Download Does the Environmental Kuznets Curve Describe How Individual

Does the Environmental Kuznets Curve Describe
How Individual Countries Behave?
Robert T. Deacon and Catherine S. Norman
ABSTRACT. We examine within-country time
series data on income and concentrations of SO2,
smoke, and particulates to see if the shapes of
pollution-income
relationships in individual
countries agree qtialitatively with predictions of the
environmental Kuznets curve. The shapes of the.se
relationships are determined non-parametricalty
for individual cotintries using recently available
data on air pollution concentrations. Eor smoke
and particulates, the shapes of within-country,
polltition-income patterns do not agree with the
EKC hypothesis more often than chance wotitd
dictate. For SO2. which generally exhibits EKCconsistent pollution-income relationships among
wealthier countries, the observed patterns are also
consistent with a simpler hypothesis. (JEL Q20,013)
I. INTRODUCTION
The environmental Kuznets curve (EKC)
is an empirical proposition about the relationship between a country's income and
its levels of pollution. Starting from an initially low income level, the EKC postulates
that, as a country's income grows, its pollution will initially rise, but will eventually
decline if growth proceeds far enough, tracing out an inverted-U relationship. There
is now an extensive empirical literature on
the EKC. We contribute to this literature
by examining time series data on pollution
and income for individual countries and
pollutants to see if the EKC's qualitative
predictions are borne out with greater frequency than chance would dictate. In contrast to the majority of EKC studies, we
focus exclusively on within-country empirical relationships. We believe evidence
from within individual eountries can potentially provide compelling evidence on
Land Economics • May 2006 • 82 (2); 291-315
ISSN 0023-7639; E-ISSN 1543-8325
© 2006 by the Board of Regents of the
University of Wisconsin System
the central policy question at issue in this
literature: how will an individual nation's
environmental quality evolve if it makes
the transition from poverty to affluence?
Unlike other studies, we test a very general form of the EKC hypothesis: that a
country's pollution-income relationship can
be described by a single peaked curve, which
may be particular to an individual country
rather than common across countries.
The empirical strategy tests three implications of the EKC hypothesis: income
growth is accompanied by increased pollution in low-income countries, by decreased
pollution in high-income countries, and by
first increasing, then decreasing, pollution in middle-income eountries. To reduce
the number of shapes possible in any
pollution-income plot, within-country pollution and income data are collapsed by
taking means over three country-specific
income ranges. The three summary data
points that result are then plotted and their
shapes examined. The "shape" (increasing,
decreasing, inverted-U, etc.) of a pollutionincome plot for a particular country and
pollutant is treated as an observation. In
total, 52 such observations are examined for
consistency or inconsistency with the EKC
predictions. The tests are for "qualitative
The authors are, respectively, professor of economics, University of California. Santa Barbara and university fellow. Resources for the Future; and assistant
professor. Department of Geography and Environmental Engineering, The Johns Hopkins University,
This material is based on work supported by NSF
Grant No. 9S08696, Without implicating them tor errors,
the aulhors aeknowledge valuable comments from Ed
Barbier. Henning Bohn, Chris Costello. Bill Harbaugh,
John List, Doug Steigerwald, David Stern. David
Molloy Wilson, and two anonymous referees, Arik
Levinson kindly provided an updaled version of the
GEMS dataset. An earlier version of this paper was
included as part of Catherine Norman's PhD dissertation at the University of California, Santa Barbara.
292
Land Economics
consistency" with the EKC in the following
sense: a particular pollution-ineomc plot is
judged EKC-consistent if some singlepeaked curve, whdse peak falls in a range
of possible EKC turning points identified
from the literature, can be drawn through it.
Tlie central conclusion from this analysis is
that, with one notable exeeption, the shapes
of observed pollution-ineome relationships
do not agree with EKC predictions with any
greater frequency than random data would.
The EKC has now been studied extensively. Despite numerous critiques, its central policy message —that the simultaneous
pursuit of economic growth and a cleaner
environment ultimately need not conflictremains influential. Part of the EKCs appeal, particularly to non-specialists, is that
it seems to agree with environmental
changes observed in the world's wealthier
nations over the last 50 years. Casual empiricism suggests that environmental quality in many of these countries worsened
following World War II, but eventually improved after the late l%Os. Because incomes generally were increasing over this
period it is tempting to conclude that an
inverted-U proeess, with income as the
driving force, was at work.' We believe this
plausible eonjeeture deserves more earcful
scrutiny, and set out here to see if it is supported across a series of countries and air
pollutants by available, widely used data.
The following section reviews the EKC
literature, Seetions 3 and 4 describe the nonparametric approach and the data used to
IIS, airborne emissions of carhon monoxide and
volatile organic compounds show an invortod-LI shape
when ploltcd over time, and sulfur dioxide (SOT) displays a single peak if WWII years arc excluded (U. S.
Environmental Protection Agency 2(HM)), which roughly
agrees with the generalization in the text. Other pollutants show different patterns, however. Over available
reporting periods, IIS. air emissions were monotonieally
increasing for nitrogen oxide, decreasing for lead and
PMIO particulates, and roughly constant for finer particulates. Further, an inverted-U relationship heiween
pollution and time (as with carhon monoxide and volatile organic compounds) eomhined with an increasing relationship between income and time need not
result in an invcrtcd-LI relationship hetween pollution
and income.
May 2006
examine within-country, pollution-income
relationships and Section 5 presents tests
for nonrandomness in the shapes of withincountry, pollution-income plots. These tests
reveal that EKC-consistent behavior in our
sample largely consists of SO^ cleanup that
occurred in wealthy countries while they
were growing. Following up on this point.,
Section 6 carries out a time series analysis of within-country data to see if the
EKC model ean add explanatory power to
a simple alternative specification in which
pollution declined over time in richer countries, possibly due to changes in information, technology, or preferences.
II. EMPIRICAL AND THEORETICAL
WORK ON THE EKC
The EKC first emerged in studies of ambient concentrations of air and water pollution data collected by the World Health
Organization and reported under the name
Global Environmental Monitoring System (GEMS). Shafik and Bandyopadhyay
(1992) estimated cross-country relationships between income and air and water
pollution, deforestation and waste output,
concluding that airborne SO^ and smoke
concentrations began to diminish after an
income level of $3,{)00-$4,000 per capita
was reached. Grossman and Krueger (1993)
reached similar conclusions from an analysis of the same data, but found turning
points in the $4,()()()-$6.()0() range. Grossman and Krueger (1995) confirmed the
inverted-U pattern for some additional environmental measures, but with turning
points at different ineome levels. Panayotou
(1997) controlled for industrial structure
to allow for the possibility that production
shifts toward cleaner products as income
grows, and Torras and Boyce (1998) and
Barrett and Graddy (2000) included measures of literacy, economic inequality., and
civil and political freedoms in the analysis.
In each case, the inverted-U was confirmed
using the GEMS data set. Harbaugh,
Levinson, and Wilson (2002) (hereafter
HLW) examined an expanded and corrected version of the GEMS data used in
earlier research. ITiis later version included
82(2)
Deacon and Norman: Kuzrtets Curve and How Countries Behave
more observations and corrected some erroneous entries found in versions used in
earlier research. HLW found no clear support for the inverted-U relationship and
obtained results that were sensitive to sample, specification, and estimation method.'
While the preceding studies all examined ambient concentrations, a parallel
strand of research focused on nationwide
pollution emissions. Hilton and Levinson
(^1998) separated the policy response (pollution per unit output) from the scale effect
for lead emissions. They identified an EKC
with a turning point in the normal range.
but found it was sensitive to specification
and sample period. Seiden and Song (1994)
examined airborne emissions of SO2 and
particulates in a panel of mostly OECD
nations. They found the EKC pattern, but
with turning points in the $8,000-10,000
income range. Holtz-Eakin and Seiden
(1995) examined CO2 emissions, an unregulated pollutant at the time, and found no
point at which emissions declined with total
income. Stern and Common (2001) examined SO2 emissions and found a "turning
point" exceeding $100,000, far outside the
observed range of per capita income. They
also found different turning points for separate samples of OECD and non-OECD
countries, suggesting that searching for a
universal EKC may be inappropriate."
Relatively few empirical studies have
evaluated the EKC by looking at income
and pollution within countries, over time.
Among these, De Bruyn (1997) examined
time series data on income and sulfur emissions in four OECD nations separately and
found relationships that took a variety of
shapes, including Us and Ns, in addition
"' Antweiler, Copeland, and Taylor (2001) used the
GEMS data to examine a model of pollution generation
that separates scale, technique, and output composition
effects, but without testing the EKC hypothesis directly.
' Cole {2003) estimated :m EKC niodel that allows
emissions to respond to differences in openness :md comparative advantage and concludes that the inverted-U
is not driven by considerations of trade. Lopez and
Galinato (2005) show that the position of an EKC for
forest cover is significantly related to trade openness and
governance.
293
to the inverted-U. Vincent (1997) tested predictions from the EKC literature on a panel
of detailed data from Malaysian states. He
found that parameters from published EKC
models failed to predict the pattern of
changes in Malaysian air and water pollution; moreover, none of the Malaysian pollution measures exhibited an EKC at all.
