P.1 

OAR Box 1846 

Prepped by Ollie Stewart 

Document Number: 

77) lll-A-3 

Docket Number: 

A-90-07 

Printed 11/18/2008 7:30:17 AM Sheet 77 of 93 
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#-<=)0-07 

"HT- ^ -0 3 

Regulatory Flexibility Analysis of 

the Proposed Fuels and Fuel 

Additives Registration Program 

Final Report 

Prepared by: 

ICF Incorporated 

under 

EPA Contract No. 68-C8-0024 

Work Assignment No. 2-1, Subtask 4 

Prepared for: 

Office of Mobile Sources 

U.S. Environmental Protection Agency 

September 30, 1991 
P.3 

INTRODUCTION 

EPA is in the process of developing regulations under Section 211 of the Gean Air Act 

(CAA) which would require emissions and health effects testing of all motor vehicle fuels and fuel 

additives (F/FA). This report presents the results of a regulatory flexibility analysis of the testing 

requirements of the proposed fuels and fuel additives regulation. It is divided into eight sections: 

I) Background and Summary of the Proposed Regulation; 

II) Requirements of the Regulatory Flexibility Act; 

III) Methodology; 

TV) Definition of "Small" Manufacturer; 

. V) Characterization of Small Manufacturers in the Regulated Community; 

VI) Summary of Expected Costs of the Proposed Regulation; 

VII) Impacts on Small Fuel and Fuel Additive Manufacturers; and 

Vm) Possible Responses to the Impacts of the Proposed Regulation. 

I. BACKGROUND AND SUMMARY OF THE PROPOSED REGULATION 

Section 211(b)(2) of the 1970 CAA gives EPA the authority to require manufacturers to 

conduct tests to determine the potential health effects of their fuels and fuel additives and to require 

other information to identify the emissions produced by such fuels and additives, their effects on the 

public health and welfare, and their impacts on the performance of emission control systems. The 

CAA Amendments of 1977 added Section 211(e) which made implementation of Section 211(b)(2) 

mandatory and provided additional direction concerning the implementation of the statute's testing 

requirements. Specifically: 

• Section 211(e)(1) required implementation of the Section 211(b)(2) authority within 

one year of the enactment of the amendments. 

• Section 211(e)(2) established time limits for compliance by fuel and additive 

manufacturers. For those fuels and additives not yet registered when the Section 

211(b)(2) regulations are promulgated, the "requisite information" must be submitted 

prior to registration. For fuels and additives already registered, the information must 

be provided "no later than three years" after the new regulations are issued. 

• Section 211(e)(3) allowed EPA to exempt or make special allowances for small 

businesses, to provide for cost and/or burden sharing, and to make other specific 

exemptions when additional testing would be duplicative of adequate existing testing. 

The proposed regulation implements Sections 211(b)(2) and (e) by developing an emissions 

and health effects testing program. In developing this program, EPA has taken steps consistent with 

the Act to limit the focus of the requirements so as not to present an unnecessary burden on the 

regulated industry. 

The proposed testing program consists of three tiers. The purpose of the tiers is to provide 

a means by which additional testing will only be required if the test results of the previous tiers merit 

it. Tier 1 includes characterization of emissions, a comprehensive search and summary of available 

information sources concerning the emissions-based effects of fuels and fuel additives on public health 

and welfare, and application of data modeling techniques. Tier 2 consists of short-term screening 
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tests which address certain health endpoints.1 The number of tests required in this tier will depend 

on the extent to which the data search activities in Tier 1 provide documentation of adequate existing 

testing comparable to the screening tests included in Tier 2. Fuels and additives will be subject to 

Tier 3 requirements as determined on a case-by-case basis by EPA. to address particular areas of 

concern which arise in the earlier tiers. EPA expects that Tier 3 will often include animal inhalation 

exposures of chronic (two to three year) duration to verify and/or quantify the results of the screening 

evaluations. 

EPA is allowing industry to determine how costs are to be allocated among manufacturers. 

However, to prevent undue burden (as required in Section 211(e)(3)), the proposed regulation 

contains provisions such as cost/burden sharing, consideration of small business impacts, and 

elimination of duplicative testing. These provisions are explained in detail in the preamble to the 

proposed regulation. 

n. REQUIREMENTS OF THE REGULATORY FLEXIBILITY ACT 

The Regulatory Flexibility Act (RFA) requires EPA to analyze the requirements of proposed 

regulations to determine whether they will cause a significant impact on a substantial number of small 

entities.2 Because the RFA does not provide concrete definitions of "small entity," "significant 

impact" or "substantial number," EPA has established guidelines setting the standards to be used in 

evaluating impacts on small businesses.3 The guidelines specify that size definitions set by the U.S. 

