Under the current practice of benefit-cost analysis, the direct economic benefits produced by a newly built transit facility are assessed based on how it affects travel time and various costs that are associated with transport needs and travel behavior. However, the time-saving-based benefit calculation approach has been questioned and criticized. Given the strong correlation between accessibility and land value, we propose the access-based land value benefit assessment as an alternative, and apply this assessment method to analyzing the Second Avenue Subway project in Manhattan, New York. The primary principle of the access-based method is that the economic value of a transport project’s intangible gains is largely capitalized by nearby properties’ value appreciation, which is directly caused by improved transport accessibility. We find that: (i) the actual travel time saving is lower than originally forecast; (ii) a strong positive correlation between residential property value and job accessibility by transit is observed; (iii) the appreciation in sold property value and rented property value both far exceed total project cost; and (iv) such results support the decision to approve and construct the Second Avenue Subway.

“Uncertain futures” refers to a set of policy problems that possess some combination of the following characteristics: (i) they potentially cause irreversible changes; (ii) they are widespread, so that policy responses may make sense only on a global scale; (iii) network effects are difficult to understand and may amplify (or moderate) consequences; (iv) time horizons are long; and (v) the likelihood of catastrophic outcomes is unknown or even unknowable. These characteristics tend to make uncertain futures intractable to market solutions because property rights are not clearly defined and essential information is unavailable. These same factors also pose challenges for benefit-cost analysis (BCA) and other traditional decision analysis tools. The diverse policy decisions confronting decision-makers today demand “dynamic BCA,” analytic frameworks that incorporate uncertainties and trade-offs across policy areas, recognizing that: perceptions of risks can be uninformed, misinformed, or inaccurate; risk characterization can suffer from ambiguity; and experts’ tendency to focus on one risk at a time may blind policymakers to important trade-offs. Dynamic BCA – which recognizes trade-offs, anticipates the need to learn from experience, and encourages learning – is essential for lowering the likelihoods and mitigating the consequences of uncertain futures while encouraging economic growth, reducing fragility, and increasing resilience.

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.

Much is known about private financial returns to education in the form of higher earnings. Less is known about how much social value exceeds this private value. Associations between education and socially-desirable outcomes are strong, but disentangling the effect of education from other causal factors is challenging. The purpose of this paper is to estimate the social value of one form of higher education. We elicit willingness to pay for the Kentucky Community and Technical College System (KCTCS) directly and compare our estimate of total social value to our estimates of private value in the form of increased earnings. Our earnings estimates are based on two distinct data sets, one administrative and one from the U.S. Census. The difference between the total social value and the increase in earnings is our measure of the education externality and the private, non-market value combined. Our work differs from previous research by focusing on education at the community college level and by eliciting values directly through a stated-preferences survey in a way that yields a total value including any external benefits. Our preferred estimates indicate the social value of expanding the system exceeds private financial value by at least 25% with a best point estimate of nearly 90% and exceeds total private value by at least 15% with a best point estimate of nearly 60%.

Disruptions in routine immunization caused by COVID-19 put African countries with large vaccine-preventable disease burdens at high risk of outbreaks. Abbas et al. (2020) showed that mortality reduction from resuming immunization outweighs excess mortality from COVID-19 caused by exposure during immunization activities. We leverage these estimates to calculate benefit-cost ratios (BCRs) of disrupted immunization and apply cost of illness (COI) and value of statistical life-year (VSLY) approaches to estimate the cost of excess child deaths from eight vaccine-preventable diseases. BCRs were computed for each country, vaccine, and Expanded Program on Immunization visit. Secondary estimates that include the cost of providing immunization are presented in scenario analysis. Suspended immunization may cost $4949 million due to excess mortality using the COI approach, or $34,344 million using the VSLY approach. Likewise, excess COVID-19 deaths caused by exposure from immunization activities would cost $53 and $275 million using the COI and VSLY approaches, respectively. BCRs of continuing routine immunization are 94:1 using COI and 125:1 using VSLY, indicating that the economic costs of suspending immunization exceed that of COVID-19 deaths risked by routine immunization. When including the costs of providing routine immunization during the COVID-19 pandemic, the BCRs are 38:1 and 97:1 using the COI and VSLY approaches, respectively.

The risk of dying in fires in nursing homes is six times the risk of dying in fires at home in Sweden. One way to reduce this risk is to install fire sprinklers. This study measures the benefits using value of full lives, life years and quality adjusted life years (QALYs) for deaths and injuries. The results show that sprinklers are cost-effective in newly built nursing homes no matter what value of life is used. However, if sprinklers are installed in already existing buildings, they are cost-effective only if the value of a statistical life is used.

