads to a
rise in the price of the materials in question” (p.483).
Non-marketable regenerative resources. These resources are the types of resources
with which the discussion of environmental problems at present is usually associated,
since they appear to be the most pressing. In most cases, they are out of the market as
there are no property rights on them. At best the rights are poorly defined, rendering
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difficulty of enforcement. In other words, they are common property for which
private agents would hardly see any incentive for protection individually. The
optimism with regard to these resources is based on the following notions:
• Historical evidence suggests that the structural change accompanying economic
growth entails a higher share of services (which do the least harm to the
environment compared to the other sectors) in GDP over time.
• Affluence resulting from economic growth would bring about greater demand by
citizens for the quality of the deteriorating environment. This might be expected
to trigger definition and enforcement of property rights (where possible) on some
resources which did not seem limiting in the initial stage of growth. For those
environmental resources where it is impossible to enforce property rights,
government regulations and controls of various forms could be expected to follow.
• While many forms of control or regulative measures might be expected to bring
overall net present benefit to a society, the initial resource costs could be so high
that only developed economies (with available funds to spare for environmental
control measures, and the strength of demand by citizens) would be likely to
undertake the control programs. It is in this sense that economic growth is
considered to be part of the solution, rather than the cause, for some of the
environmental problems.
The foregoing discussion implies that it would be inappropriate to assume that
environmental degradation worsens monotonically with an increased level of
economic activity. Economic growth (with the accompanying structural changes,
alteration of preferences, the ability to spare economics resources, etc.) could be
related to the state of the environment in another way than implied by “the principle of
material balance”, i.e. fixed coefficient relationship between the scale of economic
activity and environmental degradation. This reasoning of a non-linear relationship is
the basis for the hypothesis that, while economic growth may initially lead to
environmental degradation, further growth may eventually reduce the environmental
impact of economic activity. This is what has come to be known as the
Environmental Kuznets Curve hypothesis. It purports an inverted-U relationship
between economic growth and environmental degradation similar to the relationship
noted by Kuznets for income inequality (see Module 4). The following quotation
explains well the reasoning behind the EKC hypothesis:
“We begin by noting that the environmental impact of economic activity
in subsistence economies are limited to their resource bases and to
biodegradable wastes. Now economic development has often been
accelerated by means of an intensification of agriculture and resource
extraction. In this phase, therefore, we would not only expect rates of
resource depletion to exceed their rates of regeneration, but we would also
expect the generation of wastes to increase in quantity and toxicity. At
higher levels of economic development, however, matters should be
expected to be different. Structural changes towards information-intensive
industries and services, coupled with increased environmental awareness
and expenditure (allied to stiffer enactment and enforcement’s of
environmental regulations) would be expected to result in a gradual
decline in deterioration of the environment” (Panayotou, 1994, p.13).
5
The hypothesis could take a mathematical equation of the form:
E =
β
1 +
β
2 Y +
β
3 Y2(1)
Where E is environmental impact per capita, Y is GDP per capita, and the
β
's are
parameters to be estimated, with the signs of
β
2 and
β
3 being positive and negative,
respectively.
The exclusion of some other explanatory variables (like the composition of output)
from the above equation, can be justified by the assumption that those factors are
endogenous consequences of growth, and the exclusion enables us to asses both the
direct and direct outcomes of growth. The model therefore represents a reduced-form
equation. Similar reduced-form models were used in various empirical studies of the
EKC hypothesis (see section 2.2 below).
The reasoning behind the EKC hypothesis, in particular, the mechanisms leading to
the eventual downturn of environmental degradation, can be depicted by the scheme
showed in Figure 1. In a nutshell, the scheme portrays the hypothesis that economic
growth would eventually lead to and provide resources for environmental protection.
Much of the link between income and the environment is believed to be through
induced policy changes resulting from a greater demand by societies whose members
are becoming richer. The World Bank’s World Development Report (IBRD, 1992)
also noted that “As incomes rise, the demand for improvements in environmental
quality will increase, as will the resources available for investment” (p.39). Grossman
and Kruger (1995) stated that “the available evidence on instances of pollution
abatement suggests that the strongest link between income and pollution in fact is via
an induced policy response” (pp.371-2). The greater demand from citizens triggers a
series of reactions like technical changes, and substitutions in production and
consumption. These are induced by various forms of government regulations and
control that may either take a direct form or work through the market. One of the
remaining links is through structural transformation that accompanies economic
growth, resulting in the alteration of the composition of goods and services produced.
