The objective of the electricity transmission project is to increase domestic electricity consumption by improving the availability and reliability of electricity in Nepal’s electricity grid. This investment is to be financed through a grant from the US government via the Compact between the Millennium Challenge Corporation (MCC) and the Government of Nepal at a proposed cost of US$ 530 million. In addition, the Nepal Electricity Authority (NEA) is in the process of undertaking a number of generation projects with a total cost of approximately US$ 350 million, facilitated by funding of US$ 150 million from the Asian Development Bank and several bilateral development assistance organizations. Hence, the total investment program for system improvement is approximately US$ 880 million.

First, be sure to get the basic facts surrounding your problem right. This may sound obvious, but it isn’t, and getting the basic facts right may prove to be very difficult. In the 1950s, the U.S. Government and our NATO Allies accepted as fact that NATO land and tactical air forces were hopelessly outnumbered by the forces of the USSR and its Warsaw Pact allies, so outnumbered that in the event of a Soviet attack, our only defense would be first use of nuclear weapons. The widely accepted numbers were 175 divisions on their side v. 25 on ours. This was an extremely dangerous and unnecessary strategy. John F. Kennedy criticized this as confronting the President with “a choice between Suicide or Surrender.” And when McNamara became Secretary of Defense, Kennedy ordered “Give me some better alternatives”. 

Of course, this was a huge exaggeration of Soviet and Warsaw Pact capabilities. But to debunk this myth took years of effort. K. Wayne Smith and I told the story in our book How Much is Enough?, published by RAND. And economic analysis proved to be a most valuable tool. For one example, the Joint Chiefs were counting the MIG 21, the Soviet front line tactical fighter as equally capable as our F-4. The CIA had acquired a MIG-21 from a defector and obtained an estimate that if we produced it in our factories, it would cost about a third the cost of an F-4. The F-4 could carry a much larger payload and deliver bombs with greater accuracy because of superior electronics the MIG-21 did not have. I remember McNamara explaining to the Chiefs if a MIG 21 is as good as an F-4 we are buying the wrong plane. So getting the basic facts right can take a lot of effort and willingness to challenge the accepted conventional wisdom.

Next, when analyzing a problem, always start with the grand totals, the big picture, so that your problem can be situated in the relevant context. In the Systems Analysis Office (now Cost Assessment and Program Evaluation or CAPE) we used to call this McNamara’s first law of analysis. He said this in the context of a study of the air battle in central Europe in which the whole Warsaw Pact air forces were in combat with a faction of the NATO forces. We changed the outcome by assuming that NATO would commit more of its forces.

A tool that I found useful is what Paul Samuelson called “the generalized law of diminishing marginal returns”, something familiar to every economist. In the situation in which I first recognized and used it, we had already committed to the deployment of 1000 Minuteman ICBMs, each in a concrete and steel underground silo. How many more should we buy? The Joint Chiefs were recommending a total of 2400. We also were deploying a fleet of ballistic missile-carrying submarines. How much was enough?  I plotted the curve of targets destroyed versus Minutemen deployed, and, under a wide range of assumptions, it got pretty flat above 1000. So the Secretary decided to stop at 1000. I remember an admiral coming to me and saying “Dr. Enthoven, I want you to know that our program is not on the flat of the curve.”  I replied “We’ll have to do the calculations and see.”   

President Johnson liked our work product because our analyses served as the basis for the recommendations McNamara provided him. So in 1966, he directed that all the departments in the executive branch should have a similar office. At least in some cases, the recipients of the order were at a loss as to what it meant and what to do. (Although in some cases such as DHHS, they hired a RAND alumnus to head the office who was a great success.) I was reminded that the Office of the Assistant Secretary of Defense for Systems Analysis in the Defense Department had the benefit of about 10 years of research on these issues at RAND learning how to identify the issues and to analyze them. We developed and argued out the principles of what we then called Systems Analysis. (Systems Analysis, short for Weapon Systems Analysis, was a discipline-neutral term of art that reflected the fact that the problems called for collaboration of scholars from several different disciplines.) And, of course, the success depended a great deal on the intellect of Robert McNamara who could and did ask many good and penetrating questions and who could spot weaknesses in analyses and demand improvements. 

In later years when I switched my attention from National Defense to health care, I could find many “flat of the curve situations.” With the help of a grant from the Henry Kaiser Family Foundation, I created a course on “Analysis of the Costs, Risks and Benefits of Medical Technology” in the Stanford Graduate School of Business, in collaboration with the Medical School, which has continued to this day, about 40 years later. With all the understandable concern over the costs of Medicare, Medicaid, and Health Insurance for public employees, development of BCA for medical technology seems timely and likely to grow in importance.

This paper develops a methodology for analyzing the value of environmental valuation studies, and to uncover the benefits of the information added by such studies, versus their costs. It can be claimed to launch a new branch of welfare economics: the “benefit-cost analysis (BCA) of public-good valuation work”, and thereby establish principles for how public-goods valuation activities can themselves be assessed. 

Public-good valuation studies are designed to inform decisions about whether to provide or not provide particular environmental or other public goods or services, or to protect or not protect and maintain particular natural objects, including forests, lakes, rivers, parks, and landscapes. Our paper studies the welfare gain by making public decision-making processes related to public goods more precise. Consider, for example, the decision to enact or not enact an environmental policy, or protect or not protect an object with environmental or natural resource of significance. The example used in our paper is the protection of a rainforest (or part of one). Given perfect information about both its protection value and its opportunity (or exploitation) value (for example by cutting down the forest and converting it into agricultural land), and a socially optimal decision process, no mistakes will be made: the rainforest will be saved when its protection value is greater than its opportunity or exploitation value; and it will be converted (cut down) when the exploitation value is greater. 