Perman and Stern (2(X)3) studied sulfur
emissions and, after accounting for the time
series properties of emissions data, found
no statistical support for the EKC hypothesis either in a panel or in within-country
analysis. Notably, the preceding withincountry studies were based on emissions
rather than concentrations and none confirmed the inverted-U as a valid generalization. Markandya, Pedroso. and Golub
(2(X)4) estimated within-country pollutionincome regressions for SO2 emissions in 14
European eountries. They found a quadratic, inverted-U relationship fit best in four
cases, but the other significant relationships
were complex fourth order polynomials.
Other researchers have examined panel
data from US states. Carson, Jeon, and
McCubbin (1997) estimated EKCs for air
quahty and found that most measures improved monotonically with income over
the sample range. List and Gallet (1999)
studied sulfur and nitrogen emissions in a
pane! of U.S. states over the period 19291994. They found evidence that forcing
the same specification (and hence turning
point) on all the states resulted in biased
estimates. Using the same data, Miliimet,
List, and Stengos (2003) found the EKC
peak to be highly sensitive to modeling
assumptions and rejected a parametrie specification in favor of a more flexible semiparametric approach."^ Stem (2004,1424-25)
provides a critical overview of EKC papers
examining airborne sulfur and their various
econometrie approaches.
•* Koop and Tole (1999). in their examination of
deforestation in a cross-country setting, also found
evidence that the shape of income-forest cover relalionsliips varied considerably across countries. Interestingly, they found an inverted-U relationship only
wben they imposed ii single functional form on all Ihe
countries observed.
294
Land Economics
The emergenee ofthe inverted-U in empirical work prompted a theoretical literature to explain how this shape might
arise.^ Stokey (1998) showed that it could
emerge from a model in which pollution
control effort is not expended until a pollution threshold is crossed. Andreoni and
Levinson (2001) demonstrated that strong
increasing returns to scale in pollution
abatement could give rise to an inverted-U.
Lopez and Mitra (1997) showed how corruption in government, by reducing abatement effort, could affect the shape of the
EKC and move its turning point.^ Brock
and Taylor (2004) showed that an EKClike relationship could arise naturally in a
Solow growth model, but demonstrated
that the peak could occur at virtually any
income level. Copeland and Taylor (2003,
chapter 3) examined four mechanisms that
can generate an inverted-U in the context
of a single consistent modeling framework,
effectively summarizing much of this work.^
The EKC has now been extensively researched and the associated literature has
been reviewed and critiqued. Despite this
concerted attention, the literature remains
incomplete in two respects. First, examples
of countries actually moving up one side of
the inverted-U curve and down the other
have yet to be identified in the literature.
Indeed, time series analysis of individual
countries has yielded disappointing results;
see De Bruyn (1997), Vincent (1997), and
Perman and Stern (2003). Second, several
researchers have concluded that the shapes
of pollution-income relationships may be
country specific, so generic empirical specifications may yield biased results. This point
is made by Stern, Common, and Barbier
Given the empirical emphasis in this paper, theoretical contributions are summarized only briefly. Stern
(2()()4, 1420-22) reviews mueh of this work and provides
an extensive hibliography,
" Weisch (2004) tests one implication of ihis theory
and finds a sirong, positive relationship hetween pollution and corruption when income is held eonstanl,
•' Israel and Levinson (2(X)4) use data from the
World Values Survey to test ancillary predictions from
different theoretical models that have heen used lo generate the EKC.
May 2006
(1996), by Barbier (2001) in his review of
empirical work on tropical forest cover, and
in Coondoo and Dinda's (2002) discussion
of causality in EKC studies. It is corroborated by results from Hilton and Levinson
(1998).' HLW. and Stern and Common
(2001) indicating the sensitivity of EKC
results to sample. In addition, theoretical
treatments by Lopez and Mitra (2000) and
Brock and Taylor (2004) indicate that a single
EKC function need not fit all economies.
in. EMPIRICAL APPROACH
The empirical approach followed here is
motivated by these observations on the literature. First, attention is focused exclusively on pollution-income patterns within
individual countries, over time. The appeal
of the EKC is its potential ability to predict
how a eountry's pollution will change as its
economy grows. To policymakers, the identification of countries behaving according
to the EKC proposition for specific pollutants would be compelling evidence for
the theory's value. In addition, examining
only within-country data minimizes the influence of cross-country heterogeneity, restricting this source of potential concern
to attributes that change within a country
during the sample period. While recognizing the power of panel data methods,
which have been the foundation of past
EKC research, empirical applications in the
EKC literature have required sueh heterogeneity to be additive and this may be too
restrictive. Allowing individual countries to
follow different pollution-ineome paths
may yield new insights.
Second, to impose minimal structure on
the shapes of pollution-income relationships in individual countries, a nonparametric method is used to characterize the
shapes of these relationships qualitatively,
that is. as monotone increasing, monotone
decreasing, single peaked, etc. A country's
pollution-income relationship is then characterized as EKC-consistent if it follows
some single peaked function whose peak
falls within a speeified range. The range
used in practice, while motivated by EKC
peaks reported in the literature, is very
82(2)
Deacon and Norman: Kuznets Curve and How Countries Behave
broad. According to the EKC hypothesis,
ineome growth should be associated with
increasing pollution in poor countries and
with decreasing pollution in rich countries,
where poor and rich refer to whether a
country's observed ineome series lies entirely to the left or to the right of the range
of possible EKC peaks. When a country's
income series lies partially or entirely within the range of allowed peaks, a variety of
pollution-income shapes are potentially
EKC-consistent, as explained shortly. Testing the predictive power of the EKC hypothesis involves determining whether or
not EKC-consistent shapes occur more frequently in the observed data than would be
the case in randomly generated data.
The shapes of pollution-income relationships are identified non-parametrically by
collapsing the available annual observations
on pollution and income for each pollutantcountry case examined into three summary
observations. First, the country's pollution
and ineome observations are ordered by
income, measured as real GDP per capita.
These observations are then partitioned into
tritiles. three groups that eontain the lowest,
middle, and highest third of that eountry's
GDP observations. Next, mean GDP and
mean pollution for each tritile are computed,
yielding three summary poilution-ineome
points for the country-pollutant case under
consideration. The plot of these points is then
examined to see if they ean be connected by
some single-peaked curve whose peak lies
in a specified range of possible EKC turning points. Thus, monotone-inereasing and
monotone-decreasing, pollution-income
plots can satisfy this criterion for income
ranges that lie. respectively, entirely below or
entirely above the range of possible EKC
peaks. If a country's range of GDP observations lies partially or entirely within the
range of possible turning points, more than
one shape can satisfy the criterion. Detailed
criteria and the range of EKC turning points
applied in these tests are provided following the presentation of summary statistics.
This procedure is illustrated in Figure 1
using data for four of the countr>'-pollutant
cases examined. Panels A and B show 21
annual observations (from 1972-1992) for
295
GDP and sulfur dioxide in two countries,
Canada and Japan.'^ The vertical lines show
the GDP cutoff levels that partition each
country's observations into tritiles. The circles
show mean pollution and GDP in each tritile.
Thc plot of these three points in Panel A,
depicting Canada, decreases monotonically.
This shape is EKC-consistent because Canada's GDPobservations lie above the range of
estimated SO2 turning points reported in the
literature. The plot for SO2 in Japan, however, is increasing. This is EKC-inconsistent
because Japan's GDP is also above estimated turning points for SOT. With three data
points only two other shapes are possible, an
inverted-U and a trough. The data in Panels
C and D, showing smoke in Venezuela and
particulates in Indonesia, respectively, illustrate these shapes. The inverted-U in
Panel C is EKC-consistent beeause Venezuela's income range lies within the range of
EKC turning points gleaned from the
literature. The trough shape for particulates
in Indonesia is EKC-inconsistent. Overall,
half of the four pollutant-country cases in
Figure 1 exhibit EKC-consistent shapes.
Below, we replicate this procedure for all 52
country-pollutant cases available and compare the frequencies of EKC-eonsistent
shapes to the frequencies that would arise
in randomly generated data.
Grouping the data into income categories and taking means obviously suppresses much of the information in the original
series. It has the advantage, however, of
allowing clear, robust conclusions to be
drawn regarding the shapes of pollutionincome relationships. The choice of three
categories for grouping the data rather
than a larger or smaller number is suggested by the nature of the EKC hypothesis
itself. A minimum of three data points is
required to trace out a single-peaked curve,
so a smaller number clearly will not suffice.
^ Note that real GDP per capita is not a monotone
funetion of time in most countries examined, so plotting
pollution over time could result in a different shape. Of
the 28 countries examined, only seven (China, Denmark. Hong Kong. Ireland, Italy, Japan, and Thailand)
grew monotonically during the sample periods.
Land Economics
296
Panel A
May 2006
PanelB
Japan
40
•
*
• •
30
llOJI t„oncent
rations
Can ad t
•
'I,
••
5
c.
12UO0
I4IXX]
16000
18000
6000
KOIM)
Per Capita GDP
• CPoll airiiilepoll
• CPiill 0 irjiilepol
Panel C
Panel D
Vcnc/iic"la
Indonesia
o.
d
o
0
2
c '"'"
•
•
*
•
O
c _
c
C
U
•
cC --I
o.
