Interrelationships between income, health and the environment: extending the Environmental Kuznets Curve hypothesis

Ecological Economics 36 (2001) 513–531

ANALYSIS

Interrelationships between income, health and theenvironment: extending the Environmental Kuznets Curve

hypothesis

Lata Gangadharan a,*, Ma. Rebecca Valenzuela b

a Department of Economics, Uni6ersity of Melbourne, Melbourne, Vic. 3010, Australiab Department of Economics, Monash Uni6ersity, Vic. 3145, Australia

Received 4 April 2000; received in revised form 19 September 2000; accepted 20 September 2000

Abstract

This paper examines the link between the health indicators and the environmental variables for a cross-section ofcountries widely dispersed on the economic development spectrum. While environment and income are seen to havean inverted-U shaped relationship (Environmental Kuznets Curve (EKC) hypothesis), it is also well established thatenvironment and health are positively related. Our study focuses on the implications of this for the relationshipbetween health and income. In the early phases of income growth, the gains in health and the losses in environmentalquality could cancel each other out and this challenges the idea that as incomes increase health would alwaysimprove. To empirically analyse these issues, we estimate a two-stage least squares model that focuses on the impactof income and the environment on health status, with environment being an endogenous variable. Our results showthat the environmental stress variable has a significant negative effect on health status. At the same time, grossnational product (GNP) levels are shown to vary positively with health status variables. We find that the health gainsobtained through improved incomes can be negated to a significant extent if the indirect effect of income acting viathe environment is ignored. Research findings in this regard would be a useful policy instrument towards maximisingboth the environmental and health gains that come with economic growth and development. © 2001 Elsevier ScienceB.V. All rights reserved.

Keywords: Environmental stress; Health indicators; Income levels

JEL classification: O11; Q25; C30

www.elsevier.com/locate/ecolecon

* Corresponding author.E-mail addresses: [email protected] (L. Gangadharan), [email protected] (M.R.

Valenzuela).

0921-8009/01/$ - see front matter © 2001 Elsevier Science B.V. All rights reserved.

PII: S 0921 -8009 (00 )00250 -0

L. Gangadharan, M.R. Valenzuela / Ecological Economics 36 (2001) 513–531514

1. Introduction

Is economic growth part of the solution ratherthan the cause of environmental problems? Thisquestion has been raised very often in recent yearsas empirical evidence in support of the Environ-mental Kuznets Curve (EKC) hypothesis mount.1

The EKC (Grossman, 1995; Grossman andKreuger, 1995) describes the relationship betweendeclining environmental quality and income as aninverted-U, that is, in the course of economicgrowth and development, environmental qualityinitially worsens but ultimately improves with im-provements in income levels. For instance, Torrasand Boyce (1998) show that the level of air pollu-tants (sulphur dioxide and smoke) peak at a percapita income in the neighbourhood of US $4000,after which they start falling.

One of the explanations for the EKC relation-ship is that the environment can be thought of asa luxury good. In the early stages of economicdevelopment, a country would be unwilling totrade consumption for investment in environmen-tal regulation, hence environmental quality de-clines. Once the country reaches a threshold levelof income, its citizens start to demand improve-ments in environmental quality and this leads tothe implementation of policies for environmentalprotection and, eventually, to reductions in pollu-tion. Increasing levels of pollution are thusstrongly associated with both poor and develop-ing economies, while declining levels of pollutionare more commonly observed for their developedcounterparts.2 Another explanation of the EKChypothesis is that countries pass through techno-logical life cycles, as they move from agriculture-based economies to service-based systems. As theservice sector is associated with lower environ-mental impact, this transition from high pollutingto low polluting technology leads to less environ-

mental stress. Hence, in the long run, pollutionlevels are expected to improve with incomes. Thisargument has been used to justify the pursuit ofgrowth strategies that do not give due consider-ation to their effect on the environment.

In this paper, we argue that this strategy is notjustified and provide some evidence to supportour case. We test the hypothesis that larger eco-nomic and social gains can be attained by aneconomy if the growth strategy adopted incorpo-rates, rather than ignores, environmental con-cerns. To do this, we include health as theintervening variable in the analysis. While envi-ronment and income are seen to have an inverted-U shaped relationship, it is also well establishedthat health and environment are positively re-lated. What does this imply for the relationshipbetween health and income? It is possible that inthe early phases of income growth, the gains inhealth and the losses in environmental qualitycancel each other out and this challenges the ideathat as incomes increase, health would alwaysimprove.

In view of the above, we argue in this paperthat the recorded health gains brought about bythe improvement in income levels do not repre-sent the total realisable health benefits from hav-ing higher per capita income. Without theappropriate environmental protection policies,damages to a country’s physical environment areincurred during the process of income growth andeconomic development. This negatively affects thehealth and well-being of individuals in the countryand the aggregated impact could negate some ofthe health gains already derived, and hencedampen achievement levels in the health area. Ifwe find that this argument has some empiricalsupport, it would imply that development policiesaddressing environmental issues are effectivelyalso addressing the health issues of the economy.In that case, policies that pursue economic devel-opment cannot afford to ignore environmentalissues, particularly in the early phases of eco-nomic growth.

We look at recent evidence from a cross-sectionof countries to determine if this is indeed the case.While there are some studies (for example, Crop-per et al., 1997) that look at the incidences of air

1 The original Kuznets curve refers to an inverted-U shapedrelationship between per capita income and inequality ob-served by Kuznets (1955).

2 For example, Grossman and Kreuger (1995) found evi-dence in support for the EKC hypothesis for 12 of the 14 airand water quality variables for a cross-section of countries.

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or water pollution-related diseases in a particularregion or country, this is one of the first papers toanalyse the link between the health indicators andthe environmental variables for a cross-section ofcountries widely dispersed on the economic devel-opment spectrum.

The major contributions of this paper are thefollowing.1. To establish the link between health and envi-

ronment. While this link has been suggestedbefore, to the best of our knowledge it has notbeen explored for different countries.