Small Business Administration (SBA) should be used as the initial determination of a "small entity," 

but that EPA may use an alternative definition if it better captures the point at which entities are 

adversely affected simply because of their size. The guidelines further specify that a "substantial 

number" can be either a large fraction of the affected population of small entities or a large number 

of small entities. Finally, the guidelines set four criteria for determining whether impacts will be 

significant: 

• Annual compliance costs increase total costs of production for small businesses by 

more than five percent; 

• Compliance costs as a percentage of sales for small businesses are at least ten percent 

higher than compliance costs as a percentage of sales for large businesses; 

• Capital costs of compliance represent a significant portion of capital available to small 

businesses; or 

• The requirements of the regulation are likely to result in closures of small businesses. 

As explained below in the section on methodology, this analysis employs the latter two 

criteria. 

1

 Please refer to the proposed regulation for details on health endpoints. 

2

 5 USC 601. 

3 U.S. Environmental Protection Agency, Memorandum to Assistant Administrators, 

"Compliance with the Regulatory Flexibility Act," EPA Office of Policy, Planning, and 

Evaluation, 1984. 
P.5 

m. METHODOLOGY 

This section presents the data sources and methodology used to conduct this regulatory 

flexibility analysis. The analytical assumptions and limitations ofthe data collected are also discussed. 

This section is divided into four parts: collection and adjustment of data; definition of "small" 

manufacturer for this regulation; estimation of testing costs; and estimation of financial impacts. 

A. Data 

The primary source of data for this analysis is the report entitled, "Characterization of the 

Motor Fuel and Fuel Additives Industry." ICF used data in this report to develop a characterization 

database that contains the following information for 292 fuel manufacturers and 216 additive 

manufacturers: 

Manufacturer name; 

Location; 

Corporate status; 

Name of parent company; 

Number and type of fuel and additive products; 

Indication of whether the financial information pertains to the parent company or 

affiliate/division/joint venture/subsidiary, 

Sales; 

Assets; 

Net Income; 

Number of employees; 

Four-digit Standard Industrial Classification (SIC) codes corresponding to the 

principal business activities; and 

• Comments/additional information. 

Three adjustments were made to the characterization database for this analysis. First, any fuel 

or additive manufacturers that produce only leaded fuels, blends,4 or relabeled products were 

omitted, since these manufacturers are not affected by the testing requirements of the proposed rule. 

Second, asset and net income statistics were estimated by regression for a number of 

companies. Two separate equations (one relating net income to sales and the other relating assets 

to sales) were developed for both fuel and additive manufacturers. These equations are presented 

in Appendix A 

Third, since the report did not contain information on all affected manufacturers and data in 

it are skewed toward larger manufacturers, the underlying sales distributions of F/FA manufacturers 

were estimated as follows. First, ICF Incorporated (ICF) took a random sample of 100 from the 472 

fuel and 340 additive manufacturers for which the database contained no information. Data on sales, 

number of employees, primary and secondary SIC codes, and miscellaneous other information were 

obtained for 78 of the 100 sample manufacturers. Analysis indicated that the random sample 

contained a higher percentage of small companies than the industry characterization database. 

Blends are chemicals that are added to fuels but are not registered separately by 

additive manufacturers. Because blends do not represent a unique product 

registered by a manufacturer, they were not considered in this analysis. 
P.6 

To approximate the underlying distributions of fuel and additive manufacturers, ICF used 

information obtained from the sample companies to develop "weights" for companies in industry 

characterization database. Appendix B contains tables detailing this process. These company weights 

were used throughout the analysis to project the number of small and large companies and estimated 

impacts. 

B. Definition of "Small" Manufacturer 

The Regulatory Flexibility Act specifies that Small Business Administration (SBA) definitions 

should be used as the initial determination of a small entity but that EPA may use an alternative 

definition if it better captures the point at which entities are adversely affected simply because of 

their size. 

Adoption of an alternative definition for this regulation was considered because the regulated 

community spans a large number of Standard Industrial Classification (SIC) codes. Specifically, fuel 

and additive manufacturers represented 29 and 67 different SIC codes, respectively. Six four-digit 

SIC codes (2911, 5153, 5171, 5172, 5191, and 5541) represented over 50 percent of F/FA 

manufacturers in the Industry Characterization database. However, since compliance costs are 

primarily a function of the type of product, and not the number of products or the size of a company, 

ICF was unable to develop an alternative definition without first performing an in-depth analysis of 

the structure of the fuel and additive industry. 