In practice there is however always uncertainty in such decision processes, usually mostly about the forest’s protection value. One can then make two types of mistakes under uncertainty: 1) fail to save the forest when it ought to be saved; and 2) save it when it is welfare-enhancing to convert it (when its true use value exceeds its protection value). The valuation study or set of studies makes the protection value more precise, and reduces or eliminates these mistakes, thus increasing social welfare. The key question: is this welfare gain greater than the cost of doing the study? If that is the case, the study ought to be performed.  

The paper itself is highly mathematical and I will not go into those technical details in this blog. It is more useful for readers to focus concretely on the rainforest example. Consider the Amazon, or rather a part of it being valued. Our (or “a given”) value estimate is used as a basis for saving or not saving this part of the rainforest. Our question is: do we want to carry out more valuation studies of the Amazon rainforest, to make this decision more precise?

A valuation study can be shown to have particularly high value when the resource to be valued is “highly contested”. By this we mean that the exploitation value is known to be “close” to the protection value; but it is not clear which is higher. This is intuitive: when the protection value is known to be much larger (smaller) than the exploitation value, we are quite sure that the forest ought to be protected (not protected). Information gained from a new valuation study even if the new study provides an entirely correct and quite different valuation outcome than what we initially thought, will not change our initial assessment, nor our decision. The valuation study is then of no value for this decision. When instead our initial assessment is that the two values are quite similar, it is much more likely that our relative assessment, and then also our decision, will be changed by the new study. The study can then have great impacts, and high value.

The valuation study can also have great social value when the true value of the public good is particularly large, even when the probability that the decision can be changed is relatively small. 

The paper provides a numerical example based in part on data and in part on “educated guesses” about the distributions of both exploitation and protection values for the Amazon. One assumption we make is that 10% of the rainforest is likely to be “threatened”, and we assume that protection and exploitation values are similar for this part of the rainforest. This analysis finds extremely high value of a valuation study given that it significantly improves the basis for our decision to save or not save this part of the Amazon. We consider a hypothetical case where the valuation study (or set of new such studies) removes all uncertainty; this is unrealistic but analytically useful as it provides an upper bound on the value that can be achieved from new studies. Assuming then also that information is used optimally as basis for the decision to deforest or not, the value of the study (with plausible uncertainties for both protection and exploitation values) is in the range 5-8 % of the total net benefit of protection. In reality, a set of new studies may remove perhaps half of the initial uncertainty about protection values; they will then provide at least half of this total value. Given a (very conservative) protection value of the Amazon rainforest of $5000 per hectare, the value of new valuation studies that eliminate half of this decision uncertainty is a very high number, about 3-5 % of the rainforest’s total protection value. Even if the magnitude of “contested” rainforest is far smaller (say, only 1 million hectares, approximately equal to the annual deforestation level in the Amazon in recent years), this value is still $150-250 million. 

Our analysis thus shows that a very large amount of valuation work may be efficient to carry out, to make the value of the Amazon rainforest, and the decision to save or not save it, more precise. This is not terribly surprising; but it is good to have such a conclusion verified in a rigorous way. Similar conclusions for other natural resources or environmental policies are however not equally obvious. Here, we have at least developed a robust procedure for investigating the value of such valuation work.

I discuss two recently published benefit-cost analyses of sanitation interventions in low-income countries (Radin et al. 2020a, Radin et al. 2020b). While the results of the analyses show that in some cases sanitation interventions yield benefits greater than the costs, in many instances the costs are greater than the benefits. The analyses demonstrate the importance of improving the public health sector’s understanding of the community level factors that influence intervention effectiveness. Furthermore, there is a clear need for developing new interventions that compliment sanitation interventions and increase latrine uptake and use. These innovations can help policymakers properly target new sanitation programs and maximize their benefits.     

Community-Led Total Sanitation (CLTS) has become the preferred approach for improving access to sanitation in rural areas since it was introduced in 2000 in Bangladesh (Zuin et al. 2019). CLTS works as a behavior change campaign targeting community norms through a number of activities that elicit disgust and shame with the status quo and trigger aspirational goals for a cleaner, healthier, and safer community. Community members are expected to organize and work together to end open defecation and ensure that all households gain access to and use basic facilities, most often simple pit latrines. 

I summarize the results of a benefit-cost analysis of a CLTS intervention that served as a case study for the Bill and Melina Gates Foundation funded Guidelines for Benefit-Cost Analysis (Guidelines) (Radin et al. 2020a). The Guidelines were designed to help analysts perform high quality benefit cost analyses for health policies and projects in low-income countries. The project resulted in a number of technical papers and a general guidelines report that were used in designing the CLTS benefit cost analyses (Robinson & Hammitt 2018, Robinson et al. 2018, Claxton et al. 2019, Robinson et al. 2019, Whittington & Cook 2019). 

For the CLTS case study, we assumed that the intervention was implemented in a hypothetical region in Sub-Saharan Africa. We analyzed the intervention on a regional level because the scaled version of CLTS is typically implemented across a region.  

The analysis used cost estimates for the CLTS intervention from numerous impact evaluations of CLTS interventions in Sub-Saharan Africa (Crocker et al. 2017). The costs of the intervention include the program administration, time costs of community members participating in the intervention, the costs of latrine materials, and those for ongoing operation and maintenance. We valued the time spent in the program and building latrines at 50% of the estimated local informal wage rate (Whittington and Cook 2019).

