\WPs 1 876 POLICY RESEARCH WORKING PAPER 1876 Industrial Pollution in Industrial -water pollution stabilizes witn economic Economic Development development, but there is no evidence that it declines. (Kuznets Revisited) Hemamala Hettige Muthukumara Mani David Wheeler The World Bank Development Research Group January 1998 POLICY RESEARCH WORKING PAPER 1876 Summary findings Using new international data, Hettige, Mani, and water pollution rises rapidly through middle-income Wheeler test for an inverse U-shaped, or "Kuznets," status and remains roughly constant thereafter. relationship between industrial water pollution and To explore the implications of their findings, the economic development. They measure the effect of authors simulate recent trends in industrial water income growth on three proximate determinants of pollution for industrial economies in the OECD, the pollution: the share of manufacturing in total output, the newly industrialized countries, Asian developing sectoral composition of manufacturing, and the intensity countries, and ex-COMECON economies. They find (per unit of output) of industrial pollution at the "end of roughly stable emissions in the OECD and ex- pipe." COMECON economies, moderate increases in the newly They find that the manufacturing share of output industrialized countries, and rapidly growing pollution in follows a Kuznets-type trajectory, but the other two the Asian developing countries. determinants do not. Their estimates for the 1980s suggest that Asian Sectoral composition gets "cleaner" through middle- developing countries displaced the OECD economies as income status and then stabilizes. the greatest generators of industrial water pollution. At the end of pipe, pollution intensity declines strongly Generally, however, the negative feedback from with income. The authors attribute this partly to stricter economic development to pollution intensity was regulation as income increases and partly to pollution- sufficien- to hold total world pollution growth to about labor complementarity in production. 15 percent over the 12-year sample period. When they combine the three relationships, they do not find a Kuznets relationship. Instead, total industrial This paper - a product of the Development Research Group - is parr of a larger effort in the group to understand the economics of industrial pollution control in developing countries. The study was funded by the Research Support Budget under the research project "The Economics of Industrial Pollution Control in Developing Countries" (RPO 680-20). Copies of this paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact David Wheeler, room MC2-529, telephone 202-473-3401, fax 202-522-3230, Internet address dwheelerl@worldbank.org. January 1998. (36 pages) The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Produced by the Policy Research Dissemination Center INDUSTRIAL POLLUTION IN ECONOMIC DEVELOPMENT: KUZNETS REVISITED by Hemamala Hettige Muthukumara Mani David Wheeler Development Research Group World Bank The authors are respectively Economist, Asian Development Bank, and Consultant and Principal Economist in the Infrastructure and Environment Unit. Development Research Group, World Bank. Our thanks to the many Bank staff members, consultants and officials of national environmental protection institutions who made the data for this study available to us. I 1. INTRODUCTION A number of recent studies have explored the relationship between economic development and environmental quality. Theoretical papers by Gruver (1976), John and Pecchenino (1992), and Seldon and Song (1995) have derived transition paths for pollution, abatement effort and development under alternative assumptions about social welfare functions, pollution damage, the cost of abatement, and the productivity of capital. Empirical studies (Hettige, et. al. (1992), Shafik (1994), Seldon and Song (1994) and Grossman and Krueger (1995)) have searched for systematic relationships by regressing cross-country measures of ambient air and water quality on various polynomial specifications of income per capita. This extensive body of work has been motivated by several related questions: Does pollution follow a 'Kuznets' curve, first rising and then falling as income increases? At what income level does the turnaround occur? Do all pollutants follow the same trajectory? Is pollution reduction in developed economies due primarily to structural change, or to regulation? The theoretical work has shown that a Kuznets, or inverted-U, relationship can result if a few plausible conditions are satisfied as income increases: Constant or falling marginal utility of consumption; rising marginal disutility of pollution; constant or rising marginal pollution damage; and rising marginal abatement cost. Of course, actual turnaround points depend on the relative magnitudes of the underlying parameters, as well as their signs. Although they are not explicitly captured by the theoretical models, structural change in the economy and more effective regulation are also potentially-important sources of change in pollution. The empirical results are roughly consistent with a Kuznets curve for conventional air pollutants such as suspended particulates and sulphur dioxide, but the results for water pollution are mixed. In most cases, however, the implied trajectories are sensitive to inclusion of higher- 1 order polynomial terms in income whose significance varies widely. Structural interpretation of the estimates remains ad hoc, since the existing studies have incorporated almost no evidence about actual emissions in developing countries.' This paper attempts to advance the state of the art, using new data on industrial water emissions in developed and developing countries. Our analysis decomposes total industrial pollution into four proximate determinants: National output; the share of industry in national output; the share of polluting sectors in industrial output; and end-of-pipe pollution intensities in the polluting sectors. As most of the previously-cited work has noted (without being able to resolve the issue), declining pollution at higher levels of development must be driven by some combination of income-related changes in the latter three factors. We investigate these changes in three econometric exercises. Using international panel data, we estimate the effects of economic development on industry's share of total output and the industry share of polluting sectors. To study development-related changes in end-of-pipe pollution intensity, we have collected factory-level data on industrial water pollution from national and regional environmental protection agencies (EPA's) in twelve countries: Brazil, China, Finland, India, Indonesia, Korea, Mexico, Netherlands, Philippines, Sri Lanka, Taiwan (China), Thailand and the US. Controlling for sectoral differences, we use these data to investigate the effects of income per capita, regulatory strictness and relative input prices on factory-level pollution intensity (pollution/output). In a complementary exercise, we add a measure of regulatory strictness to a cross-country labor intensity equation to test for the impact of regulation 'A partial exception is the work of Seldon and Song (1994), whose regressions employ air emissions instead of ambient air quality measures. The lack of monitoring information forces the authors to estimate air emissions from secondary sources: National fuel use data and fuel-based pollution parameters which are adjusted for conditions in countries at varying income levels. Data scarcity in developing countries is clearly 2 on the demand for labor. For our international pollution accounting exercise, these results provide two inputs: A measure of average water pollution intensity for each industry sector (an input to our study of income-related changes in polluting sectors), and an estimate of the change in sectoral pollution intensities as income per capita increases. We combine our econometric results to simulate the total effect of economic development on industrial water pollution. In this case, we do not find an overall inverse U-shaped relationship. The three factors have very different relationships with income, and their joint product with total output is asymptotic, not parabolic. Industrial water emissions rise until countries attain middle- income status, and