Pol lut
KKMXI
Per Capita GDP
•
•
•
5 c
15
*
7500
8(XK)
I21X)
Per Capita GDP
I4(K1
Per Capita GDP
rpnII 0 trililcpi.il]
I t'Poll o iriiilcpi)ll
FIGURE 1
CONSTRUCTING TRITH.K POLLUTION-INCOME PLOTS: FOUR EXAMPLES
With three points, only four shapes are
possible: monotone-increasing, monotonedecreasing, single-peaked, and singletroughed. The first three of these are
potentially consistent with the EKC hypothesis, depending on the country's ineome
range, and the fourth is EKC-inconsistent.
As the number of groups increases, the
number of possible shapes grows exponentially and the fraction that is potentially
EKC-consistent shrinks rapidly.'' Hence,
EKC-consistent patterns are most likely to
emerge if the data arc collapsed into three
points rather than a higher number.
Considering monotone inereasing and monotone
decreasing shapes to be single-peaked with the peak at
an end point, any single peaked eurve eould he EKC-
IV. POLLUTION AND INCOME DATA
The analysis requires within-country
time series data on two variables, income
(real per capita GDP) and pollution. The
pollution data are ambient concentrations
consistent, depending on a country's income level. Only
one shape out of four, the trough (which has two peaks,)
is necessarily inconsistent. With four data points, eight
shapes are possible. The four that are single peaked (one
monotone inereasing. one monotone decreasing, and two
with single interior peaks) are potentially EKC-consistent, depending on ineome. Four of Ihe eight shapes have
multiple peaks and are necessarily ineonsisteni with the
EKC. More generally, the numher of distinci shapes that
ean be observed when Ihe data are summarized by n data
points equals 2" '. The fraction of possible shapes that is
single-peaked and therefore potentially EKC-consistent
equals n/2" ', whieh shrinks rapidly as n increases.
82(2)
Deacon and Norman: Kuznets Curve and How Countries Behave
of SO2. particulates and smoke. The source
is the expanded GEMS dataset used by
HLW. It reports readings from hundreds of
individual monitoring sites in scores of
countries. Data from different sites in the
same country clearly show that some sites
are in dirtier locations than others, whieh
raises the question of how income should
be measured. If the goal were to explain
pollution at individual sites, both loeal and
national income would arguably be relevant. Local income is presumably correlated with local pollution generating
activities, such as auto transportation, and
possibly with local pollution control policies. National level income, a plausible
determinant of national air pollution regulations, is also potentially relevant. Loeal
income or production data are generally
not available for individual monitoring
sites, however. Following others who have
examined the GEMS data, we rely on a
national income measure and use per
capita real GDP (1985 dollars) from the
Penn World Tables. Following HLW, threeyear lagged averages are used to allow for
gradual responses to income changes and
to moderate short-term income fluctuations in the raw data.'^
Using a national income series logically
dictates that the pollution measures used
also reflect national time patterns. We extract national time patterns in pollution
from data reported by individual monitoring sites by estimating 52 regression equations, one for each pollutant-eountry case
examined. For a given case, for example,
SO2 in Japan, we postulate that the pollution reading (mean annual eoncentration)
from site / in year t. Pi,, equals a constant, a.
plus a site effect /i,, plus a year effect 7,,
plus a mean zero error term t,,. The site
effects should eapture site-specific attributes that are relatively constant over time,
such as topography, meteorology, and economic base. The time effects reflect temporal changes in pollution common to all
sites. Factors driving these common time
'" Using contemporaneous income rather than
lagged averages produced qualitatively similar results.
291
effects would include nationwide trends
in levels of eeonomic aetivity. in abatement
costs, in the composition of output and
in the stringency of environmental policy.
These site and time effects are estimated
from the following regression mode!:
8.,.
[1]
where D, and T, are dummy variables for
monitoring sites and years, respectively.
The national pollution series for a eountry
is compiled by adding the individual year
effects, •;„ to the sum of the constant term
and the mean of the site effects. This procedure is repeated 52 times to obtain national
pollution time series for each pollutantcountry case examined." Alternative procedures for forming national pollution
series were also considered. One involved
using the log of pollution as the dependent
variable, which allows site-specific effects to
be proportional rather than additive. Another involved dropping observations from
sites that do not report pollution over the
entire sample period and forming withincountry averages from those that remained.
Results from both procedures were similar
to those reported in the tables that follow
and are available on request.'"^
" Results from these 52 regression equations are
available on request. An alternate procedure, forming
within-country averages of observations from each
monitoring site, would provide a suitable index if all
sites operated continuously, bul this is not the case. For
example, Brussels, Belgium, reported particulates from
one site in 1976-1986 and from another in 1985 and
1986. and the second site was substantially dirtier Ihan
the first. Simply splicing these observations together to
yield a series for Belgium would give a spurious indication of increasing pollution. For a parametric model
with panel daia, one could deal with this problem by
including site-specific fixed effects.
'^ ln an earlier iteration of this analysis. Deacon and
Norman (20()4) examine within-country average pollution readings from monitoring sites that reported
continuously as within-country pollution series. Sites
Ihat did not operate continuously for at least 10 years
are dropped from the analysis. While avoiding the
possibility of regression error in constructing withincountry pollution series, this procedure requires discarding roughly one-lhird of the available smoke data
and more than half ot the available data for particulates
and sulfur dioxide. The averaged data are then subjected
to EKC-consistency checks similar to those used here.
2y»
Land Economics
May 2006
TABLE 1
SUMMARY STATISTICS
All Countries
Income categories:
t983 real per cap, income
helow %3Hm
$3800-6500
over $6500
Sulfur Dioxide
Smoke
Particulates
Income
49,3
(37,3)
5H,2
(44,4)
150.0
(109.7)
8.117
(4,440)
54,7
(32.4)
59.8
(.-(8.3)
46.6
(37.6)
7.1.1
(30.8)
103.0
(47.1)
43.2
(36,8)
207.8
(93.5)
184.t
(94.2)
102,0
(103,8)
2,702
(1.154)
4,391
(927)
10,018
3,818
Notes: These arc summary siaiisiius for estimates of tho pollution level al the average site in each country. Standard deviations are
in parentheses.
Examining individual countries over
time requires data over a span of years
long enough for pollution generation and
environmental policy to change with income. Accordingly, any pollutant-country
case with data for less than 10 years is
excluded from the analysis. Data remained
for 25 individual countries for SO2 pollution, 14 countries for particulates. and 13
countries for smoke, for a total of 32 cases.
These within-country pollution series contain 744 observations in total, 379 for SO2.
183 for particulates, and 182 for smoke.
The data cover 28 separate countries and.
on average, provide 15.2 annual observations per country for SO2, 13.1 for particulates, and 14.0 for smoke.
V. WITHIN-COUNTRY RELATIONSHIPS
BETWEEN POLLUTION AND GDP
Descriptive Statistics
Table 1 presents summary statistics on
pollution and income for countries in three
income categories, which we refer to as
low, middle, and high for purposes of discussion. (These income categories are for
Overall, the results were similar to those reported laler
in Ihis paper: the EKC hypothesis outperformed random assignment only for SO2. and these EKC-consistent
cases were largely relatively wealthy countries with
declining pollution. Detailed results are reported in
Deacon and Norman (2004),
preliminary discussion only; the task of
specifying exact pollutant-specific EKC
turning points, as required for hypothesis
testing, is taken up shortly.)'^ The pollution-income pattern in Table 1 is reminiscent of the curves estimated in the original
Grossman and Kruger (1995. figure 1)
study: a mildly single peaked plot for SO2,
a more distinct peak for smoke, and a
monotonieally declining plot for particulates. This agreement is somewhat surprising given that Grossman and Kruger (1995)
and others who examined the GEMS data
generally included either fixed or random
effects for monitoring sites, plus other conditioning factors. With site characteristics
held constant, the association between pollution and income in a panel data mode!
should reflect temporal variation. Over
time, then, one would expect to see income
growth causing SO2 and smoke pollution
to increase in poor countries and to decrease in rich countries, while income
growth should reduce particulate pollution
at all income levels.
The pollution-income relationships individual countries actually follow over time
generally do not agree with these predictions. Tables 2-4 report the numbers and
identities of countries exhibiting each of
" Vhfi income eutoffs (1983 GDP per capita in t985
dollars) used in Ihis discussion are $3,800 for low versus
middle income and $6,5(K} for middle versus high ineome.
82(2)
Deacon and Norman: Kuznets Curve and How Countries Behave
299
TABLE 2
THE SHAFH OF POLLUTION-GDP RELATIONSHIPS:
Sulfur Dioxide
Peak (invcrted-U)
Mean GDP at peak
Trough
Mean GDP at trough
Increasing
Center of GDP range
Decreasing
Center of GDP range
N
4
6,287
4
3,427
2
8,540
15
8,882
Countries
China, Ireland. Hong Kong, Italy
Chile. India, Poland, Portugal
Japan, Venezuela
Australia, Belgium. Brazil, Canada, UK.
Egypt. Finland, West Germany. Iran, U,S.
Israel, Netherlands, New Zealand, Spain, Thailand
the four possible shapes for pollutionincome plots, plus summary statisties on
their ineomes. Table 2 shows results for
sulfur dioxide pollution, with 25 countries
available to examine. The most common
shape is decreasing (pollution falls with rising income), which occurs in 15 countries.
