TO: Tom Walton

Darryl Weatherhead

FROM: Kathy Heller

Dallas Wood

DATE: June 7, 2007

SUBJECT: Cost-to-Sales Analysis of Testing and Monitoring Costs.

This memo describes our cost-to-sales analysis of potential economic impacts of testing and 

monitoring costs for new and existing solid- and liquid-fuel boilers at area sources. First, we 

describe the data used in our analysis. Next, we describe the methods and assumptions used 

during this analysis. Lastly, we report our results. 

DESCRIPTION OF DATA USED IN ANALYSIS

Testing and Monitoring Compliance Cost Data

EPA provided testing and monitoring costs for liquid- and solid-fuel boilers by 2-digit SIC code. 

To match cost data for each industry with financial data available from the U.S. Census Bureau, 

we first had to convert SIC codes to NAICS codes. To do this we used the official SIC to NAICS 

cross walk provided by the U.S. Census Bureau, which matches each 4-digit SIC code to a 6-

digit NAICS code.1 However, when aggregating to the 2-digit SIC level, we find that each 2-

digit SIC code often has several NAICS codes associated with it. For example, SIC code 12 is 

associated with three 3-digit NAICS codes (212, 213, and 238). In these cases, since we had no 

other information on the industries being affected, we assigned the NAICS code that was 

associated the greatest number of times with a particular SIC code. For example, NAICS 212 is 

associated three times with SIC 12, while NAICS 213 and 238 are only associated with SIC 12 

once. As a result, NAICS 213 was selected. 

However, converting the SIC code for a group labeled “government owned buildings” proved to 

be a special case because it had several SIC codes associated with it (ranging from 91 to 98). As 

a result, a collection of NAICS codes were required to adequately capture the entities being 

presented in these 7 SIC codes. These NAICS codes included 9211, 9212, 9213, 9214, 922, 923, 

924, 925, and 926. However, due to data availability, it was deemed appropriate to simply collect 

data on county and municipal governments as a whole, rather than attempt to collect information 

 1 U.S. Bureau of the Census, 1987 SIC to 2002 NAICS Crosswalk. Available here: 

http://www.census.gov/epcd/naics02/NAICS02toSIC87.xls
Tom Walton and 

Darryl Weatherhead

February 1, 2005

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specifically on each of the NAICS codes listed above. In the end, RTI identified 63 industries

from the EPA’s cost data that it would study as part of its cost-to-sales analysis. 

Industry Financial Information

Given the substantial number of businesses and municipalities incurring testing and monitoring 

costs, sales information could not be obtained for individual parent companies for this analysis. 

Therefore, we used data obtained from the U.S. Bureau of the Census for each industry to 

conduct a “representative entity”, cost-to-sales analysis (described in the following section). 

For industry sectors, the data sets we used were compiled from the Economic Census and the 

Survey of Current Business. These data sets present financial information for each industry by 

NAICS—specifically the total number of firms in each industry, total receipts earned by these 

firms, as well as the total number of establishments associated with them. In addition, data are

organized into groups based on either employment size total receipts of the firm. For example, 

data organized by employment size would report financial information for firms employing 

between 1 and 4 employees, 5 to 9 employees, and so on.2 This organizational structure allowed 

us to group firms into large and small size categories based on criteria established by the Small 

Business Administration, which are described below. 3 

For municipalities, we used two data sets compiled from the 2002 Census of Governments, 

which reported the number of county and municipal governments and their revenues by the size 

of the population they serve. 4 As before, this organization structure allowed us to separate large 

and small municipalities based on an established size criteria—that small governments are those 

serving a population of less than 50,000 people.5

Small Business Definitions

To group firms into small and large categories, RTI used size criteria established by the Small 

Business Administration for each NAICS code.6

 2 Data is also provided for firms employing zero employees in 2002. Most likely, this is partial information for firms that did not 

exist for the entire year. As a result, these firms are excluded from our analysis. 

 

However, since our analysis takes place at such 

3 U.S. Bureau of the Census, Statistics of U.S. Businesses. Data compiled by employment size: 

http://www.census.gov/csd/susb/usalli02.xls and by receipts: http://www.census.gov/csd/susb/usalli_r03.xls.

4 U.S. Bureau of the Census, 2002 Census of Governments. Data compiled for county governments: 

http://www.census.gov/prod/2005pubs/gc02x43.pdf and for municipal governments: 

http://www.census.gov/prod/2005pubs/gc024x4.pdf.

5 For example, see previous cost-to-sales analysis performed in A Profile of Sectors Potentially Affected by the Proposed 

NESHAP for Industrial, Commercial, and Institutional Boilers by Heller et al, 2006.

