Environment During growth: Accounting for governance and vulnerability

World Development Vol. 34, No. 9, pp. 1597–1611, 2006� 2006 Elsevier Ltd. All rights reserved

0305-750X/$ - see front matter

doi:10.1016/j.worlddev.2005.12.008www.elsevier.com/locate/worlddev

Environment During Growth:

Accounting for Governance and Vulnerability

SUSMITA DASGUPTA, KIRK HAMILTONWorld Bank, Washington, DC, USA

KIRAN D. PANDEYGlobal Environment Facility, Washington, DC, USA

and

DAVID WHEELER *

World Bank, Washington, DC, USA

Summary. — The conventional environmental Kuznets curve (EKC) model holds that pollutioninevitably increases until societies reach middle-income status, because poor countries do not havethe institutional capacity or political will to regulate polluters. Some policy makers and researchershave cited the EKC model when arguing that developing countries should ‘‘grow first and clean uplater.’’ However, new evidence suggests that this argument is incorrect because it mistakenly as-sumes that strong environmental governance is not possible for poor countries. This paper extendsthe EKC model to include a new measure of environmental governance, as well as a detailedaccounting of geographic vulnerability (climate and terrain factors). We find that these two factorscan account for much of the observed variation in developing-country air pollution levels.
� 2006 Elsevier Ltd. All rights reserved.
Key words — development, environment, pollution, Kuznets, governance

* Financial support for this study has been provided by

the World Bank’s Environment Department and Devel-

opment Research Group. Our thanks to Bart Ostro and

WHO colleagues for useful comments and suggestions,

and to Piet Buys, for his assistance with GIS applications.

The findings, interpretations, and conclusions are entirely

those of the authors. They do not necessarily represent

the view of the World Bank, its Executive Directors,

or the countries they represent. Final revision accepted:December 21, 2005.

1. INTRODUCTION

The environmental Kuznets curve (EKC)model posits a simple, predictable relationshipbetween economic development and environ-mental quality. 1 In the first stage of industrial-ization, pollution in the EKC world growsrapidly because people are far more interestedin jobs and income than clean air and water,communities are too poor to pay for abate-ment, and environmental regulation is cor-respondingly weak. The balance shifts asincome rises. Leading industrial sectors becomecleaner, people value the environment morehighly, and regulatory institutions becomemore effective. Along the curve, pollution levelsoff in the middle-income range and then fallstoward pre-industrial levels in wealthy societies.

Many empirical researchers have acceptedthe basic tenets of this model, and have focused

159

on measuring its parameters. Their regressions,fitted to cross-sectional observations acrosscountries or regions, typically suggest that airand water pollution increase with developmentuntil per capita income reaches a range of$5,000–$8,000. 2 When income rises beyondthat level, pollution starts to decline. In devel-oping countries and donor institutions, some

7

1598 WORLD DEVELOPMENT

policy-makers have interpreted such results asconveying an important message about priori-ties: Grow first, then clean up.

If the EKC model is correct, the environmen-tal prospects are extremely poor for many devel-oping countries. According to the most recentWorld Bank estimates, the average per capitaGDP at purchasing power parity (PPP) in 2002was $1,379 in 51 low-income countries and$4,858 in 47 lower-middle-income countries. 3

The low-income countries are nowhere near themaximum pollution point on the conventionalEKC curve so, in this model, they are fated to en-dure rising pollution and natural resource degra-dation for many decades. Moreover, empiricalresearch suggests that pollution costs are alreadyquite high. For example, recent World Bank esti-mates of mortality and morbidity from urban airpollution in India and China suggest annuallosses in the range of 2–3% of GDP (Pandeyet al., forthcoming).

Should we believe this model? In fact, numer-ous critics have challenged the EKC, both as arepresentation of what actually happens in thedevelopment process and as a policy tool. Somecritics argue that the EKC is actually too opti-mistic. Over time, they claim that the curve willrise to a horizontal line at maximum existingpollution levels, as globalization promotes a‘‘race to the bottom,’’ poor countries becomepollution havens, and environmental standardscollapse in industrial countries as they defendtheir competitive position. 4

The pessimists’ claims have not been bol-stered by much empirical research. In fact, re-cent empirical work has fostered an optimisticcritique of the conventional EKC. The new re-sults suggest that the curve is actually flatteningand shifting to the left, as growth generates lesspollution in the early stages of industrializationand pollution begins to fall at lower incomelevels. 5 Such work, however, continues toreinforce the deterministic worldview of theEKC model.

In contrast, recent comprehensive surveys ofthe EKC literature by Stern (2004) and Copelandand Taylor (2004) find that theoretical andempirical work on the EKC does not supportthe existence of a simple, predictable relationshipbetween pollution and per capita income be-cause many structural factors intervene. In thispaper, we present new evidence that supportsthis view. Our research suggests that most EKCmodels are incomplete because they excludetwo important factors that have been hard toquantify, particularly for developing countries:

governance and vulnerability to environmentaldamage. New evidence on governance suggeststhat the conventional EKC’s simple link betweenincome and policy is misspecified, because somepoor countries have strong policy performance,while some middle-income countries are weakin this dimension. As we will demonstrate in thispaper, governance has strong, independenteffects on environmental quality.

