6-1

CHAPTER 6

EMPLOYMENT AND ECONOMIC IMPACT ANALYSIS

In this chapter, we estimate select employment effects of the rule, for both the 

regulated industry (electric power industry) and the environmental control industry.

6.1 Employment Impacts for the MATS

In addition to addressing the costs and benefits of the MATS, EPA has estimated certain 

impacts of this on employment, which are presented in this section.1 While a standalone 

analysis of employment impacts is not included in a standard cost-benefit analysis, such an

analysis is of particular concern in the current economic climate of sustained unemployment. 

Executive Order 13563, states, “Our regulatory system must protect public health, welfare, 

safety, and our environment while promoting economic growth, innovation, competitiveness, 

and job creation” (emphasis added). Therefore, the Agency has provided this analysis to inform 

the discussion of labor demand and employment impacts. The estimates include the 

employment impacts on the regulated industry over time. Also provided are certain short-term 

and on-going employment impacts (increase in labor demand) associated with the construction 

of needed pollution control equipment, and other activities, to comply with the regulation.

The Agency has not quantified the rule’s effects on all labor in other sectors not 

regulated by the MATS, or the effects induced by changes in workers’ incomes. What follows is 

an overview of the various ways that environmental regulation can affect employment, 

followed by a discussion of the estimated impacts of this rule. EPA continues to explore the 

relevant theoretical and empirical literature and to seek public comments in order to ensure 

that such estimates are as accurate, useful and informative as possible.

From an economic perspective, labor is an input into producing goods and services. If 

regulation requires that more labor be used to produce a given amount of output, the

additional labor is reflected by an increase in the cost of production.2 Moreover, when the 

economy is at full employment, we would not expect an environmental regulation to have an 

impact on overall employment because labor is being shifted from one sector to another. On 

the other hand, in periods of high unemployment, an increase in labor demand due to 

 

1

See the employment impacts appendix included in this RIA. 

2

It should be noted that if more labor must be used to produce a given amount of output, then this implies a 

decrease in labor productivity. A decrease in labor productivity will cause a short-run aggregate supply curve to 

shift to the left, and businesses will produce less, all other things being equal. 
6-2

regulation may result in a short-term net increase in overall employment due to the potential 

hiring of previously unemployed workers by the regulated sector to help meet new 

requirements (e.g., to install new equipment) or by the environmental protection sector to 

produce new abatement capital. With significant numbers of workers unemployed, the 

opportunity costs associated with displacing jobs in other sectors are likely to be smaller.

To provide a partial picture of the employment consequences of this rule, EPA takes two 

approaches. First, the analysis uses the results of Morgenstern, Pizer, and Shih (2002) to 

estimate the effects of the regulation on the regulated industry, the electric power industry in 

this case. This approach has been used by EPA previously in recent Regulatory Impact Analyses. 

Second, EPA uses information derived from engineering studies and projections of pollution 

controls from the power sector modeling to generate estimates of employment impacts to the 

pollution control sector. EPA is interested in public comments on the merits of including 

information derived in this fashion for assessing the employment consequences of regulations.

Section 6.2 discusses the estimates of the employment consequences in the electricity 

sectors, using the Morgenstern, et al. approach. Section 6.3 estimates the employment 

consequences in the environmental protection sector.

6.2 Employment Impacts Primarily on the Regulated Industry: Morgenstern, Pizer, and 

Shih (2002) 

EPA examined possible employment effects within the electric utility sector using a 

peer-reviewed, published study that explores historical relationships between industrial 

employment and environmental regulations (Morgenstern, Pizer, and Shih, 2002). For context, 

in 2007, the electric power generation, transmission and distribution sector (NAICS 2211) had 

approximately 510,000 paid employees (according to the 2007 Economic Census). Estimates 

from Morgenstern et. al. study have been applied in recent RIAs to derive the employment 

effects of new regulations within the regulated industry. (See, for example, the Regulatory 

Impact Analyses for the proposed MATS and final CSAPR regulations). We believe this study is 

applicable to this employment analysis, as it was for the MATS proposal, since the pollution 

control strategies or measures that form the basis of the cost inputs in the Morgenstern et al. 

analysis are primarily add-on or end-of-line pollution controls, in general covering more than 

70% of the abatement expenditures in most years and industries analyzed as shown in 

Table 6-1. The analysis of control strategies presented in Chapter 3 of this RIA are composed 

entirely of add-on or end-of-line pollution controls. Thus, the cost inputs in the Morgenstern et 

al. analysis are consistent with the cost inputs that enter into this analysis of employment 

impacts within the regulated industry for MATS. 
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Table 6-1. Percent of Abatement Expenditures in Different PACE Studies from Add-On or 

End-of-Line Control Measures3

Industry

Percent

1979 1983 1988 1991

Pulp and Paper 84 80 61 47

Plastics 85 88 75 67

Petroleum Refining 72 57 63 61

Iron and Steel 96 93 94 92

Determining the direction of employment effects in the regulated industry is challenging 

due to competing effects. The fundamental insight of Morgenstern, et al. is that environmental 

regulations can be understood as requiring regulated firms to add a new output (environmental 

quality) to their product mixes. Although legally compelled to satisfy this new demand, 

regulated firms have to finance this additional production with the proceeds of sales of their 

other (market) products. Satisfying this new demand requires additional inputs, including labor, 

and may alter the relative proportions of labor and capital used by regulated firms in their 

production processes. Thus, Morgenstern et al. decompose the overall effect of a regulation on 

employment into the following three subcomponents: 

 The “Demand Effect”: higher production costs raise market prices, reducing 

consumption (and production), thereby reducing demand for labor within the 

regulated industry (an unambiguously negative effect); 

 The “Cost Effect”: Assuming that the capital/labor ratio in the production 

process is held fixed, as production costs increase, plants use more of all inputs, 

including labor, to maintain a given level of output. For example, in order to 

reduce pollutant emissions while holding output levels constant, regulated firms 

may require additional labor (an unambiguously positive effect) ; 

 The “Factor-Shift Effect”: Regulated firms’ production technologies may be more 

or less labor intensive after complying with a regulation (i.e., more/less labor is 

required per dollar of output). “Environmental activities may be more labor 

intensive than conventional production,” meaning that “the amount of labor per 

dollar of output will rise.” However, activities may, instead, be less labor 

 

3

U.S. Bureau of the Census. Pollution Abatement Costs and Expenditures. Washington, DC: U.S. Government 

Printing Office, various years. The pulp and paper industry is defined by SIC 2611 & 2621, plastics by SICE 282, 

petroleum refining by SIC 2911, and iron and steel by SIC 332. For pulp and paper and iron and steel industries in 

1983 the data is partially estimated based on non reported data due to disclosure reasons. The 1984 (1989) data 

for plastics (pulp and paper) is used instead of 1983 (1988) due to a lack of reported data in the original year.
6-4

intensive because “cleaner operations could involve automation and less 

employment, for example.” (p. 416) (ambiguous effect)

Decomposing the overall employment impact of environmental regulation into three 

subcomponents clarifies the conceptual relationship between environmental regulation and 

employment in regulated sectors, and permitted Morgenstern, et al. to provide an empirical 

estimate of the net impact. For present purposes, the net effect is of particular interest, and is 

the focus of our analysis. 

The demand effect is expected to have an unambiguously negative effect on 

employment, the cost effect to have an unambiguously positive effect on employment, and the 

factor-shift effect to have an ambiguous effect on employment. Without more information with 

respect to the magnitudes of these three competing effects, it is not possible to predict the net

employment effect in the regulated sector.

Using plant-level Census information between the years 1979 and 1991, Morgenstern et 

al. estimate the size of each effect for four highly polluting and regulated industries (petroleum, 

plastic material, pulp and paper, and steel). On average across the four industries, each 

additional $1 million ($1987) spending on pollution abatement results in a (statistically 

insignificant) net increase of 1.55 (+/- 2.24) jobs. As a result, the authors conclude that 

increases in pollution abatement expenditures do not necessarily cause economically significant 

employment changes. The conclusion is similar to Berman and Bui (2001) who found that 

increased air quality regulation in Los Angeles did not cause in large employment changes. 

Since the Morgenstern, et al. parameter estimates are expressed in jobs per million 

($1987)4of environmental compliance expenditures, their study offers a transparent and simple 

way to transfer estimates for other employment analysis. For each of the three job effects 

outlined above, EPA used the Morgenstern et. al. four industry average parameters and 

standard errors along with the estimated private compliance costs to provide a range (based on 

the 95th percentile of results) of employment effects in the electricity sector associated with 

the rule. By applying these estimates to annualized cost for the final rule for the electric power 

sector as shown in Chapter 3 of this RIA ($9.60 billion in 2007$) , the Agency estimated each 

effect. The results are: 

 

4

The Morgenstern et al. analysis uses “production worker” as defined in the US Census Bureau’s Annual Survey of 

Manufactures (ASM) in order to define a job. This definition can be found on the Internet at 

http://www.census.gov/manufacturing/asm/definitions/index.html
6-5

 Demand effect: −39,000 to +2,000 jobs in the directly affected sector with a 

central estimate of −18,000; 

 Cost effect: +4,000 to +21,000 jobs in the directly affected sector with a central 

estimate of +12,000; and 

 Factor-shift effect: +200 to +27,000 jobs in the directly affected sector with a 

central estimate of +14,000. 

EPA estimates the net employment effect to range from −15,000 to +30,000 jobs in the 

directly affected sector with a central estimate of +8,000.5,6EPA recognizes there will be other 

employment effects which are not considered in the Morgenstern et al. study. For example, 

employment in environmental protection industries may increase as firms purchase more 

pollution control equipment and services to meet the rule’s requirements. EPA does provide 

such an estimate of employment change later in this section in a separate analysis. 

A defensible methodology for evaluating the employment impacts beyond the 

environmental protection and regulated sectors is not yet available, though as noted before,

net effects on employment are expected to be at or very close to zero for the economy overall 

under full employment. Attempts to estimate such effects usually rely on input-output 

methodologies that hold technologies and the proportion of various inputs constant over time, 

making them inappropriate for estimating long run impacts of regulation.

6.2.1 Limitations 

The Morgenstern et al. approach to employment analysis has the advantage of carefully 

controlling for many possibly confounding effects in order to separate the effect of changes in 

regulatory costs on employment. Although the Morgenstern et al. paper provides information 

about the potential job effects of environmental protection programs, however, there are 

several caveats associated with using those estimates to analyze the final rule. First, the 

Morgenstern et al. estimates presented in Table 6-2 and used in EPA’s analysis represent the 

weighted average parameter estimates for a set of manufacturing industries (pulp and paper, 

plastics, petroleum, and steel). Unfortunately this set of industries does not overlap directly 

with the electric utility sector. Second, relying on Morgenstern et al. implicitly assumes that the 

 

5

Since Morgenstern’s analysis reports environmental expenditures in $1987, we make an inflation adjustment the 

IPM costs using the ratio of the annual consumer price index, U.S. city, all items reported by the U.S. Bureau of 

Labor Statistics: CPI1987/ CPI2007 = (113.6/207.3) = 0.55. 

6

Net employment effect = 1.55× $9,600 million × 0.55. Given the 95% confidence interval for this effect, this 

estimated net result is not statistically different from zero. 
6-6

employment estimates derived from 1979–1991 data are still applicable. Third, the 

methodology used in Morgenstern et al. assumes that regulations affect plants in proportion to 

their total costs. In other words, each additional dollar of regulatory burden affects a plant by 

an amount equal to that plant’s total costs relative to the aggregate industry costs. By 

transferring the estimates, EPA assumes a similar distribution of regulatory costs by plant size 

and that the regulatory burden does not disproportionately fall on smaller or larger plants. 