Other indirect links considered by some are an open political system and a more
equitable income distribution, both of which are assumed to accompany economic
growth in the long run.
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Figure 1. Schematic representation of the forces leading to the downward-sloping part of the Environmental Kuznets Curve
a rise in GDP per capita
availability of funds for
public sector
environmental control
affluence
environment
priority to environmental
protection
relative price changes
technical change
substitution in production changes in patterns of demand
scarcity
regulation
structural transformation
leading to higher shares of service sector and
high-tech production
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2.2. Empirical results
The empirical literature of the EKC commonly displays a cross-section/pooled time
series regression analyses on country-level data. Different contributors to the EKC
literature considered various indicators of the environment. Understandably, no
individual study has exhausted all possible lists of the indicators of the environment.
Moreover, no consistent picture was found for all the indicators considered. An
overview of major findings of relationships between environmental degradation and
economic growth until now is presented in Table 1.
As can be seen from Table 1, it could be said that empirical evidence generally
supports the EKC for sulphur dioxide, suspended particulate matter, nitrogen dioxide,
and deforestation, but not for many other environmental indicators. In interpreting the
results, however, a number of limitations should be taken into account.
The data employed in empirical tests of the EKC are in general patchy. Unavailability
of sufficient data on some of the indicators of environmental impacts has forced the
contributors of the EKC literature to resort to either unrepresentative samples or
unreliable estimations and not-very-realistic assumptions. For example, in the case of
air pollution two studies used ambient pollution data of urban areas (which obviously
is confined to impacts on health of urban population), while two other studies made
rough estimations of national levels. In the two former studies, national per capita
income (explanatory variable) was used as a proxy for city incomes. It has further
been noted that measurement sites for various pollutants in different countries are
unlikely to be properly representative. This coupled with definitional differences
regarding classification of sites considerably affects the comparability of these data
across countries.
Table 1. An overview of major empirical evidence on the relationship between
economic growth and environmental impact
INDICATORS
OF THE
ENVIRONMENT
Grossman
and
Kruger
(1995)
Shafik and
Bandyo-
padhyay
(1992)
Panayotou
(1995)
Seldon
and
Song
(1994)
Cropper
and
Griffiths
(1994)
sulfur dioxide EKC EKC EKC
suspended
particulate matter
EKC
EKC
EKC
dark matter
deforestation no effect EKC EKC
nitrogen dioxide EKC EKC
carbon monoxide EKC
municipal waste
per capita positive,
non-linear
lack of urban
sanitation and safe
water
negative,
non-linear
carbon dioxide positive,
non-linear
fecal coliform in
rivers
EKC
cubic
8
total coliform in
rivers
cubic
nitrates and
chemical oxygen
in rivers
EKC
dissolved oxygen
in rivers positive,
non-linear negative,
non-linear
number of
countries
19-42
25-153
41-55
30
64
period 1977-88 1960-89 1980 1973-84 1961-88
As regards methodology, the empirical literature on the EKC commonly uses a simple
regression model with variations in such specifics as functional forms, inclusion of
additional explanatory variables (e.g., population density), lagged income variables,
and dummies to account for location- or site-specific differences. Estimation is done
with ordinary least squares (OLS). Since the EKC hypothesis refers to the impact of
income growth with all its accompanying effects on the state of the environment, the
reduced-form model linking income to the environment is considered appropriate to
find the overall impact of income growth. In the presence of possible feedback effects
of environmental degradation on economic growth, however, OLS estimation of the
reduced-form model would entail the problem of simultaneity, resulting in biased and
inconsistent estimates of the EKC. The relevant question here is whether
environmental degradation (as measured by the indicators considered in the literature)
have a significant effect on production possibilities in the medium or long term. All
economic activity ultimately depends on the environmental resource base.
Degradation of this resource base may irreversibly reduce its capacity for generating
material production in the future. Moreover, the environmental resource base includes
assimilative capacities for waste discharges. Exceeding these assimilative capacities
may reduce the availability of and productivity of natural (and human) resources. So,
economic growth and environmental quality are to a large extent jointly determined.
Simultaneous-equations techniques should therefore be used to disentangle these
mutual interactions. The conclusion reached in empirical studies on the EKC so far
may no longer hold when such simultaneous-equations methods are used to correct for
the biases and inconsistencies in the estimated coefficients.