then remain approximately constant as they grow richer. YWhile our results do not support the Kuznets hypothesis for industrial water pollution, they do reveal a striking regularity in cross-country environmental performance. Our plant-level results suggest that pollution and labor intensities with respect to output decline continuously, and at almost exactly the same rate, as income increases. Thus, sectoralpollution/labor ratios remain approximately constant during the development process. This finding provides useful leverage for the analysis of pollution trends across countries and over time. As an illustration, we combine our estimated sectoral pollution/labor ratios with panel data on sectoral employment to simulate international trends in industrial water pollution during the past two decades. The remainder of the paper is organized as follows. Section 2 develops the models which link our three pollution factors to economic development. Section 3 introduces the data used for estimation. Section 4 discusses the results and their implications, while Section 5 provides a problem for this exercise. Of thirty countries in the estimation sample only four (China, India, Thailand, Turkey) are LDC's. 3 illustrative estimates of recent water pollution trends in a number of developed and developing countries. Section 6 concludes the paper. 2. DEVELOPMENT AND INDUSTRIAL POLLUTION The first stages of economic development typically witness the rapid growth of industrial activity and declining environmental quality in densely-populated urban areas. When new industries are pollution-intensive, their emissions can increase local ambient pollutant concentrations to harmful levels. To study this phenomenon, we decompose total industrial emissions in a particular region as follows. (1)P = rn(y)Qp(y)17(y) where P = Total industrial pollution m - Manufacturing share of total output Q Total output p = Manufacturing pollution intensity q= Degree of pollution abatement: 0 < r1 < 1 y Income per capita Equation (1) includes three parameters which we hypothesize to be functions of economic development: The manufacturing share of total output (m), the pollution intensity of manufacturing (p), and the degree of pollution abatement by industry (r1). In this decomposition, the effect of economic development on pollution depends on the signs and the magnitudes of the parameters governing the relations between m, p, rj and y. 2.1 Manufacturing Share of Total Output Numerous studies of the relationship between industrialization and economic development have suggested an inverted-U relationship between the manufacturing share of output (m) and 4 income per capita (y).2 During the first phase of economic growth, m increases as industry expands more rapidly than agriculture. As the economy begins to mature, rapid growth in services becomes the dominant factor and m declines. Over the existing range of national incomes per capita, this relationship can be approximated with a parabolic function: .(2) logm = a0 + a1 logy + aC2(logy)2 (a, > 0, a2 < 0) Our empirical analysis uses cross-country evidence for the past two decades to estimate this relationship and test its intertemporal stability. We focus particularly on changes in om/5y as development proceeds. Controlling for growth in total output, large movements in m will have a significant impact on the trajectory followed by industrial pollution. 2.2 Sector-Weighted Pollution Intensity The sectoral composition of industrial activity has an important effect on its average pollution intensity, or pollution per unit of output. Industrial processes differ greatly in their production of waste residuals which, in turn, have varying potential for creating environmental damage. Abatement costs also differ significantly by industry sector (Dasgupta, et. al. (1996), Hartman, et. al., (1997)). Even in well-regulated economies, these factors cause significant intersectoral differences in pollution intensity. For example, metals and cement are generally intensive in harmful air pollutants; food and paper production are disproportionate emitters of organic water pollutants (Hettige, et. al. (1995)). Anecdotal evidence suggests that the sectoral composition of industry follows a 'clean' trend as development proceeds. This could reflect domination of early industrialization by primary industries, which generate heavy pollution loads as they convert bulk raw materials into primary 2 For a discussion of structural change in development, see Syrquin (1989). 5 inputs (e.g. metals, paper, cement, sugar). During the development process, primary industries may lose output share to cleaner industries (e.g. vehicle and electronics assembly, instruments).3 In this paper, we test the clean trend hypothesis for industrial water pollution by fitting the following equation to an international panel dataset: pj, = ,A0 + A logyi, +fA2(logy1, )2 (3) where P =2.SkPk k j, k, t 5 Country, sector and year respectively Pj = Average pollution intensity for sector j (see Section 4.3) 2.3 Pollution Abatement The marginal cost of abating pollution from industrial sources is a function of the scale of activity, pollutant concentration in process influent,4 the degree of abatement, and local input prices.5 In static partial equilibrium, cost-minimizing firms with flexible abatement choices will control pollution to the point where their marginal abatement costs equal the 'price' exacted for pollution by affected parties.6 Characteristic production scale and process effluent intensity differ 3 See Mani and Wheeler (1997) for further discussion. 4 .Influent' refers to emissions from industrial processes before treatment (or abatement); 'effluent' refers to emissions to air, water or land after treatnent; 'concentration' refers to the quantity of pollutant per unit volume of the waste stream. 5 For recent empirical evidence, see Hartman, et. al. (1997) and Dasgupta, et. al. (1996). These may. according to the circumstances, include local administrators, pressure groups, national regulators, stockholders, and 'green consumers.' Each group is in a position to impose some cost on a firm or plant if its emissions exceed the norms adopted by that group. Thus, even where pollution charges are in effect, there is no singlc 'price' of pollution. For a detailed discussion, see Afsah, et. al. (1996). 6 significantly by sector, and abatement costs differ by location. Differences in the groups affected by pollution can also lead to significant spatial variation in emissions prices.7 2.3.1 Pollution, Employment, Regulation and Input Prices Where the environment is 'cheaper' or abatement is more expensive, the pollution intensity of production in a particular sector should be higher, ceteris paribus. However, data scarcity has made it difficult to test the magnitude of these effects, as well as the impact of spatial variation in the prices of capital, labor, energy and materials. At present, we have sufficient data to investigate these relationships in a two-equation demand system:8 in P.= ao + E dS R j KIn WK+aL In WL +a E In WE +aM InWMj+aQIn.Q InP. + m-"PDP+R Ka +WKlL InL. = 0i+XfiSLDSL+JpPInR.+IKlnWK +fILInWLj+fE lnwEj +lM lnwMj +,Q inQj where (for country j) P = Plant-level pollution L = Plant-level employment D = Vector of dummy variables for S sectors R = An index of regulatory strictness WK,L,E.M = Prices of capital, labor, energy and materials Q = Plant-level output Our dataset, described in the following section, combines information from several sources: plant- and sector-level data on emissions and employment from national and regional EPA's; sector-level information on output and employment from national census bureaus and the World Bank's international database (BESD); and data from BESD on national income, population, and 7 For recent evidence, see Pargal and Wheeler (1996), Wang and Wheeler (1996), and Hettige, et. al. (1997). 8 Data-gathering in this context is not a simple task. Even assembly of the relatively sparse dataset used for this exercise has required a massive canvass of World Bank project files, consultants' reports, and emissions reports from many national environmental protection institutions. The data are briefly surveyed in the following section, with a more detailed description in the Appendix. 