THE INCOME ELASTICITY OF AIR POLLUTION: REVISITING THE ENVIRONMENTAL KUZNETS CURVE HYPOTHESIS

Transcription

1 THE INCOME ELASTICITY OF AIR POLLUTION: REVISITING THE ENVIRONMENTAL KUZNETS CURVE HYPOTHESIS Neha Khanna Assistant Professor Economics and Environmental Studies Binghamton University LT 1004 P.O. Box 6000 Binghamton, N.Y Economics Department Working Paper Series, WP0104 August 16, 2001 Draft: please do not quote or cite without permission An earlier version of this paper was presented at the 76 th Annual Conference of the Western Economic Association International, July 4-8, 2001, San Francisco, CA, and also at the Inaugural Conference on the U.S. Society for Ecological Economics, Duluth, MN, July 11-13,

2 ABSTRACT This paper estimates the relationship between income and pollution while controlling for the influence of other relevant factors such as economic structure and trade policy, as well as socio-economic factors including race, education, housing, and propensity for collective action. The analysis is based on 1990 ambient concentrations of five Clean Air Act criteria pollutants and data for U.S. census tracts. The traditional inverted U-shaped curve is obtained in the case of PM 10 only. For NO x the curve is U- shaped. For the remaining three gases the relationship between ambient concentrations and median household income is statistically insignificant. Furthermore, while the income elasticity of pollution is negative and monotonically declining for PM 10, it is positive and rising in the case of NO x. These results call into question the global applicability of the Environmental Kuznets Curve relationship. KEY WORDS: economic growth, income elasticity, environmental Kuznets curve, ambient concentrations, criteria pollutants, census tracts, median household income, weighted least squares 2

3 Acknowledgements The author gratefully acknowledges David Mintz at the EPA for extracting data from the AIRS database, and also for answering several subsequent questions about these data. Diane Geraci at the Binghamton University Library was instrumental in obtaining the election data. Lucius Willis mapped the monitor coordinates to census tracts; Martina Vidovic provided outstanding research assistance. Duane Chapman and Florenz Plassmann provided several useful suggestions and insights at all stages of the analysis. Jean-Daniel Saphores and Mariano Torras provided comments on an earlier draft of the paper. Any remaining errors are the sole responsibility of the author. 3

4 1. Introduction The academic debate on the Environmental Kuznets Curve (EKC) was engendered by a paper by Grossman and Krueger (1992) on the effect of the North Atlantic Free Trade Agreement on environmental quality. According to this hypothesis, there is an inverted U-shaped relationship between environmental degradation and economic growth: as income levels increase, pollution worsens but eventually improves once income crosses some threshold level. The term, inverted U, is misleading insofar as it implies a symmetric relationship. The EKC hypothesizes a concave relationship between income and pollution, without any reference to its skewness. Indeed, several studies, including the original Grossman and Krueger paper, provide evidence of a highly skewed relationship. Four factors are commonly believed to account for this relationship (Selden and Song, 1994, McConnell, 1997, Barbier, 1997): 1. Economic structure or the composition effect: as the economy shifts from a subsistence to an agricultural and then an industrial economy, pollution levels increase due to a fundamental change in the nature of the production processes. However, as the economy matures and becomes service oriented, there is a downturn in pollution levels as the relatively dirty industries shift to other, industrializing economies. 2. Trade policy: the more open an economy, the greater the possibilities for importing (exporting) pollution intensive commodities, the lower (higher) the domestic pollution levels. 3. Income elasticity of environmental quality: it is hypothesized that environmental quality (pollution) has a high and positive (negative) income elasticity. As income increases, all else held constant, the demand for improved environmental quality 4

5 increases. This stimulates public pressure for stricter environmental policies, as well as the use of clean technologies and management practices. This is sometimes also referred to as the technique effect. 4. Economic scale: the larger the size of the economy, the greater the pollution, everything else remaining constant. The Grossman and Krueger paper was followed soon after by Shafik and Bandhopadhyay (1992) whose results were included in the 1992 World Development Report (World Bank, 1992, pp ). Subsequently, a large and continuously expanding empirical literature has emerged. The focus of this literature has been twofold (Barbier, 1997): estimating and statistically validating the EKC for individual pollutants, and estimating the turning point the level of per capita income at which specific pollutant levels begin to show a decline. Empirical results are mixed and the academic debate remains wide open. As Table 1 shows, the turning points are statistically significant for only some pollutants. And in some cases, there is evidence for a cubic relationship where pollution levels begin an upswing towards the upper end of the income range considered. 5

