One of the most popular sessions at the SBCA 2021 Annual Conference was on combining economics and epidemiology to understand COVID-19. Session speaker Bill Bossert shares a brief statement below. 

One of the most popular sessions at the SBCA 2021 Annual Conference was on combining economics and epidemiology to understand COVID-19. Session speaker Natalie Dean shares a brief statement below.

Past studies of Ebola, HIV, dengue, and Zika by infectious disease epidemiologists provide a road map for the use of outbreak and contact tracing data to estimate transmission parameters for application in mathematical models. There are several primary goals for modeling efforts in this context.

One of the most popular sessions at the SBCA 2021 Annual Conference was on combining economics and epidemiology to understand COVID-19. Session speaker Chris Avery shares a brief statement below.

Thomas Schelling suggested in his book Micromotives and Macrobehavior that cost-benefit choices by individuals can explain the emergence of population-level phenomena in a game theory model. We can apply Schelling’s binary choice framework to social distancing.

President Biden's regulatory-reform calls for an updating of OMB Circular A-4, the obscure technical guidance that governs how regulatory agencies perform benefit-cost analysis (BCA) and how OMB reviews agency analyses.  Here are some issues ripe for reconsideration, as A-4 has not been updated since 2003.

Rob Moore invited me to share further reflections on BCA in practice, lessons from 50 years ago but still relevant today.

Environmental valuation has over the last 40 years grown into a major field within environmental and resource economics. Sizable resources are every year put into environmental valuation work, and an entire industry of analysts is devoted to it. There is however little discussion of benefits versus costs of these studies. A small part of them are innovative and part of fundamental research, and should clearly be funded, and published. But by far most valuation studies are much more practical and aim to assess particular goods or policies with less general interest to the broader public. Their usefulness should therefore be scrutinized.

The evidence access to safe sanitation services is essential for reducing child mortality and improving public health is overwhelming (Mara et al. 2010 & Prüss-Ustün et al. 2019). The international public health and medical communities have reached a consensus that access to sanitation services is a priority. Readers of the British Medical Journal voted the “sanitary revolution” as the most significant medical achievement since 1840 (Ferriman 2007). The United Nations’ Millennium Development Goals and Sustainable Development Goals both include explicit targets for increasing access to sanitation services. Despite overwhelming support for promoting sanitation in low-income countries, however, the problem remains large as an estimated 4.2 billion people worldwide are using inadequate sanitation facilities and almost 700 million have no access to any sanitation (UNICEF and WHO 2020). 

Exposure to air pollution continues to be a major health risk, including worsening health risks related to COVID-19¹. Thus, accounting for these benefits of these avoided health risks is critical in the evaluation of policies that focus on improving air quality and also play an important role in the anticipated climate policies, where improving air quality should be considered as a major co-benefit. However, compared to the scrutiny that has been given to the relationship between exposure to air pollution and adverse health effects, modeling the transport and fate of air pollutants from the emission source to the ambient concentrations to which we are exposed is often given more limited consideration in the modeling chain from emissions to monetary valuation for air pollutants. 

An important and difficult issue in benefit-cost analysis is how to deal with the distributional impacts of policies. An approach to this issue is described in a recent article published in the fall 2020 issue of the Journal of Benefit-Cost Analysis by Anthony Boardman, Aidan Vining, David Weimer, and me. This blog summarizes our analysis.

If the Value of a Statistical Life (VSL) is observed to be a function of income and policy fixes VSL as a constant, then policy has defined welfare weights over income.

Few topics are as controversial between the public and benefit-cost analysts as placing a value on a statistically lost or shortened life, the VSL.  Recent public discourse and civil unrest are in part driven by whether some classes of lives matter more than others.  Yet with the dry logic of economists it is possible to combine evidence based VSLs that change with income, the less money you have the lower the VSL, with the public policy VSL that is chosen to be constant. 

Happiness Economics (HE) is concerned with the utility consequences of economic choices, while Experimental Economics (EE) studies choice behavior. Both HE and EE are branches of Behavioral Economics (BE) and they often lead to similar conclusions, which are at odds with assumptions of the Standard Economic Model (SEM). In the SEM the decisions maker maximizes a utility function with complete, transitive and self-regrading preferences, which are affected only by the levels of one’s own payoffs (the payoffs of other individuals and other generations are not considered). The SEM has no ethical underpinnings and no distributional concerns. For many economists, as well as scientists from other disciplines that endeavor to develop interdisciplinary frameworks and systems, which include socio-economic considerations, the SEM is unsatisfactory.

In a recent paper in the Journal of Benefit-Cost Analysis (Manski, 2020), I observed that formation of COVID-19 policy must cope with many uncertainties about the nature of the disease, the dynamics of the pandemic, and behavioral responses. I noted that these uncertainties have been well-recognized qualitatively but not satisfactorily characterized quantitatively. I argued that credible measurement of uncertainties would improve prediction of policy impacts and promote reasonable policy decisions.

In January, SBCA organized its first panel as an affiliate society for the Allied Social Sciences Association/American Economic Association annual meetings.¹ The session, titled “Regulatory Benefit-Cost Analysis -- Advice for a New Presidential Term,” featured a panel dispensing advice to the incoming administration on improving and expanding the use of benefit-cost analysis.² SBCA Vice President Glenn Blomquist chaired the panel discussion that included three former SBCA presidents (Susan Dudley, Don Kenkel, and Clark Nardinelli) as well as Professors Michael Greenstone and Howard Shelanski. Dudley (the George Washington University) and Shelanski (Georgetown University) served as administrators of the Office of Information and Regulatory Affairs (OIRA) in the Bush and Obama administrations. Kenkel (Cornell University) and Greenstone (University of Chicago) served on the staff of the Council of Economic Advisors in the Trump and Obama administrations. Nardinelli served as the Senior Economist at the Food and Drug Administration.

International studies valuing mortality risk changes often rely on stated preference estimates of the value of a statistical life (VSL). Because labor market data in most countries are not as reliable as the fatality rate statistics in the United States, stated preference evidence for the VSL provides a popular research strategy for obtaining country-specific estimates. Unfortunately, this article finds that this literature is subject to rampant publication selection effects, leading to huge biases in the estimated VSL levels.