2.3. Policy implications of the EKC
The empirical support of the EKC for some environmental indicators appears to be
subject to different interpretations and hence varying policy implications. While the
contributors to the EKC literature seem to make inferences on their findings in a
lukewarm fashion, some commentators appear to take some stronger positions. At
one extreme we find an assertion, “...there is clear evidence that, although economic
growth usually leads to environmental degradation in the early stages of the process,
in the end the best - and probably the only - way to attain a decent environment in
most countries is to become rich” (Beckerman, 1992: p.482). At another extreme,
Stern et al. (1996), emphasising ‘irreversibility’ of damages on some aspects of the
environment, seem to be of the opinion that there is no policy lesson to be drawn from
the EKC, especially if sustainability is to be a global policy objective.
9
One point to be noted in the interpretation of the EKC is that the findings do not hold
for all dimensions of the environment. In the absence of an environmental ‘index’
that might represent the state of the environmental as a whole, it would be difficult to
make any generalisation about the relationship between economic growth and the
environment.
A somewhat obvious implication of the EKC might be that environmental degradation
is an inevitable side-effect of the development process in its early stage, but once a
certain level of per capita income is reached, further growth turns out to be beneficial
to the environment. This may seem to suggest policy in-action with respect to the
environment or rather that policies (and hence resources) directed only at economic
growth would suffice. However, the issue of ‘irreversibility’ of certain types of
environmental degradation implies that, unless it is assured that the ‘turning point’
(the level of income corresponding to the peak of the environmental impact) in the
curve is reached before the threshold for the irreversibility of the damage, the mere
existence of the EKC may not be what is relevant (Stern et al., 1996). This issue of
the ‘turning point’, not just for cases of irreversibility but for all other impacts, is
perhaps what makes the findings of the relationship characterised by the EKC
disquieting. For most indicators of the environment, the turning points are such that
most countries of the world would have to go a long way to pass through the
environmentally unfavourable stage of development. Hence, quite a long time elapses
before the prevalence of a decline in global environmental impacts, and in effect
ecological optimality might not be maintained. That is to say, the threshold beyond
which environmental degradation is irreversible may be surpassed. This suspicion
seems to be the most important factor that prompted many writers on the EKC
literature to suggest collective policy action towards the environment rather than
policy-inaction.
On purely economic grounds, too, it may be argued that even though the EKC is an
empirical reality, it is not optimal. The reason is that there exist complementarities
between certain forms of environmental degradation and economic growth, and that
prevention of environmental damages today may be less costly than treatment at later
stages of development. Munasinghe (1995), supplemented this line of argument by
noting that “Even if such a curve [the EKC] characterised past growth, there is no
reason for countries to passively accept historical ‘determinism’ along their future
development path” (p.123). The relationship between economic growth and the
environment depends on the sources of growth and the characteristics of growth
strategies as well. It means that similar levels of economic growth could be achieved
from different sources, and that economic and political environments differ in their
corresponding environmental impacts (e.g., subsidised prices of natural
resources/improved institutional structures resulting in efficient use of resources).
From the foregoing, it seems clear that the EKC does in no way suggest that the
environment does not need any explicit protection, or that resources should be spared
to promote only growth and the environment would be taken care of as well. Indeed,
better management of environmental resources by a host of policy instruments and
public expenditures might flatten the EKC without compromising growth. A
simultaneous concern both for growth and the environment could be a wise policy
option for sustainability of growth itself.
10
On the other hand, as Panayotou (1995) pointed out, there might be a possible danger
of retarding growth and stretching the EKC “when developing countries prematurely
adopt the strict environmental standards of developed countries, and attempt to attain
them overnight through strict end-of-pipe emission standards and requirements for the
mandatory instalment of waste treatment facilities and pollution abatement
equipment” (p.31). We can only hope that the EKC for developing countries could be
shallower by flexible market-based instruments which make agents absorb the
opportunity costs of resource uses (Panayotou, 1995).
3. INCOME INEQUALITY AND THE ENVIRONMENT
Not only the average income level of a country, but also the degree of inequality in the
income distribution may affect the state of the environment. There are two major
arguments for this. One is that EKCs or other non-linear curves may characterise the
micro-level relationship between household incomes and environmental damage.