6 Table 1: An Incomplete Overview of the EKC Literature Study Pollutants Estimated Relationship With Income Carson et al. (1997) NO x, SO x, PM 10 (emissions per capita) Monotonically declining Cole et al. (1997) de Bruyn et al. (1998) Grossman & Krueger (1995) NO 2, SO 2, SPM, CO (emissions per capita) NO x, SO 2 (emissions per capita) SO 2, SPM (concentrations) Inverted U-shaped Log linear SO 2 : cubic SPM: monotonically declining Kaufmann et al. (1998) SO 2 (concentrations) U-shaped List & Gallet (1999) Panayotou (1993) SO 2, NO x (emissions per capita) SO 2, NO x, SPM (emissions per capita) SO 2 : inverted U-shaped, cubic NO x : U-shaped, cubic Inverted U-shaped Panayotou (1995) SO 2 (concentrations) Inverted U-shaped Selden & Song (1994) Shafik & Bandhopadhyay (1992) SO 2, NO x, SPM, CO (emissions per capita) SO 2, SPM (concentrations) SO 2, NO x, SPM: inverted U- shaped; CO: insignificant Inverted U-shaped Stern & Common (2001) SO 2 (emissions per capita) Inverted U-shaped, increasing Torras & Boyce (1998) SO 2, SPM (concentrations) SO 2 : cubic; SPM: inverted U, insignificant Vincent (1997) TSP (concentrations) Monotonically increasing Note: SPM: suspended particulate matter (heavy particles); TSP: total suspended particulates This table reviews only a subset of the literature focussing on the pollutants considered in the current paper. For a more complete review, see Stern and Common (2001) and Agras and Chapman (1999b). 6

7 2. The EKC: what do we know? Despite the mixed empirical results, and despite the warning by several early authors (e.g., Grossman and Krueger, 1992 and 1995, World Bank, 1992) that this empirical relationship, where present, is not inevitable, there is a generally optimistic feeling in policy circles that economic growth will be beneficial to the environment. There are several reasons to caution against such optimism. First and foremost, the empirical evidence in favor of the EKC is based on a reduced-form model linking pollution to per capita income. The model per se does not explain the underlying mechanism and nor does it imply that rising income by itself and automatically causes a change in environmental quality. In fact, Grossman and Krueger (1995, p. 372) suggest that a policy response due to increased public pressure for higher and more strictly enforced environmental standards has provided the strongest link between income and declining pollution. Furthermore, as Stern et al. (1996, p. 1159) point out, reduced form models do not inform policy choices either. For that, a structural model would be more appropriate. A second cause for caution stems from the nature of the data typically being used in the literature. Stern and Common (2001) point out that in most studies the estimated turning point lies well within the sample range of income. (A notable exception are global pollutants such as carbon dioxide for which the turning point is generally found at or above the upper tail of the sample income distribution.) However, these studies are typically based on data sets dominated by the recent empirical history of developed countries. In their study, these authors use a much larger and more globally representative data set and find a turning point for sulfur emissions that lies outside the 7

8 sample income distribution. In effect, the relationship is monotonically increasing. They conclude that turning point estimates based on developed country data may be downwardly biased. 1 There is yet another data related reason to doubt the optimism regarding the EKC. Several studies, including Grossman and Krueger (1992, 1995), Shafik and Bandhopadhyay (1992), Panayotou (1997), and Torras and Boyce (1998), base their analyses on the GEMS data. These data contain information on pollutant concentrations measured at specific monitoring stations and provide an estimate of the environmental quality in the immediate vicinity of the station. For example, data on ambient concentrations of various gases in India are obtained from monitors in Bombay, Delhi, and Calcutta. Variation in the air quality at specific locations in these three cities might not be representative of the situation over all of India. Furthermore, monitoring stations (especially in the case of SO 2 ) are typically far removed from the source of the pollution. For many countries included in the data set pollutant concentrations are measured at locations in the capital city alone. Regressions based on these data should ideally use per capita income data for the city or region where the monitoring stations are located rather than data on the national level. Absent this, it is possible that these studies may have miscalculated the income level at which the turning points occur, or whether a turning point exists at all. Further analysis is required to determine the robustness of the results presented in these studies. 1 Stern and Common (2001) also estimate a regression model using a sub-set of their data comprising OECD countries only. In this case, the turning point lies within the sample income range. This lends further credibility to their argument that including lower income points in the data set might raise the level at which a turning point occurs. 8

