The value of a statistical life (VSL) is the most influential single parameter used in calculating the benefits of governmental regulations. While there are some interagency differences, there is a commonality in the conceptual approach, the central role of mortality risk valuation in benefit assessment, and the general range of valuations used. Corporate risk decisions are based on a less rigorous risk analysis procedure. As typified by the General Motors ignition switch recall problems and the company’s lax corporate safety culture, there is often little systematic corporate balancing of cost and risk. This suppression of safety concerns may be attributable to the adverse experiences automobile companies had after conducting risk analyses that valued fatalities based on damages awards for wrongful death, and in response juries levied blockbuster punitive damages awards. Instead, companies should adopt the VSL in its product risk decisions. Companies should also be provided with a safe harbor reference point for responsible risk decisions. Regulatory agencies should use the VSL in setting regulatory sanctions.

The analysis of regulations affecting addictive or habitual goods has drawn considerable controversy. Some studies have suggested that such regulations have only small welfare benefits, as consumers value these goods despite health benefits from quitting, while other studies suggest that information or behavioral problems make existing consumption decisions a poor guide to welfare evaluation. We examine potential utility offsets to health benefits of regulations affecting addictive or habitual goods theoretically and empirically. Our analysis focuses on individuals who consume these goods only, ignoring other social costs and benefits. Theoretically, we show the importance of several factors including: money saved in addition to health improvements; differentiating steady-state utility losses from short-term withdrawal costs; lack of utility loss for people dissuaded from starting to consume the good; and accounting for utility consequences of explicit or implicit cost increases. Our empirical analysis considers regulations that affect smoking. To measure the welfare cost of smoking cessation, we divide the population into those with more and less rational smoking behavior and use the valuation of smoking from more rational smokers to impute values of losses for less rational smokers. Our results show that the utility cost of smoking cessation is small relative to the health gains in people for whom withdrawal costs are the main utility loss of quitting, and even among people who have some ongoing loss, the utility offsets represent 20%–25% of the health gains. While marginal smokers induced to quit by regulations can be expected to have low or no steady-state loss, even this higher estimate is far below prevailing estimates of the utility cost of smoking used by the Food and Drug Administration and other analysts.

The estimated impacts, benefits, and costs of legalizing slot machines in Maryland are analyzed building on and contrasting with results from an impact analysis. The analysis provides estimates of the components and the total net benefits to the state and its citizens; the role of uncertainty, distributional impacts, and a basic tax alternative. The results forecast mostly positive net benefits for Maryland both in comparison to doing nothing and in comparison to raising an equivalent amount in taxes. However, if slot revenue raised from the lower income population is given more weight, then doing nothing or raising taxes appears to be preferred.

Consumer protection and financial regulatory agencies such as the Federal Trade Commission (FTC), the Securities and Exchange Commission (SEC), and the Consumer Financial Protection Bureau (CFPB) regulate various types of consumer, investor and financial frauds. Whether required or not, rulemaking proceedings oftentimes include some form of benefit-cost analysis. Thus, the benefits of proposed regulations – whether fully quantified or not – are an increasingly important component of rulemaking decisions. Anecdotal evidence suggests that the impact on victims in some cases includes significant time and financial hardships and even pain, suffering, and reduced quality of life. Further, the existence of these offenses causes nonvictims to take costly precautionary behavior and might even inhibit legitimate business activities. Yet, little is known about the true costs of consumer and financial crimes other than the out-of-pocket monetary losses incurred by victims. To the extent society wishes to optimally deter such crimes, without better data on nonmonetary costs, any benefit-cost analyses of criminal justice or prevention programs designed to reduce these crimes will inevitably underestimate program benefits. This paper provides an initial framework and empirical estimates of the willingness to pay (WTP) to reduce four types of white-collar and corporate offenses – consumer fraud, financial fraud, corporate crime, and corporate financial crime. Utilizing a contingent valuation survey approach that has been used to estimate the cost of street crimes, the average WTP for a 10% reduction in each of these four offenses is estimated to range between $35 and $85 per household. In the case of consumer fraud and financial fraud, where estimates of prevalence are available, this translates into a WTP of $1200 per consumer fraud and $12,000 for financial fraud. In contrast, the out-of-pocket costs to victims of consumer fraud have been estimated to average about $100, and about $200 to $250 for various types of financial frauds. These figures also compare favorably to the WTP for a reduced household burglary of $19,000.