Most of the eountries in this group would
be classified as high income and the group
as a whole has above average income,
which agrees with the EKC hypothesis. The
group does contain two anomalies, Egypt
and Thailand, which are both poor. The remaining 10 countries show no tendency to
agree with the EKC hypothesis. Pollution
increases with income in two countries,
Japan and Venezuela, and neither is poor.
A trough, which is never EKC-consistent,
appears in four cases, exactly as often as
the inverted-U. While the inverted-U appears four times, two of these are not EKCeonsistent: China is too poor, and Italy too
rieh, for their inverted-U patterns to agree
with EKC turning points in the literature.
It might be argued that countries experiencing little GDP growth over the sample
period should be given httle weight when
assessing the EKC's predictive power.
Dropping the eight eountries that grew
less than 20% between bottom and top
income tritiles, however, does not improve
matters.''* The remaining countries still exhibit equal numbers of peaks and troughs,
three each. Two of the three peaks, China
The eight are Australia, Brazil. India. Ireland, Israel,
New Zealand, the Netherlands, and Ihe United Kingdom.
and Italy, are not consistent with the EKC.
Japan and Venezuela with their increasing
pollution-income patterns remain in the
sample. The "deereasing" group is reduced
by six, of which five are relatively well off
and one is middle income. Overall, the
pattern in Table 2 is largely preserved: the
EKC-consistent cases for SO2 consist of
rich countries that reduced pollution as
their incomes increased.'"''
The descriptions of shapes in Table 2 are
based solely on comparisons of means,
without requiring that the pollution means
in adjaeent tritiles be statistically distinguishable from one another. This is appropriate for the hypothesis testing carried out
later because these tests examine the frequencies of EKC-consistent patterns in
samples of pollution-income plots, rather
than testing for signifieant departures from
EKC-consistency in individual plots. For
descriptive purposes, however, it is of interest to know which of the plot shapes in
Table 2 are statistically distinct."* Restricting attention to countries for which a /-test
• If one looks only at countries that grew more than
50% between bottotii and top Iritiles (Egypt. Hong
Kong, Iran, Japan, and Thailand) the fit to EKC predictions actually seems worse. One country exhibits a
peak and none exhibit troughs, an apparent improvement. However, one rich country (Japan) still displays
an increasing pollution-income relationship and the
Ihree countries with decreasing relationships are either
poor (Egypt and Thailand) or of middle income (Iran),
'" Our tests for statistically distinct pollution levels in
adjacent tritiles proceed by comparing the means and
standard deviations ut ihe point estimates of pollution in
300
Land Economics
May 2006
TABLE 3
THE SHAPE O F POLLUTION-GDP RELATIONSHIPS: PARTICULATE.S
Partieulates
Peak (Inverted-L')
Mean GDP at peak
Trough
Mean GDP at trough
Increasing
Center of GDP range
Decreasing
Center of GDP range
N
2
6,231
6
4.320
1
11.324
5
S,,S05
indicates that the first tritile mean is statistically different (at 10%) from the second tritile. and the second from the third,
leaves only seven SO2 cases.'^ All are highincome countries for which SO:^ declines
as income increases, hence all are EKCconsistent. When pollution means are significantly different for one pair of adjacent
tritiles but not the other, a comparison of
two points is possible. Both increasing and
decreasing plots, the only possible outcomes
with two data points, are EKC-consistent
for a middle income country, so sueh twopoint comparisons are informative only for
poor and wealthy countries. Seven significant two-point comparisons are possible
for SOi. Four of these agree with the EKC
and three do not. For poor countries we
observe one significant increasing plot
(India) and one decreasing plot (China),
and for rich countries we observe two in-
each tritile, A referee correctly points out that two additional sources of error are present in our tritile pollution
figures: the sampling error that results front computing
annual average pollulion levels from daily monitoring readings and the estimation error present in the
regression equations used to form our within eounlry
pollution series from Ihe moniloring site data. Incorporating these sources of error into our analysis would
require Information on the distributions of individual
monitoring sile observations, which are unavailable to
us. The regression results used to form our within country pollution series, Ineluding estimated standard errors,
are available to the interested reader on request. Clearly,
incorporating these additional sources of error would
reduce the number of statistically significant pollutionincome plots available to consider,
'^ Tlie countries are Australia, Belgium, Canada,
West Germany, Spain, the United Kingdom, and the
United Stales.
Countries
China, Finland
Belgium. India, Indonesia, Iran
Malaysia. Portugal
Denmark
Australia, Brazil, Canada, Japan,
ITiailand
creasing plots (Japan and Venezuela) and
three deereasing plots (Finland, Italy and
New Zealand). Overall, the only systematic agreement with EKC predictions
among statistieally significant cases is SO2
reductions accompanying income growth
in rich countries, and this agreement is
evident only in three-point comparisons.
Table 3 shows results for total suspended
partieulates, with 14 countries available, Tlie
most common shape is a trough, which is
never consistent with EKC behavior. The
two countries exhibiting an inverted-U.
China and Finland, are quite poor and quite
wealthy, respectively, rather than of middle
income. The one country exhibiting an
increasing pollution-income relationship,
Denmark, is quite rieh; the EKC hypothesis
predicts that it should be poor. The group for
which pollution decreases as income grows
has above average ineome. which agrees with
the EKC hypothesis, but it includes two
relatively low-ineome states, Brazil and
Thailand, On balanee, the nine eountries
exhibiting peaks, troughs, and increasing
relationships are all EKC-inconsistent and
two of the five with decreasing relationships
are EKC-ineonsistent as well. Dropping
countries that experienced little eeonomie
growth does not improve matters."^ Restricting attention to countries with three
Dropping countries for which GDP growth is less
than 20% wouid eliminate the trough for India and the
increasing pollulion-income relationship for Denmark,
bolh anomalies. Overall, troughs outnumber peaks by
five to two and countries with decreasing pollutionincome relationships are a mixture of rich and poor.
82(2)
Deacon and Norman: Kuznets Curve and How Countries Behave
301
TABLE 4
THE SHAPE OF POLLUTION-GDP RELATIONSHIPS: SMOKE
Smoke
Peak (Invcrted-U)
Mean GDP at peak
Trt)ugh
Mean GDP al trough
Increasing
Center of GDP range
Decreasing
Center of GDP range
N
Counlries
3
4.408
3
5,798
2
9.L'i5
5
7.861
Statistically distinct tritile pollution means
leaves us with two troughs, Malaysia and
Indonesia, and one rich country with a
declining pollution-income plot. Canada.
Three statistically significant two-point
comparisons are possible and all involve
wealthy countries: two of these plots are
decreasing (Australia and Belgium) and
one is increasing (Denmark).
Results for smoke, presented in Table 4,
also show no clear agreement with the EKC
hypothesis. There are as many troughs as
peaks, three eaeh. One of the three countries exhibiting a peak, Egypt, is too poor to
be crossing an EKC turning point, based on
estimates in the literature. The group for
which pollution increases with output is
relatively wealthy, not poor as the EKC
predicts. Four of the five countries where
pollution decreases as income rises are
indeed rich, which agrees with the EKC
hypothesis. Overall, however, the adherence to EKC predictions is poor at best.
Dropping countries that grew less than
20% would eliminate roughly equal numbers of anomalies and EKC-consistent
cases.''* There are no smoke cases for
which all three tritile points are statistically
distinct. There are six significant two-point
' This would eliminate the following countries:
Brazil, Denmark, Ireland, New Zealand, and the United
Kingdom, The two increasing cases in Table 4 are thus
removed. For the decreasing cases, two instances of
EKC agreement would be removed. New Zealand and
the United Kingdom, plus one case of disagreement,
Brazil. The reduced set of comparisons would stiil show
three troughs, and the EKC-inconsistent peak, Egypt,
would remain.
Egypt. Poland. Venezuela
Beliiium, Chile, Iran
Denmark, Ireland
Brazil, Hong Kong. New Zealand.
Spain. United Kingdom
relationships. Contrary to EKC predictions, these cases show identical qualitative
behavior by rich and poor countries. Brazil
and Poland, both poor, have decreasing
pollution-ineome plots, as do Belgium and
the UK, which are rich. Two countries display significant increasing pollution-income
plots: Chile, which is poor, and Denmark,
which is rich.
Hypothesis Tests
The first null hypothesis examined is that
the frequency of EKC-consistent observations in the data is no different than
would occur by chance. Other tests consider
related hypotheses. The tritile data on
pollution and income for a single countrypollutant case are treated as an observation.
Each observation includes an income range
and a shape for the pollution-income plot.
Depending on where the income range lies
relative to the EKC turning point, the observation is either qualitatively consistent
or inconsistent with the EKC hypothesis. Accordingly, the frequency of EKCconsistent observations in the data ean be
computed. Of course, even randomly generated data will agree qualitatively with the
EKC hypothesis in some fraction of cases.
Once the latter fraction has been determined, the y^ distribution can be exploited
to test the null hypothesis stated above.
