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.

Examination of estimates of the income elasticity of the value of a statistical life based on international stated preference studies yields an average between 0.94 and 1.05 overall and 0.65 and 0.80 after controlling for covariates. Quantile regression estimates indicate that the income elasticity is about 0.55 for more affluent countries and 1.0 for lower income nations, i.e., those countries that have estimates of the value of a statistical life below $2 million or per capita income levels below $3212. The estimates distinguish the values of the income elasticity across country either by income level or by the value of a statistical life. These elasticities are similar to those found in revealed preference labor market studies. The estimates are robust, controlling for possible sample selection bias and the influence of covariates, such as the type of risk.

Agencies are frequently required to analyze the impact of their decisions, particularly in the context of regulatory policy. Advocates of analysis have championed the transparency benefit of these requirements. But there has been very little attention paid to the effectiveness of analysis in spurring useful participation in practice. This article examines how analysis can hinder and motivate public participation. Interviews were conducted with 48 analysts (including economists, risk assessors, and environmental impact assessors). In addition I conducted a case study on a unique method for using analysis in partnership with participation, the use of panels of small business owners to evaluate a regulatory proposal by the Occupational Safety and Health Administration (OSHA). I find that participation in agency decisions as a result of traditional analytical requirements has been very uneven. Examples of success exist but so do cases where participation may be deterred by the density and complexity of analysis as well as cases of massive letter-writing campaigns ignored by decision-makers. I recommend a move toward simpler and earlier analysis, and the use of panels (such as described in the case study) to better take advantage of the potential synergy between analysis and participation.

The European Union’s regulation for chemical safety (REACH) addresses the registration, evaluation, assessment, and consequent authorization (or restriction) of chemicals which are potentially harmful for both public health and the environment. The current study aims at ascertaining the costs and benefits of the REACH framework for the Austrian economy under major uncertainties, and draws on a wide range of databases on public and workplace health, chemical accidents in households, and the potential environmental impacts of harmful chemicals.

The uncertainties in the REACH system assessments of the effects of chemicals on health lie not only in the insufficiency of scientific evidence but also in the economic evaluation of effects on health, especially in regard to the value of statistical life (VSL), and the economic value of diseases attributed to chemicals.

This benefit-cost analysis (BCA) of the REACH system in Austria therefore takes into account these manifold uncertainties by designing a conservative baseline scenario and by varying all determinants in comprehensive sensitivity analyses. Projected over a period of about 30 years, this paper provides evidence that the REACH system most probably leads to net benefits for the Austrian economy (benefit-cost ratio of about 10.6) even though many benefits are still highly uncertain or unknown.

Food and Drug Administration (FDA) published a final regulation in 2004 that requires pharmaceutical manufacturers to place linear bar codes on certain human drug and biological products. The intent was that bar codes would be part of a system where healthcare professionals would use bar code scanning equipment and software to electronically verify against a patient’s medication regimen that the correct medication is being given to the patient before it is administered, which could ultimately reduce medication errors. In the 2004 prospective regulatory impact analysis, FDA anticipated that the rule would stimulate widespread adoption of bar code medication administration technology among hospitals and other facilities, thereby generating public health benefits in the form of averted medication errors. FDA estimated that annualized net benefits would be $5.3 billion. In this retrospective analysis, we reassess the costs and benefits of the bar code rule and our original model and assumptions. Employing the most recent data available on actual adoption rates of bar code medication administration technology since 2004 and other key determinants of the costs and benefits, we examine the impacts of the bar code rule since its implementation and identify approaches to improve the accuracy of future analyses. In this retrospective study, we use alternative models of health information technology diffusion to create counterfactual scenarios against which we compare the benefits and costs of the bar code rule. The magnitudes of the costs and benefits of the 2004 rule are sensitive to assumptions about the counterfactual technology adoption rate, with the upper-bound range of calculated annualized net benefits between $2.7 billion and $6.6 billion depending on the baseline scenario considered.

Disclaimer: The findings, interpretations, and conclusions expressed in this article are those of the authors in their private capacities, and they do not represent the views of the Food and Drug Administration.

Practitioners of benefit-cost analysis face many difficulties. Despite the best training, guidance, and intentions, practitioners can stumble: actual benefit-cost analysis is hard and mistakes get made. Over the years, I have collected the mistakes I have seen in actual benefit-cost analyses. Many of the same mistakes occur over and over: the pitfalls of practical benefit-cost analysis. In this paper, I describe common pitfalls and suggest ways to avoid them.