2. To explore the differential impact on health ofseveral environmental stress variables. Thestandard practice is to focus on just one vari-able, normally CO2 emissions. We attempt tomake our findings and conclusions more ro-bust by including several other pollutants andenvironmental damage indicators in theanalysis.

3. To analyse the shape of the EKC curve andthe health relationship using alternative func-tional forms to determine which among thembest fits the data.

The rest of the paper is organised as follows.Section 2 surveys the related literature on therelationship between income levels and environ-mental stress and the link between income levelsand health status. Section 3 describes the analyti-cal framework and the estimation methodologyused in the paper. Section 4 summarises the dataused in the analysis. Section 5 discusses the resultsfrom the estimation, and Section 6 concludes.

2. Related literature survey

Panayotou (1993), Selden and Song (1994) andGrossman and Kreuger (1995) presented initialevidence that some pollutants follow an inverted-U shaped curve with respect to income. This waswidely interpreted (for example, World Bank,1992) to mean that the surest way to improve acountry’s physical environment is to increase in-come levels. More recent work has focused onfactors other than income as explanatory vari-ables in analysing variations in environmentalstress in different countries. Kaufmann et al.

(1998), Torras and Boyce (1998), Suri and Chap-man (1998)and Agras and Chapman (1999) arguethat the EKC’s previously estimated could be dueto important missing variables. Kaufmann et al.(1998) stress the importance of spatial intensity ofeconomic activity, Torras and Boyce (1998) ex-plore the effects of social factors like civil rights,income inequality and education, while Suri andChapman (1998)and Agras and Chapman (1999)find that trade-related variables and the price ofenergy have significant explanatory power. Mostof the papers mentioned use linear or a log linearfunctional relationship between emissions and in-come. An exception is Galeotti and Lanza (1999),which studied relationships based on the gammadistribution. The current state of the research onthe EKC is unable to conclude if the EKC hy-pothesis is confirmed or rejected.

With regards to health, there exists a largeliterature that has analysed the relationship be-tween income and health using cross-country data(for example, Gerdtham et al., 1992; Chakrabartiand Rao, 1999). A number of previous studies inthis literature have found an economically andstatistically significant and negative income elas-ticity of infant mortality rate (see for example,Flegg, 1982; Parpel and Pillai, 1986; Hill andKing, 1992; Kakwani, 1993; Subbarao andRaney, 1995; Pritchett and Summers, 1996). Simi-larly, research on life expectancy and income hasshown that there is a positive relationship betweenincreases in income and life expectancy, with in-come elasticity of life expectancy estimated to besignificant and positive (Preston, 1980; Hill andKing, 1992). Most of these studies also control forother factors that affect health status such as theaccessibility of health services and education lev-els of the population.

3. Analytical framework

In this paper, we are interested in how theinterplay between income and the environmentaffect the health outcomes of a population. Gen-erally, it is assumed that health outcomes for apopulation improve as the economy grows anddevelops. Such improvements are facilitated by


the rise in general standard of living, includingimproved access to educational opportunities andhealth services. One’s health is also seen as depen-dent on the quality of his or her physical environ-ment — such as the amount of air pollution orthe quality of drinking water. At the same time,the quality of a country’s physical environment isa result of certain growth factors in the economy.These include, for instance, the more intensive useof land, forest and water resources to increaseoverall economic production. Air pollution levelsare also bound to increase as production levelsrise. Increase in population numbers is anotherimportant factor in this context.

The relationships discussed above are sum-marised in the following general model:

Hi= f(Xi, Ei(Xi, Zi), Wi) (1)

which states that an economy’s health status (Hi)depends on its level of economic growth (Xi), thequality of its environment (Ei) and other socialfactors (Wi) including the provision and access tohealth facilities. Zi is used to denote the factorsthat determine the quality of the environment.Within this framework, we test the relevance ofthe EKC hypothesis, captured in the term Ei (Xi,Zi), and how it impacts on the health outcomesfor a country’s population.

To empirically analyse these issues, the follow-ing econometric model is formulated for countryi :

Ei=b0+b11Xi+b12Xi2+b13Xi

3+b2Zi+ei (2)

Hi=a0+a1Xi+a2Wi+a3Ei+ui (3)

where Ei refers to the overall level of environmen-tal stress in the economy; Hi refers to healthstatus of the population; Xi pertains to the coun-try’s level of economic growth; Zi, are non-incomevariables that impact on the environment; Wi, arevariables that directly influence health such asprovision and access to medical facilities, etc.; ui,ei, are the error terms.

Eq. (2) is the EKC, where the dependence ofenvironmental quality on economic growth is rep-resented in a cubic relationship. The inverted-Ushaped EKC requires b11 to be positive and b12 tobe negative. A cubic income term is added to test

the proposition from recent research that environ-mental quality tends to increase once again withextremely high incomes (De Bruyn et al., 1998).The upward bend of the Kuznets curve at the veryhigh-income levels will be captured by the b13

term, which is expected to have a positive sign.The Zi term captures the effects of non-incomevariables such as population levels, literacy ratesand income inequality, which are thought to sig-nificantly influence environmental outcomes. Forexample, countries with higher population densi-ties are expected to suffer from greater environ-mental stress as there would be more peoplesharing the existing environmental resources. Acountry’s level of urbanisation is also thought tonegatively impact on the environment, although itis possible that urbanisation can bring improvedwaste disposal and sanitation provisions for theurban areas, and hence mitigate the detrimentalenvironmental effects of an increase in popula-tion. Further, there are strong grounds for believ-ing that the education levels of the population arepositively associated with better environmentalquality, while is not clear in which direction thegap between the rich and the poor influences theenvironment. In previous research, increases inthe inequality of income distribution have beenassociated with higher levels of pollution, as thosewho benefit from pollution-generating activitiesare better able to prevail against those who bearthe costs (Torras and Boyce, 1998). However, thishypothesis has been challenged by Scruggs (1998),who suggests that it is impossible to make gener-alisations about the effect of income distributionon environmental degradation without havingmore information about the preferences of differ-ent income groups.

