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5.3 PEIS Modeling Approach: Operational Issues

This section addresses the practical issues that must be resolved to provide an empirical analysis of economic impacts and social costs using the behavioral approach. The key information needed to develop this approach relates to the baseline characterization of affected producers and markets, engineering estimates of the regulatory control cost for affected entities, and modeling of the economic behavior of affected entities (e.g., position and shape of the supply and demand functions) and market outcomes.

   5.3.1    Baseline Characterization

To perform the economic impact analysis, the model must allow for a comparison between the baseline equilibrium conditions and the counterfactual or with-regulation equilibrium conditions that the model produces under a changed policy regime. The assumption of an "observable" baseline equilibrium leads directly to the need for and construction of a data set that fulfills the equilibrium conditions for the industry or entities subject to a regulatory action. The issues of selecting and specifying the time period of analysis are discussed in detail in Section 8. This section addresses how to develop an analytical baseline that appropriately accounts for the entities, products, and markets to be affected by a regulatory action in a manner consistent with the available data from the industry profile and engineering cost analysis. Based on the EIA conducted for the Polymers and Resins II NESHAP, Table 5-1 provides an example of the typical components of a baseline scenario for use in an EIA.


5 EIA Framework

 5.0 Intro

 5.1 Alternative
   for Economic
   Impact Analysis

 5.2 PEIS Modeling

 5.3 PEIS Modeling

 5.4 Economic
   Impact Results

Table 5-1.  Baseline Characterization of U.S. Amino and Phenolic Resin Markets:  1991
Amino Resins
Phenolic Resins
Market price ($/lb)
Market quantity(million lbs/yr)a
Domestic production

a     Market quantity is the sum of domestic production and imports (exports are included within domestic production).

Another case where the analyst will need different measures of regulatory impact occurs when the regulatory action affects a factor of production (e.g., boilers or degreasers). In the majority of these cases, data are not sufficient for the analyst to model the final product markets or there are too many markets to be modeled. The direct impact measures most often used in these cases address the relative impact of the costs imposed on these inputs and the ultimate burden placed on users of these inputs (i.e., producers of final product). These measures include the following:
  • annual control cost share of annual costs for the affected factor(s) and
  • annual control cost share of value of shipments of the final product.

In lieu of a market analysis, the first measure above can be used to determine the likelihood that the factor will be substituted away from by the producer (i.e., comparison with costs of substitute inputs/processes), while the second measure can be used to project potential price increases to consumers of the final good or service (see the EIA conducted for NSPS on utility and industrial boilers).    Producer Characterization

The industry profile and industry responses to the EPA's Information Collection Request (ICR) form the basis for identifying and characterizing all known sources in the affected industry (e.g., manufacturing facilities or plants) or other affected entities (e.g., federal, state, or local governments). For inclusion in the economic model, these potentially affected entities must be either individually characterized or grouped together in terms of their technical and economic activities. The level of resolution for which these entities are included in the EIA depends on the data available from the industry profile and ICR survey and the engineering cost analysis methodology. Obviously, the economic analysis cannot identify and characterize the known sources beyond the limits imposed by the available data from the industry profile or ICR survey responses. However, a less obvious constraint on the economic analysis involves the level at which EPA is able to estimate the compliance costs of the regulatory action.

The most detailed and accurate method to determine compliance costs is to examine the requirements of each individual entity in the affected industry. Thus, the best case scenario occurs when the economic and engineering analysis are not limited in their ability to fully characterize individual facilities. If data are not sufficient to determine facility-specific requirements, the next best approach is to estimate these costs for model plants and to map the model plants (and related control costs) directly to actual facilities. If model plants are used, then they should appropriately capture all types of facility characteristics in the industry without any unnecessary limits (e.g., production process, capacity, vintage). The economic analyst should review the characterization of model plants and assignment to the industry to verify that it agrees with industry structure (as set out in the industry profile). Again, in this case, individual facilities are able to be fully characterized. However, in cases where the number of affected entities is very large (e.g., 20,000 dry cleaners) and/or limited information is available, the model plant costs may be allocated to facility groupings based on a distribution rule (e.g., one-third of the affected facilities are represented by model plant A). In this case, the economic analyst is limited by the engineering analysis and can only characterize groups of suppliers within the affected industry. Similarly, those entities that are not directly affected by the regulatory action may be grouped together and modeled with single representative agents.

