NSF Org:	SES Division of Social and Economic Sciences
Recipient:	AMERICAN UNIVERSITY
Initial Amendment Date:	July 27, 2021
Latest Amendment Date:	July 27, 2021
Award Number:	2116716
Award Instrument:	Standard Grant
Program Manager:	Robert O'Connor roconnor@nsf.gov  (703)292-7263 SES  Division of Social and Economic Sciences SBE  Directorate for Social, Behavioral and Economic Sciences
Start Date:	July 1, 2021
End Date:	June 30, 2023 (Estimated)
Total Intended Award Amount:	$29,876.00
Total Awarded Amount to Date:	$29,876.00
Funds Obligated to Date:	FY 2021 = $29,876.00
History of Investigator:	Mary Hansen (Principal Investigator) mhansen@american.edu Robert Feinberg (Co-Principal Investigator) Elissa Cohen (Co-Principal Investigator) Alexandra Mislin (Co-Principal Investigator)
Recipient Sponsored Research Office:	American University 4400 MASSACHUSETTS AVE NW WASHINGTON DC  US  20016-8003 (202)885-3440
Sponsor Congressional District:	00
Primary Place of Performance:	American University 4400 Massachusetts Avenue, NW Washington DC  US  20016-8003
Primary Place of Performance Congressional District:	00
Unique Entity Identifier (UEI):	H4VNDUN2VWU5
Parent UEI:
NSF Program(s):	Decision, Risk & Mgmt Sci
Primary Program Source:	010V2122DB R&RA ARP Act DEFC V
Program Reference Code(s):	075Z, 102Z, 9179
Program Element Code(s):	132100
Award Agency Code:	4900
Fund Agency Code:	4900
Assistance Listing Number(s):	47.075

ABSTRACT

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).

Every day, people consider tradeoffs they are willing to make to reduce risks. The Value of a Statistical Life (VSL) describes the tradeoffs in aggregate. The VSL is used to evaluate safety regulations across many areas of life. Accurate measurement is critical, but VSL estimates vary widely. This dissertation advances the national health, prosperity, and welfare by investigating three sources of variability in the VSL. First, it compares the methods of discovering people?s risk preferences. This is important because preferences are a key input into the VSL metric. Second, it examines whether people?s risk preferences differ across contexts when the probability of death does not. This is important because VSL estimates derived from labor market data are used to guide regulation in unrelated areas. Finally, it explores people?s subjective experiences of objective risks. This is important because use of the VSL requires that people accurately comprehend risk. The project contributes to the advancement of the decision sciences and economics by testing prevailing assumptions about the nature and measurement of risk preferences. Findings promote a more complete theory of how perceptions of risk guide decision making. Findings may also reshape how regulatory agencies approach cost-benefit analyses of safety regulations. This is of particular importance in promoting the health, prosperity, and welfare of vulnerable populations, who are more susceptible to fatality risks and health hazards.

The Value of a Statistical Life (VSL) captures the trade-offs people are willing to make to reduce the probability of death. The VSL is used widely in safety regulation. Questions remain, however, regarding how well the VSL captures risk preferences. First, VSL estimates vary considerably by measurement approach (revealed vs. stated preferences), reflecting different assumptions about how people evaluate risk. Second, VSL estimates from labor contexts are leveraged for non-labor regulations, which may not be justifiable if people value risk mitigation differently across contexts. Finally, use of the VSL relies on the assumption that people accurately comprehend risk; this ignores differences in subjective experiences of risk. To address the first two issues, subjects complete a revealed preferences (RP) survey about the labor market and three discrete choice experiments (DCE) eliciting stated preferences (SP) to reduce risks in other contexts. To assess the VSL?s criterion validity, RP responses are predicted from the labor SP DCE. To test the stability of preferences across contexts, comparisons of people?s wealth/risk trade-offs across the DCEs are examined with machine learning techniques. To address the third question, baseline risk preferences for each subject and their perceptions of risk in their jobs, and in each DCE are measured and compared. The data are used to develop and test a model of risk preferences in contexts significant to VSL estimation. The work advances understanding of how decision making is shaped by context and how subjectivity in experiencing risk affects the VSL over and above the objective risky features of one?s environment. Ultimately these findings have the potential to inform how governments allocate public resources to increase safety.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

PROJECT OUTCOMES REPORT

Disclaimer

This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.

The Value of a Statistical Life (VSL) captures the trade-offs people are willing to make to reduce the probability of death. While VSL is widely used by policymakers to assess safety regulations, questions remain about how to estimate it. First, VSL estimates from labor contexts are used to assess non-labor regulations, which may not be justifiable if people value risk mitigation differently across contexts. Second, VSL estimates vary by measurement approach. One approach (called revealed preference) is to observe the extent to which similar people require higher wages to take riskier jobs. The second approach (called stated preference) is to ask people how much they are willing to pay to avoid risk. To find out whether the VSL really does differ by context or by approach, this project conducted an extensive survey of over 600 people who work in jobs with different risks of death at work. The survey asked questions about their actual wages, and it also presented them with a series of alternative hypothetical scenarios in which they were asked to state their preferred choice between different scenarios.  The survey results were used to compute VSLs. People’s health VSLs are significantly larger than their labor or transportation VSLs, suggesting regulators may be underestimating people’s willingness to pay for safety regulation in health care. The average size of people’s VSLs using the revealed preference approach is bigger than using the stated preferences approach, but the revealed preference approach produces such a wide range of values that the difference is not statistically meaningful. This provides some support for claims that the stated preference approach provides a meaningful complement to, or substitute for, the revealed preference approach.

Last Modified: 07/01/2023
Modified by: Mary Hansen

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