To estimate the benefits, we drew on the results of fourteen recent randomized control trials testing the impact of CLTS and similar interventions in low-income countries. Incorporating this body of evidence into the economic analysis is an important contribution as many other prior benefit cost analyses studies have relied on general assumptions to estimate impacts (Whittington et al. 2020). This new evidence also shows that villages within targeted regions have heterogeneous responses to CLTS interventions; meaning that some villages in targeted regions experience low uptake of improved sanitation while others have higher adoption rates. We incorporate this heterogeneity by assuming a distribution of villages with three groups: low, medium and high response. Unfortunately, more nuanced targeting of programs to locations where success is most likely remains difficult because the CLTS literature has not identified a clear set of population and village characteristics that can influence uptake rates. 

While there are numerous health and non-health benefits to gaining access to sanitation, due to data limitations, we only monetized the benefits of reducing mortality and morbidity due to diarrheal disease, and the time savings from reduced time spent walking to open defecation sites. The mortality and morbidity benefits are valued through a VSL approach and a cost-of-illness approach (Robinson & Hammitt 2018, Robinson et al. 2018). Time savings are valued at the same rate as time costs. We also considered the impact of a sanitation externality, which is the benefit households in a village derive due to other households within the same village adopting latrines. The sanitation externality exits because one household’s use of latrines reduces the overall environmental contamination, thereby benefiting all households in the village. 

We find that a traditional CLTS intervention does pass a benefit–cost test in many situations, with a benefit-cost ratio B greater than one in 75% of the trials in a Monte Carlo analysis, but that the results are less favorable than many other analyses have found (Whittington et al. 2020). Furthermore, we find that incorporating the benefits of a sanitation externality in this hypothetical region has positive but limited effects.  

One of our main conclusions is that sanitation benefit cost analyses need to be location specific rather than for a general or hypothetical region. We followed this recommendation and produced a subsequent analysis for a CLTS intervention in Ghana (Radin et al. 2020b). In the Ghana specific analysis, we also modelled the potential impacts of subsidies for latrine construction that could be justified due to the public good nature of the sanitation externality. In the Ghana analysis, we found that subsides are necessary for the CLTS intervention to generate sufficient uptake to pass a benefit-cost test.  

Ultimately, we conclude that while improving access to sanitation remains a priority, the cost of a CLTS intervention may outweigh the benefits in many locations. Public health practitioners and sanitation experts need to invest resources into identifying the attributes that most influence the response to a CLTS intervention so that high-uptake villages can be identified. In such villages, the benefits of sanitation and capture of positive externalities are greater. We also encourage more work on complementary policies and innovations that can improve sanitation adoption. 


References

Claxton, K., Asaria, M., Chansa, C., Jamison, J., Lomas, J., Ochalek, J., & Paulden, M. (2019). Accounting for Timing when Assessing Health-Related Policies. Journal of Benefit-Cost Analysis, 10(S1): https://doi.org/10.1017/bca.2018.29. 

Crocker, J., Saywell, D., Shields, K. F., Kolsky, P., & Bartram, J. (2017). The true costs of participatory sanitation: Evidence from community-led total sanitation studies in Ghana and Ethiopia. Science of the Total Environment, 601, 1075-1083.

Ferriman A. (2007). BMJ readers choose the “sanitary revolution” as greatest medical advance since 1840. BMJ: British Medical Journal, 334(7585), 111. https://doi.org/10.1136/bmj.39097.611806.DB

Mara, D., Lane, J., Scott, B., & Trouba, D. (2010). Sanitation and Health. PLoS Medicine, 7(11), e1000363. https://doi.org/10.1371/journal.pmed.1000363

Prüss-Ustün, A., Wolf, J., Bartram, J., Clasen, T., Cumming, O., Freeman, M. C., Gordon, B., Hunter, P. R., Medlicott, K., & Johnston, R. (2019). Burden of disease from inadequate water, sanitation and hygiene for selected adverse health outcomes: An updated analysis with a focus on low- and middle-income countries. International Journal of Hygiene and Environmental Health, 222(5), 765–777. https://doi.org/10.1016/j.ijheh.2019.05.004

Radin, M., Jeuland, M., Wang, H., & Whittington, D. (2020a). Benefit–Cost Analysis of Community-Led Total Sanitation: Incorporating Results from Recent Evaluations. Journal of Benefit-Cost Analysis, 11(3), 380-417.

Radin, M., Wong, B., McManus, C., Sinha, S., Jeuland, M., Larbi, E., Tuffuor, B., Biscoff, N.K., & Whittington, D. (2020b). Benefits and costs of rural sanitation interventions in Ghana. Journal of Water, Sanitation and Hygiene for Development, 10(4), 724-743.

Robinson, L. A., & Hammitt, J.K. (2018). Valuing Nonfatal Health Risk Reductions in Global Benefit-Cost Analysis. Guidelines for Benefit-Cost Analysis Project, Working Paper No. 2. https://cdn2.sph.harvard.edu/wp-content/uploads/sites/94/2017/01/Robinson-Hammitt-Nonfatal-Risks.2018.03.121.pdf. 

Robinson, L. A., Hammitt, J.K., & Adler, M. (2018). Assessing the Distribution of Impacts in Global Benefit-Cost Analysis. Guidelines for Benefit-Cost Analysis Project, Working Paper No. 3. https://cdn2.sph.harvard.edu/wp-content/uploads/sites/9 4/2017/01/Robinson-Hammitt-Adler-Distribution-2018.03.07.pdf. 

