however, to the change in market power expected as one moves from sector 6 to sector 10.

In general, the market structure analysis revealed that a discernable gap exists in the estimated parameters around rank 4/ 5 and around rank 14/ 15 for most indicators ( see Table B. 6). For each indicator, two small groups, each containing about four to B 14 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 MP& M EEBA: Appendices five sectors, therefore seem to have relatively low and high market power. A much larger group of about nine to ten sectors exhibit average market power. Appendix B: Cost Pass Through Analysis Table B. 6: istribution of Estimated Parameters for Market Structure Variables Rank 8 firm Concentration Ratio Import Penetration Export Dependence Average Annual Growth Rate Risk Normalized ROA 19.26 77.36% 50.17%  7.6% 7.21 24.52 51.85% 49.85%  7.3% 7.50 35.00 33.18% 46.43%  1.9% 9.58 35.07 32.40% 43.41%  1.7% 11.38 a 41.16 27.56% 29.62%  1.5% 12.02 41.87 24.55% 24.04% 0.4% 12.30 42.51 22.74% 23.64% 1.1% 12.31 44.22 21.99% 23.07% 1.5% 13.19 47.27 21.42% 17.57% 1.5% 13.44 a 54.22 18.92% 17.07% 1.8% 14.43 54.27 17.71% 17.02% 2.1% 14.62 58.20 15.33% 15.74% 2.6% 16.11 58.56 15.16% 11.37% 2.8% 16.15 61.38 14.31% 10.26% 3.1% 16.78 a 71.00 6.49% 6.48% 3.1% 17.18 76.90 4.54% 3.04% 3.7% 18.10 77.30 2.86% 1.32% 4.8% 18.13 85.32 0.75% 0.75% 5.1% 19.64 92.29 0.00% 0.00% 7.6% 26.60 D a Highlighted rows mark the natural gaps in the various indicators.

Source: U. S. EPA analysis The aggregate market structure scores for all sectors range from a low of 19 to a high of 71. Apart from the lowest score ( precious metals and jewelry) and the highest score ( railroad), all the other scores are uniformly distributed with no clear breaks in their distribution that can be used for classifying sectors by their CPT potential ( see Table B. 5). EPA therefore used an alternative classification system for the market structure analysis. Based on the average aggregate score of 50 ( average rank of 10), EPA assigned sectors with an aggregate score of 40 or below ( average rank of 8 or less) to the low CPT category,

and assigned sectors with an aggregate score of 60 or above ( average rank of 12 or more) to the high CPT category. EPA assigned sectors with aggregate scores between these cutoffs to the average CPT category. Table B. 7 shows the categorization of all 19 sectors by their CPT potential based on this classification system. In total, EPA classified six, eight,

and five sectors in the low, average, and high CPT categories, respectively. The classification cutoffs, though somewhat arbitrary, result in a sector classification similar to the trends witnessed for most individual indicators, such that about five sectors are classified in the low and high CPT categories and the remaining sectors are classified as having average CPT potential.

B 15 MP& M EEBA: Appendices Appendix B: Cost Pass Through Analysis Table B. 7:

Low CPT Average CPT High CPT Precious Metals & Jewelry Aircraft Job Shop Printed Circuit Boards Iron & Steel Motor Vehicle Ordnance Other Metal Products Aerospace Household Equipment Stationary Industrial Equipment Bus & Truck Office Machine Hardware Railroad Electronic Equipment Instruments Mobile Industrial Equipment Ships & Boats Classification of MP& M Sectors by CPT Ability Source: U. S. EPA analysis Although recognizing the limitations of the ranking scale, EPA believes that it is useful for presenting the results succinctly and provides a basis for validating the estimated CPT coefficients. Analyzing the relative importance of each indicator for each of the sectors is beyond the scope of this analysis.

B. 4 VALIDATION OF ECONOMETRICALLY ESTIMATED CPT COEFFICIENTS The econometric analysis provides a quantitative assessment of what the cost pass through ability of each sector appears to be. The market structure analysis yields a judgment of what the pass through ability of each sector ought to be. In this section the two analyses are brought together, with the results of the market structure analysis used to validate the CPT coefficients estimated by the econometric analysis.

Table B. 8 shows a comparison of each sector s CPT classification based on the econometric analysis and the market structure analysis. The two analyses classify 13 of the 19 sectors in the same CPT category. For these sectors, the market structure analysis appears to validate the CPT coefficient derived using the econometric analysis. No econometric estimate is available for one sector ( aerospace); for this sector, EPA used only the market structure analysis. For the remaining five sectors,

however, the two analyses assign sectors to different CPT categories. EPA undertook a more detailed analysis of these sectors  market structure to validate their CPT coefficient. Specifically, EPA examined the following two factors affecting firm s market power in a given industrial sector:

  Whether any ( i. e., one or more) of the five structural indicators may be extremely important or irrelevant for a particular sector, and therefore whether its effect on market power is being under weighted or over weighted,

respectively.

  Whether other factors affecting market power for these sectors have not been included in the market structure analysis, but which possibly have substantial effects on market power/ CPT ability in particular sectors.

The discussion below summarizes EPA s review and conclusions for each of these six sectors.

B 16 MP& M EEBA: Appendices Appendix B: Cost Pass Through Analysis Table B. 8: Comparison of Sectoral Classification Based on Econometric and Market Structure Analysis Sector Econometric Analysis Market Structure Analysis CPT Categorization Matches Electronic Equipment Low Low Household Equipment Low Low Office Machine Low Low Ordnance Low Low Precious Metals and Jewelry Low Low Printed Circuit Boards Low Low Hardware Average Average Instruments Average Average Iron and Steel Average Average Ships and Boats Average Average Stationary Industrial Equipment Average Average Bus & Truck High High Railroad High High CPT Categorization Does Not Match Other Metal Products Low Average Job Shop Low High Motor Vehicle Average High Aircraft High Average Mobile Industrial Equipment High Average CPT Comparison Not Possible Aerospace N/ A High Source: U. S. EPA analysis.

B. 4.1 Other Metal Products This sector is assigned to the low category by the econometric analysis and the average category by the market structure analysis. EPA believes that the estimated CPT coefficient for this sector is accurate and that the market structure score for this sector is somewhat misleading because of the exceptionally high risk normalized ROA derived for it. A priori, there appears to be no reason why firms in this sector should be able to earn significantly higher returns than in other sectors, and the high risk normalized ROA estimated is likely an artifact of the small sample of firms for which financial data were available to estimate risk normalized returns for this sector. The other four indicators of market power suggest below average CPT for this sector, which agrees with the CPT coefficient estimated from the econometric analysis.

B. 4.2 Job Shops EPA assigned this sector to the low category by the econometric analysis and the high category by the market structure analysis. EPA believes that the market structure analysis may be misleading due to the high CPT ranks assigned to the Import Penetration and Export Dependence indicators of market power for this sector. These two indicators of market power are not relevant for this sector, however, because the sector is not trade oriented. EPA expects the level of domestic competition among job shops to be the single most important factor that determines market power and the ability of firms to pass through costs in the sector. The Job Shop sector has the lowest concentration ratio among all the sectors, suggesting that the sector is characterized by a substantial number small firms ( see Table 3.8 in the MP& M Industry Profile) that are most likely engaged B 17 MP& M EEBA: Appendices Appendix B: Cost Pass Through Analysis in intense competition among each other. The estimated, low, CPT coefficient for this sector therefore appears to be appropriate.

B. 4.3 Motor Vehicle This sector is assigned to the average category by the regression analysis and the high category by the market structure analysis. EPA believes that this sector is characterized by average cost pass through potential due to the extremely competitive nature of the motor vehicle industry both domestically and in international markets. In recent years, in a bid to remain or become more competitive, the trend in this industry has been towards the continual consolidation of firms into globalized manufacturers. In fact, motor vehicle manufacturers are no longer constrained within national boundaries, as mergers and joint ventures include some of the largest firms from different countries. In addition, manufacturers have increasingly standardized the design of motor vehicles and their parts, changes that have resulted in much less product differentiation ( but greater product quality) among manufacturers. The increasing intensity of global competition and the move towards decreasing product differentiation are likely to limit the ability of domestic producers to pass through significant portions of their cost increases associated with this regulation. Therefore, the finding of an average cost pass

through coefficient appears to be justified.

B. 4.4 Aircraft This sector is assigned to the high category by the econometric analysis and the average category by the market structure analysis. Based on the unique nature of the global aircraft industry, EPA believes that the estimated CPT coefficient for this sector is appropriate. Not only is the industry concentrated domestically ( concentration ratio of 85.3), but this is also true of the global aircraft manufacturing industry. In recent years, the industry has witnessed substantial restructuring through mergers and consolidation, both nationally and internationally ( see section 3.2.2 in the MP& M Industry Profile). The highly concentrated nature of the industry, combined with the sizeable share of the domestic market that is controlled by domestic aircraft manufacturers, suggests that firms in this sector have the ability to pass through a significant portion of their cost increases.

B. 4.5 Mobile Industrial Equipment EPA assigned this sector to the high category by the econometric analysis and the average category by the market structure analysis. EPA believes that this sector is more appropriately characterized by average CPT because the sector has witnessed certain trends in recent years that suggest that firms in this sector do not have a high ability to pass through cost increases.

Specifically, growth rates in the construction and the farm and machinery equipment industries started to level off or even declined in recent years after a sustained period of growth ( see section 3.2.10 in the MP& M Industry Profile). These declining trends are not fully represented in the regression analysis because the last year of analysis is 1996. EPA therefore revised the CPT coefficient for this sector to equal the average CPT value for all sectors classified in the average category based on the regression analysis.

B. 4.6 Aerospace Since the market structure analysis categorizes the Aerospace sector in the high CPT category, EPA estimated the CPT coefficient for this sector as the average CPT value for all sectors classified in the high category based on the regression analysis ( excluding Mobile Industrial Equipment whose CPT coefficient was revised based on the market structure analysis).

B. 5 ADJUSTING ESTIMATES OF COMPLIANCE CPT POTENTIAL The CPT values estimated above reflect sector level CPT potential. The methodology must consider that ability to pass on cost increases through price increases will differ at the industry level versus the facility level. Cost increases that affect all facilities in an industry are more likely to be recovered through industry wide price increases, whereas cases where only a few facilities in an industry incur cost increases are less likely to result in price increases. This analysis must therefore take into account the proportion of an industry that will experience cost increases when applying industry level cost pass through coefficients.

B 18 MP& M EEBA: Appendices Appendix B: Cost Pass Through Analysis For the final MP& M rule, EPA will use the method used in the Phase I analysis where EPA adjusted the industry level cost pass through coefficient downward in proportion to the percentage of sector output bearing compliance cost. The ratio of the revenues in water discharging facilities affected by the rule divided by total revenues in the MP& M sector provided a measure of the fraction of production in the MP& M sector likely to be affected by cost increase. That is, a cost pass through percentage of 90 percent would be reduced to 72 percent if 80 percent of the sector output was subject to the regulation (. 80 × .90 = .72). EPA applied this adjusted pass through percentage to the percentage cost increase experienced by the regulated facilities only ( i. e., sum of compliance costs divided by the sum of baseline costs for the facilities subject to the rule). Table B. 9 presents the adjusted CPT coefficients estimated for each sector.

Table B. 9: Adjusted Estimates of Compliance Cost Pass Through Potential by MP& M Sector Sector Unadjusted Cost Pass

Through Potential Estimated Fraction of Sector s Revenue Subject to Regulation (%) Adjusted Cost Pass Through Potential Aerospacea 0.98 100.00 1.00 Aircraftb 1.20 100.00 1.00 Bus & Truck 0.86 100.00 0.96 Electronic Equipment 0.39 100.00 0.42 Hardware 0.77 33.50 0.26 Household Equipment 0.64 100.00 0.64 Instruments 0.77 100.00 0.77 Iron and Steel 0.77 100.00 0.77 Job Shop 0.57 43.70 0.25 Mobile Industrial Equipmentc 0.79 100.00 0.79 Motor Vehicle 0.82 44.10 0.36 Office Machinesd ( 9.33) 34.50 0.00 Ordnance 0.59 100.00 0.59 Other Metal Products 0.63 100.00 0.63 Precious Metals & Jewelry 0.64 42.90 0.27 Printed Circuit Boards ( 0.34) 53.60 0.00 Railroad 0.88 100.00 0.88 Ships and Boats 0.82 100.00 0.82 Stationary Industrial Equipment 0.79 32.20 0.25 a CPT coefficient for the Aerospace sector estimated based on the market structure analysis.

b For the Aircraft sector, the cost pass through potential is capped at 100%. CPT coefficient for the Mobile Industrial Equipment sector revised based on the market structure analysis.

d For the Office Machine and Printed Circuit Boards sectors, the cost pass through coefficients are set to zero based on both the estimated negative regression coefficient and the results of the market structure analysis.

Source: U. S. EPA analysis B 19 c MP& M EEBA: Appendices Appendix B: Cost Pass Through Analysis ATTACHMENT B. A: SELECTED REVIEW OF CPT LITERATURE To support the CPT analysis, EPA undertook a selected review of previous CPT analyses. The two most studied areas in the literature deal with exchange rate pass through and tax pass through. Unfortunately, neither of these study types is useful in assessing the reliability of the MP& M CPT results. Sections B. A. 2 and B. A. 3 provide a brief summary of this studies. One study ( Ashenfelter et al, 1998) estimates the pass through rate for cost changes faced by an individual firm and compares it with passes through of cost changes common to all firms in an industry. This appears to be the most relevant to the analysis of compliance costs pass through. Section B. A. 1 provides a brief summary of findings from this study.