Additionally, the intent of the Regulatory Flexibility Act is to determine the disproportionate 

impacts of regulations on small companies relative to their large competitors (which, as in this case, 

may be different than all companies within the regulated community). This suggests that impact 

analyses should consider the relative size of each company with respect to its own industry. 

For these reasons, EPA has adopted SBA definitions of "small" for this analysis. Furthermore, 

while this analysis separates fuel and additive manufacturers, it combines all SIC codes. ICF 

judgement is that the results are robust with respect to the size definition used in this analysis. 

C. Estimation of Testing Costs 

For purposes of this analysis, EPA used estimates of laboratory testing costs, the number of 

products and manufacturers in the regulated community, and any information currently available 

about the toxicity of each product to develop a total expected cost and an expected cost per 

manufacturer of the testing requirements. EPA estimated compliance costs for 715 fuel 

manufacturers and 517 additive manufacturers. In developing these estimates, EPA made several 

simplifying assumptions. First, testing requirements will impose one-time costs that will be incurred 

over a three year period. Second, one product per product group will be selected for testing and the 

costs of testing that product will be divided equally among the number of products in that group (as 

opposed to the number of manufacturers having products in that group). 

D. Estimation of Impacts 

To assess the impacts of the proposed testing requirements and to discern whether small 

companies are impacted disproportionately, ICF analyzed the effects of compliance costs on each 

company's return on assets (net income/assets). Return on assets (ROA) is a general indicator of 

financial health and is frequently used to compare the financial health of companies within an 

industry. The financial industry often uses ROA as one of the criteria for determining the borrowing 
P.7 

potential of a company. In this manner, analysis of the change in a company's ROA can indicate 

whether comphance costs represent a significant portion of the capital available to a small business 

and, ultimately, whether compliance costs are likely to result in closure of that company. As 

explained below, this can be done by utilizing standard ROA thresholds indicating different levels of 

financial health. 

In general, a company with a ROA less than 2.5 percent is considered to be in financial 

distress. At this level, a company may experience difficulties obtaining additional loans or other 

capital. A ROA less than -4. percent indicates that a company is in severe financial distress. Finally, 

a ROA less than -30 percent generally indicates closure. 

ICF used slightly different methods to estimate the impacts on companies for which actual 

data was available and on companies for which assets and net income were estimated. For companies 

with actual data, the analysis was fairly straightforward. For each company, its initial ROA served 

as a baseline. Since compliance costs are assumed to be incurred over three years, ROA after 

comphance costs were calculated by subtracting one-third of compliance costs from net income. ROA 

after costs were then compared with the baseline to determine the overall magnitude of the change 

in ROA and whether costs had driven ROA across any of the three thresholds. 

For companies with estimated asset and net income information, the initial ROA were, by 

definition, point estimates. Actual ROA for these companies are expected to fall within a given 

range around those point estimates. To account for this uncertainty in the analysis, ICF calculated 

the standard deviation of ROA for companies with actual data and used this value to estimate the 

range in which each initial ROA could be expected to vary and the probability that a company's ROA 

was already below a threshold. This same process was applied to calculating ROA after comphance 

costs. The baseline and after cost ROA were then compared as described above. Note that the 

estimates of the uncertainty in impacts for these companies will not be strictly accurate, as the 

estimates of the change in ROA will be subject to the same uncertainty. ICFs judgement is that this 

additional uncertainty does not materially affect the results. 

There are several limitations to this analysis. First, it assesses the impact of the start-up 

program only. It does not account for future unique products nor how manufacturers of new 

products in currently existing groups will pay for previous testing. Second, it assumes that a company 

is willing to use profits and resources from the entire company to cover compliance costs of this 

regulation. How realistic this assumption is depends, in part, on how integral fuel and additive 

products are to the core business of the company. Third, some of the financial information was for 

subsidiaries while other was for parent companies. To assess impacts, ICF matched subsidiary 

financial information with subsidiary compiiance costs and parent financial information with parent 

compliance costs. 

IV. DEFINITION OF "SMALL" MANUFACTURER 

As stated in the section on methodology, EPA has adopted SBA definitions of small for this 

analysis. These definitions are based on SIC codes and are presented in Exhibit 1. The most 

frequent definitions of small are companies having less than or equal to 100 and 500 employees. 

