Corporate Decision-Making with Macroeconomic Uncertainty: Performance and Risk Management

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Log In. Don't have an account? Sign Up. Update your profile Let us wish you a happy birthday! Add your birthday. The increased costs borne by regulated entities and public agencies charged with enforcement may also be associated with increased costs borne by other private-sector entities and public agencies at different levels of government. An increase in enforcement effort will generally raise compliance rates and, subsequently, the costs borne by the regulated parties Shimshack and Ward, , Measuring the capital costs and operating costs incurred by private parties in order to estimate compliance costs can be a difficult task for agencies with the responsibility of implementing a regulation.

For example, under a cost-of-service regulation, an agency sets a price per unit of output and must gauge whether the regulated price it sets is sufficient to yield an adequate return to the regulated entity. At the same time, the agency must ensure that the price is not set so high that returns are excessive Breyer, Compliance costs are often estimated using engineering models, with the models often being based on expert opinions of the relationships between input use and outputs for a particular industry or application within.

It is not always made clear, however, from what settings the estimates were drawn.

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There may often be substantial variability in compliance costs depending on the characteristics of the regulated entity, including its scale and the age of its plants and equipment. In still other cases, if the cost of compliance is too great, facilities may be closed.

It is also possible that, unknown to the EPA, particular facilities may have already been targeted for closure by company management even without the arrival of the new environmental requirements. A lack of such information is a common problem in the area of regulation and adds to the total uncertainty.

EPA typically conducts surveys of facilities to determine the different types of technologies that are in place EPA, , b. Cost estimates are usually based on estimates of the changes that would be required in the different types of facilities to comply with the standards.

ACCA P5 Risk and Uncertainty

For example, the summary of a regulatory impact analysis for arsenic in drinking water included tables listing the monetized health benefits from avoided cases of bladder and lung cancers and containing estimated compliance costs EPA, b. The table with the monetized benefits contains lower and upper estimates of benefits, which were based on the lower and the upper estimates of bladder cases avoided. No estimates that took sources of uncertainty other than human health risks into account are displayed.

Estimates of the costs were provided in the summary table for two discount rates 3 percent and 7 percent , for two different plant categories, and for four different maximum contaminant levels. No analysis of other factors was displayed. As a result, the estimates did not reflect the overall variability in the cost of complying with the rule.

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Strategy Under Uncertainty

A first step in dealing with this source of uncertainty would have been greater transparency in how the estimates were derived. The use of engineering models for estimating costs raises a number of other technical issues as well. Conceptually, the relevant compliance. There are issues of how joint costs or products are treated in the determination of engineering cost estimates. For example, the removal of one type of contaminant may be much less costly if industries have already installed treatment processes for other contaminants; similarly, if a control technology will decrease the emissions of a number of pollutants, it is difficult to know what portion of the costs of installing and maintaining that control technology should be attributed to regulating just one of those pollutants.

In such cases the marginal cost of removing the contaminant can be substantially overstated if the other pollution control activities are not considered. For example, EPA uses a model that accounts for the control of multiple pollutants sulfur oxides, nitrogen oxides, directly emitted particulate matter, and carbon dioxide in its regulatory impact analysis for mercury EPA, b. There is also likely to be uncertainty concerning the number of households, firms, or systems for example, water systems 16 that may be affected by a rule and also concerning the methods that the regulated entities will use to comply with the rule.

Uncertainty is even greater when EPA sets a national standard and agencies at a lower level of government, such as state agencies, implement the rule. In such instances, in addition to the issue of how firms will actually change to meet the new standards, there is additional uncertainty concerning how other units of government will implement the new standard. Once again, however, systematic inaccuracy is unlikely to occur, except in those cases in which a problem with compliance is known or anticipated.

Other sources of uncertainty that are sometimes relevant are the level of enforcement, the productivity of such enforcement efforts, and, subsequently, the compliance with the rule. The simplest approach for dealing with such uncertainty is to assume complete compliance—in other words, percent enforcement. This level of enforcement may be higher than either the level that is socially optimal that is, the one at which the marginal cost of.

Estimates made with this assumption, therefore, should be considered to be high estimates of enforcement cost. Alternatively, EPA could estimate a range of costs using different percentages of firms complying with the laws. Whatever level of enforcement is assumed should be assumed throughout the analysis, including in the computation of benefits.