B. A. 1 Ashenfelter et al. ( 1998),   Identifying the Firm Specific Cost Pass Through Rate.

As noted above, Ashenfelter et al. ( 1998) examines the pass through rate for cost changes faced by only an individual firm ( Staples, an office superstore chain), and distinguishes that rate from the rate at which a firm passes through cost changes common to all firms in an industry. Based on their analysis, they find the combined firm specific and industry wide pass

through rate ( i. e., with no distinction between cost changes specific to the individual firm and those applicable to the entire industry) to be 57 percent. Conversely, the pass through rate estimated for only firm specific cost changes is about 15 percent and the pass through rate for only industry wide cost changes is close to 85 percent. The finding of a high CPT rate for industry wide cost changes lends support to EPA s finding of similarly high historical CPT rates for many of the MP& M sectors.

B. A. 2 Exchange Rate Pass Through The exchange rate pass through literature examines the response of local currency import prices to variation in the exchange rate between exporting and importing countries. Based on seven studies covering the period 1970 to the mid 1980s, Menon ( 1995) finds that the estimated aggregate pass through of exchange rate changes to import prices ranges from a low of 48.7 percent to a high of 91 percent. The mean value for pass through for the sample of studies he considered is 69.9 percent. In contrast, Feinberg ( 1989) considers the impacts of exchange rate movements on U. S. domestic prices and finds an average pass through of 16 percent in real terms. The pass through is close to complete for industries that are heavily reliant on imported inputs and producing goods highly substitutable for imports. Pass through rates are much lower for capital intensive and concentrated industries and those protected by barriers to entry. The exchange rate pass through scenario, however, is not comparable to the nature of compliance cost changes expected under the MP& M regulation and the resultant pass through responses from domestic producers because the studies focus primarily on the impact of exchange rate changes on prices of imported goods and not on prices of domestically produced goods. Feinberg s study appears to be more relevant, but he does not present pass through rates for individual industries, and does not explain why pass through rates are much lower for capital intensive and concentrated industries and those protected by barriers to entry.

B. A. 3 Tax Pass Through The literature on tax pass through examines the impact of excise tax changes on prices. Of the several studies that addressed the issue of tax pass through, the majority report pass through rates slightly in excess of a 100 percent ( Ashenfelter et al.,

1998). This literature is not entirely relevant to the CPT scenario being analyzed for this rule because most of these studies analyze changes in excise tax rates in the cigarette industry. In addition, excise tax changes on final goods do not affect manufacturing costs, and they have a uniform impact on the entire industry. Excise taxes do affect domestic producers,

however, by altering final demand and therefore revenues received.

B. A. 4 Studies Cited Ashenfelter, Orley, et al. ( 1998),   Identifying the Firm Specific Cost Pass Through Rate,  FTC Working Paper No. 217,

January.

Feinberg, Robert M ( 1989),   The Effects of Foreign Exchange Movements on U. S. Domestic Prices,  The Review of Economics and Statistics.

Jaffe, A. et al., ( 1995),   Environmental Regulation and the Competitiveness of U. S. Manufacturing: What does the Evidence Tell Us?  Journal of Economic Literature, XXXIII ( March): 132 63.

B 20 MP& M EEBA: Appendices Appendix B: Cost Pass Through Analysis Menon, Jayant ( 1995),   Exchange Rate Pass Through,  Journal of Economic Surveys, 9( 2).

Waldman, Don E. and Elizabeth J. Jensen ( 1997), Industrial Organization: Theory and Practice. Addison Wesley.

B 21 MP& M EEBA: Appendices Appendix B: Cost Pass Through Analysis ACRONYMS CAPM: Capital Asset Pricing Model CPT: cost pass through ECI: Employment Cost Index PPI: Producer Price Index ROA: risk normalized return on assets B 22 MP& M EEBA: Appendices Appendix C: Summary of Moderate Impact Threshold Values by Sector INTRODUCTION Facilities subject to moderate impacts from the rule are expected to experience financial stress short of closure.

This analysis uses two financial indicators: ( 1) Pre Tax Return on Assets ( PTRA) and ( 2) Interest Coverage Ratio ( ICR). These threshold values were compared to pre and post compliance PTRA and ICR values for sample facilities to determine if facilities choosing to remain in business after promulgation of effluent guidelines would Appendix C: Summary of Moderate Impact Threshold Values by Sector APPENDIX CONTENTS C. 1 Developing Threshold Values for Pre Tax Return on Assets ( PTRA) ................. ................. .. C 1 C. 2 Developing Threshold Values for t Coverage Ratio ( ICR) ................. ................. .... C 2 C. 3 ary of Results ................. ............. C 4 References ................. ................. ....... C 5 Interes Summ experience moderate impacts on their ability to attract and finance new capital. The remainder of this appendix describes the sources and methodology used to derive sector specific moderate impact threshold values.

EPA calculated the thresholds using income and financial structure information by 4 digit SIC code from the Risk Management Association ( RMA) Annual Statement Studies for eight years 1994 2001 ( RMA, 2001; RMA 1998). This source provides quartile values derived from statements of commercial bank borrowers and loan applicants for firms having less than $ 250 million in total assets. These criteria may introduce bias, since firms with particularly poor financial statements might be less likely to apply to banks for loans, and some types of firms may be more likely to use bank financing than others. However, the RMA data offers the advantage of being available by 4 digit SIC codes and for quartile ranges.

RMA did not provide data for all 4 digit SIC codes associated with an MP& M sector. Out of 174 manufacturing SIC codes and 50 non manufacturing SIC codes, 52 manufacturing SIC codes ( 30 percent) and 13 non manufacturing SIC codes ( 26 percent), had no years of data available. RMA did not compile data for any SIC codes in two manufacturing sectors,

Ordnance and Aerospace and one non manufacturing sector, Precious Metals and Jewelry. When data were not available for any SIC codes within the sector, EPA calculated an average manufacturing or non manufacturing threshold to use as a proxy.

The 4 digit SIC code data were consolidated into weighted sector averages, weighted by 1997 value of shipments from the Economic Censuses ( U. S. DOC, 1997). For each sector and impact measure, a separate threshold was calculated for manufacturing and non manufacturing SIC codes. The use of the RMA data for calculating the threshold values for pre tax return on assets and interest coverage ratio is outlined below.

C. 1 DEVELOPING THRESHOLD VALUES FOR PRE TAX RETURN ON ASSETS ( PTRA)

Pre tax return on total assets measures the effectiveness of management in employing the resources available to it. A low ratio may indicate that a borrower would have difficulty financing treatment investments and continuing to attract investment.

The following data from Risk Management Association Annual Statement Studies were used to calculate PTRA:

  % Profit Before Taxes / Total Assets25th Ratio of profit before taxes divided by total assets and multiplied by 100 for the lowest quartile of values in each 4 digit SIC code.

  Operating Profit Gross profit minus operating expenses.

  Profit Before Taxes Operating profit minus all other expenses ( net).

RMA provides a measure of pre tax return on assets that approximates the measure that EPA defined for the moderate impact analysis. As defined by RMA, this measure is the ratio of pre tax income to assets, designated ROARMA:

ROARMA = Pre Tax Income ( EBT) / ASSETS25th C 1 MP& M EEBA: Appendices Appendix C: Summary of Moderate Impact Threshold Values by Sector However, as defined by EPA for its analysis, the numerator of the PTRA measure requires the use of earnings before interest and taxes ( EBIT) instead of pre tax income ( EBT). Defined as EBIT, the PTRA numerator will capture all return from assets,

whether going to debt or equity. To derive a pre tax, total return value, EPA adjusted RMA s measure of PTRA using the median percentage values of EBIT and EBT available from RMA. This adjustment yields the PTRA measure that EPA used in the moderate impact analysis, designated ROAMP& M :

ROAMP& M = ROARMA * EBIT / EBT Negative values are included in the weighted sector PTRA averages but a different method is used to adjust the ROA values reported in RMA to the value used in the moderate impact analysis. Specifically, using only those observations ( i. e., 4 digit SIC code and year combinations) with positive values for % Profit Before Taxes / Total Assets, Operating Profit, and Profit Before Taxes, EPA calculated an adjustment factor by subtracting the difference between ROAMP& M and ROARMA as follows:

ROAMP& M ROARMA = adjustment factor.

Those values were consolidated into sector specific adjustment factors, weighted by 1997 value of shipments from the Economic Censuses ( U. S. DOC, 1997). Each negative PTRA observation from RMA was adjusted by its sector specific adjustment factor to approximate the measure used in the moderate impact analysis:

ROARMA + sector specific adjustment factor = ROAMP& M The sector specific adjustment factors average 0.47 for manufacturing sectors and range from 0.13 for the Office Machines sector to 0.60 for the Aircraft and Motor Vehicle sectors. The sector specific adjustment factors average 0.22 for non

manufacturing sectors and range from 0.15 for the Motor V ehicle sector to 0.74 for the Railroad sector.

C. 2 DEVELOPING THRESHOLD VALUES FOR INTEREST COVERAGE RATIO ( ICR)

Interest coverage ratio is a measure of a firm's ability to meet current interest payments and, on a pro forma basis, to meet the additional interest payments under a new loan. A high ratio may indicate that a borrower would have little difficulty in meeting the interest obligations of a loan. This ratio also serves as an indicator of a firm's capacity to take on additional debt.

The following data from Risk Management Association Annual Statement Studies were used to calculate ICR:

  EB IT/ Interest25th Ratio of earnings ( profit) before annual interest expense and taxes ( EBIT) divided by annual interest expense for the lowest quartile of values in each 4 digit SIC code.

  % Dep r., Dep., Amort./ Salesmed Median ratio of annual depreciation, amortization and depletion expenses divided by net sales and multiplied by 100.

  Op erating Profit Gro ss profit minus ope rating expense s.

RMA provides a measure of interest coverage that approximates the measure that EPA defined for the moderate impact analysis. As defined by RMA, this measure is the ratio of earnings before interest and taxes to interest, designated ICRRMA :

ICRRMA = EBIT / INTEREST25th However, as defined by EPA for its analysis, the numerator of the ICR measure requires the use of earnings before interest,

taxes, depreciation, and amortization ( EBITDA) instead of earnings before interest and taxes ( EBIT). Defined this way, the ICR numerator will include all operating cash flow that could be used for interest payments. To derive the desired ICR value ( designated ICRMP& M ), EPA adjusted the RMA value as outlined below:

ICRMP& M = EBITDA / INTEREST Therefore, ICRMP& M = ICRRMA * ( EBIT + DA) / EBIT * { 1+ [( DA / SALES) / ( EBIT / SALES)]} or ICRMP& M = ICRRMA C 2 MP& M EEBA: Appendices Appendix C: Summary of Moderate Impact Threshold Values by Sector For consistency of calculation, EPA used the median values available from RMA for the adjusting both the numerator ( DA / SALES) and denominator ( EBIT / SALES) terms. 1 EPA used the same method as described above to adjust the negative ICR values reported in RMA to the value used in the moderate impact analysis. Including only those observations with positive values for EBIT/ Interest, % Depr., Dep.,

Amort./ Sales, and Operating Profit, an adjustment factor was calculated by subtracting the difference between ICRMP& M and ICRRMA as follows:

ICRMP& M ICRRMA = adjustment factor.

A sector specific adjustment factor was calculated for ICR values similar to the PTRA. Each negative ICR observation from RMA was adjusted by its sector specific adjustment factor to approximate the measure used in the moderate impact analysis:

ICRRMA + sector specific adjustment factor = ICRMP& M The sector specific adjustment factors average 0.59 for manufacturing sectors and range from 0.28 for the Precious Metals and Jewelry sector to 0.79 for the Printed Circuit Board sector. The sector specific adjustment factors average 0.50 for non

manufacturing sectors and range from 0.24 for the Office Machines sector to 1.85 for the Aircraft sector.

1 Numerator (% Depr., Dep., Amort./ Sales) is available for quartile values; denominator ( Operating Profit) only for median values.

C 3 MP& M EEBA: Appendices Appendix C: Summary of Moderate Impact Threshold Values by Sector C. 3 SUMMARY OF RESULTS Table C. 1 shows the resulting threshold values for PTRA and ICR by sector. The PTRA values for manufacturers range from zero percent for the Office Machine sector to 2.8 percent for the Aircraft and Household Equipment sectors and for the non

manufacturers the values range from 0.3 percent for the Office Machine sector to 3.1 percent for the Railroad sector. The ICR values for manufacturers range from 1.4 for the Office Machine and Railroad sectors to 2.3 for the Hardware, Household Equipment, and Printed Circuit Board sectors and for the non manufacturers the values range from 1.2 for the Office Machine sector to 2.9 for the Aircraft sector.

In assessing moderate impacts, EPA used the non manufacturing threshold for facilities that reported 100 percent of their revenues came from rebuilding and maintenance; otherwise, EPA used the manufacturing threshold.

