Economic Growth and its Impact on Environment: A Panel Data Analysis

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Ecological Indicators 24 (2013) 324–333

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Ecological Indicators

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Economic growth and its impact on environment: A panel data analysis

Ahmet Atıl As ıcı ∗

Istanbul Technical University, Is letme Fakültesi, Mac ka, 34367 Istanbul, Turkey

a r t i c l e i n f o

Article history:Received 6 September 2011Received in revised form 18 June 2012Accepted 24 June 2012

JEL classification:Q01Q32Q56

Keywords:Adjusted Net SavingEnvironmental sustainabilityEconomic growthRace-to-the-bottom hypothesis

a b s t r a c t

This paper aims to explore the relationship between the economic growth and the pressure on naturefrom the environmental sustainability perspective. The pressure on nature is measured by the naturaldisinvestment component of the Adjusted Net Savings data of the World Bank; which is the sum ofenergy, mineral, net forest depletions and carbon dioxide damage, all measured in US dollars. Our panelconsists of 213 countries and spans the period between 1970 and 2008. We employ a panel Fixed-EffectsInstrumental Variable (IV) methodology. Regression analysis reveals that there is a positive relationshipbetween income and pressure on nature, yet the effect is much stronger in middle-income than in lowand high-income countries, and also differs across subcomponents. While increase in income decreasesthe pressure on forests, CO2 damage and mineral extraction increase. We found that increasing trade, allelse equal, increases the pressure on nature, hence our findings support Race-to-the-bottom hypothesis.

© 2012 Elsevier Ltd. All rights reserved.

1. Introduction

Human activities are altering the global environment on anunprecedented level. The concentration of green house and ozonedepleting gases in the atmosphere, the accelerated extinction ofspecies, the breakdown of biogeochemical cycles, deforestation,and natural resource depletion are undeniably related to the humanactivity (Spangenberg, 2007). The question of how economic activ-ities affect environment has become popular among scholars by1960s, although historical records show that human related envi-ronmental catastrophes are not a rare event. For example, evenby 800 B.C. terracing for rice farming led to a massive deforesta-tion in China, and during the height of the Roman Empire, land andwater around Rome became highly contaminated by human relatedactivities (Kula, 1998, p. 194). Yet, by those times, as Hahnel (2010)argues, the world was “empty” and environmental problems werelocal, which allowed humans to continue their unsustainable wayof living without bothering with such questions. However, espe-cially after the Industrial Revolution in the 18th century, the worldpopulation started to grow rapidly and ecological scarcities startedto felt themselves. In 1966, the publication of a short chapter enti-tled “The economics of the coming Spaceship Earth” (Boulding,1966) changed radically the economic thought. Viewing the earth

∗ Tel.: +90 2122931300x2250; fax: +90 2122407260.E-mail address: [email protected]

as a small spaceship where all economic activities take place,Boulding argues that as population and economic activity continueto increase, the scarcity and the waste problems upon the space-ship will worsen (Kula, 1998, p. 199). The term “sustainable” inmodern ages was first spelled in the famous report of Club of Romeentitled The Limits to Growth (Meadows et al., 1972), and sincethen it has been gaining increasing interest from scholars. In 1980,“sustainable development” concept was introduced by the Inter-national Union for Conservation of Nature and Natural Resourcesreport (IUCN, 1980), but it was after the publication of the Our Com-mon Future, commonly known as Brundtland Report (WCED, 1987),the concept gained popularity.

Environmental sustainability, along with economical and socialsustainability constitutes the three pillars of sustainability (Moldanet al., 2011) and is defined by Goodland (1995) as “improvinghuman welfare by protecting the sources of raw materials usedfor human needs and ensuring that the sinks for human wastes arenot exceeded in order to prevent harm to humans”.

Environment both serves for environmental and economicalsustainability. In low and middle-income countries especially, nat-ural resources constitute a fairly big share of exports (Muradian andMartinez-Alier, 2001). Although, UN’s declaration of MDGs (Mil-lennium Development Goals) in the year 2000 “recognized thefull integration of human development and the environment asmutually reinforcing development goals” (Costantini and Monni,2007, p. 868), it is still questionable that the world will reachthe targets in the near future, let alone by 2015 (UN, 2011). Yet,

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A.A. As ıcı / Ecological Indicators 24 (2013) 324–333 325

accumulating scientific evidence does not portray an optimist pic-ture. Ewing et al. (2010) claims that the human demand has led to anenvironmental degradation that surpasses the Earth’s ecologicalcapacity to regenerate already in the mid 1970s, and this “over-shooting” is growing ever since. Coupled with the recent globaleconomic crisis, discontent over the conventional developmentparadigm is rising (Schneider et al., 2010; Jackson, 2009). Hence, thequestion of how environmentally sustainable the economic growthprocess becomes more and more important for policy design giventhe strong aspirations of low and middle-income countries tocatch-up with the level of life standards enjoyed by high-incomecountries. The issue at stake is whether we will be able to decou-ple environmental pressure from economic growth. Decoupling canoccur either relatively or in absolute terms. Absolute decouplingrequires environmental pressure to be stable or decreasing whilethe economic driving force is growing, whereas, relative decou-pling occurs when the rate of environmental pressure is positive butless than the economic growth rate (OECD, 2002). In this study ouraim is to investigate the casual relationship between income andpressure on nature from the domestic environmental sustainabilityperspective. Economic growth has consequences on environmentboth domestically and globally. In this paper we concentrate ondomestic consequences and therefore, we employ natural disin-vestment components of Adjusted Net Savings (ANS) (called alsoas Genuine Savings) data constructed by Hamilton and Clemens(1999). Our panel consists of 213 low, middle and high-incomecountries during the period between 1970 and 2008. This studyaims to provide new insights to the ongoing discussion by employ-ing a new dataset within a panel fixed-effects IV (InstrumentalVariable) regression framework.