9 The 2001 Stern and Common study finds empirical support for an earlier argument by Stern, Common, and Barbier (1996) that the historical experience of some economies cannot be extrapolated to the global economy. According to these authors, one of the problems attending the EKC literature is that while stricter environmental standards in currently developed countries may encourage polluting industries to locate elsewhere, the same opportunity may not be available to developing countries. When currently developing countries apply more stringent pollution standards they may have no choice but to abate pollution domestically since unregulated industries may have nowhere to migrate to. They suggest analyzing the historical data of individual countries. Grossman and Krueger (1995, p. 372) also present a similar argument in the conclusion of their paper, though they believe that cross-country differences in environmental standards are not a major determinant of international trade. An analogous argument can be applied to the role of structural change in determining a global EKC. Currently developed countries have experienced a transition from an industrialized economy (and earlier, an economy relying primarily on agriculture) to an economy where services play a dominant role. According to World Bank data for 1996, the industry and services sectors account for approximately 31% and 63%, respectively, of total value added in high-income countries. 2 This shift in economic structure has been facilitated by the growth of industry in developing countries, especially those in Asia, Latin America, and Africa. (In low-income countries, the value added share of industry and services in 1996 was about 38% and 43%, respectively). 2 Of course, while the share of industry may have declined over time, this does not imply that the absolute level or scale of production has declined simultaneously. On the contrary, World Bank data indicate that the manufacturing/industry sector in these countries has been growing. 9

10 Would such a structural transition be possible for the currently developing countries in the future? Or would the share of industry in the total value added of these countries continue to rise? Only a few studies test the EKC hypothesis using data on individual countries. Based on 1990 state level data, Carson et al. (1997) show that pollution levels in the U.S. have been steadily declining. However, List and Gallet (1999) find results that contradict Carson et al. Using state level data on NO x and SO 2 emissions from , they find a statistically significant cubic relationship with the curve turning upwards at the upper tail of the income distribution. 3 Furthermore, while the turning point for NO x is well within the sample income range, for SO 2 it lies at the upper boundary. In addition, their results show that socio-economic characteristics (population density, education, etc) tend to affect the location of the EKC and the value of the turning point. Vincent (1997) tested the EKC hypothesis using data for the Malaysian states. He found no evidence in support of the hypothesis. Ambient concentrations were either not statistically significantly related with income, or there was a monotonically increasing relationship. 4 The debate may be better served by focussing on its constituent parts (see also Stern, 1998, especially pp ). This would also overcome some, though not all, of the problems mentioned earlier which arise due to the reduced form models typically used. However, there is a paucity of papers empirically analyzing the individual role of 3 List and Gallet (1999) estimate several models. These results pertain to the model typically used in the EKC literature which is a fixed effects model that allows for intercept heterogeniety but assumes the slope coefficients are homogenous across all groups (U.S states in this case, and countries in other studies.) 4 Stern and Common (2001) report a study by Dijkgraaf and Volleburgh (1998) with time series regression models for carbon emissions estimated separately for each country. The results are mixed: linear, inverted-u, U-shaped, and cubic relationships are found, even though the panel data set as a whole yields the traditional inverted-u shaped curve. 10

11 the three factors underlying the EKC. de Bruyn (1997) used a decomposition analysis to determine the role of structural change and environmental policy in the reduction in SO 2 emissions in many developed countries. 5 His analysis substantiated Grossman and Krueger s original statement that environmental policy, caused by the increased demand for improved environmental quality at higher income levels, has been the primary driver of the downturn in emissions. Selden et al. (1999) analyzed the reductions between 1970 and 1990 in the emissions of six pollutants covered under the U.S. Clean Air Act (CAA). They decompose the decline in the emissions into scale, composition, and technique effects. 6 They fail to support the hypothesis that composition change was, by itself, sufficient to generate the downturn in aggregate emissions in the United States (p. 14). Their overwhelming result is that technique effects other than changes in energy intensity and energy mix were necessary for aggregate emissions downturns to have occurred (p. 15). Furthermore, the CAA played a critical role in the observed emissions declines (p. 18). One way to interpret these decomposition results is that pollution has a high and negative income elasticity. The demand for improved environmental quality at higher income levels could have been at least partly responsible for the CAA and the technological improvements that it engendered. The same argument may be applied to other non-caa related technique changes. 5 de Bruyn et al. (1998) also attempt a decomposition analysis using data for UK, USA, West Germany, and The Netherlands. They found evidence for a significant role for structural change in explaining the change in emissions of CO 2, SO 2, and NO x. However, Stern (1998, p. 190) questions the interpretation of their results. In addition, de Bruyn et al. state that where there is a significant observed decline in emissions, it may be entirely due to stricter environmental regulation (1998, p. 171). 6 effects. They further decompose technique effect into energy intensity, energy mix, and other technique 11