Eq. (3) postulates that the population’s overallhealth and general well-being is dependent onthree factors, the country’s level of economicgrowth; the availability and accessibility of medi-cal facilities; and the quality of the country’sphysical environment. Xi and Ei are the samevariables defined in Eq. (2) while Wi captures allother variables thought relevant to health such asnumber of doctors and other medical workers,immunisation levels, as well as literacy rates andother population-related factors.


The quality of the physical environment is in-cluded here as an endogenous variable. We expectthat health indicators would show an improve-ment with better availability of health care ser-vices and with higher rates of child immunisation.Urbanisation being an indicator of modernisationcould lead to improved health, as there might beeasier access to sophisticated health facilities.However, it is also possible that the same couldimpact negatively on health quality as urbanisa-tion very often leads to overcrowding, poor sani-tation levels and hence more health ailments. Weexpect higher educational levels to improve healthquality as education lowers the cost of informa-tion, and people with higher levels of educationmight have a better understanding of the value ofpublic health infrastructure and are better able tolocate and utilise these services. In particular,education has often been cited for its strong effecton reducing child mortality (for example, Melling-ton and Cameron, 1999).

In the above model, the structural equations areclearly identified given that Ei and Hi are the onlyendogenous variables in the system while the rest(Xi, Zi and Wi) are taken to be exogenous. Theequations are then estimated using general two-stage least squares estimation methods. The re-sults obtained are subjected to a robustness testwith regards to functional forms and to differentassumptions made regarding the type and natureof the variables used. The coefficients from theestimated equations will indicate if environmentalvariables play an important role in improvinghealth outcomes in a country.

4. Data

The data used in this paper are obtained fromthe World Development Indicators (1998), com-piled by the World Bank. Summary statistics ofthese data and the units of measurement used arepresented in Table 1. There are a total of 51countries included in the analysis, covering morethan 70% of the world’s population (Appendix A).3

Twenty-two of the 51 countries (43%) come fromthe high-income OECD set, but the populationcomposition is dominated by those in the low-in-come countries, with the inclusion of China andIndia in this group.

There are a number of environmental stressvariables that are available for analysis. The airpollutants for which data are available are carbondioxide (CO2), sulphur dioxide (SO2), nitrogenoxides (NOx) and total suspended particulates(TSP).4 Commercial energy use (ENPC) is takento be another environmental indicator. For waterpollution, we use data on emission levels of or-ganic pollutants (EMW) while data on deforesta-tion rates (DEFRTE) have also been obtainedfrom the World Bank. CO2 emissions and ENPCare very high for richer countries compared withlow-income countries. Other air pollutants likeSO2, NOx and TSP are significantly higher forlow-income countries than for the high-incomecountries in the sample. This could be due to thefact that richer countries have already in placeenvironmental regulations targeting these pollu-tants, while this has yet to be implemented forpoorer countries. EMW and DEFRTE are muchhigher for low-income countries for similarreasons.

The link of the environment with the country’slevel of economic growth is analysed using thecountry’s gross national product (GNP) per cap-ita, purchasing power parity, as the proxy vari-able for the latter.5 Population factors arecontrolled for the estimation through the coun-

4 The data for SO2, NOx and TSP were provided at the citylevel not the country level. We obtain the country level datausing the city’s population as a proportion of the country’spopulation.

5 It is acknowledged here that gross national product is justone aspect of economic growth and development, and thatother more broad based measures (for example, the HumanDevelopment Index or HDI) are available. We nonetheless useGNP in this analysis to facilitate comparison with previousEKC studies. It is noted that GNP is highly correlated withHDI values for the countries covered in this study (r=0.82),therefore, results should be robust between these two vari-ables. For more details on the importance of alternativemeasures of income and growth on the EKC relationship, seeMunasinghe (1999).

3 The countries in the sample are listed in the Table ofAppendix A.

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518Table 1Variable means and summary statisticsa

Income groupb RegionCode AllVariablecountries

Medium Low Africa Asia Eastern Latin Middle OECDHighEastEurope America

Totals16 12 3 9 8Number of 7Total 2 2251 23

numbercountries6 18 16 1445 4100 4331 24Share (%)

4326 875 706 2745 83 2685 260 374Size of 122Total 802numberpopulation

(inmillions)

2 62 6 9 3Share (%) 19100 20 16 63

MeansPopulation 206 354 65 112 53.33 751.11 89.88 30.00 50.00 117POPDEN

density(numberper km2)

65 48 11.03 100.74 23.28UPOP 41.8141 31.90 7476Urbanpopulation(%)

20151 7514 3228 3457 10 252GNP 5579GNP 7830 4110 18 73312 204purchasingpowerparity p.c.

37 52 39 27GINI 5336 38 3232Gini 43coefficient

LEXP 77 71 66 61 70 70 71 68 76Life 72expectancy(years)

71 62 58 42 63 65HLE 64Healthy life 60 7165expectancy(years,1997–1999)

202 257 157 19 26 72 309 147 126 263DOCPhysicians/100 000(1993)

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Table 1 (Continued)

Income groupb RegionCode AllVariablecountries

Africa Asia Eastern MiddleLatinLow OECDMediumHighEurope EastAmerica

IMM 87 88 87 84 57 91 93 84 95 87Childimmunisation rate (%of allunder 12months)

86 109 71 61Gross 48EDU 69 84 56 72 109enrollmentratio (% ofschool-agechildrenc)

Infant 19IMR 6 22 40 59 27 13 26 44 8mortalityrate (per1000 livebirths)

9CMR 24 7 26 53 89Child 34 17 31 52mortalityrate (per1000childrenunder age5)