7 a number of other variables. For cross-country consistency, we use summary data by sector.9 Plant-level relations between scale (Q) and pollution intensity (P/Q) are not relevant for sectoral aggregates, so we impose the assumption of constant returns (aQ = /IQ = 1).1o This implies estimation of the pollution and labor equations in intensity form, with P/Q and L/Q as the dependent variables. Using the BESD database, we estimate sectoral average L/Q ratios for each sample country. We construct sectoral average P/L ratios (sectoral pollution intensities w.r.t. labor) from the data provided by national and regional EPA's. We estimate PIQ (pollution intensity w.r.t. output) by multiplying L/Q and P/L for each sector and country. The results permit us to estimate the following equations: P. (5) InOJ=0 5SPSP +RInR.+KInWK +aLIn L +aEn WE +C. L In 0 g flSLDSL +6P In Rj +fl6 In WK. +AL In WLj + flE In WEj + vj In some cases, we have clear prior expectations about parameter signs: Regulation: Ceteris paribus, we expect stricter regulation to have a negative impact on pollution intensity. We have no clear prior about its impact on labor intensity at the sector level. Labor Price: We naturally expect increasing wages to reduce the labor intensity of industrial output. The effect of wages on pollution intensity is less transparent. Econometric estimates of KLEM (capital, labor, energy, materials) models has suggested that (K,E) and (L,M) See Appcndix I for a description of dala sources in each country. to Marginal abatement costs declinc with treatment scale for most pollutants, because abatement capital is lumpy. Thus, the estimated output clasticity of emnissions in a plant-level equation is generally less than one. For cvidence from Asia, see Pargal and Wheeler (1996). At the sectoral level, however, the constant-returns assumption seems appropriate for cross-country work. It is possible that characteristic plant scale is larger in countries with greater sectoral output, but we have no way to test this proposition with the available data. 8 are complements in production, while the pairs KE and LM are gross substitutes."1 If these relations hold, a wage increase should have the following effects on emissions: (1) Materials use and the volume of polluting residuals should decline; (2) Labor use should decrease in both processing and pollution abatement activities, with some increase in pollution from the latter effect. However, our prior expectation is that the materials- reducing effect should dominate: A wage increase should reduce water pollution intensity. Energy Price: If labor and energy are gross substitutes in production, then an increase in the price of energy should increase the labor intensity of production. In the case of pollution, an energy price increase will reduce energy use for both processing and pollution abatement. Abatement activity should therefore fall, and water pollution intensity should rise. Capital Price: A capital price increase should also increase labor intensity. For pollution, an increase in the interest rate or the price of equipment should reduce capital and energy use as well as pollution abatement, while increasing the use of labor and materials in processing. Both reduced abatement and increased materials use should lead to more water pollution. 2.3.2 Pollution Intensity and Economic Development Regulatory strictness and some input prices (e.g., wages) change systematically as per capita income increases. To assess the overall impact of economic development, we also estimate our intensity equations in reduced form: p . (6) In J = P = Yo + 2 5SpDsp +P, In y + j Qi ~~s L . In Q] = A} = ro + E YSLDSL tYy In y + v} These equations have two specific roles to play in our analysis. First, they provide an estimate of py, the elasticity of end-of-pipe pollution intensity with respect to income per capita. We use this to construct the index il in Equation (1). The results also provide estimates of 5 See Christensen, et al. (1973). 9 average sectoral pollution intensities (8sp) across countries. We combine these intensities with our panel data on sector shares by country to construct estimates of p for use in Equation (3). 3. DATA 3.1 Industrial Pollution To our knowledge, this is the first comparative international study of industrial pollution which uses direct observations on emissions. We have obtained the data from environmental protection agencies in Brazil, China, Finland, India, Indonesia, Korea, Mexico, Netherlands, Philippines, Sri Lanka, Taiwan (China), Thailand and the LJS. Descriptions of the data sources are provided in Appendix I. We use the pollution information and complementary employment data to estimate emissions intensities by industry sector in kilograms per day per employee. We focus on organic water pollution because it provides the most plentiful and reliable source of comparable cross-country emissions infornation. Water pollution data are the most plentiful because developing countries have traditionally begun industrial pollution control programs with regulation of organic water emissions. They are relatively reliable because sampling techniques for measuring water pollution are more widely understood and much less expensive than those for air pollution. 3.2 Environmental Regulation Some comparable measure of regulatory strictness is necessary for estimation of our cross- country equations. However, credible indices of environmental regulation are difficult to find. Even in the US, comparative analyses of state-level regulatory 'outputs' have generally used input-based measures such as expenditures on monitoring and enforcement, or total employment 10 of inspectors. 12 Such measures may have at least some justification for within-country analyses, since quality- and price-adjustment problems are not too serious. For international comparisons, however, they would be problematic even if comparable data were available. Most developing countries do not have such data, so input-based comparisons are not possible in any case. A more promising approach has been taken by recent econometric work on the sources of variation in regulatory strictness. This work is helping to identify robust proxies which can be used as instruments in cross-country comparisons. The best instrument is undoubtedly per capita income, which has been shown to affect both formal and informal regulatory pressure on polluters in the US and Asia (McConnell (1992), Pargal and Wheeler (1996), Hartman, et. al. (1997), Hettige, et. al. (1996) and Wang and Wheeler (1996)). Dasgupta et. al. (1995) have advanced the state of the art by developing quantitative indices of regulatory development from reports filed for the U.N. Conference on Environment and Development (UNCED - Rio de Janeiro, 1992). Their results suggest that international differences in pollution regulation are well-explained by a model which incorporates the effects of per capita income, urbanization, population density, and manufacturing share in national output. We have adopted the Dasgupta model to produce a cross-country pollution regulation index for this paper. Six of our thirteen country cases have actually been scored by the Dasgupta exercise. For the remaining seven cases, we have calculated the pollution regulation index values using the Dasgupta equation. 12 See for example Levinson (1994) and Beede (1993). 11 3.3 Input prices We have computed wages (in $US 1990 per worker) by ISIC sector from UNIDO's reported sectoral totals for employment and payrolls. Our electricity tariff rates for the OECD and developing countries have been drawn from International Energy Agency data and the World Bank's Power Sheets database, respectively. The World Bank's World Development Indicators database has provided our national real interest rate measures. 3.4 Employment, Income and Output Estimation of equations (2), (3), (5) and (6) requires cross-country data on total output, industrial output, employment, income, population and a number of other variables. We have obtained the relevant panel data from the World Bank's international database (BESD). We have used Summers-Heston estimates as our measure of income per capita. 