12 This paper focuses on the pure income effect on pollution i.e., the relationship between income and pollution after controlling for the influence of economic structure and trade policy. The income elasticity of pollution is estimated using a reduced-form regression model and data for five air pollutants included in the U.S. CAA. According to the EKC hypothesis, beyond a threshold the income elasticity of pollution should be negative and declining. This paper examines the empirical evidence from the U.S. and finds that this is not always the case. In addition to the EKC literature there are several studies relating the distribution of pollution in the U.S. to demographic and political variables. Table 2 provides an overview of this literature. Clearly, income is only one of the determinants of exposure to pollution. Other important factors include race, education, structural composition of the workforce, housing tenure, and population density. Brooks and Sethi (1997) and Arora and Cason (1999) examine the determinants of exposure to air toxins other than the criteria air pollutants. They report qualitatively similar results. In addition, both studies find that populations with a greater propensity for collective action tend to be less exposed to pollution, ceteris paribus. The current analysis accounts for the influence of such demographic and political factors in order to isolate the impact of income on air pollution. 12

13 Table 2. The Distribution of Air Pollution in the U.S. Study Pollutants Geographic Key Determinants of Exposure Coverage Freeman (1972) SO 2, particulates Kansas City, Washington D.C., St. Louis Income (-), Non-white population (+) Zupan (1973) SO 2, particulates - concentrations - emissions NYC zip codes NYC metropolitan area Income (-) Kruvant (1975) CO, hydrocarbons Washington D.C. Income (-), Rent (-), proportion of professional and managerial workers (-), proportion of black residents (+) Asch and Seneca (1978) Particulates 284 cities in 23 states Population (+), population density (+), proportion of nonwhite residents (+), income (-), education (-) Brajer and Hall (1992) O 3, PM 10 South Coast Air Basin of California Source: As reported in Brooks and Sethi (1997). Proportion of black residents (+), proportion of hispanic residents (+), income (-), population density (-), education (-) Signs in parentheses indicate whether the variable is positively or negatively related to exposure to pollution. 13

14 3. The empirical model and data The model used in the current analysis is represented by the following equation: Ln 2 ( A ) ji = C j + β 1 j Ln( inci ) + β2 j Ln( inc i ) + X iφ j + εi (1) where A ji is the ambient concentrations of pollutant j in region i inc i is the median household income in region i X i is a vector of demographic, political, and other control variables N j is the corresponding vector of slope coefficients for each pollutant j g i is a randomly distributed error term, and C j is the intercept. Separate models were estimated for five criteria pollutants carbon monoxide (CO), ozone (O 3 ), nitrogen oxides (NO x ), sulfur dioxide (SO 2 ), and particulate matter (PM 10 ). Data on the annual ambient concentrations in 1990 were obtained from the EPA s AIRS data base, along with the number of observations taken at each monitor. 7 In some cases, there are multiple monitors for the same gas at a given site. In these cases, a weighted average of the ambient concentrations at all monitors at the same site was used, with the number of observations at each monitor as the weights. The AIRS data base also provides the exact geographic coordinates (latitude and longitude) for each monitor. These were used to map the monitors to census tracts. All socio-economic data, including median household income, are for the census tract in which the corresponding monitor is located. These data were obtained from the 1990 U.S. Census. The choice of variables included in the vector of controls was based on a review of the literature on the distribution of the air pollutants covered in this study. 7 In some cases, the number of observations at a monitor did not meet the National Ambient Air Quality Standards data completeness requirements. These observations are excluded from the data set used in this analysis (Mintz, 2000). 14

15 Following Brooks and Sethi (1997), the propensity for collective action is measured by the ratio of the number of registered voters in the 1992 Presidential elections to the estimated voting age population. 8 Unfortunately, the smallest geographic unit for which these data are available are individual counties. It is, therefore, assumed that voting behavior is uniformly distributed over all census tracts within a county. Table 3 shows the variables included in the model. Also shown are the variable means and standard deviations for each of the five data sets separately. Note that the EPA does not monitor all pollutants at each location. Thus, the data sets include a different set of census tracts in each case. At first sight, there are no perceptible differences between the socio-economic characteristics across data sets. The 95% confidence intervals around the mean value of each variable overlap for all gases. In this sense, the average values are not statistically different across the data sets. 8 Data for Wisconsin and Alaska were not reported. These values were predicted via an auxiliary regression model using data for the entire U.S. Furthermore, North Dakota does not require voter registration. In this case, the ratio of voter turn-out to voting age population was used. For the District of Columbia, estimates of voting age population were unavailable. Hence, the ratio of voter turn-out to number of voters registered was used instead. Many jurisdictions do not report voter turn-out data. Therefore, the ratio of voter turn-out to voter registration could not used be to capture collective action in all cases. 15