The other frequently excluded factor is localvulnerability to environmental damage. Envi-ronmental outcomes can be significantly affectedby the sectoral composition of economic activ-ity, as well as the geographic features of eachlocus of activity. Using newly available data,we incorporate these factors into a more com-plete model of environmental change. 6

The remainder of the paper is organized asfollows. Section 2 reviews recent research oneconomy–environment links, with a particularfocus on pollution. Sections 3 and 4 introducenew evidence on factors that have often beenexcluded from EKC research: environmentalgovernance, local vulnerability and the sectoralcomposition of economic activity. In Section 5,we incorporate these factors into an economet-ric analysis of the most recent internationalevidence on air pollution. Section 6 employssimulation to explore the implications of ourfindings, and Section 7 provides a summaryand conclusions.

2. THEORETICAL AND EMPIRICALWORK ON THE EKC 7

Theoretical papers on the EKC have derivedtransition paths for pollution, abatement effortand development under alternative assumptionsabout social welfare functions, pollution dam-age, the cost of abatement, and the productivityof capital. This research has shown that an in-verted-U EKC can arise under the followingconditions: As income increases in a society,the marginal utility of consumption is constantor falling; the disutility of pollution is rising;the marginal damage of pollution is rising; andthe marginal cost of abating pollution is rising.Most theoretical models do not incorporate gov-ernance quality, since they implicitly assume theexistence of a public agency that regulates pollu-tion with full information about the benefits andcosts of pollution control. They seldom incorpo-rate variations in vulnerability to environmentaldamage, and they generally assume that the pol-lution externality is local, not cross-border. In

ENVIRONMENT DURING GROWTH 1599

the latter case, there would be little local incen-tive to internalize the externality.

Lopez (1994) demonstrates that if producerspay the social marginal cost of pollution, thenthe relationship between emissions and incomedepends on the properties of technology andpreferences. Under homothetic preferences, anincrease in output will result in an increase inpollution. If preferences are nonhomothetic,however, the response of pollution to growthwill depend on the degree of relative risk-aver-sion and the elasticity of substitution in produc-tion between pollution and conventional inputs.

Selden and Song (1995) derive an inverted-Ucurve for the relationship between optimal pol-lution and the capital stock, assuming that opti-mal abatement is zero until a given capital stockis achieved, and that it rises thereafter at anincreasing rate. John and Pecchenino (1994),John, Pecchenino, Schimmelpfennig, and Sch-reft (1995) and McConnell (1997) derive similarinverted-U curves by using overlapping genera-tions models. In an interesting departure thathas particular significance for this paper, Lopezand Mitra (2000) analyze the effect of gover-nance on environmental quality. Their theoreti-cal results show that for any level of per capitaincome, the pollution level corresponding tocorrupt behavior is always above the sociallyoptimal level. Further, they show that the turn-ing point of the environmental Kuznets curvetakes place at income and pollution levels abovethose corresponding to the social optimum. Re-cent theoretical and empirical work by Farzinand Bond (in press) complements this work byconsidering the impact of democratic govern-ment on pollution regulation.

Numerous empirical studies have tested theEKC model. Most have regressed cross-countrymeasures of ambient air and water qualityon various specifications of income per capita.Most studies rely on air pollutant concentrationdata from the Global Environmental Monitor-ing System (GEMS), an effort sponsored bythe United Nations. Others (e.g., Stern, Auld,Common, & Sanyal, 1998) have used a sulfuremissions database produced for the US Depart-ment of Commerce by ASL (Lefohn, Husar, &Husar, 1999), or data on greenhouse gasemissions from the US Oak Ridge National Lab-oratories (Marland, Boden, & Andres, 2001).

Empirical researchers are far from agreementthat the EKC provides a good fit to the avail-able data, even for conventional pollutants.In reviews of the empirical literature, Stern(1998, 2004) argues that the evidence for the

inverted-U relationship is highly varied and,in any case, applies only to a subset of envi-ronmental measures (e.g., air pollutants suchas suspended particulates and sulfur dioxide).Since Grossman and Krueger (1993) find thatsuspended particulates decline monotonicallywith income, even Sterns’ subset is open tocontest. In related work, Stern et al. (1998) findthat sulfur emissions increase through the exist-ing income range. Results for water pollutionare similarly mixed.

3. ENVIRONMENTAL GOVERNANCE

Until recently, direct measures of environ-mental governance have not been available fordeveloping countries. Empirical research oninstitutional determinants of the EKC hastherefore focused on political background vari-ables that are hypothesized to affect pollutionindirectly, through their effect on governance.The evidence from these studies is mixed.Barrett (2000) finds that an increase in civiland political freedoms reduces some pollutants(including suspended particulates), but not oth-ers. Torras and Boyce (1998) find that literacy,political rights and civil liberties have significantpollution-reducing effects in low-income coun-tries, but weaker effects in higher-income econ-omies. Harbaugh, Levinson, and Wilson(2000) find a negative, significant associationbetween suspended particulates and a measureof democratic participation. However, Scruggs(1998) finds that reduced political and economicinequalities do not necessarily reduce pollution.