In addition, other economic considerations should be accounted for in characterizing affected entities. These considerations include issues such as the scale and scope of production. Potentially affected producers may be part of vertically integrated operations in which the directly affected product is captively produced and consumed within the firm for use in the production of another intermediate or final product. For example, the pulp and paper industry is characterized by both nonintegrated and vertically integrated mills. Vertically integrated mills rely mostly on their own production of pulp to produce paper and paperboard products, while nonintegrated mills include pulp mills that produce market pulp as well as paper mills that purchase market pulp to produce paper and paperboard products. Thus, in conducting the EIA for the Pulp and Paper Cluster Rule, the ISEG analysts categorized mills in this manner to appropriately define their market activities and interactions (i.e., supplier versus demander) and, thus, better estimate the economic impacts. Furthermore, in the case of joint production, the specification of product lines at affected facilities depends not only on the available industry profile or survey data but also on the resolution of compliance cost estimates. For example, in the Pulp and Paper EIA, the integrated and paper-only mills jointly produced a myriad of final paper and paperboard products. The level at which the economic model addressed the production of individual product lines was determined by the ability of the engineers or economic analysts to appropriately assign or allocate compliance costs at a given mill across its products.     Market Characterization

In the cases where markets are well defined and impacts clearly should be addressed, the EIA evaluates the economic impacts in terms of market adjustments and the associated producer and consumer surplus measures welfare change, as described in Section 5.3. The market characterization process involves two main activities

  1. Determining the markets that are affected by the regulation
  2. Determining the competitive structure of affected markets

The determination of markets potentially affected by the regulation requires identifying the products that are produced at the affected facilities and linking them with specific markets in which they are traded. The product identification process will be determined to some extent by self-evidence (e.g., regulation of the Portland cement industry) and to some extent by the availability of product-level data at the affected facilities. The latter may occur either through the Agency information collection process or, in some cases, by secondary data.

Once affected products are identified, linking them to markets requires a clean definition of the relevant markets. For the purposes of this discussion, markets can be defined along the dimensions of substitutability and geography.

The extent to which products serve as close substitutes has a large effect on whether they should be considered part of the same market. For instance, two herbicide products that are both used on corn are fairly close substitutes and would normally be considered in the same market. However, herbicides for corn and steel beams are not substitutes and would not be considered in the same market. The analyst is often aided in the market definition task by the existence of standard industry and product codes where substitutability is implicit in the definitions (e.g. SIC codes). In addition, industry trade publications often provide information on end use markets that is more detailed than SIC codes.

Once the analyst has determined the substitution dimension, the geographic extent of the markets must be defined. In many cases, the argument can be made for national markets for potentially affected products. However, regional or local markets may exist for products where the ability of producers to compete across regions is limited by physical or economic barriers to transport the product (e.g., cement and dry cleaning). In such cases, these regional or more localized markets should be defined and characterized to the extent available data allow.

Once the potentially affected markets are defined, the economic analyst must determine the practicality of including all potentially affected markets in the economic model. To aid in this determination, a screening analysis can be employed to avoid unnecessary inclusion of too few or too many markets. For example, information on the proportion of all producers (output, capacity) in the market that are potentially affected and the relative magnitude of their compliance costs can provide some insights on the merits of including that market in the analysis. In general, observing smaller proportions of affected producers and/or relative compliance costs reduces the value of modeling a particular market. In some cases, the screening analysis may suggest little need to conduct a market analysis altogether; that is, no market adjustment is expected in response to regulation or the expected adjustment is small enough to ignore.