Robinson, L. A., Hammitt, J.K., & O’Keeffe, L. (2019). Valuing Mortality Risk Reductions in Global Benefit-Cost Analysis. Journal of Benefit-Cost Analysis, 10(S1): https://doi.org/10.1017/bca.2018.26.

United Nations Children’s Fund (UNICEF) and the World Health Organization (WHO). (2020). State of the World’s Sanitation: An urgent call to transform sanitation for better health, environments, economies and societies. New York, New York: United Nations Children’s Fund (UNICEF) and the World Health Organization.

Whittington, D., & Cook, J. (2019). Valuing Changes in Time Use in Low- and Middle-Income Countries. Journal of Benefit-Cost Analysis, 10(S1): https://doi.org/1 0.1017/bca.2018.21.

Whittington, D., Radin, M., & Jeuland, M. (2020). Evidence-based policy analysis? The strange case of the randomized controlled trials of community-led total sanitation. Oxford Review of Economic Policy, 36(1), 191-221.

Zuin, V., Delaire, C., Peletz, R., Cock-Esteb, A., Khush, R., & Albert, J. (2019). Policy diffusion in the rural sanitation sector: lessons from community-led total sanitation (CLTS). World Development, 124, 104643.

Financial transfers from taxpayers to program recipients (such as Temporary Assistance to Needy Families, or TANF, in the US), are treated as having no effect on net benefits in benefit cost analysis, because, in dollar terms, the benefit they generate for recipients is exactly offset by the cost to taxpayers.  But if poverty has the effect of reducing the rationality of recipients relative to taxpayers, and if getting out of poverty increases it, then transfers may actually generate a non-zero net benefit, which could be positive or negative. 

BE brings psychology into economics analysis with the basic premises that cognitive limitations lead people to apply heuristics and routines that yield outcomes which individuals consider satisfactory, not optimal. Everything else being equal an agent that has better algorithms and heuristics could make more “rational” (more optimal) decisions than one that has poorer heuristics and algorithms. For example, advances in technology (artificial intelligence and big data analytics) expand the bounds that define the feasible rationality space, also social networks structures in socio-ecological systems drive towards improved rationality (Campbell and Smith, 2020; Smith and Wilson, 2019; Kahneman, 2003).  

While psychologists started exploring happiness already in the late 1940s, the economics of happiness is a more recent field. Economists started studying happiness, or more broadly Subjective Wellbeing (SWB), to clarify issues in welfare economics, since SWB can serve as a proxy for the fundamental economic notion of utility previously deemed unobservable. Since 1970s HE has changed our understanding of the structure of the utility function.

There exist three central unresolved scientific questions that drive the search of an alternative, deeper, more mature understanding of the structure of the utility function. First, the need to recognize that a substantial fraction of the people is also motivated by fairness concerns. People do not only differ in their tastes for chocolate and bananas, but also with regards to how selfish or fair-minded they are, which has important economic consequences. Second, the need to understand how people make decisions over time and under uncertainty. With regards to the latter economics has two separate frameworks: the subjective expected utility framework (where the decision maker weights probabilities) and the discounted utility framework (where the decision makers weights discount factors based on time delays). However, time and uncertainty are correlated, while uncertainty often takes the form of ambiguity (when probabilities of uncertain events are unknown) so we need a unifying framework, which must be derived from a deeper understanding of the structure of human preferences. 

Finally, we need to understand human preferences with regards to public goods, such as education, health, security, and the environment. Currently we are facing  a triple crisis: the health crisis of the COVID-19 pandemic, the unprecedented macroeconomic recession deriving from attempts to contain the spread of the COVID-19 virus, and the mother of all crisis, the Climate Crisis, speculated to have contributed to the emergence of the pandemic (deforestation and biodiversity loss bringing wild life and humans very close, which increases the probability of zoonotic viruses to make the cross species leap)¹²³ and affecting each and every country in the world via natural disasters that translate in billions of dollars in economic losses and millions of human lives lost. An immense amount of effort (from research and innovation, policy-making and politics, business and NGOs, the civil society) is invested in avoiding to “waste this triple crisis” and use this moment of clarity to effectively reboot development towards a people-centric, inclusive, rights-based, participatory and green development envisioned in the United Nations 2030 Agenda with the 17 Sustainable Development Goals and the Paris Climate Agreement (Lancet, 2020). The recovery needs to be transformative with regards to our social, economic, financial and political systems, so that they become human-centric, climate neutral and resilient, and be based on a sustainable digitalized economy and an up-skilled labor force that can embrace the impressive technological advancements in renewable energy production and storage, circular economy, energy efficiency, digitalization, e-mobility, smart food production, protection of human health and biodiversity (Koundouri, Pitits, Smartzis, 2020). We need to understand human preferences and decisions-making, and all the main elements affecting human well-being. We need to move away from imperfect measurements of growth, like the “Gross National Product” measurement, and focus on inclusive measurements of the well-being of nations. The World Happiness Report (https://worldhappiness.report/ed/2020/) is a landmark survey of the state of global happiness that ranks 156 countries by how happy their citizens perceive themselves to be, while there exist a number of efforts for robust measurement of Natural Capital.⁴

This important book brings together a number of exceptional contributions on Economics, Well-Being and Happiness, and redirects economics research to one important quest that dominated the classical Political Economy and formed the basis of the moral statute of Political Economy in the late modernity: “The telos of Political Economy is to reduce unhappiness by means of reducing material poverty and ignorance, increasing the wealth of nations”. 