Fourth, the Morgenstern et al. analysis makes particular assumptions about the role of imports 

and the effect of previous regulation on plant closures. While imports are not an issue for 

MATS, the stringency of the current regulation is expected to result in a number of power plant 

closures due to early retirement of coal-fired EGU capacity in 2015, as indicated in Chapter 3 of 

this RIA. Finally, the Morgenstern et.al. methodology does not examine the effects of 

regulation on employment in sectors related to, but outside of the regulated sector, but it does 

suggest that it is not appropriate to assume that the employment impact in any sector is 

directly proportional to increased costs.

Table 6-2. Employment Impacts Within the Regulated Industry Using Peer-Reviewed Study 

Estimates using Morgenstern et al. (2002) 

Estimates Using Morgenstern, et. al (2002)

Demand Effect Cost Effect Factor Shift Effect Net Effect

Change in Full-Time Jobs per Million 

Dollars of Environmental Expenditurea

−3.56 2.42 2.68 1.55

Standard Error 2.03 0.83 1.35 2.24

EPA Estimate for Ruleb−39,000 to +2,000 +4,000 to +21,000 +200 to +27,000 −15,000 to +30,000c

a

Expressed in 1987 dollars. Adjustment of dollars from 2007 to 1987 is accomplished through use of the annual 

Consumer Price Index – All Urban Consumers, found on the Internet at 

ftp://ftp.bls.gov/pub/special.requests/cpi/cpiai.txt. U.S. Department of Labor, Bureau of Labor Statistics. 

Washington, D.C. 

b

According to the 2007 Economic Census, the electric power generation, transmission and distribution sector 

(NAICS 2211) had approximately 510,000 paid employees. Both the midpoint and range for each effect are 

reported in the last row of the table.

c

EPA has used this study to estimate the mean net employment impact of this rule, and provided the 95% 

confidence interval results to reflect the high degree of uncertainty regarding the effect on employment within 

the regulated industry. The confidence interval includes zero indicating we are uncertain as to the sign of the 

effect, but the interval itself does reveal information on the magnitude of the effect. 
6-7

6.3 Employment Impacts of the MATS-Environmental Protection Sector Approach by 

20157

Regulations set in motion new orders for pollution control equipment and services. 

New categories of employment have been created in the process of implementing regulations 

to make our air safer to breathe. When a regulation is promulgated, one typical response of 

industry is to order pollution control equipment and services in order to comply with the 

regulation when it becomes effective. With such a response by industry as a basis, this section 

presents estimates for short term employment needed to design, construct, and install the 

control equipment in the three or four year period leading up to the compliance date. Revenue 

and employment in the environmental technology industry have grown steadily between 2000 

and 2008, reaching an industry total of approximately $300 billion in revenues and 1.7 million 

employees in 2008.8 While these revenues and employment figures represent gains for the 

environmental technologies industry, they are costs to the regulated industries required to 

install the equipment. As with any pool of labor, the gross size of the labor pool does not reflect 

the net impact on overall employment after adjusting for shifts in other sectors.

Regulated firms hire workers to design and build pollution controls. Once the 

equipment is installed, regulated firms hire workers to operate and maintain the pollution 

control equipment – much like they hire workers to produce more output. Of course, these 

firms may also reassign existing employees to do these activities. Environmental regulations 

also support employment in many basic industries. In addition to the increase in employment 

in the environmental protection industry (to fill increased orders for pollution control 

equipment placed by the regulated sector), environmental regulations also support 

employment in industries that provide intermediate goods to the environmental protection 

 

7

EPA expects that the installation of retrofit control equipment in response to the requirements of this proposal 

will primarily take place within 3 years of the effective date of the final rule, but there may be a possibility that 

some installations may occur within 4 years of the effective date. 

8

In 2008, the industry totaled approximately $315 billion in revenues and 1.7 million employees including indirect 

employment effects, pollution abatement equipment production employed approximately 4.2 million workers in 

2008. These indirect employment effects are based on a multiplier for indirect employment = 2.24 (1982 value 

from Nestor and Pasurka - approximate middle of range of multipliers 1977-1991). Environmental Business 

International (EBI), Inc., San Diego, CA. Environmental Business Journal, monthly (copyright). 

http://www.ebiusa.com/ EBI data taken from the Department of Commerce International Trade Administration 

Environmental Industries Fact Sheet from April 2010: 

http://web.ita.doc.gov/ete/eteinfo.nsf/068f3801d047f26e85256883006ffa54/4878b7e2fc08ac6d85256883006c45

2c?OpenDocument . The definition of the environmental technology industry from the Department of 

Commerce’s ITA FY 2010 environmental technologies industries assessment is not according to NAICS code but 

instead is “all goods and services that generate revenue associated with environmental protection, assessment, 

compliance with environmental regulations, pollution control and prevention, waste management, renewable 

energy, remediation of contaminated property, design and operation of environmental infrastructure, and the 

provision and delivery of environmental resources.”
6-8

industry. For example, an investment in capital expenditures to reduce air pollution involves 

the purchase of abatement equipment. The equipment manufacturers, in turn, order steel, 

tanks, vessels, blowers, pumps, and chemicals to manufacture and install the equipment. A 

study by Bezdek, Wendling, and DiPernab (2008) found that “investments in environmental 

protection create jobs and displace jobs, but the net effect on employment is positive.”

9

The focus of this part of the employment analysis is on short-term employment related 

to the compliance actions of the affected entities. This analysis estimates the employment 

impacts due to the increased demand for pollution control equipment in response to MATS.10 

Results indicate that the MATS has the potential to result in a net increase of labor in these 

industries, driven by the high demand for new pollution controls. Overall, the results of the 

environmental protection sector approach indicate that the MATS could support an increase of 

about 46,000 job-years11 by 2015.

6.3.1 Overall Approach and Methodology for Environmental Protection Sector Approach

EPA developed estimates of the potential employment changes for the Environmental 

Protection Sector using a bottom-up engineering based methodology combined with 

macroeconomic data on industrial output and productivity, to estimate employment impacts. 

The approach relies heavily on the projections and costing analysis from the IPM model, which 

uses industry specific data and assumptions to derive compliance costs and energy impacts (See 

Chapter 3). Central to the approach are prior EPA studies on similar issues, and in particular,

data and information from extensive engineering studies that the Agency has commissioned.

12

 

The analysis develops employment estimates by relying on IPM projections from the MATS 

analysis for the specific types of pollution control technologies expected to be installed to 

comply with the rule.13 More specifically, the analysis includes estimates for the labor needed 

 

9

Environmental protection, the economy, and jobs: National and regional analyses, Roger H. Bezdek, Robert M. 

Wendling and Paula DiPerna, Journal of Environmental Management Volume 86, Issue 1, January 2008, Pages 63-

79.

10 For more detail on methodology, approach,and assumptions, see TSD as part of the Toxics Rule Docket: 

“Employment Estimates of Direct Labor in Response to the Proposed Toxics Rule in 2015.”

11 Numbers of job years are not the same as numbers of individual jobs, but represents the amount of work that 

can be performed by the equivalent of one full-time individual for a year (or FTE). For example, 25 job years may 

be equivalent to five full-time workers for five years, twenty-five full-time workers for one year, or one full-time 

worker for twenty-five years.

12 Engineering and Economic Factors Affecting the Installation of Control Technologies for Multipollutant Strategies 

EPA-600/R-02/073 (2002) and Engineering and Economic Factors Affecting the Installation of Control 

Technologies – An Update (2011).

13 Detailed results from IPM for the MATS can be found in Chapter 3 of the RIA.
6-9

to design, manufacture and install the needed pollution control equipment over the 3 to 4 

years leading up to compliance in 2015.

For construction labor, the labor needs are derived from an update to a 2002 EPA 

resource analysis for building various pollution controls (FGD – Flue Gas Desulfurization or 

scrubbers, SCR- selective catalytic reduction, ACI – activated carbon injection, DSI - dry sorbent 

injection, and FF - Fabric Filters) and are further classified into different labor categories. These 

categories include boilermakers, engineers and a catch-all “other” installation labor. For the 

inputs needed (e.g., steel), the updated 2002 resource study was used to determine the steel 

demand for each MW of additional pollution control, combined with labor productivity data 

from the Economic Census and BLS for relevant industries. More detail on methodology,

assumptions, and data sources can be found in Appendix 6A for this RIA. Projections from IPM 

were used to estimate the incremental retrofit capacities projected in response to the final rule.

These additional pollution controls are shown in Table 6-3, and reflect the added pollution 

controls needed to meet the requirements of the rule. Additional information on the power 

sector impacts can be found in Chapter 3 of the RIA.

Table 6-3. Increased Pollution Control Installations due to MATS, by 2015 (GW)

Retrofit Type IPM Projected Additional Pollution Control

FGD 17

ACI 99

DSI 44

FF 102

6.3.2 Summary of Employment Estimates from Environmental Protection Sector Approach

Table 6-4 shows the results of employment impacts resulting from the additional 

demand for the aforementioned pollution controls. The results indicate that MATS could 

support or create roughly 46,000 one-time job-years of increased cost of direct labor, driven by 

the need to design and build the pollution control retrofits.
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Table 6-4. Employment Effects Using the Environmental Protection Sector Approach for the 

MATS (in Job-Years)14

Employment Incremental Employment

One-Time Employment Changes for Construction

1. Boilermakers 20,000

2. Engineers 5,000

3. General Construction 21,000

Total 46,000

6.3.3 Other Employment Impacts of MATS

There are also likely to be other employment impacts associated with MATS. These 

include changes resulting from additional labor needed to operate the needed pollution 

controls, increased demand for materials used in pollution control operation, shifts in demand 

for fuel in response to the rule, changes in employment resulting from additional coal 

retirements, and changes in other industries due to changes in the price of electricity and 

natural gas. The EPA has provided estimates of some of these effects below, which can be 

found in Appendix 6A. The most notable of the ones that the Agency is unable to estimate is

the impacts on employment as a result of the increase in electricity and other energy prices in 

the economy. Nor is the Agency able to quantify all the employment changes in industries that 

support and supply the pollution control industry. Because of this inability to estimate all the 

important employment impacts, EPA neither sums the impacts that the Agency is able to 

estimate for these other employment impacts or make any inferences of whether there is a net 

gain or loss of employment across these categories. A summary of the other employment 

impacts can be found in Table 6-5, with additional detail provided in Appendix 6A.

 

14 Numbers are rounded to nearest thousand.
6-11

Table 6-5. Other Employment Impacts of MATS (in Job-Years)

Employment Impacts from Increased Demand for Pollution Control Operating Inputs

Lime (FGD) 280 

Activated Carbon (ACI) 460

Trona (DSI) 3,130 

Baghouse material (FF) 20 

Employment Impacts from Pollution Control Operation 4,320

Employment Impacts from Retirements of Existing Coal Capacity (2,500)

Employment Impacts from Changes in Coal Demand (430)

Employment Impacts from Changes in Natural Gas Demand 670 

Note: See Appendix 6A for more detail.

6.4 Summary of Employment Impacts

The employment approaches used by EPA rely on different analytical techniques and are 

applied to different industries during different time periods, and they use different units of 

analysis. These estimates should not be summed because of the different metrics, length and 

methods of analysis. The Morgenstern estimates are used for the ongoing employment 

impacts for the regulated entities (the electric power sector). The short term estimates for 

employment needed to design, construct, and install the control equipment in the three or four 

year period leading up to the compliance date are also provided. Finally some of the other 

types of employment impacts that will be ongoing are estimated.

