Reported expenditures for environmental protection in the U.S. are estimated to exceed $150 billion annually or about 2% of GDP. This estimate is often used as an assessment of the burden of current regulatory efforts and a standard against which the associated benefits are measured. This makes it a key statistic in the debate surrounding both current and future environmental regulation. Little is known, however, about how well reported expenditures relate to true economic cost. True economic cost depends on whether reported environmental expenditures generate incidental savings, involve uncounted burdens, or accurately reflect the total cost of environmental protection. This paper explores the relationship between reported expenditures and economic cost in a number of major manufacturing industries. Previous research has suggested that an incremental $1 of reported environmental expenditures increases total production costs by anywhere from $1 to $12, i.e., increases in reported costs probably understate the actual increase in economic cost. Surprisingly, our results suggest the reverse, that increases in reported costs may overstate the actual increase in economic cost. Our results are based a large plant-level data set for eleven four-digit SIC industries. We employ a cost-function modeling approach that involves three basic steps. First, we treat real environmental expenditures as a second output of the plant, reflecting perceived environmental abatement efforts. Second, we model the joint production of conventional output and environmental effort as a cost-minimization problem. Third, we calculate the effect of an incremental dollar of reported environmental expenditures at the plant, industry, and manufacturing sector levels. Our approach differs from previous work with similar data by considering a large number of industries, using a cost-function modeling approach, and paying particular attention to plant-specific effects. Our preferred, fixed-effects model obtains an aggregate estimate of thirteen cents in increased costs for every dollar of reported incremental pollution control expenditures, with a standard error of sixty-one cents. This single estimate, however, conceals the wide range of values observed at the industry and plant level. We also find that estimates using an alternative, random-effects model are uniformly higher. Although the higher, random-effects estimates are more consistent with previous work, we believe they are biased by omitted variables characterizing differences among plants. While further research is needed, our results suggest that previous estimates of the economic cost associated with environmental expenditures have been biased upward and that the possibility of overstatement is quite real. Key words: environmental costs, fixed-effects, translog cost model
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Costs of Air Quality Regulation
July 1999
Working Paper Number:
CES-99-09
This paper explores some costs associated with environmental regulation. We focus on regulation pertaining to ground-level- ozone (O) and its effects on two manufacturing industries - industrial organic chemicals (SIC 2865-9) and miscellaneous plastic products (SIC 308). Both are major emitters of volatile organic compounds (VOC) and nitrogen oxides (NO), the chemical precursors to ozone. Using plant-level data from the Census Bureau's Longitudinal Research Database (LRD), we examine the effects of regulation on the timing and magnitudes of investments by firms and on the impact it has had on their operating costs. As an alternative way to assess costs, we also employ plant-level data from the Pollution Abatement Costs and Expenditures (PACE) survey. Analyses employing average total costs functions reveal that plants' production costs are indeed higher in (heavily-regulated) non-attainment areas relative to (less-regulated) attainment areas. This is particularly true for younger plants, consistent with the notion that regulation is most burdensome for new (rather existing) plants. Cost estimates using PACE data generally reveal lower costs. We also find that new heavily-regulated plants start out much larger than less-regulated plants, but then do not invest as much. Among other things, this highlights the substantial fixed costs involved in obtaining expansion permits. We also discuss reasons why plants may restrict their size.