The book starts with an excellent historical perspective on economics, well-being and happiness, where Lauigino Bruni explains when and how happiness/eudaimonia has been reduced to utility/pleasure, while in chapter 2 Ruut Veenhoven proceeds with the presentation of the status-quo of archiving of this literature, namely “World Database of Happiness”. In chapter 3, Mona Ahmadiani et al. present and analyze the puzzle of “spatial variation in Life Satisfaction”, namely although income has a positive impact on SWB at a point in time, there is little effect of economic growth after a level of GDP per capita. This puzzle is revisited in chapter 6, where David Maddison and Katrin Rehdanz explain cross-country variations in subjective well-being explained by the climate. In chapter 4 Heinz Welsch explains how Happiness is integrated in Environmental Economics, while in chapter 5 Jianjun Tang et al. present how the concept subjective wellbeing is used in the valuation system of environmental quality. Valuation Environmental Economics relies upon utility maximization which is assumed to sufficiently and accurately capture an individual’s decision-making framework. This chapter suggests that subjective wellbeing embedded in an Environmental Social Science framework which allows estimation by structural equation methods that can handle latent and observable variables simultaneously, is an alternative to valuation methods based on neoclassical premises.  

In chapter 7 Michael Berlemann et al. present empirical evidence on natural disasters and self-reported well-being, which focuses on extreme rainfall in the UK. This evidence shows that an increase in disaster risk, which is associated with Climate Change, has a direct negative effect on economic costs, but also happiness and life satisfaction. In chapter 8, Benjamin A. Jones, focuses on happiness and forest-attacking invasive alien species, in chapter 9, Arik Levinson analyzes happiness and air pollution, a subject that is also visited by Xin Zhang et al. in chapter 10 with a special empirical focus on China. In chapter 11 Daniel Fujiwarw and Ricky N. Lawton focus on yet another externality, namely noise, and empirically analyze a panel data set that allows estimation of its effects on subjective wellbeing. In chapter 12 David Fujiwara measure the wellbeing and health impacts of sewage odour, while in chapter 15 Peter Howley investigate legacy effects and individual heterogeneity in the relationship between health and wellbeing. In chapter 13 Teresa Ruckelshaub econometrically compares subjective and objective measures of the effect of green areas on life satisfaction, in chapter 14 George MacKerron and Susana Moourato highlight the value of understanding localized patterns in subjective wellbeing both at the individual level and for policy and planning purposes, while in chapter 16 Christian Krekel showcases the estimation of energy infrastructure externalities by using wellbeing and hedonic price data. In chapter 17 Heinz Weilsch investigates the effects of nuclear risks on wellbeing, in chapter 18 Kate Lafffan studies the relationship between pro-environmental behavior and subjective wellbeing. In chapter 19 Heinz Welsch empirically investigates the available evidence on the relationship between green lifestyle and wellbeing, while in chapter 20 Tetsuya Tsurumi et al. empirically show for the case of Japan that although there are no satiation points concerning the consumption-well-being relationship, there are satiation points for people who have the perspectives on environmental ethics concerning “irreversibility” or ‘intergenerational equity”. In chapter 21 Carmen Amelia Coral-Guerrero et al. empirically assess the indigenous “sumac Kawsay” (living well) for people’s subjective well-being, while in chapter 22, Shashi Kant et al. attempt an assessment of Aboriginal wellbeing based not only materials aspects, but also on Aboriginal people’s wellbeing. Finally, in chapter 23, Frey S. Bruno summarizes the remarkable results over the last years of the research on subjective well-being, in short “happiness”, and connects it to important contributions showing the relationship between the natural environment and happiness. This book is a joy to read and a firm basis for navigating through the most exciting areas of economic theory: those that serve the need to understand what makes people happy, that is understand those important factors that increase human well-being. A must read!

¹Lancet COVID-19 Commission Statement on the occasion of the 75th session of the UN General Assembly. The Lancet COVID-19 Commissioners, Task Force Chairs, and Commission Secretariat. Lancet 2020; 396: 1102–24 Published Online September 14, 2020 https://doi.org/10.1016/ S0140-6736(20)31927-9
²Lancet COVID-19 2nd Commission Statement: https://covid19commission.org/enhancing-global-cooperation
³Priorities for the COVID-19 pandemic at the start of 2021: statement of the Lancet COVID-19 Commission. The Lancet, February 12, 2021DOI: https://doi.org/10.1016/S0140-6736(21)00388-3
⁴See for example, Bringing Health and the Environment into Decision-Making: The Natural Capital Approach. Rockefeller Foundation Economic Council on Planetary Health, 2018 https://valuing-nature.net/sites/default/files/images/Bateman%20%20Wheeler%202018%20-%20Rockefeller%20Nat%20Cap-%20website.pdf and http://www.naturalcapital.vn/measuring-natural-capital/

References
Bringing Health and the Environment into Decision-Making: The Natural Capital Approach. Rockefeller Foundation Economic Council on Planetary Health, 2018 https://valuing-nature.net/sites/default/files/images/Bateman%20%20Wheeler%202018%20-%20Rockefeller%20Nat%20Cap-%20website.pdf

Michael J. Campbell; Vernon L. Smith (2020). "An elementary humanomics approach to boundedly rational quadratic models". Physica A. 562: 125309. doi:10.1016/j.physa.2020.125309.

