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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, competiveness, and 

job creation “ (emphasis added). Therefore, and consistent with recent efforts to characterize 

the employment effects of economically significant rules, the Agency has provided this analysis 

to inform the discussion of labor demand and employment impacts. 

The analysis includes two sets of estimates. The first involves the employment impacts 

on the regulated industry over time. The second involves 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. EPA estimates 

that the net employment effect will range from −15,000 to +30,000 jobs, with a central 

estimate of +8,000. This aggregate figure includes potential job losses from increased costs as 

well as potential job increases as a result of additional hiring for compliance. In the 

environmental sector, EPA estimates an increase of 46,000 job-years. EPA also provides a 

qualitative discussion of other potential employment effects, including both increases and 

decreases. These sets of estimates should not be added in an attempt to characterize the 

overall employment effect.

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, which continues to evolve, and to seek public 

 

1

See the employment impacts appendix included in this RIA. 
6-2

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 leads to more labor being used to produce a given amount of output, the additional 

labor is reflected by an increase in the cost of production.2 When an increase in employment

occurs as a result of a regulation, it is a cost to firms. 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, employment effects (both positive and negative) are possible. 

For example, an increase in labor demand due to 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 pollution control 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. And, in general, if a regulation imposes high costs and does not increase 

the demand for labor, it may lead to a decrease in employment.

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

investigates the expected consequences for the regulated sector and for the pollution control 

sector. 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. 

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 pollution control sector.

 

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. 
6-3

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). With certain 

qualifications, we believe that this study is relevant 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. It should be 

noted that the electric utility sector is less labor-intensive than the industries examined by 

Morgenstern et al. (2002). To this extent, it is possible that the positive employmentimpact 

estimates are high.

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

 

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

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

due to competing effects. A regulation that imposes costs may, for that reason, have an 

adverse effect on employment, but if a regulation leads to the hiring of additional workers, it 

may, for that reason, have a positive effect on employment. 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 

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 
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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 can have positive effects on employment and 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. 

Ideally, the EPA would first apply the methodology of Morgenstern et al. to current 

pollution expenditure and market data for the regulated firms to identify the relationship 

between abatement costs and employment, then use this relationship to extrapolate the effect 

of new projected abatement costs on these firms. Unfortunately, current firm-level abatement 

cost and market characteristics are not available. Therefore, the EPA has used the estimated 

relationship from the Morgenstern et al. data to extrapolate the employment impact of the 

new projected abatement costs without accounting for the industry and firm differences. 

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: 

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

central estimate of −18,000; 

 

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
6-6

 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 that are not considered in the Morgenstern et al. study. For example, 

employment in pollution control 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 pollution 

control 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 

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 

 

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. 
6-7

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. EPA 

also assumes that the net employment impact can be linearly extrapolated from the abatement 

cost (i.e., that every million 1987 dollars generates a central estimate of 1.55 jobs). 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. However, it does 

suggest that the relationship between the employment impact in any sector and increased 

costs due to regulation is ambiguous.

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. 
6-8

6.3 Employment Impacts of the MATS-Pollution Control 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 

environmental regulations. 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, while closure of plants that choose not to comply occurs after the 

compliance date. 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. Environmental regulation may 

increase revenue and employment in the environmental technology industry. While these 

increases represent gains for that 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 pollution control 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 pollution control 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.”

8

 The majority of the jobs associated with 

added pollution controls (e.g., boilermakers, general construction workers, etc.) will provide 

domestic employment opportunities, but some goods and services demanded and/or provided 

to the pollution control industry (e.g., steel, cement, etc.) are internationally traded goods.

 

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

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.
6-9

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.9 

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 

pollution control sector approach indicate that the MATS could support an increase of about 

46,000 job-years10 by 2015.

6.3.1 Overall Approach and Methodology for Pollution Control Sector Approach

EPA developed estimates of the potential employment changes for the Pollution Control

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.

11

 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.12 More 

specifically, the analysis includes estimates for the labor needed 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 

 

9

For more detail on methodology, approach, and assumptions, see Appendix 6A.

10 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.

11 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).

12 Detailed results from IPM for the MATS can be found in Chapter 3 of the RIA.
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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 Pollution Control 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.

Table 6-4. Employment Effects Using the Pollution Control Sector Approach for the MATS 

(in Job-Years)13

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

 

13 Numbers are rounded to nearest thousand. MATS is not anticipated to result in any notable new capacity in 

response to the rule, and thus is not considered as part of this analysis.
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6.3.3 Other Employment Impacts of MATS

In addition to the employment impacts estimated for the regulated sector and pollution 

control sectors, there are also likely to be other employment impacts associated with MATS. 

These include changes resulting from 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.14 The EPA 

has provided estimates of some of these effects below, which are discussed in more detail 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.

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 

 

14 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).
6-12

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 

pollution control 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)

Pollution Control 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. These employment estimates should 

not be summed because of the different metrics, length and methods of analysis. 

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

6.5 Potential Effect of Electricity Price Increase on Economy-Wide Production Costs

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. 