10.07 5.61Carbon 2.92CO2 2.93 5.88 7.48 3.49 2.95 9.306.99dioxideemissionp.c.c

0.394 0.992 6.411 0.834Total 7.931TSP 1.061 1.118 1.143 0.4261.997suspendedparticlesemissionc

0.411 1.174 0.159 1.377 0.371SO2 0.2790.477 1.173 0.1900.160Sulfurdioxideemissionc

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Table 1 (Continued)

All RegionCodeVariable Income groupb

countries

Africa Asia EasternHigh Latin Middle OECDMedium LowAmerica EastEurope

0.558 1.176 0.289 1.417 0.456 0.687 0.00 0.603Nitrogen 0.5340.693NOx

dioxideemissionc

382 217 800 148 1092Emission of 217EMW 216 150 336430organicwaterpollutantsc

1940 1020 869 2067 2852 11694437 9852850 4144Commercial ENPCenergy usep.c.c

0.19 −0.36 0.53 0.80Deforestation 0.60DEFRTE 1.13 −0.09 0.76 0.90 −0.39rate(average%change,1990–1995)

a Data were derived from World Development Indicators, WDI (1998), except for HLE and DOC which were taken from the World Health Organisation. Allvariables are for year 1996 unless otherwise indicated. Gini coefficients for a few countries (missing in the WDI) were obtained from Deininger and Squire (1996).

b Denote x as the per capita income. This grouping thus classifies countries in the high income category if (x]$12 000), medium if ($45005xB$12 000) and lowif (xB$4500).

c EDU refers to secondary level (1995), CO2 expressed in metric tons (1995), TSP, SO2 and NOx in kg/m3 (1995), EMW in kg/day (1993), and ENPC in kg of oilequivalent (1995).


try’s population density levels (POPDEN) and theoverall degree of urbanisation (UPOP). Secondaryschool enrollment ratios (EDU) proxy for theliteracy rate in the country, and the gap betweenthe rich and the poor as measured by the Ginicoefficient (GINI) is used as the indicator ofincome inequality in the country.

The main health status indicators used are lifeexpectancy (LEXP) and infant mortality rates(IMR). Life expectancy is a popular indicator ofhealth although it is not without problems.Feachem et al. (1992) show that the causes ofdeath in adults are much less likely to decreasewith increases in per capita income; it may, infact, increase. For example, many adult deathscould be due to motor vehicle accidents, use oftobacco and alcohol, excessive consumption offood products related to heart disease, and allthese tend to rise with income. Infant mortality isa good alternative indicator as it avoids the po-tentially more severe reverse causation problemsassociated with the relationship between adulthealth and income growth. The mortality rate ofchildren under 5 years of age is usually used as anindicator of child well-being (UNICEF, 1991 and1992). This welfare measure, which we refer to aschild mortality rate (CMR), is used as anothermain health indicator here.6 To account for illhealth in life expectancy, we use a new variabledeveloped by the World Health Organisationcalled healthy life expectancy (HLE). To calculateHLE, the years of ill health are weighted accord-ing to severity and subtracted from the expectedoverall life expectancy to give the equivalent yearsof healthy life. This indicator is also known as the

disability adjusted life expectancy (DALE) and itsummarises the expected number of years to belived in what might be termed the equivalent of‘full health’.7

For all the countries in the sample, the averagerate of infant mortality is 19 deaths per 1000 livebirths. However, this rate shoots up to 59 deathsper 1000 live births in Africa and 44 deaths per1000 births in the Middle East. The African rateis extremely high, and far exceeds the averagelevels computed for all the other regions — morethan double the rate for Asia and Latin Americaand seven times higher than that of the advancedcountries of Europe and North America. Incomeinequality, measured using Gini coefficients, wassupplemented from Deininger and Squire (1996).The data show that income inequality is highest inLatin America and Africa, and lowest in Europeand North America.8

Table 2 presents the correlation between theenvironmental indicators relating to air pollution.CO2 emissions and ENPC are noted to have avery high positive correlation, while ENPC andCO2 emissions have a weak negative relationshipwith the emission levels of SO2, TSP and NOx,which are local pollutants. This may be a bit

Table 2Correlation between environmental indicators (air pollution)

TSPCO2 SO2 NOxENPC

CO2 1.01.00.8909ENPC

TSP 1.0−0.3193−0.28711.00.8905−0.2648−0.2176SO2

−0.1060 −0.1451 0.8216 0.8677 1.0NOx

6 There exist alternative data sources for the variables usedin this paper. For example, the World Resources Institute(WRI) provides data on the environment, health and theeconomy (http://www.wri.org/wri/facts/data-tables.html).WRI data on air pollution is very similar to the kind we use asit is available for a few years, but not over time. The data onwater pollution is of better quality in our current source (i.e.World Development Indicators, 1998) compared with that ofthe WRI. Further, WRI data for water pollution is for differ-ent years for different countries, with a time span difference ofup to 13 years in some cases. Such disparate data would notlend itself to cross-country comparisons, as we require in thisstudy.

7 We would like to thank an anonymous referee for bringingthis to our attention.

8 The Gini coefficient does have some limitations as a mea-sure of income inequality. Torras and Boyce (1998), Magnani(2000) discuss some of these limitations, and it is sometimessuggested that the ratio of the income shares of the first andfourth quintile of the income distribution might be a bettermeasure of income inequality. In this paper, however, the useof a general measure such as the Gini coefficient is sufficient,as the focus of this paper is not on inequality but on impact ofenvironmental stress on health outcomes.


Fig. 1. Estimated relationship between GNP and CO2, ENPClevel.

coefficients are jointly significant.Fig. 1 shows how CO2 emissions and ENPC

levels change with increasing per capita income. Atincome levels below $5000, environmental stress isseen to increase with energy use increasing muchmore than carbon dioxide emissions. These emis-sion levels peak at around the $6000 mark, afterwhich energy use plateaus while CO2 emissionsdecrease until income levels reach $18 000. Afterthis critical income point, rapid rates of increase inenergy use and CO2 emission levels are observed.These results contrast with the standard EKC curvein that we do not find an inverted-U curve.10

Rather, we find that the curve is a flattenedinverse-S shaped curve where the slope is mostlypositive everywhere, except for the inflection pointwhere the slope is zero. The inverse-S shape isobserved for both CO2 and ENPC variables, withENPC levels showing larger rates of change overthe income scale, that is, the CO2 curve is flatter andless variable.