For all markets that will be modeled, the analyst must characterize the degree of competition within these markets. The discussion generally focuses on perfect competition, or price-taking behavior, and imperfect competition, or the lack of price-taking behavior. Although most EIAs conducted by ISEG involve perfectly competitive markets (or, at least, assume this market structure), some EIAs have been conducted for imperfectly competitive industries, such as pharmaceuticals and cement. In fact, the OMB guidelines for EO 12866 specifically mention that elements of market power should be addressed in conducting EAs and measuring the social costs of regulatory actions. Therefore, it is important for the economic analyst to select the appropriate market structure for the subject industry because the projected market outcomes can vary greatly by specification of market structure.     Characterizing Impacts Outside a Market Setting

In cases where markets are not well defined or impacts are negligible and not explicitly addressed, the analyst needs different measures of regulatory impact. The majority of these cases occur when a regulatory action directly affects government-owned and nonprofit entities by imposing control requirements and the associated compliance costs (e.g., landfills, municipal waste combustors [MWCs], publicly owned sewerage treatment works [POTWs], municipally owned electric companies). The direct impact measures most often used in these cases address the relative impact of the costs imposed on these entities and the burden placed on citizens or consumers of the affected services. These measures include the following:

  • annual control cost share of annual costs for the affected service(s),
  • annual control cost share of annual revenues of the governmental unit, and
  • per-household (or per capita) annual control cost share of median household (or per capita) income.

For some affected services, in lieu of a market analysis, the first measure above can be used to project increases in fees charged to local consumers of the service (see the EIA conducted for MWCs) or the likelihood that the service will be discontinued by the governmental agency (i.e., use of closure decision similar to that for private firms). In addition, baseline measures of the economic and financial status of the affected entity or community can provide insights on their ability to absorb the required expenditures.

   5.3.2     Regulatory Control Costs

Before an EIA can be performed, quantitative estimates must be made of the control costs that will be incurred by the affected entities in complying with the regulatory alternatives. For stationary sources, pollution control cost estimates are typically developed by the Emission Standards Division (ESD). These cost estimates are determined for each control option and modified for any source-specific factors to estimate the compliance cost per source. The ESD analysis sums the source-specific costs to compute the aggregate, or national, compliance cost. Economic analysts (working for ISEG), in turn, use these costs to evaluate the economic and financial impacts of a regulatory action. The use of these costs for the EIA is summarized below.

As shown in Table 5-2, the engineering analysis typically estimates both fixed and variable control costs for each regulatory alternative. Fixed control costs include the costs associated with purchasing and installing pollution control equipment and other costs that are periodically recurring but do not vary with the level of emissions or production (e.g., fixed overhead, property taxes, insurance, and other administrative costs). Alternatively, those control costs that do vary with the level of emissions or production include the regularly recurring expenditures required to operate the control equipment as well as any costs incurred due to a change in operating practices as a result of regulatory action. For example, if a facility uses a substitute input in response to a regulatory action, the additional annual costs associated with using the substitute are included in the operating cost component. Net operating costs may include credits for reduced consumption of energy or material resources associated with pollution control. Also shown in Table 5-2 are monitoring, reporting, and recordkeeping (MR&R) costs. These costs may have a variable and/or fixed component. In absence of engineering determination of these components, these costs are typically included with other fixed costs.

Table 5-2.  Required Compliance Cost Elements for Affected Entities
Fixed control costs (do not vary with emissions level)
Capital investment
Initial capital cost (total installed costs of compliance equipment)
Annualized capital (include separate info on equipment life and interest rate)
Other fixed costs
Overhead allocation
Property taxes
Administrative fees/charges
Variable control costs (vary with output level)
Raw materials
Waste treatment disposal fees
Replacement parts
Product recovery factors (may lead to cost savings)
Monitoring, reporting, and recordkeeping costs (may have fixed and variable components)

The engineering estimates of fixed capital and variable operating and maintenance (O&M) costs provide the measure of total annual compliance costs without accounting for behavioral responses. Annualized costs represent the total yearly pollution control expenditures and consist of the sum of the annual O&M control cost and the annualized capital control cost. To compute the annualized capital control cost, partitioning the capital cost into the equivalent of annual payments is necessary so that the total capital cost is not assessed against any single year in the operational life of the control equipment (see Figure 5-6). The capital costs are annualized over the expected lifetime of the capital equipment using the opportunity cost of capital. Although firm-specific costs of capital are desired for this approach, these rates are difficult to obtain from publicly available sources and are not generally available for use in the EIA. Therefore, in the absence of firm-specific estimates of the cost of capital, the EIA typically uses 7 percent based on the OMB Circular A-94. Please see the discussion in Section 8 on discounting for more information on the appropriate cost of capital to annualize capital investment required by regulation.