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Pollution Abatement Expenditures and Plant-Level Productivity: A Production Function Approach
August 2003
Working Paper Number:
CES-03-16
In this paper, we investigate the impact of environmental regulation on productivity using a Cobb-Douglas production function framework. Estimating the effects of regulation on productivity can be done with a top-down approach using data for broad sectors of the economy, or a more disaggregated bottom-up approach. Our study follows a bottom-up approach using data from the U.S. paper, steel, and oil industries. We measure environmental regulation using plant-level information on pollution abatement expenditures, which allows us to distinguish between productive and abatement expenditures on each input. We use annual Census Bureau information (1979-1990) on output, labor, capital, and material inputs, and pollution abatement operating costs and capital expenditures for 68 pulp and paper mills, 55 oil refineries, and 27 steel mills. We find that pollution abatement inputs generally contribute little or nothing to output, especially when compared to their '''productive''' equivalents. Adding an aggregate pollution abatement cost measure to a Cobb-Douglas production function, we find that a $1 increase in pollution abatement costs leads to an estimated productivity decline of $3.11, $1.80, and $5.98 in the paper, oil, and steel industries respectively. These findings imply substantial differences across industries in their sensitivity to pollution abatement costs, arguing for a bottom-up approach that can capture these differences. Further differentiating plants by their production technology, we find substantial differences in the impact of pollution abatement costs even within industries, with higher marginal costs at plants with more polluting technologies. Finally, in all three industries, plants concentrating on change-in-production-process abatement techniques have higher productivity than plants doing predominantly end-of-line abatement, but also seem to be more affected by pollution abatement operating costs. Overall, our results point to the importance using detailed, disaggregated analyses, even below the industry level, when trying to model the costs of forcing plants to reduce their emissions.
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ENVIRONMENTAL REGULATION AND INDUSTRY EMPLOYMENT: A REASSESSMENT
July 2013
Working Paper Number:
CES-13-36
This paper examines the impact of environmental regulation on industry employment, using a structural model based on data from the Census Bureau's Pollution Abatement Costs and Expenditures Survey. This model was developed in an earlier paper (Morgenstern, Pizer, and Shih (2002) - MPS). We extend MPS by examining additional industries and additional years. We find widely varying estimates across industries, including many implausibly large positive employment effects. We explore several possible explanations for these results, without reaching a satisfactory conclusion. Our results call into question the frequent use of the average impacts estimated by MPS as a basis for calculating the quantitative impacts of new environmental regulations on employment.
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Misallocation or Mismeasurement?
February 2020
Working Paper Number:
CES-20-07
The ratio of revenue to inputs differs greatly across plants within countries such as the U.S. and India. Such gaps may reflect misallocation which hinders aggregate productivity. But differences in measured average products need not reflect differences in true marginal products. We propose a way to estimate the gaps in true marginal products in the presence of measurement error. Our method exploits how revenue growth is less sensitive to input growth when a plant's average products are overstated by measurement error. For Indian manufacturing from 1985'2013, our correction lowers potential gains from reallocation by 20%. For the U.S. the effect is even more dramatic, reducing potential gains by 60% and eliminating 2/3 of a severe downward trend in allocative efficiency over 1978'2013.
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Environmental Regulation, Abatement, and Productivity: A Frontier Analysis
September 2013
Working Paper Number:
CES-13-51
This research studies the link between environmental regulation and plant level productivity in two U.S. manufacturing industries: pulp and paper mills and oil refineries using Data Envelopment Analysis (DEA) models. Data on abatement spending, emissions and abated emissions are used in different DEA models to study plant productivity outcomes when accounting for abatement spending or emissions regulations. Results indicate that pulp and paper mills and oil refineries in the U.S. suffered decreases in productivity due to pollution abatement activities from 1974 to 2000. These losses in productivity are substantial but have been slowly trending downwards even when the regulations have tended to become more stringent and emission of pollutants has declined suggesting that the best practice has shifted over time. Results also show that the reported abatement expenditures are not able to explain all the losses arising out of regulation suggesting that these abatement expenditures are consistently under-reported.
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Measuring Cross-Country Differences in Misallocation
January 2016
Working Paper Number:
CES-16-50R
We describe differences between the commonly used version of the U.S. Census of Manufactures available at the RDCs and what establishments themselves report. The originally reported data has substantially more dispersion in measured establishment productivity. Measured allocative efficiency is substantially higher in the cleaned data than the raw data: 4x higher in 2002, 20x in 2007, and 80x in 2012. Many of the important editing strategies at the Census, including industry analysts' manual edits and edits using tax records, are infeasible in non-U.S. datasets. We describe a new Bayesian approach for editing and imputation that can be used across contexts.