The results imply that we can partition theenvironmental stress experience of countries intodistinct phases. During the first phase when percapita incomes are low, environmental stress isshown to increase but at a diminishing rate. Duringthe second phase when per capita incomes arehigher, environmental stress levels appear con-trolled and no increases are observed. The thirdphase occurs at extremely high incomes whenemissions increase again and escalate rapidly. Thisimplies that the impact of income on the environ-ment is more significant at the extreme ends of theincome scale. In particular, the results show thatvery low and very high-income countries tend toexperience increasing stress levels in their environ-mental conditions, while there is relatively littlechange in the environmental stress levels for themiddle-income countries.

The results further show that population densitylevel and levels of urbanisation are both positivelyrelated to environmental stress while the level ofincome inequality is inversely related to environ-mental quality. Hence, as a country gets more

surprising, as we would expect that a rise in energyconsumption would be accompanied by a rise inpollutant emissions. Suri and Chapman (1998)explain this seemingly inconsistent result by sug-gesting that it is possible for energy consumptionto keep rising but for emission levels of localpollutants to fall, as would be the case whenend-of-pipe technology like scrubbers are used toreduce local pollutants. As the existing policies toabate local pollution often concentrate on end-of-pipe methods and not on reducing energy consump-tion or emission levels, it should not surprising thatenergy use and CO2 emissions are not being re-duced along with reductions in the levels of localpollutants.

5. Results

Table 3 presents results from the estimation ofthe environmental equation (Eq. (2)). It is seen thatper capita income, population density, country’slevel of urbanisation, inequality in the distributionof income as well as level of education exertsignificant influences on a country’s level of envi-ronmental stress. The results are particularly strongwhen CO2 emissions and ENPC are used as thedependent variables. In the case of CO2, we findthat when all other influences are taken to beconstant, a $1000 increase in per capita GNPincreases the per capita CO2 emission level by 1metric ton. For ENPC, a 255 kg in oil equivalentincrease in energy use results from a $1000 increasein per capita GNP.9 The F-test shows that the 10 We do not find an inverted-U shaped relationship be-

tween income and any of the pollutants. In the literature, theEKC relationship is supported for pollutants like SO2 andNOx, but not always for CO2, which is usually seen to increaseover income.

9 The coefficients do not come out to be statistically signifi-cant for ENPC, however, the sign of the income coefficientsare very similar to the signs of the income coefficients for CO2

emissions.

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Table 3Impact of GNP and other explanatory variables on the environmenta

SO2 NOx EMW DEFRTECO2Variable ENPC TSP

844416.11.034 1.51313.760Constant −0.895 0.485, 0.651−233.047(1.011)(7.941)b(897.500) (697726.6)(3.401) (1.519)

0.001 −0.002GNP −1.93E-040.255 −1.85E-04 −129.00 −4.05E-05(201.261)(0.001) c (2.33E-04)(0.208) (2.61E-04)(0.001) (2.40E-04)

−2.11E-05 6.91E-09−1.12E-07GNP2 1.07E-081.21E-07 0.002 −9.46E-10,(0.016)(2.01E-08) 1.52E-08(1.88E-05) (1.70E-08)(5.62E-08)c (1.11E-07)

−5.38E-14−2.41E-12 −1.65E-136.53E-10 1.13E-073.02E-12GNP3 6.71E-14,3.12E-13(3.68E-07)(2.38E-12) (4.65E-13)(4.81E-10)(1.47E-12)c (3.60E-13)

0.199 9.84E-050.001POPDEN −7.21E-050.001 −152.317 −5.45E-05,(143.116)(1.82E-04) 1.20E-04(0.213) (1.43E-04)(0.001)b (0.001)

−9.9161989.4335.034 −1.421UPOP −1.016 −1010368 0.201, 1.288(1.956)(795.405)b (1152394)(1.736)(2.869)b (7.185)

GINI 0.0110.017 11058.34 0.018, 0.015−36.024 0.020−0.090(0.021)(0.096)(14.988)b (0.024)(0.059) (14480.17)

6086.6750.010 0.0020.012 −0.008, 0.00818.411EDU 0.034(0.016)(0.011)(0.046) (7135.055)(0.034) (7.757)b

4.44 0.53 1.08 2.456.96 0.71F-test 0.83

a Figures in parenthesis indicate robust S.E.b Significant at 10% level.c Significant at 5% level.


crowded (more people on a fixed area of land), thehigher will be their CO2 emissions and per capitaenergy use. This can be due to the fact that aspopulation density increases, there is increasingpressure to use the existing land more intensively.The creation of multi-storey residential and com-mercial buildings in high population density coun-tries is a good example of this problem. Lifestyleadjustments for residents in these countries implymore energy consumption and this leads to abnor-mally high levels of CO2 emissions. Singapore is acase in point; its population density in 1996 was4990 persons per km2 and the commercial energyuse was 7162 kg of oil equivalent per capita. Incontrast, the corresponding average levels for oursample of 51 countries are 206 and 2850, respec-tively. Clearly, the high population density inSingapore exerts a major influence on its extremelyhigh level of energy use. Further, the percentage ofpopulation living in urban areas (UPOP) impactspositively on the levels of CO2 and ENPC, withemissions rising as urban population increases.

