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.

The study provides comparative risk analyses of Australia’s three Victorian dairy regions. Historical data were used to identify business risk and financial viability. Multivariate distributions were fitted to the historical price, production, and input costs using copula models, capturing non-linear dependence among the variables. Monte Carlo simulation methods were then used to generate cash flows for a decade. Factors that influenced profitability the most were identified using sensitivity analysis. The dairies in the Northern region have faced water reductions, whereas those of Gippsland and South West have more positive indicators. Our analysis summarizes long-term risks and net farm profits by utilizing survey data in a probabilistic manner.

Nuclear war is clearly a global catastrophic risk, but it is not an existential risk as is sometimes carelessly claimed. Unfortunately, the consequence and likelihood components of the risk of nuclear war are both highly uncertain. In particular, for nuclear wars that include targeting of multiple cities, nuclear winter may result in more fatalities across the globe than the better-understood effects of blast, prompt radiation, and fallout. Electromagnetic pulse effects, which could range from minor electrical disturbances to the complete collapse of the electric grid, are similarly highly uncertain. Nuclear war likelihood assessments are largely based on intuition, and they span the spectrum from zero to certainty. Notwithstanding these profound uncertainties, we must manage the risk of nuclear war with the knowledge we have. Benefit-cost analysis and other structured analytic methods applied to evaluate risk mitigation measures must acknowledge that we often do not even know whether many proposed approaches (e.g., reducing nuclear arsenals) will have a net positive or negative effect. Multidisciplinary studies are needed to better understand the consequences and likelihood of nuclear war and the complex relationship between these two components of risk, and to predict both the direction and magnitude of risk mitigation approaches.

Managing large-scale, geographically distributed, and long-term risks arising from diverse underlying causes – ranging from poverty to underinvestment in protecting against natural hazards or failures of sociotechnical, economic, and financial systems – poses formidable challenges for any theory of effective social decision-making. Participants may have different and rapidly evolving local information and goals, perceive different opportunities and urgencies for actions, and be differently aware of how their actions affect each other through side effects and externalities. Six decades ago, political economist Charles Lindblom viewed “rational-comprehensive decision-making” as utterly impracticable for such realistically complex situations. Instead, he advocated incremental learning and improvement, or “muddling through,” as both a positive and a normative theory of bureaucratic decision-making when costs and benefits are highly uncertain. But sparse, delayed, uncertain, and incomplete feedback undermines the effectiveness of collective learning while muddling through, even if all participant incentives are aligned; it is no panacea. We consider how recent insights from machine learning – especially, deep multiagent reinforcement learning – formalize aspects of muddling through and suggest principles for improving human organizational decision-making. Deep learning principles adapted for human use can not only help participants in different levels of government or control hierarchies manage some large-scale distributed risks, but also show how rational-comprehensive decision analysis and incremental learning and improvement can be reconciled and synthesized.

There is strong interest in both developing and developed countries toward expanding health insurance coverage. How should the benefits, and costs, of expanded coverage be measured? While the value of reducing the financial risks that result from insurance coverage have long been recognized, there has been less attention in how best to measure such benefits. In this paper, we first provide a framework for assessing the financial value from health insurance. We focus on three distinct potential benefits: Pooling the risk of unexpected medical expenditures between healthy and sick households, redistributing resources from high- to low-income recipients and smoothing consumption over time. We then use this theoretical framework and an illustrative example to provide practical guidelines for benefit-cost analysis in capturing the full benefits (and costs) of expanding health insurance coverage. We conclude by considering other potential financial effects of broad insurance coverage, such as the ability to consolidate purchases and thus lower input prices.

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.

This paper builds a multi-generation household model of consumption, accumulation, and risk management to assess the dynamic consequences of climate risk exposure. The model incorporates long-term impacts of consumption shortfalls, induced by optimal ‘asset smoothing’ coping behavior of the vulnerable, on poverty. The analysis shows the long-term level and depth of poverty can be improved by incorporating ‘vulnerability-targeted social protection’ into a conventional social protection system. The paper further shows insurance-based vulnerability-targeted social protection dominates (in economic growth and poverty reduction measures) both in-kind transfers and asset-based vulnerability-targeted protection. We then stress test social protection mechanisms and find the relative performance of insurance-based vulnerability-targeted social protection improves when subjected to current pessimistic projections about increasing drought risk. However, if drought risk increases beyond current climate change projections, then even the vulnerability-targeted policy loses its ability to stabilize the extent and depth of poverty.

Regulatory impact analyses (RIAs) weigh the benefits of regulations against the burdens they impose and are invaluable tools for informing decision makers. We offer 10 tips for nonspecialist policymakers and interested stakeholders who will be reading RIAs as consumers.

1. 1. Core problem: Determine whether the RIA identifies the core problem (compelling public need) the regulation is intended to address.

2. 2. Alternatives: Look for an objective, policy-neutral evaluation of the relative merits of reasonable alternatives.

3. 3. Baseline: Check whether the RIA presents a reasonable “counterfactual” against which benefits and costs are measured.

4. 4. Increments: Evaluate whether totals and averages obscure relevant distinctions and trade-offs.

5. 5. Uncertainty: Recognize that all estimates involve uncertainty, and ask what effect key assumptions, data, and models have on those estimates.

6. 6. Transparency: Look for transparency and objectivity of analytical inputs.

7. 7. Benefits: Examine how projected benefits relate to stated objectives.

8. 8. Costs: Understand what costs are included.

9. 9. Distribution: Consider how benefits and costs are distributed.

10. 10. Symmetrical treatment: Ensure that benefits and costs are presented symmetrically.