Table C. 1: Summary of Moderate Impact Thresholds by Sector Sector Pre Tax Return on Assets ( PTRA) Interest Coverage Ratio ( ICR)

Manufacturing Non

Manufacturing Manufacturing Non

Manufacturing Hardware b 2.6% 1.6% 2.3 1.9 Aircraft 2.8% 0.4% 2.2 2.9 Electronic Equipmentb 2.1% 1.6% 2.2 1.9 Stationary Industrial Equipment 2.1% 2.5% 2.1 2.8 Ordnancea 2.2% 1.6% 2.1 1.9 Aerospacea 2.2% 1.6% 2.1 1.9 Mobile Industrialb 2.6% 1.6% 2.1 1.9 Instrument 2.2% 2.0% 2.1 2.0 Precious and Non Preciousa 1.8% 1.6% 1.7 1.9 Ships and Boats 1.7% 1.0% 1.6 2.0 Household Equipment 2.8% 2.6% 2.3 2.0 Railroadb 1.1% 3.1% 1.4 2.7 Motor Vehicle 2.4% 1.5% 2.0 1.7 Bus and Truck 2.3% 1.7% 2.0 2.8 Office Machine 0.0% 0.3% 1.4 1.2 Printed Circuit Boardb 2.5% 1.6% 2.3 1.9 Job Shopb 2.3% 1.6% 2.2 1.9 Other Metal Products 1.0% 1.7% 1.6 1.8 Iron and Steel 2.4% N/ A 2.2 N/ A Unknown Sectora 2.2% 1.6% 2.1 1.9 a When data were not available for any SIC codes within the sector, EPA calculated an average manufacturing or non manufacturing threshold to use as a proxy. b There are no non manufacturing SIC codes in several sectors, but in these sectors there are some facilities who reported that all of their revenue came from rebuilding and maintenance. In these cases, EPA used the average non manufacturing thresholds in that sector as a proxy for the non manufacturing threshold.

Source: RMA, 2001; RMA, 1998; U. S. Economics Census, 1997; U. S. EPA Analysis, 2002.

C 4 MP& M EEBA: Appendices Appendix C: Summary of Moderate Impact Threshold Values by Sector REFERENCES U. S. Department of Commerce. 1997. Bureau of the Census. Census of Manufacturers, Census of Transportation, Census of Wholesale Trade, Census of Retail Trade, Census of Service Industries.

Risk Management Association ( RMA). 1997 1998. Annual Statement Studies.

Risk Management Association ( RMA). 2000 2001. Annual Statement Studies.

C 5 MP& M EEBA: Appendices Appendix C: Summary of Moderate Impact Threshold Values by Sector THIS PAGE INTENTIONALLY LEFT BLANK C 6 MP& M EEBA: Appendices Appendix D: Estimating Capital Outlays for MP& M Discounted Cash Flow Analyses INTRODUCTION The economic impact analysis for the Metal Products &

Machinery Industry ( MP& M) final regulation involved calculation of the business value of sample facilities on the basis of a discounted cash flow ( DCF) analysis of operating cash flow as reported in facility questionnaires.

Business value is calculated on a pre and post compliance basis and the change in this value serves as an important factor in estimating regulatory impacts in terms of potential facility closures. For proposal, the business value calculation was based only on cash flow from operations and did not recognize cash outlays for capital acquisition as a component of cash flow. EPA Office of Water ( OW) previously identified that the omission of capital acquisition cash outlays from the DCF analysis may lead to overstatement of the business value of sample facilities and, as a consequence, understatement of regulatory impacts in terms of estimated facility closures. Appendix D: Estimating Capital Outlays for MP& M Discounted Cash Flow Analyses APPENDIX CONTENTS D. 1 ncepts Underlying Analysis of Capital Outlays ................. ............. D 2 D. 2 ying Variables for the Analysis ................ D 4 D. 3 et ............. D 7 D. 4 ................. D 8 D. 4.1 r Model Specification ................. .. D 9 D. 4.2 Linear Model Specification .............. D 10 D. 4.3 vity Analysis ................. ....... D 12 D. 5 tion ................. .............. D 12 Attachment D. A: Bibliography of Literature Reviewed for this Analysis ................. ........... D 17 Attachment D. B: istorical Variables Contained in the Value Line Investment Survey Dataset ................ D 18 Analytic Co Specif Selecting the Regression Analysis Datas Specification of Models to be Tested Linea Log

Sensiti Model Valida H In response to this omission, the Office of Management and Budget suggested the adoption of depreciation as a surrogate for cash outlays for capital replacement and additions. However, for several reasons EPA believes depreciation is a poor surrogate. First, depreciation is meant to capture the consumption/ use of previously acquired assets, not the cost of replacing,

or adding to, the existing capital base. Therefore, depreciation is fundamentally the wrong concept to use as a surrogate for capital outlays for capital replacement and additions. Second, depreciation is estimated based on the historical asset cost,

which may understate or overstate the real replacement cost of assets. Third, both book and tax depreciation schedules generally understate the assets  useful life. Thus, reported depreciation will overstate real depreciation value for recently acquired assets that are still in the depreciable asset base, and conversely, understate the real depreciation value of assets that have expired from the depreciable asset base but still remain in valuable use. Finally, depreciation does not capture the important variations in capital outlays that result from differences in revenue growth and financial performance among firms.

Businesses with real growth in revenues will need to expand both their fixed and working capital assets to support business growth, and all else being equal, growing businesses will have higher ongoing outlays for fixed and working capital assets.

Similarly, the ability of businesses to renew and expand their asset base depends on the financial productivity of the deployed capital as indicated by measures such as return on assets or return on invested capital. As a result, businesses with   strong

asset productivity will attract capital for renewal and expansion of their asset base, while businesses with   weak  asset productivity will have difficulty attracting the capital for renewal and expansion of the business  asset base. All else being equal, businesses with strong asset productivity will have higher ongoing outlays for capital assets; businesses with weak asset productivity will have lower ongoing outlays for capital assets.

As an approach to addressing the omission of capital acquisition cash outlays from the DCF analysis, EPA undertook to estimate a regression model of capital outlays using capital expenditure and relevant explanatory financial and business environment information for public reporting firms in the MP& M industry sectors. The estimated model was then used to estimate capital outlays for facilities in the MP& M sample dataset. The estimated capital outlay values were used in the DCF analyses to calculate business value of sample facilities and estimate regulatory impacts in terms of facility closures.

D 1 MP& M EEBA: Appendices Appendix D: Estimating Capital Outlays for MP& M Discounted Cash Flow Analyses This appendix reports the results of this effort, including: an overview of the analytic concepts underlying the analysis of capital outlays; specific variables included in the regression analysis; summary of data selection and preparation; general specification of regression models to be tested; and the findings from the regression analyses.

D. 1 ANALYTIC CONCEPTS UNDERLYING ANALYSIS OF CAPITAL OUTLAYS On the basis of general economic and financial concepts of investment behavior, EPA began its analysis by outlining a framework relating the level of a firm s capital outlays to explanatory factors that:

  can be observed for public reporting firms either as firm specific information or general business environment information and thus be included in a regression analysis; and   for firm specific information, are also available from the MP& M sample facility dataset.

To aid in identifying the explanatory concepts and variables that might be used in the analysis and as well in specifying the models for analysis, EPA reviewed recent studies of the determinants of capital outlays. EPA s review of this literature generally confirmed the overall approach in seeking to estimate capital outlays and helped to identify additional specific variables that other analysts found to contribute important information in the analysis of capital outlays ( e. g., the decision to test capacity utilization as an explanatory variable, see below, resulted from the literature review). Articles reviewed are listed in Attachment D. A to this appendix.

Table D. 1 beginning below and continuing the following two pages summarizes the conceptual relationships between a firm s capital outlays and explanatory factors that EPA sought to capture in this analysis. In the table, EPA outlines the concept of influence on capital outlays, the general explanatory variable( s) that EPA identified to capture the concept in a regression analysis, and the hypothesized mathematical relationship ( sign of estimated coefficients) between the concept and capital outlays. Table D. 2 identifies the specific variables included in the analysis, including any needed manipulations and the correspondence of the variables to MP& M survey information.

Table D. 1: Summary of Factors Influencing Capital Outlays Explanatory Factor/ Concept To Be Captured in Analysis Translation of Concept to Explanatory Variable( s) Expected Relationship Availability of attractive opportunities for additional capital investment. rm s owners, or management acting on behalf of owners, should expend cash for capital outlays only to the extent that the expected return on the capital outlays whether for replacement of, or additions to, existing capital stock are sufficient to compensate providers of capital for the expected return on alternative, competing investment opportunities, taking into account the risk of investment opportunities. Historical Return On Assets of establishment as a indicator of investment opportunities and management effectiveness, and, hence,

of desirability to expand capital stock and ability to attract capital investment. plicitly assumes past performance is indicative of future expectations. Positive Business growth and outlook as a determinant of need for capital expansion and attractiveness of investment opportunities. se equal, a firm is more likely to have attractive investment opportunities and need to expand its capital base if the business is growing and the outlook for business performance is favorable. Revenue Growth, from the prior time period( s) to the present,

provides a historical measure of business growth and is a potential indicator of need for capital expansion. se of a historical variable implicitly assumes past performance is indicative of future expectations. Positive Clearly, the theoretical preference is for a forward looking indicator of business growth and need for capital expansion. Options EPA identified include Index of Leading Indicators and current Capacity Utilization, by industry. her current Capacity Utilization may presage need for capital expansion. Positive A fi Use of a historical variable im All el U Hig D 2 MP& M EEBA: Appendices Appendix D: Estimating Capital Outlays for MP& M Discounted Cash Flow Analyses Table D. 1: Summary of Factors Influencing Capital Outlays Explanatory Factor/ Concept To Be Captured in Analysis Translation of Concept to Explanatory Variable( s) Expected Relationship Importance in capital in business production. intensive the production activities of a business, the greater will be the need for capital outlay to replenish, and add to, the existing capital stock. More capital intensive businesses will spend more in capital outlays to sustain a given level of revenue over time. The Capital Intensity of production as measured by the production capital required to produce a dollar of revenue provides an indicator of the level of capital outlay needed to sustain and grow production.

As an alternative to a firm specific concept such as Capital Intensity of production, differences in business characteristics might be captured by an Industry Classification variable. Positive Life of capital equipment in the business.

All else equal, the shorter the useful life of the capital equipment in a business, the greater will be the need for capital outlay to replenish, and add to, the existing capital stock. No information is available on the actual useful life of capital equipment by business or industry classification. ever, the Capital Turnover Rate, as calculated by the ratio of book depreciation to net capital assets, provides an indicator of the rate at which capital is depleted, according to book accounting principles:

the higher the turnover rate, the shorter the life of the capital equipment. However, the measure is imperfect for reasons of both the inaccuracies of book reporting as a measure of useful life, and as well the confounding effects of growth in the asset base due to business expansion which will tend to lower the indicated turnover rate, all else equal, without a real reduction in life of capital equipment.

As above, an alternative to a firm specific concept, differences in business characteristics might be captured by an Industry Classification variable. Positive,

generally, but with recognition of the potential for counter trend effects The cost of financial capital. which capital both debt and equity is made available to a firm will determine which investment opportunities can be expected to generate sufficient return to warrant use of the financial capital for equipment purchases. se equal, the higher the cost of financial capital, the fewer the investment/ capital outlay opportunities that would be expected to be profitable and the lower the level of outlays for replacement of, or additions to, capital stock. Preferably, measures of cost of capital would be developed separately for debt and equity.

The Cost of Debt Capital, as measured by an appropriate benchmark interest rate, provides an indication of the terms of debt availability and how those terms are changing over time. Preferably, the debt cost/ terms would reflect the credit condition of the firm, which could be based on a credit safety rating ( e. g., S& P Debt Rating). While such information would be available for public firms, EPA judged that developing a comparable concept for MP& M sample facilities would not be possible within the scope of this analysis. Negative The cost of equity capital is more problematic than the cost of debt capital since it is not directly observable for either public reporting firms or, in particular, private firms in the MP& M dataset.

However, a readily available surrogate such as Market to Book Ratio provides insight into the terms at which capital markets are providing equity capital to public reporting firms: the higher the Market to Book Ratio, the more favorable the terms of equity availability. lthough such information would not be available for private firms in the MP& M sample, EPA judged that it would be possible to develop a industry level value for use with the MP& M facility analysis. Negative All else equal, the more capital How The cost at All el A D 3 MP& M EEBA: Appendices Appendix D: Estimating Capital Outlays for MP& M Discounted Cash Flow Analyses Table D. 1: Summary of Factors Influencing Capital Outlays Explanatory Factor/ Concept To Be Captured in Analysis Translation of Concept to Explanatory Variable( s) Expected Relationship The price of capital equipment.

of capital equipment in particular, how capital equipment prices are changing over time will influence the expected return from capital outlays. ll else equal, when capital equipment prices are increasing, the expected return from incremental capital outlays will decline and vice versa.

However, although the generally expected effect of higher capital equipment prices is to remove certain investment opportunities from consideration, the potential effect on total capital outlay may be mixed.

expected returns are such that the demand to invest in capital projects is relatively inelastic, the effect of higher prices for capital equipment may be to raise, instead of lower, the total capital outlay for a firm. Index provides an indicator of the change in capital equipment prices. Negative,

generally, but with recognition of the potential for counter

trend effects The price A If Source: U. S. EPA analysis.

