Cannabis is the most commonly used illicit drug worldwide. In countries with repressive drug policies, the costs of its prohibition plausibly outweigh the benefits. We conduct a cost–benefit analysis of cannabis legalization and regulation in the Czech Republic, taking into consideration alternative scenarios designed using parameters from the known effects of cannabis legalization in selected U.S. states, Canada, and Uruguay. Our analysis focuses on tax revenues, law enforcement costs, the cost of treatment and harm reduction, and the value of Quality Adjusted Life Years (QALYs). Under all the projected scenarios, the identified benefits of legalizing cannabis for personal use exceed the potential costs. The estimated net social benefit of legalization is in the range of 34.4 to 107.6 million EUR per year (or between 3.2 and 10.1 EUR per capita), depending on the size of the cannabis market and the development of cannabis prices after legalization.

This paper studies the vulnerability of the pivotal mechanism with respect to manipulation by groups. In a lab experiment, groups decide on the implementation of various alternatives, some of which imply opposite interests for the two subgroups. We investigate the occurrence of tacit and explicit collusion by allowing for communication within subgroups in one treatment and prohibiting it in another. Even though all agents’ preferences are common knowledge and there exists a simple symmetric collusive strategy for one subgroup, we find little evidence for tacit collusion. Only when explicit communication is allowed, collusion is established. A behavioral model using quantal response equilibrium in which subjects have beliefs over the correlation of errors of same-type subjects helps explain the main features of our data.

We conduct laboratory experiments for the multi-unit Vickrey auction with and without advice to subjects on strategy-proofness. The rate of truth-telling among the subjects without advice stays at 20%, whereas the rate increases to 47% among those who have received advice. By conducting similar experiments for the pay-your-bid auction, which is not strategy-proof, we confirm that the increase in truth-telling is due significantly to the net advice effect (i.e., the effect beyond the so-called experimenter demand effect). Moreover, we find that providing advice improves efficiency in the Vickrey auction, particularly in the early periods, when the subjects are less experienced. In general, subjects tend to overbid in Vickrey auction experiments. Our results indicate the possibility that providing simple advice decreases such overbidding by promoting a better understanding of the strategy-proofness of the Vickrey auction. Strategy-proof mechanisms are sometimes criticized because players often fail to recognize the benefit of telling the truth. However, our observations show that introducing advice on the property of strategy-proofness helps them behave “correctly.”

This paper introduces a new matching mechanism that is a hybrid of the two most common mechanisms in school choice, the Boston Mechanism (BM) and the Deferred Acceptance algorithm (DA). BM is the most commonly used mechanism in the field, but it is neither strategyproof nor fair. DA is the mechanism that is typically favored by economists, but it is not Pareto efficient. The new mechanism, the Secure Boston Mechanism (sBM), is an intuitive modification of BM that secures any school a student was initially guaranteed but otherwise prioritizes a student at a school based upon how she ranks it. Relative to BM, theoretical results suggest that sBM is an improvement in terms of strategyproofness and fairness. We present experimental evidence using a novel experimental design that confirms that sBM significantly increases truth-telling and fairness. Relative to DA, theoretical results suggest that sBM can be a Pareto improvement in equilibrium but the efficiency comparison of sBM and DA is theoretically ambiguous. We present simulation evidence that suggests that sBM often does Pareto dominate DA when DA is inefficient, while sBM and DA very often overlap when DA is efficient. Overall, our results strongly support the use of sBM over BM and suggest that sBM should be considered as a viable alternative to DA.

Healthcare technologies are often appraised under considerable ambiguity over the size of incremental benefits and costs, and thus how decision-makers combine unclear information to make recommendations is of considerable public interest. This paper provides a conceptual foundation for such decision-making under ambiguity, formalizing and differentiating the decision problems of a representative policy-maker reviewing the results from an economic evaluation. A primary result is that presenting information to regulators in an incremental cost-effectiveness ratio or cost-effectiveness analysis (CEA) format instead of a net monetary benefit or cost–benefit analysis (CBA) framework may induce errors in decision-making when there exists ambiguity in incremental benefits and decision-makers use well-known decision rules to combine information. Ambiguity in incremental costs or the value of the cost-effectiveness threshold does not distort decision-making under these rules. In reasonable contexts, I show that the CEA framing may result in the approval of fewer technologies relative to CBA framing. I interpret these results as predictions on how the presentation of information from economic evaluations to regulators may frame and distort recommendations. All the results extend to non-healthcare contexts.