4. ECONOMETRIC RESULTS 4.1 Manufacturing Share in National Output Table 4.1 reports panel estimates for equation (2). We provide comparable results for OLS, fixed-effects (without time dummies) and random effects -models. We prefer the random effects model, but the choice of estimator does not have a major effect on the results. They are consistent with an inverted-U model for manufacturing share in national output. Our results also suggest some structural change in the relationship, since the interactions of time with income and income squared both satisfy classical significance criteria. During the past two decades, the 'inverted-U' appears to have steepened somewhat and shifted downward. To illustrate the implied relationship, we have calculated median manufacturing shares by income class for all 1,717 observations in our sample. The result (Figure 4.1) suggests that 12 manufacturing share rises steeply with income until a country reaches middle-income status;'3 from around 10% in countries with less than $1000 per capita (Summers-Heston income, in $US 1990) to around 25% in countries with incomes of $5,000-$6,000. Then the manufacturing share slowly declines to around 20% in countries with $20,000 or more. Figure 4.1: Manufacturing Share in GDP vs. Per Capita Income, 1975-1994 25.0 - 20.0 15.0 W 10.0 ' a 5.0 o. o 0 0 0 a o 0 0 0~ 0D 0D CD 0C0D to LO LO Lo to 0 0 0 0 o4 o CD oCooO o o1 o (N X~ t (0 X0 O N t It- 0 ,- 1 ~ ~ v cm( Income Per Capita ($US) 4.2 Changes in Sectoral Composition Table 4.2 reports results for our analysis of changes in sectoral composition. We have employed panel techniques to estimate Equation (3) in log-log formn, using the log of share- weighted average BOD intensity as the dependent variable. Again, the fixed-effects and random- effects estimates tell the same story: As income per capita increases, overall pollution intensity declines because relatively 'clean' sectors grow more quickly. However, our results suggest that 3 we have smoothed the series with a three-interval moving average: Each share observation on the graph is the average for the previous, corresponding, and succeeding income groups. 13 the rate of decline also decreases. We find no evidence ol'a structural change, except for a very slight (but significant) upshift in compositional pollution intensity. In Figure 4.2, we provide an illustration of the relationship between overall pollution intensity and income during the sample period. 14 The figure is based on median values of overall intensity for each income group in the set of 2,210 observations. It suggests that sector-weighted average water pollution intensity declines from nearly 6 Kg. to 4Kg. Per $US 1 million per day, or about 30%, as income increases to around $5,000 per capita. Then it remains approximately stable over the higher-income range. Figure 4.2 Industrial BOD Intensity vs. Income Per Capita . . ..........................................-. .................................................................................. ~5.. 4.5 4 ~3.5 3 Income Per Capita ($US) 14 We have also used three-interval smoothing for Figure 4.2. See Footnote 13. 14 4.3 End-of-Pipe Pollution Intensity Tables 4.3 - 4.5 report cross-country regression results for Equations (5) and (6). We use dummy variables to control for sectoral differences in average poflution intensity; dummy variable controls are also introduced for national differences in reporting procedures and measures of organic water pollution. The majority of environmental protection agencies (EPA's) have reported emissions of biological oxygen demand (BOD), which is a measure of oxygen removal from water by bacteria which are oxidizing organic materials. However, three EPA's - for China, Netherlands and Taiwan (China) - have reported COD (chemical oxygen demand). COD incorporates the effect of other pollutants on the rate of oxidization; it is systematically larger than BOD measures. We have controlled for the measurement problem by introducing a dummy variable for COD- based emissions reports. As expected, the estimated COD dummy is positive, large and highly significant in all pollution intensity equations. Our sectoral dummy variable results are also in accord with prior expectations: Food and Paper have the highest average organic water pollution intensities; Metals and Mineral Products have the lowest. In the case of labor intensity, Textiles, Food and Wood Products are highest (along with Other Manufacturing, the numeraire sector); Metals and Chemicals are the lowest. We have also controlled for the possible impact of differences in emissions reporting procedures. In several cases (China, India, Indonesia, Netherlands, Philippines, Sri Lanka, Taiwan (China), Thailand) the plant-level information provided by the EPA's includes employment data. This has enabled us to estimate sectoral pollution/labor ratios directly from the EPA data. In the other cases (Brazil, Finland, Korea, Mexico and the US), the EPA's have provided summary pollution data by sector. We have obtained summary employment data by 15 sector from other national or regional sources, and have used the two summaries to calculate sectoral pollution/labor ratios. We recognize the possibility of systematic differences in the results generated by these two approaches. EPA's in developing countries focus on large polluters, so the average pollution intensity of these facilities will be reflected in estimates based on plant samples. The situation is potentially quite different when EPA-reported sectoral emissions are divided by census-reported sectoral employment. Plants which ignore pollution regulations (and whose reported pollution is therefore zero) may nevertheless be registered in an employment census. This might impart a downward bias to summary-based intensities. In addition, all five countries for which we employ summary data (Brazil, Finland, Korea, Mexico, US) are in the middle or high income category. Thus, failure to control for the sampling difference might also produce a downward bias in the estimated effect of income or wages on pollution intensity. We have introduced a dummy variable to control for this difference, but it is not significant in our regressions. In fact, we are not overly surprised by this result because effective coverage of industrial facilities by both census-takers and regulators is a ftinction of development. 15 4.3.1 The Effects of Pollution Regulation and Relative Input Prices As expected, the estimated wage-elasticity of labor intensity is large (around -.70) and highly significant. The wage elasticity of pollution intensity is also negative, large (-1.71) and highly significant. In the pollution intensity equation, our results are consistent with the hypothesis that 15 Both approaches may underestimate 'true' sectoral pollution intensities in developing countries, because e.xisting research suggests that medium and large plants have lower pollution per unit of output than smaller facilities (ceteris paribus). Sincc smaller plants are covered by regulators in developed econonies, our econometric result may actually understate the effect of income on pollution intensity. We accept the plausibility of this hypothesis. but we have no way to test it at present. 16 labor and pollution are complements in production. However, the converse is not true. Our index of regulatory strictness is not significant in the labor intensity equation. While the latter result is not particularly surprising, we also find that our regulatory strictness index is not significant in pollution intensity regressions which control for wages. Does this imply that market forces alone drive pollution, and that regulation is irrelevant? Although our results are consistent with this interpretation, we reject it for several reasons. First, our wage and regulation variables are highly collinear because they are both correlated with per capita income. As Table 4.4 shows, each variable is significant in equations which exclude the other. Second, a large body of empirical work suggests that industrial pollution is responsive to pressure from local communities (Pargal and Wheeler, 1996; Hettige, et. al., 1997, Hartman, et. al., 1996), as well as formal regulation. Both forms of regulation are strongly affected by income, reflecting increasing preferences for environmental quality and higher valuation of pollution damage. We believe that the estimated wage elasticity in our pollution intensity regression is capturing cross- country income effects on formal and informal regulation, as well as the effect of complementarity with pollution in production. With currently-available information, we cannot distinguish clearly between these two effects. However, their joint effect clearly shows the impact of rising income on pollution intensity. Our results for energy and capital prices are considerably weaker. Surprisingly, neither variable is significant in the labor intensity equation when both are included. In the pollution intensity regression, the estimated electricity price elasticity is positive, large and highly significant. The real interest rate elasticity is also positive, and close to significance at the 5% level. However, these results are not robust to changes in right-hand variables or sample composition. Dropping the real interest rate increases the sample size, because we do not have 17 real interest rate data for Mexico, Brazil and Taiwan (China). However, with the larger sample the electricity price elasticity loses significance in the pollution intensity equation, while becoming large, negative, and highly 'significant' in the labor intensity equation. We conclude that our results for capital and energy prices are highly sensitive to outliers, and we see no reason to draw any clear conclusions from our results. 