The current challenge of education systems is learning. Across low-income countries (LICs) and lower-middle-income countries (LMCs), 62 % of 10-year-olds could not read at a minimally sufficient level in 2015. This study provides an overview of recent spending on education and its correlation with learning outcomes. We show that the relationship between education spending and learning is historically weak. From 2000 to 2015, LICs and LMCs increased spending on education in primary schools by ~$137 per student, an 80 % inflation-adjusted increase, with no corresponding change on the average learning outcomes. We then conduct a benefit-cost analysis of candidate interventions that could increase learning at low cost. Two interventions – structured pedagogy and, teaching at the right level, with and without a technology component generate large benefit-cost ratios. If deployed uniformly to reach 90 % of the 467 million students in LICs and LMCs, these interventions would cost on average $18 per student per year or $7.6 billion annually, generating $65 in benefits for every $1 spent. The economic logic behind this finding is that the hard and costly work of getting children into primary schools has mostly been accomplished, leaving open the possibility of learning interventions that improve the efficiency of the existing education system at low cost. Our results show that increasing education expenditure by just 6 % could increase learning by 120 % if directed toward these highly cost-effective interventions.

Under the 1990 Clean Air Act Amendments, the U.S. Environmental Protection Agency (EPA) was required to establish standards limiting air toxics emissions from industrial plants. This paper examines the effects of five of the largest-cost rules issued by EPA in the initial round of air toxics rulemaking over the 1995 to 2000 period. Our estimates suggest that plants in the printing and publishing and pulp and paper industries realized important reductions in their air toxics emissions in the period between publication of the final rule and the effective date for compliance with the rule – although the reduction in air toxics emissions by pulp and paper mills fell short of EPA’s ex ante projections. However, our estimates also suggest that plants in three other industries – petroleum refining, pharmaceuticals, and wood furniture – achieved little or no additional reduction in air toxics emissions over the compliance period in response to EPA’s rules. Finally, the paper explores steps that EPA should take in setting up future retrospective analyses.

Two important developments in recent policy analysis are behavioral economics and subjective-well-being (SWB) surveys. What is the connection between them? Some have suggested that behavioral economics strengthens the case for SWB surveys as a central policy tool, e.g., in the form of SWB-based cost-benefit analysis. This article reaches a different conclusion. Behavioral economics shows that individuals in their day-to-day, “System 1” behavior are not expected utility (EU-) rational – that they often fail to comply with the norms of rationality set forth by EU theory. Consider now that the standard preference-based view of individual well-being looks to individuals’ rational preferences. If the findings of behavioral economics are correct, an individual’s answer to a question such as “How satisfied are you with your life?” is not going to tell us much about her rational (EU-compliant) preferences. Behavioral economics, by highlighting widespread failures of EU rationality, might actually argue for an objective-good (non-preference-based) view of well-being. However (except in the limiting case of an objective-good view positing a single mentalistic good, happiness), SWB surveys will not be strong evidence of well-being in the objective-good sense. In short, SWB surveys are no “magic cure” for the genuine difficulties in inferring rational preferences and measuring well-being underscored by behavioral economics.

The Office of Information and Regulatory Affairs (OIRA) in the Executive Office of the President coordinates the federal government’s regulatory agenda, reviews executive branch agencies’ draft regulations, and oversees government-wide information quality, peer review, privacy, and statistical policies. Remarkably, its regulatory oversight functions, and the benefit-cost framework underlying them, have not changed significantly through six very different presidential administrations. This article examines the evolution of executive regulatory oversight and analysis from the 1970s to today, exploring the reasons for its durability and whether the current imposition of a regulatory budget challenges the bipartisan nature of regulatory practice.

A commonly held perception is that an elite graduate degree can “scrub” a less prestigious but less costly undergraduate degree. Using data from the National Survey of College Graduates from 2003 to 2017, this article examines the relationship between the status of undergraduate degrees and earnings among those with elite postbaccalaureate degrees. Few graduates of non-selective institutions earn postbaccalaureate degrees from elite institutions, and even when they do, undergraduate institutional prestige continues to be positively related to earnings overall as well as among those with specific postbaccalaureate degrees including business, law, medicine, and doctoral. Among those who earn a graduate degree from an elite institution, the present value of the earnings advantage to having both an undergraduate and a graduate degree from an elite institution generally greatly exceeds any likely cost advantage from attending a less prestigious undergraduate institution.

