Conventional benefit–cost analysis plays an important role in informing policy decisions, encouraging systematic investigation of the positive and negative impacts of alternative policies. It is based on strong normative assumptions, however. To measure individual wellbeing, the conventional approach relies on individuals’ willingness to exchange their income for the outcomes they experience. To measure societal welfare, it relies on simple aggregation of these values across individuals. In this “Ethics and Benefit–Cost Analysis” special issue, we explore alternative conceptions of individual and societal welfare, their application, and the implications, from both practical and ethical perspectives.

Jerry Ellig was a unique character and a great economist. He believed in one thing, using economic analysis to help solve problems. He became an expert at Regulatory Impact Analyses and how they helped governments to choose the best option to do just that, all the while recognizing the problems that government has with necessarily much less information than markets. He believed in holding governments to account for achieving results including periodic lookbacks to see what they were doing. What was great about Jerry is that he had fun doing all of this both on the job and at his beloved Tiki bars.

In 2017, the European Union Emissions Trading System underwent a policy intervention that resulted in a surge in carbon prices. Using this setting as a quasi-natural experiment, we focus on employment, productivity, and emission outcomes among covered enterprises. Results show that emission-intensive private firms, particularly those with financial constraints, are more likely to downsize by divesting production assets, reducing both workforce and emissions. Smaller, cash-strapped listed firms are also prone to downsize by decreasing their operating leverage while maintaining emission output and asset levels. Positive productivity outcomes indicate that both private and listed firms become leaner postintervention.

In response to tire failures and vehicle rollover accidents, most notably those experienced by Ford Explorers with Firestone Tires, the 106th Congress and President Clinton enacted the Transportation Recall Enhancement, Accountability, and Documentation (TREAD) Act of 2000. Section 13 of the TREAD Act requires vehicle manufacturers to install tire pressure monitoring systems (TPMS) that alert drivers when a tire is significantly under-inflated. This paper first discusses the political economy that ultimately led to the final TREAD Act TPMS regulation. Then, relying on the variation of model-year TPMS introduction, I investigate whether and to what extent TPMS reduces all vehicle fatalities and those associated with tire failure and improper inflation. I find that the introduction of TPMS is associated with just over 11 fewer tire failure-related deaths per year, resulting in a net benefit of −$752 million to −$1,876 million (in $2001) per year. I find no change in the number of tire inflation-related fatalities with the introduction of TPMS.

Greenwashing cases holding businesses to account for false or misleading eco-claims are an increasingly visible component of the business and human rights landscape globally. In the European Union (EU), the Unfair Commercial Practices Directive is the centrepiece of regulation for business-to-consumer claims. Within the European Green Deal initiative, the EU is revising this framework, first with the Directive to ‘Empower Consumers for the Green Transition,’ and second the pending proposal for a ‘Green Claims Directive,’ introducing detailed requirements on the substantiation and communication of ‘green claims’ to consumers. If fully adopted, this fundamental reform will impose greater restraints on the discretion of any authority charged with the assessment of green claims and provide more uniform criteria across the EU, resulting in more accurate environmental claims and greater clarity for consumers and businesses alike.

This article documents a trend of declining flexibility in share repurchase policies over the last 4 decades. We show that repurchases have become particularly sticky for firms with repurchase programs in place. We also exploit the additional inflexibility within existing repurchase programs to show that repurchase stickiness can have real effects for firms. Using the 2008 financial crisis as a shock to firms’ ability to raise capital, we find that firms with ongoing share repurchase programs ending after Dec. 2007 reduced investment, employment, and R&D spending by more than similar firms with programs ending before the onset of the crisis.

This article provides the first comprehensive evidence that the return extrapolation behavior of investors leads to biases in the expectations of volatility. Lower past returns are associated with higher expectations of volatility when using the physical, risk-neutral, and survey measures to estimate volatility expectations. Consistent with the return extrapolation framework, recent past returns have a larger impact than distant past returns on volatility expectations. Biases in volatility expectations are i) distinct from extrapolating past realized volatility, ii) asymmetrically induced by recent past negative returns, and iii) lead investors to pay more to insure against the perceived higher expected volatility.

We show theoretically and empirically that the cross-section of stock return idiosyncratic volatilities contains useful information about the ICAPM. We construct a proxy cross-sectional bivariate idiosyncratic volatility (CBIV) for the covariance risk between the market and the unobserved hedge portfolio under the ICAPM. Consistent with the ICAPM pricing relation, CBIV is a robust and significant predictor of the equity risk premium. We further show that the return predictability of the tail index in Kelly and Jiang (2014) can be explained by the ICAPM covariance risk.

This study utilizes U.S. Patent Office data to explore potential improvements in the patent examination process through machine learning. It shows that integrating machine learning with human expertise can increase patent citations by up to 26%. Using machine learning predictions as benchmarks, I find that the early expiration rate of granted patents positively correlates with examiners’ false acceptance rates. These errors negatively impact public companies’ operational performance and reduce successful IPO or M&A exits for private firms. Overall, this study highlights significant social and economic benefits of incorporating machine learning as a robo-advisor in patent screening.

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.