D. 2 SPECIFYING VARIABLES FOR THE ANALYSIS Working from the general concepts of explanatory variables outlined above, EPA defined the specific explanatory variables to be included in the analysis. A key requirement of the regression analysis is that the firm specific explanatory variables included in the regression analysis later be able to be used as the basis for estimating capital expenditures for facilities in the MP& M dataset. As a result, in defining the firm specific variables, it was necessary to ensure that the definition of variables selected for the regression analysis using data on public reporting firms be consistent with the data items available for facilities in the MP& M dataset.

Also, EPA s selection of firm specific variables was further constrained by an earlier decision to use the Value Line Investment Survey ( VL) as the source of firm specific information for the regression analysis. The decision to use VL as the source of firm specific data for the analysis was driven by several considerations:

  Considerably lower price than alternatives. VL data were available at a price of $ 95 for a one time data purchase; the price for other data sources such as Bloomberg and Standard & Poor s ranged from $ 7,000 to $ 11,000.

  Reasonable breadth of public reporting firm coverage. The VL dataset includes 7,500 firms.

  Reasonable breadth of temporal coverage. VL provides data for the most recent 10 years i. e., 1991 2000.

Although ideally EPA would have preferred a longer time series to include more years not in the   boom

investment period of the mid to late 1990s.

  Timeliness of access. The VL data are provided as a standard package and thus could be available within a week of ordering while other data sources ( e. g., Bloomberg) would have required more time because data would have provided as a custom purchase.

  Reasonable coverage of concepts/ data needed for analysis. The VL data includes a wide range of financial data that are applicable to the analysis ( VL provides 37 data items over the 10 reporting years; see Attachment DB).

However, because of the pre packaged nature of the VL data, it was not possible to customize any data items to support more precise definition of variables in the analysis. In particular, EPA found that certain balance sheet items were not reported to the level of specificity preferred for the analysis. Overall, though, EPA expects the consequence of using more aggregate, less refined concepts should be minor.

D 4 MP& M EEBA: Appendices Appendix D: Estimating Capital Outlays for MP& M Discounted Cash Flow Analyses The decision to use VL data for the analysis constrained, in some instances, EPA s choice of variables for the analysis.

Table D. 2 reports the specific definitions of variables included in the analysis ( both the dependent variable and explanatory variables), including any needed manipulations, the data source, the MP & M estimation analysis equivalent ( either the corresponding variable( s) in the MP& M questionnaire or other source outside the questionnaire), and any issues in variable definition.

Table D. 2: Variables For Capital Expenditure Modeling Analysis Variables for Regression Analysis MP& M Analysis Equivalent Comment / IssueVariable Source Calculation Dependent Variable Gross expenditures on fixed assets:

CAPEX, includes outlays to replace, and add to, existing capital stock Value Line Obtained from VL as Capital Spending per Share.

CAPEX calculated by multiplying by Average Shares Outstanding. None: to be estimated based on estimated coefficients. This value and all other dollar values in the regression analysis were deflated to 1996 ( base year for MP& M regulatory analysis) using 2 digit SIC PPI values.

Explanatory Variables Firm Specific Variables On Assets:

ROA Value Line ROA = Operating Income /

Total Assets.

Operating Income, defined as Revenue less Operating Expenses ( CoGS+ SG& A),

and Total Assets were obtained directly from VL. From Survey: Revenue less Total Operating Expenses ( Material &

Product Costs +

Production Labor +

Cost of Contract Work + Fixed Overhead + R& D +

Other Costs &

Expenses) Would have preferred a post tax concept in numerator and a deployed production capital concept in denominator.

no tax value per se and would require calculation of tax using an estimated tax rate, which could introduce error. neither VL nor MP& M survey data provide sufficient information to get at deployed production capital. Both However, VL provides Also D 5 MP& M EEBA: Appendices Appendix D: Estimating Capital Outlays for MP& M Discounted Cash Flow Analyses Table D. 2: Variables For Capital Expenditure Modeling Analysis Variables for Regression Analysis MP& M Analysis Equivalent Comment / IssueVariable Source Calculation Revenue Growth:

RVGR Value Line Primary formulation tested for linear models was percentage change in revenue over two years prior to current year: RVGR = ( REVt   REVt 2) / REVt 2. provides 10 years of financial statement values 1991

2000, including Revenue by year.

For log linear models, the growth concept was dropped and REV was used as the explanatory variable ( see below and also see later discussion under model specification). No equivalent needed.

Analysis proposed to set this value to zero in estimating capital outlay values for MP& M facilities. The use of a zero growth value is consistent with estimating the replacement capital expenditures in a no

growth steady state. Using a revenue growth term in the analysis defined over the prior two years requires three years of revenue data ( e. g., current year plus trailing two years) and effectively eliminates two observation years from the analysis ( 1991 and 1992). iven that these data years occurred at the end of a recession period and before the mid to late 90s economic boom period, EPA was very concerned about the potential loss of these years from the analysis dataset.

In the end, the use of a log linear model eliminated the need to construct the lagged difference variables and thus mooted the concern over loss of early year observations. se of the log linear model, however, also eliminated the potential to set the growth term to zero in estimating baseline capital outlays for MP& M facilities.

Revenue:

REV Value Line In the linear models, REV included as a scale variable together with REVGR, as outlined above. or log linear models, retained only REV as the explanatory variable.

REV, captures the percent change/ growth concept in the log linear formulation. From Survey: Revenue Using REV only and not REVGR in the log linear model restored the two data years at the beginning of the analysis period ( 1991 and 1992) to the analysis dataset.

including data for the first two observation period years is important for the generality of the analysis.

Also tested Total Assets as a scale variable, which provided good, but not as strong, an explanation, as REV.

Capital Turnover Rate: CAPT Value Line CAPT = Depreciation /

Total Assets. Depreciation and Total Assets directly available from VL. From Survey:

Depreciation / Total Assets Would have preferred denominator of net fixed assets instead of total assets.

However, VL provides detailed balance sheet information for only the four most recent years. possible to separate current assets and intangibles from total assets.

Capital Intensity:

CAPI Value Line CAPI = Total Assets /

Revenue. l Assets and Revenue directly available from VL From Survey:

Total Assets / Revenue As above, would have preferred net fixed assets instead of total assets, but needed data are not available from VL for the full analysis period.

Market to

Book Ratio:

MV/ B Value Line MV/ B = average market price of common equity ( Price) divided by book value of common equity ( Book Value per Share). Price and Book Value per Share directly available from VL. Use average of MV/ B for firms by MP& M industry group in regression analysis dataset; calculated at time of MP& M industry survey. Ultimately found MV/ B highly correlated with other, more important explanatory variables, which makes sense, given that equity terms would be derived from more fundamental factors,

such as ROA. MV/ B from the analysis eliminated the need to define an approach to use this variable with MP& M survey data. VL G The u F The simple variable, EPA believes Not Tota Omitting D 6 MP& M EEBA: Appendices Appendix D: Estimating Capital Outlays for MP& M Discounted Cash Flow Analyses Table D. 2: Variables For Capital Expenditure Modeling Analysis Variables for Regression Analysis MP& M Analysis Equivalent Comment / IssueVariable Source Calculation General Business Environment Variables Interest on 10 year, A

rated industrial debt:

DEBTCST Bloomberg Financial Services DEBTCST = annual average of rates for each data year Use average of DEBTCST rates at time of MP& M industry survey. 10 year maturity, industry debt selected as reasonable benchmark for industry debt costs. 10 years became   standard  maturity for industrial debt during 1990s.

Index of Leading Indicators:

ILI Conference Board Monthly index series available from Conference Board.

For linear models, ILI =

percent change from beginning to end of current year.

For log linear models, ILI =

geometric mean of current year values. For linear formulation, use average of year to

year percent change in ILI at time of MP& M industry survey.

For log linear formulation, use average of ILI values at time of MP& M industry survey.

Capacity Utilization by Industry:

CAPUTIL Federal Reserve Board ( Dallas Federal Reserve) Monthly index series available from Federal Reserve. For linear models,

CAPUTIL = percent change in annual average values from prior year to current year.

For log linear models,

CAPUTIL = current year average value. For linear formulation,

use average of year to year percent change in CAPUTIL at time of MP& M industry survey.

For log linear formulation, use average of CAPUTIL values at time of MP& M industry survey.

Producer Price Index series for capital equipment:

CAPPRC Bureau of Labor Statistics Annual average values available from BLS.

For linear models,

CAPPRC = percent change from prior year to current year.

For log linear models,

CAPPRC = current year average value as reported by BLS. For linear formulation,

use average of year to

year percent change in CAPPRC at time of MP& M industry survey.

For log linear formulation, use average of CAPPRC values at time of MP& M industry survey. BLS reports PPI series for capital equipment based on   consumption bundles  defined for manufacturing and non manufacturing industries. or this analysis, EPA used the PPI series based on the manufacturing industry bundle. F Source: U. S. EPA analysis.

D. 3 SELECTING THE REGRESSION ANALYSIS DATASET In addition to specifying the variables to be used in the regression analysis, EPA also needed to select the public firm dataset on which the analysis would be performed.

As noted above, EPA used the Value Line Investment Survey as the source for public firm data. VL includes over 7,500 publicly traded firms and identifies firms  principal business both by a broad industry classification ( e. g., Electrical Equipment, Machinery) and by an SIC code assignment. In most instances, the SIC codes assignment is only at the 2 digit level. To build the public firm dataset corresponding to the MP& M industry sectors, EPA initially selected all firms included in the following 2 digit SIC code families:

D 7 MP& M EEBA: Appendices Appendix D: Estimating Capital Outlays for MP& M Discounted Cash Flow Analyses   2500: Furniture and fixtures,

  3300: Primary metal industries,

  3400: Fabricated metal products,

  3500: Industrial machinery and equipment,

  3600: Electrical and electronic equipment,

  3700: Transportation equipment, and   3800: Instruments and related products.

From manual inspection, EPA deleted firms in four digit SIC code 3579, which, in the VL classification, was comprised only of software manufacturers. In addition, in SIC code group 3300, EPA included firms only in the ferrous metal processing sectors: SIC codes 3311, 3312, 3315, 3316, 3317, and 3398.1 As a result of this selection, EPA developed an initial dataset of 1,015 firms. On inspection, EPA found that a substantial number of firms did not have data for the full 10 years of the analysis period. The general reason for the omission of some years of data is that the firms did not become publicly listed in their current operating structure whether through an initial public offering, spin off, divestiture of business assets, or other significant corporate restructuring that renders earlier year data inconsistent with more recent data until after the beginning of the 10 year data period. 2 As a result, the omission of observation years for a firm always starts at the beginning of the data analysis period. This systematic front end truncation of firm observations in the dataset could be expected to bias the analysis in favor of the capital expenditure behavior nearer the end of the 1990s decade. To avoid this problem, EPA removed all firm observations that have fewer than 10 years of data.

As a result, the dataset used in the analysis has a total of 3,900 yearly data observations and represents 390 firms.

Table D. 3 presents the number of firms by industry classifications.

Table D. 3:

SIC Industry Classification Number of Firms 2500: Furniture and fixtures 13 3300: Primary metal industries 27 3400: Fabricated metal products 24 3500: Industrial machinery and equipment 119 3600: Electrical and electronic equipment 101 3700: Transportation equipment 65 3800: Instruments and related products 41 Number of Firms by Industry Classifications D. 4 SPECIFICATION OF MODELS TO BE TESTED On the basis of the variables listed above and their hypothesized relationship to capital outlays, EPA specified a time series,

cross sectional model to be tested in the regression analysis. EPA s dataset consisted of 390 cross sections observed at 10 years ( 1991 through 2000). The general structure of this model was as follows:

CAPEXi, t = f( ROAi, t , REVi, t , CAPTi, t , CAPIi, t , DEBTCST i, t , CAPPRCt , CAPU TILj, t )

1 These 4 digit SIC codes include all MP& M sectors in SIC 2 digit code 33 plus 4 digit SIC code 3311, to capture information for the steel manufacturing industry.

2 When VL adds a firm to its dataset, it fills in the public reported data history for the firm for the lesser of 10 years or the length of time that the firm has been publicly listed and thus subject to SEC public reporting requirements.

D 8 MP& M EEBA: Appendices Appendix D: Estimating Capital Outlays for MP& M Discounted Cash Flow Analyses Where:

CAPEXi, t = capital expenditures of firm i, in time period t; 1 t = year ( year = 1991, . . . , 2000);

i = firm i ( i= 1, . . . , 390);

j= industry classification j ROAi, t = return on total assets for firm i in year t;

REVi, t = revenue ($ millions) for firm i in year t;

CAPTi, t = capital turnover rate for firm i in year t;

CAPIi, t = capital intensity for firm i in year t;

DEBTCSTt = financial cost of capital in year t;

CAPPRCt = price of capital goods in year t;

CAPUTILj, t = the Federal Reserve Board s Index of Capacity utilization for a given industry j in year t.

EPA tested both linear and log linear model specifications. Both models fit quit well, achieving overall correlation ( R2 ) in the upper 80 percent/ low 90 percent range. However, the pattern of coefficient significance was better in the log linear model. In addition, the log linear model offered advantages in terms of retention of early time period observations and variable specification, as discussed below. Therefore, EPA selected a log linear specification as the final model. The following paragraphs briefly discuss testing of both linear and log linear forms of the model. Parameter estimates are presented for the final log linear model only because this specification appeared to be superior to a linear model.