To judge EKC-consistency for each
country-pollutant case, EKC turning points
for each pollutant must be identified. This is
complicated by the fact that the literature
reports a broad range of turning points for
each pollutant. Rather than impose con-
302
Land Economics
sensus where none exists, we specify upper
and lower bounds for "the" EKC turning
point for a given pollutant and adapt our
testing proeedure aeeordingly. The bounds
identified for EKC turning points (GDP per
capita in 19S5 dollars) are drawn from five
EKC studies that have examined data on
concentrations for the air pollutants eonsidered here."" Table 5 identifies the studies
and the point estimates for turning points
reported by eaeh. The table also reports the
mean and standard deviation of these point
estimates for eaeh pollutant. For SO2 and
smoke the lower and upper bounds used in
hypothesis tests are set at the mean point
estimate plus and minus one standard deviation. For particulates. the standard deviation is so large that applying the same
procedure would cause the income tritile
data for nearly half of the available observations to fall entirely between the lower
and upper bound. As explained shortly,
when the range of possible turning points
encompasses the entire set of tritile observations for a given eountry, the EKC's
prediction on pollution-ineome plots is relatively uninformative —only the U-shape
is ruled out."' To render our tests for this
pollutant more informative, we arbitrarily
set the range for possible turning points at
the mean of the point estimates plus and
minus $1,500." The lower and upper
bounds used in hypothesis tests are thus:
Pollutant
SO2
Partieulates
Smoke
Lower Bound
Upper Bound
$3,012
$4,140
$4,855
$6,1S8
$7,140
$8,140
^" None of the studies examined interacts income
with other variables, hence the turning point estimates
are meant lo apply uniformly to all countries.
Additionally, only Iwo turning point estimates are
available for partieulates and bolh are from a single,
early EKC study,
^^ We cheeked the sensitivity of our central conclusions on EKC consistency for particulates by recomputing our test statistics using the mean plus and
minus one standard deviation, as reported in Table 5,
These cheeks, deseribed more precisely later, revealed
that our cenlral conclusions are not sensitive to this
choice of turning point ranges for particulates.
May 2006
TABLE 5
E S T I M A T E S O F EKC
TURNING POINTS
(GDP Pl-R CAPITA IN 1985 $)
Author
Panayotou (1997)
Grossman and
Kruger (1995)
Barrett and
Graddy (2()(H))
Torras and
Boyee (1999)
Shafik and
Bandyopatay (1992)
Mean
Std, Dev.
Mean - 1 std. dev.
Mean -1- 1 sid. dev.
SO2
Partic,
4,135
4,053
4.202
5.187
3,890
3,360
3,670
8,30.5
4,600
1,588
3,012
6,188
Smoke
6.151
8,097
7,392
4,350
H.OOO
3,280
5,640
3,338
2,302
8,978
6.498
1.642
4.855
8.140
Tests for qualitative consistency with the
EKC are adapted to this range as follows:
A pollution-ineome plot is judged EKCeonsistent if it is possible to draw a singlepeaked curve through the plot, such that the
curve's peak lies within the range specified
above. Figure 2 illustrates how this criterion
is applied. The income levels for lower and
upper bounds on turning points are denoted
TPi^ and TP,/. respectively. Let /,/, /,,-w, and
/,// be country /*s lower, middle, and upper
ineome tritile values. If /,// < TPi_. the
eountry is low ineome and EK.C-consistency
requires an increasing pollution-income
plot. In Figure 2 the income range for this
case is illustrated by the bracketed set of
income points labeled ''1," and the term
"increasing" below this set of points indicates the shape of EKC-eonsistent plots.
Given this country's income range, only an
increasing pollution-income plot could be
part of a single-peaked curve whose peak lies
between TP^ and TPH. If TP,i < I,L. the
country is high ineome and its income range
is illustrated by the plot labeled ease "2."
EKC consistency requires a "decreasing"
pollution-income plot. In the data we examine, 35 ofthe 52 cases available fall into either
case 1 or case 2.
The same eriterion for EKC-consisteney—
that a single peaked curve with a peak between TPi and TP^ can be drawn through
a country's tritile points —is applied to
eountries exhibiting a set of income points
82(2)
Deacon and Norman: Kuznets Curve and How Countries Behave
303
Pollution
5a. [ •
•
• ]
Incr, deer., or invcited-U
or inverted-U
5c. [ •
t
Incr, deer,, or inVcrted-U
5d. I •
•
.1
., deer., or itivertcd-U
TPH
TPL
GDP per capita
FIGURE 2
EKC-CONSISTENT POLLUTION-INCOME PLOTS
that lies wholly or partially within the
range TP/ to TP,^. which we refer to as the
range of uncertainty. Consider the case
labeled " 3 " in Figure 2. Two of this country's income observations lie below TPi
but the third lies above it. EKC-consistency
clearly requires that the first two pollutionincome points display an inereasing relationship, but the third point, which lies in
the range of uncertainty, could be either
above or below the second. Accordingly,
monotone '"increasing"- and "inverted-U"shaped plots are EKC-consistent for case
3. Similarly, case "4" shows a country for
which two income observations lie above
TP/i. but one lies below it. In this case,
"decreasing" and "inverted-U" shapes are
EKC-consistent. Cases 5a-5d cover the remaining possibilities. In each of these
cases, the country's middle observation on
income, 1^- lies within the range of uncertainty for EKC turning points. "InvertedU"- shaped plots are clearly EKC-consistent
for these cases. "Increasing" and "decreasing" pollution-income plots can also lie
along inverted-U shaped curves peaking
within the range of uncertainty, however.
Thus, increasing, decreasing, and invertedU pollution-income plots are all judged
EKC-consistent for such cases.
For hypothesis testing, it is necessary
to determine the proportion of cases that
would be judged EKC-consistent with randomly generated pollution and income
data. Two alternative specifications of randomness are considered. The first, termed
"changes independent," postulates that
the level of income gives no information
May 2006
Land Economics
304
about the direction in which pollution will
change in response to a ehange in income.
Regardless of whether income is low or high
initially, an increase in income is equally
likely to be associated with iticreased or
decreased pollution under this form of randomness. Because the probability of observing no change in pollution is virtually
zero given the level of resolution in the data
used here, the possibility of no change in
pollution is ruled out. With three data points,
this process implies a one-fourth probability
for eaeh of the four possible shapes.^"*
The second version, called "levels independent," postulates that a country's pollution level is independent of its income.
In this case, the pollution values in a given
pollution-income plot are three pollution
draws from the same distribution. Label
the pollution levels in these draws X, Y,
and Z, and assume X < Y < Z. If X is
drawn when ineome is low, Z when income
is middle, and Y when income is high,
the resulting pollution-ineome plot is an
inverted-U. The shape is also an invertedU, however, if Y is drawn when income
is low, Z when ineome is middle, and X
when income is high. In total there are six
different orderings for X, Y, and Z so the
probability of drawing an inverted-U is
one-third. A symmetric argument demonstrates that the probability of drawing a
trough under this set of assumptions is also
one-third. Similar reasoning shows that
the probabilities of monotone increasing
or monotone deereasing pollution-income
plots are one-sixth each with this proeess.
The first test examines a simple null hypothesis: the overall number of EKC-eonsistent plots observed in the data is no different
than would occur if the data were randomly
generated. The test requires the total numbers
of EKC-eonsistent and EKC-inconsistent
plots expeeted under random assignment,
denoted C« and Ni^. respeetively, and the
numbers observed in the data, denoted Co
and Nr). When computing the number of
EKC-eonsistent plots under random assignment, the known proportion of observations
in each ofthe ineome categories (Cases 1,2,3,
4, 5a-5d in Figure 2) is imposed. For example, under changes independent randomness
we specify that one-fourth ofthe observations
for Case 1 (low ineome) eountries take on
each of the four possible shapes, and similarly for other income categories.
The test statistic is
No)'
- CoY
which follows the x~ distribution under the
null hypothesis. With only two outeomes to
eonsider, consistent vs. inconsistent, there
is one degree of freedom. Applying this test
separately to eaeh of the three pollutants
and using the two different versions of randomness yields the following results:
Ho' EKC-consistency observed with same frequency as with random data
H^: H() is false.
Changes Independent
Levels Independent
% EKC Consistent
Pollutant
SO2
Particulates
Smoke
% EKC-Consistent
Observed
Random
y' (1)
Prob,
Observed
Random
y (I)
Prob.
60%
21%
.38%
38%
34%
42%
5,14
0,98
0,0s
2.3%
32%
78%
60%
21%
38%
31%
9.55
0.25
0,37
0.20%
62%
85%
^"' To sec this, plot the three data points with pollution
on the vertical axis and income on the horizontal axis.
Given Ihe first data point, pollution in the second will be
above the first with probability (1,5, and pollution in the
third will be above the second with probability 0,5. Hence.
27%
36%
the probability of a monotone inereasing plot with this
form of randomness is 0,25. Similarly, the probability of
a monotone decreasing plot is also 0.2.'i, Using the same
reasoning, it is easy to show that the probabilities of
single-peaked and single-troughed plots are 0.25 as well.
82(2)
Deacon and Norman: Kuznets Curve and How Cotmtries Behave
The first two columns of numbers show,
respeetively, the percentages of EKCconsistent cells observed in the data and
under the changes independent version of
randomness- Thus, the percentage observed to be EKC-consistent in the data
ranges from 21% for particulates to 60%
for SO2; EKC-consistent percentages with
ehanges-independent random data range
from 34% to 42%. The next two columns
show the /^ statistics and the probability
of observing the fraction consistent in the
data under random assignment. The last
four columns of numbers report corresponding statisties for the levels independent form of randomness.
