Environmental Kuznets curves for US hazardous waste sites

2. Environmental Kuznets curves for US hazardous waste sites

Preliminary evidence of an EKC for hazardous waste in the US has recently been identified using continuous measures as dependent variables such as waste generation Berrens et al., 1997, and assessed risk scores for National Priority List NPL ‘Superfund’ sites Wang et al., 1998. Both of those analyses used cross-sectional data at the US county level. This research is extended here to investigate the EKC relationship for the number of hazardous waste sites. While the available data on hazardous waste sites USEPA, 1992 restricts us to a cross-sectional investigation, we use two different samples: i 3141 US counties, and ii 748 Metropolitan Statistical Areas MSAs. The first sample includes all counties, while the second sample represents relatively densely populated ur- ban areas. Further, appropriate econometric methods are employed to handle the count data nature of hazardous waste sites, and the potential endogeneity of the per-capita income terms. 2 . 1 . Modeling approach The econometric models examine the relation- ship between counts of hazardous waste sites in a region the dependent variable and a set of ex- planatory variables, which includes per capita in- come and its squared term. Two count measures of hazardous waste sites are used in the economet- ric models: i the number of NPL sites, which are the high-risk ‘Superfund’ sites, and ii the total number of sites NPL plus non-NPL. 2 Our haz- ardous waste data includes all sites ‘facilities’ where there has been a release of hazardous waste as defined under the Comprehensive Environmen- tal Response, Cleanup and Liability Act CER- CLA of 1980. Site data were extracted from the US Environmental Protection Agency CERCLA information system CERCLIS; for 1992 there were 37 640 total sites, of which 1237 were NPL sites. Population, housing and income data were taken from the 1990 Census of Population and Housing. A more detailed description of the data is provided in Appendix A. Count models are specifically designed to han- dle data in the form of non-negative integers, and will typically outperform ordinary least squares regression applied to the same data. Given the nature of our dependent variable discrete counts of sites in a region, count data models such as the commonly used Poisson and Negative Bino- mial NB are likely modeling choices. We use a generalized NB model Gurmu and Trivedi, 1996, which is highly flexible and includes a variety of other models as special cases without imposing them by assumption. The generalized NB model for the number of hazardous waste sites y i in region i, given a set of explanatory variables x i , is given as: fy i x i , g = Gy i + 8 i G8 i Gy i + 1 8 i m i + 8 i 8 i m i m i + 8 i y i , y i = 0, 1, . . . 1 where the location-specific and other parame- ters m i , 8 i, g , and k are interpreted as follows: m i is the conditional mean of y i x i ; and ln m i is modeled as being linearly related to the set of explanatory variables x i , including a per capita income vari- able and its squared term, so that m i = expx i b . The vector b is the set of coefficients on x i to be estimated. The precision parameter 8 i = 1gm k , where g ] 0 is a dispersion parameter and k is some constant. The conditional mean and vari- ance of y i are, respectively: Ey i x i = m i , and Vary i x i = m i + gm i 2 − k . The commonly used Poisson model is the special case where g = 0. Estimation of the generalized NB model allows a test of g = 0. Two other special cases to be tested include the ‘type 1’ NB model k = 1 and ‘type 2’ NB model k = 0. Table 1 contains definitions of all the explana- tory variables the vector x from Eq. 1, as well as descriptive statistics across the two samples county and MSA. The issue variables are per capita income PC-INC and its squared term PC-INC 2 . Specifically, the estimated coefficients on PC-INC and PC-INC 2 determine the shape and position of the EKC. If the quadratic term is negative and the linear term is positive, an in- 2 See Hird 1990 for an explanation of the ranking system used to identify NPL sites. Table 1 Variable definitions and descriptive statistics for count models of EKC relationship a Variable Definition County sample MSA sample Mean S.D. Mean S.D. 11.98 34.1 31.1 ALL-SITES Total number of hazardous waste sites NPL+non-NPL, 1990 57.1 2.38 0.394 1.31 1.221 NPL-SITES Number of hazardous waste sites on National Priority List, 1990 Proportion White, 1990 0.84 0.17 0.82 WHITE 0.16 13 770 2724 3143 11 164 PC-INC Per capita income, 1989 Proportion with at least high school graduation, 1990 0.70 0.10 0.75 HSGRAD 0.08 0.45 0.11 0.05 0.40 PC-HU Housing units per capita, 1990 Proportion of total occupied housing