Date of Award:
Master of Science (MS)
Economics and Finance
Randy T. Simmons
Randy T. Simmons
Ryan M. Yonk
The Value of a Statistical Life represents how much a population values reducing the probability of death. American citizens and government agencies use the Value of a Statistical Life estimates in benefit-cost analysis to pass life-saving policies. The public uses this measurement as a scientific and objective tool to identify potentially favorable policy from ineffective and inefficient policy. Institutional incentives, however, are aligned for agencies to exaggerate Value of a Statistical Life calculations and overregulate markets. This thesis summarizes how the Value of a Statistical Life data sources, methods of estimation, and inconsistent behavioral reference points distort the statistical calculations. Despite the distorted estimation, agencies still rely heavily on the Value of a Statistical Life as a tool to pass policy. Public choice theory explains that agencies employ distorted information as a tactic to pass regulation. The theory demonstrates that regulators are self-interested not unlike the general public. This thesis provides a public choice analysis and concludes that agencies are incentivized to employ distorted data sources, methods of calculation, and public risk perceptions to inflate the Value of a Statistical Life and overregulate. As such, the Value of a Statistical Life will continue to be biased and inaccurate with the current methods of calculation and addressing political incentives.
Hunter, Alecia M., "Fear-based Policymaking: How Government Agencies Exploit Mortality Risk Perceptions" (2016). All Graduate Theses and Dissertations. 4885.
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