A BioBlitz is a rapid and intensive survey of a specific geographic area that brings together experts and often lay participants to assess biodiversity, typically of macrobiota that are easily observed and identifiable on-site. This concept has become popular across taxonomic fields, attracting interest globally to increase knowledge of local biodiversity. Inspired by the success of the approach, we undertook a ‘ParasiteBlitz’ at an unexplored locality (Stono Preserve, Charleston, South Carolina, USA) to determine its feasibility for parasites, whose assessment of diversity is largely neglected worldwide. We assembled a team of parasitologists with complementary expertise. Over 12 days (3 days in each habitat) in April 2023, we intensively screened fishes and aquatic invertebrates for parasites, and sampled sediment and water for environmental DNA (eDNA) metabarcoding from four aquatic habitats: wetland, freshwater pond, brackish impoundment, and tidal creek. We incorporated assistance from non-parasitologists and students. Details on methodologies and results are provided in individual papers in this Special Collection. Traditional methods revealed the presence of ca. 100 species of seven major metazoan parasite taxa, and the eDNA survey yielded over 1,000 amplicon sequence variants identified as parasites, most with sequences unmatched in GenBank, and resulting in only a few species identified as named species in the one-year post-Blitz timeframe we imposed upon ourselves for identification. Limitations and challenges of the ParasiteBlitz are discussed, and our results support that this approach can be effective for rapid discovery of the dimensions of parasite assemblages in an understudied environment and contribute to parasitology knowledge.

This paper builds on Hsieh and Klenow’s (2009) model to offer a refined analysis of how input misallocations impact aggregate total factor productivity (TFP). We enhance the original model by relaxing the assumption of uniform input prices and adopting an econometric approach to estimate parameters using firm-level data. Estimation of model parameters and allocation efficiency is based on the system of input demand and the production function. We use an indirect inference approach to estimate the system to avoid maximum likelihood estimation, which often faces convergence issues, when there are numerous constraints. We demonstrate our model using the US firm-level manufacturing panel data from 1975 to 2010. Our final sample contains 55,518 observations. We divide the manufacturing industry into seven major categories. Our findings indicate that between 1975 and 2010, the average productivity growth rate was 2.8% but could have reached 3.2% without misallocation, highlighting the substantial gains possible through better resource allocation.

Public sector allocative decisions should reflect, as far as possible, the preferences of those affected by the decisions. Conventional benefit–cost analysis (BCA) will simply aggregate individuals’ private willingness-to-pay (WTP) over all affected individuals to estimate the total benefits of a policy that delivers a public good. Given the nature of a public good, it is not unreasonable to consider that an individual may have altruistic preferences over the consumption of the public good by others. In this paper, we set out the theoretical underpinnings for a new citizen-based WTP, informed by political philosophy. Our model extends the standard social utility model (Bergstrom, 2006) of WTP for a public good when individuals are altruists by incorporating a Veil of Ignorance (VoI; Harsanyi, 1955). Our findings show that our WTP (Citizen) correctly includes altruistic as well as distributional preferences of individuals in society into WTP for use in a BCA. When WTP (Citizen) are aggregated for use in a BCA, equal weight is given to each individual’s preference and the BCA will correctly identify potentially Pareto-improving projects in a consistent manner.

Over the last century, the United States has witnessed three approaches to achieving better regulatory outcomes: the removal of “economic” regulations in certain sectors; regulatory impact analysis (RIA) of new “social” regulations; and retrospective analysis of existing regulations. This article reviews the rationale for each approach, the results to date, and the remaining challenges. It finds that both institutional and technical factors influence the success of reform efforts.

The so-called Flyvbjerg database is the largest source of data on the performance of major investment projects. It has generated influential analyses of the magnitude of and reasons for cost overruns and demand shortfalls in major projects. Those analyses have demonstrated, among other things, the systematic presence of large forecast errors in both construction costs and in user demand in the first year of operation. They have also linked those results to the social welfare consequences of the underlying projects, suggesting that the large and systematic forecast errors are indicative of welfare destruction. Given how influential those analyses have been, this paper examines the link between the database, empirical analyses thereof, and social benefit–cost analysis (BCA). To that end, both the measurement of variables in the database and the estimation of forecast errors are contrasted against BCA. The conditions for the estimated forecast errors to approximate those obtained from a BCA are spelled out, and the scope for drawing welfare conclusions based on those estimates is discussed. Furthermore, numerical simulations are presented to explore whether the estimated forecast errors do indeed imply likely welfare destruction. The simulations suggest that as large as the forecast errors are, welfare destruction is no foregone conclusion.