6 US Small Business Administration, Table of Small Business Size Standards Matched to North American Industry 

Classification System Codes: http://www.sba.gov/idc/groups/public/documents/sba_homepage/serv_sstd_tablepdf.pdf
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Darryl Weatherhead

February 1, 2005

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a disaggregated level (3-digit NAICS), there were often several potential criteria for defining a 

small business for each of our industry groups. For example, firms categorized under NAICS 

112111 are defined as small if they earn revenue below $0.75 million per year. However, firms 

categorized under NAICS 112112 (which shares the same 3-digit NAICS) are defined as small if 

they earn revenue below $1.5 million per year. In these cases, RTI used the median of all criteria 

associated with each NAICS code to define small businesses in its analysis (For example, the 

median criteria for a small businesses categorized under NAICS 112 is $0.75 million per year). 

It should be stressed that since this is a “representative firm” analysis using Economic Census 

data (as opposed to data for each affected parent company) it is likely to overstate the number 

and typical sales of small firms affected by testing and monitoring costs in each industry for 

several reasons. Primarily, it is likely to overstate the number of affected small businesses 

because of limitations associated with reported sales and employment categories used by the 

Census. 7 For example, the median small business criteria for Food and Beverage Stores 

(NAICS 445) is any business earning $6 million in revenue or less. However, the sales category 

that most closely matches this size standard in the dataset used is $10 million dollars or less. As a 

result, there will be many firms that earn more than $6 million per year, but that we are 

classifying as small businesses. 

METHOD OF ANALYSIS

In this section we discuss the representative entity, cost-to-sales analysis we conducted to 

evaluate the economic impact of testing and monitoring costs. This analysis essentially compares 

the estimated total compliance costs incurred by an average (or “representative”) entity to its 

revenue for each industry. First, we describe how the revenue of a representative entity is 

calculated. Next, we discuss how total compliance costs are computed for representative entities. 

Lastly, we describe how these two pieces estimates are combined to calculate a cost-to-sales 

ratio

Creating the Representative Entity

Since individual parent company data was not available for firms in each industry, RTI used U.S. 

Census data to estimate the sales of “typical” or “representative” small and large firms. This was 

done by taking the total revenue received by either small or large firms in a particular industry 

and dividing it by the total number of firms in that industry. For example, 12,174 small firms 

involved in forestry and logging activities earned a total of $8.38 billion in revenue in 2002. 

 7The Economic Census provides the following definition of the firm: “A firm is a business organization or entity consisting of

one domestic establishment (location) or more under common ownership or control. All establishments of subsidiary firms 

are included as part of the owning or controlling firm.” For the economic census, the terms “firm” and “company” are 

synonymous. EPA comparisons of Census data and detailed analysis of ownership configurations in affected industries 

suggest the Census data likely overstate the number of small ultimate parent companies.
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Darryl Weatherhead

February 1, 2005

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Therefore, revenue for a “representative” small firm in this industry would be $688 thousand 

dollars per year.

Similarly, we computed average revenue for large and small government entities by taking total 

revenue received by these governments and dividing them by the total number of relevant 

government entities. For example, 2,178 small counties in the United States received a total of 

$36.64 billion in revenue in 2002. Therefore, revenue for a “representative” small county would 

be $16.82 million per year. 

In addition, we also made the assumption that new firms would be identical to existing ones. As 

a result, there is no difference between representative firms for new or existing sources. 

Computing Compliance Costs for a Representative Entity

Based on the data provided by the EPA, RTI determined that compliance costs for each source 

would total $50,000 per year for both new and existing solid- and liquid-fuel boilers. 

Unfortunately, RTI did not have information regarding the number of sources per entity. 

Therefore, to compute the cost incurred by firms and local governments we had to make the 

following assumptions.

For industry sectors, we assumed that each establishment in each industry would be associated 

with one source. Therefore, to calculate the compliance costs incurred by a representative firm, 

we must multiply the costs imposed on each source ($50,000), by the number of sources per 

establishment (1), by the number of establishments per firm (computed from U.S. Census Data).

A similar assumption was made for government entities. Since RTI did not have information on 

the number of municipal buildings (e.g. courthouses, prisons) that would be affected, we 

assumed that each county and municipality had only one source. In other words, that the 

representative county or municipal government would incur a compliance cost of exactly 

$50,000 per year. 