In this paper, we are able to address environ-mental governance directly, using a measuredeveloped by the World Bank. The bank hascommitted itself to an annual, quantitativeassessment of country policies and institutionalcapacity for environmental governance. 8 TheWorld Bank’s CPIAE (Country Policy and Insti-tutional Assessment for Environment) ratescountries from 1 to 6, in ascending order of effec-tiveness in environmental governance. Table 1tabulates the most recent CPIAE ratings byincome group for 134 developing and newlyindustrialized countries. As the table shows, theCPIAE rises moderately with income, from amean rating of 2.9 for low-income countries to4.2 for upper-middle-income countries. How-ever, the detailed tabulation of ratings indicatesthe actual degree of dispersion: low-income coun-tries vary from 1 to 4.5; lower-middle-incomecountries from 2.5 to 4.5, and upper-middle-in-come countries from 2.5 to 6. 9

Table 1. Distribution of institutional capacity rating by world bank income group

Income group No. of countries Mean rating Capacity rating for environmental institutions

1 2.5 3 3.5 4 4.5 5 6

% by performance class

Low income 58 2.89 5 29 43 19 2 2 0 0Lower middle income 49 3.41 0 10 27 39 20 4 0 0Upper middle income 27 4.24 0 4 15 15 26 7 19 15

Total 134 3.35 2 17 31 25 13 4 4 3


This dispersion may have two sources. On the‘‘supply side,’’ the effectiveness of environmentalinstitutions may reflect the overall institutionaleffectiveness, which may in turn reflect a varietyof social and political factors that correlate onlyroughly with development. On the ‘‘demandside,’’ countries with more serious environmen-tal problems may devote more resources to envi-ronmental institutions, given their levels ofincome and overall institutional effectiveness.In any case, the ratings in Table 1 suggest animportant message: low levels of developmentdo not prevent countries from having effectiveenvironmental institutions and policies.

4. VULNERABILITY TO POLLUTION

(a) Geographic factors

This paper focuses particularly on suspendedparticulate matter created by combustion

Figure 1. Urban vulnerability to

and other processes, since inhalation of theseparticles creates much of the human healthdamage attributed to air pollution. 10 Intui-tively, it seems likely that once small particlesare emitted, they will stay airborne for shorterperiods in areas that are rainy and windy. Moresubtle factors (temperature, sunshine, air pres-sure, surrounding terrain) may also affect theairborne suspension of particulates. Usinginformation from thousands of air-qualitymonitoring reports, a recent World Bank –WHO study has quantified these factors andcombined them into an environmental vul-nerability index for approximately 3,200 citieswhose population exceeds 100,000 (Pandeyet al., forthcoming).

Figure 1 displays the results, shaded by quin-tile, while Figure 2 uses city populations asweights to display national vulnerability indi-ces. The results suggest great variation in theatmospheric impact of fine-particulate emis-sions. Across cities, the 1st- and 99th-percentile

emissions of fine particulates.

Figure 2. National vulnerability to emissions of fine particulates.


index values are 15 and 83, respectively. Byimplication, the impact of particulate emissionsvaries more than fivefold from cities with theleast natural vulnerability to those with themost. The maps also show that vulnerabilityis highly varied, both within and across regions:All continents have regions of low and highvulnerability.

Table 2 provides a breakdown of worldurban population by atmospheric vulnerabilityand income group. In low-income countries,approximately 100 million people live in areaswhere climates and terrain features indicatelow vulnerability (0–40), 130 million live atmedium vulnerability (41–60), and 200 millionat high vulnerability (61–100). In high-incomecountries, by contrast, most people live underconditions of low or medium vulnerability.We emphasize that, given the population distri-bution, these conditions are simply given bynature: In high-vulnerability cities (and dispro-

Table 2. World urban population (millions) bygeographic vulnerability and income

Vulnerability World bank incomegroup

Total

Low Middle High

0–20 8 25 12 4521–40 93 193 194 48041–60 133 627 293 1,05361–80 150 169 32 35181–100 50 0 0 50

Total 434 1,014 530 1,978

portionately in low-income countries), a unit ofparticulate emissions pollutes the air muchmore than in low-vulnerability cities.

(b) Economic structure

For economic–environmental analysis, mea-sures of aggregate economic output can be verymisleading because the composition of outputis critical for understanding potential environ-mental impacts. To illustrate, consider theimpact of industrial development on air andwater pollution. Intuitively, it seems clear thatnot all industry sectors are equal sources ofenvironmentally damaging emissions: A shirtfactory is not a steel mill. In recent years, wehave been able to quantify the dimensions ofthis difference by industry sector, for a largenumber of pollutants. 11 We have found thatof 28 industry sectors coded at the three-digitinternational classification (ISIC), only sevenpersistently account for at least 90% of globalemissions for major air and water pollutants.Table 3 displays the estimated percent contri-butions by sector and pollutant. Besides reveal-ing the large aggregate contribution, it indicateseven more sectoral concentration for individualpollutants. 12

Comparative advantage and governmentpolicies have both affected the internationaldistribution of industrial activity during thepast several decades.

