COVID-19 has created a dual set of stresses on health care systems worldwide: a rise in expensive intensive care services and a dramatic decline in elective services. The U.S. government has responded with both grant and loan programmes to help health care providers weather the storm.
Many stated-preference studies that estimate the willingness-to-pay (WTP) for reductions in mortality or morbidity risk suffer from inadequate scope sensitivity. One possible reason is that the risk reductions typically presented to respondents are too small to be meaningful.
Survey responses may thus not accurately reflect respondents' preferences for health and safety. In this paper we propose a novel way of estimating the value per statistical life (VSL) or the value per statistical case (VSC) based on risk reductions measurable as percentages.
While such non-marginal risk reductions are easier to understand, they introduce well known feedback effects. We develop a methodology to de-bias VSL and VSC estimates derived from the evaluation of non-marginal risk reductions and prove the concept using simulated data.
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