The paper is organized as follows. The coming part is devotedto the review of relevant literature and sustainability indicators.In Section 3 we describe the data and the methodology. Section 4presents our baseline regression results and a battery of robustnesschecks. And finally Section 5 concludes.

2. Literature review

2.1. Human activity and environment

The question of how human activity interacts with environmentcan be traced back to the times of Malthus. In his famous 1798book, titled as, “An Essay on the Principle of Population” Malthusproved that the growth of population will eventually reach thelimit of resource base in the absence of technological progress.In early 1970s, a debate between Commoner, Ehrlich and Holden(1971) gave rise to the development of a formula, called as IPAT(Commoner et al., 1971), which summarizes the impact of humanactivity on the environment. This formula states that total envi-ronmental impact (I) is the multiplicative product of population(P), affluence (A) and technology (T) (see Marin and Mazzanti,2009).

Conventional wisdom sees economic growth as indispensible inincreasing human well-being. Yet, this view has been challengedrecently especially after the global economic crisis in 2008. Forexample the proponents of “degrowth movement” put the paradig-matic proposition as “human progress without economic growth ispossible” (Schneider et al., 2010, p. 512), by also adding that sus-tainable degrowth does not necessarily mean degrowth in all andevery sector or regions. Jackson (2009) also accepts that currentdevelopment paradigm is unsustainable but he also criticizes thedegrowth proposal as being “unstable” in today’s circumstances,for it may lead to “rising unemployment, falling competitivenessand a spiral of recession” (p. 46), and proposes zero growth. Pro-ponents of green economy, on the other hand, see the triple crisis

as an opportunity and argue that green investments in key sec-tors, like energy, construction etc., are able to create green jobs andto replace carbon-based economy with that of renewable-energy(Barbier, 2010). For them, “green Keynesian” (or popularly knownas Green New Deal) policies have a capacity to heal the triple crisisin the short-run and to pave the way towards sustainability in thelong-run, a promise which is attacked by many scholars as naïveon the grounds that they do not take into account the so-calledrebound effect at least (Schneider et al., 2010).

Throughout the sample period, global economic system haschanged radically. Thanks to the economic globalization, tradeamong nations increased dramatically. For example, the averageopenness to trade ratio increased from 54.3 in 1970 to over 100in 2008 in middle-income countries (see footnote 4 for incomeclassification methodology). The question of how this economictransformation affected the environmental sustainability of indi-vidual countries is interesting. In the literature it has been arguedthat opening up to trade could affect environment through severalchannels. It may be that, after opening up to trade, countries mayprefer to adopt looser standards of environmental regulation inorder to gain or preserve international competitiveness. This viewis called as Race-to-the-Bottom Hypothesis (see Daly, 1993; Ayres,1996; Frankel and Rose, 2005). Even in the absence of relaxed regu-lations, opening up to trade may have negative consequences overenvironment through relative price changes. For example, ResourceCurse Hypothesis (RCH), in particular Dutch Disease argument (VanWijnbergen, 1984), suggests that opening up to trade may lead toan increase in natural resource prices in the domestic economy, andhence a real exchange appreciation which would contract manu-facturing exports or displace capital and labor factors away frommanufacturing towards the extractive industries (Costantini andMonni, 2007). At the other extreme, Gains-from-Trade Hypothesisstates that openness may lead firms to adopt higher environmentalstandards and allows for technological and managerial innovations(see Eskeland and Harrison, 2002) which help to reduce the pres-sure on nature.

The interaction between environment and economic growthhas been a subject of different approaches. The effects of natu-ral resources endowment on economic growth are emphasizedby studies based on RCH.1 Environmental Kuznets Curve (EKC)approach, on the other hand, analyzes the effects of economicgrowth on different dimensions of environmental quality. Initi-ated by Grossman and Krueger (1991), studies in the EKC literaturehypothesize that the negative scale effect tend to prevail in theinitial stages of economic growth, but after a threshold level ofdevelopment it should be outweighed by the positive structuraland technological effects. Obviously environmental quality has sev-eral dimensions, yet aggregating them into a single indicator isproblematic. Hence, different studies in the EKC literature employsdifferent indicators such as carbon dioxide, sulfur dioxide emis-sions (i.e. Boulatoff and Jenkins, 2010; Grossman and Krueger,1991); urban air quality (i.e. Esty and Porter, 2005); deforestation(i.e. Ehrhardt-Martinez et al., 2002), heavy metal contamination(i.e. Grossman and Krueger, 1995) and waste (i.e. Mazzanti et al.,2009). The empirical evidence is mixed however. It is not possibleto talk about a unique curve for all types of environmental degra-dation, which raises doubts about the generalizability of the EKChypothesis (Özler and Obach, 2009). In our view, natural disinvest-ment component of ANS is superior to these single environmentalquality indicators employed in EKC literature in its capacity to rep-resent more broadly the changes in environmental sustainability.