The total annualized control cost estimated for each affected entity is calculated on a per unit of output basis, typically the quantity of the good/service provided. These average control cost estimates are used in the economic impact modeling to project market adjustments in response to a regulatory action. However, it is important to note that the method used in the engineering analysis to estimate control costs and the magnitude of these estimates will affect the way in which the economic analyst is able to conduct the EIA. Specifically, the engineering analysis may affect the way in which the economic analysis is able to model the affected entities and markets. As discussed above, the economic analyst must consider the preliminary engineering cost estimates in developing the appropriate economic methodology.

   5.3.3     Modeling Economic Behavior

Building from the baseline characterization and regulatory control costs (as described above), the ISEG analyst can employ standard concepts in microeconomics to develop the behavioral economic model. The main elements are

  • characterize supply of each product at the individual and market levels,
  • characterize demand for each product, and
  • solve for the new with-regulation equilibrium.

This section provides an overview of these key operational issues in conducting EIAs.

   Figure 5-6     Supply Characterization and Responses

Historically, most of the EIAs prepared by ISEG have been applied in situations where supply side issues have been critical. In these cases, implementation of a proposed regulatory action will affect the costs of production at existing sources of air emissions, which is expected to induce some facilities to alter their current level of production or to close. As a result, much of the attention has been placed on using information from the engineering analysis and industry profiles to characterize supply from potentially affected entities. The need to specifically address the distribution of producer impacts pursuant to RFA/SBREFA and UMRA suggests that supply-side factors will become even more prominent in future analyses.

Supply Function Specification. The supply function relates the quantity of a good supplied as a function of its price and other factors, such as the price of inputs, the price of substitute outputs, and technological factors. As with the demand function, factors other than the product's price are typically held constant in the supply analysis. Therefore, the focus of the supply function specification is to establish the quantitative relationship between price and quantity supplied in the affected market. The market-level supply functions presented in the conceptual discussion can be viewed as the aggregate sum of the supply functions of all individual suppliers in the market. The change in market supply in response to price then is the sum of all suppliers' responses to price.

First consider the polar case of the horizontal market supply function presented in Figure 5-7. In this case, all suppliers in the market have identical and constant marginal cost of production. Therefore, it is assumed that the market can supply an unlimited supply of product X at the given unit cost. This implies that all suppliers are identical (possess the same production technology) and that no factors raise marginal cost as output increases (e.g., diminishing returns to a fixed capital stock, inclusion of higher cost supply sources as market output expands). This type of supply function is often associated with the long run where technology and input prices ultimately determine the market price, not the level of output in the market. Given the previously referenced problems with taking a purely long-run view in the analysis of economic impacts, the horizontal aggregate supply function is, nonetheless, sometimes all that the data can support. In those cases, all that is needed is the baseline price level and the assumption that supply is perfectly elastic at that price. This may be further supplemented with data on baseline costs of production and output levels to compute average

   Figure 5-7
cost levels and compare with price. If market price and average cost differ substantially, this may cast some doubt on the validity of the horizontal supply function.

Alternatives to the horizontal supply function include the following:

  • econometric estimation of supply,
  • prototype supply function with parameter inputs, and
  • step supply function.

The first option, econometric estimation, uses econometric methods applied to data on producer quantities, price, and the other theoretically relevant variables. For example, in conducting the EIA for the Pulp and Paper Cluster Rule, the Agency econometrically estimated product-specific supply curves for each affected commodity based on pooled time-series, cross-sectional production data for each affect facility. An additional econometric approach is simultaneous estimation of supply and demand systems (see Kennedy [1994] for an overview). Direct econometric estimation of supply functions has theoretical and empirical appeal but may be difficult to implement due to data and project resource limitations.

