Benefit-cost analysis (BCA) and related economic evaluation methods (cost analysis and cost-effectiveness analysis) have increasingly been applied to prevention and intervention programs for youth and young adults to assess their costs as well as the gains that may be anticipated from investing in these programs. This work reflects in part the growing prominence of evidence-based programs, policies, and practices as well as evidence-informed decision making. The papers included in this special issue represent a range of topics and issues, including the need for accurate and comprehensive assessment of program costs, high-quality BCAs of prevention and intervention programs, increasing recognition of the importance of monetizing noncognitive outcomes, and the role of BCA in pay for success financing arrangements. This introduction (a) describes the evidence-based context in which this work plays a role, (b) summarizes the practical and theoretical contributions of the papers, and (c) identifies the common themes.

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.

This paper is mainly concerned with weighted-average measures of the social discount rate, where the components of the average are the marginal productivity of investment (measured by its gross-of-tax rate of return), and the marginal rate of time preference (measured by the net-of-tax yield of capital). We believe that these components should best be measured using data (the national accounts) that span the whole economy and reflect the product actually produced and the rewards actually perceived. We use a methodology based on just four familiar parameters to generate productivity estimates applicable to a wide range of countries. In the process, we make an adjustment for infrastructure investment, also excluding income from land, monopoly markups, supra-marginal returns due to increases in total factor productivity (TFP), and returns to capital in financial intermediation. The end products are estimates of social discount rates averaging around 8% for the advanced countries, and 10% for healthy developing countries and Asian Tigers.

The prospect of improving “noncognitive” skills through intervention increases the need to understand how to represent them in evaluations. Economic assessment of such efforts rarely incorporates these factors, especially when a benefit-cost approach is employed. Programs targeting such skills are more likely to be assessed through approaches that do not monetize noncognitive ability (e.g., using cost-effectiveness analysis). This could lead to ineffective policy formulations in situations where policy is swayed toward programs that can show monetized effects. Benefit-cost analyses (BCAs) that are employed for programs that target noncognitive competencies currently may underestimate the true economic impact if such skills are left out of the equation. The limitations in valuing these skills impede thorough economic assessment for important and effective programs that target noncognitive competencies. This is especially the case for programs for younger children where readily monetized outcomes are few. The targeted outcomes in programs for children are often noncognitive skills, skills that are perceived as vital to healthy human development and valued by parents, teachers, and educators.

In this paper, we review the state of valuation of key noncognitive skills that are often targeted in social policy intervention directed toward children in youth. We examine the state of valuation of noncognitive skills through a summary of the frameworks in research for characterizing noncognitive ability and by considering the measurement approaches for noncognitive skills in terms of origin (interpersonal versus intrapersonal) and measurement type (observed versus assessed). We review examples of recent BCAs that have employed shadow prices for certain noncognitive skills. Finally, we consider what research is necessary to facilitate valuation in BCA in the future. Shadow price methodology should be carried out in a rigorous manner that recognizes uncertainty in cost projections. Improved methodologies in this area will increase the potential for more comprehensive BCA in evaluations of programs for children and youth.

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.

Benefit-cost analysis (BCA), a discipline best known for guiding policy choices, can also guide personal decisions. In either application, traditional BCA tallies benefits and costs using market values or willingness to pay. When future outcomes are uncertain, as they are across a wide array of situations, BCA must call as well on the methods of decision analysis. Thus, von Neumann–Morgenstern utilities, subjective probabilities, and sequential decision strategies are brought into play.

Traditional decision analysis distinguishes between risk and uncertainty. With risk, the probabilities of possible outcomes are known; with uncertainty, those outcomes are known, but not their probabilities. We introduce the concept of ignorance: a third, less tractable category. With ignorance, even the possible outcomes of decisions cannot be identified. Ignorance takes particular importance when high payoffs are associated with these unidentified outcomes, as is often the case. We identify such outcomes as consequential amazing developments (CADs). In the policy realm, the 2008 financial meltdown or the Arab Spring would represent a CAD. For an individual, a CAD might be that one’s secure tenured position had been inexplicably terminated, or that one’s trusted business partner had long been shuttling corporate secrets to a competitor. We distinguish between unrecognized and recognized ignorance. In the latter category, we identify specific cognitive biases that impair decision making.

Consequential ignorance cannot be studied in a controlled laboratory setting, since its payoffs are high, its time delays often long, and merely introducing the subject would tend to give away the game. Thus we develop a descriptive understanding of ignorance drawing on great works of literature, from antiquity to the present day, positing that skilled writers understand how humans make decisions and respond to unanticipated outcomes. Shakespearean examples would be Hamlet’s ignorance of his father’s killer, and Macbeth’s lack of awareness of the tragic ramifications following his actions to seize the Scottish crown.

Following this descriptive analysis, we turn to prescription. We draw on decision analysis to develop a formula for calculating consequential ignorance; it incorporates the expected magnitudes and assessed base rates for CADs. Finally, we propose a decision-analytic framework for measured decision making, given ignorance. Our recommended approach explicitly recognizes decision-making costs, and thus proposes when to use quick and intuitive as opposed to more deliberative approaches to decision, or the labels Kahneman has popularized as System 1 and System 2.

