Research and development (R&D) planners in homeland security agencies would like to be able to prioritize investments in projects based on costs versus future safety and security benefits. While costs are often readily available, estimates of safety and security benefits are fraught with uncertainty. To address these challenges, a benefit–cost model of technological change is adapted to the homeland security context. Data are sparse; therefore, estimation is facilitated by developing a familiar linear welfare model using derivatives of cost and risk reduction functions to estimate areas of costs and benefits. The theoretical model is applied to two homeland security projects involving airport patrols and the assignment of U.S. federal air marshals to international flights. Retrospective data are available for most periods. Welfare-based rates of return are reported for the two cases, each of which is estimated to return large present value net benefits. Extensive sensitivity and Monte Carlo simulation explores uncertainties. Two important findings are that (i) given the rationality assumption, relative increases in security levels can be valued, even if the absolute level of security is not known; and (ii) large uncertainties about risk reduction exist but can be bounded by parametric sensitivity and uncertainty analysis.

Nepal has suffered from the worst electricity shortages in South Asia. This study is an attempt to measure the willingness to pay for an improved service using a model of revealed preference. Respondents are asked about the actions they are taking to reduce the impact on their household or business of scheduled and unscheduled outages and more stable voltage. We estimate the averting expenditures that were being incurred to compensate for the lack of reliability of the electricity service. The estimated cost of the averting actions as a percentage of the electricity bills is 53 % for households, 47 % for small businesses, 46 % for medium businesses, and 35 % for large businesses. Based on the estimations, we find that in 2017 the annual benefit from improving the reliability of the electricity service would be approximately US$ 188 million with a present value over 20 years of US$ 1.6 billion.

Jim Tozzi has for several decades been among the most dedicated and perceptive commentators on the White House Office of Information and Regulatory Affairs (OIRA). Indeed, Tozzi is arguably better suited than anyone to be the keeper of the OIRA flame: from his position in charge of the Office of Regulatory and Information Policy, OIRA’s predecessor organization, Tozzi contributed to the development of Executive Order (EO) 12291 and then stepped in as OIRA’s first Deputy Administrator. Since that time, he has been a steadfast defender of OIRA’s critical role in bringing rigorous analysis, quality control, and policy discipline to Executive Branch regulation. I share Tozzi’s view, and indeed that of all former OIRA officials I have spoken with, of OIRA’s importance to the regulatory state. Preservation of the strength and independence of the office is to me the principal criterion by which policy proposals that effect OIRA should be judged.

This paper provides a Consent Justification for benefit–cost analysis (BCA). The Consent Justification is based on a tendency toward actual compensation. A substantial justification for using BCA as a tool is the actual Pareto test, called the Consent Justification, in combination with the net present value criterion for individual projects. The traditional justification, the potential compensation test (PCT), is unsatisfactory on several grounds. In addition, the PCT occupies the uneasy position of being the source of extended criticisms in the economic literature and especially in the legal and philosophy literature. The argument for the Consent Justification lies not only in the deficiencies of the PCT, but also, especially, in a showing through simulation that all tend to gain across a portfolio of projects which is not large but rather robust with respect to errors and assumptions.

In 1992, Congress passed The Elwha River Ecosystem and Fisheries Restoration Act with the goal of “full restoration of The Elwha River Ecosystem and native anadromous fisheries.” As part of that act, the federal government was required to produce a benefit-cost analysis on dam removal of the Elwha and Glines Canyon dams, which was published in 1994. This article revisits that initial 1994 benefit-cost analysis; background on its methods and assumptions is given, comparisons are made to current state-of-the-art techniques in benefit-cost analysis, and an ex post benefit-cost analysis of the project is conducted for comparison purposes. We find that the cost and scope of the project exceeded original expectations, the cost of the foregone electricity generation was less than expected, and that anticipated recreational and fisheries benefits were both delayed, and lower, than expected. Furthermore, issues such as the value of hatchery-spawned versus wild anadromous fish seem not to have been anticipated in the original analysis, highlighting the fact that in doing an ex ante analysis, researchers must expect that unexpected factors may influence the ex post results of any project.

Various methods have been applied to evaluating the economic viability of public investments in tourism. In this article, we capitalize on the strengths of computable general equilibrium and cost-benefit analytical techniques and develop an integrated approach to evaluating public investments in tourism. We apply the approach to the evaluation of a US$6.25 million investment in tourism in Uruguay from the perspective of a multilateral development bank and a beneficiary government. These perspectives differ in a cost-benefit analysis (CBA) due to the timing of the costs incurred. The integrated approach is powerful in that it captures first and subsequent rounds of investment impacts of benefits and costs; resource diversion and constraints are accounted for, and the estimation of benefits is consistent with the welfare economics underpinnings of CBA.

Households in the USA spend about $70 billion annually on pets. Dogs, the most common pet, can be found in nearly half of American households. An important shadow price in the analysis of policies affecting human mortality is the value of statistical life (VSL), which is imputed from how people make decisions involving tradeoffs between small mortality risks and other goods. The value of statistical dog life (VSDL) is also an important, but until now unavailable, shadow price for use in regulation of such goods as pet foods and environmental toxins. Additionally, an estimate of the VSDL would have uses outside the regulatory process in valuing programs involving zooeyia, in setting tort awards for wrongful dog death, and in property divisions in divorce settlements where joint custody of dogs is not feasible. In order to estimate the VSDL, we conducted a contingent valuation of a national sample of dog owners that elicited willingness-to-pay for changes in mortality risk for pet dogs. Specifically, respondents were asked about willingness-to-pay for a vaccine that would reduce the risk of canine influenza. The design included both quantity (different magnitudes of risk reduction from the offered vaccine) and quality (differences in nature of death from the influenza) treatments as scope tests. It also included treatments involving spillover effects to other dogs and a priming question about disposable income. Based on the analysis and consideration of its assumptions, we recommend $10,000 as the VSDL.