When such micro relationships exists and are non-linear indeed, then the
corresponding environmental damage for the country as a whole is related both to the
average income level and the degree of income inequality. The second reason is that a
more equitable income distribution makes it easier for policy makers to implement
environmental protection policies. This section will examine these two arguments,
and present some empirical evidence on the impact of income inequality on the
environment.
3.1. Household incomes, income inequality, and the environment
Emissions of some types of air and water pollutants, such as SO2, are directly related
to the production of certain industrial sectors and the technologies used in these
industries. Other types of environmental damage, especially those related to the
consumption of commodities, are a direct consequence of household decisions and are
therefore related to the income levels received by households. What does the
relationship between household income and the environment look like for these
aspects?
Households, in urban and rural areas alike, consume energy of different sources and
magnitude. Rural households (and many poor urban households in developing
countries) essentially require energy for cooking. This demand may be met by fuel
wood, crop residue, and animal dung, all of which would be a burden on some aspects
of the environment especially on soil. Fuel wood has, in addition, large carbon
emissions. It has been argued that the demand for fuel wood is related to the level of
household income in a non-linear fashion. Demand for fuel wood as an energy source
may initially rise with income, but could eventually be expected to fall with income,
as modern sources of energy are substituted.
The waste generated by households is evidently linked to their income levels as well.
Not only does the level of consumption increase with the income level, the degree of
processing and packaging of consumption goods increases with welfare growth. As a
result, the waste generated through household consumption may increase more than
11
proportionally. At higher income levels, however, environmental awareness may also
increase, leading to less waste generation and recycling activities.
Sanitation and safe drinking water are among the physical environmental dimensions
household expenditures would be allotted to. While the supply of these services is in
large measure a case of public goods to be provided by the state, actual access to them
could be influenced by household income. As these services might have a nature of
necessities (a question of survival), households may meet these needs early in the
growth of their incomes, and with higher incomes the share of expenditures on them
in their total expenditure might get lower. Here again, we may expect a negative but
non-linear relationship between income and (lack) of access to these services.
Besides consumption-related damage, production activities undertaken by households
may also affect the environment. This relationship is basically relevant in the
agricultural sector, especially in developing countries where much of production
activities are undertaken by households. Households with relatively more income can
afford intensive agricultural practices based on the use of purchased inputs, resulting
in less demand for expansion of cultivable land. In addition, they can afford the
investments needed for taking soil conservation measures (like the construction of
terraces, adoption of agro-forestry practices, etc.) and they may be more interested in
making such investments. In the absence of long-term credit markets, the discount
rate for a household will depend on the rate of time preference, investment
opportunities, and risk premiums. The personal discount rate depends on income
since wealthy farmers will be relatively more willing to forego current consumption
for future benefits, and wealthy farmers may attach lower risk premiums on future
outcomes. As a lower discount rate makes investments to increase future production
levels more attractive, a positive relationship may exist between soil conservation
investments and household income.
With respect to the demand for environmental quality, it can be hypothesised that
higher household income will enable more education, which in turn, by raising
awareness, might positively affect the environment. Lack of awareness or education
may have to do with the inability to appreciate the real consequences of environmental
degradation and eventual action.
So, it follows that, especially for those types of environmental quality that are directly
related to the consumption levels of households or the (agricultural) production
activities of households, there exist strong links between the income levels of
households and the resulting environmental damage. In many cases, these micro
relationships may be non-linear and in some cases (like for example the use of fuel
wood for cooking) they are likely to resemble a micro-level EKC. What are the
implications for the corresponding aggregate relationship at the macro level? This can
be examined in the same way as was done for the impact of income inequality on
economic growth in Module 4.
Suppose that the micro-level relationship between household income Y and the
environmental damage in question, E, follows a function f which is the same for all
households except for a random disturbance term:
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EfY
ii
=()+ ui for i = 1,..,n(2)
where
Ei = value of the measure of environmental damage caused by household i;
Yi = income level of household i;
ui = random disturbance term; and
n = total number of households.
Then, aggregating over all households gives:
EnfY nufYnfY fY nu
i
i
n
i
i
n
i
i
n
i
i
n
−=+=
−+−
−+
== = =
11 1 1
11 1 1
( ) () ( ) () (3)
or
EfY VY u
f
−− −
=+ +() () (4)
with
VY nfY fY
fi
i
n
() ( ) ()=−
−
=
1
1(5)
EnEi
i
n
−==
1
1, YnYi
i
n
−==
1
1, unui
i
n
−==
1
1, Eu() ,
−=0 Eu i
()
−=
22
σ
(6)
E
− = average environmental damage per household,
Y
− = average household income level, and
Vf(X) = bias term reflecting the degree of nonlinearity in the function f.