In the face of strong policy interest in the possible regulation–jobs linkage and weak analytical evidence to support a generalizable conclusion, what should a regulatory agency like the Environmental Protection Agency do in a regulatory impact analysis (RIA)? Initially, an RIA should start with a clear concept of what the regulatory agency is trying to estimate. Much of the popular debate is looking for a total job effect. Yet one thing we do know is that, in aggregate, there will not be a net job change unless the economy deviates from its normal rate of full employment. The gist of our literature review suggests that looking to historic data for stable statistical relationships between regulatory spending and job changes, even in a single industry, is tenuous at best. However, the intuition is relatively easy to trace out with certain assumptions: (1) added costs imply added activity that entails added jobs; (2) higher product prices or other regulatory limits imply less production that entails fewer jobs. Taking an average employment rate per dollar of relevant economic activity, coupled with an assumed demand elasticity, these effects can be multiplied out into job changes, although such simple calculations must be tested by validating key assumptions or exploring the estimates sensitivity to alternatives. New estimates by Belova, Gray, Linn and Morgenstern [(2013a). Environmental Regulation And Industry Employment: A Reassessment. Center for Economic Studies, U.S. Census Bureau Discussion Paper, CES 1336, July.] indicate that extending and expanding the widely cited approach by Morgenstern, Pizer and Shih [(2002). Jobs Versus the Environment: An Industry-Level Perspective. Journal of Environmental Economics and Management, 43, 412–436] is unlikely to be successful. Finally, more effort is needed to inform the public about the potential job impacts of new regulations, especially the distinction of these impacts from long-term technological and economic trends.

A requirement for project proponents to include a benefit-cost analysis (BCA) as a component of their environmental impact assessments is implied in planning approval legislation in New South Wales, Australia. Fulfilling that requirement in the context of three large-scale expansions of coal mines has led to the application of choice modeling to estimate values for the main environmental, social, and heritage impacts. A number of particular issues have emerged in those applications: the framing of choice sets so that incentive-compatible willingness-to-pay questions are asked; the inclusion of “existence values” associated with employment opportunities provided by mines; and the incorporation of environmental offsets as part of the choice task given to respondents. The benefit-cost analyses of the coal mining projects have proven controversial. While the government agency responsible for administering the project approval process has used them as an input to decision-making, in some cases its recommendations have been “over-ridden” by the imposition of subjectively determined administrative rules. In one case, an appeal through legal channels against an approval was successful in part because the judge who heard the appeal dismissed the BCA findings because it was contrary to his own viewpoint of the merits of the case. In response, the state government has introduced legislation that requires greater “weight” to be given to the development benefits of coal mining. These responses have left the role of BCA and nonmarket valuation in the decision-making process in “limbo,” with practitioners and policy makers unsure as to the future of the methods in politically charged contexts.

Although there is growing interest in applying benefit-cost analysis (BCA) to public policy questions, limited information is available on states’ use of this methodology. The nationwide assessment presented here begins to fill that void and finds that states and the District of Columbia are increasingly conducting BCAs and using the results to inform their policy choices. The numbers of reports released by the states and statutory mandates to conduct these studies increased substantially between 2008 and 2011. An analysis of the studies released by states shows that most lack some recommended technical features of rigorous BCA, but the reports are having a reported impact on state policy and budget decisions. Like other forms of policy research, BCA faces challenges including resource and data limitations, timing problems, and gaining policymaker buy-in for the approach and findings.

Applying benefit-cost analysis in the White House regulatory oversight process served as a basic mission of the Council on Wage and Price Stability (CWPS) during its seven-year lifespan (1974–1981). This paper reviews that CWPS experience, which involved filing comments in over 300 proceedings at more than 25 federal regulatory agencies. The paper draws on those CWPS public comments (filings), identifying persistent and pervasive deficiencies in the economic analysis regulators then and now often use as support for new regulation. CWPS filings fostered greater acceptance of benefit-cost analysis in regulatory decisions; such analysis is now required by executive order.

Recent federal regulations require new light-duty vehicles to have lower greenhouse gas emissions and better fuel economy. This paper presents the reasoning used by the U.S. Environmental Protection Agency (EPA) in its benefit-cost analysis of the standards. According to EPA, many available technologies could achieve these goals without affecting other vehicle qualities, and fuel savings would pay for the increased technology costs with short payback periods. This lack of market adoption of cost-effective energy-saving technologies has been termed the energy efficiency gap or energy efficiency paradox. It suggests that either there are additional costs, such as changes in vehicle qualities, not considered in cost estimates, or markets for energy-saving technologies are not achieving all cost-effective savings. EPA argued that, even if consumers do not accurately consider expected future fuel savings when buying new vehicles, consumers are projected to receive those savings; the latter measure should reflect the impacts of the rule on fuel expenditures. For the cost side, EPA used a measure of technology costs required to meet the standards while maintaining baseline (2008) vehicle attributes. Estimates of how these costs would be affected by changes in vehicle attributes were not included.