1 See Costantini and Monni (2007) for a survey of RCH studies.


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2.2. Human well-being and sustainability

The development of indicators was essential to pursue poli-cies in line with sustainable development principles, and it wasone of the main target of the Agenda 21 adopted by the UNWorld Conference on the Environment and Development in Riode Janeiro in 1992 (UN, 1992). In the years following the confer-ence, several steps have been taken to measure the environmentalconsequences of economic activity through developing indicators,metrics and embedding environment into conventional accountingframework. Increasing dissatisfaction from the conventional mea-sures of well-being like GDP per capita can be argued to lead tothe development of alternative measures of well-being and sus-tainability. Neo-classical economic growth theory links economicgrowth to the accumulation of physical capital. But the conven-tional saving rate which solely depends on accumulation of thephysical capital lacks many dimensions on which the well-beingof people is assumed to rest, i.e. natural resources, human capitaland environmental quality. That is, rising GDP per capita may notnecessarily reflect in an equal increase in the well-being of peo-ple (Hamilton and Clemens, 1999; De Haan, 2004). Rennings andWiggering (1997), Wilson et al. (2007), Siche et al. (2008), Singhet al. (2012) provide lists and comparative analysis of differentindicators and metrics.

Relevant indices on the income-environment interaction canbe listed as follows: Environmental Sustainability Index (ESI) (WEF,2001); Environmental Performance Index (EPI) (Bohringer andJochem, 2007); Environmental Vulnerability Index (EVI) (Singh et al.,2012), Index of Sustainable and Economic Welfare (ISEW) (CES, 2000);Green Net National Product (UNEP, 2000); Ecological Footprint (EF)(Rees, 1992; Wackernagel et al., 1999); and Adjusted Net Sav-ings (ANS) (Hamilton and Clemens, 1999). The discussion is stillgoing on regarding the best approach to the design and use ofsustainable development indicators methods (Wilson et al., 2007,p. 300).

On the income-environmental sustainability nexus, appropriateindicators are EF and ANS, cause other indicators mentioned aboveare either more appropriate to measure quality of life (for exampleISEW) or to assess the likelihood of potential damages caused byenvironmental problems (i.e. EVI), or are based on undertaken poli-cies (such as EPI) (Singh et al., 2012), Yet the problem with EF is that,it accounts for resource use (consumption) regardless of the coun-try of origin where extraction/production takes place. Given thefact that “some consumers are capable of displacing the ecologicalconsequences associated with their consumption through trade”(Özler and Obach, 2009, p. 86), EF measure is not suitable for ouraim in this paper. In contrast, the natural disinvestment componentof the ANS is measured within the country where production andextraction take place, which makes it possible to observe the effectof the income growth on the domestic environment and domes-tic environmental sustainability. To our knowledge, regarding theinteraction between economic growth and environment, naturaldisinvestment components of ANS has not been employed beforein any other study, and it has a potential to bring useful insights onthe issue.

ANS combines the investments on three forms of capital, physi-cal, human and natural, all measured in current $US. More formally,

ANS = NNS + E − R − P

where NNS stands for net national saving, E for current educationexpenditures, R for resource rents (depletion of energy, miner-als and forests), and P for carbon dioxide (CO2) damage. NNS is

computed as gross national saving net of depreciation of fixedcapital.2

The superiority of ANS over the conventional savings rate inreflecting the true well-being of people has been shown by severalstudies in the literature. Ferreira et al. (2008) finds a positive corre-lation between current per capita Adjusted Net Savings and futurechanges in per capita consumption. Gnegne (2009) concludes thatthere is a positive and significant relationship between ANS andwelfare, which is defined by Human Development Index and infantmortality rate.

ANS is the fruit of the notion called weak-sustainability (Gowdyand O’Hara, 1997) which assumes substitutability of differenttypes of capital forming the fundamental basis of human exis-tence (Gnegne, 2009, p. 1129). By referring to the Hartwick-rule(Hartwick, 1977), a country is assumed to be weakly sustainableif she manages to channel fully the rents extracted from naturalresources to physical and/or human capital formation. More for-mally, weak sustainability requires total net capital investment notto be persistently negative (Hamilton, 2003). Weak sustainabilityrests on the belief “that natural resources are super-abundant, ortechnological progress can increase the productivity of the natu-ral capital faster than it is being depleted” (Dietz and Neumayer,2007, p. 618). It also assumes perfect substitution among physi-cal, human and natural capital, and neglects irreversibility issue(Bridger and Luloff, 1999; Ekins et al., 2003). Strong sustainability,on the other hand, challenges most of these assumptions. Specifi-cally, it argues that environment is not only a direct determinantof human well-being, more importantly it has a specific functionof holding everything together (Turner and Pearce, 1994). Hence,strong sustainability maintains that “certain forms of natural cap-ital cannot be substituted by other forms of capital”, and “a subsetof total natural capital be preserved in physical terms so that itsfunctions remain intact” (Dietz and Neumayer, 2007, p. 619).