Vernon L. Smith and Bart J. Wilson (2019). Humanomics: Moral Sentiments and the Wealth of Nations for the Twenty-First Century. Cambridge University Press. doi:10.1017/9781108185561. ISBN 9781108185561

Kahneman, Daniel (2003). "Maps of Bounded Rationality: Psychology for Behavioral Economics". The American Economic Review. 93 (5): 1449–1475. doi:10.1257/000282803322655392. ISSN 0002-8282. JSTOR 3132137.

Koundouri, P., Pittis, N., Samartzis, P., 2020. Never Waste a Good Crisis: COVID-19, Macroeconomic Effects and the Way Forward, Perspectives on the Economics of the Environment in the Shadow of Coronavirus Environmental and Resource Economics volume 76, pages 447–517(2020)

Lancet COVID-19 Commission Statement on occasion of the 75th session of the UN General Assembly. The Lancet COVID-19 Commissioners, Task Force Chairs, and Commission Secretariat. Lancet 2020; 396: 1102–24 Published Online September 14, 2020 https://doi.org/10.1016/ S0140-6736(20)31927-9

Natural Capital Accounting http://www.naturalcapital.vn/measuring-natural-capital/

The World Happiness Report, 2020. https://worldhappiness.report/ed/2020/

Incredible Certitude in Epidemiological and Macroeconomic Modeling

Epidemiological models of disease dynamics, sometimes combined with models of macroeconomic dynamics, have been used to reach conclusions about optimal COVID-19 policy. However, researchers have done little to appraise the realism of their models, nor to quantify uncertainties. Hence, I find little basis to trust the policy prescriptions that have been put forward.

Epidemiological modelers have sought to determine COVID-19 policy that would be optimal from a public health perspective if specified models of disease dynamics were accurate and public health were measured in specified ways. However, epidemiological modeling has only considered impacts on health. Policy assessment should consider the full health, economic, and social impacts of alternative options. Recognizing this, macroeconomists have sought to expand the scope of optimal policy analysis by joining epidemiological models with models of macroeconomic dynamics and by specifying welfare functions that consider both public health and economic outcomes.

A serious underlying problem in both epidemiological and macroeconomic modeling has been the dearth of evidence available to inform model specification and estimation. Studies of disease and macroeconomic dynamics are largely unable to perform randomized trials. Modeling necessarily relies on observational data, which are difficult to interpret. Lacking much evidence, epidemiologists and macroeconomists have developed models that are sophisticated from mathematical and computational perspectives but that have little empirical grounding. These modeling efforts may perhaps be useful if interpreted cautiously as computational experiments studying policy making in hypothetical worlds. However, their relevance to the real world is unclear.

I have persistently argued for forthright communication of uncertainty in research that aims to inform public policy (Manski, 2019). I have criticized the prevalent practice of policy analysis with incredible certitude. Exact predictions of policy outcomes are routine. Expressions of uncertainty are rare. Yet predictions often are fragile, resting on unsupported assumptions and limited data. Expressing certitude is not credible. Incredible certitude has been prevalent in both epidemiological and economic modeling.

There is an urgent need for epidemiologists and economists to join forces to develop credible integrated assessment models of epidemics. Even with the best intentions, this will take considerable time. There is some reason to hope that epidemiologists and economists may be able to communicate with one another because they share a common language for mathematical modeling of dynamic processes. However, each group has in the past exhibited considerable insularity, which may impede collaboration. Moreover, neither discipline has shown much willingness to face up to uncertainty when developing and applying models.

Adaptive Policy Diversification

I think it misguided to make policy that is optimal in hypothetical scenarios but potentially much less than optimal in reality. It is more prudent to approach policy as a problem in decision making under uncertainty. Facing up to uncertainty, one recognizes that it is not possible to guarantee choice of optimal policies.

While one cannot guarantee optimality under uncertainty, one may still make decisions that are reasonable in well-defined respects. I have suggested adaptive diversification of COVID-19 policy. Adaptive policy diversification was proposed in Manski (2009, 2013). Akin to financial diversification, policy is diversified if a planner randomly assigns treatment units (persons or locations) to different policies. At a point in time, diversification avoids gross errors in policy making. Over time it yields new evidence about policy impacts, as in a randomized trial. As evidence accumulates, a planner can revise the fraction of treatment units assigned to each policy in accord with the available knowledge. This idea is the ideal form of adaptive diversification.

Explicitly random assignment of policies may not be feasible in practice. Nevertheless, it may be possible to vary policy across time or place to approximate adaptive diversification. Justice Brandeis suggested something of this sort close to a century ago. In a famous dissent on a Supreme Court case, he referred to the states as the “laboratories of democracy.”

To illustrate, consider the choice between suppression and mitigation of COVID-19. Suppression may be the better policy if it were known that this policy has strong positive health impacts and only small negative economic impacts. On the other hand, mitigation may be the better policy if suppression has weak positive health impacts and large negative economic impacts. Policy diversification, with some locations implementing suppression and others implementing mitigation, gives up the ideal of optimality in order to protect against making a gross error in policy choice.

When diversifying, what fraction of locations should implement each policy option under consideration? This depends on the welfare function that society uses to evaluate options and on the uncertainties that afflict prediction of policy impacts. Manski (2009) studied adaptive diversification when social welfare is utilitarian, and a planner uses a simple dynamic version of the minimax-regret criterion to cope with uncertainty. The result is a simple diversification rule. I think it would be productive to specify an appropriate welfare function, characterize the relevant uncertainties, and adapt this analysis to diversify COVID-19 policy.