D. 4.1 Linear Model Specification EPA first tested linear models of CAPEX as a function of the proposed explanatory variables. In testing linear models of CAPEX, EPA tested a number of structural modifications within the overall hypothesized framework of explanatory variables. These included:

  Testing the influence of industry classification on the estimation of the coefficients for certain of the explanatory variables: e. g., using the product of an industry classification dummy variable and CAPPRC to test whether certain industries in particular,   high tech  vs.   traditional  industries responded differently to change in price of capital equipment over time.

  Testing contemporary vs. lagged specification of certain explanatory variables: e. g., using prior, instead of current, period revenue, REV, as an explanatory variable.

  Testing scale normalized specification of the dependent variable: e. g., using CAPEX/ REV as the dependent variable instead of simple CAPEX.

  Testing flexible functional forms that included quadratic terms.

  Testing additional explanatory variables including the index of 10 leading economic indicators ( ILI) and market

to book ratio ( MV/ B).

EPA also tested the data for autocorrelation, heteroscedasticity, and multicollinearity.

Cross sectional, time series datasets typically exhibit both autocorrelation and group wise heteroscedasticity characteristics.

Autocorrelation is frequently present in economic time series data as the data display a   memory  with the variation not being independent from one period to the next. Heteroscedasticity usually occurs in cross sectional data where the scale of the dependent variable and the explanatory power of the model vary across observations. Not surprisingly, the dataset used in this analysis had both characteristics.

The collinearity diagnostic showed that several independent variables are collinear. In particular, EPA found that the index of leading economic indicators ( ILI) and the price of capital equipment ( CAPPRC) variables are highly correlated. EPA further found that the market to book ratio variable ( MV/ B) was highly correlated with both capital turnover ( CAPT) and return on

assets ( ROA) variables. To address the multicollinearity issue, EPA substituted capacity utilization ( CAPUT IL) for the index of leading economic indicators ( ILI) and dropped the market to book ratio ( MV/ B) variable in the final model.

1 All dollar values were deflated to 1996 ( base year for MP& M regulatory analysis) using 2 digit SIC PPI values.

D 9 MP& M EEBA: Appendices Appendix D: Estimating Capital Outlays for MP& M Discounted Cash Flow Analyses D. 4.2 Log Linear Model Specification The main advantage of the log linear model is that it incorporates directly the concept of percent change in the explanatory variables. Specifying the key regression variables as logarithms permitted us to estimate directly as the coefficients of the model, the elasticities of capital expenditures with respect to firm financial characteristics and general business environment factors. In addition, by eliminating the need to use percent change variables, EPA was able to avoid losing early year observations in the analysis dataset. Finally, the logarithmic transformations helped to reduce outlier effects in the model.

EPA specified a log linear model, as follows:

ln( CAPEXi, t) =   +   [   x ln( Xi, t)] +   [   y ln( Yt)] +   W here:

CAPEXi, t = capital expenditures of firm i, year t;

  x = elasticity of capital expenditures with respect to firm characteristic X;

Xi, t, = a vector of financial characteristics of firm i, year t;

  y = elasticity of capital expenditures with respect to economic indicator Y;

Yt = a vector of economic indicators, year t; for CAPU TIL, Y is also differentiated by industry classification   = an error term; and ln( x) = natural log of x Based on this model, the elasticity of capital expenditures with respect to an explanatory variable, for example, return on assets is calculated as follows:

Because the log linear specification incorporates directly the concept of percent change in the explanatory variables, EPA dropped the   change  specification variables i. e., revenue growth ( REVGR), year to year change in the Index of Leading Indicators ( ILIGR), and year to year change in the Capital Equipment Price Index ( CAPPRC) from the analysis. For these variables, EPA used the logarithm of the simple, unadjusted values in the log linear specification.

One disadvantage of the specified log linear model is that the logarithmic transformation is not feasible for negative and zero values. This means that negative and zero values require linear transformation to be included in the analysis. The following variables in the sample required transformation:

  CAPEX: four firms in the sample reported zero capital expenditures at least in one time period. EPA set these expenditures to $ 1.

  REVENUE: one firm reported negative revenue ($ 1,018) in one time period. Because this is likely due to accounting adjustments from prior period reporting, EPA set the firm s revenue in the current time period to $ 1.

  ROA: the values for return on assets in the public firm sample range from  1.1 to 0.6. Approximately 25 percent of the firms in the dataset reported negative ROAs in at least one year. To address this issue while reducing potential effects of data transformation on the modeling results, EPA used the following data transformation approach:

R EPA excluded 12 firms with any annual ROA values below the 99th percentile of the ROA distribution ( i. e.,

ROA    0.31).

R EPA used an additive data transformation to ensure that remaining negative ROA values were positive in the logarithm transformation. The additive transformation was performed by adding 0.31 to all ROA values.

D 10 MP& M EEBA: Appendices Appendix D: Estimating Capital Outlays for MP& M Discounted Cash Flow Analyses The analysis tested several specifications of a log linear model, including models with slope dummies for different industrial sectors and models with the intercept suppressed. The model presented below was most successful at explaining firms

investment behavior.

EPA estimated the specified model using the generalized least squares procedure. This procedure involves the following two steps:

  First, EPA estimated the model using simple OLS, ignoring autocorrelation for the purpose of obtaining a consistent estimator of the autocorrelation coefficient (   ) ;

  Second, EPA used the generalized least squares procedure, where the analysis is applied to transformed data.

The resulting autocorrelation adjustment is as follows:

Zi, t = Zi, t    Zi, t 1 where Zit is either dependent or independent variables.

EPA was unable to correct the estimated model for group wise heteroscedasticity due to computational difficulties. The statistical software used in the analysis ( LIMDEP) failed to correct the covariance matrix due to the very large number of groups ( i. e., 390 firms) included in the dataset. Application of other techniques to correct for group wise heteroscedasticity was not feasible due to time constraints. The estimated coefficients remain unbiased; however, they are not minimum variance estimators.

Table D. 4 presents model results. The model has a fairly good fit, with adjusted R2 of 0.89. All coefficients have the expected sign and all but two ( constant and capital price) are significantly different from zero at the 95th percentile.

Table D. 4: ime Series, Cross Sectional Model Results Variable Coefficient t Statistics Constant  2.077  0.97 Ln( ROA) 0.618 9.353 Ln( REV) 1.025 113.867 Ln( CAPT) 0.6 20.285 Ln( CAPI) 0.976 27.342 Ln( DEBTCST)  0.205  2.653 Ln( CAPPRC)  0.478  0.939 Ln( CAPUTIL) 0.904 3.176 Autocorrelation Coefficient r 0.413 27.842 T The empirical results show that the output variable ( REV) is a dominant determinant of firms  investment spending. A positive coefficient on this variable means that larger firms invest more, all else equal, which is clearly a simple expected result. Very important for the MP& M analysis, as expected, firms with higher financial performance and better investment opportunities ( ROA) invest more, all else equal: for each one percent increase in ROA, a firm is expected to increase its capital outlays by 0.62 percent. Other firm specific characteristics were also found important and will aid in differentiating the expected capital outlay for MP& M facilities according to firm specific characteristics. Firms that require more capital to produce a given level of business activity ( i. e., firms that have high capital intensity, CAPI) tend to invest more: a one percent increase in capital intensity leads to a 0.98 increase in capital spending. Higher capital turnover/ shorter capital life ( CAPT)

also has a positive effect on investment decisions: a one percent increase in capital turnover rate translates to a 0.60 percent in capital outlays.

The model also shows that current business environment conditions play an important role in firms  decision to invest. The D 11 MP& M EEBA: Appendices Appendix D: Estimating Capital Outlays for MP& M Discounted Cash Flow Analyses most influential factor is capacity utilization in manufacturing facilities. A one percent increase in the Federal Reserve Index of Capacity Utilization for the relevant industrial sector ( CAPUTIL) leads to a 0.90 percent increase in capital investment.

Negative signs on the debt cost ( DEBTCST) and capital price ( CAPPRC) variables match expectations, indicating that less costly credit and falling ( either relatively or absolutely) capital equipment prices are likely to have a positive effect on firms

capital expenditures. That these systematic variables are significant in the regression analysis means that EPA will be able to control for economy and industry wide conditions in estimating capital outlays for MP& M facilities.

D. 4.3 Sensitivity Analysis To examine the degree to which the estimated model was affected by transformation of ROA values and inclusion/ exclusion of firms with the lowest ROA values, EPA ran two additional models. First, EPA estimated a model based on a subset of data that includes only firms with positive ROA values. Second, EPA estimated a model based on a complete dataset that includes the 12 firms with the lowest ROA values. Although all three models produced compatible results, the first model shows some notable differences in the estimated coefficients compared to the model presented in the preceding section. EPA found that when firms with the lowest negative ROAs are excluded from the analysis:

  The magnitude of the ROA effect on capital expenditures decreases;

  The magnitude of the debt cost effect on capital expenditures decreases slightly;

  The coefficient on the capital price term becomes significant.

These differences can be expected since firms with negative ROAs are weak performers and therefore are less likely to have large capital outlays. Not surprisingly, general economic indicators that affect firms  decisions to invest can be less or more important if a firm s financial performance/ asset productivity is weak. For financially weaker firms, the financial cost of capital is a more important factor compared to firms that are strong financially. This finding indicates a strong   threshold of adequate financial performance  effect: capital outlays fall off severely at the lowest financial performance levels but the marginal effect of financial performance becomes more moderate as asset productivity moves into a more acceptable i. e.,

positive return range. Price of capital goods appears to be an insignificant factor in firms  decision to invest when weak firms are included in the analysis. At first, this finding seems to be counterintuitive: previous studies of investment behavior found a strong capital price effect on firms  decision to invest in high tech equipment. However, because financially weak firms are less likely to invest in general, it is reasonable to assume that they will not respond as strongly to changes in capital equipment prices. Thus, their investment decisions were relatively less affected by falling high tech equipment prices in the last decade.

D. 5 MODEL VALIDATION To validate the results of the regression analysis, EPA used the estimated regression equation to calculate capital expenditures and then compared the resulting estimate of capital expenditures with actual data. EPA used two methods to validate its results:

  EPA used median values for explanatory variable from the Value Line data as input to estimate capital expenditures and then compared the estimated value to the median reported capital expenditures, and   EPA used MP& M survey data to estimate capital expenditures and then compared the estimated values to depreciation reported in the survey.

First, EPA estimated capital expenditures for a hypothetical firm based on the median values of the four dependent variables from the Value Line data and the relevant values of the three economic indicators. The estimated capital expenditures for this hypothetical firm are $ 10.9 million. EPA then compared this estimate to the median value of capital expenditures from the Value Line data. The median capital expenditure value in the dataset is $ 11.3 million, which provides a very close match to the estimated value. This is not surprising since the same dataset was used to estimate the regression model and to calculate the median values used in this analysis.

EPA also used MP& M survey data to confirm that the estimated capital expenditures seem reasonable. Because the MP& M survey does not provide information on capital expenditures, EPA compared the capital expenditure estimates to the D 12 MP& M EEBA: Appendices Appendix D: Estimating Capital Outlays for MP& M Discounted Cash Flow Analyses depreciation values reported in the survey. Depreciation had been proposed as a possible surrogate for cash outlays for capital replacements and additions. However, depreciation does not capture important variations in capital outlays that result from differences in firms  financial performance.

For this analysis, EPA chose a representative facility from each of the nineteen MP& M sectors for model validation. The selected facility for each sector corresponds as closely as possible to the hypothetical median facility in the sector based on the distribution of facility revenues and facility return on assets. For each of the nineteen facilities, EPA estimated capital expenditures using the estimated regression equation and facility financial data. Table D. 5 shows the estimated regression coefficients, financial averages for the nineteen MP& M sectors, estimated facility capital expenditures, reported facility depreciation, and the comparison of capital expenditures and depreciation.

As shown in Table D. 5, the estimated model provides reasonable estimates of capital expenditures. A facility s size, as indicated by revenue, is a principal determinant of the general range of value for capital expenditures, all else equal ( i. e.,

greater revenues correspond to greater predicted capital expenditures). However, the size of capital expenditures relative to the depreciation allowance depends substantially on a facility s return on assets. Facilities with lower return on assets tend to invest less than indicated by depreciation while facilities with higher return on assets tend to invest more than depreciation.

This finding is consistent with the expectation that businesses with higher financial performance will have relatively more attractive investment opportunities and are more likely to attract the capital to undertake those investments. To highlight this relationship between capital expenditure, depreciation allowance, and a facility s return on assets, EPA presents graphs for the Hardware, Iron & Steel, Job Shops, and Printed Circuit Board sectors that plot MP& M survey facilities in these sectors along with linear trend lines for each sector s depreciation and capital expenditures with respect to return on assets. 4 4 For presentation purposes, some outlier facilities were excluded from the graphs.