Firms can respond to price changes by making changes to their processes, raising their prices, 

reducing production, etc. However, electricity expenditures are only a modest component of 

overall economic activity. 

On an expenditures-weighted basis, electricity comprises only 0.75% of total production 

expenditures across all sectors in 2002 (BEA, 2007b, 2007c).15 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. Therefore, the upper estimate of the initial increase 

 

15 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” (Stewart et al., 2007). For more detail, see BEA 2007a and 2009.
6-13

in production costs across all sectors from direct electricity expenses is 0.023% ( =[0.031 * 

0.0075]*100% ).16 This 0.023% increase in average production expenditures represents a 

credible upper estimate of the average direct effect of higher electricity expenses because it 

assumes that production, consumption, and input levels do not change in the economy.17 In 

reality, we know that producers and consumers can often use less electricity-intensive 

substitute goods and services to avoid a significant portion of these costs even in the short-run, 

which would mitigate these production cost increases. We also know that producers of 

intermediate goods and services that adjust to higher electricity prices can also make changes 

that lead to price adjustments for final goods and services sectors (as discussed below, indirect 

electricity price effects are not included in this illustrative estimate). Taking into account the 

fact that these numbers represent an upper estimate of initial production costs from the direct 

increase in electricity expenditures, EPA does not expect that increases in average production 

expenses from direct electricity price changes in this range are sufficient to cause significant 

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

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

This estimate has a number of limitations. First, as mentioned above, it reflects an 

upper estimate on the initial change to the average production cost of goods and services from 

direct electricity expenses because the calculation used to estimate these changes assumes

that production, consumption and input use will not change in response to higher electricity 

prices. Second, as mentioned above, 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 indirect effect of electricity price pass-through on factors relies even more 

 

16 Note that we are only performing simple calculations for upper estimate increases in per unit production costs 

as a direct consequence of higher electricity prices. 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. 

17This means that all other inputs, including capital, labor, and materials are assumed to be fixed when generating 

an upper estimate per unit production expense from direct electricity prices for the industries included in this 

analysis. 
6-14

heavily on assumptions about the inability of sectors to change their factor mix in response to 

relative factor prices changes. Third, important differences across sectors, regions and 

consumer classes may be masked by the nationwide estimated 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 estimates on 

per unit production expenses, regional and consumer class price differences cannot be 

calculated. Similarly, there are sectors that will have a meaningfully higher or lower maximum 

increase in average production expenditures within the context of the national average. 

Fourth, the share of electricity used 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 may lead to a 

slight underestimate of the increase in average production expenditures, averaged across the 

entire economy, as a result of this rule. 

While there are several caveats to this approach, this calculation suggests that 

electricity prices under MATS are not expected to have a large impact on production costs for 

the economy as a whole. Initial production cost impacts of less than 0.023% from direct 

electricity expenditures are unlikely to lead to significant impacts on the overall economy and 

would fall within the normal variability range of input price variation observed by producers in 

the past. This is consistent with the overall history of the implementation of the Clean Air Act

(Jaffe et al., 1995). 

This upper estimate of average initial production cost increases from direct electricity 

expenditures 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 do not 

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. Changes in employment (both increases and decreases) in downstream sectors will 

reflect the balance of all of these interactions.

An evaluation of the employment impacts beyond the pollution control and regulated 

sectors is not yet available, though as noted before, net effects on employment are expected to 
6-15

be at or very close to zero for the economy overall under full employment. In the case of this 

rule, labor may be a complement or a substitute to electricity in production, depending on the 

sector. It is also the case that environmental regulation may increase labor productivity by 

improving health, which may increase employment (via an increase in overall economic 

productivity, see the discussion in Chapter 5). Attempts to estimate such economy-wide effects 

by holding technologies and the proportion of various inputs constant over time are 

inappropriate for estimating long run impacts of regulation and an inaccurate representation of 

the behavior of real-world firms.

6.6 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 
6-16

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.

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.
6-17

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.

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.18 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).19 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. 

 

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

units.

19 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. MATS is not anticipated to result in any 

notable new capacity in response to the rule, and thus is not considered as part of this analysis.

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.20 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 

 

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

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

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

the 2002 analysis.21 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.22 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:23 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. 

 

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

Technology Partners and ICF International. October 20, 2011. 

22 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). 

23 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) 24 17

Activated Carbon Injection (ACI) 99

Dry Sorbent Injection (DSI) 44

Fabric Filter (FF) 25 102

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 

 

24 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. 

25 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

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

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

results.26 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.27

 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.28

 

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

other controls. 

27 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. 

28 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 $9529

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. 

 

29 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 units30 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.31 

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.32 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

 

30 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.

31 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. 

32 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.33 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.34,35 

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

below. 

 

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

March 2001.

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

2008. 

35 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 Requirement36

Pollution 

Control Type

Incremental GW 

Installed

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

FGD 37 17 1730 40 20 40

ACI 99 10 50 17 33

DSI 44 55 50 17 33

 

36 See Chapter 3 for more detail.

37 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. 
6A-12

FF 38 102 780 45 7 48

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 

 

38 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-13

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 

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.39

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

 

39 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

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.

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
6A-15

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.

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.