OBJECTIVES: The search for “value for money” represents a fundamental motivation underlying comparative health economic evaluations. Value is frequently conceptualized as willingness-to-pay (WTP) per quality-adjusted life year (QALY) gained. However, currently used benchmarks for the WTP/QALY are controversial and lack robust empirical support. Against this background, we conducted a systematic review of economic studies providing empirical data on the value of a statistical life (VSL), which were published between 1995 and 2015.

METHODS: Our literature search (using the EconBiz and EconLit databases) identified 120 studies reporting original data, yielding 133 unique VSL estimates. We transformed these estimates into VSLY (value of a statistical life year, expressed in year 2014 Euros), using WHO life expectancy tables, a 3% discount rate, consumer price indices for inflation adjusting, and purchasing power parities for currency conversion. We performed a regression analysis to measure the effect of different drivers on VSLY estimates, including region, method (stated preference, SP: contingent valuation, CV; discrete choice experiment, DCE; revealed preference, RP: wage risk, WR; Other), study design (cross-sectional; panel), GDP per capita and fatality risk.

RESULTS: The median VSLY was €164,400 (mean VSLY, €223,500), more than six times GDP per capita. Regression results indicate that studies with North American data sources report significantly higher VSLY estimates (median, €271,200 vs. Europe, €158,500; Asia, €43,000; Other, €80,400), as well as those valuated with the RP/WR method (median, €259,200 vs. RP/Other, €179,200; SP/CV, €124,000; SP/DCE, €186,600) and with higher GDP per capita. In our analysis, study design as well as fatality risk were not significant at conventional levels.

CONCLUSIONS: Our results strongly suggest that VSLY estimates based on empirical data exceed benchmarks commonly used in the context of Health Technology Assessments. WTP in North American studies was significantly higher compared to Europe and Asia, even after adjustment for GDP/capita.