D 13 MP& M EEBA: Appendices Appendix D: Estimating Capital Outlays for MP& M Discounted Cash Flow Analyses Table D. 5: ted by Revenue and ROA Percentiles Sectors Pre Tax Return on Assets ( ROA) Revenue Capital Turnover Rate Capital Intensity Cost of Debt Price of Capital Goods Capacity Utilization Estimated Capital Expenditures Depreciation Difference between Depreciation and Capital Expenditures Coefficient Intercept ( 2.077) 0.62 1.03 0.60 0.98 ( 0.21) ( 0.48) 0.90 Aerospace 0.02 90.66 0.02 1.29 7.11 135.4 73.67 2,113,741 1,821,434  0.14 Aircraft 0.05 18.39 0.06 0.54 9.8 115.87 80.01 440,385 558,478 0.27 Bus &

Truck 0.06 58.09 0.03 0.25 7.11 135.4 73.69 471,199 503,124 0.07 Electronic Equipment 0.05 36.85 0.12 0.4 7.11 135.4 86.37 1,100,627 1,730,023 0.57 Hardware 0.03 11.99 0.06 0.61 9.8 115.87 81.93 311,085 403,535 0.3 Household Equipment 0.05 18 0.05 0.8 7.11 135.4 84.24 624,804 745,476 0.19 Instruments 0.15 62.47 0.04 0.47 7.11 135.4 77.21 1,195,144 1,139,873  0.05 Iron & Steel 0.12 23.17 0.06 0.47 6.4 136.9 90.82 617,740 613,834  0.01 Job Shop 0.03 2 0.07 0.26 7.11 135.4 81.92 25,146 37,250 0.48 Mobile Industrial Equipment 0.07 37.6 0.03 0.63 9.8 115.87 79.45 670,447 586,609  0.13 Motor Vehicle 0.1 104.44 0.06 0.46 7.11 135.4 81.24 2,473,215 2,810,386 0.14 Office Machine 0.1 28.95 0.06 0.43 7.11 135.4 85.02 661,715 748,972 0.13 Ordnance 0.05 27.08 0.04 0.65 9.8 115.87 79.77 674,446 770,051 0.14 Other Metal Products 0.08 27.78 0.17 0.44 7.11 135.4 80.01 1,100,691 2,034,831 0.85 Precious Metals &

Jewelry 0.04 13.5 0.03 0.62 7.11 135.4 77.21 224,438 226,708 0.01 Estimation of Capital Outlays for MP& M Sample Facilities: Median Facilities Selec For facilities that responded to the Phase 1 survey, EPA calculated a 3 year average of the non facility specific information over the years in which survey data were collected ( 1987 1989). Likewise, for facilities that responded to the Phase 2 survey, EPA calculated a 3 year average of the non facility specific information for the years 1994 1996. Since the Iron and Steel sector was surveyed in 1997, EPA calculated a 3 year average of the non facility specific information for the years 1995 1997.

Source: U. S. EPA analysis D 14 MP& M EEBA: Appendices Appendix D: Estimating Capital Outlays for MP& M Discounted Cash Flow Analyses Figure D. 1: Comparison of Estimated Capital Outlays to Reported Depreciation for MP& M Survey Facilities in the Hardware Sector Source: U. S. EPA analysis.

Figure D. 2: Comparison of Estimated Capital Outlays to Reported Depreciation for MP& M Survey Facilities in the Iron & Steel Sector Source: U. S. EPA analysis.

D 15 MP& M EEBA: Appendices Appendix D: Estimating Capital Outlays for MP& M Discounted Cash Flow Analyses Figure D. 3: Comparison of Estimated Capital Outlays to Reported Depreciation for MP& M Survey Facilities in the Job Shop Sector Source: U. S. EPA analysis.

Figure D. 4: Comparison of Estimated Capital Outlays to Reported Depreciation for MP& M Survey Facilities in the Printed Circuit Board Sector Source: U. S. EPA analysis.

D 16 MP& M EEBA: Appendices Appendix D: Estimating Capital Outlays for MP& M Discounted Cash Flow Analyses ATTACHMENT D. A: BIBLIOGRAPHY OF LITERATURE REVIEWED FOR THIS ANALYSIS As noted above, EPA relied on previous studies of investment behavior to select critical determinants of firms  capital expenditures. Empirical results from these studies suggest that investment is most sensitive to quantity variables ( output or sales), return over cost, and capital utilization ( R. Chirinko). Empirical results from more recent studies further found that increasing depreciation rates and capital equipment prices were of first order importance in the equipment investment behavior in the 1990 ( T. Tevlin, K. Whelan). Specifically, declining prices of micro processor based equipment played a crucial role in the investment boom in the 1990.

Chirinko, Robert S. 1993.   Business Fixed Investment Spending: A Critical Survey of Modeling Strategies, Empirical Results and Policy Implications.  Journal of Economic Literature 31, no. 4: 1875 1911.

Goolsbee, Austan. 1997.   The Business Cycle, Financial Performance, and the Retirement of Capital Goods.  University of Chicago, Graduate School of Business Working Paper.

Greenspan, Alan. 2001.   Economic Developments.  Remarks before the Economic Club of New York, New York, May 24.

Kiyotaki, Nobuhiro and Kenneth D. West. 1996.   Business Fixed Investment And The Recent Business Cycle In Japan.

National Bureau of Economic Research Working Paper 5546.

McCarthy, Jonathan. 2001.   Equipment Expenditures since 1995: The Boom and the Bust.  Current Issues In Economics And Finance 7, no. 9: 1 6.

Opler, Tim and Lee Pinkowitz, Rene Stulz and Rohan Williamson. 1997.   The Determinants and Implications of Corporate Cash Holdings.  Working paper, Ohio State University College of Business.

Tevlin, Stacey and Karl Whelan. 2000.   Explaining the Investment Boom of the 1990s.  Board of Governors of the Federal Reserve System Finance and Economics Discussion Paper no. 2000 11 Uchitelle, Louis. 2001.   Wary Spending by Companies Cools Economy.  New York Times, May 14, p. A1.

D 17 MP& M EEBA: Appendices Appendix D: Estimating Capital Outlays for MP& M Discounted Cash Flow Analyses ATTACHMENT D. B: HISTORICAL VARIABLES CONTAINED IN THE VALUE LINE INVESTMENT SURVEY DATASET All variables are provided for 10 years ( except where a firm has been publicly reported for less than 10 years):

  Price of Common Stock   Revenues   Operating Income   Operating Margin   Net Profit Margin   Depreciation   Working Capital   Cash Flow per share   Dividends Declared per share   Capital Spending per share   Revenues per share   Average Annual Price Earnings Ratio   Relative Price Earnings Ratio   Average Annual Dividend   Return Total Capital   Return Shareholders Equity   Retained To Common Equity   All Dividends To Net Worth   Employees   Net Profit   Income Tax Rate   Earnings Before Extras   Earnings per share   Long Term Debt   Total Loans   Total Assets   Preferred Dividends   Common Dividends   Book Value   Book Value per share   Shareholder Equity   Preferred Equity   Common Shares Outstanding   Average Shares Outstanding   Beta   Alpha   Standard Deviation D 18 MP& M EEBA: Appendices Appendix E: Calculation of Capital Cost Components Appendix E: Calculation of Capital Cost Components INTRODUCTION APPENDIX CONTENTS E. 1 Calculation of One Time Capital Cost Estimates ......... E 1 E. 1 CALCULATION OF ONE TIME CAPITAL COST COMPONENTS EPA used the engineering estimates of total one time capital costs to calculate the purchase cost paid to manufacturers of compliance equipment, and the costs of shipping, installation, insurance, engineering, and consultants. Two components of capital costs were used to estimate job gains due to compliance requirements: ( 1) the estimated direct capital equipment cost and ( 2) the labor cost of installation. Table E. 1 shows the cost components that comprise the total capital costs attributed to the regulation.

Table E. 1: Components of Total Installed Capital Costs ( millions, 2001$, before tax) a Cost Component Option I:

Selected Option Option II:

Proposed/

NODA Option Option III:

413 to 433 Upgrade Option Option IV:

All to 433 Upgrade Option ( a) Total installed direct capital costs $ 4,407,590 $ 802,051,833 $ 95,552,532 $ 148,434,303 ( b) Direct capital equipment cost $ 3,070,680 $ 558,773,471 $ 66,569,538 $ 103,411,210 ( c) Shipping ( 20% of a) $ 881,518 $ 160,410,367 $ 19,110,506 $ 29,686,861 ( d) Labor cost of installation ( 7% of f) $ 455,392 $ 82,867,995 $ 9,872,488 $ 15,336,232 ( e) Indirect costs: insurance, engineering &

consultants ( 47.6% of a) $ 2,098,013 $ 381,776,672 $ 45,483,005 $ 70,654,728 ( f) Total installed capital costs $ 6,505,602 $ 1,183,828,505 $ 141,035,538 $ 219,089,032 a Excludes costs for baseline and regulatory closures.

Source: U. S. EPA analysis.

The components of total capital costs for the final rule in Table E. 1 are discussed below in reverse order of the table presentation.

  Total installed capital costs: EPA estimated the total one time capital cost for each facility expected to comply with the regulation. 1 Compliance costs are discussed in more detail in Chapter 5: Facility Level Impact Analysis of this EEB A. The national estimate of capital costs for the regulation is $ 6.5 million ($ 2001). 2 1 See the Technical Development Document for a description of the methods used to estimate capital costs.

2 The $ 6.5 million is the sum of one time outlays for purchasing and installing the capital equipment needed to comply with the final rule. This expense is not the annual equivalent of that capital investment. The capital outlay is annualized in the economic impact analysis over a 15 year period. The resulting value, which is part of the total annual cost of compliance, is $ 0.7 million.

E 1 MP& M EEBA: Appendices Appendix E: Calculation of Capital Cost Components   Indirect Costs: MP& M project engineers estimate that indirect costs such as insurance, engineering, and consulting are 47.6% of installed direct capital cost. EPA calculated the total direct and indirect cost using the total capital cost.

The national estimate of indirect costs for the regulation is $ 2.1 million.

  Total Installed Direct Capital Costs: The direct capital costs include the cost of compliance equipment, shipping,

and the labor cost of installation. The national estimate of direct costs for the regulation is $ 4.4 million. MP& M project engineers estimate that shipping costs might be as much as 20 percent of the total installed direct capital cost.

The estimated one time shipping cost is $ 0.9 million for the final regulatory option. Installation labor costs are estimated by the engineers to be seven percent of the total installed capital costs. The estimated one time cost of installation labor is $ 0.5 million for the final regulatory option. Therefore, the direct capital equipment cost is $ 3.1 million, the remainder of the total installed direct capital cost when the cost of shipping and installation are subtracted out.

E 2 MP& M EEBA: Appendices Appendix F: Administrative Costs Appendix F: Administrative Costs INTRODUCTION Effluent guidelines and limitations are implemented by Federal, State, and local government entities through the NPDES permit program ( for direct dischargers) and the General Pretreatment Regulations ( for indirect dischargers). A new effluent guideline rule may require that facilities: ( 1) be permitted for the first time; ( 2) be issued a different form of permit, if they already have a permit in the baseline; and ( 3) be repermitted sooner than would otherwise be required. In these cases, the permitting authority will incur additional costs to implement the effluent guideline rule.

This appendix provides information on the unit costs of these permitting activities and describes the calculation of government permitting costs for the final MP& M rule and regulatory alternatives. EPA expects no additional costs for permitting direct dischargers under the final rule. Costs for issuing permits for indirect dischargers are based on information reported by publicly owned treatment works ( POTWs) in the Metal Products and Machinery ( MP& M)

POT W Survey. EPA also used the data provided in the Association of Metropolitan Sewerage Agencies ( AMSA)

survey to supplement information from the MP& M POTW Survey. EPA evaluated POTW administrative costs for pretreatment options for the final rule. As discussed in Section VI of the preamble to the final rule, EPA is not establishing any pretreatment standards in the final rule. APPENDIX CONTENTS F. 1 luent Guidelines Permitting Requirements ...... F 1 F. 1.1 ermit Program .... F 1 F. 1.2 ................. .. F 2 F. 2 TW Administrative Cost Methodology ........ F 2 F. 2.1 ................. ........ F 2 F. 2.2 Overview of Methodology ............... F 3 F. 3 .............. F 4 F. 3.1 .......... F 4 F. 3.2 ion ................. ........... F 7 F. 3.3 ................. .......... F 7 F. 3.4 ement ................. ......... F 9 F. 3.5 itting ................. ........ F 10 F. 4 dministrative Costs by Option ......... F 10 Appendix F Exhibits ................. ........... F 12 References ................. ................. . F 25 Eff NPDES Basic Industrial P Pretreatment Program PO Data Sources Unit Costs of Permitting Activities Permit Application and Issuance Inspect Monitoring Enforc Reperm POTW A The remainder of this appendix is organized as follows: Section F. 1 provides an overview of permitting requirements under the NPDES Permit Program and the General Pretreatment Regulations. Section F. 2 describes the MP& M POTW Survey and the methods used to develop annualized cost estimates for permitting indirect dischargers. Section F. 3 presents the estimates of unit costs by permitting activity for indirect dischargers. The final Section F. 4 lists the steps involved in applying these unit costs to calculate administrative costs for regulatory options evaluated by EPA for the final rule.

F. 1 EFFLUENT GUIDELINES PERMITTING REQUIREMENTS Any facility that directly discharges wastewater to surface water is required to have a permit issued under the National Pollution Discharge Elimination System ( NPDES) permit program. Facilities that discharge indirectly through a POTW are regulated by the General Pretreatment Regulations for Existing and New Sources of Pollution ( 40 CFR Part 403). The major portion of government administrative costs associated with implementing an effluent guidelines rule are the costs of managing the NPDES and Pretreatment programs.

F. 1.1 NPDES Basic Industrial Permit Program Best Practical Technology ( BPT), Best Control Technology ( BCT), and New Source Performance Standards ( NSPS) for effluent limitations guidelines are implemented through the NPDES industrial permit program. However, EPA does not expect the administrative costs associated with the NPDES industrial permit program to increase as a result of the final rule.