In 2023, the U.S. Office of Management and Budget (OMB) issued guidance documents that specified new procedures for assessing prospective government regulations (Circular A-4) and economic policies more generally (Circular A-94). These revisions to long-standing guidance were not minor updates but shifted policy analyses from an efficiency-oriented perspective to a redistributive approach. OMB broadened the guidelines for reporting distributional consequences of policies and also specified how policy impacts on different income groups should be weighted. The weights assume that the social welfare function is governed by the sum of identical individual utility functions, each of which exhibits a substantial rate of diminishing marginal utility of income. The resulting weights provide a premium for households below the median-income level and a considerable penalty for those at higher-income levels. Application of the weights to property losses creates potentially substantial inefficiencies. If based on current empirical evidence on the income elasticity of the value of a statistical life rather than assuming that there is a complete offset of the weights, application of the weights to mortality risk valuation would generate inequities in protection.

There is a need to value health technologies in a way that accommodates their broader economic impacts and competing approaches for doing so have emerged. The Pareto principle (PP) requires policymakers to resolve intrapersonal trade-offs by deferring to the preferences of the individuals facing those trade-offs. Many broad value frameworks such as cost-utility analysis and its extensions, health-centric multicriteria decision analysis, and poverty-free life expectancy are not sufficiently deferential to these preferences, violating PP. I propose using the health-augmented lifecycle model (HALM) to value health technologies in a way that flexibly incorporates the interactions among health and economic factors – specifically mortality and morbidity risks, paid and unpaid work, consumption, leisure, and public and private transfer inflows and outflows--over the life course. It relies on individual preferences, satisfying PP. It is compatible with cost-benefit analysis, social welfare functions, and equivalent income approaches. I calibrate the HALM for the US setting and apply it to a pediatric vaccine.

Compromised kidney function is associated with an array of environmental contaminants and pathogens that may be considered for regulation. However, there are few valuation estimates for kidney effects for use in benefit–cost analyses, particularly willingness-to-pay estimates. This paper is one of several surveys valuing morbidity developed by the OECD Surveys to elicit Willingness-to-pay to Avoid Chemicals-related negative Health Effects project, which aims to improve the basis for benefit–cost analyses. We report the results of a stated preference survey valuing reduced the risk of symptomatic chronic kidney disease, filling an important gap in the valuation literature and addressing a need for applied benefits analysis of chemical regulation. The survey was administered to representative samples in each of 10 countries: Canada, Chile, China, Denmark, Germany, Italy, Norway, Türkiye, the United Kingdom, and the United States. The mean (median) WTP for an average reduction of 3.5 in 1,000 of the risk of serious kidney disease over 5 years is $2,609 ($764), corresponding to a mean (median) value per statistical case (VSC) of chronic kidney disease of $805,000 ($224,000). The mean VSC varies between $700,000 for Canada and $1,200,000 for Türkiye.

Both Republican and Democratic administrations make regulatory and funding decisions with close reference to benefit–cost analysis (BCA). With respect to regulation, there has been a great deal of academic discussion of BCA and its limits, but almost no attention has been paid to the role of BCA in government funding. That is a serious gap, not least in connection with climate-related risks, such as wildfire, drought, extreme heat, and flooding. Office of Management and Budget (OMB) Circular A-94 sets out guidelines for the BCA required when people are applying to many federal discretionary grant programs. Through Circular A-94, OMB has long required applicants to demonstrate that the benefits of their projects would exceed the costs. But under Circular A-94 as it stood for many years, efficiency-based BCA could produce results that fail to maximize welfare and that are also highly inequitable. The 2023 revision of Circular A-94 focuses more directly on welfare and equity, which are now – not uncontroversially – being brought directly into policy. At the same time, the new Circular A-94 raises fresh questions about how best to promote welfare, and to consider equity, in practice. This article explains the economic foundations for promoting welfare through distributional weighting – and how the old BCA guidance fell short. It then offers recommendations on how to operationalize distributional weighting on the ground specifically for government spending programs – and for BCA more broadly.

Unweighted benefit–cost analysis (BCA) based on aggregate willingness to pay might be, at long last, falling into disrepute, as it is widely recognized that it exhibits a bias toward the wealthy, and as alternatives are appearing more and more practicable. However, the choice of alternatives is often framed in terms of choosing an alternative metric to willingness to pay in money, such as willingness to pay in healthy life years, or a measure of subjective well-being. It is argued in this paper that (i) a simple summation of individuals’ willingness to pay in any numeraire (e.g., money, healthy life years) is bound to generate non-transitivity issues in a similar way as money-based BCA, and (ii) a metric such as subjective well-being involves distributional value judgments that are too specific to reflect the relevant spectrum in the public debate. The “orthodox” weighted BCA method, which links BCA to an underlying social welfare function, offers more flexibility and guarantees transitive choices. Fortunately, in some relevant cases, these various methods may provide similar results, and the main options currently proposed all give greater weight to the worse off in the population than does unweighted BCA.