16 Table 3: Statistical Summary Means and Standard Deviations Regression Model CO O 3 NO x SO 2 PM 10 Variable Median household income ($) 25,385 (13,119) 30,807 (12,606) 29,877 (13,378) 26,751 (11,051) 25,630 (11,758) Population density (persons/square mile) 5,696 (9,546) % population minorities 27.1 (25.6) % labor force unemployed a 8.9 (6.7) % labor force employed in manufacturing a 11.1 (5.9) % population with high school degree 17.2 (6.1) % voting age population registered to vote b 72.6 (10.2) % houses renter occupied % female-headed households Proportion monitors in urban areas 57.5 (26.1) 6.4 (5.9) 0.91 (0.29) 2,599 (5,804) 17.8 (22.1) 7.0 (5.7) 14.6 (6.4) 19.4 (6.4) 72.9 (10.6) 37.0 (22.5) 5.6 (4.5) 0.60 (0.49) 5,217 (11,040) 25.3 (27.0) 8.3 (6.7) 12.9 (6.1) 17.5 (6.4) 72.0 (10.2) 45.9 (23.1) 6.3 (5.1) 0.73 (0.44) 3,219 (8,187) 18.1 (25.4) 8.6 (6.4) 14.9 (6.4) 21.0 (6.6) 75.0 (10.7) 39.0 (23.8) 6.2 (5.4) 0.59 (0.49) 3,176 (7,009) 18.5 (23.2) 8.6 (6.4) 13.5 (6.6) 20.0 (6.4) 75.9 (11.8) 44.4 (23.2) 6.6 (6.3) 0.59 (0.49) CO (ppm) (2.6) O 3 (ppm) (0.03) NO x (ppm) (0.01) SO 2 (ppm) (0.005) PM 10 (ug/m 3 ) (38.64) # of observations a Labor force refers to the population that is 16 years or older. b Refers to 1992 Presidential elections. Values shown in the table exclude the predicted data for Wisconsin and Alaska. Standard deviations in parenthesis. 16

17 A preliminary analysis of the data using correlation coefficients supports the results of the earlier literature (see Table 4). In general, pollution is found to be negatively related with income, education, and propensity for collective action. On the other hand, pollution levels tend to increase with population density, proportion of minorities, unemployment rate, proportion of female-headed households, and the proportion of renter occupied houses. Surprisingly, the correlation coefficient between the proportion of working age population employed in manufacturing and ambient concentrations is negative, though it is statistically insignificant in three out of five cases. Furthermore, while median household income is generally negatively and statistically significantly related with the ambient concentrations, it is positively correlated with ozone levels, and not significantly related with NO x levels. 17

18 Table 4: Correlation Coefficients Variable CO O 3 NO x SO 2 PM 10 Median household income ^ Population density ^ ^ % population minorities ^ % labor force unemployed ^ % labor force employed in ^ ^ ^ manufacturing % population with high school degree % voting age population registered to ^ ^ vote % houses renter occupied % female-headed households ^ Correlation coefficients are statistically significant at the 5% level unless otherwise noted. ^ indicates insignificant at the 5% level. 18

19 4. The EKC and the income elasticity of pollution The pollution-income relationship was estimated using the reduced-form equation shown in (1). Two separate models were estimated. In the first model, A, the coefficients were estimated using weighted least squares, with the number of observations at each site as the weights. 9 In model B, ordinary least squares were used. Due to the presence of heteroscedasticity, White s heteroscedasticity consistent standard errors are reported for the second model and these are also used to determine statistical significance. Before proceeding with any further analysis there is an important detail about the data. By including the percentage of working age population employed in manufacturing, the regression model controls for the impact of economic structure on environmental quality. But what about the role of trade? There are two important distinctions in this context. The first relates to trade policy. The EKC literature has typically captured the impact of trade policy by using a national level variable, such as the ratio of import and/or exports to GDP, to represent the openness of the economy to international trade (see, for instance, Grossman and Krueger, 1992, 1995). However, since the current analysis uses data for census tracts, which are governed by a common Federal trade policy, the degree of openness is invariant. Hence, the current data set allows the author to run a controlled experiment where the trade policies of the economies are held constant by design. 9 The number of observations varies substantially from site to site, and also across the different gases at any given site. Presumably, ambient concentrations based on a larger number of observations are more accurate than those based on a smaller set of monitor readings. 19