Moreover, estimation of only an aggregate market supply function will not allow for direct mapping of impacts to individual suppliers that may be differentially affected by the regulation.

The solution to the econometric implementation and producer heterogeneity issues just referenced may come in the form of characterizing the aggregate supply function as a collection of prototype supply functions, each of which captures a distinct segment of the supply side of the market. For example, a Cobb-Douglass specification is

Micrografx Picture Publisher 6.0 Image a Cobb-Douglass specification
where i indexes each supply segment modeled. The price elasticity of supply is given by e, and b is a parameter representing the scale of the output-price relationship. In the extreme, there is a unique supply function for each supplier (firm, facility) in the market. More typically, there will be distinct model supply units characterized by factors such as technology, region, or other characteristics that differentiate supply conditions. This upward-sloping supply curve is illustrated in Figure 5-8. Based on the selected functional form, the economic analyst then proceeds to the issue of parameter estimation. Supply function elasticity parameters can be econometrically estimated or based on previous econometric estimates in the literature. However, in the absence of these estimates, assumed elasticity values for the commodities of interest may be used and are good candidates for sensitivity analysis.

While the collective prototype functions approach has the advantage of accounting for differences across suppliers, there may be conceptual or empirical reasons arguing against the use of smooth continuous upward-sloping supply functions as shown in Figure 5-8 to represent each supply unit. These might include inflexible production processes not easily varied in response to prices and binding capacity constraints. If such concerns are warranted,

   Figure 5-8
then one instead can use a step supply function approach, as illustrated in Figure 5-9. Here, Step 1 represents the average cost of supply for producer 1, the lowest cost supplier. For any price at or above P1, producer 1 is assumed to supply a fixed amount of output, Q1. Supplier 2 is the next lowest cost supplier and will provide Q2 for any price at or above P2. As the price rises, more steps (higher-cost suppliers and quantities) are brought into the market and output expands. Based on this approach, the marginal producer defines the market price (i.e., the highest observed average variable cost of production is assumed to be the baseline market price).

Employing a step supply function requires information on the opportunity cost and output level for all potential suppliers (or model supply segments) in the market. In the short run, opportunity costs may be crudely approximated by the average variable cost (AVC) of production. These data may be difficult to obtain for individual suppliers in the market because of confidentiality constraints, unless the data were obtained through an ICR and maintained through Confidential Business Information (CBI) procedures. In other situations, secondary data may be available through industry or government sources to approximate costs by supplier type. Examples of this approach include the engineering cost analysis conducted

   Figure 5-9
to estimate baseline AVC for individual coke batteries for use in the Coke Ovens EIA ( Research Triangle Institute, 1992) and the fixed coefficient AVC function estimated for individual cement kilns for use in the Portland cement EIA ( Research Triangle Institute, 1996a). An additional method described in Arnold's (1995) review of the asbestos EA is to estimate the value of the fixed capital stock for different supply segments in the market to proxy for the steps in the supply function.

The implicit assumption with the step supply function is that each producer has a supply limit due to capacity constraints. While the absolute capacity constraints provide some notion of the short-run rigidities that are elemental to an analysis of economic impacts, the constraints are rather rigid. Suppliers, even in the short run, usually have enough flexibility to modify production levels at least somewhat, hence the conceptual advantages of some sort of upward price responsiveness in the short run. Nonetheless, as a first approximation, the step supply function provides a substantial improvement to the horizontal aggregate supply function (i.e., a one-step/infinite-width supply function) by at least accounting for cost differences across suppliers, which further enables a better characterization of impacts across suppliers.