References

Manski, C. (2009), “Diversified Treatment under Ambiguity,” International Economic Review 50, 1013-1041.

Manski, C. (2013), Public Policy in an Uncertain World, Cambridge, MA: Harvard University Press.

Manski, C. (2019) “Communicating Uncertainty in Policy Analysis,” Proceedings of the National Academy of Sciences, 116, 7634-7641.

Manski, C. (2020), “Forming COVID-19 Policy under Uncertainty,” Journal of Benefit-Cost Analysis, 11, 341-356.

The Department of Justice recently released an opinion on “Extending Regulatory Review Under Executive Order 12866 to Independent Regulatory Agencies.”¹ The memorandum, dated October 8, 2019, concludes that “The President may direct independent regulatory agencies to comply with the centralized review process prescribed in Executive Order 12866.” The opinion means that the President can require the independent agencies to perform benefit-cost analyses of all significant regulations and submit the regulations for review to the Office of Information and Regulatory Affairs in the Office of Management and Budget. The opinion is the latest development in a 40-year-old debate over whether executive orders on benefit-cost analysis of administrative rules could or should be extended to rules issued by the independent regulatory agencies. A typical independent agency differs from executive agencies in that it is headed by a commission or a board appointed by the President. Their members have staggered terms and cannot be removed except for cause, such as (in the case of the Federal Trade Commission) “inefficiency, neglect of duty, or malfeasance in office.” 

The question is: Do the constraints on the president’s ability to remove members mean their regulations must be exempt from requirements of presidential executive orders? The statutes establishing the agencies did not indicate any independence beyond “for cause” removal. Indeed, although the President cannot remove commissioners and board members, he can remove other employees of independent agencies.² 

Publication selection biases may arise at different stages of the research process. If, for example, researchers and editors use U.S. estimates of the VSL as the reference point for reasonable estimates of the VSL, the U.S. value will influence which estimates researchers choose to submit to journals and which estimates are accepted for publication and ultimately published. Previous research has found that publication selection effects plague the VSL literature overall (Stanley and Doucouliagos, 2012), but do not significantly affect labor market estimates of the VSL based on the Census of Fatal Occupational Injuries data (Viscusi, 2015). 

This article examines the potential presence of publication selection effects using a sample of 1,148 stated preference estimates of the VSL from throughout the world. If there are no biases, estimates of the VSL should be symmetrically clustered around the true VSL level. In the case of the stated preference data, the distribution is highly skewed, with many precisely estimated values being near zero, and with a long tail of imprecisely estimated high values of VSL. Using standard statistical tests to adjust for publication selection effects, the article finds that the mean VSL level is reduced from $8.5 million (in 2015 USD) to a bias-adjusted value of $3.2 million. The extent of the publication selection biases is not uniform. High international VSL estimates are most seriously affected. At the 90th percentile of the distribution of VSL estimates, the mean estimate is $15.8 million, but the bias-corrected value is $4.2 million.

Similar biases also affected the labor market estimates in the U.S. literature. However, in that case, it was feasible to identify a subset of studies not significantly affected by such biases. To explore whether international stated preference studies similarly have a set of studies that is not distorted by publication selection effects, the authors also considered ten different subsamples of studies, such as stated preference studies published in peer reviewed journals, estimates pertaining to health risks or environmental risks, and estimates from lower and medium income countries. All these groups were subject to substantial publication selection biases, which sometimes reduced the estimated VSL by up to 80%.

What then are researchers and policy analysts to do when valuing mortality reductions outside the U.S.? The approach advocated by the authors is to use the U.S. estimates based on the CFOI data as the baseline, and then to extrapolate these values to other countries using their estimated international income elasticity of 1.0. Tables of such estimates appear in Masterman and Viscusi (2017) and Viscusi (2019). Notwithstanding the downward adjustments for income relative to the U.S., the projected VSL levels generally exceed the VSL amounts currently used outside the U.S. (Viscusi, 2018).

Using an average international income elasticity value has the advantage that the procedure is readily transferable and is based on income differences, which are a principal driver of variations in the VSL. The international elasticity value of 1.0 is also robust, as it is reflected in both stated preference and revealed preference estimates. However, the procedure ignores elasticity differences that may arise because of international differences in attitudes toward risk, such as religious and cultural factors (Hammitt and Robinson 2011).

The evidence of biases in the stated preference literature on the VSL does not imply that stated preference studies have no valid policy role. Other health risks that have been the focus of stated preference studies may not be subject to biases such as those that arise from anchoring on the revealed preference VSL estimates. There are many health risks such as risks of cancer for which reliable stated preference evidence is not available. Stated preference studies can continue to serve a complementary role (Alberini, 2019; Viscusi and Dalafave, 2020).

There are two forces that, ceteris paribus, would seem to be eroding productivity, and thus diminishing future economic growth: the quality of K-12 education and maternal workforce participation. For both mothers interrupting their careers to meet the educational needs of their children during the pandemic and those children, these forgone earnings represent a loss of future productivity to the economy at large. 

A comprehensive assessment of the costs—and benefits—of a virtual K-12 education rather than a face-to-face education in 2020-2021 will take years to fully study and appreciate, but we know that teacher quality, even for just one year, can have profound impact on students’ future earnings. Despite teachers’ unquestioned hard work, it seems reasonable to suppose that a rapid implementation of a completely novel medium of instruction will reduce teacher quality during the transition. 