4.3.2 Economic Development and Pollution Intensity We have also estimated reduced-form intensity equations which control for per capita income, sector and COD reporting. The results are summarized in Table 4.5 for three intensities: labor/output, pollution/output and pollution/labor. In all three equations, the results for the sectoral dummies replicate the pattern of results in Tables 4.3-4.4. As before, the dummy variable for COD is positive and significant in the pollution equations. The results for per capita income suggest a striking regularity across countries. The income elasticities of pollution/output and labor/output are both negative, and not significantly different from one. In the third equation, we test for the equality of pollution and labor elasticities (w.r.t. income) by regressing pollutionAabor on the same set of right-hand variables (this amounts to differencing the coefficients in the first two equations). Te resulting elasticity of pollution 'labor with respect to income per capita is not significantly different from zero. Of course, we cannot generalize from one sample for one pollutant to all industrial emissions. However, for industrial water pollulion, our results suggest that sectoral emissions/labor ratios Lare approximately constant across countries at all income levels. Developing economies generate much more pollution per unit of output than developed economies, but they also employ much more labor per unit of output, and in the same proportion. 18 Figure 4.3 and Table 4.6 portray the estimated relationship between pollution intensity (per unit of output) and income per capita. For ease of interpretation, we normalize to an intensity value of 100 for the poorest income category ($500 per capita). The cross-country evidence suggests a sharp drop in pollution intensity with income growth, as manufacturers respond to higher wages and regulatory pressures with end-of-pipe abatement and process change. From an emissions irdex value of 100 at $500 per capita, pollution abatement is about 60% at $1,500, 80% at $3,000, 90% at $7,000 and 95% at $15,000. Table 4.6: Income and Pollution Abatement Income % Per Capita Abatement 1,500 60 3,000 80 7,000 90 15,000 95 Figure 4.3: Water Pollution Intensity vs. Income Per Capita .~80 70 .2 60 = 50 0 0. 40 4'30- ~20 10 0 0 2000 4000 6000 8000 10000 12000 14000 16000 18000 20000 Per Capita Income ($US) 19 5. IMPLICATIONS OF THE RESULTS 5.1 The Kuznets Hypothesis Our estimation exercises have suggested three distinct patterns of response to economic development. Industry's share of national output rises sharply through middle-income status and then slowly declines. Sectoral composition follows a 'clean' trend for low-income developing countries, but exhibits little or no trend beyond the middle income range. End-of-pipe pollution intensity, by contrast, declines continuously with income. We use simulation to project the net result of changes in these three factors. Our four simulation variables are in columns 1-4 of Table 5.1. Column 1 includes a broad range of incomes, from $US 500 to $US 20,000 per capita. Columns 2 and 3 replicate the information on manufacturing output shares and average pollution intensities in Figures 4.1 and 4.2. Column 4 reproduces the pollution intensity index in Figure 4.3, re-normalized to one for the lowest income level. We assume a unit population for convenience, so income per capita also serves as a measure of total output. We simulate the overall relationship between economic development and industrial pollution by multiplying the four column entries in each row. The result combines the effects of changes in total output, manufacturing share, sectoral composition, and end-of-pipe pollution intensity. Column 5 and Figure 5.1 portray the total pollution estimate, which has been normalized to an index value of 100 at the lowest income level. Our result suggests that the inverted U-shaped story is only half right for industrial water pollution: Total emissions rise sharply in the range [$500 - $7,000], but remain constant as income increases further. 20 Figure 5.1 Industrial Pollution and Economic Development 300 ° 250 . °200 150 A- ~100 o8 888 8 v Per Capita Income ($US) To assess the contribution of each factor to the overall result, we perforn three counterfactual simulations which are tabulated in columns 6-8 and illustrated in Figure 5.2 Each simulation allows one of columns 2-4 to vary while holding the other two constant at the lowest- income level. The experiment in column 6 holds sectoral composition and end-of-pipe pollution intensity constant, while allowing the share of manufacturing to vary with income. The result is rapid growth of pollution over the whole income range, and an estimated pollution load at $20,000 which is eighty times the initial load. To produce column 7, we allow sectoral composition to vary while holding the other two factors constant. Pollution growth is considerably moderated by comparison with column 6, but the projected load at $20,000 is still 40 times the initial load. Finally, we test the effect of end-of-pipe change in column 8. This experiment clearly identifies the most important factor: Projected emissions at $20,000 are only I .8 times the initial load, if manufacturing share and sectoral composition are held constant. 21 We conclude that pollution levels off in the middle income range because end-of-pipe pollution intensity responds to rising wages and stricter regulation. By comparison, the manufacturing share and sectoral composition are minor players. For industrial water pollution, the inverted-U pattern does not emerge because declining pollution intensity almost exactly balances output growth, while manufacturing share and sectoral composition remain constant beyond the middle income range. Figure 5.2: Counterfactual Simulations 7,000 Vanable: = 6~~~.,00 Ponution Iniensity : -u-Manuf. Share 5,000 - --Sector Mix 4,000 Pw3,000 ~ cJ2,000 /_k = 1,000.i t ttttt o 00 0 0 0 0 0 0 0 L _ to Li 0 0 0 0 0 0 0 (N () ~ (0 co 0 ' ?r Per Capita Income ($US) 5.2 Trends in International Emissions To explore the real-world implications of our results, we estimate pollution loads for a set of large industrial economies during the period 1977 - 1989. Powerful leverage is provided by our finding that sectoral pollution per unit of labor (PIL) remains approximately constant across the entire range of incomes. This allows us to use commonly-available sectoral labor/output (LIQ) ratios to predict international changes in industrial water pollution. As an illustration, we use the World Bank's BESD database to estimate sectoral L/Q ratios for fifteen countries during the 22 period 1977-1989. To estimate BOD loads by sector, we multiply the L/Q ratios by sectoral P/L coefficients calculated from our regression results for P/L.i6 We have chosen the fifteen countries to represent large industrial economies in four major groups: OECD (represented by the US, Japan, France and Germany (former F.R.); the NIC's (Mexico, Brazil, Taiwan, Korea, South Africa, Turkey); Asian LDC's (China, India, Indonesia); and the ex-COMECON countries (Poland, former USSR). The results are tabulated in Table 5.3 (Appendix II) and