Using these definitions, ICF estimates that there are 405 (78 percent) small additive 

manufacturers, and 538 (75 percent) small fuel manufacturers, currently registered for unleaded gas 

and diesel products. 
P.8 

Exhibit 1 

SBA DEFINITIONS OF SMALL BY SIC CODE FOR F/FA MANUFACTURERS 

SIC 

1094 

1221 

1311 

1381 

1382 

1389 

1542 

1711 

2011 

2022 

2023 

2046 

2048 

2061 

2085 

2086 

2096 

2329 

2339 

2435 

2436 

2611 

2657 

2671 

2673 

2678 

2813 

2819 

2821 

2822 

2824 

2833 

2834 

2841 

2842 

2843 

2844 

2851 

2865 

2869 

2873 

2874 

2875 

2879 

2891 

2892 

2899 

2911 

2952 

2992 

» OF EMPLOYEE S O R 

MILLIONS OF $ 

500 

500 

500 

500 

$3.5 

$3.5 

$17.0 

$7.0 

500 

500 

500 

750 

500 

500 

750 

500 

500 

500 

500 

500 

500 

750 

750 

500 

500 

500 

1000 

1000 

750 

1000 

1000 

750 

750 

750 

500 

500 

500 

500 

750 

1000 

1000 

500 

500 

500 

500 

750 

500 

1500 

750 

500 

SIC 

3081 

3089 

3172 

3411 

3429 

3432 

3433 

3469 

3479 

3499 

3519 

3523 

3524 

3531 

3556 

3561 

3569 

3592 

3599 

3694 

3711 

3714 

3715 

3724 

3795 

3799 

3861 

4221 

4226 

4612 

4613 

4953 

4959 

5012 

5013 

5014 

5015 

5072 

5082 

5083 

5085 

5122 

5153 

5159 

5169 

5171 

5172 

5191 

5198 

5211 

» OF EMPLOYEE S O R 

MILLIONS OF $ 

500 

500 

500 

1000 

500 

500 

500 

500 

500 

500 

1000 

500 

500 

750 

500 

500 

500 

500 

500 

750 

1000 

500 

500 

1000 

1000 

500 

500 

$12.5 

$12.5 

1500 

1500 

$6.0 

$3.5 

100 

100 

100 

100 

100 

100 

100 

100 

100 

100 

100 

100 

100 

100 

100 

100 

$3.5 | 

SIC 

5251 

5261 

5399 

5411 

5531 

5541 

5571 

5699 

5912 

5941 

5983 

5984 

6221 

6531 

7389 

7538 

7549 

7699 

8734 

* OF EMPLOYEE S O R 

MILLIONS OF S 

$3.5 

$3.5 

$3.5 

$13.5 

$3.5 

$3.5 

$3.5 

$3.5 

$3.5 

$3.5 

$6.0 

$3.5 

$3.5 

S10.0 

$3.5 

$3.5 

$3.5 

$3.5 

$3.5 
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v. CHARACTERIZATION OF SMALL MANUFACTURERS IN THE REGULATED COMMUNITY 

The characteristics of small F/FA manufacturers are significantly different than those of large 

F/FA manufacturers. Additionally, the characteristics of fuel manufacturers differ from those of 

additive manufacturers. Exhibit 2 illustrates these differences by comparing weighted averages of 

several pertinent statistics. 

First, weighted average sales and assets of large fuel manufacturers are approximately 30 times 

larger than the weighted average sales and assets of small fuel manufacturers. The weighted average 

sales of large additive manufacturers are almost 200 times larger than the weighted average sales of 

small additive manufacturers. The weighted average assets of large additive manufacturers are 377 

times greater than the weighted average assets of small additive manufacturers. Interestingly, small 

additive companies tend to be smaller than small fuel companies while large additive companies tend 

to be larger than large fuel companies. These differences indicate that, for a given level of 

comphance cost, small manufacturers are likely to be impacted more than large manufacturers and 

small additive manufacturers may be impacted more than small fuel manufacturers. 

Second, small additive companies tend to be stronger financially than small fuel companies. 

For a given level of sales, additive companies generally have higher net incomes, assets, and ROA 

than fuel companies. Furthermore, ROA are fairly constant for small and large additive companies 

while ROA increase with size for fuel companies. This implies higher economies of scale for fuel 

companies. This also indicates that, for a given level of compliance cost, fuel companies may be 

impacted more than additive companies. 