20 A second concern is the physical flow of goods from one economy to another (Suri and Chapman, 1998). To the extent that pollution levels in a census tract are related to the volume of goods imported and/or exported from there, the model must also account for this factor. However, data on the flow of goods from one census tract to another are unavailable. Therefore, the estimates presented here are somewhat biased. In addition to the explanatory variables shown in Table 5, each model also includes dummy variables to represent the 10 EPA regions. The coefficients on these shift variables are generally significant at the 5% level. (Estimates for these coefficients and their standard errors are available from the author upon request.) Finally, in each case, 2-3 dummy variables were also included to account for highly influential observations. 10 Table 5 shows the estimated slope coefficients obtained from the models including these additional dummy variables. 11 (Table i in the Appendix reports the results from the models without the additional dummy variables for the highly influential observations.) Both models yield qualitatively similar results. For simplicity, the results from Model A are discussed in the remainder of the paper. 10 Influential observations refers to data points with unusually large Studentized residuals, DFFITS, DFBETAS, and/or unusual observations on the partial regression plots. 11 As mentioned at the bottom of Table 5, all variables are in natural logs. However, in the case of some control variables, the levels value was zero, i.e., x=0. These dependent variables were defined as log(1 + x) rather than log(x) since the log is undefined for x=0. 20

21 Median household income (0.997) Median household income squared (0.051) Population density 0.156* (0.016) % population minorities 0.043* (0.022) % labor force unemployed (0.039) % labor force employed in 0.068** manufacturing (0.038) % population with high school degree (0.045) % voting age population 0.223** registered to vote (0.119) % houses renter occupied (0.045) % female-headed households (0.028) Dummy variable for 0.304* urbanized areas (0.081) Table 5: Regression Results CO O 3 NO x SO 2 PM 10 A B A B A B A B A B (0.866) (0.044) 0.157* (0.021) 0.057* (0.022) (0.056) (0.042) (0.046) 0.277* (0.119) (0.048) (0.026) 0.264* (0.001) (0.595) (0.030) 0.016* (0.006) 0.026* (0.010) (0.020) 0.035** (0.019) (0.026) (0.047) (0.010) (0.018) (0.024) (0.467) (0.074) 0.018* (0.006) 0.024** (0.009) (0.022) (0.020) (0.024) (0.040) (0.021) (0.018) 0.004* (0.002) * (1.262) 0.233* (0.063) 0.121* (0.016) 0.084* (0.023) (0.053) 0.163* (0.046) (0.058) * (0.147) (0.051) * (0.036) 0.256* (0.071) * (1.202) 0.234* (0.059) 0.121* (0.019) 0.084* (0.022) (0.054) 0.167* (0.049) (0.051) * (0.134) (0.050) * (0.031) 0.251* (0.010) (1.362) (0.068) 0.058* (0.015) * (0.024) (0.050) (0.052) (0.065) 0.259** (0.138) (0.049) (0.038) (0.060) (1.229) (0.062) 0.053* (0.016) * (0.025) (0.053) (0.054) (0.066) 0.316* (0.146) (0.053) (0.040) (0.016) 2.970* (0.723) * (0.039) 0.016* (0.007) (0.014) 0.083* (0.025) 0.050** (0.027) * (0.033) (0.077) * (0.030) (0.020) 0.138* (0.032) 2.547* (0.627) * (0.032) 0.024* (0.009) (0.014) 0.068* (0.025) (0.028) * (0.036) ** (0.070) (0.033) (0.020) 0.113* (0.004) R-squared All variables are in natural logs. Model A uses weighted least squares; Model B uses ordinary least squares. White s heteroscedasticity consistent standard errors are reported for Model B. Both models also include dummy variables for the EPA regions and for influential observations. * indicates significance at the 5% level; ** indicates significance at the 10% level. Standard errors are in parentheses.

22 The coefficients on the income terms are statistically significant for only NO x and PM 10. For PM 10 the traditional inverted-u shape is obtained. However, in the case of NO x, concentrations first decrease and then increase with increases in median household income. In both cases, the turning point, peak and trough, respectively, lies within the income range. Population density is the single most important determinant of ambient concentrations. In all cases, the coefficient is positive and significant at the 1% level. Pollution levels also increase with the proportion of minorities in the population. The exception is SO 2 where the coefficient is negative and statistically significant. Unemployment and education are generally not statistically significant, except in the case of PM 10 where the anticipated sign is obtained. As expected, census tracts with a higher proportion of the workforce employed in manufacturing have worse air quality, on average. The coefficients on the percentage of the population residing in renter occupied houses are generally insignificant, except in the case of NO x where the sign on the coefficient is not only contrary to what might be expected, it is also statistically significant. In general, urban areas tend to be more polluted. In all models, the socioeconomic variables are jointly statistically significant. The coefficients on the variable capturing the propensity for collective action vary across the pollutants. A priori, we would expect a negative sign. Politically active communities are in a position to lobby for legislation protecting their local environments, even in the case where the pollutants are transported over large distances. However, positive and statistically significant coefficients are obtained in the case of CO and SO 2. Recall that data on this variable are available at the county level. It is possible that the