Investment decision rules in risk situations have been extensively analyzed for firms. Most research focus on financial options and the wide range of methods based on dynamic programming currently used by firms to decide on whether and when to implement an irreversible investment under uncertainty. The situation is quite different for public investments, which are decided and largely funded by public authorities. These investments are assessed by public authorities, not through market criteria, but through public Cost-Benefit Analysis (CBA) procedures. Strangely enough, these procedures pay little attention to risk and uncertainty. The present text aims at filling this gap. We address the classic problem of whether and when an investment should be implemented. This stopping time problem is established in a framework where the discount rate is typically linked to GDP, which follows a Brownian motion, and where the benefits and cost of implementation follow linked Brownian motions. We find that the decision rule depends on a threshold value of the First Year Advantage/Cost ratio. This threshold can be expressed in a closed form including the means, standard deviations and correlations of the stochastic variables. Simulations with sensible current values of these parameters show that the systemic risk, coming from the correlation between the benefits of the investment and economic growth, is not that high, and that more attention should be paid to risks relating to the construction cost of the investment; furthermore, simple rules of thumb are designed for estimating the above-mentioned threshold. Some extensions are explored. Others are suggested for further research.

The effective design and implementation of interventions that reduce vulnerability and poverty require a solid understanding of underlying poverty dynamics and associated behavioral responses. Stochastic and dynamic benefit streams can make it difficult for the poor to learn the value of such interventions to them. We explore how dynamic field experiments can help (i) intended beneficiaries to learn and understand these complicated benefit streams, and (ii) researchers to better understand how the poor respond to risk when faced with nonlinear welfare dynamics. We discuss and analyze dynamic risk valuation experiments in Morocco, Peru, and Kenya.

Survey-based contingent valuation (CV) techniques are commonly used to value the potential effects of a policy change when market-based valuation of those effects is not possible. The results of these analyses are often intended to inform policy decisions, which are made within the context of formal policymaking institutions. These institutions are typically designed to reduce the large number of potential options for addressing any given policy problem to a binary choice between the continuation of current policy and a single, specified alternative. In this research we develop an approach for conducting CV exercises in a manner consistent with the decision structure typically faced by policymakers. The data generated from this approach allow for an estimate of willingness to pay (WTP) for a defined policy alternative, relative to leaving policy unchanged, which we argue is of direct interest to policymakers. We illustrate our approach within the context of policy governing the storage of used nuclear fuel in the United States. We value the policy option of constructing an interim storage facility relative to continuation of current policy, wherein used nuclear fuel is stored on-site at or near commercial nuclear generating plants. We close the paper with a discussion of the implications for future research and the role of CV in the policymaking process.

Individuals who are compromised in their ability to either believe in or plan for their future will make very different valuations of future benefits than will those who have more means and capabilities. These valuations could apply across a wide spectrum, from health care and insurance to investments in education to retirement savings. Data based on time trade-offs and other hypothetical questions will lead to large gaps in contingency valuations which, taken at face value, would lead to regressive outcomes. Individuals who discount the future are unlikely to be responsive to information intended to mitigate risk or to nudges designed to guide behavior away from risky choices. For example, these differential responses result in particular preventive policies having much less than the intended benefit values. Subjective well-being metrics can help circumvent the problem by comparing the reported well-being of individuals who are actually in different arrangements, such as those who have taken up health insurance or not, or in different work arrangements. Still, subjective well-being metrics are a compliment and not a substitute for the standard data that is used in BCA.

Production function models for cotton lint yields, seed yields, turnout, and lint quality characteristics are developed for the Texas High Plains. They are used to evaluate the impacts of quality considerations and of climate/weather information on the management decisions and on the profitability and risk of irrigated cotton production systems. It is concluded that both quality considerations and improved climatic/weather information could have substantial effects on expected profitability and risk. These effects mainly occur because of changes in optimal variety selection and irrigation water use levels. Quality considerations in particular result in significantly lower irrigation water use levels regardless of the climate/weather information assumption, which has important scarce-resource use implications for the Texas High Plains.

This paper focuses on Enterprise Risk Management (ERM) and strategic business management for health insurance companies in our world of ‘unknown unknowns’ and the emergence of unexpected risks over time. It illustrates how Chief Risk Officers (CROs) can focus on ‘risk and opportunity management’ through an ERM framework, and thereby balance risks against opportunities, whilst being resilient against ‘unknown unknowns’ and their emergence over time as ‘known unknowns’ and ‘known knowns’. The paper has been designed to meet the broad requirements of health insurers that would like to implement an ERM framework for the effective risk management of their health insurance lines of business. Risk management for health insurers in the context of Solvency II and broader European Commission regulatory requirements is also discussed. The authors discuss how insurers can develop and apply risk management to build resilience in the face of the storms and shocks that may lie ahead.

The authors have reviewed over 60 texts on the subject of Enterprise Risk Management (ERM). In this paper they set out a summary of ERM based on three of those sources, selected for their relevance and breadth of view. The paper observes that the approaches described vary widely in nature. A separate ‘on-line” source is provided, which summarises key readings from the 60 texts. Combining findings from these texts with the authors' own experiences, the paper suggests some best practice checklists, designed to enable organisations to take stock of their current ERM frameworks. It discusses other aspects of ERM for practitioners, including extreme events, opportunity management and the link with corporate strategy. The paper looks at immediate and longer-term implications for actuaries in the United Kingdom, and then poses questions about future professional development and education. It suggests an emerging role for the ‘ERM actuary’, and, finally, it suggests future work to progress the development of ERM and the actuaries' role.

Does climate affect behaviour and social process? In this case study, powerful scientific, anthropological and archaeological arguments are deployed to show that it can. The capricious climate of the latest centuries of the Marquesas Islands was instrumental in transforming a chieftain society into less hereditary and more flexible polities by the time of European contact.