units that are owned, 1990 0.72 0.08 0.69 OWNHOME 0.10 0.50 0.12 0.39 Number of manufacturing establishments, 1987 thousands MNFEST 1.02 Median gross monthly rent of renter occupied units, 1990 323.1 99.0 422.2 109.4 RENT 0.32 0.11 0.34 WEST 0.14 US Bureau of the Census West Region 0.34 0.47 0.26 0.44 MIDWEST US Bureau of the Census Midwest Region 0.07 0.25 0.16 0.36 NORTHEAST US Bureau of the Census Northeast Region a US per capita income in 1989 was 14 400 current dollars. The simple average for PC-INC reported here is lower in the county sample. The population-weighted average across counties yields 14 400. Similarly for the MSA sample. verted-U shaped curve for the data is obtained; i.e. up to a threshold level of per capita income the number of sites increases, but beyond that decreases. The choice of other explanatory variables is motivated by controlling for factors that may otherwise influence the variation in site data. A set of regional dummy variables WEST, MID- WEST, NORTHEAST, with SOUTH as the base category is included to control for the regional disparity in the emergence and location of the types of industries that have heavily influenced the location of hazardous waste sites. Since the de- pendent variable changes with the intensity of production activity across counties, the number of manufacturing establishments MNFEST is in- cluded to control for scale effects. A set of vari- ables controlling for socio-economic factors in a region is also included. These variables, which include the proportion of the county population that is White WHITE, proportion of high school graduates HSGRAD, per capita housing units PC-HU, and proportion of owned homes OWNHOME, distinguish counties by skill and economic status. Finally, in estimating the generalized NB model an important consideration is the potential endo- geneity of PC-INC term. The distribution of PC- INC across counties may itself be endogenously determined by the migration decisions of individ- uals, as well as the number of hazardous waste sites. If sites are disamenities, then this effect must be reflected in income differences across regions in a spatial equilibrium Roback, 1982. Thus, the presence of PC-INC terms in both the NB model of sites and the net outmigration equations Sec- tion 3 requires a correction for the endogeneity. 3 The method of Kelejian 1971 is used to perform this correction. Fitted values for PC-INC and relevant transformations i.e. PC-INC 2 are gener- ated by regressing these variables on all the right- hand side variables and their squared terms. 2 . 2 . EKC results The EKC relationship between hazardous waste sites and the set of independent variables was estimated using the generalized NB model. In addition, type 1 NB, type 2 NB, and Poisson models were also estimated separately. The pre- ferred model was selected based on three criteria: the Akaike information criterion AIC, likeli- 3 In Section 3, net outmigration equations across US coun- ties are estimated to see whether migration decisions are influenced by the number of hazardous waste sites in the area. The linear model includes an interaction term between PC- INC and a sites variable. Endogeneity in the PC-INC term is also corrected there. Table 2 Generalized negative binomial model estimates for the EKC relationship a NPL-SITES Variable ALL-SITES Scale factor COUNTY, MSA, MSA, COUNTY, n = 748 n = 3141 n = 748 n = 3141 23.946 1.240 PC-INC –FIT 10 − 4 33.239 5.973 32.277 4.714 18.809 7.274 − 5.899 −1.346 − 5.323 −8.901 PC-INC2 –FIT 10 − 8 − 8.432 −4.619 − 8.578 −5.682 0.265 0.167 − 0.920 −1.161 − 1.803 −7.429 WHITE 1 − 3.452 −5.216 − 18.144 −1.038 − 8.950 −3.928 HSGRAD − 15.285 −3.150 1 − 16.867 −3.627 − 1.569 −0.968 − .639 −2.486 PC-HU − 2.634 −2.102 1 − 1.643 −1.664 − 2.891 −1.807 − 1.441 −3.864 − 5.954 −1.213 OWNHOME 1 0.067 0.081 10.457 0.867 35.936 5.527 MNFEST − 0.240 −0.600 1 0.035 0.120 0.791 1.582 WEST 1 2.074 5.228 1.205 4.873 0.901 4.536 0.866 4.163 MIDWEST 1 2.093 5.193 1.049 4.072 0.875 1.461 0.979 11.501 0.418 1.100 NORTHEAST 1.011 4.662 1 1.714 9.821 − 0.583 −0.227 − 3.741 −5.262 Constant 1 − 13.165 − 13.061 −4.278 −5.457 0.210 6.936 0.579 16.821 g 1.189 5.220 1.533 7.963 − 0.340 −10.069 − 0.085 −0.490 k − 0.170 −12.656 − 0.132 −2.194 − 3010.9 − 9315.7 Log-likelihood − 976.7 − 1929.2 0.261 0.560 Maddala’s R 2 0.628 0.249 a Variables scaled to be uniform in size. To interpret the coefficients in terms of Table 1 units, scale the estimate by the scale factor. Numbers in parentheses are asymptotic t-statistics, computed from the heteroscedastic-consistent covariance matrix. If g\0 then there is heterogeneity in variances. The fitted values of PC-INC –FIT and PC-INC 2 – FIT are generated by regressing these variables on all the right-hand side independent variables and their