23 assumption of uniformly distributed voting behavior is incorrect. In this case, the coefficients obtained here would be misleading. Figure 1 shows the observed and predicted values for ambient concentrations, as well as the estimated regression line for all five pollutants. 12 For the three non-point source pollutants NO x, CO, and O 3 the estimated curve is either horizontal or U- shaped. (Recall that the income coefficients are statistically insignificant for CO and O 3.) For these gases, a major source of emissions is motor vehicles. In 1990, more than 60% of all CO emissions came from transportation; the corresponding shares for NO x and volatile organic compounds (VOCs) were approximately 38% and 34%, respectively (based on USEPA, 1991). This points to a possible explanation for the observed pollution-income relationship. The demand for cars (vehicle miles traveled) has a high income elasticity, with estimates varying from about 0.5 to over So, while increases in income mean that consumers use more efficient cars, and hence the initial decline in ambient concentrations, growth in income also leads to a greater use of private vehicles. In other words, scale effects more than outweigh the technology effects, causing the curve to turn upwards at higher income levels. 12 The regression line is estimated by holding all variables other than median household income and its square at their sample means. 13 See Agras and Chapman (1999a) for a review of elasticity estimates. 23

24 Figure 1: Pollution Income Relationships 24

25 Somewhat similar results are reported by Torras and Boyce (1998). Using GEMS data from in conjunction with national level economic and political data they estimate EKC relationships which include three variables to account for power inequality. Their primary hypothesis is that greater power inequality is, on average, associated with greater pollution. They find that once the variables capturing power inequality are included, the statistical significance of the income variables generally decreases in their cross-sectional models, and especially in the case of smoke and heavy particles where the coefficients become statistically insignificant. Furthermore, they find that higher levels of adult literacy as well as a greater degree of civil and political liberties are generally associated with lower pollution levels. However, in no case do they obtain a statistically significant U-shaped relationship. Curiously, Selden and Song (1994) discount their cross-sectional results which yield U-shaped curves for SO 2, particulate matter, and CO emissions per capita. They argue that since the null hypothesis that the cross-country effects are homogenous is rejected, these results are inefficient at best, and may yield biased coefficients (p. 151). While this may be true, the present author is not completely convinced that the signs obtained on the coefficients are also misleading. The U-shape obtained here is not necessarily inconsistent with the results obtained by List and Gallet (1999). Using historical data for the U.S. states these authors found statistically significant cubic relationships between per capita NO X and SO 2 emissions and per capita income. It is possible that the current model and data are picking up the section of the EKC around the trough. 14 Most of the income data points in 14 Gajwani (2001) reports similar U-shaped curves for SO 2 and suspended particulate concentrations. She used the air quality data published in YCELP (2001), which were originally obtained from GEMS. 25

26 the sample lie above the turning points reported elsewhere (see Stern and Common, 2001, for an overview of estimated turning points). Furthermore, when the slope coefficients are allowed to vary across states, these authors generally find a similar U-shape for NO x emissions per capita, and an inverted U-shape in the case of per capita SO 2 emissions. The income elasticity of pollution is depicted in Figure 2. In three out of the five cases, it is not statistically different from zero. In the case of ambient NO x concentrations, the income elasticity is monotonically increasing. At the lower end of the income range, the estimated elasticity is negative. However, beyond $23,500 it becomes positive. These cases do not support the basic hypothesis underlying the EKC that pollution has a negative (and declining) income elasticity. The only case which supports this hypothesis is PM 10 where the income elasticity is statistically significantly different from zero, monotonically declining, and negative over most of the sample income range. 26

