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Journal of Benefit-Cost Analysis

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2152-2812
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Skills of the trade: valuing health risk reductions in benefit-cost analysis

Lisa A. Robinson
  • Corresponding author
  • Senior Fellow, Mossavar-Rahmani Center for Business and Government, Harvard Kennedy School, USA
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  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ James K. Hammitt
  • Harvard University (Center for Risk Analysis), USA and Toulouse School of Economics (LERNA-INRA), France
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
Published Online: 2013-03-28 | DOI: https://doi.org/10.1515/jbca-2012-0006

Abstract

Many public policies and private actions affect the risk of injury, illness, or death, yet changes in these risks are not easily valued using market prices. We discuss how to value these risk reductions in the context of benefit-cost analysis. We begin with a pragmatic focus, describing the analytic framework and the approaches currently used for valuation, including estimates of willingness to pay, cost of illness, and monetized quality-adjusted life years. We then turn to some conceptual issues that illustrate areas in need of further exploration.

Keywords: benefit-cost analysis; cost of illness; health risk valuation; value per statistical life; willingness to pay; quality-adjusted life year

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About the article

Corresponding author: Lisa A. Robinson, Senior Fellow, Mossavar-Rahmani Center for Business and Government, Harvard Kennedy School, USA


Published Online: 2013-03-28


This trade-off also can be represented by individual willingness to accept compensation (WTA): the smallest amount an individual would accept to forego the improvement. WTP is the compensating variation and WTA is the equivalent variation for the improvement. In the sections that follow, we refer to WTP for convenience, because WTP studies dominate the health risk valuation literature and policy analyses typically address improvements from the status quo rather than compensation for forgoing the improvement. We discuss the distinction between WTP and WTA in the final section.

Another revealed preference approach considers averting behaviors; i.e., defensive measures or consumer products used to protect against perceived health risks (see, for example, reviews by Blomquist, 2004; Viscusi and Aldy, 2003). These studies are applied infrequently in benefit-cost analysis due to concerns about their limitations, including the difficulty of estimating the size of the risk change associated with many behaviors and the need to separately estimate the value of key inputs such as the time spent in the activity.

For example, for US regulatory analysis, best practice guidance is provided in U.S. Office of Management and Budget (OMB) (2003) and US Environmental Protection Agency (EPA) (2010b). Boardman, Greenberg, Vining, and Weimer (2011) also discuss related issues.

This is true only in the short run because everyone eventually dies. Policies extend lives rather than saving them; the deaths prevented by the policy are delayed to a later date and possibly shifted to another cause.

VSL can also be viewed as aggregating individual WTP across a population; i.e., a $600 average individual WTP×10,000 affected individuals=$6.0 million. Because both the risk reduction and WTP are likely to vary by individual, it is more accurate to first determine each individual’s WTP for the risk reduction he or she would receive then aggregate. However, the data needed to calculate these values on an individualized basis are often unavailable.

As noted below, an increasing number of studies directly estimate the value of life extension, but more work is needed to establish robust estimates suitable for use in policy analysis.

Viscusi and Aldy (2003) review over 60 wage-risk studies conducted globally. The Lindhjem et al. database [described in more detail in Organisation for Economic Co-operation and Development (OECD), 2012], includes 74 stated preference surveys from around the world, of which 37 address traffic safety, 30 address public health or health risks from unidentified causes, and 17 address environmental risks. (Some surveys address more than one risk type.)

One Canadian dollar was worth roughly 1 US dollar at the time this manuscript was prepared.

All values are converted to US dollars based on current exchange rates using the Google currency converter (which relies on Citibank data), as viewed May 2, 2012.

In practice, VSL is generally not adjusted for income differences within a particular population, but is adjusted for population-wide income changes over time or income differences across countries. In the U.S., there has been substantial debate about adjusting VSL to reflect age differences, particularly over the use of lower values for older individuals (i.e., the “senior discount”) (Robinson, 2007, 2009; Viscusi, 2009). While this lack of adjustment for population characteristics is often framed as an equity issue, applying the same value regardless of the characteristics of those affected ignores their preferences for spending on health risk reductions rather than on other goods and services.

EconLit: http://www.aeaweb.org/econlit/index.php; EVRI: https://www.evri.ca/Global/HomeAnonymous.aspx.

In particular, Lund et al. (2009) provides a comprehensive inventory of sources of cost estimates. For injuries, Finkelstein, Corso, and Miller (2006) present incidence-based estimates of lifetime costs per case, and the Centers for Disease Control and Prevention (CDC) provide an injury cost calculator as part of the Web-based Injury Statistics Query and Reporting System (WISQARS) website (http://www.cdc.gov/injury/wisqars/index.html).

The cost of this treatment is nevertheless a social cost, and savings in this cost can be added to estimates of WTP when calculating the total benefits of the policy as long as care is taken to avoid double-counting.

A closely related measure, the disability-adjusted life year (DALY), was developed as an egalitarian indicator of the gap between current and “ideal” health to support assessment of the global burden of disease (Murray & Lopez, 1996). The DALY approach is currently undergoing revision (see: http://globalburden.org/).

In cost-effectiveness analysis, valuation is implicit, because monetary thresholds are needed to compare with the cost-effectiveness ratio to determine whether the intervention is worth implementing.

The Tufts CEA Registry can be accessed at:https://research.tufts-nemc.org/cear4/.

More information on these and other indices, including cross-index comparisons, is available on the National Health Measurement Study website: http://www.healthmeasurement.org/NHMS.html.

This article, and a calculator that allows users to retrieve EQ-5D scores by International Classification of Disease code and demographic characteristics, are available online: http://www.ohsu.edu/epc/mdm/webResources.cfm. The underlying scoring function used to calculate HRQL from the EQ-5D for the US population was developed by Shaw, Johnson, and Coons (2005).

While some analysts add COI estimates to QALY estimates, arguing that the later incorporates the value of averting pain and suffering, such an approach can lead to double-counting. For example, a QALY estimate that includes effects on usual activities may overlap with estimates of productivity losses.

This issue is distinct from the question of whether the outcome is a public or private good. In economics, public goods are nonexclusive (individuals cannot be prevented from consuming them) and nonrival (consumption by one does not reduce the amount available for others). However, public programs can provide private goods (e.g., health care consumed by the individual) as well as public goods (e.g., air pollution abatement that affects the community as a whole).

This issue is not the same as considering internalities and externalities. An externality is an outcome not reflected in market interactions, and is the traditional rationale for government intervention. However, government may intervene for other reasons; for example, to address the need for accurate information or to promote equity.

Adler and Posner (2006) refer to this as “laundering” preferences.


Citation Information: Journal of Benefit-Cost Analysis, ISSN (Online) 2152-2812, ISSN (Print) 2194-5888, DOI: https://doi.org/10.1515/jbca-2012-0006.

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