The resulting differences have significantenvironmental implications: Industrial econo-mies focused on light, labor-intensive assembly

Table 3. Percent contribution to global industrial emissions in 1990: seven industry sectors (with ISIC codes)

Industry sector(ISIC code)

Iron &steel(371)

Petroleumrefineries

(353)

Foodproducts

(311)

Industrialchemicals

(351)

Paper &products

(341)

Non-ferrousmetals (372)

Cement(369)

Total

Air pollutants

Particulate airpollution (PM-10)

21.8 1.0 7.9 1.8 2.0 0.7 60.3 95.4

Sulfur dioxide 15.3 19.5 3.2 12.3 6.8 15.0 14.5 86.6Toxic chemicals 4.3 4.8 0.7 40.1 6.5 5.9 1.7 64.1

Water pollutants

Biochemicaloxygen demand

0.1 1.7 31.2 24.5 25.9 8.1 0.1 91.6

Toxic chemicals 8.0 1.9 1.0 73.0 8.6 1.2 0.1 93.8


(e.g., apparel, electronics, furniture) are far lesssusceptible to pollution than those with a heavyconcentration of activity in the seven ‘‘dirty’’industry sectors. Rapid growth in the firstgroup may have very modest environmentalimpacts, while growth in the second mayendanger thousands of lives annually.

5. THE EKC REVISITED: ACCOUNTINGFOR GOVERNANCE AND

VULNERABILITY

For our empirical work, we focus on one formof air pollution, suspended particulate matter(TSP), because suspended particulates havesignificant health impacts, and TSP data areavailable for many developing countries. Wecompare results for a conventional EKC model,in which the atmospheric concentration of TSPis a function of income per capita alone, and anextended model that includes measures of gov-ernance, geographic vulnerability, and the pol-lution intensity of industrial activity. We alsoinclude population, which proxies the scale oflocal pollution-generating activities. Populationdensity expands with city size, so we would ex-pect the spatial intensity of emissions-generat-ing activities to be greater in larger cities. Wealso allow for the possibility of an exogenoustrend in pollution, reflecting the internationaldiffusion of cleaner technology and (perhaps)environmentalist values during the 1990s.

We employ the latest available TSP datasetfrom WHO, which includes time series from1986 to 1999 for 340 individual air quality mon-itors in 170 cities. Of these, 209 monitors and85 cities are in developing, newly industrializedor Eastern European countries. For exact com-

parison over time, we match air quality mea-sures from specific monitors in differentperiods. 13

We draw our measures of country income percapita and city population from World Bankand UN databases. We also use the city-specificgeographic vulnerability indices and shares ofthe seven ‘‘dirty’’ industry sectors that we intro-duced in Section 4. 14 Our panel data span 14years, and we cannot depend on recently com-puted World Bank CPIAE ratings to proxy envi-ronmental governance since the mid-1980s. Theonly related panel data available for the entireperiod are country corruption indices publishedby Transparency International (TI). However,we find that TI’s corruption index for 2003 ishighly correlated with the 2003 CPIAE: the lin-ear regression result is CPIAE = 2.06 (10.2) +0.48 TI (8.0); R2 = .41, N = 92 (t-statistics inparentheses). We therefore employ the TI cor-ruption index for 1986–99 as our governanceproxy. 15 Table 4 provides descriptive samplestatistics for income, population, geographicvulnerability, the ‘‘dirty’’ sector share and theTI corruption index.

Using the matched monitoring data, Table 5presents panel estimates for the conven-tional EKC and the extended model. 16 Allregressions are in log form. We present ran-dom- and fixed-effects results for both the con-ventional EKC model, which includes onlyincome, and our extended EKC model that alsoincludes governance, geographic vulnerability,population and industry structure. The fixed-ef-fects results introduce controls for all monitor-ing sites in the data. By removing the influenceof cross-sectional variation in the sample, theyprovide useful evidence on marginal (within-site) relationships.

Table 4. Sample statistics

Percentile Incomeper capita

(PPP, $2000)

TI corruptionindex

Geographicvulnerability

index

Population % Share ofpollution-intensive

industry sectors

Minimum 894 0.7 6.1 107,958 7.625th 3,735 3.2 36.4 610,333 13.3Median 7,799 4.5 42.6 1,720,434 15.075th 22,414 8.2 55.7 4,337,934 17.1Maximum 32,626 9.8 84.9 18,400,000 47.3

Mean 11,806 5.5 45.8 3,465,161 16.0

Table 5. Determinants of urban TSP concentrations panel regressions: matched monitors

(1) (2) (3) (4) (5)

Random Fixed Random Random Fixed

Dependent variable: Log TSP (lg/m3)

Log GDP per capita 2.897 2.757 2.806 2.229 2.342(5.87)** (4.97)** (4.85)** (3.76)** (3.36)**