27 Figure 2: Estimated Income Elasticity of Pollution 27

28 5. Conclusions and suggestions for further research This paper approaches the EKC debate from a slightly different perspective than typically adopted in the literature. The main focus of this paper is to estimate the pure income effect on pollution, i.e., to determine the relationship between pollution and household income after controlling for economic structure, trade policy, and eight demographic and political factors that are related to the distribution of pollution. The analysis is based on ambient concentrations for five criteria pollutants under the U.S. CAA. These are combined with socio-economic data for the census tract in which the pollutant monitors are located. The traditional inverted U-shaped curve is obtained in the case of PM 10 concentrations only. In the case of NO x, the curve declines and then rises. In the remaining three cases, the coefficients on the income variables are statistically insignificant, indicating that changes in income are, on average, not related with changes in pollution levels, everything else remaining constant. Therefore, except in the case of PM 10, the results obtained here do not unambiguously support the EKC hypothesis. Furthermore, there is some evidence to indicate that in the case of non-point source pollutants, ambient concentrations may increase at very high income levels. A mixed bag of results is also obtained for the estimated income elasticity of pollution. In three cases, the elasticity is monotonically increasing. For the two point source pollutants, the income elasticity declines with income. However, it is statistically different from zero only in the case of NO x and PM 10. Further analysis is necessary to validate the results obtained here. U.S. Census 2000 data should be soon available. These data can be used to test the results obtained 28

29 using 1990 data. Between 1990 and 2000, the U.S. economy grew steadily. At the same time, the Clean Air Act Amendments were passed in These Amendments facilitated the use of innovative market based instruments for pollution abatement. The 2000 data might, therefore, strengthen some of the current results. It will be particularly interesting to see whether the statistically insignificant coefficients on the income variables change sign and/or become significant. The U.S. experience cannot be directly applied to other countries, or to the global economy. Therefore, further research should also incorporate the detailed analysis of data from other countries, where available. Other papers have also reported results that are inconsistent across the different gases. This raises the question of the relationship between income growth and overall air quality. The EPA measures overall air quality using the Pollutant Standards Index. Recently, YCLEP (2001) and Khanna (2001) have proposed two new methodologies for aggregating pollutants into overall measures of environmental quality. It would be interesting to see if these alternative measures yield consistent results for the EKC as it relates to aggregate environmental quality. Ekins (1997, pp ) showed that most of the world s population lies on the upward sloping portion of the EKCs that have been estimated. This implies that in the foreseeable future, income growth will lead to a worsening in global pollution rather than an improvement. Selden and Song (1994) found that in all cases (SO 2, suspended particulate matter, NO x, and CO) emissions continue to grow rapidly through the first half of the 21 st century. A similar result was obtained for global SO 2 emissions by Stern et al. (1996). 29

30 These projections of increasing global emissions are primarily the result of the rapid increase in the emissions from currently developing countries which more than offset the potential decline in developed country emissions. However, the results of the current analysis indicate that even for developed countries, such as USA, which have already achieved a significant transition to a service-based economy and have very open trade policies, there is no guarantee that ambient concentrations will decline with future economic growth. In fact, it is entirely possible that the contrary might be true. This underscores the need for a continued and across the board emphasis on stronger and more strictly enforced environmental policies, rather than relying on EKC type arguments. These policies, in turn, would provide the impetus for further research on less pollution intensive production and consumption processes. 30

31 Appendix Table i: Regression Results (all models estimated without dummy variables for highly influential observations) CO O 3 NO x SO 2 PM 10 Median household income ** (1.051) (0.065) * (1.495) (1.456) 3.160* (0.730) Median household income squared (0.053) 0.053** (0.032) 0.171* (0.074) (0.073) * (0.037) Population density 0.172* (0.017) 0.020* (0.006) 0.178* (0.017) 0.068* (0.016) 0.023* (0.007) % population minorities 0.048* (0.023) 0.026* (0.011) 0.080* (0.027) * (0.025) (0.014) % labor force unemployed (0.041) (0.022) (0.061) (0.054) 0.071* (0.025) % labor force employed in manufacturing (0.041) 0.039** (0.021) 0.137* (0.054) (0.056) (0.028) % population with high school degree (0.048) (0.028) (0.069) (0.069) * (0.033) % voting age population registered to vote (0.126) * (0.051) * (0.165) (0.145) (0.077) % houses renter occupied (0.047) (0.022) (0.060) ** (0.053) * (0.030) % female-headed households (0.029) (0.020) * (0.043) (0.041) (0.021) Dummy variable for urbanized areas 0.258* (0.085) (0.027) 0.159** (0.083) (0.064) 0.127* (0.032) R-squared Note: Standard errors in parentheses. *: significant at 5%; ** significant at 10%. Models also include 9 dummy variables for the EPA regions. This table shows the estimates from the weighted least squares regression (model A). The ordinary least squares estimates are qualitatively similar. Detailed OLS estimates are available from the author upon request. 31

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