In Table 6-6, we show the employment impacts of the MATS as estimated by the 

environmental protection sector approach and by the Morgenstern approach.

Table 6-6. Estimated Employment Impact Table for the MATS

Annual (Reoccurring)

One Time (Construction 

During Compliance Period)

Environmental Protection Sector approacha Not Applicable 46,000

Net Effect on Electric Utility Sector Employment 

from Morgenstern et al. approachc

8,000b

-15, 000 to +30,000d

Not Applicable

a

These one-time impacts on employment are estimated in terms of job-years.

b

This estimate is not statistically different from zero.

c

These annual or recurring employment impacts are estimated in terms of production workers as defined by the 

US Census Bureau’s Annual Survey of Manufacturers (ASM).

d

95% confidence interval
6-12

6.5 Potential Effect of Electricity Price Increase on Downstream Sectors

6.5.1 Introduction

As with any input into production, the new price of electricity, reflecting the costs of 

MATS, will be absorbed in some fraction by industries that use electricity in their operations. 

This section contains a screening analysis that presents upper-bound estimates of the increase 

in per unit production expenses of industries other than the electricity sector. These cost 

increases represent credible upper-bounds on price increases due to higher electricity prices 

because they assume that production, consumption, and input levels do not change in the 

economy. In reality, we know that producers and consumers can always substitute for less 

electricity-intensive goods and services to avoid a significant portion of these costs. EPA 

conducted this scoping or “upper-bound” analysis to determine if the electricity price increase 

in 2015 attributable to MATS will lead to substantial increases in the average production 

expenses of any other goods and services produced in the U.S. 

In this section, using readily available data collected and prepared by the Bureau of 

Economic Analysis (BEA), we estimate an upper-bound on percentage increase in the per unit 

production costs attributable to higher electricity prices due to MATS. By upper-bound, we 

mean an estimate of the increase in the per unit production costs making the extreme 

assumption that there are no changes in the production and use of any goods, services, and 

intermediate inputs in the economy.15 Note that we are only performing simple calculations to 

estimate these upper-bound increases in per unit production costs. A modeling approach would 

require assumptions about behavioral response to price changes, and we are assuming for this 

analysis that there is no behavioral response to higher electricity prices. If quantities of 

production, consumption, and input use changed in response to a higher electricity price, the 

effects on average production expenses would be less than the upper-bound estimate we 

calculate. We show that the upper-bound increase in average production expenses of goods 

and services across the economy attributable to the increase in the electricity prices as a result 

of MATS across the economy is 0.023% (using a total output weighted average), and is no 

greater than 0.241% for any industrial sector. Taking into account the fact that these numbers 

represent an upper bound with no adjustments by firms to higher electricity prices, EPA does 

not expect that increases in average production expenses in this range are sufficient to cause 

significant shifts in economic activity outside the electricity sector and its major input markets. 

 

15This means that all other inputs, including capital, labor, and materials are assumed to be fixed when estimating 

an upper bound per unit production expense for the industries included in this analysis. 
6-13

6.5.2 Bureau of Economic Analysis Benchmark Input-Output Accounts

With the exception of the forecast change in the national average electricity price in 

2015, all of the data used to calculate an upper-bound on the increase in average production 

expenses are drawn from the 2002 BEA benchmark input-output (I/O) summary-level accounts

(BEA 2002a, 2002b). The benchmark I/O accounts are intended to “facilitate the study of 

economic activity by providing a highly detailed looks at interindustry activity” (Stewart et al., 

2007). 

The BEA’s benchmark I/O summary-level data includes information on the share of 

expenditures by industry spent on 135 commodity categories for 133 different sectors. These 

data provide a “comprehensive picture of inner workings of the economy“ (Ibid.). That is, they 

represent a full balancing of the use and production of goods and services in the entire U.S. 

economy. For each sector, the sum of expenditure shares across all 135 commodities equals 1. 

The entire output of each sector is also accounted for. That is, the output of each sector is the 

sum of production for inputs used by other sectors, production for final demand, and 

production for export. BEA’s sector and commodity classification system for the benchmark I/O 

accounts is described in BEA 2007a and 2009.

The most recent benchmark I/O data available that are suitable for calculating an upperbound on the increase in per unit production costs reflect economic conditions in 2002 and 

were published in 2007. 16 In the benchmark I/O summary data one of the commodities is 

“electricity generation, transmission and distribution”. As discussed in Chapter 2 of this RIA, 

generation, transmission and distribution are the three main services used to provide electricity 

to consumers, and therefore are the three main services reflected in electricity rates. For 

brevity, in this section we collective refer to the average price for these three services as the 

“electricity price” and expenditures on these three services as “electricity expenditures”. 

 

16 BEA also reports I-O accounts data on an annual basis (Stewart et al., 2007). The most recent annual I/O 

accounts data represent economic activity in 2010. We cannot use the annual I/O accounts data because they do 

not report the share of electricity consumption at the sector level independent of other commodities. Rather, 

expenses on electricity are combined with expenses on other utilities such as water and sewage treatment in the 

annual I/O accounts data. Aggregating utility expenditures together prevents us from using more recent 

expenditure data as it is not reasonable to assume that all utility expenses reflect electricity expenses exclusively. 

Furthermore, the 2002 benchmark I/O data are the most recent benchmark I/O data available. The benchmark 

I/O data are based on data collected on the Economic Census, which is conducted by the U.S. Census Bureau and 

occurs every five years. The publication of the benchmark I/O data by BEA lags the Economic Census by five years. 

Therefore benchmark I/O data for the 2007 Economic Census, the most recent conducted, have not been 

published yet by BEA. 
6-14

For each sector, our calculation of the upper-bound increase in average production 

expenses accounts for the increase in expenditures from direct electricity consumption. It does 

not account for changes in factor prices attributable to higher electricity prices.17 The share of 

direct electricity expenditures for each sector is reported in the Commodity-by-Industry Direct 

Requirements after Redefinitions, 2002 table (BEA, 2007b). The share of production 

expenditures on electricity for each of the 133 sectors is reported in Table 6B-1 in Appendix 6B 

of this RIA. The share of expenditures on electricity as a share of total expenditures ranges from 

0.010% for Software Publishers to 8.029% for Metal Ores Mining. On an expenditures-weighted 

basis, electricity comprises only 1.7% of total production expenditures across all sectors in 2002 

(BEA, 2007c). 

6.5.3 Upper-bound Increase in Per Unit Production Expenses from Electricity Purchases 

In order to estimate an upper-bound percentage increase in per unit production 

expenditures in 2015, for each sector we multiply the forecast percent change in electricity 

price in 2015 by the share of production expenditures on electricity. As reported in Chapter 3, 

the Retail Electricity Price Model forecasts a 3.1% increase in the contiguous U.S. electricity 

price in 2015 (see Table 3-12) as a result of MATS.

18 The share of production expenditures on 

electricity in each of the 133 sectors, as described above, comes from BEA. 

For example, according to the 2002 benchmark I/O accounts, in the Forging and 

Stamping sector 1.42% of production expenditures are on electricity. Again, EPA forecasts that 

MATS will increases average electricity prices by 3.1% in 2015. Therefore, the upper bound on 

the percentage increase in production costs in this sector (from direct electricity expenses) is 

0.044% ( =[0.031 * 0.01423]*100% ). 

Because we assume that production, consumption and input use does not change in this 

analysis, the calculated percent change in total production expenses equals the upper-bound 

 

17 For sectors that use energy-intensive inputs the effect of higher electricity prices on the prices of other factors 

might be important. However, applying the BEA data to estimate an upper-bound that accounts for electricity 

price pass-through on factor prices would rely even more heavily on the assumption that factor mixes do not 

change in response to relative factor prices changes, and thus we would not find the upper-bound increase in 

average production costs calculated in this way credible. See Section 6.5.4 for further discussion. 

18 We use the national average electricity price change for this calculation because we do not have sufficiently 

detailed regional production expenditure data from BEA to calculate regional sectoral estimates of upper-bounds 

on increases in average production expenses.
6-15

on the percent change in per-unit or average production. By average production expenditures 

we mean the change in production expenditure per unit of output by the sector.19

Note that this per unit percent cost increase does not reflect potential other economic 

effects of this rule. For example, the increased expenditures on pollution abatement 

equipment could create more demand for labor in those industries. Alternatively, as producers 

switch away from electricity-intensive inputs, the demand for other inputs may increase, 

changing the cost of production for those factors of production. 

Table 6-7 summarizes the upper-bound average production expense increase for the 

thirteen largest private sectors, which represent just over 50% of total private economic 

activity, in terms of value added. We focus on the private sector as the effect of MATS on 

private economic activity is of significant public interest. We focus on the largest sectors by 

BEA’s categorization given their collective importance to the U.S. economy. The average of the 

upper-bound change in average production expenditure, weighted by total output, for these 

sectors is 0.024%. Table 6B-1 reports the total output of these sectors. 

 

19 Output in these sectors is measured in dollars as output needs to be normalized for product and price 

heterogeneity within the sector as described in BEA, 2009.
6-16

Table 6-7. Share of Production Expenditures on Electricity and Upper-Bound Percentage 

Increase in Production Expenditures for 13 Largest Sectors in Terms of Value 

Added (2002)

Sector

Share of 

Production 

Expenditures on 

Electricitya

Upper-Bound Increase 

in Average Production 

Expendituresb

Share of 2002 Total 

Value Added from 

Private Sectorc

Real estate 1.783% 0.055% 7.39%

Retail trade 1.328% 0.041% 7.08%

Wholesale trade 0.450% 0.014% 6.97%

Monetary authorities, 

credit intermediation 

and related activities

0.096% 0.003% 5.23%

Ambulatory health care 

services 0.350% 0.011% 3.82%

Management of 

companies and 

enterprises

0.602% 0.019% 3.14%

Food services and 

drinking places 1.894% 0.059% 2.88%

Insurance carriers and 

related activities 0.073% 0.002% 2.79%

New nonresidential 

construction 0.363% 0.011% 2.77%

Telecommunications 0.334% 0.010% 2.71%

Hospitals 1.142% 0.035% 2.45%

New residential 

construction 0.325% 0.010% 2.19%

Computer systems 

design and related 

services

0.811% 0.025% 1.99%

a

Source: BEA 2007b. Electricity expenses are those on “electric power generation, transmission, and distribution"

which is BEA commodity sector 2211. The correspondence between BEA’s sector classification and NAICS codes 

is available from BEA 2007a.

b The forecast change in average production expenses is an upper-bound and assumes that production and use of 

all goods and services in the economy remain unchanged. See chapter discussion for details.

c

Source: BEA 2007c . These percentages are calculated by dividing the total value added for the sector by the 

total value added for the sectors that are not government and not owner-occupied dwellings. See footnote 20

for further explanation. 

Table 6B-1 reports the upper-bound per unit production cost increase for all 133 sectors 

in the BEA benchmark I/O data. The sector with the highest average production expense 
6-17

increase, 0.241% (less than 3 tenths of 1 percent), is metal ores mining. The next highest is 

nonmetallic mineral mining and quarrying, which is 0.125%. Seven sectors have an upperbound average production expense increase greater than 0.100%. Twenty two have an upperbound average production expense greater than 0.050%. Table 6-8 reports the upper-bound 

per unit production cost increase for the 10 sectors with the highest upper-bounds, along with 

their share of value added in 2002. These 10 sectors represent 2.38% of total value added from 

the private sector in 2002.20 The average of the upper-bound change in average production 

expenditures for all 133 sectors, weighted by total output, is 0.023%. 