Studying ignorance through literature has important implications for BCA. Given the potential for ignorance, there are two key goals for prudent decision making. First, steps should be taken to recognize when ignorance might be present. Second, efforts should be made not to respond in a suboptimal fashion when ignorance is anticipated or when a CAD springs upon us. Great literature can provide the equivalent of widespread life experience, and can help a decision maker reach both goals. At its heart, this essay represents a benefit-cost approach to dealing with ignorance. Numerous connections to BCA are made throughout.

There is a growing body of research emphasizing the advantages of teaching students social and emotional (SE) skills in school. Here we examine the economic value of these skills within a benefit-cost analysis (BCA) framework. Our examination has three parts. First, we describe how the current method of BCA must be expanded to adequately evaluate SE skills, and we identify important decisions analysts must make. Second, we review the evidence on the benefits of SE skills, again noting key methodological issues with respect to shadow pricing. Finally, we perform BCA of four selected social and emotional learning (SEL) interventions: 4Rs; Second Step, Life Skills Training; and Responsive Classroom. These analyses illustrate both methodological and empirical challenges in estimating net present values for these interventions. Even with these challenges, we find that the benefits of these interventions substantially outweigh the costs. We highlight promising areas of research for improving the application of BCA to SEL.

Purpose: There is limited research on the costs of social and emotional learning (SEL) interventions [Crowley, Jones, Greenberg, Feinberg & Spoth (2012). Resource Consumption of a Diffusion Model for Prevention Programs: The PROSPER Delivery System. Journal of Adolescent Health, 50 (3), 256–263]. This paper describes a comprehensive methodology for determining the costs of a successful universal, school-based SEL intervention that was implemented in nine public schools over 3 years.

Methods: Resource costs were identified using the Cost–Procedure–Process–Outcome Analysis Model [Yates (1996). Analyzing Costs, Procedures, Processes, and Outcomes in Human Services. Thousand Oaks, CA, US: Sage Publications, Inc.; Yates (1999). Measuring and Improving Cost, Cost-Effectiveness, and Cost-Benefit for Substance Abuse Treatment Programs. No. NIH 99-4518, 135] and the ingredients model [Levin (Ed.) (1983). Cost-Effectiveness A Primer (Vol. 4). Beverly Hills, CA: Sage; Levin & McEwan (2001). Cost-Effectiveness Analysis: Methods and Applications. (2nd ed.). Thousand Oaks, CA: Sage Publications]. This involved careful identification of resource use, finding the cost per unit for each resource by intervention activity, and ultimately calculating the total resource cost (resource use $\times$ cost per unit).

Results: Our analysis estimated the overall cost of this 3-year SEL and literacy intervention to be $1,831,296 for nine schools. This averages to $67,825 yearly per school and $130 yearly for each student. The analysis estimated the first year of the intervention to be the costliest ($683,106) and then decreasing in Year 2 ($581,764) and Year 3 ($566,426).

Conclusion: This research emphasizes the need to study the costs of SEL interventions. By providing a detailed and standardized methodology, this cost analysis can provide added support for implementing an effective social and emotional learning intervention in a school setting. Furthermore, it provides groundwork for more advanced cost analyses, such as a cost–effectiveness analysis or a benefit-cost analysis (BCA).

In this study, we conduct a cost-effectiveness analysis of nine air quality regulations recently issued by the U.S. Environmental Protection Agency (EPA). Taking emission reductions in the Regulatory Impact Analyses (RIAs) for these regulations as given, we independently assess uncertainty about the compliance costs of the regulations and the lives the regulations are estimated to save. The latter evaluation is based on a formal uncertainty analysis that integrates expert judgments about the effects of fine particle exposures on mortality risks. These expert judgments were given in an EPA-sponsored elicitation study conducted in 2006. The integrated judgments are used to generate probability distributions for several types of cost-effectiveness ratios, including the gross and net cost per life saved, net cost per life year saved, and net cost per quality-adjusted life year (QALY) gained. The results show that the cost-effectiveness ratios exhibit considerable uncertainty individually and also vary widely across regulations. Within a simulated 90% confidence interval for the gross cost per life saved, for example, there is both the possibility that benefits from lifesavings alone are sufficient to cover the rules’ costs and the possibility that no lives will be saved and cost-effectiveness ratios will be infinite. The wide ranges for the confidence intervals suggest the need for better information about the effects of fine particle exposures on mortality risks.

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.