[Log GDP per capita]2 �0.190 �0.156 �0.176 �0.153 �0.165(6.76)** (4.92)** (5.20)** (4.37)** (3.93)**

Log TI governance index �0.239 �0.290 �0.303(2.63)** (3.17)** (3.02)**

Log city population 0.070 0.087 0.539(2.71)** (2.90)** (2.10)*

Log pollution-intensive 0.162 0.150 0.127(2.75)** (2.50)* (1.99)*

Log vulnerability index 0.914(6.74)**

Year �0.021 �0.027 �0.016 �0.013 �0.015(11.58)** (14.20)** (6.60)** (5.31)** (4.47)**

Constant 36.161 46.802 22.248 22.460 19.619(8.75)** (10.74)** (4.13)** (4.10)** (2.81)**

Observations 1337 1337 959 959 959Number of siteno 122 122 118 118 118

R-squareda

Within .15 .18 .18 .18 .19Between .67 .007 .81 .74 .63Overall .64 .04 .79 .71 .65

Hausman v2 74.36 1.74Prob > v2 0.00 0.94

Absolute value of z statistics in parentheses.a R-squared statistics are interpreted as follows: ‘‘Within’’ refers to variation among observations for individualmonitors), ‘‘between’’ refers to variation across monitors, and ‘‘overall’’ refers to total variation.* Significant at 5%.** Significant at 1%.


We present Hausman tests for both conven-tional and extended EKC models. For the con-ventional model, our test results suggest that therandom effects estimator is inconsistent (Haus-man v2 = 74.36, Prob > v2 = 0.00). However,

the significance of all non-income variables inour extended model indicates that the conven-tional model is misspecified in any case.

We cannot perform a standard Hausmanspecification test for the full extended model,


since the random-effects equation includes atime-invariant site-specific variable (vulnerabil-ity). For an insight into the specification issues,however, we perform a Hausman test on thefixed-effects and random-effects results for amodel with vulnerability excluded. Here the teststatistics indicate that the random-effects resultis consistent and efficient (Hausman v2 = 1.74,Prob > v2 = 0.94).

We obtain similar results for the income com-ponent of the model in all four results: Both lin-ear and quadratic terms are highly significant,with the negative sign on the quadratic termsuggesting that the pollution-reducing effect ofadditional income increases with income. 17

Table 6 presents elasticities estimated for sam-ple-country income percentiles, using the linearand quadratic income terms from the extendedfixed-effects model (Table 5, column 5). The sig-nificant time trends indicate that technologydiffusion and/or spreading environmentalismhave had a significant overall impact on air pol-lution. Both the fixed- and random-effectsresults (Table 5, columns 3–4) suggest anannual trend decline of 1.5%, or a total declineof 18%, during the sample period (1986–99). Ifthis trend has continued, air pollution levels inthe sample cities are currently about 25% belowtheir levels in 1986.

The governance effect is highly significant inboth the random-effects and fixed-effects mod-els, suggesting a change in urban TSP concen-tration of .2–.3% for each 1% improvement inthe Transparency International governanceindex. The geographic vulnerability result inthe random effects model is also large andhighly significant, suggesting that urban TSPconcentration increases by about .9% for each1% increase in the vulnerability index.

The positive, significant results for pollution-intensive sectors also suggest that urban pollu-tion is related to industrial structure: the higher

Table 6. Income elasticities of TSP concentration forsample-country income percentiles

Percentile Incomeper capita

(constant $US PPP)

Elasticity

Min $1,422 �0.0510 $1,743 �0.1225 $5,461 �0.5050 $8,074 �0.6375 $22,585 �0.9790 $24,081 �0.99Max $26,950 �1.02

the share of pollution-intensive sectors inindustrial output, the higher the urban TSPconcentration, ceteris paribus. Population isalso significant in the random- and fixed-effectsmodels, although its estimated elasticity issubstantially higher in the fixed-effects result.

To summarize, panel estimation results forthe extended EKC model suggest that incomeeffects are strongly complemented by gover-nance, geographic vulnerability, populationand industry structure.

6. DETERMINANTS OF URBAN AIRPOLLUTION: PRESENT AND FUTURE

Using our econometric results, we performtwo sets of simulation experiments to assessthe relative impact of income, governance, vul-nerability and population on urban air pol-lution. Our random- and fixed-effects modelsdiffer by one time-invariant factor (geographicvulnerability), so we include simulations forboth models.

(a) Comparative impacts

In the first set of experiments, we use the ran-dom effects model to predict air pollution levelsfor developing-country cities whose characteris-tics represent low, medium, and high valuesof the four determinants. Table 7 displays therelevant ranges from the sample dataset.

