Federal Regulation and Mortality in the 50 States

Abstract

Previous research speculates that some regulations are counterproductive in the sense that they increase (rather than decrease) mortality risk. However, few empirical studies have measured the extent to which this phenomenon holds across the regulatory system as a whole. Using a novel U.S. state panel data set spanning the period 2000–2014, we estimate the effect of U.S. federal regulation on state‐level mortality. We find that a 1% increase in federal regulation of state economies is associated with an increase in an index of state mortality of between 0.53% and 1.35%. These findings are robust to the form of mortality measure, choice of covariates, and the inclusion/exclusion of various regions, states, and industries. We also provide an update of the “cost‐per‐life saved cutoff,” which is the counterproductive risk threshold for expenditures. We find that expenditures in excess of $38.6 million (2019 dollars) per life saved can be expected to increase mortality risk. This article fills an important gap in the empirical literature and boosts the credibility of mortality risk analysis, whereby public policymakers weigh both the expected lives saved and lost due to a proposed regulation or other policy.

Valuing Human Impact of Natural Disasters: A Review of Methods

“…These differences could be due to the level of income of the country where the disaster occurred [ 40 ]. The method of calculation could be another reason for such differences, for example, as consumers optimize their lifetime utility, thus neglecting intergenerational (long-term) utility, using willingness to pay (WTP) methods for a reduction of risk can often lead to overestimated values [ 44 , 45 ]. It could also simply be due to the differences in cultural norms between countries [ 40 ].…”

A Mortality Risk Analysis for OSHA’s COVID-19 Emergency Regulations

“…It seems likely that the direct method may underestimate the cutoff value, whereas the indirect method may overestimate it. Therefore, as a lower bound our analysis uses a USD 39.1 million (2020 USD) per expected death estimate arrived at using the direct method by Broughel and Chambers (2022), and as an upper bound our analysis uses a USD 109.8 million (2020 USD) per expected death estimate arrived at using the indirect method by Broughel and Viscusi (2021). The midpoint of these values, USD 74.5 million (2020 USD), is the central cutoff estimate in our analysis.…”

“…Countervailing increases in risk arise from nearly any expenditure because some risk-reducing expenditures are displaced when resources are commandeered and used in a different manner. This gives rise to a phenomenon known as the "mortality cost of expenditures" (Broughel and Viscusi 2021;Broughel and Chambers 2022). Given this tradeoff between spending and death risks, a key question for policymakers is whether regulations reduce risk sufficiently to offset increases in countervailing mortality risk from regulatory expenditures.…”

“…Researchers estimate the cutoff using two methods: (a) a direct approach that relies on the statistical association between income and mortality after controlling for confounding variables, or (b) an indirect method based on consumer preferences and economic theory. A recent estimate of the cutoff for the United States made using the direct approach is USD 38.6 million (2019 USD) (Broughel and Chambers 2022), whereas a study using the indirect approach estimates the cutoff at USD 108.5 million (2019 USD) (Broughel and Viscusi 2021).…”

“…A form of economic analysis known as "mortality risk analysis" identifies the degree to which regulatory costs generate opportunity costs that offset their lifesaving benefits (Broughel and Viscusi 2021;Broughel and Chambers 2022). A properly conducted mortality risk analysis calculates a regulation's cost-effectiveness (in this case, the cost per life saved) and tracks this cost-effectiveness over time.…”