summarized in Table 5.2 below. Taken together, they illustrate the main implications of our empirical analysis. In the OECD, despite modest continued economic growth, estimated BOD emissions remain almost constant. In our view, this reflects the countervailing effects of output growth and increases in wages and regulation; manufacturing shares and the 'clean' sector share change very little. The COMECON economies are in relative stagnation during the sample period, so there is little movement in their estimated emissions. The story for the NIC's is quite different. Their estimated pollution increases by about 25% during the sample period - substantially less than their growth in per capita income. The increase is relatively moderate because rapid output growth is offset by three factors: the negative impact of increased wages and regulation on industrial pollution intensity; the first stage of the decline in manufacturing share; and the last stage of the 'clean' trend in sectoral composition. The Asian LDC experience is also distinctive. Estimated BOD emissions grow by approximately 55% in these lower-income economies, because rapid output growth and 16 For this application, we regress log (P/L) on dummy variables for COD and the industry sectors. Since income is insignificant, we impose a parameter value of zero by dropping it from the equation. To calculate sectoral P/L ratios, we assume the BOD case (COD=O), add the constant term to the estimated parameters for the sector dummies, and calculate the antilogs of the results. 23 increasing manufacturing share dominate the clean compositional trend and the first effects of rising wages and regulation on pollution intensity. To a striking degree, BOD growth in our intemational sample is due to increased emissions in developing Asia. The overall result of these changes is a significant shift in group shares of total pollution. Table 5.2 shows that the OECD and COMECON countries drop significantly in share; the NIC's increase marginally; and the Asian LDC's jump from 29% to 37% ofthe total. Equally impressive, however, is the apparently moderate growth in total BOD emissions during the period when world concern over environmental damage was reaching a peak. While economic development was sparking greater interest in pollution, is was also setting the stage for real improvements in environmental performance. From 1977 to 1989, we estimate that total industrial BOD emissions grew by only 16% in these fifteen major industrial countries. Table 5.2: Trends in International Emissions: Selected Countries, 1977 - 1989 l____ _ BOD Emissions ('000 Kg./Dv) Region 1977 1980 1983 1986 1989 OECD 5,776 5,847 5,501 5,403 5,523 NIC's 1,565 1,917 1,848 2,19'7 2,188 ASIN LDC'S 4,617 5,030 5,566 6,183 6,883 COMECON 4,127 4,218 4,302 4,228 4,039 TOTAL 16,085 17,012 17,217 18,011[ 18,633 % of Sample Total 1977 1980 1983 1986 1989 OECD 36 34 32 30 30 NIC's 10 11 11 12 12 ASIAN LDC'S 29 30 32 34 37 COMECON 26 25 25 23 22 TOTAL 100 100 100 100 100 24 6. SUMMARY AND CONCLUSIONS In this paper, we have used new international data to investigate the relationship between industrial pollution and economic development. To test for a Kuznets effect, we measure the effect of income growth on three proximate determinants of pollution: The share of manufacturing in total output; the sectoral composition of manufacturing; and the intensity (per unit of output) of industrial pollution at the end-of-pipe. We find that the manufacturing share follows a Kuznets-type trajectory, but the other two determinants do not. Sectoral composition gets 'cleaner' through middle-income status and then stabilizes. At the end-of-pipe, pollution intensity declines strongly with income. We attribute part of this to stricter regulation as income increases, and partly to pollution-labor complementarity in production. Our results suggest that income elasticities of both pollution- and labor-intensity are approximately minus one. The remarkable implication is that a sector 'spollution/labor ratio is conistant across countries at all income levels. Our findings motivate two illustrative simulation exercises. First, for a set of income benchmarks, we simulate total pollution by combining representative measures of manufacturing share in output, sectoral composition, and end-of-pipe pollution abatement. We do not see a Kuznets-type story in the result, since total pollution rises rapidly through middle-income status and remains approximately constant thereafter. In three counterfactual experiments, we assess the relative importance of the three proximate determinants. Our results highlight the domninance of end-of-pipe reductions as wages and regulation increase with development. The combined influence of changes in manufacturing share and sectoral composition is lower by almost two orders of magnitude. Our second simulation uses international panel data to explore the implications of constant sectoral pollution/labor ratios. We estimate recent trends in water pollution for fifteen major 25 industrial nations in the OECD, the NIC's, Asian LDC's and the ex-COMECON economies. We find approximately stable emissions in the OECD and ex-COMECON, moderate increases in the NIC's and rapidly-growing pollution in the Asian LDC's. During the 1980's, our estimates suggest that the latter group displaced the major OECD economies as the world's largest generator of organic water pollution. Overall, however, the negative feedback from economic development to pollution intensity was sufficient to hold total world pollution growth to around 15% during a twelve-year sample period. In closing, it is worth asking whether these results are cause for optimism or pessimism. The appropriate answer seems to be 'both.' It is comforting to see that industrial water emissions level off in richer economies because pollution intensity has an elastic response to income growth. Unfortunately, unitary elasticity implies that total emissions remain constant unless other factors intervene. Of course, industry tends to deconcentrate over time as infrastructure improves and prosperity spreads. Constant total emissions may therefore be consistent with improving water quality in at least some areas. However, the continued existence of many seriously-polluted waterways, even in the most prosperous countries, suggests that economic development remains far short of a Kuznets-style happy ending in the water sector. 26 REFERENCES Afsah, Shakeb, Benoit Laplante and David Wheeler, 1996, "Controlling Industrial Pollution: A New Paradigm," World Bank, Policy Research Department Working Paper Beede, David, David Bloom and David Wheeler, 1992, "Measuring and Explaining Cross- Establishment Variation in the Generation and Management of Industrial Waste, World Bank (mimeo.) Birdsall, Nancy and David Wheeler, 1993, "Trade Policy and Industrial Pollution in Latin America: Where are the Pollution Havens? Journal of Environment and Development, 2, 1, Winter, 137-149. Christensen, L., D. Jorgensen and L. Lau, 1973, "Transcendental Logarithmic Production Frontiers," Review of Economics and Statistics, 55:28-45 Dasgupta, Susmita and David Wheeler, 1996, "Citizen Complaints as Environmental Indicators: Evidence from China," World Bank, Policy Research Department Working Paper, December Dasgupta, Susmita, Ashoka Mody, Subhendu Roy and David Wheeler, 1995, "Environmental Regulation and Development: A Cross-Country Empirical Analysis," World Bank, Policy Research Department Working Paper, No. 1448, April Dasgupta, Susmita, Mainul Huq, David Wheeler and C.H.Zhang, 1996, "Water Pollution Abatement by Chinese Industry: Cost Estimates and Policy Implications," World Bank, Policy Research Department Working Paper Grossman, Gene and Alan Krueger, 1995, "Economic Growth and the Environment," Quarterly Journal of Economics, May, 353-377 Gruver, G.W., 1976, "Optimal Investment in Pollution Control Capital in a Neoclassical Growth Context," Journal of Environmental Economics and Management, 5, 165-177. Hartman, Raymond, Mainul Huq and David Wheeler, 1997, "Why Paper Mills Clean Up: Determninants of Pollution Abatement in Four Asian Countries," World Bank, Policy Research Department Working Paper, Number 1710. Hartman, Raymond, Manjula Singh and David Wheeler, 1997, "The Cost of Air Pollution Abatement," Applied Economics (forthcoming) Hettige, H., R.E.B. Lucas and D. Wheeler, 1992, "The Toxic Intensity of Industrial Production: Global Patterns, Trends and Trade Policy," American Economic Review Papers and Proceedings, 82, 478-481. 