 

20 There is no clear distinction in the BEA data between private and public sector activities. However, we use 

“private sector” as shorthand to indicate that we are excluding those sectors that BEA characterizes as 

“government industries”. These are sectors Federal Government enterprises, State and local government 

enterprises, General Federal defense government services, General Federal nondefense government services, and 

General state and local government services. We also exclude the owner-occupied dwellings sector in what we 

are calling the private sector. The maximum percent change in average production expenditures for the 

government industries and owner-occupied dwellings sectors is reported in Table 6B-1 in the Appendix to this 

chapter.
6-18

Table 6-8. Share of Production Expenditures on Electricity and Upper-Bound Percentage 

Increase in Production Expenditures for 10 Sectors with Largest Upper-Bound 

Average Production Expenditures Increase (2002)

Sector

Share of 

Production 

Expenditures on 

Electricitya

Upper-Bound Increase 

in Average Production 

Expendituresb

Share of 2002 Total 

Value Added from 

Private Sectorc

Metal ores mining 8.029% 0.241% 0.048%

Nonmetallic mineral 

mining and quarrying

4.158% 0.125% 0.121%

Basic chemical 

manufacturing

3.519% 0.106% 0.243%

Iron and steel mills and 

manufacturing from 

purchased steel

3.412% 0.102% 0.187%

Nonferrous metal 

production and 

processing

3.396% 0.102% 0.148%

Pulp, paper, and 

paperboard mills

3.346% 0.100% 0.326%

Accommodation 3.141% 0.094% 0.737%

Foundries 2.905% 0.087% 0.130%

Warehousing and 

storage

2.615% 0.078% 0.296%

Textile mills 2.409% 0.072% 0.146%

a

Source: BEA 2007b. Electricity expenses are those on “electric power generation, transmission, and distribution”

which is BEA commodity sector 2211. The correspondence between BEA’s sector classification and NAICS codes 

is available from BEA 2007a.

b The forecast change in average production expenses is an upper-bound and assumes that production and use of 

all goods and services in the economy remain unchanged. See chapter discussion for details.

c

Source: BEA 2007c . These percentages are calculated by dividing the total value added for the sector by the 

total value added for the sectors that are not government and not owner-occupied dwellings. See footnote 20

for further explanation. 

Tables 6-7 and 6-8 illustrate that, even when considering an unrealistic worst case 

scenario where the economy fails to respond in any way to the change in electricity prices, the 

change in average production expenditures for most sectors is modest. Averaged over the 13 

largest sectors, which represent over half of total output, the average electricity price impact of 

the rule leads to, at most, an only 0.024% increase in average production expenditures. 

Averaged across all sectors, the upper-bound increase in average production expenditures 

would be a very similar 0.023%. Such small changes are unlikely to lead to significant impacts 
6-19

but rather ones which likely cannot be detected in the noise and variability of the real national 

economy.

6.5.4 Limitations

This approach suffers from a number of limitations. First, these estimates reflect an 

upper-bound on the change the average production cost of goods and services prices because 

the calculation used to estimate these changes assume that production, consumption and input 

use will not change in response to higher electricity prices. For example, sectors that use 

electricity may mitigate the impact of the higher price of electricity by shifting to other inputs. 

This limitation is why we characterize this approach provides an upper bound on the potential 

increase in average production costs outside the electricity sector. 

Second, this analysis also does not account for the effect that higher electricity prices 

have on the factors used by other sectors (e.g. the cost of components and other inputs). For 

sectors that use both electricity and other energy-intensive inputs the effect of higher 

electricity prices on input prices can be important but applying the BEA data to account for the 

effect of electricity price pass-through on factors relies even more heavily on assumptions 

about the inability of sectors to change their factor mix in response to relative factor prices 

changes. 

Third, important differences across regions and consumer classes may be masked by the 

nationwide estimates of upper-bound average expenditure changes. However, because the 

Retail Electricity Price Model does not estimate price changes for different customer classes, 

and because the BEA data does not provide a regional decomposition of the economic accounts 

suitable to calculating regional upper-bounds on per unit production expenses by sector, 

regional and consumer class price differences cannot be calculated. Similarly, there may be 

subsectors that would have a meaningfully higher or lower maximum increase in average 

production expenditures than their corresponding BEA sector, but we cannot identify these 

cases as we are limited to using BEA’s allocation of industries to sectors for this analysis. Such 

regional impacts are highly sensitive to the actual pathway to compliance taken by firms as well 

as the steps individual sectors take to adjust to any changes in electricity price.

Fourth, the share of electricity used in each sector may have changed since 2002. In 

general, electricity consumption per dollar of gross domestic product fell from 2002 through 

2009, but electricity expenditures relative to gross domestic product rose slightly over this time

(EIA, 2011; BEA 2011). Not accounting for this change over time in expenditures on electricity 
6-20

may lead to a slight underestimate of the upper bound increase in average production 

expenditures, averaged across the entire economy, as a result of this rule. 

Fifth, MATS is expected to meaningfully change a number of factor demands in the 

electricity market in addition to electricity supply as described in Chapter 3. These changes in 

factor demands may in turn affect average production expenditures of goods in sectors that are 

not regulated, but only the effect on average production expenditures associated with the 

electricity price increase is accounted for in the upper bound calculations.

Finally, our calculation of upper bounds on average production cost increases does not 

account for the effect of health benefit impacts of the regulation on prices and output (e.g., 

health care consumers and effects on markets from changes in labor productivity from reduced 

health damages), though this is a common limitation for other economic tools for estimating 

price changes in response to regulations (e.g. partial equilibrium models, CGE models). 

While there are several caveats to this approach, this screening analysis has the virtue of 

being transparent and provides an upper bound on the effect of MATS on average production 

expenses attributable to higher electricity expenditures within a sector as a result of the rule. 

The results suggest that electricity prices under MATS will not have a large impact on 

production costs across the entire economy. This is consistent with the overall history of the 

implementation of the Clean Air Act (Jaffe et al., 1995). 

6.5.5 Impacts of Electricity Price Induced Changes in Production Costs on Employment in 

Downstream Sectors

With respect to the impact of electricity prices, the analysis using the benchmark I/O 

data from BEA suggests that the MATS will only have modest impacts on production costs 

across the entire economy. These upper-bound estimates of average production cost increases

cannot be used to estimate changes in employment as a result of the regulation, either 

nationally or for individual sectors. First, as noted above, these calculations overstate the 

actual impact of retail electricity prices by failing to account for the ability of the real economy 

to adjust to changes in price through input substitution, technological innovation, or other 

means. It is necessary to account for changes in production, consumption, and input use to 

estimate the change in total employment. Second, this approach does not account for changes 

in consumer and producer behavior as they adjust the quantity of goods and services supplied 

or demanded in all of the markets affected by the regulation. 
6-21

6.6 Alternative Approach to Estimating Social Cost and Economic Impacts

In the Transport Rule proposed in the summer of 2010 and in other rulemakings, EPA 

used a different model to estimate the social cost and economic impacts of the regulatory 

approach than the model applied in this RIA. That model, EPA’s EMPAX, is a CGE model that 

dynamically cascades the cost of a regulation through the entire economy. Since that rule was 

proposed, a different version of EMPAX was used to estimate the social cost of the Clean Air Act 

in a new EPA report entitled “The Benefits and Costs of the Clean Air Act from 1990 to 2020” 

(EPA, 2010, herein referred to as the Section 812 report). This version of EMPAX accounts for 

the benefits of reducing pollution on labor productivity and on the demand for health care, 

which significantly influenced the model’s estimates of the social cost and economic impacts of 

the Clean Air Act relative to an analysis using EMPAX in which these benefits-related effects 

were not accounted for. In December 2010, in its review of the 812 Report EPA’s Science 

Advisory Board (SAB) found that “The inclusion of benefit-side effects (reductions in mortality, 

morbidity, and health-care expenditures) in a computable general equilibrium (CGE) model 

represents a significant step forward in benefit-cost analysis” (SAB, 2010). A description of the 

changes to the model and implications are described in detail in chapter 8 of the Section 812 

report. EPA has determined that it needs to update the EMPAX model version used for RIAs to 

account for these beneficial effects of reducing pollution prior to its use in any additional 

regulatory analysis. The EMPAX model version used for the Section 812 report cannot be used 

for this rulemaking because it contains energy and economic data that are consistent with the 

multi-year timeframes and energy scenarios of the 812 study but not with the single target 

year, analysis timeframe, and energy scenario most appropriate for this current rulemaking 

analysis. For example, much of the energy data in the EMPAX model employed in the Section 

812 report is from the Energy Information Administration’s Annual Energy Outlook 2005. With 

these impacts of reducing pollution on labor productivity and the demand for health care now 

in the process of being incorporated into the model, the SAB’s perspective on the desirability of 

accounting for these effects in the CGE analysis for the 812 study, and the typical practice by 

EPA’s Office of Air and Radiation of having analyses within RIAs to be consistent in design with 

those included in the most recent available Section 812 report, EPA will not use EMPAX for this 

RIA.

6.7 References

Berman, E., and L. T. M. Bui. 2001. Environmental Regulation and Labor Demand: Evidence from 

the South Coast Air Basin. Journal of Public Economics 79(2):265-295.
6-22

Bezdek, Roger H. , Robert M. Wendling and Paula DiPerna, Environmental protection, the 

economy, and jobs: National and regional analyses. Journal of Environmental 

Management Volume 86, Issue 1, January 2008, Pages 63-79.

Environmental Business International (EBI), Inc., San Diego, CA. Environmental Business Journal, 

monthly (copyright). http://www.ebiusa.com/

Jaffe, Adam B., Steven R. Peterson, Paul R. Portney, Robert N. Stavins. (1995). “Environmental 

Regulation and the Competitiveness of U.S. Manufacturing: What Does the Evidence Tell 

Us?” Journal of Economic Literature, 33(1), pp. 132-163

Morgenstern, R. D., W. A. Pizer, and J. S. Shih. 2002. Jobs versus the Environment: An IndustryLevel Perspective. Journal of Environmental Economics and Management 43(3):412-

436.

Stewart, Ricky L., Jessica Brede Stone and Mary L. Streitweisser. 2007. “U.S. Benchmark InputOutput Accounts, 2002.” Survey of Current Business. October, 2007. 

US Bureau of Economic Analysis (BEA). 2007a. Appendix B.--Classification of Value Added and 

Final Uses in the 2002 Benchmark Input-Output Accounts. Available in: 2002 Summary 

Tables, 2002 Benchmark Input-Output Data. Retrieved from 

http://www.bea.gov/industry/io_benchmark.htm#2002data.

US Bureau of Economic Analysis (BEA). 2007b. Commodity-by-Industry Direct Requirements 

after Redefinitions, 2002. Available in: 2002 Summary Tables, 2002 Benchmark InputOutput Data. Retrieved from 

http://www.bea.gov/industry/io_benchmark.htm#2002data.

US Bureau of Economic Analysis (BEA). 2007c. “The Use of Commodities by Industries after 

Redefinitions, 2002.” Available in: 2002 Summary Tables, 2002 Benchmark Input-Output 

Data. Retrieved from http://www.bea.gov/industry/io_benchmark.htm#2002data.

US Bureau of Economic Analysis (BEA). 2009. Concepts and Methods of the U.S. Input-Output 

Accounts. Retrieved from: http://www.bea.gov/papers/pdf/IOmanual_092906.pdf.

US Bureau of Economic Analysis (BEA). 2011. Gross Domestic Product. In National Income and 

Product Account Tables (Table 1.1.5). Retrieved From 

http://www.bea.gov//national/nipaweb/DownSS2.asp.