ANS is criticized not only because it is often employed as ameasure of a controversial concept like weak sustainability, but itinvolves some methodological problems related to measurement,aggregation and coverage. In developing composite indicators, theneed to find a common measurement unit comes at the expenseof some limitations. Valuing depletion and degradation withinan accounting framework is still controversial. For example, oneapproach chooses maintenance cost (UN, 1993) as a measure-ment unit, while marginal social cost is preferred in theoreticalapproaches (Hamilton and Clemens, 1999). Sutton et al. (2012),on the other hand, offers a different methodology for quantifying(or monetizing) anthropogenic environmental impact by using twoproxy measures; Anthropogenic Impervious Surface Area (whichrepresents human demand on the planet) and Net Primary Pro-ductivity (which represents “supply” provided by the planet). Theoptimality and sustainability of the prices used in ANS computa-tion is very crucial given the assumption that the economy followsan efficient growth path in theoretical models upon which ANS isbuilt. Yet, in practice only current prices, which are neither sustain-able nor optimal, are available (Nourry, 2008). Moreover, Pillarisetti(2005) concludes that “natural capital should be treated indepen-dent of physical and human capital” (p. 609) after finding out thatANS data is heavily driven education expenses. Another problem-atic issue is the coverage. Not all the pollutants and minerals arecovered in the indicator. For example, energy depletion consists ofthe depletion of oil, coal and natural gas only. Mineral depletionconsiders only the depletion of bauxite, copper, iron, lead, nickel,phosphate, tin, zinc, gold and silver. Pollution considers only car-bon dioxide damage and neglects other pollutants. Other resources,

2 See World Bank (2002) for a detailed explanation of the computation of ANS.



Table 1Natural disinvestment components of ANS (% of GNI).a

Observation Mean Standard deviation Min. Max.

CO2 damageHigh income 1513 0.37 0.40 0 4.4Middle income 2962 0.7 0.8 −0.3 7.7Low income 1765 0.56 0.9 0 9.7

Net deforestationHigh income 1179 0.01 0.07 0 1.1Middle income 2569 0.2 0.95 0 19.4Low income 1766 1.34 2.3 0 20.1

Mineral depletionHigh income 1474 0.13 1.1 0 32.5Middle income 2962 0.57 1.88 0 34.3Low income 1765 0.79 2.5 0 27.6

Energy depletionHigh income 1513 3.8 10.3 0 98.2Middle income 2962 4.8 12 0 150.7Low income 1765 2.7 9.3 0 113.9

a Note: See footnote 4 for the determination of income groups.

which forms the biophysical environment (i.e. water quantity andquality, air quality, sediments and soil nutrients, wildlife, habitatand vegetation, biota, species at risk, acoustics environment) arenot included because they are more difficult to evaluate (Gnegne,2009, p. 1129).

3. Methodology

3.1. Data and descriptive analysis

With a panel of 213 low, middle and high-income countries,between 1970 and 2008, we employ a panel regression analysisto investigate the relationship between log real per capita incomeand log real pressure on nature. Our dependent variable, per capitapressure on nature, in constant 2005 US $, is defined as;

Pressure on nature p.c. = carbon dioxide damage p.c.

+mineral depletion p.c.

+energy depletion p.c.

+net forest depletion p.c.

Unless otherwise indicated, all variables are extracted fromWorld Development Indicator (WDI) database3 of the World Bank(World Bank, 2012), and are summarized in Table 2. See Table A1for a detailed explanation and sources of all variables.

Table 1 shows that the pressure on nature takes different formsin different income groups. Comparatively, net deforestation andmineral depletion in low-income countries, energy depletion andCO2 damage in middle-income countries and energy depletion inhigh-income countries constitute the major sources of the pressureon nature.

When we look at the shares of each component in the total pres-sure on nature, we come up with a similar picture. In Fig. 1 we seethat, in high and middle income countries CO2 damage and energydepletion constitute the majority of the total pressure on nature,whereas in low income countries, it is dominated by forest deple-tion, followed by CO2 damage and mineral depletion. This unevendistribution of components across different income groups requiresmore attention and we will turn back to this issue in the regressionanalysis part.

3 WDI data set has been provided for free online by the World Bank.

Fig. 1. Distributions of components across income groups: 1970–2008 averages.

Fig. 2. Pressure on nature and income: 1970–2008 (Country Averages). Notes: SeeTable A1 for data definitions. Values are averaged by country in 1970–2008 periodwhen both income and pressure on nature data exist together. The line representsLowess function estimated with a bandwidth of 0.8.

Preliminary cross-country analysis, by using the plot diagram inFig. 2, reveals that there is a positive relationship between incomeand pressure on nature. In other words, as countries grow richer, sodo their pressure on nature. However, the relationship is not linearacross different income groups.

Due to the possible existence of endogeneity and omitted vari-able biases cross-country relationship does not necessarily provecausation. Consider for example Turkey and Finland. Finland isricher and exerts less pressure on her nature, so a simple cross-country comparison would suggest that higher per capita incomecauses less pressure on nature. But the right question to ask shouldbe whether a country is more likely to exert less pressure on natureas it becomes richer or not. In Fig. 3, we plot the changes in logper capita income against changes in pressure on nature between1970 and 2008. This helps to eliminate the time-invariant country-fixed effects. But even after eliminating them, the positive relationbetween income and pressure on nature remains.