For a back-of-the-envelope calculation of the educational costs associated with virtual learning, let us suppose that the effect on students’ future earnings, is analogous to the difference between having a teacher who is 1.0 standard deviation above average compared to an average teacher. If that supposition is correct, that will mean that students in online classes this year will have $12,263 lower lifetime earnings in net present value (2020 dollars; Hanushek estimated $10,600 per student in 2011 dollars). Multiply this impact across 54 million K-12 students, and we can expect a decline in earnings of over $662 billion, net present value, over the course of the next 30 to 40-odd years. 

Second, early evidence indicates that women’s careers are being disproportionately affected by the pandemic generally, but school closures in particular. To the extent that this scenario is analogous to women interrupting careers for motherhood in general, it is reasonable to expect that working mothers’ earnings will also be diminished if and when they return to the workforce after schools reopen. Based on a model for maternal time off for child rearing from the Center for American Progress, an estimate of the effects on lifetime earnings is likely between $100,000 and $150,000 for most women (e.g. a 30-year-old woman working since age 22 and earning $44,000 who leaves the workforce for one year would lose $130,564 in total lifetime income; the model isn’t transparent, but I’m assuming it’s adjusted to net present value). 

Estimates of how many women have or will temporarily leave the workforce due to school closures are conflated with the high numbers of layoffs generally. However, in 2018, the Census Bureau reported that there were 23.5 million mothers of children under 18 working full-time; if we assume 1.0% of those mothers left the workforce voluntarily due to school closures, that would conservatively be a decrease of $2.35 billion in lifetime earnings for this group.

Combined, these two effects could decrease the net benefit of lockdowns by approximately $0.665 trillion—roughly 12% of the Thunström estimate—if lockdowns include closing K-12 education. These back-of-the-envelope calculations are intentionally conservative, so it is not surprising that others have found potentially larger costs to school closures in the U.S. and elsewhere. Even more concerning, the long-term rate of GDP growth of 1.75% used by Thunström, et al. is likely overly optimistic for scenarios in which schools are primarily remote or virtual for one or more years because it does not include probable decreases in future productivity and growth, further attenuating the net benefits of lockdowns. This analysis does not address the costs of daycare closures or virtual college learning. Nor does it address the racial and wealth disparities associated with all of these costs, which are undoubtedly substantial. 

I am not advocating that we abandon all physical distancing policies. A 12% decline in the net present value of these wages losses is quite impactful, but it does not change the results of the analysis: physical distancing policies have a net positive economic benefit; especially in the medium term. Rather, I am arguing that the policy mix within the umbrella of physical distancing matters. The closure of restaurants and bars (for example) has a very well-appreciated immediate impact on the economy, but the closure of schools, on the other hand, will have much larger long-term effects than is often appreciated. Even a small change in future productivity can and will have very long-lasting costs, indeed.

For over 50 years, economists have developed and refined methods to value environmental and other nonmarket goods to provide benefit estimates that are commensurate with goods that are exchanged in markets. Without these estimates, benefit cost analysis of environmental regulations risk erroneous conclusions regarding the net benefits of a regulation. The methods can be broadly categorized into revealed preference (that infer values from behavioral clues) and stated preference (which directly elicit values through surveys). Despite their longer history and the many documented shortcomings, revealed preference methods have not been subject to the intense validity and reliability challenges as their stated preference counterparts. And, unlike stated preference methods (see Johnston et al.2017), there has not previously been an effort for scholars of the approaches to develop a set of “best practice” guidelines for the implementation and reporting of these analyses.

This post is a summary of a paper I’ve written called “What’s in, what’s out? Towards a rigorous definition of the boundaries of benefit-cost analysis,” forthcoming in Economics and Philosophy. Students are typically told that benefit-cost analysis is an application of the potential Pareto criterion, which defines net benefit as the difference between the willingness to pay of winners for their gains from a policy and the willingness to accept of losers for their losses. If the difference is positive, the policy is a potential Pareto improvement, and we say that it generates positive net benefits. Economic philosophers have presented many objections to this definition, but none of these objections refutes the basic logic.

I start with some substantial evidence for appraisal optimism in project evaluations. I then discuss the alleged two main drivers: cognitive biases and incentives in government appraisal processes.  Finally, I discuss some remedies. But I am not optimistic that the virus will entirely disappear. 

Since electronic cigarettes were introduced, in 2007, they have presented controversial public health tradeoffs. E-cigarettes provide users with the addictive chemical nicotine but without exposing them to the harmful combustion-generated toxicants in cigarette smoke. On the one hand, because smoking combustible cigarettes is estimated to lead to almost 500,000 deaths each year, e-cigarettes have great potential as a harm reduction strategy. In particular, evidence from randomized clinical trials shows that vaping e-cigarettes helps adult smokers quit. On the other hand, the growing popularity of vaping among teens raises concerns about nicotine addiction, the possibility that vaping is a gateway to smoking, and unknown future health consequences. More teens now vape e-cigarettes than smoke cigarettes. In the National Youth Tobacco Survey, the fraction of high school students reporting vaping within the past 30 days increased from 11.7 percent in 2017 to 27.5 percent in 2019, before dropping to 19.6 percent in 2020.  Some public policies – increasing the legal purchase to 21 and banning e-cigarette flavors popular with teens – try to target teen vaping. Other policies, most notably e-cigarette excise taxes, discourage both adult and teen vaping.

Novel threats call us to action. Witness the response to the 9/11 attack and more recently to the coronavirus. Such risks may be particularly compelling if citizens do not understand how to deal with the harm or because of ambiguity around the probability that the threat will be realized.