For particulates and smoke, the levels
of EKCT-consistency observed in the data
could easily have occurred by chance
under either version of randomness. For
particulates, both forms of randomness
actually produce more EKC consistent
cases than occur in the data. A sensitivity
cheek reveals that this finding is not sensitive to an alternative choice of upper and
lower bounds for EKC turning points.*^"^
This is unsurprising in light of the f'act that
eight of the 14 particulate eases follow a Ushaped pattern, which is EKC inconsistent
regardless of the turning point. For SO2.
however, the data are significantly more
compatible with the EKC hypothesis than
either version of randomness would yield.
As discussed below, however, this agreement is of a very specialized
^*
^•* The test was re-computing using $2,302 and $8,978
as upper and lower bounds, whieh correspond to the
mean plus and minus one standard deviation of turning
point estimates reported in Table 5, With ihese bounds
the tesl statistics revealed that the probability of finding
the observed degree of EKC consistency in random data
is 78% with changes independent randomness and 84%
with levels independent randomness.
" One might naturally consider a different procedure: test the null of random assignment against the alternative of EKC-consistency using a likelihood ratio
test. This strategy would not be informative, however,
EKC theory, strictly interpreted, predicts that certain
patterns actually observed in the data are not possible,
e,g.. troughs, so the sample would have zero likelihood
under the EKC hypothesis. Specifying a stoehasiic variant of the EKC theory is not something we have attempted.
305
The preceding tests only examine the
"overall" predictive power the EKC hypothesis: Is the proportion of cells found to
be EKC-consistent greater than would
occur by chance? One might also wonder
how well the EKC hypothesis predicts the
shapes of pollution-income relationships
for individual income categories. This is of
interest because the EKC's predictive performance seems to be different for different
income categories. To address this question,
use the superscript / = 1..J to denote
income categories. Then let C;^ and N^
denote the number of EKC-consistent and
inconsistent cells expected in income category / with random assignment and let C^
and /V/, denote the number of EKCconsistent and inconsistent cases in income
category/ in the observed data. Under the
null hypothesis that C^= C/^for all y, we
have the y~ statistic:
+•
This statistic assigns significance to departures from randomness within individual
income categories, while the earlier test only
looked at overall performance across all income categories. The test considers 2J frequencies, hence there are 2J - I degrees of
freedom. In praetice, certain income categories have no observations for some pollutants, so degrees of freedom differ from one
test to another. As before, the known proportions of observations in various income
categories are imposed when computing the
frequencies with random data. Performing
this test separately for each of the three pollutants yields the following results:
Ho- EKC-consistency in income categories
observed with same frequency as with
random data
H^: Ho is false.
Changes
Independent
Pollutant
SO2
Particulates
Smoke
y^ (d.f.)
Prob,
37.35
9.33
5.44
<0.OI%
23%
61%
Levels
Independent
(d.f.)
56.74
[n.8O
5.90
Prob.
<0.(H'
16%
.55%
306
Land Economics
Again, the SO2 results are highly consistent with the EKC hypothesis but the
patterns for particulates and smoke could
easily have arisen by chance.^^ The increased significance for sulfur dioxide results from the fact that a very high
proportion of cases in one income group,
the high ineome category, are EKC-consistent. This pattern was, of course, evident
in the deseriptive statisties.
The last hypothesis examined considers
whether the plot shapes and income
categories observed in the data exhibit
any non-randomness at all. This involves
ignoring EKC-eonsistency or ineonsisteney
and simply testing whether the numbers
of observations falling into the various
"plot shape/income category" cells differs
from what would occur with random
data. While the SO2 patterns are known
to be non-random, it is of interest to check
for non-randomness for the other two
pollutants. Examining frequeneies in all
possible eells seems exeessively detailed in
light of the numbers of observations available. To simplify, the number of ineome
groups is reduced to three by eombining
cases 3, 4, and 5a-5d into a single "middleincome" group. There remain 12 possible
combinations of plot shapes and income
categories. A finding that the frequencies of observations in eaeh differ from
what random data would produce would
not indicate that the EKC has predictive power, of course, because there are
many non-random assignments and EKCconsistent assignments are only a subset
of these.
Let the superscript k = 1...12 index the
cells (four shapes and three combined
income categories) and let Eo and £«
denote the numbers of observations occurring in cell k in the data and under ran-
Performing Ihe lest for particulates using the
alternative bounds for turning points revealed that the
observed EKC consistency in the data eould easily have
arisen by chanee, with 74% probability for changes
independent randomness and with 64% probability for
levels independent randomness.
May 2006
domness, respectively. The appropriate
test statistic is
which follows a y^ (II) distribution. Results for the three pollutants are
Ho: Observed cell frequencies equal frequencies wiih ramiom data
H^: HQ is false.
Changes
Independent
Pollutant
SO2
Particulates
Smoke
y_
11)
26.71
10.00
4.60
Levels
Independent
Prob.
) ^ (11)
Prob.
<0.0t%
53%
95%
45.29
10.50
7.70
<0.01%
49%
74%
Only sulfur dioxide can be regarded
as non-random; the observed pattern of
ineome ranges and poUution-ineome-plots
for particulates and smoke are not significantly different than what one would
get by rolling a 12 sided, appropriately
weighted die.^''
Looking aeross all three pollutants and
considering the detailed criteria in Figure 1,
the EKC fares poorly as a predictive proposition. In total, 23 of the 52 available
observations are eonsistent with the EKC,
while 29 are ineonsistent. The inverted-U
oeeurs nine times in the data while a Ushape, which is never EKC-consistent, occurs 13 times. Five of the 52 cases exhibit
a positive pollution-income relationship,
but none of these are low-income eountries.
The one bright spot for EKC-consistency
is the frequency with which high income
countries reduced their pollution as income
grew. These eases account for 15 of the 23
EKC-consistent cases for all three pollutants
' The last set of tests was examined for robustness
to alternative ways of defining income groups based on
1983 income levels. Results did not depend on the choice
of income culoffs.
82(2)
Deacon and Norman: Kuznets Curve and How Countries Behave
combined, and for 11 of the 14 EKCconsistent observations for SO2.
Discussion and Extensions
Our findings agree with List and Gallet's (1999) observation (from individual
U.S. states) that pollution-income relationships tend to vary across political units.
When they allowed for state-specific intercept and income slope terms in a panel
data model, they found a wide range of
SO2 emission turning points, from $2,989
{Rhode Island) to $69,047 (Texas) using a
quadratic specification, and from $6,428
(Arizona) to $95,703 (Texas) with a cubic
specification. Only a small fraction of the
turning points obtained from these statespecific EKCs were within one standard
deviation of the turning points they obtained by estimating a "traditional" EKC
model, allowing only for state-specific
intercepts. This led them to conclude that
a "one size fits all" approaeh may result in
bias. List and Gallet's (1999) results could
be more thoroughly compared to ours by
determining the shapes of state-specific
pollution-income relationships over the
income ranges actually observed in their
datasets. This would no doubt reveal that
some states exhibit deereasing, others increasing, still others possibly u-shaped,
pollution-income relationships.
In the dataset we examine. EKCconsistent behavior within countries is
largely confined to high-income nations.
This is noteworthy because high-income
countries are overrepresented in the GEMS
dataset. Countries with 1983 per capita GDP
greater than $7,000 represent exactly onehalf of all the country-pollutant cases in
the GEMS data we examine, but only
22% of the 144 countries in the Penn
World Tables. Countries with incomes below $3,500 eomprise 21% of the countrypollutant pairs in our sample, but 58% of
observations in the Penn World Tables.
The fact that richer countries are overrepresented, together with the observation
that EKC-consistent behavior is largely
confined to richer countries, implies that
the cards are stacked in favor of accepting
307
the EKC hypothesis as a general proposition in the GEMS dataset. This is unfortunate because a primary aim of EKC
analysis is to predict the environmental
implications of growth in poorer countries,
thai is, the group that is underrepresented
and for which the EKC predicts poorly.
The potential role of European Union
(EU) policy also merits discussion. Ten of
the 28 countries examined here were EU
members during the sample period, including three that joined during this
period. This is potentially signifieant because EU Direetive 80/779/EEC required
both EU members and prospective members to harmonize air quality standards for
SO2 and particulates in urban areas. It also
directed EU members to uphold domestic
laws consistent with the "environmental
acquis," the body of laws and standards
agreed to by the union. The compliance
date, 1983. falls roughly in the middle of
the sample period."^ Contrary to the EKC
hypothesis, this and other EU environmental directives required a single policy
response of all members regardless of income level or growth. In addition, because
the environmental acquis applied to prospective as well as existing members, a
desire for EU membership may have motivated more cleanup than would otherwise
have occurred among countries such as
Spain and Portugal, which joined during
the sample period and after the initial
directive was established. Additionally, the
EU subsidized the pollution control of
poorer member states, including Ireland,
Portugal and Spain, so for these states EU
policy increased the benefits of pollution
control (by tying them to the other benefits
of membership) while simultaneously reducing the eosts.^"
It is instructive to reconsider the descriptive results for Ireland, Spain, and
The regulation was revised by Directives 89/427/
EEC and 91/692/EBC. which also fall witbin the sample
period. Information on EU poliey was taken from Kraus
(1997),
^^ The EU Structural Fund and Ihe EU Cohesion
Fund adminislered these subsidies; Hansen and Rasmussen (2001),
308
Land Economics
Portugal in this light. Ireland appears in the
sample for smoke and for SOi. Ireland's
smoke, which was not covered by EU regulations, rose steadily with income, nearly
doubling between first and third tritiles.