While differencing variables helps to remove the time-invariantcharacteristics of countries, it does not necessarily heal the simul-taneity bias. That is, the positive relationship emerged in theplot-diagram may be arisen due to some other factor affecting botheconomic growth and pressure on nature. The preliminary analy-sis of the data by plot diagrams presented in Figs. 2 and 3 helpsus to find the right econometric method to study the economicgrowth-environmental pressure relationship. We will come back



Table 2Descriptive statistics.a

Variable Mean

All countries High incomecountries

Middle incomecountries

Low incomecountries

Log real pressure on nature p.c. (t) 3.86 5.31 4.26 2.122.12 1.58 1.79 1.76

Log real GNI p.c. (t − 1) 7.81 10.2 7.9 5.941.6 0.43 0.72 0.53

Rule of law (t) −0.08 1.32 −0.22 −0.860.98 0.56 0.64 0.51

Log openness (t) 4.3 4.4 4.4 40.55 0.6 0.5 0.53

Log population density (t) 3.87 4.3 3.8 3.91.5 1.8 1.4 1.32

Democracy (t) 0.55 0.58 0.6 0.450.29 0.15 0.3 0.3

Education (t) 6.8 9.8 6.9 3.93.2 2.1 2.3 2.3

Enrollment rate (t) 62.3 85.7 63.9 28.527.7 12.3 21 20.7

a Notes: Standard deviations are reported in italic below mean values. For detailed definition and sources, see Table A1.

Fig. 3. Change in pressure on nature and income between 1970 and 2008. Notes: SeeTable A1 for data definitions. Changes are total difference between the latest and theearliest years at which both income and pressure on nature data exist together in1970–2008 period. The line represents Lowess function estimated with a bandwidthof 0.8.

to this issue in the next subsection. Table 2 presents the descriptivestatistics of the observations included in the regression analysis.

3.2. Panel data econometric model

Consider the following simple econometric model, which willbe the basis of our analysis:

nit = ˛git−1 + xit−1 + �t + ıi + uit, (1)

where nit is the log real per capita pressure on nature of country iin period t. The main variable of interest is git−1, the lagged value oflog real income per capita. The parameter ˛ therefore captures thecausal effect of income on pressure on nature. Vector xit−1 containsall other potential covariates. In addition, the ıi’s denote a full setof country dummies and the �t’s denote a full set of time dum-mies that captures common shocks to pressure on nature levels ofall countries; uit is an error term, capturing all other omitted fac-tors, with E(uit) = 0 for all i and t. We construct five-year and annualpanels: for the five-year panels, we take the observation every fifthyear. We prefer using levels rather than averages, since averaging

introduces additional serial correlation which complicates infer-ence and estimation.

For comparison purposes we start by estimating (1) in a pooledOLS model with time and country dummies. However, the esti-mates of the pooled OLS model are biased and inconsistent whenthe ıi’s are correlated with xit−1. In order to investigate the causaleffect of economic growth on nature we employ two strategies. Thefirst strategy is to control for country-specific factor affecting botheconomic growth and pressure on nature by including country-fixed effects. Let, xj

it−1 denote the jth component of the vector xit−1

and let Cov denote the population covariances. Then, if Cov (xjit−1,

ıi + uit) /= 0 for some j, the OLS estimator will be inconsistent. Incontrast, even when this covariance is nonzero, the fixed-effectestimator will be consistent if Cov (xit−1, uit) = 0 for all j. Yet, it failsto measure the casual effect of income on pressure on nature sim-ply because of the possibility that Cov (git−1, uit) /= 0 because ofthe reverse effect of pressure on nature on income, or because bothchanges in income and changes in pressure on nature are causedby a third, time-varying factor. To solve this problem, we imple-ment a fixed-effect IV strategy. One important issue needs to beaddressed is the stationarity of dependent and independent vari-ables. Standard regression analysis assumes that the dependent andindependent variables are all stationary. Note that, the regressionanalysis employ five-year panel, hence for each cross-sectional unit(country) we have at most 8 observations during the 1970–2008period. The short time dimension allows us to continue with thefixed-effects model without bothering about the stationarity ofvariables.

How sensible are the conclusions to the inclusion or exclusionof some countries, or some variables, or to the income groups?Following the baseline model, a battery of robustness checks willbe presented to test the validity of earlier conclusions. Note thatour panel is unbalanced due to missing data belonging to someobservations. Hence, the first robustness check will employ thebalanced panel. Secondly, it is natural to expect that the effect ofincome growth on environment in a high-income country wouldbe different than in middle and low-income countries (Muradianand Martinez-Alier, 2001). After classifying countries by usingincome ranges provided by the World Bank,4 we will run the

4 For example, countries with p.c. income between 480 and 6000 US dollars wereclassified as middle income countries in 1987. The ranges are updated frequantly



Table 3Fixed effects results.a

All countries, 1970–2008

Five-year data Annual data

Pooled OLS Fixed effects OLS Fixed effects IV Pooled OLS Fixed effects OLS Fixed effect IV1 2 3 4 5 6

Dependent variable is log real per capita pressure on natureLog real GNI (t − 1) p.c. 0.38*** (0.09) 0.379** (0.17) 0.262*** (0.1) 0.72*** (0.04) 0.725*** (0.055) 0.67*** (0.041)