We observe a positive coefficient for the educa-tion variable (EDU), which runs counter to expec-tations. The results show that higher levels ofeducation aggravate, rather than improve, environ-mental conditions. On the other hand, any im-provement in the inequalities between the rich andthe poor is found to be detrimental to the environ-ment. While counter intuitive in the first instance,this makes empirical sense because a move towardsmore equal standards of living implies more peopleare able to afford the use of electricity, cars andother luxuries — which leads to increased energyuse.11 For such a cross-section of countries, the

explanatory power of these two models is fairlyhigh (adjusted R2=67% for CO2 and 79% forENPC).

Eq. (2) was also estimated using data on otherspecific pollutants such as TSP, SO2, NOx, EMWand DEFRTE. The magnitude and signs of theestimated coefficients are very sensitive to thepollutant used, and are very unstable. Further, theexplanatory power of the models are greatly re-duced with F-test results simultaneously indicatinginappropriate models. We note that many environ-mental studies used CO2 and ENPC preciselybecause the data on these variables are well devel-oped. Also, we note here that trend results aresimilar for CO2 and ENPC because CO2 is a majorcomponent of ENPC. As seen in Table 2, these twoenvironmental stress variables have a high andpositive correlation between them.

5.1. Impact on health

Results of the two-stage least squares (2SLS)estimation of Eq. (3) are presented in Tables 4 and5. In these estimations, we use alternative indicatorsof a population’s health status — namely, lifeexpectancy (LEXP), healthy life expectancy (HLE),infant mortality rate (IMR) and child mortalityrate (CMR) — and treat the environmental stressvariable as endogenous. We find that if we ignorethe potential endogeniety of the environmentalvariables, the results obtained are inconsistent. TheDavidson and MacKinnon (1993) augmented re-gression test shows that the null hypothesis of anexogenous environmental stress variable is stronglyrejected for all the alternative types of pollutants.12

Table 6 presents the coefficients of the environmen-tal stress variables for the different health indica-tors and compares the OLS and the 2SLS estimates.The coefficients obtained from the 2SLS estimationhave signs in the expected direction and the magni-tudes are larger compared with the coefficientsfrom the OLS estimation. This implies that theimpact of the environmental stress variable on

11 This issue has been analysed in greater detail in Torras andBoyce (1998), Scruggs (1998) with mixed results. While Torrasand Boyce (1998) find that more equitable distributions ofincome and power tend to result in better environmental quality,Scruggs (1998) shows that equality does not necessarily lead tolower environmental degradation. Magnani (2000) finds thathigher levels of income would increase environmental qualityprovided the negative effect of production of goods and serviceson pollution levels is more than counterbalanced by the positiveeffect of growth on the demand for pollution abatement policy.The demand for environmental quality will be affected byinequality levels in the country. As the level of per capita incomeincreases above a critical level, income equality becomes posi-tively correlated with environmental protection; however, be-yond a certain threshold level of income, the correlation betweenincome and environmental protection turns negative.

12 The augmented regression is formed by including thepredicted value of the endogenous right-hand side variable asa function of the all exogenous variables, in a regression of theoriginal model.

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Table 4Impact of GNP and environment on health: with LEXP and HLE as dependent variablesa

SO2 NOx EMW DEFRTEVariable ENPCCO2 TSP

(A) Dependent 6ariable: life expectancy

62.728 (6.948)b 65.456 (12.590)b 63.381 (5.336)b 58.002 (3.962)bConstant 57.228 (3.597)b 53.078 (3.250)b 62.143 (5.048)b

−4.152 (5.439) −4.220E-06 (0.470E-06)c −1.803 (3.430)c−5.144 (5.156)−0.623 (0.440)Environmental −0.002 (0.001)b−0.380 (0.156)b

stress variable

3.3E-04 (1.2E-04)b 4.6E-04 (1.4E-04)b 5.01E-04 (1.1E-04)b 4.0E-04 (8.7E-05)b0.001 (1.6E-04)cGNP 4.1E-04 (7.4E-05)b0.001 (9.3E-05)b

0.063 (0.069) 0.087 (0.081)0.056 (0.104)0.110 (0.082)0.095 (0.062)IMM 0.064 (0.038)c 0.091 (0.033)b

0.012 (0.009)0.009 (0.004)b 0.007 (0.004)c −0.001 (0.017)0.013 (0.004)b 0.006 (0.005) 0.005 (0.007)DOC

−0.004 (0.040)0.012 (0.022) 0.006 (0.025) 0.002 (0.041)0.030 (0.023) 0.008 (0.022) 0.016 (0.021)EDU

−3.737 (4.363) 3.664 (4.506)−4.054 (6.700)−5.541 (5.614)UPOP −5.383 (4.526)4.932 (3.301)3.846 (2.813)

9.40 9.43 12.59 21.6117.27F-test 16.1623.60

(B) Dependent 6ariable: healthy life expectancy

47.126 (9.874)b 41.164 (6.873)b37.139 (5.767) 44.883 (8.480)b 47.426 (10.820)b 52.610 (20.266)b40.236 (6.759)bConstant

−0.326 (0.237) −7.256 (7.622) −6.626 (8.987) −5.890E-06c (3.690E-06) 0.320 (4.680)−0.002b (00.001)Environmental −0.603 (0.627)

stress variable

3.053E-04 (1.807E-04)c 5.029E-04 (2.108E-04)b 5.604E-04 (1.665E-04)b 4.652E-04 (1.564E-04)b7.393E-04 (1.678E-04)bGNP 4.352E-04 (1.115E-04)b0.001 (1.357E-04)b

0.146 (0.123) 0.112 (0.099)0.120 (0.172)0.196 (0.138)IMM 0.157 (0.097)c0.146 (0.061)b0.126 (0.070)c

0.028 (0.016)c0.022 (0.008)b 0.020 (0.009)c 0.021 (0.026)0.025 (0.006)b 0.019 (0.009)b 0.017 (0.011)DOC

−0.004 (0.048) 0.014 (0.039)−0.020 (0.070)0.012 (0.042)0.004 (0.038)EDU 0.008 (0.039) 0.023 (0.037)