squared terms. Denotes significance at the 10 level. Denotes significance at the 5 level. hood ratio LR tests, and t-tests. The computed LR statistics for NPL sites data NPL-SITES favor the generalized NB model over the Poisson model LR = 686 and 478, respectively, from the two samples, the type 1 NB model LR = 53 and 27.6, and the type 2 NB model LR = 18, and 13.6. All LR statistics exceed the critical 1 x 2 cut-off at 6.64 1 df. The t-tests also reject the Poisson hypothesis that g = 0, the type 1 NB hypothesis k = 1, and the type 2 NB hypothesis k = 0. A comparison based on the smallest AIC value, which penalizes excessive parameterization, also favors the generalized NB model. Model comparison diagnostics from the ALL-SITES data also arrive at the same conclusions. Thus, the generalized NB is selected as the preferred model, and used for making inferences. Evidence of the EKC is presented in Table 2, using the generalized NB model. The first two columns report estimates using NPL site data for the County and MSA samples, respectively. The model fits the cross-sectional NPL data, which is characterized by numerous zero values 58 of the MSA sample and 82 of the County sample have no NPL sites fairly well. The Maddala’s R 2 values are 0.25 for the County sample and 0.26 for the MSA sample, respectively. 4 The signs and statistically significant estimates on per capita income and its squared term PC- INC and PC-INC 2 bear out the inverted-U rela- 4 Maddala’s R 2 = 1 − L F L N 2n , where L F is the likelihood of the full model and L N is the likelihood of the null model with just the intercept, and n is the sample size. Fig. 1. Fitted EKCs with NPL sites data. tionship. The EKC from the County sample has a turning point at a per capita income level of 19 375, which is 2.56 S.D. above the sample mean. In our sample of 3141 counties, 1.62 or 36 counties have average per capita incomes that exceed 19 375. 5 A review of these counties shows them to be heavily urbanized high income coun- ties. The EKC from the MSA sample has a turn- ing point at a similar per capita income level of 19 145, which is 1.66 S.D. above the MSA sam- ple mean. In our sample of 748 MSAs, 6.02 or 42 MSAs have per capita incomes exceeding 19 145. In summary, given the high income turn- ing point, only small percentages of US counties 5 Since PC-INC is endogenous this calculation is based on the predicted number of sites. and MSAs are on the downward slope of the estimated EKC. After controlling for the effects of the other explanatory variables, Fig. 1 presents the fitted EKCs using the NPL sites data for both the County sample and the MSA sample. Estimated coefficients on other significant ex- planatory variables also have expected signs. Of note, the variable WHITE proportion of the population that is White is estimated with a statistically significant negative coefficient in the County sample. This is consistent with the evidence found in Berrens et al. 1997 for hazardous waste generation, and Wang et al. 1998 for the assessed risk of NPL sites. The last two columns of Table 2 report estimates from the total counts of all hazardous waste sites ALL-SITES. Since the total data have far fewer zeros than the NPL data, the models fit the cross-sectional data well. Specifically, the total site count data is denser it has few zeroes; 3 of the MSA sample and 15 of the County sample have no sites. The Maddala’s R 2 values are 0.56 for the County sample and 0.63 for the MSA sample, respectively. Again the signs and statistical signifi- cance of estimates on per capita income and its squared term PC-INC and PC-INC 2 , bear out the inverted-U shape of the EKC relationship. Also, the WHITE variable is again estimated with a significant negative coefficient in the County sam- ple. In the County sample, the EKC turning point occurs at a per capita income level of 17 670, which is 2.15 sample S.D. above the sample mean. In our sample, 3.02 or 95 counties lie on the downward part of the EKC. In the MSA sample, there is only weak evidence of the EKC from the ALL-SITES data, since both the linear and quadratic PC-INC terms are imprecisely measured. Regardless, estimates indicate an income turning point for the EKC of 20 300, which is 1.93 sample S.D. above the sample mean. In our sample, 4.68 or 35 MSAs lie on the downward part of the EKC. Again, given the high income turning point, only small percentages of US counties and MSAs are on the downward slope of the estimated EKC. 6 In summary, the count modeling evidence from all four samples County and MSA crossed with NPL-SITES and ALL-SITES indicates the pres- ence of the EKC relationship for hazardous waste sites, with similar per capita income turning points ranging from 17 670 to 20 300.

3. Exploring a migration explanation for the EKC