Observations 1067 1067 931 5023 5023 4749Countries – 164 163 – 164 164R-squared (within) 0.91 0.17 0.18 0.91 0.19 0.18

a Pooled cross-sectional OLS regression with time dummies in columns 1 and 4, with robust standard errors are in parenthesis. Fixed effects OLS regressions in columns 2and 5, with time dummies and robust standard errors clustered by country in parenthesis. Columns 3 and 6 employ the fixed-effect IV method; we instrument for GNI percapita using a double lag. Unbalanced panel, 1970–2008, with data at five-year intervals, where the start date of the panel refers to the dependent variable (i.e. t = 1975, sot − 1 = 1970) in columns 1–3. Columns 4–6 use annual data, where, as before, the start date of the panel refers to the dependent variable (i.e., t = 1971, so t − 1 = 1970). Timedummies and constants are not reported. For detailed data definitions and sources, see Table A1.* Significance at 90%.

** Significance at 95%.*** Significance at 99%.

baseline model for each income group. What about the compo-sition of environmental pressure in different income groups? AsFig. 1 shows, environmental degradation takes different forms indifferent income groups. Therefore, it may be too broad to claimthat economic growth has the same effect over, say CO2 damage andnet deforestation (Bae et al., 2012; Rudel et al., 2005). To clarify thispoint, we will run a third check by estimating the baseline modelwith different components of pressure on nature as independentvariables separately.

The fourth robustness check will be conducted by includingsome structural and institutional variables, i.e. openness, democ-racy, education, etc., to the baseline model. On the question ofhow openness to trade interacts with pressure on nature, there areseveral competing hypotheses mentioned in the literature reviewsection. Therefore we expect that the effect will be either positiveor negative. The regime characteristic (i.e. democracy or authoritar-ian) is another important factor to take into account. In democraticsocieties it can be expected that governments would be moreresponsive to the demands for clean environment, which mayresult in decreasing the pressure on nature. Yet the evidence ismixed. Knight and Rosa (2011) study finds no significant effectof democracy on life satisfaction as measured by environmentalefficiency of well-being. York et al. (2003b) and Marquart-Pyatt(2010) report that the relationship between democracy and EF iseither non-significant or positive. The regulatory framework is asimportant as the governance structure. Tragedy of the commons(Hardin, 1968) describes perfectly the link between the environ-mental protection and the quality of the regulatory framework.The unsustainable management of natural resources in low-incomecountries can be due to the lack of clearly defined property rightsand lax (or non-existent) environmental standards (Deacon, 1994).However one should also take into account the fact that the exis-tence of rules and regulations does not necessarily guarantee theirenforceability. Nevertheless, we expect that pressure on naturedecreases as countries’ ability to enforce rule of law improves.Another important factor is the average level of education in thesociety. In the literature, education is considered to have positiveeffects on environmental quality. Societies with longer averageyears of schooling are more likely to demand a cleaner environ-ment (Alam, 2010; Torras and Boyce, 1998). Therefore, we expecta negative sign for the coefficient of education variable.

and by 2009 the lower bound became 995 and upper bound 12,195 US dollars. Forhistorical classification of countries see http://data.worldbank.org/about/country-classifications/a-short-history.

4. Fixed-effects estimation results

Table 3 presents the fixed effects estimation results.5 In columns1–3 we use the five-year sample, whereas in columns 4–6 we useannual observations for comparison purposes. Log per capita realincome is highly significant in all regressions and indicates thepositive relationship between the income and pressure on nature,however the effects are much stronger in the annual sample thenin the five-year sample, suggesting a lower impact on the longerrun. Starting from the five-year sample, the coefficient 0.38 in col-umn 1 of Table 3 implies that 10% increase in GNI p.c. is associatedwith a 3.8% increase in the per capita pressure on nature. Yet,the pooled OLS estimates are potentially biased. After eliminatingtime-invariant country effects, in column 2 we see that the rela-tionship between income and pressure on nature remains almostunchanged and 10% increase in income leads to a 3.8% increasein pressure on nature. In columns 4–6 we do the same exercisewith annual data. The relationship between income and pressureon nature remains highly significant and positive, and the effect isalmost doubled (from 0.379 in column 2 to 0.725 in column 5). Incolumns 3 and 6, we instrument for GNI p.c. using a double lag toaccount for the bias possibly introduced by the existence of thirdfactor affecting both income and pressure on nature. As comparedto fixed-effect estimates, fixed-effect IV estimates are smaller buthave the same signs, and that 10% increase in GNI p.c. increasesthe pressure on nature by 2.6% in the five-year sample and 6.2%in the annual sample. All the regressions in Table 3 are estimatedwith time dummies. Although not presented here, time-effects arefound to be increasing over time. It could be that the valuation ofthe impacts has increased over time. Overall, the regression resultssuggest that there is a significant positive relationship betweenper capita income and pressure on nature. As countries growricher so do their pressure on nature, and it is increasing overtime. This finding is in line with York et al. (2003a) which findsa monotonically increasing impact of affluence over environment(as measured by CO2 emissions and energy footprint). But as Fig. 1indicates, pressure on nature is dominated by different componentsfor different income groups. Hence it can be expected that incomegrowth may have different effects on different components of theindicator.

5 Hausman specification test results rejects the null hypothesis is that the dataare generated by random effects, with a chi-square value of 102.5 and an associatedprobability of 0.