−4.771 (10.689)6.504 (3.639)c −3.463 (6.259) 4.662 (4.797)7.176 (3.348)b −2.234 (7.330) −5.738 (8.015)UPOP

7.72 7.14 9.43 17.9719.50F-test 14.1118.16


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Table 5Impact of GNP and environment on health: with IMR and CMR as dependent variablesa

EMWVariable DEFRTECO2 ENPC TSP SO2 NOx

(A) Dependent 6ariable: infant mortality rate59.30874.065 56.816 75.41682.829Constant 45.71660.419

(15.342)b (12.086)b(46.084)b(13.419)(11.594)b (12.095)b(20.929)b

2.113 16.07916.439 1.540E-05 12.8890.173Environmental stress 0.004variable (14.902) (10.253)(18.806)(1.178)c(0.003)(0.619) (8.420E-06)c

−1.270E-03−0.001 −0.001−0.001−0.001GNP −0.001−0.002(3.391E-04)b(3.586E-04)c (4.523E-04)b(2.181E-04)b (3.580E-04)b(0.001)b(3.285E-04)b

IMM −0.388−0.214 −0.216 −0.283−0.275 −0.348 −0.442(0.209)c (0.349)(0.159)b (0.141)b (0.256)c(0.129)c (0.135)b

−0.051−0.066−0.056 −0.047DOC −0.073 −0.048 0.003(0.016)b(0.031)b (0.053)(0.020)b(0.013)b (0.014)b(0.011)b

−0.038−0.091−0.064 −0.066EDU 0.011 −0.023 0.034(0.074)(0.071) (0.133)(0.066) (0.132)(0.064) (0.066)

−17.604 11.772−13.266UPOP 11.14412.711 10.838 −24.073(15.122)(22.007) (16.440)(10.654)c (16.183)(9.619) (12.768)

22.62 7.64 16.53 13.3917.98 17.64F-test 10.85

(B) Dependent 6ariable: child mortality rate113.82090.029 71.22689.890127.268 82.166112.428Constant

(36.188)b(23.089)b (27.228)b (71.380) (22.437)c(21.052)b (21.322)b

0.416Environmental stress 3.4330.007 24.473 23.110 17.8542.190E-05(15.862)(28.130)(22.826) (1.250E-05)c(1.948)c(0.851) (0.005)cvariable

−0.001GNP −0.001−0.002 −0.001 −0.001 −0.002 −0.001(0.001)b(0.001) (4.825E-04)c(3.176E-04)b (0.001)b(0.001)b(4.639E-04)b

DPT −0.467−0.468 −0.723 −0.779−0.571 −0.5120.683(0.419)b(0.363)b (0.547)(0.299)b (0.266)c(0.227)b(0.223)b

−0.095 −0.059 0.011−0.057DOC −0.062−0.088−0.071(0.021)b (0.080)(0.030)c(0.017)b (0.046)b(0.020)b (0.021)b

−0.111 0.068−0.063 0.043 −0.010SEC −0.022 0.071(0.095) (0.195)(0.094) (0.107) (0.208)(0.103)(0.095)

13.22420.191 15.370−22.663 −36.789UPOP 12.990−29.605(19.265) (24.261)(33.370)(15.172)c(13.364)c (21.196)(24.349)

10.61 10.8217.97 5.3611.99 6.4413.91F-test


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Table 6Comparative test results from alternative estimation methods useda

Health indicatorEnvironmental

stress variable

CMRIMRLEXP HLE

t-Valueb OLS 2SLSOLS t-Valueb OLS 2SLS t-Valueb2SLS t-Valueb OLS 2SLS

3.51c −0.22 (0.20) 0.17 (0.62) 3.78c −0.08 (0.39)−0.35c (0.20) 0.42 (0.85)CO2 4.19c−0.33 (0.24)6.63c−0.38b (0.16)−0.16c (0.09)

−6.02E-04c (3.67E-04) −0.002b (0.001) 3.43c 3.00E-04 (8.12E-04) 0.004 (0.003) 3.76c 1.50E-03 (1.09E-03) 0.007c (0.005) 4.03c−0.002b (0.001) 6.45cENPC −1.33E-03b (5.75E-04)

0.42 (0.33) −7.26 (7.62) 3.81c 0.53 (1.11) 16.44 (14.90) 3.56c 2.34E-03 (1.40) 24.47 (22.83) 4.06c−5.14 (5.16)SO2 8.35c0.21 (0.24)

4.01c 0.31 (0.98) 16.08 (18.81) 3.62c −0.19 (1.35) 23.11 (28.13)−6.63 (8.99) 4.13c8.48c 0.46 (0.31)NOx 0.28 (0.19) −4.15 (5.44)

−0.60 (0.63)0.02 (0.07) 3.74c 0.33 (0.37) 2.11c (1.18) 3.45c 0.34 (0.53) 3.43c (1.95) 3.96c−0.62 (0.44) 8.45c 0.10 (0.10)TSP

3.53cEMW 1.17E-06 (1.53E-06)6.86E-08 (4.58E-07) 1.54E-05c (8.42E-06) 3.56c 1.20E-06 (2.11E-06) 2.19E-05c (1.25E-05) 4.21c−4.22E-06c (2.47E-06) 6.98c 3.45E-07 (5.99E-07) −5.89E-06c (3.69E-06)

3.43c 0.03 (2.10) 12.89 (10.25) 3.70c −0.27 (3.08) 17.85 (15.86)0.32 (4.68) 4.29c−0.16 (0.77)DEFRTE −0.25 (0.44) −1.80c (3.43) 7.46c

a Figures in parenthesis indicate robust S.E. *, Significant at 10%; **, significant at 5%.b t-Value from Davidson–Mackinnon augmented regression test for exogeneity.c Indicates the null hypothesis of exogeneity is rejected at 5% significance level.


health is bigger when we take the endogenietyinto account. The t-statistic from the Davidson–Mackinnon augmented regression test for exo-geneity shows that the null hypothesis ofexogeneity is rejected at the 5% significancelevel.