In other words, the average value of E equals the value that the function f assumes at
the average income level plus the value of a nonlinearity bias Vf(Y). It has been
shown that (Heerink and Folmer, 1994):
• when the function f is linear, then Vf(Y) equals zero;
• when the function f is strictly convex, then Vf(Y) is larger then zero and an
equalisation of incomes reduces the value of Vf(Y); and
• when the function f is strictly concave, then Vf(Y) is smaller then zero and an
equalisation of incomes increases the value of Vf(Y).
In other words, Vf(Y) is a measure of income inequality in the case of strictly convex
function f. In the case of strictly concave micro-level relationships f, Wf(Y) = -Vf(Y)
is a measure of income inequality. The exact shape of the function f determines which
inequality measure should be used at the macro-level. In the case of a quadratic
function, as used to represent the EKC in section 2 above, the corresponding
inequality measure Vf(Y) equals the variance of the income distribution. Notice that
the EKC has the shape of a concave function, since the environmental damage first
increases with income but, after a certain threshold income level, is assumed to
decrease. This means that an equalisation of incomes will increase the level of
environmental damage if the EKC holds at the micro-level. So, it may be concluded
that for those environmental aspects that are directly related to the behaviour of
households, the degree of income inequality may be an important determinant of the
total (and average) environmental damage in a country.
3.2. Income inequality and environmental policy
13
A more equitable distribution of income, ceteris paribus, may make it easier for
policy makers to implement environmental protection policies for the following
reasons:
• There is a possibility that these policies have a more broad-based and stronger
support. It may be relatively easier to implement environmental policies, since the
reaction of certain disadvantaged groups (due to unfavourable redistribution
effects) may be relatively less. The assumption here is that the reaction by the poor
will be stronger since what they forego might be basic material goods, while what
they receive are relatively luxury goods of the environment.
• A more equitable distribution of power and wealth enhances the influence on
environmental policy of those who bear the costs of pollution, relative to those who
benefit from pollution-generating activities.
• Policy makers may be apt to give priority to visible current problems with solutions
clearly observed. This seems to disfavour environmental problems, especially
when income inequality is high. This is because the relatively more visible
problems of lack of basic needs fulfilment (which is a positive function of income
inequality) would stand in the way of public expenditure for environmental quality.
• Public funds for environmental protection measures may face relatively less
competition with other public equality-enhancing programs or projects.
• In countries where the chronically poor ‘mine’ local environmental resources, it
would be both infeasible and unfair to tax the access of the poor to common
resources. Income redistribution, by reducing the number of the very poor, could
make public regulation on common resources more feasible.
It should be noted that there are diverse forms of government, major differences in the
behaviour of the civil society, and different sorts of interest groups with varying
degree of organisation and political activity in different countries. Evidently, all of
these will affect the degree to which environmentally favourable public decisions are
linked to a more equitable income distribution
3.3. Empirical evidence
Empirical evidence on the relationship between income inequality and the
environment comes from cross-national regression analyses. Data availability on
environmental indicators as well as income inequality indicators hinders the
application of time-series or pooled cross-section time series analysis at the moment.
The data sets used for the empirical analysis are: the Shafik and Bandhyopadhay
(1992) data set for all environmental indicators, except for soil nutrients balances, and
for average income; the data set of Stoorvogel and Smaling (1990) for soil nutrients
balances; and the data set of Deininger and Squire (1996) for income inequality. The
first two data sets are available through the World Bank Economic Project site on the
World Wide Web. All observations refer to countries. Besides environmental data, the
availability of good-quality data on income inequality is a major bottleneck. The
sample sizes for all environmental indicators are therefore partly determined by the
availability of observations on income inequality.
Table 2 shows the results for a number of indicators of environmental damage that are
commonly used in the literature on the EKC (for the precise definitions of each
14
indicator, see exercises). In addition, two indicators of soil nutrient depletion in Sub-
Saharan Africa are included in the table. The Gini coefficient is used to estimate the
total impact of income inequality on the environment. This gives the following
specification of the regression equations:
ln (ENV) = α0 + α1 ln (Y) + α2 ln{(Y)}2+ α3 ln (G) (7)
where
ENV = measure of environmental quality;
Y = Real per capita GDP in purchasing power parity (PPP) terms; and
G = Gini-coefficient of inequality in income distribution.