Cost-to-Sales Ratio Analysis

The financial impacts of potential compliance costs are assessed for representative entities in 

each industry and for each size category using the ratio of compliance costs to the average 

representative entity revenue (cost-to-sales ratio or CSR).8 This ratio is calculated using the 

following formula:

 8This approach assumes affected firms absorb the control costs, rather than pass them onto consumers in the form of higher 

prices.
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Darryl Weatherhead

February 1, 2005

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j

n

i

TR

TACC

CSR

∑

= (5.1)

where 

TACC = total annual compliance costs incurred by the representative enity,

i = indexes the number of affected sources owned by representative entity j,

n = number of affected sources, and

TRj = total annual revenue of a representative entity j in each industry

RESULTS OF ANALYSIS

In this section we report the results of our analysis of the potential economic impacts of testing 

and monitoring costs for new and existing boilers at area sources. More detailed information is 

available in Appendix A. 

It should be noted that since the cost per source are the same for new as well as existing boilers 

($50,000 per year)--and we are assuming that existing representative entities will be identical to 

new ones--that, the CSRs for both new and existing entities will be the same. Therefore, the 

difference between our analyses of existing units is that there are different industries expected to 

incur costs for new and existing.

Existing Entities

Table 1 compares the frequency of CSRs for existing small and large entities. As one can see, for 

small entities, almost half of the cost-to-sales ratios (29/63) are either equal to zero (meaning that 

no costs were incurred for those industries) or unable to be calculated due to insufficient data. 

Representative firms for which total compliance costs exceed 3% of average firm revenues

account for over 60% of remaining CSR’s (21/34). This would seem to suggest that testing and 

monitoring costs have a significant impact on many industries. This conclusion seems to be 

supported by the distribution of CSRs found in Table 2, which reports that the median CSRs 

computed during our analysis is approximately 1% and that estimates can reach as high as 19%. 

These conclusions contrast sharply with the information computed for existing large entities. As

with the small entities, almost half of the CSRs for these entities (29/63) are either equal to zero 

or unable to be calculated due to insufficient data. However, now, over 85% of the remaining 

CSRs are less than 1%. The range and median of CSRs for existing entities is also much lower 

than those of small entities. However, the range does reach a maximum of 4.98%. The

combination of this evidence suggests that testing and monitoring costs would not have a 
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Darryl Weatherhead

February 1, 2005

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significant impact on most existing large firms, but there are at least several that could witness a 

dramatic change in costs. 

Table 1. Frequency Comparison of Cost-to-Sales Ratio for Existing Small and Large 

Entities

Cost-to-Sales Ratio

Small Entities

(#)

Large Entities

(#)

Equal to Zero 20 18

Less than 1% 28 47

Between 1 and 3% 5 3

Greater than 3% 21 2

Insufficient Data 9 11

Table 2. Minimum, Median, and Maximum Cost-Sales Ratio for Existing Small and 

Large Entities

Cost-to-Sales Ratio Small Entities Large Entities

Minimum 0.00% 0.00%

Median 0.97% 0.13%

Maximum 18.64% 4.98%

New Entities

Table 3 compares the frequency of CSRs for new small and large entities. As one can see, the 

number of small and large representative entities incurring compliance costs (CSRs equal to 

zero) is much larger for new entities than it was for existing entities. However, new small entities 

with CSR’s greater than 3% account for 71% of total representative entities for which CSRs 

could be calculated (15/21). Again, this seems to suggest that the testing and monitoring costs 

have a significant impact on small firms. While the measure of the median CSR for these new 

small entities is lower when compared to that reported for existing small entities, this is most 

likely due to the increase in the number of industries no longer incurring compliance costs. 

As with existing entities, the results for new small entities vary greatly from results computed for 

large entities. When one excludes the entities for which CSR could not be computed, we find that 

representative entitles with CSRs less than 1 account for 71% of those remaining (16/21). In 

Table 4, regarding the distribution of CSRs, we also see that the median has dropped to zero, but 
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Darryl Weatherhead

February 1, 2005

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that the maximum has remained 4.98%. This suggests that many, if not most, of the new large 

entities will be largely unaffected by testing and monitoring costs.

Table 3. Frequency Comparison of Cost-to-Sales Ratio for New Small and Large 

Entities

Cost-to-Sales Ratio

Small Entities

(#)

Large Entities

(#)

Equal to Zero 33 31 

Less than 1% 37 47

Between 1 and 3% 2 3 

Greater than 3% 15 2 

Insufficient Data 9 11 

Table 4. Minimum, Median, and Maximum Cost-Sales Ratio for New Small and Large 

Entities

Cost-to-Sales Ratio Small Entities Large Entities

Minimum 0.00% 0.00%

Median 0.00% 0.00%

Maximum 18.64% 4.98%