Some regulations do not only reduce human deaths, injuries, and illnesses; they also protect nonhuman animals. Regulatory Impact Analyses, required by prevailing executive orders, usually do not disclose or explore benefits or costs with respect to nonhuman animals, even when those benefits or costs are significant. This is an inexcusable gap. If a regulation prevents dogs, horses, or cats from being killed or hurt, the benefits should be specified and quantified. This proposition holds even if those benefits are in some sense incidental to the main goal of the regulation. At the same time, turning the relevant benefits into monetary equivalents raises serious challenges, akin to those raised by the valuation of statistical children.

This study develops a generalized evaluation framework that can be used to quantify the financial, economic, stakeholder, and environmental impacts of renewable energy support programs. The application of this framework is demonstrated by evaluating the feed-in tariff (FIT) program for solar distributed energy resources (DER) in Ontario, Canada. Our analysis reveals that Ontario’s FIT program has successfully promoted the adoption of solar DER across communities. However, the program has caused inequitable societal outcomes through a cross-subsidization with a present value of 9 CAD billion, paid for by the electricity consumer base for the benefit of only the 0.06 percent of electricity consumers who could install solar systems. The cost imposed on the Canadian economy ranges from 2.86 to 5.37 CAD billion, depending on the discount rate applied. The sensitivity analysis results indicate that the burden of this program on the Canadian economy would have been reduced by 50 percent if the program had been delayed and implemented in 2016 instead of 2010 due to the declining trend in solar system investment costs. The lessons from this analysis provide insights for designing future environmental and emission reduction policies.

Asthma is a non-communicable and non-curable lung disease that affects 10% of children and 4% of adults worldwide and is associated with an array of environmental contaminants and chemicals. This article offers values suitable for use in cost–benefit analyses of the willingness to pay (WTP) for reduced severity of asthma in adults and children and in reduced probability of getting asthma for these two population groups, all in the context of reducing chemical exposures. To this end, an online survey was administered between November 2021 and May 2022 to 12 727 respondents from seven countries of the Organisation for Economic Co-operation and Development (OECD). This article applies two stated preference methods for eliciting WTP: the contingent valuation method for reduced asthma severity and choice experiments for reduced probability of getting asthma of various severities. The context for such elicitations was a set of household products that contain fewer hazardous chemicals than what is currently available in supermarkets but are more expensive. The study finds that the WTP for reducing asthma severity in adults by one step, e.g. from “moderate plus” to “moderate”, is USD2022 529 per year on average. The parental WTP for reducing asthma severity in their children is USD2022 PPP 948 per year and is on average 1.8 times higher than their WTP for themselves. The mean value of a statistical case (VSC) of adult asthma which would be applied to predictions of new cases of asthma avoided by a regulation equals USD2022 280 000, while the mean VSC of childhood asthma equals USD2022 430 000.

President Biden’s first-day memo “Modernizing Regulatory Review” directs the Office of Management and Budget to “propose procedures that take into account the distributional consequences of regulations… to ensure that regulatory initiatives appropriately benefit and do not inappropriately burden disadvantaged, vulnerable, or marginalized communities.” This paper makes two contributions. First, it discusses how economic analysis can transparently provide the information needed to make value-judgments about what distributional effects are appropriate and inappropriate. Second, it discusses the distributional consequences of regulations that are either designed to reduce internalities or might have the additional benefit of reducing internalities. Examples include tobacco product regulations, appliance energy efficiency standards, and automobile fuel efficiency standards. In many cases, the regulations will increase the prices or decrease the availability of goods that disadvantaged consumers prefer. This paper discussed how to determine whether restricting their consumption opportunities creates net benefits or net costs for disadvantaged consumers. Inframarginal consumers who do not change their consumption face higher opportunity costs but do not receive any benefits from reduced internalities. Empirical challenges include the need to quantify the fraction of inframarginal consumers and the size of the internalities.

Animal welfare is often ignored in decision-making, despite widespread agreement about its importance. This is partly because of a lack of quantitative methods to assess the impacts of policies on humans and nonhumans alike on a common scale. At the same time, recent work in economics, philosophy, and animal welfare science has made progress on the fundamental theoretical challenge of estimating the well-being potential of different species on a single scale. By combining these estimates of each species’ well-being potential with assessments of how various policies impact the quality of life for these species, along with the number of animals affected, we can arrive at a framework for estimating the impact of policies on animal health and well-being. This framework allows for a quantifiable comparison between policies affecting humans and animals. For instance, it enables us to compare human QALYs to animal QALYs tailored to specific species. Hence, the intrinsic value of animal welfare impacts of policies can be monetized on the same scale as market and non-market impact for humans, facilitating benefit–cost analysis. Many challenges remain though, including issues of population ethics, political feasibility, and new complexities in addressing equity and uncertainty.