Third, while large manufacturers tend to have more products than small manufacturers, the 

average large additive company has significantly more products than the average small additive 

company and fuel companies in general. This indicates that additive companies potentially face larger 

comphance costs than fuel companies. 

5

 All of the trends discussed in this section are also supported by a comparison of median 

statistics for small and large F/FA manufacturers. 
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Exhibit 2 

Characteristics of Small and Large Manufacturers 

Fuel Manufacturers 

Weighted Average: 

# of Employees 

Sales (000) 

Assets (000) 

Net Income (000) 

ROA 

# of Products 

Additive Manufacturers 

Weighted Average: 

# of Employees 

Sales (000) 

Assets (000) 

Net Income (000) 

ROA 

# of Products 

Small 

56 

$50,000 

$17,000 

$929 

2.9% 

1.8 

Small 

91 

$18,000 

$10,000 

$603 

4.4%* 

2.9 

Laree 

2,641 

$1,474,000 

$584,000 

$39,669 

5.6% 

2.9 

Large 

11,177 

$3,418,000 

$3,767,000 

$168,568 

5.5% 

9.4 

* One outlier was dropped. 
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VI. SUMMARY OF EXPECTED COSTS OF THE PROPOSED REGULATION 

The total expected cost of the testing requirements is $83 million. Expected costs per 

manufacturer range from just $497 to over $2 million. Exhibit 3 provides summary statistics of 

comphance costs. This table allows one to compare small manufacturer comphance costs to large 

manufacturer comphance costs. For both fuel and additive manufacturers, the median compliance 

cost to large companies is higher than the median compliance cost to small companies. Moreover, 

all median comphance cost values are sufficiently small as to not cause significant financial impacts. 

There are some interesting differences between weighted average statistics for small and large 

companies as well as for fuel and additive companies. First, compliance costs for additive companies 

tend to be higher than for fuel companies. This partially reflects the fact that additive companies 

typically have more products than fuel companies. Second, and importantly for this analysis, the 

average cost per product is slightly higher for small additive manufacturers than for large additive 

manufacturers. Given the differences in the financial profiles of small and large companies, this 

statistic indicates that small additive manufacturers may experience disproportionate financial impacts. 

It also indicates that small additive manufacturers may produce more unusual or "niche" products. 

vn. IMPACTS ON SMALL FUEL AND FUEL ADDITIVE MANUFACTURERS 

As indicated in the discussion of methodology, ICF estimated impacts by analyzing the change 

in ROA directly attributable to the proposed regulation. Companies in the characterization database 

were assigned "weights" to enable ICF to project impacts over all companies currently registered as 

producing unleaded and diesel products. Exhibit 4 presents the results of this impact analysis. 

Effects on Small Companies are Relatively Greater than on Large 

This analysis shows unambiguously that, for both fuel and additive companies, the effects on 

small companies are greater than on large. The chances that otherwise healthy fuel manufacturers 

will experience financial distress (i.e., have their ROA driven below 2.5%) are 15 times greater for 

small companies than for large; the comparable ratio for additive manufacturers is 140 times as great. 

This pattern also holds if one compares the impacts in terms of severe financial distress (a loss equal 

to four percent of assets). Only two firms (both small additive manufacturers) are projected to fail 

as a result of the proposed regulation if they are forced to absorb these costs. 

Comparison of the total magnitude of changes in ROA shows that all but one of the 

companies predicted to suffer heavy impacts are small. Clearly, any tendency for smaller companies 

to have smaller comphance costs is not strong enough to outweigh the relatively greater vulnerability 

resulting from their much smaller levels of assets and earnings. 

Expected comphance costs per manufacturer have been revised since these statistics were 

calculated. Since the changes were relatively minor, ICF does not expect that these 