We explore the economic welfare effects of direct and indirect government-induced changes in employment under varying market conditions. We begin with a discussion of those policy-induced employment changes that seamlessly reshuffle workers among jobs in an efficient (i.e., full-employment, full-information) economy; generally such changes create few, if any, net changes in economic welfare not captured in changes in wage bills. We then turn to the effects of policy-induced employment changes in economies with two market distortions: (1) inflexible wages set by law or custom that result in involuntary unemployment during periods of deficient aggregate demand, and (2) illiquidity resulting from imperfect capital markets that prevent people from borrowing against future earnings. Induced employment changes in these circumstances impose real net social costs or generate real net social benefits beyond changes in the wage bill. We also assess the likely magnitude of the social opportunity cost of labor in the case of involuntary unemployment and imperfect liquidity, and address how the welfare effects of such employment changes should be valued. Based on currently available empirical research, we develop estimates of the opportunity costs of hiring or releasing an employee during periods of high unemployment with and without other market distortions. In contrast to conventional benefit-cost analysis practice, which treats releasing workers as having a negative opportunity cost, we estimate an opportunity cost for firing that is positive and equal to about 73% of pre-firing compensation, primarily because of the “scarring effect” of unemployment. Also in contrast to conventional practice, we estimate an opportunity cost for hiring an unemployed worker that is less than the worker’s opportunity cost of time.

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.

Talent Search was created to improve high school completion and college enrollment for disadvantaged students. Since the program’s inception in 1967, there has not been a valid study on its economic value. In this paper, we perform a full economic evaluation, yielding direct information on the value of Talent Search and highlighting key methodological issues relating to economic evaluations of education programs. We provide rigorous estimates of social costs using the ingredients method. Using prior estimates of impacts from Constantine et al. [(2006). Study Of The Effect of The Talent Search Program On Secondary And Postsecondary Outcomes In Florida, Indiana And Texas: Final Report From Phase II of The National Evaluation. Washington, DC: U.S. Department of Education Office of Planning, Evaluation and Policy Development, Policy and Program Studies Service], we perform a cost-benefit analysis based on new estimates of shadow prices. Finally, to examine site-specific differences in impacts and costs, we undertake cost-effectiveness analysis and derive confidence intervals that illustrate key sensitivity issues. Regarding costs, we find significant resource use beyond federal funding amounts; but we also find that the present value benefits of Talent Search almost certainly exceed the present value of costs by a substantial margin. With regard to cost-effectiveness, we find significant differences across sites and extremely wide confidence intervals. We conclude with an outline of key research issues that need to be addressed to enhance future economic evaluations in educational settings with wide site-specific variation.

This paper reports on a benefit-cost analysis of a targeted intervention program, the YouthBuild USA Offender Project (YBOP), aimed at low-income, criminal offenders who are 16–24 years old. Using data on 388 participants, we find: (1) evidence of reduced recidivism and improved educational outcomes that exceed our expectations based on similar cohorts and (2) evidence consistent with a positive benefit-cost ratio, indicating that every dollar spent on the YBOP is estimated to produce a return on investment between $7.20 and $21.60, with benefits to society ranging between $174,000 and $281,000 per participant at a cost to society between $13,000 and $24,000.

Regulatory policies designed to improve societal welfare by “nudging” consumers to make better choices are increasingly popular. The application of benefit-cost analysis (BCA) to this sort of regulation confronts difficult theoretical and applied issues. In this analysis we contribute a worked example of behavioral BCA of U.S. anti-smoking policies. Our conceptual framework extends the standard market-based approach to BCA to allow for individual failures to make lifetime-utility-maximizing choices of cigarette consumption. We discuss how our market-based approach compares to the health benefits approach and the “consumer surplus offset” controversy in recent BCAs of several health-related regulations. We use a dynamic population model to make counterfactual simulations of smoking prevalence rates and cigarette demand over time. In our retrospective BCA the simulation results imply that the overall impact of anti-smoking policies from 1964 to 2010 is to reduce the total cigarette consumption by 28%. At a discount rate of 3% the 1964–present value of the consumer benefits from anti-smoking policies through 2010 is estimated to be $573 billion ($2010). Although we are unable to develop a hard estimate of the policies’ costs, we discuss evidence that suggests the consumer benefits substantially outweigh the costs. We then turn to a prospective BCA of future anti-smoking Food and Drug Administration (FDA) regulations. At a discount rate of 3%, the 2010–present value of the consumer benefits 30 years into the future from a simulated FDA tobacco regulation is estimated to be $100 billion. However, the nature of potential FDA tobacco regulations suggests that they might impose additional costs on consumers that make it less clear that the net benefits of the regulations will be positive.

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.

Increasing access to effective preschool programs is a high priority at local, state, and federal levels. Recently, two initiatives to expand preschool programming in Illinois and Utah have used funds from private investors to scale up existing programs. Private-sector social impact investors provide funding to nonprofit or public preschool providers to increase the number of children served. If the measured outcomes from preschool participation meet predetermined goals, then the estimated government cost savings arising from these preschool interventions are used to repay the investors. Social impact investing with a “Pay-for-Success” contract can help budget-constrained governments expand proven or promising preventive interventions without the need to increase taxes. Benefit-cost analysis (BCA) plays a crucial role in helping to identify which social, educational, or health interventions are suitable for this type of innovative financing. Benefit-cost analysts are needed to design the structure of the success payments that the government will make to the private investors. This paper describes social impact borrowing as a new method for financing public services, outlines the contribution of BCA, and discusses the innovative use of social impact financing to promote scaling evidence-based Child-Parent Centers and other early childhood programs.