TO: Tom Walton Darryl Weatherhead

FROM: Kathy Heller

Dallas Wood

DATE: June 7, 2007

SUBJECT: Cost

- to

- Sales Analysis of Testing and Monitoring Co sts

.

This memo describes our cost

- to

- sales analysis of potential economic impacts of testing and

monit oring costs for new and existing solid

- and liquid

- fuel boiler s at area sources. First, we describe the data used in our analys is. 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 t esting and monitoring cost s for liquid

- and solid

- fuel boilers by 2

- digit SIC code.

T o 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 provide d 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 ha s several NAICS codes associated with it. For example, SIC code 12 is

ass ociated with three 3

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

other information on the industr ies being aff ected, 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 21 3 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, 921 2, 921 3, 921 4

, 922, 923, 924, 925, and 926

. However, due to data availability, it was deemed appropriate to simply collect data on county and m unicipal 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.xlsTom Walton and Darryl Weatherhead February 1, 2005 Page 2

specifically on each of the NAICS codes listed above. In the end, R TI 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 th e substantial number of businesses and municipalities in curring testing and monitoring

costs , sales info rmation could not be obtained for individual parent companies for this analysis. Therefore, we used data obtain ed from the U.S. Burea u of the Census for each industry to conduct a “representative entity”

, cost

- to

- sales analysis ( described in the following section)

.

For industry sectors , t he data set s we used were compiled from the Economic Census and the Survey o f Current Business

. The se 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 to tal number of establishments associated with them. In addition, data are organized into groups b ased 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 s o 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 set s compiled from the 2002 Census of Governments, which reported the number of county and municipal governments and their revenues by the siz e 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 Admin istration for each NAICS code. 6

2 Data is also provided for firms employing zero employ ees 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. H owever, since our analysis takes place at such

3 U.S. Bureau of the Census, Statistics of U.S. Businesses. Data compiled by employment siz e: 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 Boi lers 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.pdfTom Walton and Darryl Weatherhead February 1, 2005 Page 3

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 business es categorized under NAICS 112 is $0.75 million per year ).

It should be stressed that since this is a “ representative firm ” analysis using E conomic 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 indu stry 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 f or 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 us ed is $10 million dollars or less . As a result, there will be many firm s 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 mon ito ring 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 no t 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 d ividing 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.

7 The Economic Census provides the following definition of the firm: “A firm is a business organization or entity consisting of one domestic establishment (lo cation) 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 de tailed analysis of ownership configurations in affected industries suggest the Census data likely overstate the number of small ultimate parent companies.Tom Walton and Darryl Weatherhead February 1, 2005 Page 4

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 exampl e, 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 th e 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, t o compute the cost incurred by firms and local govern ments 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 informati on 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 co st 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

rep resentative entity revenue (cost

- to

- sales ratio or CSR). 8 This ratio is calculated using the following formula :

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

.Tom Walton and Darryl Weatherhead February 1, 2005 Page 5

j n

i TR

TACC

CSR 

# ∑

**=** (5.1)

where

TACC = total annual compliance costs incurred by the representative enity

, i = indexes the number of affe cted 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 econom ic impacts of testing and monitoring costs for new and existing boilers at area sources. More detailed information is available in Appendix A.

I t 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 . Th erefore, the

difference between our analyses of existing units is that there are differe nt 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 equ al to zero (meaning that

no costs were incurred for those industries) or unable to be calculated due to insufficient data. Representative firms for which tot al compliance costs exceed 3% of average firm revenue s 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 analy sis is approximately 1% and that estimates can reach as high as 1 9%.

These conclusions co ntrast 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 e qual 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 rea ch a maximum of 4.98 %

. Th e combination of this evidence suggests that testing and monitoring costs would not have aTom Walton and Darryl Weatherhead February 1, 2005 Page 6

significant impact on most existing large firms , but there are at least several that could witness a dramatic change in costs.

Table 1. F requency 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 tha n 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 Ent ities

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 in crease 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 comput ed, 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, butTom Walton and Darryl Weatherhead February 1, 2005 Page 7

that the maximum has remained 4.98%. This sugges ts 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%