The Clean Water Act prohibits discharge of any pollutant to a water of the U. S. except as permitted by a NPDES permit.

Therefore, every facility that discharges wastewater directly to surface water must hold a permit specifying the mass of pollutants that can be discharged to waterways. The final rule will affect the terms of the permits but is unlikely to increase the administrative costs associated with permitting.

The final rule may decrease the administrative burden of NPDES permits. The TDD and rulemaking record for the final rule F 1 MP& M EEBA: Appendices Appendix F: Administrative Costs provide valuable information to permitting authorities that may reduce the research required to develop Best Professional Judgment ( BPJ) permits. 1 Further, establishing discharge standards may reduce time spent by permitting authorities establishing limits and the frequency of evidentiary hearings. The promulgation of limitations may also enable EPA and the authorized States to cover more facilities under general permits. General permits are single permits covering a common class of dischargers in a specified geographic area.

F. 1.2 Pretreatment Program The General Pretreatment Regulations ( 40 CFR Part 403) establish procedures, responsibilities, and requirements for EPA,

States, local governments, and industry to control pollutant discharges to POTWs. Under the Pretreatment Regulations,

POTWs or approved States implement categorical pretreatment standards for existing sources ( PSES) and new sources ( PSNS).

Discharges from an MP& M facility2 to a POTW may already be permitted in the baseline. 3 For example, industrial users subject to another Categorical Pretreatment Standard would have a discharge permit. Other significant industrial users ( SIU)

that are typically permitted by POTWs include industrial users that:

  discharge an average of 25,000 gallons per day or more of process wastewater to a POTW,

  contribute a process waste stream that makes up five percent or more of the average dry weather hydraulic or organic capacity of the POT W treatment plant, or   have a reasonable potential for adversely affecting the POTW   s operation or for violating any pretreatment standard.

As discussed in Section VI of the preamble to the final rule, EPA did not establish or revise any pretreatment standards in the final rule. Consequently, there are no POTW administrative costs associated with the final rule. Under the options evaluated for the final rule, which include options for setting pretreatment standards, EPA expects no increase in permitting costs for indirect dischargers that already hold a permit in the baseline. However, governments will incur additional permitting costs for unpermitted facilities ( under the NODA/ Proposal option only) and to accelerate repermitting for some indirect dischargers that currently hold permits. The remainder of this appendix estimates these cost increases. As with direct industrial dischargers, promulgation of the MP& M rule may cause some administrative costs to decrease. For example, control authorities will no longer have to repermit facilities that are estimated to close as a result of some of the options EPA evaluated for the final rule. These cost savings are reflected in estimates of total government administrative costs associated with the regulatory options considered for the final rule.

F. 2 POTW ADMINISTRATIVE COST METHODOLOGY F. 2.1 Data Sources EPA collected information from POTWs to support development of the MP& M effluent guideline ( see Section 3 of the TDD). Of 150 surveys mailed, EPA received responses to 147, for a 98 percent response rate. The POTW Survey asked respondents to provide information on administrative permitting costs for indirect dischargers, sewage sludge use and disposal costs and practices, and general information ( including number of permitted users and number of known MP& M dischargers).

The administrative cost information included the number of hours required to complete specific permitting and repermitting,

1 Permits issued to facilities not covered by effluent guidelines or water quality based standards are developed based on BPJ ( see NPDES  permit writers manual).

2 MP& M facilities are defined on the basis of three considerations: ( 1) they produce metal parts, products, or machines for use in one of the 19 industry sectors evaluated for coverage in the MP& M point source category; ( 2) they use operations in one of the eight regulatory subcategories evaluated for coverage in the MP& M point source category; and ( 3) they discharge process wastewater, either directly or indirectly, to surface waters. In this document, the term   MP& M facilities  refers to all facilities meeting the above definition, regardless of whether a facility s industrial sector, subcategory, or discharger category is covered by the final regulation.

3 Under the General Pretreatment Program, a facility's discharges may be controlled through a " permit, order or similar means". For simplicity, this document refers to the control mechanism as a permit.

F 2 MP& M EEBA: Appendices Appendix F: Administrative Costs inspection, monitoring, and enforcement activities. Respondents were also asked to provide an average labor cost for all staff involved in permitting activities. EPA used the survey responses on administrative costs to estimate a range of costs incurred by POTWs to permit a single MP& M facility.

The Association of Metropolitan Sewerage Agencies ( AMSA) also provided data on administrative costs to EPA ( see Section 3 of the TDD). EPA used the data provided in the AMSA survey to verify and, in some cases, supplement its own analyses of POTW administrative costs for regulatory options evaluated for the final rule. AMSA provided EPA with comments on the proposed MP& M rule and supplemented these comments with a spreadsheet database. The database contains data from an AMSA formulated survey and covers responses from 176 POTW s, representing 66 pretreatment programs. The AMSA survey was conducted to verify data from EPA's survey of POTW s and therefore included similar, although fewer, variables compared to EPA's survey. Elements EPA verified using the AMSA survey include: ( 1) the estimated number of indirect dischargers; and ( 2) the unit costs of certain permitting activities, including permit implementation, sampling, and sample analysis. Elements EPA added to its analysis using the AMSA data include: ( 1) screening costs for POTW s that do not currently operate under a pretreatment program; and ( 2) oversight costs associated with implementing the MP& M regulation.

F. 2.2 Overview of Methodology EPA estimated the annualized costs of permitting indirect dischargers under the different regulatory options evaluated for the final rule using the following steps:

  Determine the number and characteristics of indirect dischargers that will be permitted under each regulatory option evaluated for the final rule. Only the NODA option includes costs for permitting an MP& M facility for the first time. The final rule does not cover indirect dischargers while the other regulatory options only regulate those indirect dischargers that already hold permits in the baseline. For the NODA option, EPA determined how many new permits would be issued. The NODA option requires only concentration based permits, no mass

based permits.

  Use the data from the POTW Survey to determine a high, middle, and low hourly burden for permitting a single facility. EPA defined the low and high estimates of hours such that 90% of the POTW responses fell above the low value and 90% of responses fell below the high value. The median value is used to define the middle hourly burden.

  Use the data from the POTW Survey to determine the average frequency of performing certain administrative functions. For administrative functions that are not performed at all facilities, survey data were used to calculate the portion of facilities requiring these functions. For example, the survey data show that on average 38.5% of facilities submit a non compliance report.

  Multiply the per facility burden estimate by the average hourly wage. EPA determined a high, middle, and low dollar cost of administering the rule for a single facility by multiplying the per facility hour burden by the average hourly wage. The POTW Survey reported an average hourly labor rate of $ 39.33 ($ 2001) for staff involved in permitting. This is a fully loaded cost, including salaries and fringe benefits.

  Calculate the annualized cost of administering the rule. The number of facilities, hourly burden estimate,

frequency estimates, and hourly wage estimates are all combined to determine the total cost of administering the rule.

The type of administrative activities required varies over time and the total administrative cost is calculated over a 15 year time period. EPA calculated the present value of total costs using a seven percent discount rate, and then annualized the present value using the same seven percent discount rate.

F. 3 UNIT COSTS OF PERMITTING ACTIVITIES This section presents unit costs for the following permitting activities:

  Permit application and issuance: developing and issuing concentration based permits at previously unpermitted facilities; providing technical guidance; and conducting public and evidentiary hearings;

  Inspection: inspecting facilities both for the initial permit development and to assess subsequent compliance;

F 3 MP& M EEBA: Appendices Appendix F: Administrative Costs   Monitoring: sampling and analyzing permittee s effluent; reviewing and recording permittee s compliance self

monitoring reports; receiving, processing, and acting on a permittee s non compliance reports; and reviewing a permittee s compliance schedule report for permittees in compliance and permittees not in compliance;

  Enforcement: issuing administrative orders and administrative fines; and   Repermitting.

EPA believes that theses functions constitute the bulk of the required administrative activities. To these costs, EPA added a provision for managerial oversight of 25 percent. 4 There are other relatively minor or infrequent administrative functions ( e. g., providing technical guidance to permittees in years other than the first year of the permit, or repermitting a facility in significant non compliance), but their costs are likely to be insignificant compared to the estimated costs for the five major categories outlined above. EPA also added a cost for identifying facilities to be permitted for POTWs that do not currently operate under a Pretreatment Program. EPA estimates this cost to be approximately $ 0.8 million. This cost only applies to the NODA/ Proposal Option since facilities subject to the upgrade options already hold permits.

For each major administrative function, this section provides below: ( 1) a description of the activities involved, ( 2) the estimated percentage of facilities that require the administrative function; ( 3) the frequency with which the function is performed, and ( 4) high, middle, and low estimates of per facility hours and costs. All costs are presented in year 2001 dollars.

F. 3.1 Permit Application and Issuance Before issuing a wastewater discharge permit to a facility, the permit authority typically inspects the facility, monitors the facility s wastewater, and completes pollutant limits calculations and permit paperwork. This section discusses the costs of completing limits calculations and paperwork; subsequent sections address inspection and monitoring costs. This section also discusses the costs of technical assistance that the control authority may provide facilities to facilitate compliance with new limits. Finally, this section includes the costs of public and evidentiary hearings that may be required for some permits.

a. Issue a concentration based permit at a previously unpermitted facility To issue a concentration based permit, permit authorities first review permit applications for completeness. If an application is incomplete, the authorities notify the applicant and request the missing information. Completed applications are assigned to permit writers, who review the applications in more detail as they develop permit conditions. The effort required to complete these activities depends, in part, on the extent to which the permit authority has automated the permitting process.

EPA assumed that one third of facilities will be permitted in each of the three years following the rule s effective date because compliance is mandated within three years of the date the standard is effective ( 40 CFR Section 403.6). EPA further assumed that facilities are repermitted in five year cycles. ( The administrative costs of repermitting are discussed separately below.)

The actual number of facilities that are permitted each year is likely to differ somewhat from EPA s simplifying assumption.

These minor differences in permit timing are not expected to significantly change the estimated administrative costs.

4 The 25 percent oversight cost provision is based on comments and data received from the Association of Metropolitan Sewerage Agencies ( AMSA).

F 4 MP& M EEBA: Appendices Appendix F: Administrative Costs Table F. 1: Administrative Activity: Develop and issue a concentration based permit at a previously unpermitted facility Percent of facilities for which activity is required Frequency of activity Typical costs ( 2001$)

Low Median High 100% of unpermitted MP& M facilities ( applicable to NODA/ Proposal option only) One time 4.0 hours;

$ 122 10.0 hours;

$ 304 40.0 hours;

$ 1,217 Source: U. S. EPA analysis of POTW Survey responses; U. S. Department of Labor, Bureau of Labor Statistics.

b. Issue a mass based permit for a previously unpermitted facility5 The same administrative activities required to issue a concentration based permit are also required for a mass based permit.

In addition, for mass based permits issued under the MP& M rule, the permit writer must determine whether the facility practices pollution prevention and water conservation methods equivalent to those specified as the basis for BPT. If so, the permitting authority must determine the facility s historical flow rate. If not, the authority must derive a mass based limit based on other factors such as production rates. When a facility matches BPT water conservation practices and provides historic flow data, development of a mass based permit is a relatively straight forward process. However, the task will be more challenging at a facility practicing only limited water conservation, particularly if the facility has multiple production units and generates integrated process and sanitary wastewaters.

Table F. 2: Administrative Activity: Develop and issue a mass based permit at a previously unpermitted facility Percent of facilities for which activity is required Frequency of activity Typical costs ( 2001$)

Low Median High 100% of MP& M facilities being issued a new mass based permit ( estimates used for the proposed rule) One time 4.0 hours; $ 122 13.0 hours; $ 396 40.0 hours; $ 1,217 Source: U. S. EPA analysis of POTW Survey responses; U. S. Department of Labor, Bureau of Labor Statistics.

c. Issue a mass based permit for a facility with a concentration based permit6 Some of the activities described above for issuing a mass based permit will be simplified in cases where the facility already holds a concentration based permit. For example, much of the basic information required in the permitting application will already be in the permitting authorities  records. However, the potentially labor intensive task of determining the flow basis for the permit remains.

5 None of the regulatory options considered for the final rule require issuance of mass based permits for previously unpermitted facilities. However, since these costs were developed for the proposed rule, they are presented in this appendix even though they are not used in the administrative costs estimates.

6 None of the regulatory options considered for the final rule require conversion of a concentration based to a mass based permit.

However, since these costs were developed for the proposed rule, they are presented in this appendix even though they are not used in the administrative costs estimates.

F 5 MP& M EEBA: Appendices Appendix F: Administrative Costs Table F. 3: Administrative Activity: Develop and issue a mass based permit at a facility holding a concentration based permit Percent of facilities for which activity is required Frequency of activity Typical costs ( 2001$)

Low Median High 100% of MP& M facilities with permit conversion ( estimates used for the proposed rule) One time 2.0 hours;

$ 61 8.0 hours;

$ 243 20.0 hours;

$ 608 Source: U. S. EPA analysis of POTW Survey responses; U. S. Department of Labor, Bureau of Labor Statistics.

d. Provide technical guidance to a permittee Technical guidance is frequently provided by permit authorities to permittees concurrent with the issuance of a new permit.