Table 4Robustness check: balanced panel (1970–2008).a

Five-year data Annual dataFixed effects IV Fixed effects IV1 2

Dependent variable is log real per capita pressure on natureLog real GNI (t − 1) p.c. 0.1 (0.12) 0.49*** (0.05)

Observations 672 3456Countries 96 96R-squared 0.18 0.17

a The values in parenthesis are the standard errors. Time dummies and constantsare not reported.* Significance at 90%.** Significance at 95%.

*** Significance at 99%.

Table 5Robustness check: income groups.a

Five-year dataFixed effects IV

High incomeonly

Middle incomeonly

Low incomeonly

1 2 3

Dependent variable is log real per capita pressure on natureLog real GNI (t − 1) p.c. 0.14 (0.27) 0.66*** (0.14) 0.32 (0.28)

Observations 158 329 202Countries 28 75 42R-squared 0.29 0.14 0.14

a The values in parenthesis are the standard errors. Time dummies and constantsare not reported.* Significance at 90%.** Significance at 95%.


4.1. Robustness checks

In this section we will present a battery of robustness checksto validate our results obtained from the baseline model. Afterexcluding countries entirely with either of the dependent or theexplanatory variable is missing at any year between 1970 and2008, we see that the coefficient estimates continue to be positive(Table 4).

Yet, in the five-year sample the effect now becomes insignif-icant. This is possibly due to the fact that most of the excludedcountries from the panel (55 of 67) are either middle or low-incomecountries for which the effect can be expected to be higher thanthat of for high-income countries. To test whether the effect willdiffer across different income groups, we run the baseline modelin column 3 of Table 3 for each income group. Table 5 presentsthe estimation results. Remember that we found in our baseline

regression that 10% increase in GNI p.c. increases the pressure onnature by 2.6%.

Compared with the coefficient estimates in Table 5, we seethat this effect is much larger in middle-income group countries(almost 7%). The coefficient estimates for high and low incomecountries become insignificant though, nevertheless they still havepositive signs. Income growth has the highest negative impact onenvironment in middle-income countries, shedding doubts on thesustainability of the income generation process. This result is in linewith the conclusions of Muradian and Martinez-Alier (2001), suchthat economic growth in high-income countries “goes togetherwith increasing consumption of non-renewable resources com-ing from developing countries; and worsening terms of trade forexporting countries specialized in non-renewable resources” (p.289).

Economic growth may have different effects on different dimen-sions of the environmental pressure, say on CO2 damage and netdeforestation. Table 6 presents the results of the baseline modelestimated with each component separately. We see that the posi-tive relationship holds for all but the net forest depletion. Incomegrowth reduces the per capita pressure on forests. In other compo-nents, like mineral depletion and CO2 damage, regression resultsindicate a negative scale effect of increasing income. As for energydepletion, we do not find a statistically significant effect.

Lastly, we investigate the influences of some structural and insti-tutional covariates. Table 7 presents the fixed-effects IV modelresults. The results are fairly supportive of the race-to-the-bottomhypothesis which asserts that countries tend to lower down theirenvironmental standards in order to attract more investment.

Increasing integration to the global system through tradeincreases the pressure on environment. We found that 10% increasein openness ratio increases the per capita pressure on nature by9.5%. This is in line with the conclusion reached by Borghesi andVercelli (2003). The regression results support our hypothesis thatthe governance structure is positively related with environmentalsustainability which also confirms the findings of the earlier studiesmentioned above. More specifically, we found that a unit increasein the rule of law indicator decreases the per capita pressure onnature by 0.5%. Together with the effects of increasing opennessto trade, the positive relationship between rule of law (or qual-ity of institutions) and environmental protection calls for a closerlook at the current globalization patterns. The environmental con-sequences of deregulation efforts by international institutions likeIMF, WB and WTO during the sample period is worth to mention.As Tisdell (2001) and Esty (2001) argue, existing environmentaland social constraints were gradually eroded by the indiscriminatederegulation of world trade. In the same spirit, Daly (1993) arguesthat free trade promotes competition that results in lowering ofenvironmental standards as well as wages, which in turn, increasesenvironmental degradation in developing and unemployment in

Table 6Robustness check: components of pressure on nature.a

Five-year dataFixed effects IV

Net forest depletion Energy depletion Mineral depletion CO2 damage1 2 3 4

Dependent variable is log real per capita pressure on natureLog real GNI (t − 1) p.c. −0.34*** (0.05) 0.14 (0.10) 0.27*** (0.07) 0.23*** (0.06)

Observations 901 901 901 901Countries 160 160 160 160R-squared 0.08 0.15 0.14 0.16

a The dependent variables are logarithms of real per capita figures. The values in parenthesis are the standard errors. Time dummies and constants are not reported.* Significance at 90%.** Significance at 95%.




Table 7Robustness check: structural and institutional factors.a

Five-year data (1970–2008)Fixed-effects IVDependent variable is log real per capita pressure on nature

1 2 3 4 5

Log real GNI (t − 1) p.c. 0.24** (0.1) 0.3*** (0.11) 0.13 (0.25) 0.18 (0.14) 0.54*** (0.2)Log openness (t) 0.95*** (0.11)Democracy (t) 0.24* (0.14)Rule of law (t) −0.54*** (0.17)Education (t) −0.08 (0.08)Enrollment rate (t) 0.01** (0.006)

Observations 894 812 464 570 315Countries 161 146 162 118 129R-squared 0.26 0.19 0.21 0.11 0.28

a The values in parenthesis are the standard errors. Time dummies and constants are not reported. See Table A1 for a detailed explanation of variables.* Significance at 90%.