The Biden administration has made equity a priority when issuing regulations, encouraging agencies to ensure that their regulations appropriately benefit and do not inappropriately burden disadvantaged groups. But scholarly examinations of agencies’ practices to date on understanding the distributional consequences of their regulations and on promoting equity have revealed significant gaps. In particular, agencies pay very little attention to the incidence of the costs of their regulations. The U.S. Environmental Protection Agency, for example, rarely considers the incidence of regulatory costs among disadvantaged groups, despite being an agency that conducts relatively complete benefit–cost analyses and explicitly analyzes environmental justice implications of its regulations. But this cost-blindness is a mistake; it presents a missed opportunity to use the current equity-focused momentum to make real improvements for disadvantaged groups that could have long-lasting effects. This essay calls for agencies to give more attention to the incidence of regulatory costs in order to identify needs and opportunities for grants and investments to disadvantaged groups. This approach could provide much-needed direction for a program like the Biden administration’s Justice40 initiative.

Law enforcement agencies are adopting a variety of new surveillance technologies at a fast pace. These technologies could have substantial benefits in terms of public safety, but, for many of them, their ability to reduce crime is unknown. Although a small experimental literature addresses some of these technologies, many of the implementations have been too small to provide an accurate measurement of their potential. In this paper, I explore the advantages and make general suggestions about the use of quasi-experimental methodologies in estimating the public safety benefits of police technology. I also consider the specific case of license plate readers and provide some examples of difference-in-differences approaches that could be used to study their efficacy.

This paper sets out to explore to what extent integrating employment effects, equity, and risk aversion within cost–benefit analysis (CBA) affect the economic appraisal of a climate change adaptation project designed to protect against flood risk in a region of Bilbao (Basque Country, Spain). Four CBAs are conducted: (i) a standard CBA; (ii) a standard CBA considering equity; (iii) a standard CBA considering equity and employment; and (iv) a standard CBA considering equity, employment and risk aversion. All CBAs are conducted using a time frame of 2014–2080 and considering a 100-year return period under a middle of the road emission scenario (RCP4.5). A sensitivity analysis is also undertaken. Results suggest that the economic efficiency of the adaptation investment is contingent on what types of considerations are included within CBA. Integrating elements of employment, equity and risk aversion can strengthen or weaken the case for action (leading to higher or lower net-present values) and (depending on the discount rate chosen) may even be the deciding factor for determining whether a particular action should be carried out or not (whether the net-present value is positive or negative).

In 1948, the RAND Corporation, formed to connect military planning with research and development decisions, became an independent nonprofit organization. Before then, cost-effectiveness analysis, benefit-cost analysis, and systems analysis had no established home in the federal government. In the 1950s, under the leadership of Charles Hitch, Chief, RAND Economics, undertook a program of activities they called “systems analysis,” including evaluation of the costs and effectiveness of weapon systems. In 1961, Robert McNamara appointed Hitch to be the Comptroller of the Department of Defense and invited Hitch to carry out his vision he described as “Programming and Systems Analysis.” Programming became the Planning, Programming, Budgeting System (PPBS) and the Five-Year Defense Program that linked strategies to forces to budgets. Systems Analysis assisted the Secretary to make choices of weapon systems and strategies. In 1965, Hitch returned to California and ultimately became President of the university. McNamara wanted Systems Analysis to report directly to him, and on his recommendation, President Lyndon Johnson appointed me Assistant Secretary for Systems Analysis. In 1966, the President directed that all departments in the executive branch establish offices based on the Systems Analysis model. In 1967, Henry S. Rowen became President of the RAND Corporation. He broadened RAND’s scope beyond the military to include Health Services, education, urban problems including homelessness, ethics in scientific research, and climate research. In 1970, Rowen led the establishment of the Pardee RAND Graduate School, offering a doctoral degree in Public Policy Analysis to extend widely the application of the RAND Systems Analysis approach to many fields.

Given that President Obama’s Executive Orders on regulation have emphasized the importance of retrospective analysis and review of existing federal rules, I survey the state of retrospective analysis and review of federal regulations. I first ask how much is known about the economic merit of past federal regulatory decisions, based on retrospective economic analyses of their effects. I review reports of the Office of Management and Budget and related literature, but unlike those reports I find only five rules, issued by the National Highway Traffic Safety Administration (NHTSA), for which retrospective analyses provide estimates of both costs and reasonably good proxies for benefits (e.g., adverse health effects avoided). Other retrospective studies of federal rules estimate are incomplete, estimating only the compliance costs, or only the benefits, or only costs and measures of effectiveness, like emissions reductions, which do not closely relate to people’s welfare.