An alternative, less elaborate approach to the step supply function of Figure 5-9 may be employed when data are not available to fully characterize this function. The alternative approach implicitly utilizes the step supply framework; but in the absence of actual data to parameterize the function, the analyst employs knowledge of the subject industry, the applicability of controls across the affected population, and economic theory to develop sensitivity scenarios that qualitatively address potential economic impacts of a proposed regulatory action. With a step supply function, the marginal (highest cost) producer determines the market price. Therefore, the compliance costs of the marginal supplier determine the increase in market price. If the marginal producer is expected to incur a higher level of compliance cost than the inframarginal producers, this suggests that producers, in aggregate, can more than pass on regulatory costs to consumers. Here, the expected price increase is higher than the per-unit compliance costs of inframarginal producers so these producers are expected to gain as a result of the regulatory action at the expense of consumers paying higher prices. Alternatively, if the marginal producer is expected to incur a lower relative level of compliance costs, then the regulatory burden is likely to fall more heavily on inframarginal producers as the expected price increase is lower than their per-unit compliance costs. This approach allows the analyst to inform the regulatory development process in cases with severe lack of data or time and resources.

Incorporating Import Supply.The discussion above focuses on domestic sources of supply for the affect product(s). However, if foreign imports account for a significant share of U.S. apparent consumption of the regulated product, then the ISEG analyst should attempt to incorporate them into the economic model. In this case, the preferred approach is to define the relevant markets as the United States and to include imports as a source of supply that is added to and competes with domestic sources of supply. In addition to providing insights on the regulatory effect on foreign competition, accounting for foreign imports will allow the market model to better measure the impacts on domestic suppliers in aggregate and their distribution across individual producers or producing segments. For example, if the supply curve for imports is perfectly elastic (i.e., a horizontal foreign import supply curve), then the U.S. market price is fixed at the world market price and will not change in response to the proposed regulatory action. This occurs because foreign imports will totally offset any reductions in domestic production as they are deemed perfect substitutes and unconstrained in terms of availability. Under this scenario, domestic producers would have to absorb the entire regulatory cost burden and be more likely to close. A more plausible scenario is one with an upward-sloping supply curve for imports that reflects the ability of these sources to partially offset reductions in domestic production with increases in the U.S. market price.

This foreign source of supply is typically characterized using a prototype supply function with parameter inputs as described above for domestic suppliers. Therefore, the analyst must determine the elasticity of the import supply function through econometric estimation or appropriate literature estimates. The difficulty in econometrically estimating import and/or export elasticities from the international trade data has long been recognized. Orcutt (1950) demonstrated that elasticity estimates derived from regressions of a country's import (export) quantity on historical prices understate the true price responsiveness of imports and exports, typically by a substantial magnitude. Part of the difficulty derives from the identification problem in estimating a supply/demand system, which is more pronounced given the difficulty in procuring measures of exogenous demand and supply factors for foreign consumers and producers. However, Orcutt demonstrates how to derive the total elasticity of imports/exports, which accounts for the effect of the change in price on all (foreign and domestic) producing and consuming parties. In the absence of literature estimates of foreign trade elasticities, the ISEG analyst can employ this type of method to numerically compute the elasticities of import supply (export demand) as done for the EIA conducted in support of the Pulp and Paper Cluster Rule.

Regulation-Induced Shift in Supply Functions. The starting point for assessing the market impacts of a regulatory action is to incorporate the regulatory compliance costs into the production decision of affected producers. In other words, the focus here is to quantify the shift in the supply function caused by the regulation. An upward shift in the supply function represents the additional cost per unit of producing output because of compliance activities on a "before-tax" basis. In most analyses, this shift is quantified by taking the total annual compliance cost estimate and dividing it by baseline output quantity and computing the average cost per unit of output. Then, the supply function is shifted up by that amount. In essence, computing the supply shift this way treats compliance costs as the conceptual equivalent of a unit tax on output, and the computation of consumer and producer welfare effects follows the classical treatment of tax burden distribution in the public finance literature (e.g., Harberger, 1974).