summary statistics would change significantly. 
Exhibit 3 

Summary of Compliance Costs 

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Fuel Manufacturers 

Median Cost/Manufacturer 

Weighted Average Cost/Manufacturer 

% with Costs < $10,000 

% with Costs > $10,000 

Weighted Average Cost/Product 

Additive Manufacturers 

Median Cost/Manufacturer 

Weighted Average Cost/Manufacturer 

% with Costs < $10,000 

% with Costs > $10,000 

Weighted Average Cost/Product 

Small 

$1,085 

$26,012 

84.6% 

15.4% 

$8,874 

Small 

$1,057 

$67,715 

80.5% 

19.5% 

$20,204 

Large 

$2,170 

$73,971 

73.0% 

27.0% 

$22,033 

Urge 

$1,990 

$121,589 

70.2% 

29.8% 

$16,915 
Exhibit 4 

Estimated Impacts* 

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Type of Manufacturer 

Fuel Manufacturers 

Small 

# in Database 

Weighted ft 

Weighted % 

Large 

# in Database 

Weighted tt 

Weighted % 

Additive Manufacturers 

Small 

# in Database 

Weighted tt 

Weighted % 

Large 

If in Database 

Weighted tt 

Weighted % 

ROA 

<2.5% 

1 

4 

0.76% 

0 

0 

0.05% 

7 

23 

5.73% 

0 

0 

0.04% 

ROA 

<-4% 

0 

0 

0.04% 

0 

0 

0.00% 

3 

18 

4.38% 

0 

0 

0.00% 

ROA 

<-30% 

0 

0 

0.00% 

0 

0 

0.00% 

0 

2 

0.58% 

0 

0 

0.00% 

* Interpretation: 

ROA < 2.5% indicates financial distress. 

ROA < -4% indicates severe financial distress. 

ROA < -30% indicates closure. 

Fuel percentages axe based on 538 small and 177 large fuel manufacturers. 

Additive percentages are based on 405 small and 112 large additive manufacturers. 

All manufacturers that were impacted at the -30% and -4% thresholds were also 

impacted at the 2.5% threshold. 
P.14 

Actual Impacts are Insignificant for Fuel Manufacturers 

The fact that comphance costs have relatively greater effects on small companies, while not 

an unimportant consideration for evaluating impacts on small manufacturers, is probably not of great 

concern unless the actual magnitude of the impacts is significant. For fuel manufacturers, there is 

no evidence that this is the case. The projected chance that a given small fuel manufacturer will be 

driven over the threshold separating an adequate return on assets to the point of financial distress 

is only about one in 130. The analysis shows that no fuel companies would be driven into closure 

by the proposed regulation. Comparison of the magnitude of changes in ROA shows that only two 

fuel manufacturers in the characterization database have compliance costs large enough to reduce 

their ROA by one percent. 

Impacts may be Significant for Small Additive Manufacturers 

A different picture emerges from an examination of the effects on additive manufacturers. 

Projected impacts are, again, extremely minor for large firms, and skewed by effects on a single, 

unusually small "large" company (one with only 400 employees and sales of $17 million). Looking at 

small companies, however, it appears that there could be a significant number of companies driven 

into financial distress as a result of the proposed regulation. The analysis projects that almost six 

percent of small additive manufacturers will be pushed over the threshold into financial distress. 

Impacts are also seen at other thresholds. 

These findings are not based on one or two unusual companies. Comparison of the 

magnitude of changes in ROA shows that eight small additive companies in the characterization 

database are projected to experience reductions of at least three percentage points in their ROA 

A decrease of three percentage points in ROA is significant because the weighted average ROA is 

4.4 percent for smaU additive companies, and 5.5 percent for large additive companies. Thus, if an 

additive manufacturer experiences a decrease in ROA of three percent, it is likely to fall into financial 

distress if forced to absorb those costs. 

VIII. RESPONSES TO THE IMPACTS OF THE PROPOSED TESTING REQUIREMENTS 

Companies projected to experience impacts from the comphance costs will not necessarily be 

left with the reductions in their rates of return indicated by this analysis. Instead, they will have open 

to them a number of courses of action that could reduce the impacts of the regulations. Two likely 

responses, price increases and reductions in the number of products, are discussed below. These 

actions, however, will have secondary effects of their own. 

A. Price Increases 

The analysis presented above estimates impacts on returns assuming no changes in prices or 

sales. In many cases, however, regulatory costs can be passed on to consumers, or back to suppliers. 

The scope of this analysis precluded any attempt to make detailed predictions of the price changes 

that would result. Several general observations, however, can be made. First, the compliance costs 

represent an increase in the fixed costs of producing the products, rather than the variable costs. The 

distinction lies in the fact that if a firm increases its output of a given product, its total compliance 

costs do not increase; in fact, the average comphance costs drop somewhat with every additional unit 

sold, as the fixed comphance costs are spread over more units. This means that competing additive 

manufacturers will have an incentive to keep sales of their products high to help pay for their testing 

12 
P.15 

costs. This pressure to sell could make it less likely that price increases will stick, as attempts to raise 

prices by one firm are undercut by others seeking greater market share, unless or until some 

competing producers withdraw from the market. 