Ireland's SO2. which was covered by EU
policy, exhibits an inverted-U relationship,
declining sharply in the final tritile. Spain,
a prospective member in 1980, also reports
data for smoke and SO2. Both pollutants
fell sharply in Spain between the first and
third tritiles of observations and, because
Spain is relatively rich, this is eonsistent
with the EKC hypothesis. Spain's most
dramatie reductions were for SO2. however, which was covered by the EU directive and for which Spain reeeived EU
subsidies, and these reductions were deepest after the EU policy went into effect,
Portugal reports data for SO2 and particulates. both of which were regulated by the
EU. Portugal's plot for both pollutants is a
trough, whieh reached bottom the mid1980s and jumped sharply in 1989-1992,
Without claiming that EIJ polieies were
responsible for any of the EKC-eonsistent
eases found in the data, these instances
illustrate the difficulty of separating the air
quality effeets of EU polieies, whieh are
not part of the EKC story, from the
income-driven processes emphasized in
the EKC literature.'"
One advantage of the within-eountry
approaeh —minimizing the influence of unobserved heterogeneity —is lost if a country's attributes ehange. A country's system
of government is an attribute that sometimes shifts, and it is potentially important
because democratic and non-democratic
governments may well provide different
levels of pollution eontrol. Because ineome
and political institutions are highly correlated, failure to control for political change
might bias the shapes of estimated pollu-
•"' These policy responses would conform to the
EKC story if Ihe LLI were trealed empirically as a single
slate, bul this has not been the practice in the EKC
literature, DeBruyn (1997) also addresses the potcnlial
role of EU policy for pollution in member countries.
May 2006
tion income relationships."" In addition,
poliey processes driven by income growth
may operate differently in demoeraeies
and autocraeies and give rise to different
EKC responses."''^
The importance of this consideration
was assessed by examining each country's
"polity score," a variable indieating the
presenee of demoeratie attributes such as
constraints on the chief executive, competition for political offiee, and openness to
political participation, versus autocratic
attributes (see Marshall and Jaggers 20()0).
Polity scores can range from 0, indieating
autocracy, to 1, indicating demoeraey.
Countries experiencing substantial politieal
change during the sample period were first
identified and dropped from the sample.
Tests of the EKC's predietive power were
then earried out separately for democracies
and autocracies. "Politieal changers" were
defined to be countries whose polity scores
changed by at least 0.3 during the sample
period." Four countries met this criterion:
Brazil, Poland, Spain, and TTiailand. Nonehangers were classified as autocracies if
their mean polity scores were .35 or below
and as demoeraeies if their polity seores
averaged 0.9 or above.^"^
^' While various authors have ineluded political
indicators ;imong Ihe independent variables in reduced
form EKC models, they have not interacted Ihese terms
with income. In effect, pollution was allowed to be higher
or lower under dictatorship th;in democracy, but the
shape of the income-pollution curve is the same for both,
-" See Lopez and Mitra (21)00), List and Slurm (2004)
find thai changes political competition within democracies can cause changes in the stringency tif environmental
policy. They use term limits as a vehicle for representing
variations in political competition: competition is reduced during an elected politician's final term. Our
examination of political ehiinge looks at shifts between
aulocracy and democracy, but does not consider electoral
rules and Ihc timing of governmental leaders' terms of
offiee wilhin democracies at this level of detail,
•'•^ This cutoff resulted in a fairly sharp distinction. Of
countries classified as non-changers, only two experienced a change in polity of as much as 0,2 and changes in
the remaining non-changers were all 0,1 or less.
'"* Five eountries met the autocracy criterion: China,
Chile. Egypt, Indonesia, and Iran. Seventeen met the
democracy criterion: Australia, Belgium. Canada. Denmark. Finland. India, Ireland, Israel, Italy. Japan, New
Zealand. Netherlands. Portugal, the United Kingdom,
82(2)
Deacon and Norman: Kuznets Curve and How Countries Behave
Because the results do not differ markedly from those reported earlier, they are
only summarized here and details are
available on request. For particulates and
smoke, the /^ statistics that examine EKCconsistency in individual income categories
indicate that the probability of getting an
equal or better match to EKC predictions in
randotn data is 0.60 for autocracies and 0.24
for democracies. The SO2 results are more
interesting. For autocracies, agreement with
EKC predictions is substantially worse than
would occur by chance, so the EKC hypothesis elearly is not supported. For democracies, agreement with EKC predietions is
very strong, but this agreement is dominated by one now-familiar pattern —
wealthy countries that reduced pollution
as their incomes increased. Indeed, 10 of
the 11 instances of EKC-consistent behavior among democracies are of this type. The
only EKC-consistent demoeraey showing
different behavior is Ireland, a middleincome country displaying an inverted-U/^^^
The consideration of political attributes thus
allows the set of cases exhibiting EKCconsistent behavior to be narrowed further, to wealthy democracies that reduced
SO2 pollution.
Changes in pollution may result from
factors that change over time but are
unrelated to income, such as better information on health risks and improvements
in pollution control technology. Panel data
EKC models often include a trend variable
or year dummies to account lor temporal
factors that are common to all countries. In
such models, the EKC hypothesis amounts
to an inverted-U relationship between
income and the variation in pollution that
is not explained by trend-related factors.
The hypothesis tests performed earlier did
the United States. Venezuela, and West Germany. Two
countries were dropped from this part of the analysis:
Hong Kong because no polity score is reported and
Malaysia because ils polity score (U.7) fell between the
bounds sel for autocracy and democracy,
" A very slight change in Ireland's lower income
tritile would have moved it into the high income
category, in which case its inverled-U would have been
EKC-inconsistent.
309
not eontrol for purely trend-related influences on pollution and this might have
affected the data's conformance to EKC
predictions. To examine this possibility,
purely time-related movements in eaeh
pollutant, common to all countries, were
estimated using panel data EKC models
for each pollutant. The models included
fixed effects for years and countries, third
order polynomials in per capita GDP and
eaeh observation's polity score as independent variables. The yearly fixed effects
represent the time-related movements in
pollution that remain after controlling for
movements in income and politics. These
time-related movements were removed
from each pollution series by subtracting
the yearly f'ixed effects. Tests for qualitative EKC-eonsistency were then recomputed with these detrended pollution data.
Using detrended pollution data did not
improve the EKC's predictive performance. For smoke, with 13 cases to
examine, the number classified as EKCconsistent dropped from five to four. For
SO2, with 25 cases to eonsider. the number
of EKC-consistent cases fell from 15 to 14.
For particulates, with 14 available cases,
the number agreeing with the EKC declined from three to two. The probabihties
that the EKC-consisteney observed in the
data could have oecurred by chance
increased commensurately.
VI. REDUCTIONS IN SO2: DECLINING
TREND OR INCOME EEFECT?
The one EKC-consistent pattern in the
data is a strong tendency for wealthier
eountries to reduce SO2 pollution as their
ineomes grow. Pollution and income were
both trended during the 1970s and 1980s,
however, so the pollution reduction could
also be attributed to any other trended
determinant. One such determinant is a
shift in preferences toward environmental
protection, caused perhaps by better information on environmental risks and improve^'^ Detailed results, including parameter estimates
for the panel data model, are available on request.
310
Land Economics
ments in pollution eontrol technology. Highly publicized oil spills and eiaims regarding
the effects of pesticides and industrial
chemicals in the late 196(}s were followed in
the United States and other wealthy
countries by the first observance of Earth
Day, the 1972 UN. Conference on the
Human Environment, and by legislation to
control air and water pollution, proteet endangered species, and control land use
changes."'^ These events happened abruptly,
suggesting a shift in public preferences, but
the goal could only be achieved gradually as
control technology developed and political
hurdles were overcome. This line of argument suggests a different explanation for the
downward trend in SO2 and other pollutants
evident in the 197()s and 1980s. Perhaps it
was part of a transition from an old "low
environmental awareness equilibrium" to a
new equilibrium where environmental proteetion was high on the public agenda. While
this alternative story does not stress income
growth as a key faetor in reducing pollution, higher-income countries may well have
adopted more ambitious environmental
goals and achieved them more rapidly. The
power of this explanation, which attributes
pollution reductions to unobserved trended
factors, versus the income-driven EKC explanation for SO2 reductions, is examined in
what follows.-^^
The within-country time series data
were used to estimate simple models, one
model for eaeh eountry in the SO2 dataset,
in which SO2 is a function of a trend. The
estimated trends were then examined to
see if SO2 reductions were more rapid in
wealthier eountries. To check the power of
the EKC model, a third order polynomial
in per capita GDP was then added to eaeh
"'' See Portney (1990,) for a review of pre- and post1970 air pollution poliey in the U.S, According to
Portney (1990, 48-51,) U,S. emissions of particulates
dropped rapidly after iy70 and sulfur dioxide emissions,
which had peaked in 1970. fell steadily thereafter.
• The idea that observed abatement in the developed countries may be time related ralher than a
product of income growth is noted in Stern and
Common (2001); see also Stern (2002, 201).