27 Hettige, Hemamala, Paul Martin, Manjula Singh and David Wheeler, 1995, "IPPS: The Industrial Pollution Projection System," World Bank, Policy Research Department Working Paper, February. Hettige, Hemamala, Mainul Huq, Sheoli Pargal and David Wheeler, 1996, "Determinants of Pollution Abatement in Developing Countries: Evidence from South and Southeast Asia," 1996, World Development, December. Hettige, Hemamala, Manjula Singh, Sheoli Pargal and David Wheeler, 1997, "Formal and Informal Regulation of Industrial Pollution: Evidence from the US and Indonesia," World Bank Economic Review, Fall. John, A. and R. Pecchenino, 1992, "An Overlapping Generations Model of Growth and the Environrment," Department of Economics, Michigan State University (mimeo.). Levinson, Arik, 1996, "Environmental Regulations and Manufacturers' Location Choices: Evidence from the Census of Manufactures," Journal of Public Economics, 62, 5-29. Mani, Muthukumara S. and David Wheeler, 1997, "In Search of Pollution Havens? Dirty Industry in the World Economy, 1960-1995," World Bank, Policy Research Department Working Paper (forthcoming) Mani, Muthukumara S., 1996, "Environmental Tariffs on Polluting Imports: An Empirical Study," Environmental and Resource Economics, 7, 391-411 Mody, Ashoka and David Wheeler, Automation and World Competition: New Technologies, Industrial Location, and Trade (London: Macmillan Press, 1990). Pargal, S. and D. Wheeler, 1996, "Informal Regulation in Developing Countries: Evidence from Indonesia," Journal of Political Economy, December. Robison, David H., 1988, "Industrial Pollution Abatement: The Impact on the Balance of Trade," Canadian Journal of Economics, 21, 702-706. Seldon, Thomas and Daqing Song, 1994, "Environmental Quality and Development: Is There a Kuznets Curve for Air Pollution Emissions?" Journal of Environmental Economics and Management, 27, 147-162. Seldon, Thomas and Daqing Song, 1995, "Neoclassical Growth, the J Curve for Abatement, and the Inverted U Curve for Pollution," Journal of Environmental Economics and Management, 29, 162-168. Shafik, Nemat, 1994, "Economic Development and Environmental Quality: An Econometric Analysis," Oxford Economic Papers, 46, 757-773 28 Syrquin, Moshe, 1989, "Patterns of Structural Change," in Handbook of Development Economics, Vol. 1, H. Chenery and T.N. Srinivasan, eds., (Amsterdam: North-Holland) Tobey, James A., 1990, "The Effects of Domestic Environmental Policies on Patterns of World Trade: An Empirical Test," Kyklos 43, Fasc. 2, 191-209. Wang, Hua and David Wheeler, 1996, "Pricing Industrial Pollution in China: An Econometric Analysis of the Levy System," World Bank, Policy Research Department Working Paper, No. 1644. Wheeler, David and Ashoka Mody, 1992, "International Investment Location Decisions: The Case of U.S. Firms," Journal of International Economics, 33, 57-76. Wheeler, David, 1991, "The Economics of Industrial Pollution Control: An International Perspective," 1991, World Bank, Industry and Energy Department Working Paper, No. 60, January. 29 Appendix I: DATA SOURCES Brazil: The water pollution data for the Sao Paulo Metropolitan region of Brazil were collected by CETESB, the environmental agency for Sao Paulo State. Our pollution estimates are based on CETESB's 1250-plant database, which includes measures of BOD loads in kg/day. The corresponding employment data cane from the Sao Paulo State Ministry of Labor, which provided 2-digit sectoral information from 1991 on nearly 41,000 plants and 2.15 million workers. China: Water pollution data for China were obtained from the National Environmental Protection Agency (NEPA), which maintains a comprehensive database on major sources of industrial pollution in China. Our estimates are based on NEPA's 1993 emissions data for 269 factories scattered throughout China. Finland: The Finnish economic data, aggregated at the 3-digit ISIC level, were provided by the Central Statistical Office, covering both white and blue collar workers for 1989. The pollution data were provided by the Industrial Waste Water Office of the National Board of Waters and the Environment. They cover water emissions in 1992 from 193 large water-polluting factories. India: The India data are from the state of Tamil Nadu. Plant-level pollution data and employment data for 1993-94 were provided by the Tamil Nadu Pollution Control Board, which monitors air and water pollution for all the manufacturing units in the state. Indonesia: The Indonesia data came from two different soturces. The plant-level emissions data were provided by BAPEDAL, Indonesia's National Pollution Control Agency in the Ministry of Environment. The economic data are from Indonesia's Central Statistics Bureau (BPS). Korea: Korean pollution data were provided by the National Pollution Control Agency. They cover water emissions by 13,504 facilities in 1991. Complementary employment data have been drawn from Korea's National Statistical Yearbooks and the LO's International Labor Statistics, 1991. Mexico: Data for water ernissions in the Monterrey Metropolitan Area were provided by the State Water Monitoring Authority. The data cover emissions from 7,500 facilities in 1994. Complementary employment data were provided by Mexico's Census Bureau (INEGI). Netherlands: Water emissions and employment data for approximately 700 regularly-monitored facilities in 1990 were provided by the Emissions Inventory System maintained by the Ministry of Housing, Spatial Planning and the Environment (VROM). Philippines: Water emissions and employment data for factories in the Metro Manila Area (MMvIA) were provided the Philippines Department of Natural Resources (DENR) and the Laguna Lake Development Authority. 30 Taiwan (China): Water emissions and employment data for 1,800 plants were provided by the Water Quality Protection division of the Taiwan Environment Protection Agency. Thailand: Seatec International, a private-sector environmental consulting firm in Bangkok provided plant-level data from two industrial estates in Rangsit and Suksawat. The dataset contained information on water emissions and employment for approximately 450 facilities in 1992. Sri Lanka: Water pollution and employment data for Sri Lanka were obtained from a study of waste water treatment options for the Ekala/Ja-ela Industrial Estate, which includes 143 industrial establishments with 21,000 employees. The data were collected by a joint project of the World Bank's Metropolitan Environment Improvement Program and the Sri Lankan Board of Investment. Ekala/Ja-Ela industrial estate is one of the two major industrial estates in Sri Lanka. U.S.A.: The information for the United States were drawn from two main sources. The water emissions data have been collected from regional databases which monitor industrial water discharges as part of the U.S. Environmental Protection Agency's NPDES system. Employment data are from the U.S. Census Bureau's Longitudinal Research Database. 