To assess the partial effect of each deter-minant, we use the random-effects (RE) andfixed-effects (FE) results in columns (3) and(5) of Table 4 to estimate TSP concentrationsfor a city with ‘‘worst-case’’ conditions forincome, governance, vulnerability and popula-tion. Then we re-estimate concentrations atmedium and high levels for each of the four fac-tors, while holding the other three factors attheir ‘‘worst-case’’ levels. We present both REand FE simulations because inclusion of geo-graphic vulnerability in the RE equation leadsto a substantially different income effect. TheRE turning point for the partial income-pollu-tion relationship is about $3,000, while it isabout $1,200 in the FE equation. As Table 8indicates, this leads to differences in both theestimated maximum pollution level and the ef-fect of increased income on TSP concentration.The estimated maximum TSP concentration is417 lg/m3 for FE (at $1,000 per capita) and302 for RE (at $3,000 per capita, because theincome-pollution turning point is higher for

Table 7. Simulation range for random effects results: characteristics of non-OECD cities

Incomeper capita

($US ’000, PPP)

TI governanceindex

Locationalvulnerability

index

Population (’000)

Low 1,000 1.5 15 250Medium 3,000 4.5 50 1,000High 8,000 7.5 85 5,000


RE). The ratio of maximum estimated concen-tration to the current median concentration inOECD cities (49 lg/m3) is 8.5 for the FE re-sults, and 6.2 for the RE results.

For consistent interpretation of low, mediumand high levels in Table 8, we reverse themeasures of geographic vulnerability (to‘‘locational advantage’’) and population (to‘‘population advantage’’). Thus, ‘‘locationaladvantage’’ is high in cities with low geographicvulnerability, and ‘‘population advantage’’ ishigh in cities with small populations. Amongthe four determinants, locational advantageclearly has the greatest impact across its rangein the sample data. Holding income, gover-nance, and population constant at ‘‘worst-case’’ levels for RE, changing locationaladvantage from low to high reduces predictedair pollution from 302 to 62 lg/m3. This resultsuggests that geographic factors alone are suf-ficient to determine whether a poor, over-crowded, poorly governed city will suffer fromcrisis-level air pollution, or experience pollutionnear the upper bound for air pollution inOECD cities (see Table 11). Governancechanges have similar effects in the two models.Moving from low to high governance reducesthe estimated TSP concentration by 32% inRE and 39% in FE. Population effects differsubstantially, although they are important in

Table 8. Partial imp

Index level Income Governance L

(a) Random-effects model

Low 248 302Medium 302 233High 252 206

(b) Fixed-effects model

Low 417 417Medium 368 299High 235 256

a Measured as declining vulnerability index values (85 ) 5b Measured as declining population (5,000,000 ) 1,000,00

both models. Relative to the large city (popula-tion 5,000,000), pollution in the small city (pop-ulation 250,000) is 19% lower in RE and 80%lower in FE. Income effects also differ, becausethe estimated turning point for the EKC rela-tionship is $3,000 for RE and $1,200 for FE.As a result, the estimated TSP concentrationactually increases slightly (by 1.6%) from lowto high income in RE, while it falls by 43% inFE (because our ‘‘worst-case’’ income, $1,000,nearly coincides with the EKC turning pointin this case).

Table 9 adds another perspective, by measur-ing the joint impacts of TSP determinants. Forjointly low, medium and high values, gover-nance and locational advantage in RE reduceTSP from 302 to 143 to 42. This result indicatesthat in a large developing-country city with astrong locational advantage, good governancecan hold air pollution below the OECD median(49), despite the effects of poverty. In combina-tion, all four determinants in RE reduce TSPfrom 302 to 105 and 29 for medium and highvalues. In FE, the combined effect of income,governance and population reduces TSP from417 to 111 to 29.

We should stress that these results are notartifacts of our regressions. They reflect actualpatterns of variation in the urban air qualitydata. The comparative statistics in Table 11

acts on TSP levels

ocational advantagea Population advantageb

302 302186 27062 245

41717583

0 ) 15).0 ) 250,000).

Table 9. Joint impacts on TSP levels

Joint index values TSP determinants: joint effect

Governance +location

Income,governance,location,population

(a) Random effects

Low 302 302Medium 143 105High 42 29

(b) Fixed effects

Low 417Medium 111High 29


show that many non-OECD cities already haveair quality that falls within the current rangeof OECD TSP concentrations (15–109). Oureconometric analysis has suggested why thisshould be the case: Despite high levels of pov-erty and large populations, many non-OECDcities have benefited from good governanceand locational advantages that have given themlow vulnerability to air pollution. Nevertheless,as the distributional statistics for 1996/99 in Ta-ble 11 make clear, at least 75% of non-OECDcities currently have TSP concentrations that

Figure 3a. TSP distributions in non-OECD

would be considered crisis levels in the OECD.Problems with governance and vulnerabilityexplain a major part of this disparity.

(b) Predictions for 2025

In the second set of experiments, we predictfuture TSP levels under alternative assumptionsabout the time paths of income, governanceand population in our sample cities. In thebaseline experiment, we use trends during the1990s in each city to forecast income, gover-nance and population in 2025. With these fore-cast values, we predict TSP levels in 2025 forboth random and fixed-effects models. In the‘‘policy reform’’ experiment, we assume thatreal per capita income grows at a 5% annualrate in each sample country; the governanceindex reaches the current 25th-percentile valuefor OECD countries; and the growth rate ofthe urban population is one-half of the actualrate during the period 1995–2000. Figures 3aand 3b display the results for the random andfixed-effects models, and Table 11 summarizesthe same information, along with comparativestatistics for OECD cities in the sample dataset.