US Bureau of the Census. 2007 Economic Census, Washington, DC: U.S. Government Printing 

Office, http://www.census.gov/econ/census07/.

US Bureau of the Census. Pollution Abatement Costs and Expenditures Washington, DC: U.S. 

Government Printing Office, various years.
6-23

US Bureau of Labor Statistics, United States Department of Labor. ―Industry Labor Productivity 

and Cost Data Tables, Annual Percent Changes. (2010).

US Energy Information Administration (EIA), Annual Energy Review 2010. ―Coal Mining 

Productivity By State and Mine Type.

US Energy Information Agency (EIA). 2011. Electricity End Use, 1949-2010. In Annual Energy 

Review 2010 (Table 8.9). Retrieved From 

http://www.eia.gov/totalenergy/data/annual/index.cfm#electricity

US Environmental Protection Agency. Engineering and Economic Factors Affecting the 

Installation of Control Technologies for Multipollutant Strategies, EPA-600/R-02/073 

(2002).
6A-1

APPENDIX 6A

EMPLOYMENT ESTIMATES OF DIRECT LABOR IN RESPONSE TO THE MERCURY AND AIR TOXICS 

STANDARDS IN 2015

This appendix presents the short-term employment estimates of the Mercury and Air 

Toxics Standards (MATS), henceforth referred to as the final MATS. The focus of the 

employment analysis in this study is only on the first order employment impacts related to the 

compliance actions of the affected coal-fired entities within the power sector.21 It does not 

include the ripple effects of those impacts on the broader economy (i.e., the “multiplier” 

effect), nor does it include the wider economy-wide effects of the changes to energy markets 

(such as higher electricity prices).22 Moreover, this study provides only a static snapshot of the 

impacts for 2015 and does not account for the dynamic adjustments of the affected entities as 

they adapt to the final MATS, such as those arising from technological innovation and learningby-doing. This analysis is also independent of other techniques used by the U.S. EPA to 

estimate certain employment effects of particular regulations.

The estimates of the employment impacts are divided into several categories: job gains 

due to the increased demand for pollution control equipment; job losses due to retirements of 

coal capacity; and job shifts due to changes in demand for fuels. The various employment 

metrics can also be distinguished by one-time employment changes (e.g., pollution control 

construction), and ongoing employment changes (e.g., fuel use changes and pollution control 

operation or coal retirements). Results indicate that the final MATS has the potential to 

provide significant short-term employment opportunities, primarily driven by the high demand 

for new pollution control equipment. The employment gains related to the new pollution 

controls are likely to be tempered by some losses due to certain coal retirements, although, as 

discussed below, some of these workers who lose their jobs due to plant retirements could find 

replacement employment operating the new pollution controls at nearby units. Finally, job 

losses due to reduced coal demand are expected to be offset by job gains due to increased 

natural gas demand, resulting in very small positive (i.e., less than three hundred) net change in 

employment due to fuel demand changes. Overall, the preliminary results indicate that the 

final MATS could support a net of slightly over 46,000 one-time job-years and a net of about 

6,000 ongoing job-years in 2015. These results are summarized in Table 6A-1 below. 

 

21 This analysis does not include potential employment effects resulting from projected impacts on oil/gas-fired 

units.

22 For more detail on the economic impacts of the proposed rule, see Chapter 3 of the Regulatory Impact Analysis 

accompanying final MATS. 
6A-2

Table 6A-1: Net Employment Changes for 2015 (job-years)a,b

One Time Ongoing

New Pollution Control Equipment 46,120 8,210

Retirements of Generating Units - (2,500)

Changes in Fuel Use - 240

Net Effect 46,120 5,950

a Total job years of labor for controls projected to be installed by 2015.

b Totals may not add due to rounding.

The job-years estimated here is a snapshot of the first order employment effect of the 

final MATS in 2015. While there is no temporal dimension to this study, some of these jobs are 

likely to be spread over several years, and some will last longer. Most of the construction 

related labor demand, for example, is expected to provide a short-term, temporary boost to 

employment that could last two or three years, along with any “multiplier effects” (i.e., 

secondary employment supported in upstream sectors) that are not included in these job-year 

estimates. Most of the operational labor needs and labor shifts resulting from fuel changes are 

likely to be longer term. Thus, in terms of the impacts of the final MATS on economy-wide 

employment over time, this analysis shows there could be a significant temporary increase to 

employment levels starting well before 2015, which would likely recede thereafter as the 

construction phases for the needed pollution controls wind down. Over time, the operational 

jobs will continue to provide a small boost to employment over “business as usual” baseline 

employment levels. Note that this synopsis does not account for other employment impacts of 

the final MATS, such as those resulting from higher energy prices. 

6A.1 Overall Approach 

The estimates for the near-term employment effects of the final MATS utilize studies 

conducted by EPA on engineering and resource requirements for various compliance activities, 

such as installing pollution control equipment, switching fuels, or ceasing plant operations as 

they become uneconomic. Some of the information used here was obtained from a 2002 EPA 

engineering study for multi-pollutant control strategies.23 This study was also the basis for the 

employment analysis for the proposed MATS. For the final MATS, EPA has undertaken a 

separate study to update the 2002 analysis in order to refine and update the Agency’s 

understanding of the resource requirements (labor and materials) of various compliance 

activities, including estimates for newer pollution control equipment that were not included in 

 

23 Engineering and Economic Factors Affecting the Installation of Control Technologies for Multi-pollutant 

Strategies. EPA-600/R-02/073 (October, 2002).
6A-3

the 2002 analysis.24 For example, the 2002 study focused on pollution control technologies that 

directly address SO2 and NOx emissions, while the updated study also includes pollution control 

technologies that reduce mercury emissions and hazardous air pollutants as well. Collectively, 

these studies are referred to as EPA pollution control studies in this appendix. This employment 

analysis is based on information from the updated study where available, as well as data from 

the original 2002 study, where updated information was unavailable. 

The basic approach involved using power sector projections and various energy market 

implications under the final MATS from modeling using EPA’s data and assumptions with the 

Integrated Planning Model (IPM), along with data from secondary sources, to estimate the first 

order employment impacts for 2015. Throughout this analysis, incremental employment is 

measured in job-years, since there is no temporal dimension to this analysis.25 Also, this 

appendix does not include estimates of total employment impacts over time, though there is a 

distinction between short-term construction related labor needs and more long-term 

operational labor needs for new pollution controls (though these operational labor 

requirements are also measured in 2015 job-years only).

6A.1.2 Employment Changes due to New Pollution Control Equipment 

EPA’s IPM projections for the final MATS policy case were used to estimate the 

incremental pollution control demand. These are shown in Table 6A-2 below:26 Note that the 

capacity estimates shown in Table 6A-2 do not include EPA’s projections for ESP and FGD 

upgrades on existing units. Because the engineering studies used in this employment analysis 

do not include resource estimates for these technologies, EPA chose not to analyze the 

employment impacts for these technologies. This exclusion is likely to understate the total 

employment impacts for the final MATS. 

 

24 Engineering and Economic Factors Affecting the Installation of Control Technologies – An Update. Andover 

Technology Partners and ICF International. October 20, 2011. 

25 A job-year is defined as the amount of work that can be performed by the equivalent of one full-time individual 

for one year (or FTE). 

26 According to IPM, there is some overlap between the different types of pollution control equipment demand at 

individual facilities. To the extent that there could be some efficiency gains at plants installing multiple controls 

due to economies of scale, the job estimates presented here could overstate the impacts. 
6A-4

Table 6A-2: Increased Pollution Control Demand due to the final MATS, 2015 (GW)

Pollution Control Type IPM Projected Additional Pollution Control

Scrubbers (FGD) 27 17

Activated Carbon Injection (ACI) 99

Dry Sorbent Injection (DSI) 44

Fabric Filter (FF) 28 102

The employment impacts due to increased pollution control demand are divided into 

three categories, one of which is associated with the construction and installation labor 

requirements, while the remaining two are associated with the resources required for the 

ongoing operation of the pollution control equipment. The labor needed for constructing and 

installing these controls are for construction-related sectors, such as boilermakers, engineers, 

and other installation labor. The two categories of labor needs for ongoing resource 

requirements include employment in sectors that supply resources needed to run these 

pollution controls (such as reagents); and utility sector jobs to operate the control equipment. 

The following sections discuss the approach for each: 

 For the construction labor estimates, per-unit labor needs were taken from the 

pollution control studies, which included man-hours required per MW for each of the 

control technologies listed above. The total installation labor was then sub-divided into 

different labor categories, such as boilermakers, engineers and a catch-all “other 

installation labor”, using estimated shares of the different labor types in the EPA 

pollution control studies. 

 For the longer term labor associated with operating the pollution controls, per-unit 

estimates of the main resources needed for the particular types of equipment (see Table

6A-3 below for a list of the resources) are also taken from EPA’s pollution control 

studies. Resources needed for FF (such as the filter bags) were estimated from the 

 

27 In addition to the scrubber capacity shown in Table 2, EPA also projects an additional 2.6 GW of dry scrubbers on 

units burning waste coal and pet. coke and have existing baghouses. This capacity is not included in the table 

above, however, employment impacts associated with these controls are included in this appendix as discussed 

below. 

28 This number includes the total incremental fabric filters, as reported in Chapter 3. For the purpose of estimating 

the construction jobs from fabric filters this appendix uses an incremental capacity of 84 GW (i.e., incremental 

fabric filters that are standalone, or installed with DSI or ACI+Toxecon). To avoid double counting, the remaining 

18 GW of fabric filters that represent those installed on units with dry scrubbers are excluded from the 

employment analysis, under the assumption that the labor estimates for dry scrubbers include resources required 

for both the scrubber as well as the fabric filter that goes with it.
6A-5

incremental Variable Operation and Maintenance (VOM) costs from EPA’s IPM modeling 

results.29 These were then multiplied by the incremental GW for each pollution control 

to obtain the total (physical) quantity of resources needed. Total tonnage for each 

resource was then converted to dollars of increased economic output for these 

resources using price estimates developed by Sargent & Lundy for EPA’s IPM Base Case 

v4.10 modeling assumptions (see notes at the end of Table 6A-3 below). Finally, the 

labor productivity for each particular sector was used to estimate the number of jobyears these could create in 2015. Labor productivities for each sector were adjusted to 

account for increased worker productivity in 2015. Data for baseline worker 

productivity and corresponding growth rates to account for future productivities came 

from the Economic Census and the Bureau of Labor Statistics (BLS) estimates.30

 The final employment vector estimated was for the utility sector labor needed to 

operate these pollution control equipment. This estimate was based on the incremental 

Fixed Operation and Maintenance (FOM) costs from EPA’s IPM modeling results, 

excluding costs due to retirements. Thus, this study assumes that the FOM costs are a 

reasonable proxy for the payroll costs that are part of the FOM costs in EPA’s modeling 

(FOM costs are defined as the operating and maintenance costs incurred by the utility, 

such as those for payroll, irrespective of whether the equipment is operated). The FOM 

costs were then translated into employment based on estimates of payroll per worker 

for the power sector taken from the 2007 Economic Census and BLS estimates.31

 

29 Because FF requirements are not endogenously determined in IPM, it required a different approach than the 

other controls. 

30 Total value of shipments in 2007 and total employees were taken from 2007 Economic Census, Statistics by 

Industry for Mining and Manufacturing sectors. The average annual growth rate of labor productivity was taken 

from the Bureau of Labor Statistics. Average growth rate calculated for years 1992-2007, applied to 2007 

productivity to determine 2015 estimates of productivity. See the Detailed Methodology section at the end for 

more details about the data used for these calculations. 