For some indicators, the squared income variable is not included in the equations
because the data do not indicate a decline for these indicators at higher income levels
(e.g. for CO2), or because the environmental quality is expected to improve uniformly
for these indicators (e.g. lack of safe water or sanitation). For each indicator, the
results with and without the Gini coefficient are presented in Table 2.
Table 2. Regression results for EKC with and without income inequality
EXPLANATORY VARIABLES
DEPENDENT
VARIABLES 1Income
per capita Income p.c.
squared Gini
coefficient Adj.
R2N
Urban concentrations
Of SO2
3.48*
(1.88) -0.23**
(-2.04) - .62 32
Urban concentrations
Of SO2
4.01**
(2.17) -0.26**
(-2.33) -0.69
(-1.48) .64 32
Urban concentrations
Of particulate matter 1.93***
(11.3) -0.16***
(-9.71) - .51 38
Urban concentrations
Of particulate matter 1.57***
(3.20) -0.14*
(-4.22) 0.41
(0.77) .50 38
CO2 emissions
Per capita 1.49**
(17.9) --.8365
CO2 emissions
Per capita 1.45***
(17.3) - -0.71**
(-2.07) .84 65
Population without
Safe water -1.56***
(-4.78) --.4134
Population without
Safe water -1.64***
(-5.30) - 2.74**
(2.26) .47 34
Urban population
Without sanitation -1.60***
(-7.78) --.5746
Urban population
Without sanitation -1.46***
(-7.60) - 2.08***
(2.91) .63 46
Percentage change
Forest area 1961-86 80.4
(1.00) -5.49
(-1.19) - .03 52
Percentage change
In forest area 1961-86 -7.52
(-0.01) 0.22
(0.05) 48.4***
(2.67) .14 52
Nitrogen soil balance
In 1983 (SS Africa) -86.0
(-0.30) 7.21
(0.34) --.0716
15
Nitrogen soil balance
In 1983 (SS Africa) -248.0
(-1.14) 18.2
(1.16) 114.0***
(3.52) .43 16
Phosphorus soil balance
in 1983 (SS Africa) -19.8
(-0.42) 1.69
(0.50) - .04 16
Phosphorus soil balance
in 1983 (SS Africa) -46.8*
(-1.41) 3.51*
(1.47) 19.1***
(3.84) .53 16
Source: Mulatu (1998).
T-values in parentheses
1: Dependent variable is ln(ENV + 1) if the variable ENV contains zero’s; in the case of
deforestation and nutrient balances, the dependent variable is ENV
* : Significant at P < 0.10
** : Significant at P < 0.05
*** : Significant at P < 0.01
There are three indicators of air pollution in the table, namely urban concentrations of
sulphur dioxide (SO2) and suspended particulate matter (SPM), and carbon dioxide
(CO2) emissions per capita. Like the studies mentioned in Table 1, the EKC is found
to hold for SO2 and SPM, while the relation with income is positive and non-linear for
CO2. This may be explained by the fact that immediate health effects are much
clearer for SO2 and SPM, while reduction of pollution is relatively more costly for
CO2. To a certain extent, air pollution is dependent on decisions by households.
Household incomes affect the level and type of energy used as well as the form of
transportation used and the total need for transportation. The existence of possible
non-linear micro-relationships justifies the inclusion of the Gini coefficient in the
regressions. For CO2, income inequality is found to have a significant negative
impact on its level of emission, but for SO2 and SPM no significant effects are found.
This result may partly reflect the fact that households are a more important source of
CO2 pollution than of SO2 and SPM pollution. But it should also be noted that the
data on SO2 and SPM are of a poor quality. In particular, they reflect urban air
pollution only.
The results on lack of safe water and lack of urban sanitation confirm the
monotonically declining, non-linear relationship with average income that was found
in previous studies (see also Table 1). Inclusion of the Gini coefficient results in a
positive coefficient that differs significantly from zero. Hence, an income
equalisation is expected to improve the access to safe water and urban sanitation
according to these results.