Panel A in Table 4 presents the results whenlife expectancy is the health indicator used. Ingeneral, the environmental stress variables havethe correct sign (negative) and are significant forcertain pollutants such as CO2, EMW, ENPCand DEFRTE.13 GNP is always significant andincreases in income levels lead to an increase inlife expectancy. The level of immunisation(IMM) is seen to increase life expectancy whenthe pollutant used is CO2 or ENPC. Educationlevel (EDU) does not seem to be significant forimproving health. The availability of doctors(DOC) as a proportion of the population has asignificant impact on improving life expectancy,particularly for the CO2, ENPC and EMW pol-lutants. When the health indicator used ishealthy life expectancy (panel B of Table 4), theresults are similar. The availability of doctorsincreases healthy life expectancy significantly;the absolute impact (measured by size of thecoefficients) of this variable is also greater withHLE, as the dependent variable. The impact ofurbanisation (UPOP) on health is large and sig-nificant particularly for HLE as the dependentvariable. This positive impact reflects the soci-ety’s benefits from improvements in the provi-sion of better waste disposal and sanitationfacilities, which would come with urbanisation.

When infant mortality is taken as the healthindicator (panel A of Table 5), we find that in-creases in TSP emissions and water pollutantemissions levels lead to significantly high infantmortality. Coefficients derived from the estima-tion of the model using the child mortality ratesare very similar to the infant mortality results(panel B of Table 5). In general, the results

show that income, immunisation rates, access todoctors and urbanisation levels all make largepositive and significant impacts on both infantand child mortality rates. Only the educationvariable fails to make a significant impact onmortality rates.

We also use log linear models to check forrobustness of results. Per capita GNP, purchas-ing power parity, is found to be significant in allcases in improving health. Using log TSP, logSO2, log NOx and log EMW as the environmen-tal stress variable, it is found that the coeffi-cients are negative and significant (for log SO2

and log NOx) in explaining health outcomes.Hence, the environmental variable is significantin explaining changes in health levels in a popu-lation. The estimated coefficients for log CO2

and log ENPC, however, have positive coeffi-cients and are significant. This is contrary towhat we would expect. We find, therefore, thatin some cases the results could be sensitive tothe functional form used.14

6. Conclusion and further research

In this paper, we examine the links betweenhealth status, income and environmental indica-tors of a country. We first look at the relation-ship between environment and income — theEKC hypothesis. We find that low-income coun-tries cannot simply postpone attending to envi-ronmental concerns in the hope that theenvironment will eventually improve with in-creased incomes. Health is a significant interven-ing variable and isolating the impact ofenvironment on health is very important, partic-ularly in the context of developing countries.Our results show that the gains in health ob-tained through improved incomes can benegated to a significant extent if the indirecteffect of income, acting via the environ-

13 The economic relationship between deforestation andhealth indicators is a bit ambiguous. As deforestation is anindicator of ecological balance in the country, this could havelong-term effects on health, however, the impact in the shortterm is not very clear.

14 Results for log linear models are not presented in thepaper, however these are available from the authors on re-quest.


ment, is ignored. This study thus shows thatpolicy makers who have chosen to pursue rapidgrowth strategies at the expense of the environ-ment are not delivering the full realisable healthgains that can be derived from higher incomes.Also environmental damage is bound to resultin health problems for the domestic population.A less healthy labour force will not be able toincrease productivity levels, and hence result inlesser income for the economy. Addressingchronic health problems for the population isalso costly and will divert valuable resourcesfrom income generating investment projects.Clearly, policies for growth must incorporateappropriate programs for protection of thecountry’s natural environment and this does nothave to be at odds with growth and develop-ment targets.

One of the ways this research can be extendedis to obtain time series data on environmentalindicators and health status for varied countriesalong the development spectrum. As we are in-terested in different kinds of environmental andhealth indicators, obtaining data for all theseindicators for many years is quite challenging.Most developing countries do not keep recordsof environmental variables, and this hampersour objective here. However, with the continuedimprovements in data availability, over time thisproblem will be reduced and research on thiscan be encouraged. A second extension is tocreate a single indicator or index that could cap-ture the overall quality of a country’s physicalenvironment. Such an index would be useful foranalysing environmental issues within a countryand can also provide important insights forcross-country trends. A third extension would beto study the impact of environmental policies onincome or growth in a country and whether ithas an impact on the health outcomes. Thiscould help us in understanding whether the pur-suit of pro-environmental policies would havebeneficial or adverse effects on growth itself.This could be examined using a three-equationsystem with income, environmental stress andhealth being endogenous variables.

Acknowledgements

We would like to thank Pete Summers, threeanonymous referees, seminar participants at theResearch School of Pacific and Asian Studies,Australian National University, the participantsat the Conference of Economists, 1999, LatrobeUniversity and at the Sixth Biennial Meeting ofInternational Society of Ecological Economics,Canberra, 2000, for their comments. We are,however, responsible for all remaining errors.Funding for this research was provided by theFaculty Research Grant Scheme, Faculty ofEconomics and Commerce, University of Mel-bourne.

Appendix A. Countries included in the study.

Argentina1Australia2Austria3

4 BelgiumBrazil5

6 BulgariaCanada7Chile8China9Colombia10

11 CroatiaCzech Republic12

13 DenmarkEcuador14Egypt, Arab Republic15Finland16France17Germany18Greece19

20 HungaryIndia21Indonesia22Iran, Islamic Republic23Ireland24Italy25Japan26Kenya27

28 Korea, RepublicMalaysia29


30 MexicoNetherlands31

32 New ZealandNorway33

34 PhilippinesPoland35

36 PortugalRomania37Russian Federation38Singapore39South Africa40

41 SpainSweden42

43 SwitzerlandThailand44Turkey45Ukraine46United Kingdom47

48 United StatesVenezuela49

50 GhanaSlovak Republic51

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