The current method used by the US Government to calculate benefits and costs does not accurately measure the monetary value of some regulations. The problem is that the method fails to recognize the possibility that individual valuations, reflecting judgments in a relatively isolated, uncoordinated situation, might be significantly different from individual valuations in a situation of coordination. For example, people might be willing to pay $X for a good, supposing that other people have that good, but might be willing to pay $Y to abolish that good, supposing that no one will have that good. Or people might be willing to pay $X to protect members of an endangered species in their individual capacity, but far more than $X for the same purpose, assuming that many others are paying as well; one reason may be that an individual expenditure seems futile. We sketch, identify, and explain this unmeasured value, which we define as coordination value, meant as an umbrella concept to cover several categories of cases in which individual valuation measured in the uncoordinated state might be inadequate. Changing the methodology of benefit–cost analysis to consider coordination value would present serious empirical challenges, but would eliminate the estimation error.

Do people like financial nudges? To answer that question we conducted a pre-registered survey presenting people with 36 hypothetical scenarios describing financial interventions. We varied levels of transparency (i.e., explaining how the interventions worked), framing (interventions framed in terms of spending, or saving), and ‘System’ (interventions could target either System 1 or System 2). Participants were a random sample of 2,100 people drawn from a representative Australian population. All financial interventions were tested across six dependent variables: approval, benefit, ethics, manipulation, the likelihood of use, as well as the likelihood of use if the intervention were to be proposed by a bank. Results indicate that people generally approve of financial interventions, rating them as neutral to positive across all dependent variables (except for manipulation, which was reverse coded). We find effects of framing and System. People have strong and significant preferences for System 2 interventions, and interventions framed in terms of savings. Transparency was not found to have a significant impact on how people rate financial interventions. Financial interventions continue to be rated positive, regardless of the messenger. Looking at demographics, we find that participants who were female, younger, living in metro areas and earning higher incomes were most likely to favor financial interventions, and this effect is especially strong for those aged under 45. We discuss the implications for these results as applied to the financial sector.

In 1921, John Maynard Keynes and Frank Knight independently insisted on the importance of making a distinction between uncertainty and risk. Keynes referred to matters about which ‘there is no scientific basis on which to form any calculable probability whatever’. Knight claimed that ‘Uncertainty must be taken in a sense radically distinct from the familiar notion of Risk, from which it has never been properly separated’. Knightian uncertainty exists when people cannot assign probabilities to imaginable outcomes. People might know that a course of action might produce bad outcomes A, B, C, D and E, without knowing much or anything about the probability of each. Contrary to a standard view in economics, Knightian uncertainty is real, and it poses challenging and unresolved issues for decision theory and regulatory practice. It bears on many problems, potentially including those raised by artificial intelligence. It is tempting to seek to eliminate the worst-case scenario, and thus to adopt the maximin rule, which might seem to be the appropriate approach under Knightian uncertainty. But serious problems arise if eliminating the worst-case scenario would (1) impose high risks and costs, (2) eliminate large benefits or potential ‘miracles’ or (3) create uncertain risks.

Many moral judgments are rooted in the outrage heuristic. In making such judgments about certain personal injury cases, people’s judgments are both predictable and widely shared. With respect to outrage (on a bounded scale of one to six) and punitive intent (also on a bounded scale of one to six), the judgments of one group of six people, or 12 people, nicely predict the judgements of other groups of six people, or 12 people. Moreover, outrage judgments are highly predictive of punitive intentions. Because of their use of the outrage heuristic, people are intuitive retributivists. People care about deterrence, but they do not think in terms of optimal deterrence. Because outrage is category-specific, those who use the outrage heuristic are likely to produce patterns that they would themselves reject, if only they were to see them. Because people are intuitive retributivists, they reject some of the most common and central understandings in economic and utilitarian theory. To the extent that a system of criminal justice depends on the moral psychology of ordinary people, it is likely to operate on the basis of the outrage heuristic and will, from the utilitarian point of view, end up making serious and systematic errors.

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.

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.

People buy some goods that they do not enjoy and wish did not exist. They might even be willing to pay a great deal for such goods, whether the currency involves time, commitment or money. One reason involves signaling to others; so long as the good exists, nonconsumption might give an unwanted signal to friends or colleagues. Another reason involves self-signaling; so long as the good exists, nonconsumption might give an unwanted signal to an agent about himself or herself. Yet another reason involves a combination of network effects and status competition; nonconsumption might deprive people of the benefits of participating in a network and thus cause them to lose relative position. With respect to real-world goods (including activities) of this kind, there is typically heterogeneity in relevant populations, with some people deriving positive utility from goods to which other people are indifferent, or which other people deplore. Efforts to measure people’s willingness to pay for goods of this kind will suggest a welfare gain, and possibly a substantial one, even though the existence of such goods produces a welfare loss, and possibly a substantial one. Collective action, private or public, is necessary to eliminate goods that people consume but wish did not exist. Legal responses here might be contemplated when someone successfully maneuvers people into a situation in which they are incentivized to act against their interests, by consuming a product or engaging in an activity they do not enjoy, in order to avoid offering an unwanted signal. Prohibitions on waiving certain rights might be justified in this way; some restrictions on uses of social media, especially by young people, might be similarly justified.

Why are take up rates incomplete or low when the relevant opportunities are unambiguously advantageous to people who are eligible for them? How can public officials promote higher take up of opportunities? All over the world, these are challenges of the first order. There are three primary barriers to take up: learning costs, compliance costs, and psychological costs. These costs lower the net expected benefit of opportunities, and reduce participation in otherwise advantageous programs. Fully rational agents would consider these costs in their take up decisions, and in light of behavioral biases, such costs loom especially large and may seem prohibitive. Experimental and other evidence suggest methods for reducing the barriers to take up and the effects of behavioral biases. Use of such methods has the potential to significantly increase access to a wide range of opportunities that would increase individual well-being and social welfare.

Chater & Loewenstein, superb and distinguished social scientists, have misfired. Their complaint is baseless: In the real world of policymaking, behavioral science is mostly being used to reform systems, not to alter individual behavior. Nor is there empirical support for the proposition that interventions aimed at helping individuals make systemic reform less likely.