May 2006
of these within-country models and the
coefficients were tested for joint significanee. Because the "trend seenario" is a
simpler explanation for SO2 reductions
than the EKC, we apply Occam's razor and
adopt it as the null hypothesis in subsequent testing. Estimating models with
two trended series, ineome and pollution,
raises econometrie issues that are addressed as they arise.
Table 6 gives results for the 25 countries
in the SO2 dataset. Pollution was expressed
in logs to ease eomparisons of trends
aeross countries. The estimated trends are
reported in eolumn (1). All of the significant trends are negative and all but one of
these oeeur in wealthy countries. The
correlation between the trend eoefficients
and mean per eapita GDP is -0.48, which is
significant at 1%. Pollution reduction was
therefore more rapid in wealthier countries.'' GDP terms were then added and
the models were re-estimated by OLS. As
indicated in column (2), the GDP terms
were jointly significant at 5% (using an Ftest) in 10 of the 25 eases.'^" The time trend
presumably eaptures part of the effeet of
income growth beeause income generally
rose in most of these 25 eountries, leaving
income terms to explain only deviations
around that trend. While ineluding income
and a trend together makes it diffieult to
separate the effeet of ineome from a pure
trend effect, this is common practice in
EKC researeh.
Before interpreting the GDP coefficients, a number of econometrie issues
must be addressed. Income is widely believed to follow a unit root process and
pollution may follow a unit root as well.
When a regressor follows a unit root process its coefficient is not normally distributed, even in large samples, and the usual
The correlation between trends and mean polily
scores is -0,43, indicating more rapid cleanup in
democracies.
""^ Per capita GDP terms were jointly significant at
10% in four other countries: Belgium, Canada, Finland,
and Italy. Adding the GDP terms naturally changed the
estimated trends. The trend eoefficients in eolumn (1)
are those estimated from a model without GDP terms.
82(2)
Deacon and Norman: Kuznets Curve and How Countries Behave
311
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312
Land Economics
critical values for hypothesis tests do not
apply. More disturbing, if both variables
follow stochastic trends a significant estimated relationship between the two may
be spurious. The latter concern is moot,
of course, if income terms are statistically
insignifieant. To check for unit roots,
augmented Dickey-Fuller tests were performed on income and pollution for the 10
countries exhibiting jointly significant income terms.•^' The hypothesis of a unit root
in ineome could not be rejected (at 5%) for
any country and a unit root in pollution
could be rejected only for China.
Having unit root series on both sides of
an estimated equation is not a problem if
the two are cointegrated, that is, if they
follow the same stochastic trend. Cointegration was indicated for all nine eountries
displaying unit roots in both pollution and
ineome.'*^ Serial correlation is another potential concern, so Durbin-Watson tests
were performed on the OLS residuals of
these nine models. In al! eases, the statisties
either indicated rejeetion of the hypothesis
of first-order serial correlation at 5% or
fell into the indeterminate range. Accordingly, the OLS income coeffieients for these
models, reported in columns (3)-(5), are
interpreted as usual. The remaining case,
China, exhibited a unit root in income but
not in pollution."^^ First-differeneing China's
income and pollution twice yielded series
with unit roots. Further, the hypothesis of
cointegration could not be rejected for the
Dickey-Fuller tests were performed on income
levels only, and nol separately on income squared and
income cubed. Perman and Slern (2003) note that low
order polynomials retain the unit root property when
Ihe untransformed variable follows a unit root. As we
focus here on within-eounlry relationships we do not
tests for unit roots in a panel, as in Jewell et al, (2003),
Im, Pesaren. and Shin (2003) and Im, Lee, and Tieslau
(2005),
"^ The null hypothesis, that the residuals follow a
unit root, was rejected al 1% or better in all cases,
''•' It is not possible for an independent variable to
follow a unit root process while the dependeni variable
does not. However, given the short time series we
observe and the relatively low power of unit root tests,
we are not inclined to regard this as eompelling evidence
of a specification problem.
May 2006
double-differenced data and residuals
from the model with double-differeneed
data did not display positive first-order
serial eorrelation. For China, then. Table 6
reports OLS results from the mode! estimated on double-differenced data,'*'*
For the 10 eountries with jointly significant income terms, the GDP coeffieients
were used to determine turning points for
pollution-income relationships. Because
these models inelude third-order polynomials in GDP, they typieally have two
turning points. Columns (6) and (7) report
the GDP values of left and right turning
points and indieate whether a given turning point is a max (peak) or a min (trough).
To allow tests for EKC-eonsistency, each
country's income range is reported in columns (8) and (9) and its income eategory,
as defined in Figure 1. is reported in eolumn (10), Column (11) reports the shape
of each country's pollution-income funetion over the range of income observed in
the data. Next, we indicate whether or not
a country's estimated pollution-ineome
function is EKC-consistent.
Brazil provides one clear ease of EKCconsistent behavior. Its ineome range lies
entirely within the upper and lower bounds
on SO2 turning points, so increasing, decreasing, or single-peaked functions would
be eonsistent with the EKC hypothesis.
Brazil's estimated pollution-ineome function is peaked, and hence is EKC-consistent.
Relaxing the EKC-consistency eriterion
slightly, Iran eould also be regarded as
EKC-consistent. Iran's income range also
lies whhin the upper and lower bounds for
EKC turning points, so inereasing, decreasing, and peaked shapes are EKC-consistent.
Iran's pollution-income function actually
shows a trough at $4,242 and a peak at
$4,886. but there is only one data point
between these two values and the function is
nearly flat over this range. Iran's pollutionincome function is thus "nearly downward
sloping," and hence is nearly eonsistent with
the EKC hypothesis.
•" Estimating China's model with undiffercnced data
produced very similar income coeffieients.
82(2)
Deacon and Norman: Kuznets Curve and How Countries Behave
313
The remaining eight pollution-ineome
relationships are EKC-inconsistent. China,
a low-income country, has a peak followed
by a trough and both are at ineome levels
well below our range for EKC turning
points."*'' Thailand, also low-income, has a
trough shaped plot over its income range
and 13 of its 15 data points are in a region
where predieted pollution declines as income increases, eontrary to the EKC
hypothesis. Hong Kong's income range
crosses the upper bound for SO2 turning
points, so a peaked or decreasing pollutionincome curve would be EKC-consistent.
Hong Kong"s plot actually increases monotonically over the data range observed.
The five remaining cases are all highincome countries. Japan and Venezuela
have peaked pollution-income plots over
ranges of observed data, with peaks occurring at income levels well above levels
reported in the literature, $12,738 and
$8,153, respectively. Most of the data
points observed for these countries (18
out of 21 for Japan and 15 out of 16 for
Venezuela) are in a range where predicted
pollution increases as income increases,
contrary to EKC predictions for a rich
country. Israel is another high-income
country for which pollution increases with
income. The Netherlands exhibits a distinct trough over the data range. This
would not be troubling if most of the data
were on the downward sloping region,
where pollution decreases as income rises.
In actuality, 12 of the 15 observations for
the Netherlands are in a range where income growth leads to higher pollution. The
remaining eountry. New Zealand, exhibits
a trough followed by a peak, with both occurring at income levels well above peaks
reported in the EKC Uterature.^'' On balance, when one allows for purely trend
driven factors to explain within-country
pollution, the additional explanatory power
of income is typically not significant and,
where significant, its effect is not consistent
with the EKC hypothesis."*^
"''' The predieted pollution-ineome plot for China is
relatively flat, however, so income's role in determining
pollution is very minor,
•*" In New Zealand's case, the effeet of ineome on
predicted pollution is very slight.
•*' Perman and Stern (2003) reach the same general
eonclusion. Our results also agree with Stern and
Common's (2001) conclusion that reductions in sulfur
emissions are more time driven Ihan due to income
growl h.
VII. CONCLUSIONS
Looking within individual countries, at
their experiences over time with eeonomic
growth and changes in pollution, and allowing each country lo have a differing
turning point, we find only a highly specialized form of agreement with EKC
predictions. Relatively wealthy democracies
controlled their SO2 pollution as their incomes increased, which is consistent with
the EKC hypothesis. The remaining EKC
predictions, that is, pollution increasing
monotonically with income in poor countries, and middle-income countries following an inverted-U path, are not observed for
SO2. For sn^oke and particulate pollution,
instances of agreement with EKC behavior
are not observed with any greater frequency
than chance would dictate.
The SO2 reductions that occurred in rich
eountries, while consistent with the EK.C
story, are also consistent with any other
force causing a secular decline in pollution
during the 1970s and 1980s. One explanation was offered in the text, but there may
be others. Once the effect of purely
trended factors is accounted for. income
growth has no consistent predictive power
for the patterns of changes in pollution
experienced by individual countries.
The aim here was not to develop a
framework for understanding why pollution behaves as it does in individual countries. This is obviously an important task,
but one that is well outside the scope of the
present analysis. Rather, the intent was to
see if the EKC hypothesis gives reliable
qualitative predictions about the way pollution behaves within individital cotintries
that experience economic growth. Based
314
Land Economics
on an examination of the data presently
available, using robust empirical methods
to test what the EKC prediets, we find that
most of the observed patterns eould easily
have occurred by chance. In the limited set
of circumstances where the EKC's predietions are upheld, the observed behavior
has a simpler explanation.
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