THE INCOME ELASTICITY OF NON-POINT SOURCE AIR POLLUTANTS

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1 THE INCOME ELASTICITY OF NON-POINT SOURCE AIR POLLUTANTS Neha Khanna Assistant Professor Economics and Environmental Studies LT 1004, P.O. Box 6000 Binghamton University Binghamton, N.Y Economics Department Working Paper Series, WP0110 December 10, 2001 ABSTRACT This paper examines the income-pollution relationship for three non-point source pollutants in Ambient concentrations reported at monitors throughout the U.S. are regressed on median household income and other socio-economic variables for the census tracts in which the monitors were located. Contrary to earlier studies, a u-shaped relationship is obtained. KEY WORDS Ambient concentrations, criteria pollutants, census tracts, median household income, weighted least squares. JEL CLASSIFICATION CODE Q01, Q25, Q40 ACKNOWLEDGEMENTS I gratefully acknowledge David Mintz at the EPA for extracting data from the AIRS database. Diane Geraci was instrumental in obtaining the election data. Lucius Willis mapped the monitor coordinates to census tracts; Martina Vidovic provided research assistance. Duane Chapman and Florenz Plassmann provided useful suggestions and insights. Any remaining errors are the sole responsibility of the author. 1

2 1. Introduction The Environmental Kuznets Curve (EKC) debate was engendered by Grossman and Krueger (1992, 1995) who posited an inverted u-shaped relationship between environmental degradation and income. Subsequently, a vast empirical literature has emerged, mostly using pooled aggregate data for several countries and pollutants. Only recently has the focus shifted to intra-national level data (e.g., Carson et al., 1997, Vincent, 1997, Berrens et al., 1997, Kahn, 1998, and List and Gallet, 1999). Empirical results are generally mixed. This paper extends the analysis by Kahn (1998) which found an inverted u-shaped relationship between California vehicle emissions and median household income. The current analysis uses 1990 ambient concentrations of three non-point source pollutants whose primary source is vehicle emissions. In 1990, over 60% of all carbon monoxide (CO) emissions came from transportation; the corresponding shares for nitrogen oxides (NO x ) and volatile organic compounds (the major precursor for ozone) are 38% and 34%, respectively (based on USEPA, 1991). A reduced form model is estimated, relating ambient concentrations at monitors located throughout the U.S. to socio-economic data for the census tract in which the monitors were located. 2. The empirical model and data The model used is represented by: Ln 2 ( a ji ) = C j + β 1 j Ln( inci ) + β2 jln( inci ) + X iφ j + ε j (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 2

3 variables; N j is the corresponding vector of slope coefficients for each pollutant j; g j is a randomly distributed error term, and C j is the intercept. Separate models were estimated for CO, ozone (O 3 ), and NO x. 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. 1 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 was located. These data were obtained from the 1990 U.S. Census. Control variables were chosen on the basis of a review of the literature on the distribution of air pollutants. Freeman (1972), Zupan (1973), Kruvant (1975), Asch and Seneca (1978), Brajer and Hall (1992), Brooks and Sethi (1997), and Arora and Cason (1999) report that socio-economic characteristics including race, education, structural composition of the workforce, housing tenure, collective action, and population density are determinants of exposure to air pollution. 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 1 Cases where the number of observations at a monitor did not meet the National Ambient Air Quality Standards data completeness requirements were excluded from this analysis (Mintz, 2000). 3

4 population. 2 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. 3. 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. 3 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. 2 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. 3 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. 4

5 In addition to the explanatory variables shown in Table 1, 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. In each case, 2-3 dummy variables were also included to account for highly influential observations. 4 Table 1 shows the estimated slope coefficients obtained from the models including these additional dummy variables. 5 Both models yield qualitatively similar results. For simplicity, the results from Model A are discussed in the remainder of the paper. The coefficients on the income terms are statistically significant for NO x and indicate a u-shaped relationship. That is, contrary to the EKC hypothesis, ambient concentrations first decrease and then increase with increases in median household income. In the case of CO and O 3 the estimated coefficients are not statistically significantly different from zero. These horizontal pollution-income relationships do not support the EKC hypothesis either. 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. Unemployment, educational status and housing tenure are not statistically significant. As 4 Influential observations refers to data points with unusually large Studentized residuals, DFFITS, DFBETAS, and/or unusual observations on the partial regression plots. The models were also estimated without dummies for these observations: qualitatively similar results were obtained. 5 As mentioned at the bottom of Table 1, 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. 5

6 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 female headed households 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. The variable measuring propensity for collective action is statistically significant in two out of three cases, though in the case of CO it has a contrary sign. 6 In general, urban areas tend to be more polluted. In all models, the socioeconomic variables are jointly statistically significant Discussion and conclusions The u-shaped relationship between income and pollution obtained here is in contrast to Kahn (1998). Two explanations are possible. First, as argued by Kahn (1998), vehicles represent a bundled technology with fuel efficiency being only one part of the overall drivability of a car. Thus, while newer vehicles have lower emissions than older models of the same car, households may switch between car types due to increased demand for other features. For example, households may switch to SUVs due to a greater perceived safety of these vehicles despite their considerably lower fuel efficiency compared to the typical sedan of the same production year. This switching 6 Recall that data on this variable are available at the county level. It is possible that the assumption of uniformly distributed voting behavior is incorrect. 7 Models were also estimated with the coefficients on four variables % labor force unemployed, % population with high school degree, % renter occupied houses, and % female headed houses restricted to equal zero. The results obtained were qualitatively similar to those reported in Table 1. 6