The most interesting results in the table are the ones for deforestation and soil nutrient
depletion. When income inequality is not included in the equation for deforestation,
there is some evidence of an EKC although the estimated coefficients for the two
income variables do not differ significantly from zero. This is consistent with the
results of earlier studies on this issue. When, however, the Gini coefficient is added to
the equation, the EKC fully disappears (t-values are very close to zero) while the
estimated coefficient for the Gini index of income inequality is highly significant.
This result suggests that income inequality is a major explanatory variable of
deforestation rates. When this variable is not included, empirical studies that examine
the existence of an EKC may in fact estimate a conventional Kuznets curve (i.e. the
impact of income on income inequality, and hence on deforestation) instead of an
environmental Kuznets curve.
16
Finally, two measures of soil nutrient depletion (nitrogen and phosphorus balances)
are included in the table to examine the impact of income inequality on
environmental damage caused by production activities of agricultural households.
The data refer to Sub-Saharan Africa, the only region for which estimates of soil
nutrient balances are available for individual countries (Stoorvogel and Smaling
1990). Average income levels are found to have no impact when the Gini coefficient
is not included in the equations. When it is added, however, some evidence of EKC
shows up (the t-values are at the edge of significance), while the income inequality
variable is found to have a significant positive impact. In the African context of
negative nutrient balances this means that higher income inequality results in a
reduction of soil nutrient depletion. This might pose a dilemma for policy makers
interested in achieving both equitable income distribution and improvement of soil
fertility. Evidently, improving soil fertility at the expense of a worsening income
distribution may not be a wise policy option. A more plausible policy option would
be to provide assistance to poor peasants to smoothen the transition stage by
facilitating credit and supply of fertiliser at affordable prices. In this way, the soil
nutrient balances may be improved without necessarily increasing the degree of
income inequality.
4. POVERTY AND THE ENVIRONMENT
The rural poor have little margin for subsistence. They lack the productive assets
(land, capital) for obtaining sufficient income to satisfy their daily basic needs. They
occupy the most threatening environmental areas (erosion-prone hillsides, urban
neighbourhoods with inadequate water and sanitation infrastructure) in the world, and
may react to stress (caused by population pressure, economic signals from policy
makers, resource exploitation by higher income groups, and so on) or exogenous
events (such as climate changes) by further degrading the environment. In responding
to such events, they only have two options available: Either supplementing the scarce
assets by using free common-property or open-access resources, or leaving the land
and migrating to urban areas to search for better earning opportunities. The first
option results in a further degradation of the rural environment through deforestation
and overgrazing, the second may put more pressure on the urban environment.
Because poverty is also associated with poor health, i.e. low physical productivity, and
with illiteracy, the ability of poor individuals to respond to pressures is low. The
existence of poverty on its own does not necessarily mean that environmental
degradation will follow. It is the combination of poverty with population growth and
other causes of stress or with exogenous shocks that leads to the degradation of
resources.
Struggling to survive today, the poor heavily discount the future and choose
consumption today. They are preoccupied with survival on a day-to-day basis. The
ability to plan ahead is often restricted by the short time horizon. In economic terms,
poor farmers have a high rate of time preference, i.e. a high discount rate in the
implicit evaluation of conservation investments that bear fruit slowly. It should be
noted, however, that these short time horizons are partly a result of market and
institutional failures. Poverty denies peasant farmers access to credit and the resources
17
necessary for conservation of agricultural practices. Insecure land tenure arrangements
further contribute to high rates of time preference among the lowest income groups.
Poverty lowers the ability to forego consumption at present in order to use these
savings for productive investments in the future. In terms of natural resources, high
discount rates imply rapid resource extraction to meet present consumption needs.
Similarly, poor farmers are less likely to make investments in natural resource
conservation, which pay off only after a number of years.
Poor rural households face higher levels of risk than wealthier households do, and face
greater constraints in coping with these risks (Mink, 1993). Degrading agricultural
land and pastures bring increasing risks, in terms of lower and more fluctuating yields,
to poor farmers reliant on these resources. The land available to poor farmers is often
more susceptible to erosion and flooding. Other sources of risk are government
policies, which often entail considerable uncertainties about prices, marketability of
produce, and access to input. Better-off farmers may have connections that provide
alternative marketing channels or sources of input supply. When there is a transition
from common property to private property systems, conflicts over land rights may
arise. Wealthier farmers have more resources available for administrative procedures
to establish legal titles on land. These constraints on managing risk may contribute to
resource degradation. Poor households that are faced with risks in maintaining a
minimum consumption level will use the freely available natural resources as an asset
that can be exploited in times of emergency.