31 Appendix I: Tables Table 4.1: Log (Manufacturing Share of Total Output) vs. Log (Income Per Capita), 1975-1994* Independent OLS Fixed Random OLS Fixed Random Variables Effects Effects _ Effects Effects Log Income 0.9195 0.5147 0.5726 1.3585 0.7719 0.9402 (2.483) (1.815) (2.076) ('7.934) (6.815) (8.323) Log Income squared -0.0442 -0.04268 -0.0364 -0.0704 - -0.0622 -(1.796) -(2.185) -(1.923) -(6.446) 0.05988 -(8.988) _____ _ ___ ___ -(8.772) Log Income * Time 0.07527 0.0450 0.0511 (1.993) (3.126) (3.456) _ Log Income squared -0.0045 -0.0028 -0.0032 * Time -(1.858) -(3.154) -(3.538) _ Time -0.3194 -0.1614 -0.1911 -0.0120 0.0146 0.0062 -(2.196) -(2.762) -(3.205) -(4.224) (6.612) (3.190) Constant -6.1460 -3.3256 4.074 -7.9373 4.2714 -5.3585 -(4.480) -(3.254) -(4.095) -(12.013) -(8.897) -(11.415) Number of 1136 1136 1136 1136 1136 1136 Observations _ Number of Time 16 16 16 16 16 16 Periods _ Adjusted R-squared 0.299 0.151 0.015 _0.297 0.171 0.003 * T-statistics in parentheses Table 4.2: Log (Sector-Weighted BOD Intensity) vs. Log (Income Per Capita), 1975-1994* Independent OLS Fixed Random OLS Fixed Random Variables Effects Effects _ Effects Effects Log Income 0.2846 -0.3903 -0.3709 -0.0236 -0.5362 -0.5283 (1.018) -(3.566) -(3.427) -(0.184) -(12.719) -(12.616) Log Income squared -0.0234 0.01749 0.0164 -0.0019 0.0269 0.0267 -(1.321) (2.459) (2.344) -(0.249) (11.050) (10.974) Log Income * Time -0.0117 -0.00736 -0.0071 *** *** -(0.416) -(1.214) -(1.171) _ Log Income squared 0.0009 0.0005 0.0004 _ ** * Time (0.578) (1.398) (1.374) __. Time 0.03203 0.0319 0.0300 0.0034 0.0055 0.0051 (0.278) (1.232) (1.161) (2.166) (6.402) (6.351) Constant 0.6935 3.4369 3.3470 1.7679 3.9912 3.9435 (0.633) (8.168) (8.035) (3.373) (21.143) (20.989) Number of 928 928 928 928 928 928 Observations _ Number of Time 16 16 16 16 16 16 Periods _ Adjusted R-squared 0.043 0.043 0.043 0.043 0.041 0.041 * T-statistics in parentheses 32 Table 4.3: Intensity Equations for Pollution and Labor (in Prices and Regulation) Dep. Var. - Log. of: Pollutionl Polution/ Labor/ Laborl _ _________ _o_ Output utput o nO Independent Variables Coef. t-stat. Coef. t-stat. Coef. t-stat Cod. t-stat. Log Wage -1.714 -3.055** -0.015 -0.044 -0.711 -8.473** -0.379 -6.380** Log Brown Index 2.459 0.958 -2.995 -1.601 0.164 0.422 -1.467 -4.657** Log Electricity Price 6.123 3.684** 0.620 0.526 -0.098 -0.580 -0.564 -3.354** Log Real Interest Rate 0.455 1.903* 0.029 0.872 _ COD 4.308 4.829** 2.406 2.559** Food 5.658 5.044** 4.511 3.940** -0.571 -3.817** -0.813 -4.239** Textiles 4.601 4.163** 3.932 3.449** -0.018 -0.125 -0.168 -0.881 Wood Products 3.717 2.775** 3.103 2.176** -0.021 -0.140 0.053 0.280 Paper 6.864 6.102** 4.946 4.318** -0.151 -1.006 -0.231 -1.205 Chemicals 4.614 3.916** 3.236 2.785** -0.526 -3.320** -0.715 -3.669** Non-Metallic Minerals 1.290 1.118 1.023 0.879 -0.123 -0.823 -0.242 -1.263 Metals 2.312 1.910* 0.988 0.828 -0.697 -4.383** -0.771 -3.903** Metal Products 3.538 3.063** 2.232 1.920* -0.278 -1.842* -0.502 -2.612** |Constant _-27.244 -2.466** -0.702 -0.096 -5.253 -3.162** 1.933 1.479 Adjusted R-square 0.63 0.34 0.92 0.81 Number of Observations 68 99 80 116 significant at 1% confidence level ** significant at 5% confidence level * significant at 10% confidence level 33 Table 4.4: Intensity Equations for Pollution and Labor (in Prices and Regulation) Dep. Var. - Log of: Pollution/ Labor/ Pollution/ Labor/ Output Outpu Output Output Independent Variables Coef. t-stat Coef. t-stat. Coef. t-stat. Coef. t-stat Log Wage -1.211 -6.153 -0.666 -22.600 Log Brown index _ -4.885 -5.052 -2.872 -14.642 Log Electricity Price 5.634 3.565 -0.280 -1.223 3.765 2.384 -1.173 -3.871 Log Real Interest Rate 0.370 1.668 0.025 0.807 0.234 0.957 -0.058 -1.27 COD 4.375 4.923 -0.110 -0.852 4.125 4.32 -0.197 -1.071 Food 5.485 4.959 -0.585 -3.979 5.073 4.278 -0.792 -3.767 Textiles 4.586 4.153 -0.018 -0.124 4.556 3.842 -0.016 -0.074 Wood Products 3.654 2.733 -0.028 -0.188 3.429 2.392 -0.131 -0.622 Paper 6.675 6.032 -0.167 -1.132 6.222 5.247 -0.396 -1.882 Chemicals 4.246 3.815 -0.558 -3.764 3.364 2.837 -1.023 -4.866 Non-Metallic Minerals 1.114 0.979 -0.138 -0.941 0.681 0.558 -0.362 -1.720 Metal s 2.014 1.723 -0.722 4.729 1.243 0.999 -1.094 -5.045 Metal Products 3.323 2.935 -0.296 -2.011 2.784 2.299 -0.560 -2.661 Constant -16.903 -7.208 -4.403 -13.734 3.324 0.661 7.522 7.83 Adjusted R-square 0.64 0.93 0.59 0.85 Number of 68 80 68 80 .Observations _I= _ I _II _ I 34 Table 4.5: Intensity Equations for Pollution and Labor (in Income Per Capita) Dep. Var. - Log of:- Pollution/Output Labor/Output Pollution/Labor Independent variables Coef. t-stat Coef. t-stat Coef. t-stat Log Income -0.875 -3.26** -1.003 |17.041** 0.120 0.449 COD 1.908 2.542** 1.930 2.576** Food 4.629 4.096** -0.925 -4.085** 5.492 4.868** Textiles 4.055 3.588** -0.150 -0.662 4.143 3.673** Wood Products 3.315 2.350** 0.047 0.206 3.485 . 2.475** Paper 5.064 4.481** -0.350 -1.547 5.353 4.745** chemical 3.349 2.963** -0.957 -4.225** 4.244 3.762** mineral 1.151 1.003 -0.361 -1.595 1.414 1.235 metal 1.119 0.962 -0.964 4.171** 2.038 1.786 metal products 2.367 2.071** -0.635 -2.803** 2.983 2.615** constant -8.872 -3.615** -1.497 -2.828** -7.246 -2.972** Adjusted R-square 0.35 0.74 0.39 Number of 99 116 100 Observations Table 5.1: Industrial Pollution and Economic Development: Simulation Experiments Income Manuf. BOD EOP Total Variable Variable Variable ($US) Share Intens. Intens. BOD Share BOD EOP 500 11.0 5.4 1.00 100 100 100 100 1,500 13.4 4.8 0.39 128 366 268 118 2,500 16.9 4.6 0.25 167 771 428 127 3,500 18.5 4.3 0.19 177 1,179 553 133 4,500 21.0 4.0 0.15 197 1,726 670 138 6,000 24.3 4.0 0.12 237 2,663 888 144 8,000 23.5 4.2 0.09 247 3,424 1,230 150 10,000 23.3 4.1 0.08 253 4,256 1,531 155 12,000 22.6 4.2 0.07 255 4,953 1,859 160 14,000 21.2 4.2 0.06 246 5,399 2,188 163 17,000 20.3 4.3 0.05 248 6,298 2,709 168 20,000 19.5 4.3 0.04 249 7,904 3,559 175 35 Table 5.3: Estimated Industrial BOD Emissions Selected Countiies, 1977 - 1989 ('000 K&/]Day) COUNTRY 1977 1980 1983 1986 1989 UTNED STATES 2,652 2,743 2,551 2,454 2,564 FRANCE 739 716 683 666 652 GERMANY (FORIMER FR) 929 932 800 789 8(0 JAPAN 1,456 1,456 1,467 1,493 1,507 OECD 5,776 5,847 5,501 5,403 5,523 BRAZIL 611 867 771 965 91L4 MEXICO 109 131 130 179 174 KOREA, REPUBLIC OF 261 282 296 345 377 TAIWAN, CHINA 208 239 252 296 282 SOUTIH AFRICA 226 238 245 245 262 TURKEY 150 160 155 167 17'9 NIC's 1,565 1,917 1,848 2,197 2,188 CH[NA 3,118 3,358 3,957 4,551 5,023 INDIA 1,309 1,457 1,380 1,277 1,428 INDONESIA 190 214 230 355 433 DEVELOPING ASIA 4,617 5,030 5,566 6,183 6,883 POLAND 578 581 546 484 459 U.S.S.R., FORMER 3,549 3,638 3,756 3,744 3,580 EX-COMECON 4,127 4,218 4,302 4,228 4,039 TOTAL 16,085 17,012 17,217 18,011 18,633 36 Policy Research Working Paper Series Contact Title Author Date for paper WPS1856 Surviving Success: Policy Reform Susmita Dasgupta November 1997 S. Dasgupta and the Future of Industrial Hua Wang 32679 Pollution in China David Wheeler WPS1857 Leasing to Support Small Businesses Joselito Gallardo December 1997 R. Garner and Microenterprises 37664 WPS1 858 Banking on the Poor? Branch Martin Ravallion December 1997 P. Sader Placement and Nonfarm Rural Quentin Wodon 33902 Development in Bangladesh WPS1859 Lessons from Sao Paulo's Jorge Rebelo December 1997 A. Turner Metropolitan Busway Concessions Pedro Benvenuto 30933 Program WPS1860 The Health Effects of Air Pollution Maureen L. Cropper December 1997 A. Maranon in Delhi, India Nathalie B. Simon 39074 Anna Alberini P. K. Sharma WPS1861 Infrastructure Project Finance and Mansoor Dailami December 1997 M. Dailami Capital Flows: A New Perspective Danny Leipziger 32130 WPS1862 Spatial Poverty Traps? Jyotsna Jalan December 1997 P. Sader Martin Ravallion 33902 WPS1863 Are the Poor Less Well-insured? Jyotsna Jalan December 1997 P. Sader Evidence on Vulnerability to Income Martin Ravallion 33902 Risk in Rural China WPS1 864 Child Mortality and Public Spending Deon Filmer December 1997 S. Fallon on Health: How Much Does Money Lant Pritchett 38009 Matter? WPS1865 Pension Reform in Latin America: Sri-Ram Aiyer December 1997 P. Lee Quick Fixes or Sustainable Reform? 37805 WPS1866 Circumstance and Choice: The Role Martha de Melo December 1997 C. Bernardo of Initial Conditions and Policies in Cevdet Denizer 31148 Transition Economies Alan Gelb Stoyan Tenev WPS1867 Gender Disparity in South Asia: Deon Filmer January 1998 S. Fallon Comparisons Between and Within Elizabeth M. King 38009 Countries Lant Pritchett WPS1868 Government Support to Private Mansoor Dailami January 1998 M. Dailami Infrastructure projects in Emerging Michael Klein 32130 Markets Policy Research Working Paper Series Conltact Title Author Date for paper WPS1859 Risk Reducation and Public Spending Shantayanan Devarajan January 1998 C Bernardo Jeffrey S. Hammer 31 148 WPS1870 The Evolution of Poverty and Raji Jayaraman Januarv 1998 P. Lanjouw Inequality in Indian Villages Peter Lanjouw 34529 WPS1871 Just How Big Is Giobal Production Aiexande, J. Yeats january 1998 L. Tabada Sharing? 38896 WPS1872 How Integration into the Central Ferdinand Bakoup January 1998 L. Tabada African Economic and Monetary David Tarr 36896 Community Affects Cameroon's Economy: General Equilibrium Estimates WPS1873 Wage Misalignment in CFA Countries: Martin Rama January 1998 S. Fallon Are Labor Market Policies to Blame? 38009 WPS1874 Health Policy in Poor Countries: Deon Filmer January 1998 S. Failon Weak Links in the Chain Jeffrey Hammer 38009 Lant Pritchett WPS1875 How Deposit Insurance Affects Robert Cull January 1998 P. Sintim-Aboagye Financial Depth (A Cross-Country 37644 Analysis)