31 Same sources as other productivity estimates (2007 Economic Census and BLS), however, uses employees and 

total payroll rather than revenue or value of shipments. 
6A-6

Table 6A-3: Estimated Pollution Control Resource Needs (Quantity and Prices Used)

Amount of Resource Used Price Used ($/unit)

Lime, FGD (tons) 1,490,391 $9532

Activated Carbon, ACI (tons) 184,771 $1,500

Trona, DSI (tons) 10,667,613 $150

Price Sources:

 Sargent & Lundy, “IPM Model – Revisions to Cost and Performance for APC Technologies Mercury Control Cost 

Development Methodology FINAL”, March 2011, Project 12301-009, Perrin Quarles Associates, Inc.

 Sargent & Lundy, “IPM Model – Revisions to Cost and Performance for APC Technologies Dry Sorbent Injection 

Cost Development Methodology FINAL”, August 2010, Project 12301-007, Perrin Quarles Associates, Inc.

 Sargent & Lundy, “IPM Model – Revisions to Cost and Performance for APC Technologies SDA FGD Cost 

Development Methodology FINAL”, August 2010, Project 12301-007, Perrin Quarles Associates, Inc.

6A.1.3 Results

Table 6A-4 presents the estimated employment impacts in 2015 resulting from the 

additional pollution controls needed to meet the final MATS requirements. According to this 

analysis, these investments could provide the opportunity to support about 54,500 job-years to 

design, construct, and operate the needed pollution control equipment in 2015. Note, some of 

these jobs are expected to start before, and continue beyond 2015 (such as the resource 

related job-years), but this analysis only provides a snapshot for 2015. 

 

32 For FGD this study uses the price for Lime (Dry FGD) which is significantly greater than the Limestone (Wet FGD) 

price. This price was used because EPA’s modeling indicates most of the incremental FGD units are likely to be dry 

scrubbers. 
6A-7

Table 6A-4: Jobs Due to Pollution Control Equipment under the final MATS (Job-years in 

2015)

Jobs for Construction Incremental Employment

1. Boilermakers 20,190

2. Engineers 5,060

3. General Construction 20,870

Sub-Total: 46,120

Jobs for Operation

Jobs from Increased Operating Resource Use

1. Lime (FGD) 280

2. Activated Carbon (ACI) 460

3. Trona (DSI) 3,130

4. Baghouse material (FF) 20

Sub-Total: 3,890

Jobs for Pollution Control Operation 4,320

Total Labor: 54,330

Note: Totals may not add due to rounding

The number of job-years estimated for pollution control installation (i.e., “Jobs for 

Construction” in Table 6A-4 above) is driven in large part by the demand for new FFs used in 

EPA’s modeling. As shown in Table 6A-2, up to 84 GW of new FF capacity is projected to come 

online in 2015 due to the final MATS that are relevant for this employment analysis. The 

demand for new FFs is estimated to contribute nearly 70 percent of the new employment 

resulting from the installation of pollution controls. Moreover, of the labor needed due to 

increased resource use, the Trona required for DSI is estimated to support higher number of 

jobs than the other resources. This is because the DSI technology requires significantly higher 

quantities of reagents than the other pollution controls, based on EPA’s engineering estimates 

and pollution control studies. The second highest resource-related employment gains would 

likely come from the activated carbon needed in response to the final MATS. 

Of the roughly 54,500 job-years estimated in Table 6A-4, about 4,300 job-years, or 

about 8 percent, are estimated to occur within the utility sector for labor needed to operate 

the pollution controls (referred to as “Jobs for Pollution Control Operation” in Table 6A-4). The 

rest of the labor demand will benefit the pollution control industry and other economic sectors. 

The increased demand for resources and chemicals needed to operate the pollution controls 

will result in increased employment in sectors such as mining, chemicals, and other 
6A-8

manufacturing sectors. The majority of these first order employment effects, however, are 

likely to benefit construction-related sectors, such as construction, boilermaker, heavy 

engineering, and other heavy construction sectors, resulting from the construction and 

installation of the new pollution controls at affected sources throughout the country.

6A.1.3.1 Employment Changes due to Coal Retirements

Employment changes due to incremental coal plant retirements were estimated by first 

identifying the retiring coal units33 from EPA’s modeling results (for the base and the final MATS 

policy cases). EPA projects roughly 4.7 GW of additional coal retirements by 2015 with the final 

MATS in place.34 

In order to convert the retired coal capacity into potential employment losses, it was 

assumed that changes in the operating costs for the retired coal units can be used as a proxy for 

payroll expenditures and the lost economic output due to coal retirements. Thus, the changes 

in the FOM costs for these particular retiring units were derived using EPA’s IPM modeling 

results, and converted to lost jobs using data from the Economic Census and BLS output/worker 

estimates for the utility sector.35 Employment losses due to plant retirements will not only 

affect those that are directly working at the plant (i.e., plant operators), but would also affect 

administrative and other “back-office” workers for those utilities and their support 

organizations. This appendix assumes that the FOM costs related to retiring plants are a good 

proxy for these types of job losses.

Table 6A-5: Annual Job Losses due to Coal Capacity Retirements for 2015

FOM Decrease from Retirements (million) $288

Workers Per Million$ in payroll 8.7

Workers lost due to retirements (job-year): 2,500

 

33 Oil and gas steam unit emissions requirements, and potential retirements, were not directly included in EPA’s 

IPM modeling under the MATS policy scenario. An analysis of these units was conducted separately, and to the 

extent that there may be some retirements of oil and gas units, then the estimates of potential job losses due to 

retirements provided here will understate the employment losses.

34 Retirement estimates are based on IPM System Summary Reports from EPA’s modeling runs. Where applicable, 

data from IPM parsed outputs were adjusted to account for partial retirements reported in the parsed outputs. 

35 The same specific sources as cited before, however, used workers and total payroll. 
6A-9

Results indicate there could potentially be about 2,500 job losses (measured in job-years 

for 2015, but any net job losses under this category are likely to be permanent), due to coal 

retirements. However, two mitigating factors could reduce the negative employment impacts 

due to retirements. First, many of the retiring units are at plants that are likely to have other 

units operating under the policy scenario. In such cases, some of the excess labor pool at the 

retiring units could well be absorbed at other units within the same firm. 

Second, as Table 6A-4 indicates, utilities are expected to have the need to fill about 

4,300 additional job slots to operate the pollution controls needed to meet the requirements of 

the final MATS. If workers with experience at existing coal facilities become available through 

plant retirements, some of these workers could be absorbed in operating these new pollution 

controls. 

6A.1.3.2 Employment Changes due to Changes in Fuel Use

Employment impacts due to projected fuel use changes (coal and natural gas production 

shifts) were estimated using EPA’s modeling results. First, employment losses due to 

reductions in coal demand were estimated using an approach similar to EPA’s coal employment 

analyses under Title IV of the Clean Air Act Amendments.36 Using this approach, EPA’s 

projected coal demand changes (in short tons) for various coal supplying regions were 

converted to job-years using EIA data on regional coal mining productivity (in short tons per 

employee hour), using 2008 labor productivity estimates.37,38 

Results of the coal employment impacts of the final MATS are presented in Table 6A-6 

below. 

 

36 Impacts of the Acid Rain Program on Coal Industry Employment. EPA 430-R-01-002

March 2001.

37 From US Energy Information Administration (EIA) Annual Energy Review, Coal Mining Productivity Data. Used 

2008. 

38 Unlike the labor productivity estimates for various equipment resources which were forecasted to 2015 using 

BLS average growth rates, we used the most recent historical productivity estimates for fuel sectors. In general, 

labor productivity for the fuel sectors (both coal and natural gas) showed a significantly higher degree of 

variability in recent years than the manufacturing sectors, which would have introduced a high degree of 

uncertainty in forecasting productivity growth rates for future years. 
6A-10

 Table 6A-6: Annual Employment Impacts Due To Changes in Coal Use for 2015

Coal by Region Change in Coal Demand 

(MM Tons) Labor Productivity Job-year Change

 Appalachia (11.8) 2.91 (1,950)

 Interior 19.9 4.81 1,990

 West (17.3) 19.91 (420)

 Waste Coal (0.7) 5.96 (60)

Net Total (9.9) -- (430)

Notes: Used US national coal productivity for waste coal

Totals may not add due to rounding

For natural gas production, labor productivity per unit of natural gas was unavailable, 

unlike coal labor productivities used above. Most secondary data sources (such as Census and 

EIA) provide estimates for the combined oil and gas extraction sector. This appendix thus uses 

an adjusted labor productivity estimate for the combined oil and gas sector that accounts for 

the relative contributions of oil and natural gas in the total sector output (in terms of the value 

of energy output in MMBtu). This estimate of labor productivity is then used with the 

incremental natural gas demand for the final MATS to estimate the job-years for 2015. 

Table 6A-7: Annual Employment Impact due to Changes in Fuel Use (2015) 

Fuel Type Employment

Coal Job Years Lost (430)

Natural Gas 

 Incremental Natural Gas Use (MMBtu) 175,786,505

 Labor Productivity (MMBtu/job-year) 261,840

 Job-years gained 670

Net Employment Effects of Fuel use changes 240

Note: Totals may not add due to rounding 

Thus, about 430 job losses in the coal mining sector are likely to be offset by about 670 

job gains in the natural gas production related sectors, for a net effect of about 240 job-year 

gains due to the changes in fuel use. The changes in coal mining employment is driven by a 

significant increase in demand for Interior coal which leads to about the same amount of job 

gains as is lost due to the decreased demand for Appalachian coal (see Table 6A-6 above). This, 
6A-11

coupled with the fact that there is likely to be some job gains due to increased demand for 

natural gas, results in a small net job gain due to fuel use changes for the final MATS. 

6A.2 Results Summary 

Overall, the final MATS is expected to provide an increase to short-term employment 

resulting from substantial investments in new pollution control equipment. For 2015, the 

results indicate the final MATS could support or create around 46,000 job-years driven by the 

need to design and construct the needed equipment. While there could be some employment 

losses due to coal retirements that will likely have a negative effect on some utilities and the 

coal mining sector, employment gains in pollution control operation activities and the natural 

gas sector are likely to offset some of those losses. As previously discussed, this assessment 

does not account for the long-run economy-wide effects of the final MATS. 

6A.3 Detailed Methodology

This section provides more details on the data and approaches used to estimate the 

employment impacts discussed above. The section also details the sources for individual data 

elements. 

6A.3.1 Pollution Control Equipment Labor

6A.3.1.1 Installation Labor 

Table 6A-8: Installation Labor Requirement39

Pollution 

Control Type

Incremental GW 

Installed

Manhours/MW Boilermakers (%) Engineers (%) Others (%)

FGD 40 17 1730 40 20 40

ACI 99 10 50 17 33

DSI 44 55 50 17 33

FF 41 102 780 45 7 48

 

39 See Chapter 3 for more detail.

40 EPA also projects 2.6 GW of dry scrubbers on waste coal and pet. coke units with existing baghouses, which are 

not shown in this table. Employment impacts from these units, however, are included in the pollution control 

construction figures, calculated using the capital cost for these controls ($220.7 MM) and estimated manhours/$ 

capital cost (0.00598) developed from the same example dry scrubbers used to find the manhours/MW of 

capacity. 