** Significance at 95%.*** Significance at 99%.

high-income countries. The experience of Mexico, as a middle-income country receiving a good deal of foreign direct investmentespecially after the NAFTA agreement is telling. Steininger (1994)reports that lower environmental standards in Mexico played acrucial role in the concentration of maquiladoras along the US-bordering area, and this resulted in increasing unemployment inUS and environmental damage and health problems in Mexico.Coming to the education, we find a statistically significant resultbetween the secondary school enrollment rate and pressure onnature, yet the positive sign of the estimate is not as expected, possi-bly due to the very limited availability of data especially for low andmiddle-income countries. Overall, we see that even after control-ling for various structural and institutional indicators, the positiverelationship between income and pressure on nature continues tohold.

5. Concluding remarks

Our results suggest that there is a positive relationship betweenincome per capita and per capita pressure on nature. The effectis much stronger in middle-income countries than in low andhigh-income countries. After controlling for various covariates,institutional and structural, the positive effect still continues tohold. Our conclusions are fairly robust to the inclusion of thesecovariates, and to the inclusion and exclusion of countries from thesample. The regression results shed doubts on the environmentalsustainability of the growth process especially in middle-incomecountries. Increasing prosperity leads more consumption andthereby more pressure on nature especially in the form of CO2

damage and mineral depletion. However, we found an oppositeeffect on forestry resources. The institutional quality, as measuredby the extent of enforceability of rule of law, has a significant neg-ative effect on the pressure on nature along with our expectations.Our results suggest that increasing trade has a negative impacton environment and this finding clearly can be taken as a sup-port for race-to-the-bottom hypothesis. Although the formulationof MDGs clearly demonstrates that economic growth and envi-ronmental protection are mutually reinforcing, there are seriousdoubts on our ability in decoupling of economic growth from pres-sure on nature in absolute terms (Moldan et al., 2011). Our resultssupport those studies indicating that the current economic growthparadigm is unsustainable especially in middle-income countries.Given the increasing importance of these countries as recipientsof FDI flows and as producers in the global supply chain, achievingenvironmental sustainability without jeopardizing the other deter-minants of human welfare continues to be a big challenge that hasto be confronted.

Acknowledgements

I would like to thank to the two anonymous referees and tothe participants of the ESEE 2011 Conference held at the Bogazic iUniversity for their valuable comments. All remaining errors aremine.

Appendix A.

Table A1Data and sources.

Variable Description Source

Net forest depletion Net forest depletion is calculated as the product of unit resourcerents and the excess of round wood harvest over natural growth.

WDI online database. NY.ADJ.DFOR.CD

Mineral depletion Mineral depletion is equal to the product of unit resource rents andthe physical quantities of minerals extracted. It refers to bauxite,copper, iron, lead, nickel, phosphate, tin, zinc, gold, and silver.

WDI online database. NY.ADJ.DMIN.CD

Energy depletion Energy depletion is equal to the product of unit resource rents andthe physical quantities of energy extracted. It covers crude oil,natural gas, and coal.

WDI online database. NY.ADJ.DNGY.CD

Carbon dioxide damage Carbon dioxide damage is estimated to be $20 per ton of carbon(the unit damage in 1995 U.S. dollars) times the number of tons ofcarbon emitted.

WDI online database. NY.ADJ.DCO2.CD

Real pressure on nature The natural disinvestment per capita consists of energy, mineral,net forest depletions and carbon dioxide damage divided bypopulation, all measured in 2005 constant US dollars.

Author’s calculation.NY.ADJ.DNGY.CD + NY.ADJ.DMIN.GN.ZS+ NY.ADJ.DFOR.CD + NY.ADJ.DPEM.CD

Real GNI Gross National Income, Atlas method (constant 2005 US$) WDI online database. NY.GNP.PCAP.CD



Table A1Table A1 (Continued)

Variable Description Source

Population density Total population divided by land area (km2). WDI online database. SP.POP.TOTL/AG.LND.TOTL.K2Education Average total years of schooling in the population age 25 and over. WDI online database. MYS.MEA.YSCH.25UP.MFEnrollment rate Secondary school enrollment rate, total. WDI online database. SE.SEC.NENROpenness Merchandise trade (% of GDP) WDI online databank. TG.VAL.TOTL.GD.ZSRule of law Rule of law captures perceptions of the extent to which agents

have confidence in and abide by the rules of society, and inparticular the quality of contract enforcement, property rights, thepolice, and the courts, as well as the likelihood of crime andviolence. Ranging from around −2.5 to 2.5.

Worldwide Governance Indicators. Available athttp://www.govindicators.org

Capital account openness The degree of the capital account openness. Ranges from 2.5(highly open) to −0.83 (least open).

Chinn-Ito Dataset 2008. Available athttp://web.pdx.edu/∼ito/Chinn-Ito website.htm

Democracy Combined polity score. Original range −10 to 10, normalized 0–1. Polity IV Project dataset. Available athttp://www.systemicpeace.org/polity/polity4.htm

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