There are no legal requirements that a permit authority provide a permittee with technical guidance. However, such guidance is generally in the interest of all parties as it can expedite the permitting process, accelerate the permittee s compliance, and reduce the compliance burden. The extent of technical guidance provided varies dramatically among permit authorities. In some cases, a permit authority may hold a one day workshop to provide information on a new pretreatment standard to facilities. In other cases, a permit authority may meet extensively with individual permittees to educate them regarding their responsibilities under pretreatment standards. The range of technical guidance appears to depend on whether the permittee already has a wastewater permit, whether the permittee is part of a multi facility company, the resources of the permit authority, and the extent to which the permit authority has written or standardized guidance available for dissemination.

EPA assumed that permit authorities provide technical guidance to all facilities being issued a new mass based or concentration based permit under the MP& M pretreatment standards. Costs for technical guidance were estimated separately for facilities receiving a concentration based permit and facilities receiving a mass based permit. EPA assumed that technical guidance is provided in the year the initial permit is issued.

Table F. 4: Administrative Activity: Provide technical guidance to permittee on permit compliance Percent of facilities for which activity is required Frequency of activity Typical costs ( 2001$)

Low Median High 100% of MP& M facilities being issued a new concentration based permit ( applicable to NODA/ Proposal option only) One time 1.5 hours; $ 46 4.0 hours; $ 122 12.0 hours; $ 365 100% of MP& M facilities being issued a new mass based permit ( estimates used for the proposed rule) One time 2.0 hours;

$ 61 4.0 hours;

$ 122 12.0 hours;

$ 365 Source: U. S. EPA analysis of POTW Survey responses; U. S. Department of Labor, Bureau of Labor Statistics.

e. Conduct a public or evidentiary hearing on a proposed permit Federal regulations provide for a period during which the public may submit written comments on a proposed permit for direct dischargers and/ or request that a public hearing be held. Permitting authorities for indirect dischargers may have the same requirements. Thus, proposed permits for indirect dischargers may be subject to public comments and hearings.

Pretreatment public hearings are typically conducted at a scheduled local government ( e. g., City Council) meeting. The meetings may require substantial preparation.

Federal regulations also provide for evidentiary hearings following final permit determination for direct dischargers. Again,

permitting authorities for indirect dischargers may have these requirements as well. Thus, final permit determinations for indirect dischargers may be subject to evidentiary hearings.

Data from the POT W Survey indicated that a public or evidentiary hearing would be required for 3.6% of indirect dischargers being issued a new mass based or concentration based permit, on average.

F 6 MP& M EEBA: Appendices Appendix F: Administrative Costs Table F. 5: Administrative Activity: Conduct a public or evidentiary hearing Percent of facilities for which activity is required Frequency of activity Typical costs ( 2001$)

Low Median High 3.2% of MP& M facilities being issued a new mass based or concentration based permit ( applicable to NODA/ Proposal option only) One time 2.0 hours; $ 61 8.0 hours; $ 243 40.0 hours; $ 1,217 Source: U. S. EPA analysis of POTW Survey responses; U. S. Department of Labor, Bureau of Labor Statistics.

F. 3.2 Inspection Permit authorities may choose to integrate their inspection and monitoring work force or to administer these functions separately. This discussion covers inspections only; monitoring is discussed below. Inspections are performed both to assess conditions for initial permitting and to evaluate compliance with permit requirements. Inspections involve record reviews,

visual observations, and evaluations of the treatment facilities, effluents, receiving waters, etc. EPA assumed that the initial inspection would occur in the same year a new permit is issued, and that all permitted facilities would be inspected annually to assess compliance.

Table F. 6: Administrative Activity: Inspect facility for permit development Percent of facilities for which activity is required Frequency of activity Typical costs ( 2001$)

Low Median High 100% of MP& M facilities being issued a new permit ( applicable to NODA/ Proposal option only) One Time 2.2 hours; $ 66 5.0 hours; $ 152 12.0 hours; $ 365 Source: U. S. EPA analysis of POTW Survey responses; U. S. Department of Labor, Bureau of Labor Statistics.

Table F. 7: Administrative Activity: Inspect facility for compliance assessment Percent of facilities for which activity is required Frequency of activity Typical costs ( 2001$)

Low Median High 100% of MP& M facilities being issued a new permit ( applicable to NODA/ Proposal option only) Annual 2.0 hours; $ 61 3.3 hours; $ 101 10.0 hours; $ 304 Source: U. S. EPA analysis of POTW Survey responses; U. S. Department of Labor, Bureau of Labor Statistics.

F. 3.3 Monitoring Permitting authorities monitor facilities both to gather data needed for permit development and to assess compliance with permit conditions. Monitoring includes sampling and analysis of the permittee s effluent, review of the permittee s compliance self monitoring reports, receipt of non compliance reports, and review of compliance schedule reports. These activities are discussed below.

a. Sample and analyze permittee s effluent As noted above, inspection and monitoring staff may be integrated or distinct. The costs of inspection were presented above.

Federal regulations require that the permit authority   randomly sample and analyze the effluent from industrial users ...

independent of information supplied by industrial users  ( 40 CFR Part 403.8). The permit authority obtains samples required by the permit and performs chemical analyses. The results are used to verify the accuracy of the permittee s self monitoring program and reports, determine the quantity and quality of effluents, develop permits, and provide evidence for enforcement proceedings where appropriate.

F 7 MP& M EEBA: Appendices Appendix F: Administrative Costs EPA estimated sampling costs for all facilities issued a new permit under the MP& M rule, and assumed annual monitoring.

Although EPA requires only annual effluent sampling, some localities sample more frequently. EPA encourages this practice.

Table F. 8: Administrative Activity: Sample and analyze permittee s effluent Percent of facilities for which activity is required Frequency of activity Typical costs ( 2001$)

Low Median High 100% of MP& M facilities being issued a new permit ( applicable to NODA/ Proposal option only) Annual 1.0 hour;

$ 30 3.0 hours;

$ 91 17.7 hours;

$ 537 Source: U. S. EPA analysis of POTW Survey responses; U. S. Department of Labor, Bureau of Labor Statistics.

b. Review and record permittee s compliance self monitoring reports 40 CFR Part 403.12 specifies that:   Any Industrial User subject to a categorical pretreatment standard ... shall submit to the Control authority during the months of June and December ... a report indicating the nature and concentration of pollutants in the effluent which are limited by such categorical pretreatment standards.  The permit authority briefly reviews these submissions and may enter the information into a computerized system and/ or file the data.

EPA estimated the costs of handling annual self monitoring reports for all facilities being issued a new permit under the MP& M rule.

Table F. 9: Administrative Activity: Review and enter data from permittee s compliance self

monitoring reports Percent of facilities for which activity is required Frequency of activity Typical costs ( 2001$)

Low Median High 100% of MP& M facilities being issued a new permit ( applicable to NODA/ Proposal option only) 2 reports per year 0.5 hours; $ 15 1.0 hour; $ 30 4.0 hours; $ 122 Source: U. S. EPA analysis of POTW Survey responses; U. S. Department of Labor, Bureau of Labor Statistics.

c. Receive, process, and act on a permittee s non compliance report Generally, when a permittee violates a permit condition, it must submit a non compliance report to the permit authority.

Permittees report both unanticipated bypasses or upsets and violations of maximum daily discharge limits. The permit authority receives and processes both verbal and written non compliance reports. In some cases, immediate action by the permit authority is required to mitigate the problem.

Data from the POTW Survey indicate that 38.5 percent of all facilities submit at least one non compliance report annually.

Of facilities that submit at least one non compliance report, the median number of reports filed per year is five reports.

Table F. 10: Administrative Activity: Receive, process and act on a permittee s non compliance reports Percent of facilities for which activity is required Frequency of activity Typical costs ( 2001$)

Low Median High 38.5% of all indirect dischargers receiving a new permit ( applicable to NODA/ Proposal option only) 5 times per year 1.0 hour; $ 30 2.0 hours; $ 61 6.0 hours; $ 183 Source: U. S. EPA analysis of POTW Survey responses; U. S. Department of Labor, Bureau of Labor Statistics.

F 8 MP& M EEBA: Appendices Appendix F: Administrative Costs d. Review a permittee s compliance schedule report Permittees submit reports to permit authorities that state whether compliance schedule milestones contained in their permits have been met. If the facility is in compliance, the permit authority reviews and files the report.

Data from the POTW Survey indicate that approximately 17% of all facilities are issued compliance milestones. Of these facilities, 94% meet the milestones. Facilities submit an average of two compliance milestone reports per year. The cost of handling the report depends on whether the facility is in compliance with the schedule.

Table F. 11: Administrative Activity: Review a compliance schedule report Percent of facilities for which activity is required Frequency of activity Typical costs ( 2001$)

Low Median High Meeting milestones: 16.0% of all facilities issued a new permit   94% of the 17% who have compliance milestones ( applicable to NODA/ Proposal option only) 2 reports per year 0.5 hours;

$ 15 1.0 hour;

$ 30 2.7 hours;

$ 81 Not meeting milestones: 1% of all facilities issued a new permit   6% of the 17% who have compliance milestones ( applicable to NODA/ Proposal option only 2 reports per year 1.0 hours; $ 30 2.0 hours; $ 61 6.0 hours; $ 183 Source: U. S. EPA analysis of POTW Survey responses; U. S. Department of Labor, Bureau of Labor Statistics.

F. 3.4 Enforcement When a permitting authority identifies a permit violation, the authority determines and implements an appropriate enforcement action. Considerations when determining enforcement response include ( 1) the severity of the permit violation,

( 2) the degree of economic benefit obtained by the permittee through the violation, ( 3) previous enforcement actions taken against the violator, ( 4) the deterrent effect of the response on similarly situated permittees, and ( 5) considerations of fairness and equity. EPA estimated administrative costs for two levels of enforcement actions: ( 1) less severe actions such as issuing an administrative order, and ( 2) more severe activities such as levying an administrative fine.

EPA estimated that, annually, seven percent of facilities issued a new permit under the MP& M rule will require a minor enforcement action, such as issuing an administrative compliance order. In addition, EPA estimated that seven percent of facilities receiving a new permit will require more severe enforcement actions such as a fine or penalty.

Table F. 12: Administrative Activity: Minor enforcement action e. g., issue an administrative order Percent of facilities for which activity is required Frequency of activity Typical costs ( 2001$)

Low Median High 7% of MP& M facilities being issued a new permit ( applicable to NODA/ Proposal option only) Annual 1.0 hour; $ 30 3.7 hours; $ 112 12.0 hours; $ 365 Source: U. S. EPA analysis of POTW Survey responses; U. S. Department of Labor, Bureau of Labor Statistics.

Table F. 13: Administrative Activity: Minor enforcement action, e. g., impose an administrative fine Percent of facilities for which activity is required Frequency of activity Typical costs ( 2001$)

Low Median High 7% of MP& M facilities being issued a new permit ( applicable to NODA/ Proposal option only) Annual 1.0 hour; $ 30 5.0 hours; $ 152 24.0 hours; $ 730 Source: U. S. EPA analysis of POTW Survey responses; U. S. Department of Labor, Bureau of Labor Statistics.

F 9 MP& M EEBA: Appendices Appendix F: Administrative Costs F. 3.5 Repermitting The duration of permits cannot exceed five years. Renewing a permit for a facility in compliance with an existing permit is expected to be a relatively straightforward task. The data submitted in the permit application generally require few changes,

although pollutant limits may need to be recalculated in some cases. The labor required for repermitting depends, in part, on the extent to which the permit authority has automated the paperwork.

Table F. 14: Administrative Activity: Repermit Percent of facilities for which activity is required Frequency of activity Typical costs ( 2001$)

Low Median High 100% of MP& M facilities being issued a new permit ( applicable to NODA/ Proposal option only) every 5 years 1.0 hour; $ 30 4.0 hours; $ 122 20.0 hours; $ 608 Source: U. S. EPA analysis of POTW Survey responses; U. S. Department of Labor, Bureau of Labor Statistics.

In addition to repermitting MP& M facilities being issues a new permit, EPA also considered two other types of cost: ( 1) the costs associated with repermitting facilities that already hold a permit in the baseline sooner than would otherwise be required; and ( 2) cost savings associated with no longer having to permit facilities that already hold a permit in the baseline but that are estimated to close as a result of the rule. Both cost components are reflected in the POTW administrative costs presented in the next section.

F. 4 POTW ADMINISTRATIVE COSTS BY OPTION Exhibits F. 1 through F. 7 at the end of this appendix present the calculation of POT W permitting costs for the final rule and the three regulatory alternatives considered by EPA.

Exhibit F. 1 provides an overview of the permitting activities, the estimated percentage of facilities that require the administrative function, the frequency with the function is performed, and per facility hours and costs for each function.

Exhibit F. 2 contains the per facility hour burden and other assumptions described above for each of the three types of permitting ( new concentration based permit, new mass based permit, and converting a concentration based to a mass based permit.)

Exhibits F. 3 through F. 5 show hours by type of permit for the low, medium, and high estimate of per facility burden,

respectively. These exhibits also summarize costs and dollars by year and permit type.

Exhibit F. 6 presents the number of facilities requiring different types of permitting, for each of the regulatory options. The exhibit shows the total number of facilities that will be subject to requirements, the baseline permit status of those facilities,

and the number of facilities by expected post compliance permit status. These estimates are based on facility survey information about baseline permit status and the results of the facility impact analysis described in Chapter 5 of the EEBA.

The exhibit also shows the number of currently permitted facilities that are projected to close as a result of the rule, and which will therefore no longer require re permitting.