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Micro Data and the Macro Elasticity of Substitution
March 2012
Working Paper Number:
CES-12-05
We estimate the aggregate elasticity of substitution between capital and labor in the US manufacturing sector. We show that the aggregate elasticity of substitution can be expressed as a simple function of plant level structural parameters and sufficient statistics of the distribution of plant input cost shares. We then use plant level data from the Census of Manufactures to construct a local elasticity of substitution at various levels of aggregation. Our approach does not assume the existence of a stable aggregate production function, as we build up our estimate from the cross section of plants at a point in time. Accounting for substitution within and across plants, we find that the aggregate elasticity is substantially below unity at approximately 0.7. Lastly we assess the sources of the bias of aggregate technical change from 1987 to 1997. We find that the labor augmenting character of aggregate technical change is due almost exclusively to labor augmenting productivity growth at the plant level rather than relative growth in capital intensive plants.
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Estimating market power Evidence from the US Brewing Industry
January 2017
Working Paper Number:
CES-17-06R
While inferring markups from demand data is common practice, estimation relies on difficult-to-test assumptions, including a specific model of how firms compete. Alternatively, markups can be inferred from production data, again relying on a set of difficult-to-test assumptions, but a wholly different set, including the assumption that firms minimize costs using a variable input. Relying on data from the US brewing industry, we directly compare markup estimates from the two approaches. After implementing each approach for a broad set of assumptions and specifications, we find that both approaches provide similar and plausible markup estimates in most cases. The results illustrate how using the two strategies together can allow researchers to evaluate structural models and identify problematic assumptions.
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A Change of PACE: Comparing the 1994 and 1999 Pollution Abatement Costs and Expenditures Surveys
July 2004
Working Paper Number:
CES-04-09
Since 1973, the Pollution Abatement Costs and Expenditures (PACE) survey has been the principle source of information on U.S. industries' capital expenditure and operating costs associated with pollution abatement efforts. The PACE survey was discontinued after 1994 and then revived in 1999 for one year ' in a substantially different form than the preceding surveys however, making longitudinal analysis quite difficult. Conceptual differences include matters as fundamental as the scope and meaning of pollution abatement as well as the definition of operating costs. A number of other critical changes also exist, including ones of industrial coverage and sample selection. This paper is the first comprehensive effort to document the many changes in the PACE survey across these years and to provide a detailed guide for researchers and policymakers who wish to compare the 1994 and 1999 data. Overall, we find a 27% decline in environmental spending by the manufacturing sector between these two years, though there appears to be significant heterogeneity across industries. We discuss potential reasons for this dramatic decline, focusing mainly on issues of survey methodology and design. This paper should help inform current efforts to redevelop the PACE survey and re-establish it as a regular, annual survey.
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Environmental Regulation And Manufacturing Productivity At The Plant Level
March 1993
Working Paper Number:
CES-93-06
This paper presents results for an analysis of plant-level data from three manufacturing industries (paper, oil, and steel). We combine productivity data from the Longitudinal Research Database ( LRD ) with pollution abatement expenditures from the Census Bureau's Pollution Abatement Cost and Expenditures (PACE) survey, as well as regulatory measures taken from datasets maintained by the Environmental Protection Agency. We use data from 1979 to 1985, considering both labor and total factor productivity, both levels and growth rates, and both annual measures and averages over the period. We find a strong connection between regulation and productivity when regulation is measured by compliance costs. More regulated plants have significantly lower productivity levels and slower productivity growth rates than less regulated plants. The magnitude of the impacts are larger than expected: a $1 increase in compliance costs appears to reduce TFP by the equivalent of $3 to $4. Thus, commonly used methods of calculating the impact of regulation on productivity are substantially underestimated. These results are generally consistent across industries and for different estimation methods. Our other measures of regulation (compliance status, enforcement activity, and emissions) show much less consistent results. Higher enforcement, lower compliance, and higher emissions are generally associated with lower productivity levels and slower productivity growth, but the coefficients are rarely significant.
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