The baseline prediction reflects the assump-tion that recent trends at each monitoring sitewill continue through 2025. As Table 10 shows,not all of these trends are favorable.

cities: 1986–2025 random-effects model.

Figure 3b. Fixed-effects model.

Table 10. Trends in determinant values

Minimum 1st Quartile Median 3rd Quartile Maximum

Per capita income: % Change, 1986/90–1996/99 �58.3 �12.1 7.8 42.4 117.7TI governance index: Change, 1986/90–1996/99 �2.4 �1.9 �0.5 0.4 1.9City population: % Change, 1995–2000 �7.1 1.9 13.0 15.0 26.7


According to the TI index, over half thesample of non-OECD sites were in countrieswith declining governance quality in the1990s. While income grew rapidly in somecountries, it also deteriorated markedly inothers, and the median real income gain(7.8% for nearly a decade) was quite modest.Urban population continued to grow rapidlyin many sample countries, with a median in-crease of 13% during the period 1995–2000.Under these conditions, we would expect amixed set of site-level baseline projections forTSP through 2025. A positive element in ourregression-based model is the secular trenddownward in overall air pollution, which mayreflect the diffusion of clean technology andenvironmentalism. We assume that this trendwill continue until 2025.

Table 11 presents our comparative results.Under the baseline assumptions the RE results

predict continued improvement, with medianTSP for non-OECD cities falling from 161 in1996/99 to 96 in 2025. However, the FE resultspredict deterioration, with median TSP risingto 204 by 2025 – near the median level in1991–95 (214). The difference in the two resultsis explained principally by differing estimates ofthe population elasticity of pollution in Table 5for RE (.07) and FE (.54), since baselinepopulation growth remains high for mostdeveloping-country cities. For both models, themedian baseline predictions remain substan-tially higher than the current OECD medianin 2025 (twice as high for RE; four times ashigh for FE).

In the policy reform simulation, both modelssuggest a similar pattern of improvement. Med-ian TSP falls from 161 to 72 in RE and 86 inFE. Both estimates are within the current upperrange for OECD cities. Maximum levels also

Table 11. Historical and predicted TSP levels, 1986–2025 (percentiles)

Minimum 10% 25% Median 75% 90% Maximum

Non-OECD cities: historical statistics

Non-OECD TSP, 1986–90 59 76 133 220 345 503 560Non-OECD TSP, 1991–95 45 74 112 214 329 464 728Non-OECD TSP, 1996–99 9 59 82 161 256 368 470

Random-effects predictions

Non-OECD TSP 2025 (baseline) 36 42 77 96 154 171 188Non-OECD TSP 2025 (reform) 19 30 40 72 98 109 120

Fixed-effects predictions

Non-OECD TSP 2025 (baseline) 17 110 143 204 270 442 555Non-OECD TSP 2025 (reform) 8 46 73 86 138 191 240

OECD cities: historical statistics

OECD TSP, 1986–90 23 38 42 58 83 122 206OECD TSP, 1991–95 21 28 36 49 62 95 136OECD TSP, 1996–99 15 24 32 42 49 64 109


fall markedly (from 470 to 120 for RE and 240for FE). In the reform simulations, 90% of non-OECD cities have reached the current OECDrange by 2025 for RE, and about 60% havereached the current OECD range for FE. Thesepredictions reflect assumptions that, while opti-mistic, are by no means out of reach for manydeveloping countries. We conclude that, despitethe geographic vulnerability of many devel-oping-country cities, a quarter century ofsustained growth, fertility reduction and gover-nance reform could bring many of them withinrange of the air quality currently enjoyed bymost people in the OECD countries.

7. SUMMARY AND CONCLUSIONS

In this paper, we have revisited the environ-mental Kuznets curve for air pollution, usingnew data on the determinants of air quality.Using a balanced panel of air monitoring datafor the period 1986–99, we have estimated airquality models that control for governance,vulnerability, population and pollution-inten-sive economic activity, as well as income percapita. Our econometric results show variedimpacts for income, but they are unambiguousin their assignment of importance to gover-nance and geographic vulnerability. Using bothrandom- and fixed-effects estimators, we usesimulation experiments to assess the relativeimportance of income, governance, vulnerabil-ity and population as determinants of air qual-ity. We find that governance and geographic

vulnerability alone are enough to account forthe crisis levels of air pollution in many devel-oping-country cities. When their effects arecombined with those of income and popula-tion, we have a sufficient explanation for thefact that some developing-country cities al-ready have air quality comparable to levels inOECD cities.

In another simulation, we project air pollu-tion in 2025 for our sample cities using two setsof assumptions. In the baseline set, we allowcurrent trends to continue for income, gover-nance and population. This leads to substantialimprovement in air quality for the fixed-effectsmodel, and moderate improvements for therandom-effects model. In the second set, we as-sume that policy reforms produce real incomegrowth of 5% annually, improved governancesufficient to achieve parity with the currentlower quartile of OECD countries, and urbanpopulation growth at half the rate observed in1995–2000. In the reform simulation, both ran-dom- and fixed-effects models predict strongimprovements in air quality for most develop-ing-country cities. By 2025, most would haveattained air quality within the current rangeexperienced by OECD cities.