Because compliance costs are typically divided into capital and operating costs, the parallel shift illustrated previously (e.g., Figures 5-1 and 5-2) implies that both capital and operating costs vary with output levels. At least in the case of capital, this raises some questions. In the long run, all inputs (and their costs) can be expected to vary with output. But short(er)-run analysis typically holds some capital factors fixed. For instance, to the extent that a market supply function is tied to existing facilities, there is an element of fixed capital. As indicated above, the current market supply function might reflect these fixed factors with an upward slope or rising steps. Figure 5-10(a) provides the case with an upward-sloping supply curve. In this case, the MC curve will only be affected, or shift upwards, by the per-unit variable compliance costs (C1), while the ATAC curve will shift up by the per-unit total compliance costs (C2). Thus, the variable costs will directly affect the production decision (optimal output rate), and the fixed costs will affect the operating decision by establishing a new higher reservation price for the firm (i.e., Pm'). However, with regard to the capital costs of compliance, the argument can be made that these expenditures have not yet been made; therefore, their scale could, at least in principle, be thought to vary with the level of output. Thus, the imposition of capital costs as part of a parallel shift may not distort the situation too significantly. In general, the degree to which it is appropriate to include capital costs of compliance should be evaluated on a case-by-case basis and may be the subject of sensitivity analysis.

Under the special case of a step supply function, each step will rise by the amount of that segment's per-unit total compliance costs, as indicated in Figure 5-10(b). As shown, in this case, the MC curve will shift by the per-unit total compliance costs (C) to allow the new higher reservation price for the firm to appropriately reflect the fixed costs of compliance in the operating decision. This could cause a re-ordering of the steps to construct the market supply function because the compliance costs may cause some supply segments with lower

   Figure 5-10
costs than other segments without the regulation to be higher cost than those same segments with the regulation. If a supply segment gets pushed to the point that it is entirely above the demand curve, then the market price will be below that segment's threshold price and it will not willingly supply the market. In those cases, the model predicts that the supply segment in question will exit the market in response to the regulation.     Demand Characterization and Responses

The demand function relates the quantity demanded of a particular commodity to its price and other factors, such as income and the price of substitutes. The factors other than the product's price are typically held constant in regulatory analysis, unless, for example, specific evidence is given that the market price of substitutes is substantially affected by the regulation. In that case, multiple market analysis or general equilibrium analysis may be necessary. But that is the exception rather than the rule. Therefore, the focus of the demand function specification is to establish the quantitative relationship between price and quantity demanded in the regulated market.

There are three options for demand function specification:

  • econometric estimation,
  • prototype demand function with parameter inputs, and
  • step demand function.

The first option, econometric estimation, uses econometric methods applied to data on demand quantities, price, and the other theoretically relevant variables. For information on specific methods for estimating demand systems, refer to any one of a number of econometric textbooks' specific treatment of demand systems (e.g., Intriligator, 1978; Berndt, 1991) or simultaneous estimation of supply and demand systems (see Kennedy [1994] for an overview). Econometric estimation, if done correctly, has the advantage of tying together economic theory and data to produce a consistent and empirically defensible characterization of the demand function, which can then be used to simulate market outcomes and welfare effects of regulatory actions, as described above. Econometric estimation, however, is often hindered by practical concerns such as the absence of high quality data to support estimation or limited project resources. When this is the case, alternative approaches must be considered.

The first alternative to direct econometric estimation considered here is the specification of a general form for the demand function. As shown in Figure 5-11, the general form of the demand function may be linear or have constant elasticity. Again, the Cobb-Douglass form provides an example (i.e., constant elasticity form):

   Figure 5-11
The price elasticity, E, can be derived from previous econometric estimates in the literature. If no previous studies have been conducted for the market in question, then estimates from similar markets may be appropriate. The more uncertainty surrounding the true value of the demand elasticity, the more the analyst may want to rely on sensitivity analysis to capture the relevant range of parameter values. Figure 5-12 provides an example from an economic analysis of air pollution regulations for agricultural chemicals in which model results are evaluated for demand elasticities in the range found in the literature ( Research Triangle Institute, 1997). More information on how to use sensitivity analysis to reflect underlying parameter uncertainty is presented in Section 8.