7 between vehicle types may cause the vehicular emissions curve to turn upwards at higher income levels. There is some tentative evidence of this in the survey data reported by Kahn (1998 see Table 2). However, that author does not include a squared term in the regression of vehicular emissions flow on median household income. The second explanation is also couched in omitted variables. Kahn (1998) relates annual household vehicular emissions to the median household income of the zip code for the car owners. No other control variables are included in this model. It is possible that the demand for vehicle miles, and therefore, household hydrocarbon emissions, is also partly determined by other factors, such as education, employment status and population density, independent of the influence of income. The current analysis presents estimates of the income-pollution relationship using ambient concentrations throughout the U.S. Hence, it was necessary to control for other factors such as race, housing tenure, economic structure, and geographic location as well. The influence of these factors on the distribution of pollution is well documented. The results presented here question the validity of the micro EKC. At the micro level, the EKC hypothesis implies that pollution has a negative and declining income elasticity. The evidence presented here is contrary: the income elasticity of pollution is monotonically increasing and generally positive for NO x. 8 In the case CO and O 3, changes in median household income are not associated with statistically significant changes in the income elasticity. 8 The estimated income elasticity is positive when income exceeds $23,500. This comprises 206 of the 305 observations. 7

8 References Arora, S. and T. Cason, 1999, Do Community Characteristics Influence Environmental Outcomes? Evidence From the Toxic Release Inventory, Southern Economic Journal 65(4), Asch, P. and J.J. Seneca, 1978, Some Evidence on the Distribution of Air Quality, Land Economics 54, Berrens, R.P., Bohara, A.K., Gawande, K., and P. Wang, 1997, Testing the Inverted-U Hypothesis for US Hazardous Waste: An Application of the Generalized Gamma Model, Economics Letters 55, Brajer, V. and J.V. Hall, 1992, Recent Evidence on the Distribution of Air Pollution Effects, Contemporary Policy Issues 10(2), Brooks, N. and Sethi, 1997, The Distribution of Pollution: Community Characteristics and Exposure to Air Toxics, Journal of Environmental Economics and Management 32(2), Carson, R.T., Jeon, Y., and D.R. McCubbin, 1997, The Relationship Between Air Pollution and Income: US Data, Environment and Development Economics 2, Freeman, A.M., 1972, Distribution of Environmental Quality, in: Kneese, A. and B. Bower, eds., Environmental Quality Analysis, Johns Hopkins Press, Baltimore. Grossman, G.M. and A.N. Krueger, 1992, Environmental Impacts of a North American Free Trade Agreement, Discussion Paper in Economics # 158, Woodrow Wilson School, Princeton University, Princeton, NJ, February., Economic Growth and the Environment, The Quarterly Journal of Economics, , May Kruvant, W.J., 1975, People, Energy, and Pollution, in: Newman, D.K. and D. Day, eds., The American Energy Consumer, Ballinger, Cambridge, MA. List, J.A., and C.A. Gallet, 1999, The Environmental Kuznets Curve: Does One Size Fit All? Ecological Economics 31(3), Kahn, M.E., 1998, A Household Level Environmental Kuznets Curve, Economics Letters 59, Mintz, D., 2000, Personal communication via , November 3. 8

9 United States Environmental Protection Agency (USEPA), 1991, National Air Quality and Emissions Trends Report, 1990, Office of Air Quality and Radiation, EPA-450/ , November. Vincent, J.R., 1997, Testing for Environmental Kuznets Curves Within a Developing Country, Environment and Development Economics 2, Zupan, J.M., 1973, The Distribution of Air Quality in the New York Region, John Hopkins Press, Baltimore. 9

10 Median household income Table 1: Regression Results CO O 3 NO x A B A B A B (0.997) (0.866) (0.595) (0.467) * (1.262) * (1.202) Median household income squared (0.051) (0.044) (0.030) (0.074) 0.233* (0.063) 0.234* (0.059) Population density 0.156* (0.016) 0.157* (0.021) 0.016* (0.006) 0.018* (0.006) 0.121* (0.016) 0.121* (0.019) % population minorities 0.043* (0.022) 0.057* (0.022) 0.026* (0.010) 0.024** (0.009) 0.084* (0.023) 0.084* (0.022) % labor force unemployed (0.039) (0.056) (0.020) (0.022) (0.053) (0.054) % labor force employed in manufacturing 0.068** (0.038) (0.042) 0.035** (0.019) (0.020) 0.163* (0.046) 0.167* (0.049) % population with high school degree (0.045) (0.046) (0.026) (0.024) (0.058) (0.051) % voting age population registered to vote 0.223** (0.119) 0.277* (0.119) (0.047) (0.040) * (0.147) * (0.134) % houses renter occupied (0.045) (0.048) (0.010) (0.021) (0.051) (0.050) % female-headed households (0.028) (0.026) (0.018) (0.018) * (0.036) * (0.031) Dummy variable for urbanized areas 0.304* (0.081) 0.264* (0.001) (0.024) 0.004* (0.002) 0.256* (0.071) 0.251* (0.010) R-squared No. of observations 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. 10