Because the poor crucially depend on ecologically vulnerable natural resources for
survival, they are the major victims of (further) degradation of these resources. A lack
of resources makes it difficult to reduce such degradation. There is mounting
evidence that water pollution damages the health of the poor by transmitting water-
borne diseases, while indoor air pollution from biomass combustion (burning of
wood, crop residues, dung) is especially detrimental to the health of poor females.
The productivity of natural resources managed by the poor declines when these
resources degrade. To some extent, this productivity decline may not be intricately
related to poverty but may be caused by factors outside the control of the poor. For
example, water pollution by industries and municipal sewerage may reduce the scope
for fishing activities, and deforestation by settlers, loggers and ranchers may destroy
the livelihood of indigenous forest dwellers. More often, however, the declining
productivity is caused by the activities undertaken by the poor themselves. Hence,
poverty and environmental degradation strongly interact with each other.
EXERCISES
1. Read the Lotus file SOIL.WK1 into Lotus (or Excel). The columns labelled N and
P provide estimates balances of nitrogen and phosphorus in 16 countries in Sub-
Saharan Africa in 1983. The columns labelled ‘gini’ and ‘gdpch’ give the
corresponding estimates of the Gini-coefficient of income inequality and real per
capita GDP (in purchasing power parity terms) for these countries. Regress the
variable N on the variable ln(gdpch) and the variable {ln(gdpch)}2 and a constant
[NB: First derive the variables ln(gdpch) and {ln(gdpch)}2 in separate columns;
18
then use the sequence Range, Analyse, Regression in Lotus to run the regression; a
constant is automatically added to the regression]. Then run a second regression
with the variable ln(gini) added to the list of explanatory variables. Compare the
results with the results presented in Table 2.
2. Read the Lotus file FOREST.WK1 into Lotus (or Excel). The column labelled
‘forest’ represents the percentage change in forest area between 1961 and 1986 for
the 46 countries that are in data set. First regress the variable forest on the variable
ln(gdpch) and the variable {ln(gdpch)}2 and a constant, and then add the variable
ln(gini) to the equation. Next regress the variable gini on ln(gdpch), {ln(gdpch)}2,
and a constant. Which conclusions on the existence of an environmental Kuznets
curve and/or an ordinary Kuznets curve can you draw from these results?
3. In this exercise you will compose your own data set and estimate the impact of
income per capita on an indicator of environmental quality that you select yourself.
First read the MSWord file README.DOC. It describes the precise definitions of
all the environmental variables (and other variables) that were used in the study by
Shafik and Bandhyopadhyay (1992). The figures of EKCs in the World
Development Report of 1992 are based on this study. The same environmental
variables were used for the regressions that are presented in Table 2 above. Select
the environmental indicator that you want to use in this exercise. Next, open the
Lotus file ENV_WK1, ENV_WK2, or ENV_WK3 (depending on your choice of
environmental indicator). The second column gives the country codes, while the
first column gives the years of observation. Choose the year for which you want to
make the analysis (e.g. 1980). You may also limit the analysis to a certain group of
countries (e.g. Latin America). Copy the data for country, year, income (gdpch),
and the chosen environmental indicator to a separate Lotus (or Excel file). Then
run the regressions of the EKC for the data in this new file (see exercises 1 and 2).
4. Open the Lotus file GINI.WK1. This file gives the so-called ‘high-quality’ data on
Gini coefficients of income inequality from Deininger and Squire (1996). Copy the
data on the Gini coefficients for the countries and years that you selected in
exercise 3 to your data set. The data may not always be available for the year that
you selected. But income inequality tends to change slowly over time, so you may
select the Gini coefficient for the year that is closest to the year of observation on
your environmental (and income) variable. For some countries, there is no
information on income inequality available, so you need to delete these countries
from your data set. First, make the regression for the EKC for this new sample of
countries. Then, add the variable ln(gini) to the list of explanatory variables, and
run a new regression. Which conclusions can you draw from the analysis?
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by Nico Heerink and Abay Mulatu
Department of Economics and Management
Wageningen Agricultural University
Hollandseweg 1
6706 KN Wageningen The Netherlands
E-mail: nico.heerink@alg.oe.wau.nl