In addition, a requirement that any new manufacturers entering the market compensate the 

existing manufacturers confers a certain degree of protection on the existing manufacturers. To the 

extent that they had been keeping their prices low as a strategy to discourage competition, the 

potential for imposing testing costs on new competitors might allow existing firms to raise prices 

somewhat without fear that this increase will result in increased competition. In other words, prices 

might be raised to help cover compliance costs because any potential new competitors would face 

similar costs. Any price increases would, of course, impose part of the costs of the regulations on 

consumers, and could be expected to reduce sales to a certain extent 

Finally, to the extent that a product has unusual characteristics, its manufacturer may have 

considerable freedom in setting its price, and might have more success in covering its comphance 

costs. This is an important consideration, given that many of the largest comphance costs are faced 

by firms that are paying a substantial percentage of the total testing costs of that testing group. This 

may suggest that their products have few close substitutes. Thus, their unusual qualities may be able 

to support a price increase that offsets the impact of the testing costs-especially given that, as noted 

above, any potential new competitors will face high comphance costs as well. 

B. Reductions in the Number of Products 

Another response open to additive producers facing substantial testing costs is to drop the 

product or products with the greatest costs and smallest profit potential. This response is likely to 

have some residual impacts on profitability. This residual impact, however, is hkely to be relatively 

small as the firm is dropping a product with high costs and small sales/profit potential. Ultimately this 

response would reduce the number of competing products and make it easier for remaining firms to 

increase prices. 

Reductions in new products can be expected to occur as well; innovation that creates product 

diversity will be discouraged to an unknown extent since every new product will require the payment 

of testing costs. Furthermore, the more unusual the new product is, the greater its compliance cost 

is likely to be. Again, this increases the ability of existing manufacturers to raise prices enough to 

cover their costs, but at some cost in terms of product diversity. 

13 
P.16 

APPENDIX A 

EQUATIONS FOR ESTIMATING ASSETS AND NET INCOME 
Appendix A 

Equations to Estimate Assets and Net Income 

EQUATIONS TO ESTIMATE ASSETS AND 

NET INCOME FOR FUEL COMPANIES 

Log(Net Income) = • + b(Log(Sales)) 

Regression Output: 

Constant 

Std Err of Y Est 

R Squared 

No. of Observations 

Degrees of Freedom 

X Coefficient(s) 1.114 

Std Err of Coef 0.033 

t = 33.932 

Log(Assets) = a + b(Log( Sales))* 

Regression Output: 

Constant 

Std Err of Y Est 

R Squared 

No. of Observations 

Degrees of Freedom 

X Coefficient(s) 1.022 

Std Err of Coef 0.021 

t = 48.397 

0.924 

0.330 

0.888 

147 

145 

-0.542 

0.220 

0.937 

159 

157 

EQUATIONS TO ESTIMATE ASSETS AND 

NET INCOME FOR ADDITIVE COMPANIES 

Log(Net Income) = a + b(Log(Sales)) 

Regression Output: 

Constant 

Std Err of Y Est 

R Squared 

No. of Observations 

Degrees of Freedom 

X Coefficienl(s) 1.149 

Std Err of Coef. 0.024 

t = 48.583 

Log(Assets) = a + b(Log (Sales)) 

Regression Output: 

Constant 

Std Err of Y Est 

R Squared 

No. of Observations 

Degrees of Freedom 

X Coefficient(s) 1.108 

Std Err of Coef. 0.014 

t = 77.814 

1.187 

0.258 

0.961 

97 

95 

-047 7 

0.161 

0.984 

103 

101 

* Outlier was excluded 
P.18 

APPENDIX B 

DEVELOPMENT OF WEIGHTS 
Appendix B-l 

DEVELOPMENT OF WEIGHTS FOR FUEL MANUFACTURERS 

SALES DISTRIBUTION OF FUEL 

COMPANIES IN THE DATABASE 

SALES 

(SM1LLIONS) 

1 

2 

3 

4 

5 

10 

20 

30 

40 

50 

100 

SOO 

1000 

5000 

10000 

>10000 

f OF 

COMPANIES 

3 

5 

6 

6 

5 

28 

28 

20 

12 

6 

27 

34 

15 

14 

8 

5 

222 

% O F 

COMPANIES 

1.35% 

2.25% 

2.70% 

2.70% 

2.25% 

12.61% 

12.61% 

9.01% 

5.41% 

2.70% 

12.16% 

15.32% 

6.76% 

6.31% 

3.60% 

2.25% 

100.00% 

SALES DISTRIBUTION OF COMPANIES 

IN THE SAMPLE DATABASE 

SALES 

(SMILLIONS) 