Incorporating Export Demand. The discussion above focuses on domestic sources of demand for the affect product(s). However, if foreign exports account for a significant share of U.S. domestic production of the regulated product, then the ISEG analyst should attempt to incorporate them into the economic model. In this case, the preferred approach is to define the relevant markets as the United States and to include exports as a source of demand that is added to domestic demand. In addition to providing insights on the regulatory effect on

   Figure 5-12
foreign competition, accounting for foreign exports will allow the market model to better measure the impacts on market price and aggregate demand and their incidence across domestic and foreign consumers. This foreign source of demand is typically characterized using a prototype demand function with parameter inputs as described above for domestic demanders. Therefore, the analyst must determine the elasticity of the export demand function through methods similar to those discussed above for incorporating import supply.     Determine With-Regulation Equilibrium

Producers' and consumers' responses and market adjustments can be conceptualized as an interactive feedback process. Facilities face increased production costs due to compliance, which causes facility-specific production responses, the cumulative effect of which leads to a change in the market price that all producers (affected and unaffected) and consumers face, which leads to further responses by producers (affected and unaffected) as well as consumers and thus new market prices, and so on. The new equilibrium after imposition of the regulatory action requires total market supply equals total market demand, that is,

QS = QD, for all product markets.

Given the producer and consumer behavioral response functions, this new equilibrium may be solved simultaneously using analytical solution mechanisms or iteratively using numerical solution mechanisms. These solution mechanisms allow for computation of a counterfactual or with-regulation equilibrium that can be compared with the baseline scenario to derive the economic impacts of a regulatory action as discussed in the next section.

6 This scenario is the most often available and employed in conducting EIAs within ISEG.

7 For example, the pulp and paper industry produces a publication called Lockwood Post's Directory that provides some link between facilities and the type of products they produce.

8 This follows early guidance on market definition by Stigler (1955) and Scherer (1980). See Viscusi, Vernon, and Harrington (1992, p.148-149) for a synopsis of the market definition issue.

9 See Research Triangle Institute (1996b) for discussion of market structure and its importance in conducting EIAs.

10 Potentially affected government entities include states, cities, counties, towns, townships, water authorities, villages, Indian Tribes, special districts, and military bases.

11 Potentially affected nonprofit entities include nonprofit hospitals, colleges and universities, and research institutions.

12 The indirect impacts of a regulatory action on government entities are addressed separately in Section 5.3 (federal administrative, monitoring, and enforcement) and Section 8 (UMRA-related).

13 This approach is based on the properties of compound interest and the related concept of an annuity. The value of the annuity is the amount, which, if it is charged as a cost each year over the control equipment's useful life, will result in a present value equal to the total capital cost. In other words, the total capital cost will be recovered if the annuity is charged year by year for the lifetime of the control equipment. This recovered amount includes the principal value plus a return on the principal. The expected rate of return on the principal is referred to as the opportunity cost of capital, or simply the cost of capital (where capital refers to investment funds rather than equipment).

14 Ideally, the economic analyst would base the selection of functional form on technical aspects of the subject production process(es) affected by regulation to appropriately model producer behavioral responses. Because these technical aspects often do not directly relate to a particular functional form, the analyst is left to select from the most commonly used general forms such as Cobb-Douglas, constant elasticity of substitution (CES), Leontief, and Generalized Leontief. These mathematical forms can be found in intermediate or advanced texts in applied production theory ( Chambers, 1988).

15 One textbook on economic analysis of environmental regulation refers to this problem as the absence of an encyclopedic Book of Elasticities to which the analyst can always refer to get the appropriate parameter ( Arnold, 1995, page 20). That textbook provides a very useful discussion of practical modeling issues confronted in performing OPPT's EA for the asbestos rule.

16 Some economic analyses adjust the compliance costs to account for implications of the U.S. tax code on the regulatory burden (e.g., depreciation allowances). However, the extent to which these tax-related outcomes influence the behavior of affected producers is considered to be very small and therefore not typically included in the "supply shift." The after tax consequences seem to have a much greater influence on the ultimate distribution of the regulatory burden across stakeholders as producers attempt to pass along portions of their burden to U.S. Treasury or taxpayers through the tax code. The analysis of these types of impacts are more appropriately addressed in a complementary financial analysis.

17 There may be some conceptual shortcomings to treating pollution control as equivalent to an output tax. For example, the regulations may cause firms to alter their production processes to reduce the cost of compliance, in which case the output tax equivalent, computed with ex ante cost estimates may overstate the true size of the supply shift. As a practical matter though the output tax approach dominates current practice.

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