I also seek to explain differences in the practice of retrospective analysis and review between NHTSA, which appears to have the best record of retrospective analyses among federal agencies, and the Environmental Protection Agency (EPA), an important regulatory agency. I find that NHTSA regularly conducts such analyses and reviews, while EPA rarely does retrospective analysis and presents rulemakings that look like business as usual as being the result of a retrospective review. I analyze the role of data limitations, statutory authority, and institutional incentives in influencing the different behaviors of these two agencies. I conclude that differences in data availability and in particular the lack of relevant control groups, are an important barrier to retrospective analysis at EPA. This data deficiency, combined with important restrictions on EPA’s ability to consider information on net benefits or cost-effectiveness in its rule-making, are together the biggest hindrance to generating better information about the effects of its rules. I conclude with recommendations intended to generate more measurement of the actual effects of regulations.

The goal of natural resource damage assessment (NRDA) is to compensate the public for losses to natural resources from past or ongoing hazardous releases, including losses that may persist into the future. Compensation is delivered in the form of restoration projects. Resolving NRDA liability requires balancing losses and restoration benefits over multiple decades and converting them into a present value for calculating appropriate damages. For the past two decades, NRDA practitioners have used a real discount rate of 3 % to convert losses and benefits to a present value equivalent. That rate was based, in part, on real historical yields on risk-free debt (e.g., the real rate of return on 3-month Treasury bills). Declining interest rates on risk-free debt in recent years has led to suggestions to reexamine the historical consensus discount rate. This paper reviews two alternative conceptual paradigms for selecting a discount rate in NRDA cases: the social rate of time preference and discount rates for tort cases. We summarize historical data for empirically implementing the two paradigms and discuss the ramifications of the different options. Based on our review, we suggest maintaining the 3 % consensus as a practical solution to a range of empirical candidates within the two conceptual paradigms.

Our research investigates the effects of residential energy efficiency audit programs on subsequent household electricity consumption. Here there is a one-time interaction between households, which participate voluntarily, and the surveyors. Our research objective is to determine whether and to what extent the surveys lead to behavioral changes. We then examine how persistent the intervention is over time and whether the effects decay or intensify. The main evaluation problem here is survey participants’ self-selection, which we address econometrically via several non-parametric estimators involving kernel-based propensity-score matching. In the first method we use difference-in-differences (DID) estimation. Our second estimator is quantile DID, which produces estimates on distributions. The comparison group consists of households who were not yet participating in the survey but participated later. Our evidence is that the customers who participated in the survey reduced their electricity consumption by about 7%, on average compared to customers who had not yet participated in the survey. Considering the total number of high-usage households participating in the survey in 2009, we estimate that electricity consumption was reduced by an aggregate of 2 million kWh per year, which is approximately equal to the monthly consumption of 3500 typical households in California with an estimated 1527 metric tons less of carbon dioxide emissions. Because the energy audit program is inexpensive ($10–$20 per household) a key issue is that while the program is cost-effective, is it regressive? We find that as the quantiles of the outcome distribution increase, high-use households save proportionally less electricity than do low-use customers. Overall, our results imply that program designers can better target low-use and low-income households, because they are more likely to benefit from the programs through energy savings.

Traditionally, benefit-cost analyses focus on average benefits and average costs. However, heterogeneous treatment effects and/or costs are most often present, which means that there is an efficiency potential hidden in the implementation of public programs, if policies can be targeted at those who, net of costs, benefit the most. We introduce efficiency potential defined as the ratio between the net benefit achieved under perfect selection of the individuals with significant positive net benefit of program participation, and the actually realized benefits net of costs. Using data from a randomized control-trial experiment of a Danish return-to-work program combined with rich administrative records and survey data, we find that there is indeed a potential for increased efficiency. Results from the treatment literature indicate that, generally, it may be difficult to harvest the full potential. Our application corroborates this finding.

The value of the risk to life is a key element for benefit-cost analysis, enabling more rational public policy decisions in diverse areas as environmental, health, and crime. We value the risk to life in the context of crime using a discrete choice experiment (CE). The method has clear advantages in that it applies to the whole population and does not require vast data from labor markets, for example. Such data are not always available even in developed economies. Combining the stated preference approach with contingent valuation (CV), CE offer advantages yet to be explored in the context of crime. We demonstrate the application in a developing economy, where similar valuations are not available. The best estimate obtained for Argentina is an average of 1.5 million in 2015 US dollars per statistical life with a confidence interval ($1.1–$2.3). This result is consistent with estimates for the developed world, after appropriate transfer. We also analyze demographic factors in the risk to life, finding a positive influence of income, risk aversion, previous victimization experience and family size on the value of a statistical life, as well as a negative impact of individualism.