A few studies have compared the compliance costs estimated in regulatory impact analyses to estimates of actual compliance costs incurred after a regulation has been put into effect Harrington, ; Harrington et al. Those comparisons indicate that compliance costs are often overestimated. OMB also concluded, however, that benefits are overestimated to a greater extent than costs, so that economic analyses typically predict that the performance of a regulation will be better than actually occurs.

And as discussed by Harrington , these experiences also demonstrate the importance of conducting analyses after the implementation of a regulation so-called ex post analyses to evaluate and improve the methods used for predicting costs and benefits. When a rule is to be implemented over a number of years, additional uncertainties arise.

For example, input prices that is, the price of inputs to a process, such as the price of low-sulfur coal might vary with time. Moreover, as discussed earlier, the costs of the technological changes and equipment necessary for a plant to comply with the rule might change.

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For example, the promulgation of a rule on a national basis might increase the market size for an innovation that improves environmental quality. Such new technologies may be more productive in achieving a particular environmental goal, and in some cases the purchase prices of equipment incorporating the new technologies might be lower. At the time the rule is being considered, however, there is considerable uncertainty about how innovators will respond to the rule that is, the amount of investment in research and development that will be forthcoming in response to promulgation of the rule , the yield from such investment, the time frame within which any yield will occur that is, when the innovation will occur , and at which price the new technology will be marketed.

A practical solution is to assume a worst case in which no innovation takes place, but such an assumption might underestimate the net benefit of innovation to the extent that there is an overestimation of the costs. Estimates of compliance costs often must be made by estimating the number of facilities currently not in compliance with a proposed standard or rule, the magnitude by which those facilities would be out of compliance,. All of those estimates are associated with an uncertainty that is difficult to accurately quantify. That uncertainty can, however, be qualitatively described, and potential ranges of costs can be used to provide decision makers with information on the effects of potential uncertainty on the estimates of the cost of different regulatory options.

The second broad category of costs consists of those costs imposed on other parties by increased prices EPA, To the extent that prices increase, the quantities of output in other sectors are affected, which in turn affects the output for the economy as a whole i. For example, if an environmental regulation increases the cost of coal mining, the price of coal is likely to increase, which in turn could lead to a decrease in coal consumption and an increase in the use of other energy sources.

The increased price of coal would likely lead to an overall increase in energy costs, adding to the cost of manufacturing various products, which could in turn lower national production and employment. When evaluating the broader costs of regulations, a distinction is often made between partial and general equilibrium analysis. Partial equilibrium analysis examines the effects of a regulatory change on a single firm.

For example, a partial equilibrium analysis might assess the effect of a particular environmental regulation on the capital spending decisions of an individual firm. By contrast, general equilibrium analysis considers the effects of a regulatory change on all participants in a market or even in the economy as a whole.


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Individual sectors do not operate in a vacuum; if regulated firms increase the prices of their products, it may affect outputs and prices in other sectors. An analysis of those economy-wide effects, therefore, is often appropriate. Increased prices and reductions in output impose costs on society at large. However, if the potential effects of the regulatory rule are small or localized, there is little reason to assess its impacts on the economy as a whole. It would be impractical to attempt to assess the economy-wide impacts of individual regulatory rules de novo.

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Instead it is necessary to employ models that have been developed for more general purposes. CGE models are a class of economic models that use actual economic data to estimate how an economy might react to changes in policy, technology, or other external factors. CGE models can also be used to compute the distributional impacts of regulatory changes. The starting point for an analysis with a CGE model is a set of assumptions about the impacts of the proposed rule on the output prices of the firms directly affected by the rule RTI International, With the CGE model, the analyst computes prices and outputs of goods and services in various sectors and calculates the gross domestic product once the simulated economy has returned to a new equilibrium following implementation of the rule.

CGE models are based on myriad assumptions about the underlying relationships among economic sectors—that is, about the substitutability of various goods and services in the economy. Those assumptions about these interrelationships, as well as the assumptions about price changes that are the essential inputs in the calculations, are sources of uncertainty in CGE models RTI International, There are also dynamic versions of CGE models that consider a broader range of longer-term effects, including technological changes, which have the potential to capture the long-term effects of regulatory rules on labor supply, savings, the growth of classes of inputs, and input productivity RTI International, Structural changes in the economy occur over time as a result of a policy change.

For example, a policy offering financial incentives to purchase energy-efficient appliances may lead to more demand for such appliances in the short run. In the long run, new appliances are developed because there is a greater financial incentive for firms to engage in research and development to develop new even more efficient projects. Because such long-term analyses rely on future projections, however, outcomes are far more uncertain than those obtained from use of static models.