41 This number includes the total incremental fabric filters. For the purpose of estimating the construction jobs 

from fabric filters, this analysis uses an incremental capacity of 84 GW. The remaining 18 GW represent fabric 

filters installed with dry FGD units, which are excluded because the labor estimates for dry scrubbers includes the 

labor for fabric filters that is installed in conjunction.
6A-12

Source: Engineering and Economic Factors affecting the Installation of Control Technologies – An Update, Andover 

Technology Partners, 2011

Installation labor is estimated by using the incremental GW installed for each pollution 

control type from EPA’s modeling using IPM. This was then converted into total man-hours 

needed for installation using estimates of man-hours/MW primarily from EPA’s 2011 update on 

pollution control technology,. Total man-hours for each pollution control type were then 

converted into man-years assuming 2,080 working hours per year. 

Total man-years for each pollution control type were then broken down into various 

sectors using the percentages, shown in Table 6A-8. These percentages were estimated from 

the 2002 study, updated from the 2011 study, where applicable. 

6A.3.1.2 Operating Resource Labor 

Table 6A-9: Resources Needed for Operation

Pollution 

Control 

Type

Incremental 

Total GW

Resource

(Units in 

parenthesis)

Usage 

Estimates

Price 

($/unit)

Industry Assumed 

for Productivity 

Calculations

Productivity*

FGD 17

Lime 

(Tons/MWh) 0.013 95 Lime Manufacturing 2.0

ACI 99

Activated 

Carbon 

(Tons/MWh)

0.00025 1,500

Other Chemical 

Product 

Manufacturing

1.6

DSI 44 Trona 

(Tons/MWh) 0.033 150

Potash Soda and 

Borate Mineral 

Mining

2.0

FF 102

Bag-house 

Resources

*Resource Labor 

determined Using 

VOM cost for FFs

Plastics Material 

and Resin 

Manufacturing

0.6

*Workers/$Million in Output, Forecasted to 2015 

Sources: Usage:

 Sargent & Lundy, “IPM Model – Revisions to Cost and Performance for APC Technologies Mercury Control Cost 

Development Methodology FINAL”, March 2011, Project 12301-009, Perrin Quarles Associates, Inc.

 Sargent & Lundy, “IPM Model – Revisions to Cost and Performance for APC Technologies Dry Sorbent Injection 

Cost Development Methodology FINAL”, August 2010, Project 12301-007, Perrin Quarles Associates, Inc.

 Sargent & Lundy, “IPM Model – Revisions to Cost and Performance for APC Technologies SDA FGD Cost 

Development Methodology FINAL”, August 2010, Project 12301-007, Perrin Quarles Associates, Inc.

Labor related to resources used in operating pollution control equipment was estimated 

using the total incremental GW of pollution control capacity which was first converted to total 

MWh of incremental capacity assuming 85 percent capacity factor. For each pollution control 

type, the next step involved choosing the primary operating resource. This approach is 
6A-13

consistent with prior EPA’s analyses on similar topics. The next step involved estimating the 

resource needs by each control type, generally in tons of material using the resource usage 

estimates as shown in Table 6A-9. Using the total usage for each pollution control input (in 

tons) and associated average prices, total expenditure by each resource type was then 

calculated. This total expenditure was then converted to labor using workers per $Million in 

total output for the industry associated with producing each respective input material.42

6A.3.1.3 Operating Labor 

Table 6A-10: Operating Labor Assumptions

Incremental FOM from IPM Parsed ($ Billion) 2.20

FF and other Capital Costs Included in FOM ($ Billion) 1.70

Remaining FOM used to find O&M Labor ($ Million) 496.5

Productivity* 8.7

*Workers per $Million in Payroll for Electricity Generating Sector, Forecast to 2015

Sources: Productivity from 2007 Economic Census and Growth Rate from BLS.

Labor requirement to operate the controls is estimated for all equipment types 

combined, using the incremental FOM costs from IPM. The IPM incremental FOM cost estimate 

included capital costs for fabric filters and scrubber improvement costs, which were first 

subtracted to obtain the true FOM costs ($496.5 million). Resulting FOM cost estimate was 

then converted to labor needs using the workers/$ Million in total payroll for the Electric 

Generating Sector. 

6A.3.2 Retirement Labor

Table 6A-11: Inputs to Labor from Retirements

Capacity of Incremental Retirements in SSR (MW) 4.7

O&M Decrease scaled to SSR Retirements ($MM) (To account for Partial Retirements) 288.2

Workers Per $Million in payroll, forecast to 2015 8.7

Sources: Productivity from 2007 Economic Census and Growth Rate from BLS.

 

42 Fabric filters follow a different pattern. Instead of a resource usage estimate, we used the VOM cost for FFs and 

converted this to jobs using the workers per million dollars output for the relevant manufacturing industry sector. 
6A-14

Retirement labor was estimated by first identifying the retiring units from EPA’s 

modeling using IPM parsed outputs (using incremental retirements in the policy case). The next 

step involved estimating the capacity of incremental retirements as well as the change in the 

FOM costs due to these retirements. Because of the discrepancies between partial retirements 

in EPA’s parsed outputs and System Summary Reports (SSR), FOM costs were scaled 

proportionately to reflect the lower SSR-based estimates, as shown in Table 6A-11 above. FOM 

cost decreases were then converted to job-years lost due to retirements using workers per 

$Million in payroll. 

6A.3.3 Fuel Use Labor

Table 6A-12: Inputs to Labor for Fuel Use

Coal by Region 2015 Incremental Fuel USE (Tons) 2008 Short Tons/Employee hour

Appalachia -11,770,000 2.9

Interior 19,870,000 4.8

West -17,260,000 19.9

Waste Coal -700,000 6.0

Natural Gas 

EIA Total Natural Gas Production 2007 (TCF) 24.664

EIA Total Crude Oil Production 2007 (Barrels) 1,848,450,000

EIA Natural Gas Heat Content 2007 (Btu/cf) 1,027

EIA Petroleum Heat Content (MMBtu/Barrel) 6.151

Total Crude Oil and Natural Gas Production (MMBtu) 36,699,744,000

Economic Census 2007 Oil and Gas Extraction Employees 140,160

MMBtu per Man-year for Oil and Gas Extraction 261,842

Incremental Natural Gas from IPM (TCF) 0.171

Incremental Natural Gas from IPM (Converted to MMBtu) 175,786,505

*Workers per $Million in Payroll for Electricity Generating Sector, Forecast to 2015

Note: Heat Contents from EIA are assumed to be for fuels used in Electric Power Sector

Sources: Short Tons per hour from US EIA, Coal Industry Annual. Total Production from 2009 EIA Annual Energy 

Review. Heat Contents from EIA, Heat Content of Natural Gas Consumed and 2009 Annual Energy Review. 

Employment Data from 2007 Economic Census.
6A-15

Fuel use related employment impacts were estimated by using IPM results for 

incremental changes in coal and natural gas use (policy case over the base case). For coal, 

estimates of coal use in tons by region from IPM were used in conjunction with labor 

productivity estimates from the EIA for each region (in short tons/ employee hour), to calculate 

the change in job-hours needed. These were then converted to job-years, assuming 2,080 

working hours per year. As discussed above, because of the high variability in coal mining labor 

productivity in recent years, no attempt was made to forecast coal (and natural gas, for 

consistency) productivities, instead the most recent historical estimates were used in this 

appendix (which was the 2008 labor productivity for coal). 

For natural gas, the first step was estimating labor productivity since such information 

was not available directly from any reliable source. EIA production data from the Annual 

Energy Review for natural gas and crude oil (in TCF and barrels, respectively), along with EIA 

heat content estimates were used to find total crude oil and natural gas production in MMBtu 

for 2007. Labor productivity in MMBtu per job-year for the Oil and Gas Extraction sector was 

then estimated using data from the Census on oil and gas extraction employment. Then, the 

incremental natural gas demand from EPA’s IPM modeling results (in TCF) was converted to 

MMBtu of natural gas demand using EIA data on natural gas heat content. This was then used 

with the labor productivity estimated above to calculate the total job-years needed for 

increased natural gas demand for the final MATS. 
6A-16

6A.4 References

Sargent & Lundy, “IPM Model – Revisions to Cost and Performance for APC Technologies 

Mercury Control Cost Development Methodology FINAL”, March 2011, Project 12301-

009, Perrin Quarles Associates, Inc.

Sargent & Lundy, “IPM Model – Revisions to Cost and Performance for APC Technologies Dry 

Sorbent Injection Cost Development Methodology FINAL”, August 2010, Project 12301-

007, Perrin Quarles Associates, Inc.

Sargent & Lundy, “IPM Model – Revisions to Cost and Performance for APC Technologies SDA 

FGD Cost Development Methodology FINAL”, August 2010, Project 12301-007, Perrin 

Quarles Associates, Inc.

United States Department of Labor. Bureau of Labor Statistics. Industry Labor Productivity and 

Cost Data Tables, Annual Percent Changes. 2010. 

US Census Bureau. 2007 and 2002 Economic Census, 2000 Annual Survey of Manufacturers, 

Manufacturing and Mining: Detailed Statistics by Industry for the US and Utilities: 

Summary Statistics for the US.

US Energy Information Administration (EIA). Annual Energy Review 2009, Assorted Data for 

Coal, Natural Gas, and Petroleum. 2009. 

US Energy Information Administration (EIA). Natural Gas 2009, Heat Content of Natural Gas 

Consumed 2009.

US EPA. Clear Skies Act, Technical Package, Section D. “Projected Impacts on Generation and

Fuel Use”. 2003. 

US EPA. Clean Air Interstate Rule, Technical Support Document. “Boilermaker Labor Analysis 

and Installation Timing.” OAR-2003-0053 (March, 2005).

US EPA. “Impacts of the Acid Rain Program on Coal Industry Employment.” EPA 430-R-01-002 

(March, 2001).

US EPA, Office of Research and Development. “Engineering and Economic Factors Affecting the 

Installation of Control Technologies for Multi-pollutant Strategies.” EPA-600/R-02/073 

(October, 2002).

US EPA, Engineering and Economic Factors Affecting the Installation of Control Technologies: 

An Update. Prepared by Andover Technology Partners and ICF International. October, 

2011. 
6B-1

APPENDIX 6B

POTENTIAL EFFECT OF ELECTRICITY PRICE INCREASE ON AVERAGE PRODUCTION 

EXPENSES, DETAILED RESULTS

Table 6B-1. 2002 Share of Production Expenditures on Electricity and Maximum Percentage 