In light of these results, we believe that policymakers should be wary of the conventionalEKC model. Our results offer no support forthe view that air quality deteriorates duringthe first phase of economic growth. Nor doour results support the EKC-motivated viewthat citizens of poor countries necessarily facea long wait for major improvements in air


quality. Significantly improved governance ispossible in poor countries, and our results sug-gest that policy reform alone is sufficient to re-duce air pollution significantly, even inovercrowded, geographically vulnerable citiesin countries with very low incomes.

Although our results have an optimistic cast,we feel compelled to close with some notes ofcaution. Our results for geographic vulnerabil-ity suggest that increases in air emissions aremuch more dangerous in some cities than inothers. In light of this finding, urban plannersmay want to take vulnerability into accountas they consider national and regional policies

for the next round of urban development. Weshould also note that nothing in our hopefulpredictions is preordained. Our results implythat air quality will become worse in cities withstagnating or falling incomes, deterioratinggovernance, and rapidly growing populations.And even in the reform simulation, improve-ments by 2025 cannot save many people whowill die due to dangerous air pollution duringthe next quarter century. We see no conflict be-tween urban air quality and economic growth(the converse, in fact), but improved environ-mental governance seems to provide our besthope for rapid improvement.

NOTES

1. Kuznets’ name was apparently attached to the curveby Panayotou (1993), who noted its resemblance toKuznets’ inverted-U relationship between incomeinequality and development.

2. See, for example, Grossman and Krueger (1993),Shafik and Bandyopadhyay (1992), Panayotou (1993),Kaufmann, Davidsdottir, Garnham, and Pauly (1998).

3. GDP per capita at PPP in constant 2000 dollars(from the World Bank’s World Development Indica-tors).

4. Daly (2000) has forcefully defended the view thattrade and investment are affected by pollution havens.At a union convention in 1999, Congressman DavidBonior offered the following critique of the World TradeOrganization (WTO): ‘‘The WTO, as currently struc-tured, threatens to undo internationally everything wehave achieved nationally – every environmental protec-tion, every consumer safeguard, every labor victory’’(Bonior, 1999). In a similar vein, the Nader-for-Presi-dent, 2000 campaign characterized the North AmericanFree Trade Agreement as follows: ‘‘Such one-dimen-sional monetized logic tramples long-standing effortsaround the world – some very successful – to protect theenvironment because environmental safeguards are veryoften considered ‘non-tariff barriers to trade’ and thusbecome targets for removal.’’ (Nader-for-President,2000). Critics of trade liberalization have also raisedthe prospect of agricultural pollution havens, where low-cost production with unregulated pesticide use poisonsagricultural workers, as well as consumers in countriesthat import their contaminated products (Rauber, 1997;Sagaris, 1999).

5. See Dasgupta, Laplante, Wang, and Wheeler (2002).

6. Previous EKC studies (e.g., Grossman and Krueger(1993) have incorporated some geographical informa-tion, mostly for developed countries. This paper drawson recent Geographic Information Systems (GIS) workat the World Bank that has computed climate- andtopographically related environmental vulnerability forover 3,000 cities in developed and developing countries.See Pandey et al. (forthcoming).

7. This section draws heavily on Dasgupta et al. (2002).

8. For related work at other institutions, see Esty andCornelius (2002).

9. For 136 countries, bivariate linear and log regres-sions of the environmental institutions rating on GDPper capita yield R2’s of only .32 and .29, respectively.

10. For a review of the evidence, see Pandey et al.

(forthcoming).

11. For a detailed discussion, see Hettige, Singh,Martin, and Wheeler (1995).

12. For the derivation of emissions estimates, seeHettige, Mani, and Wheeler (2000).

13. Air quality measures can differ substantially acrossmonitors within cities, and across cities within countries.Changing site composition can introduce both randomerror and systematic bias into intertemporal compari-sons using city or country averages, the latter becauseinitially monitored sites tend to be more polluted thanlater additions.


14. We have computed the ‘‘dirty’’ sector shares fromUN data, which are only available at the national level.Since we do not have city-specific measures, our sectorshares should be considered proxies.

15. Since the TI index reflects perceived corruption, italso provides evidence for testing the previously men-tioned corruption-pollution hypothesis of Lopez andMitra (2000).

16. We have estimated the model using Stata, Version8. We should note that these results are contingent onthe cointegration of model variables. Our thanks to ananonymous referee for pointing this out.

17. The five estimates imply significantly differentturning points. For columns (1)–(5), respectively, theturning point incomes are $2,046 (about Ghana’scurrent level), $6,881 (Thailand), $2,980 (Nicaragua),$1,457 (Senegal) and $1,208 (Rwanda).

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Environment During growth: Accounting for governance and vulnerability

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