1 

2 

3 

4 

5 

10 

20 

30 

40 

50 

100 

500 

1000 

5000 

10000 

tfOF 

COMPANIES 

10 

5 

5 

3 

1 

8 

3 

4 

4 

1 

3 

3 

1 

1 

0 

52 

% OF 

COMPANIES 

19.23% 

9.62% 

9.62% 

5.77% 

1.92% 

15.38% 

5.77% 

7.69% 

7.69% 

1.92% 

5.77% 

5.77% 

1.92% 

1.92% 

0.00% 

0.00% 

100.00% 

COMBINED SALES DISTRIBUTION 

PROJECTED 0 OF 

SAMPLE COMPANIES 

95 

47 

47 

28 

9 

76 

28 

38 

38 

9 

28 

28 

9 

9 

0 

0 

493 

PROJECTED # OF 

COMPANIES 

98 

52 

53 

34 

14 

104 

56 

58 

50 

15 

55 

62 

24 

23 

8 

5 

715 

PROJECTED 

PERCENT 

13.68% 

7.33% 

7.47% 

4.82% 

2.03% 

14 52% 

7.89% 

8.10% 

6.98% 

2.17% 

7.75% 

8.73% 

342% 

328% 

1.12% 

0.70% 

100.00% 

WEIGHTS FOR DATABASE 

COMPANIES 

SALES 

(SMILLIONS) 

1 

2 

3 

4 

5 

10 

20 

30 

40 

50 

100 

500 

1000 

5000 

10000 

>10000 

WEIGHTS 

32.60 

10.48 

8.90 

574 

2.90 

3.71 

2.02 

2.90 

4 16 

2 58 

2 05 

1.84 

1 63 

1.68 

1 00 

1 00 
Appendix B-2 

DEVELOPMENT OF WEIGHTS FOR ADDITIVE MANUFACTURERS 

SALES DISTRIBUTION OF ADDITIVE 

COMPANIES IN THE DATABASE 

SALES 

(SMILLIONS) 

1 

2 

3 

4 

5 

10 

20 

30 

40 

50 

100 

500 

1000 

5000 

10000 

>!0000 

#OF 

COMPANIES 

3 

4 

7 

3 

6 

16 

24 

15 

8 

7 

12 

24 

12 

18 

4 

6 

169 

%OF 

COMPANIES 

1.78% 

2.37% 

4.14% 

1.78% 

3.55% 

9.47% 

14.20% 

8.88% 

4.73% 

4.14% 

7.10% 

14.20% 

7.10% 

10.65% 

2.37% 

3.55% 

100.00% 

SALES DISTRIBUTION OF COMPANIES 

IN THE SAMPLE DATABASE 

SALES 

(SMILLIONS) 

1 

2 

3 

4 

5 

10 

20 

30 

40 

50 

100 

500 

1000 

5000 

10000 

>10000 

jf OF 

COMPANIES 

11 

4 

0 

1 

1 

1 

3 

0 

0 

0 

2 

2 

0 

0 

0 

0 

25 

% OF 

COMPANIES 

44.00% 

16.00% 

0.00% 

4.00% 

4.00% 

4.00% 

12.00% 

0.00% 

0.00% 

0.00% 

8.00% 

8.00% 

0.00% 

0.00% 

0.00% 

0.00% 

100.00% 

COMBINED SALES DISTRIBUTION 

PROJECTED § OF 

SAMPLE COMPANIES 

153 

56 

0 

14 

14 

14 

42 

0 

0 

0 

28 

28 

0 

0 

0 

0 

348 

PROJECTED # OF 

FUEL COMPANIES 

156 

60 

7 

17 

20 

30 

66 

15 

8 

7 

40 

52 

12 

18 

4 

6 

517 

PROJECTED 

PERCENT 

30.20% 

11.54% 

1.35% 

327% 

3.85% 

5.79% 

12.72% 

2.90% 

1.55% 

1.35% 

7.71% 

10.03% 

2.32% 

348% 

0.77% 

1.16% 

100.00% 

WEIGHTS FOR DATABASE 

COMPANIES 

SALES 

(SMILLIONS) 

1 

2 

3 

4 

5 

10 

20 

30 

40 

50 

100 

500 

1000 

5000 

10000 

>10000 

WEIGHTS 

5204 

14.92 

1.00 

5.64 

3.32 

1 87 

2.74 

1.00 

1 00 

1.00 

3.32 

2.16 

1 00 

1 00 

1 00 

1 00