Increase in Production Expenditures

Sector

Share of 

Production 

Expenditures on 

Electricitya

Upper-Bound

Increase in Average 

Production 

Expendituresb

Total Value 

Added in 2002

(MM 2002$)c

Total Industry 

Output in 2002

(MM 2002$)d

Crop production 2.243% 0.070% $46,172 $119,523

Animal production 1.636% 0.051% $27,504 $100,916

Forestry and logging 0.033% 0.001% $11,396 $32,259

Fishing, hunting and trapping 0.074% 0.002% $2,955 $5,424

Support activities for agriculture and 

forestry 0.424% 0.013% $7,556 $13,059

Oil and gas extraction 1.467% 0.045% $52,741 $104,084

Coal mining 2.090% 0.065% $10,431 $20,390

Metal ores mining 8.029% 0.249% $4,184 $7,976

Nonmetallic mineral mining and 

quarrying 4.158% 0.129% $10,542 $19,750

Support activities for mining 0.325% 0.010% $14,122 $32,356

Electric power generation, 

transmission, and distributione 0.001% 0.000% $152,179 $224,934

Natural gas distribution 0.033% 0.001% $27,426 $83,256

Water, sewage and other systems 0.307% 0.010% $4,923 $7,519

New nonresidential construction 0.363% 0.011% $240,950 $445,033

New residential construction 0.325% 0.010% $190,199 $438,434

Maintenance and repair 

construction 0.262% 0.008% $78,440 $148,896

Food manufacturing 1.168% 0.036% $97,176 $451,935

Beverage manufacturing 0.796% 0.025% $20,992 $71,627

Tobacco manufacturing 0.252% 0.008% $35,407 $47,465

Textile mills 2.409% 0.075% $12,697 $44,684

Textile product mills 0.788% 0.024% $10,109 $29,891

Apparel manufacturing 0.567% 0.018% $14,720 $39,559

Leather and allied product 

manufacturing 0.688% 0.021% $1,809 $5,786

Wood product manufacturing 1.363% 0.042% $25,910 $88,746

Pulp, paper, and paperboard mills 3.346% 0.104% $28,322 $69,961

Converted paper product 

manufacturing 0.979% 0.030% $26,547 $81,164

Printing and related support 

activities 0.993% 0.031% $45,594 $98,453

Petroleum and coal products 

manufacturing 0.781% 0.024% $21,047 $210,398

Basic chemical manufacturing 3.519% 0.109% $21,144 $105,185

Resin, rubber, and artificial fibers 

manufacturing 2.115% 0.066% $10,905 $59,517

Agricultural chemical manufacturing 1.550% 0.048% $5,311 $18,661
6B-2

Sector

Share of 

Production 

Expenditures on 

Electricitya

Upper-Bound

Increase in Average 

Production 

Expendituresb

Total Value 

Added in 2002

(MM 2002$)c

Total Industry 

Output in 2002

(MM 2002$)d

Pharmaceutical and medicine 

manufacturing 0.452% 0.014% $63,756 $135,402

Paint, coating, and adhesive 

manufacturing 0.590% 0.018% $6,723 $26,356

Soap, cleaning compound, and 

toiletry manufacturing 0.382% 0.012% $26,468 $60,511

Other chemical product and 

preparation manufacturing 0.871% 0.027% $9,720 $35,968

Plastics and rubber products 

manufacturing 1.836% 0.057% $63,199 $169,013

Nonmetallic mineral product 

manufacturing 2.218% 0.069% $39,717 $92,407

Iron and steel mills and 

manufacturing from purchased steel 3.412% 0.106% $16,231 $61,396

Nonferrous metal production and 

processing 3.396% 0.105% $12,846 $49,644

Foundries 2.905% 0.090% $11,348 $26,039

Forging and stamping 1.423% 0.044% $7,137 $21,033

Cutlery and handtool manufacturing 0.956% 0.030% $5,089 $10,056

Architectural and structural metals 

manufacturing 0.624% 0.019% $21,469 $58,094

Boiler, tank, and shipping container 

manufacturing 1.116% 0.035% $7,627 $22,955

Ordnance and accessories 

manufacturing 1.150% 0.036% $3,016 $5,158

Other fabricated metal product 

manufacturing 1.217% 0.038% $56,484 $122,911

Agriculture, construction, and 

mining machinery manufacturing 0.478% 0.015% $14,080 $44,654

Industrial machinery manufacturing 0.708% 0.022% $10,039 $28,165

Commercial and service industry 

machinery manufacturing 0.498% 0.015% $7,272 $19,765

HVAC and commercial refrigeration 

equipment manufacturing 0.550% 0.017% $11,874 $30,838

Metalworking machinery 

manufacturing 1.072% 0.033% $11,506 $24,246

Engine, turbine, and power 

transmission equipment 

manufacturing

0.537% 0.017% $13,264 $34,721

Other general purpose machinery 

manufacturing 0.647% 0.020% $19,782 $53,959

Computer and peripheral 

equipment manufacturing 0.231% 0.007% $17,951 $67,600

Audio, video, and communications 

equipment manufacturing 0.278% 0.009% $19,749 $70,744

Semiconductor and other electronic 

component manufacturing 1.112% 0.034% $43,181 $108,191
6B-3

Sector

Share of 

Production 

Expenditures on 

Electricitya

Upper-Bound

Increase in Average 

Production 

Expendituresb

Total Value 

Added in 2002

(MM 2002$)c

Total Industry 

Output in 2002

(MM 2002$)d

Electronic instrument 

manufacturing 0.515% 0.016% $33,232 $89,668

Manufacturing and reproducing 

magnetic and optical media 1.130% 0.035% $2,470 $7,373

Electric lighting equipment 

manufacturing 0.667% 0.021% $5,282 $11,757

Household appliance manufacturing 0.469% 0.015% $7,911 $21,073

Electrical equipment manufacturing 0.537% 0.017% $13,219 $29,252

Other electrical equipment and 

component manufacturing 1.091% 0.034% $14,582 $35,712

Motor vehicle manufacturing 0.208% 0.006% $51,523 $237,729

Motor vehicle body, trailer, and 

parts manufacturing 0.651% 0.020% $65,811 $222,484

Aerospace product and parts 

manufacturing 0.528% 0.016% $45,155 $121,440

Other transportation equipment 

manufacturing 0.458% 0.014% $14,760 $41,125

Furniture and related product 

manufacturing 0.711% 0.022% $29,629 $73,343

Medical equipment and supplies 

manufacturing 0.491% 0.015% $32,265 $58,729

Other miscellaneous manufacturing 0.616% 0.019% $27,230 $64,817

Wholesale trade 0.450% 0.014% $606,156 $871,529

Retail trade 1.328% 0.041% $616,158 $905,320

Air transportation 0.085% 0.003% $43,101 $98,612

Rail transportation 0.051% 0.002% $19,621 $40,537

Water transportation 0.514% 0.016% $7,745 $26,389

Truck transportation 0.250% 0.008% $92,509 $203,596

Transit and ground passenger 

transportation 0.772% 0.024% $17,889 $31,425

Pipeline transportation 1.127% 0.035% $5,603 $22,316

Scenic and sightseeing 

transportation and support activities 0.487% 0.015% $34,859 $63,843

Couriers and messengers 0.465% 0.014% $35,669 $61,599

Warehousing and storage 2.615% 0.081% $25,729 $42,563

Newspaper, periodical, book, and 

directory publishers 0.376% 0.012% $70,937 $139,878

Software publishers 0.010% 0.000% $64,378 $104,043

Motion picture and sound recording 

industries 0.486% 0.015% $49,890 $84,176

Radio and television broadcasting 0.212% 0.007% $21,790 $49,756

Cable networks and program 

distribution 0.155% 0.005% $11,360 $25,639

Internet publishing and 

broadcasting 0.094% 0.003% $4,339 $8,445

Telecommunications 0.334% 0.010% $236,169 $433,191

Internet service providers, web 

search portals, and data processing 0.110% 0.003% $43,267 $71,847
6B-4

Sector

Share of 

Production 

Expenditures on 

Electricitya

Upper-Bound

Increase in Average 

Production 

Expendituresb

Total Value 

Added in 2002

(MM 2002$)c

Total Industry 

Output in 2002

(MM 2002$)d

Other information services 0.820% 0.025% $4,550 $6,777

Monetary authorities, credit 

intermediation and related activities 0.096% 0.003% $454,896 $681,748

Securities, commodity contracts, 

investments, and related activities 0.212% 0.007% $159,906 $285,116

Insurance carriers and related 

activities 0.073% 0.002% $242,318 $452,443

Funds, trusts, and other financial 

vehicles 0.088% 0.003% $21,539 $88,019

Real estate 1.783% 0.055% $642,766 $815,660

Owner-occupied dwellings 0.000% 0.000% $703,125 $959,446

Automotive equipment rental and 

leasing 0.277% 0.009% $22,491 $38,811

Consumer goods and general rental 

centers

1.220% 0.038% $19,448 $32,612

Commercial and industrial 

machinery and equipment rental 

and leasing

0.288% 0.009% $25,915 $46,901

Lessors of nonfinancial intangible 

assets

1.121% 0.035% $101,280 $124,372

Legal services 0.186% 0.006% $152,002 $205,482

Accounting, tax preparation, 

bookkeeping, and payroll services 0.285% 0.009% $76,034 $101,979

Architectural, engineering, and 

related services 0.194% 0.006% $109,962 $176,744

Specialized design services 0.315% 0.010% $15,156 $21,611

Computer systems design and 

related services 0.811% 0.025% $172,781 $254,150

Management, scientific, and 

technical consulting services 0.119% 0.004% $86,247 $127,971

Scientific research and development 

services 0.606% 0.019% $59,858 $105,529

Advertising and related services 0.368% 0.011% $53,337 $80,504

Other professional, scientific, and 

technical services 0.325% 0.010% $48,877 $73,753

Management of companies and 

enterprises 0.602% 0.019% $272,922 $440,898

Employment services 0.236% 0.007% $113,200 $138,221

Travel arrangement and reservation 

services 0.223% 0.007% $16,564 $27,997

All other administrative and support 

services 0.371% 0.012% $167,088 $275,522

Waste management and 

remediation services 0.346% 0.011% $29,525 $53,150

Educational services 2.167% 0.067% $80,329 $141,829

Ambulatory health care services 0.350% 0.011% $332,366 $519,818

Hospitals 1.142% 0.035% $213,215 $380,472
6B-5

Sector

Share of 

Production 

Expenditures on 

Electricitya

Upper-Bound

Increase in Average 

Production 

Expendituresb

Total Value 

Added in 2002

(MM 2002$)c

Total Industry 

Output in 2002

(MM 2002$)d

Nursing and residential care 

facilities 1.396% 0.043% $84,168 $127,094

Social assistance 0.672% 0.021% $59,063 $102,033

Performing arts, spectator sports, 

museums, zoos, and parks 0.698% 0.022% $42,786 $75,962

Amusements, gambling, and 

recreation 1.931% 0.060% $59,972 $92,438

Accommodation 3.141% 0.097% $64,137 $107,298

Food services and drinking places 1.894% 0.059% $250,895 $468,666

Automotive repair and maintenance 0.869% 0.027% $98,087 $176,375

Electronic, commercial, and 

household goods repair 0.649% 0.020% $62,000 $95,436

Personal and laundry services 1.125% 0.035% $79,997 $127,208

Religious, grantmaking, giving, and 

social advocacy organizations 0.199% 0.006% $38,982 $91,988

Civic, social, professional and similar 

organizations 1.351% 0.042% $22,731 $63,418

Private households 0.000% 0.000% $12,516 $12,516

Federal Government enterprises 0.405% 0.013% $64,552 $83,473

State and local government 

enterprises 0.932% 0.029% $63,668 $161,513

General Federal defense 

government services 0.407% 0.013% $219,737 $380,797

General Federal nondefense 

government services 0.314% 0.010% $124,580 $209,856

General state and local government 

services 0.591% 0.018% $797,163 $1,240,559

a

Source: BEA 2007b. “Electric power generation, transmission, and distribution“ is BEA commodity sector 2211. 

The correspondence between BEA’s sector classification and NAICS codes is available from BEA 2007a.

b The forecast change in average production expenses is an upper-bound and assumes that production and use of 

all goods and services in the economy remain unchanged. See chapter discussion for details.

c

Source: BEA 2007c. This information is used in the calculation of percentage of total value added in Tables 6-7 

and 6-8.

d

Source: BEA 2007c . This information is used in the calculation of output-weighted average maximum average 

production expense increases reported in Section 6.5.3

e

The upper-bound increase in average production expenditures in the Electric power generation, transmission, 

and distribution sector reported in this table are strictly those due to higher electricity prices and are calculated 

using the BEA benchmark I/O accounts as described in Section 6.5.3. Chapter 3 reports EPA’s estimate of the 

effect of MATS on production costs in the electricity sector.