Who is hurt by procyclical mortality?

Abstract

There is renewed interest in understanding how fluctuations in mortality and in health are related to fluctuations in economic conditions. The traditional perspective that economic recessions lower health and raise mortality has been challenged by recent findings that reveal mortality is actually procyclical. The epidemiology of the phenomenon - traffic accidents, cardiovascular disease, and smoking and drinking - suggests that socioeconomically vulnerable populations might be disproportionately at risk of "working themselves to death" during periods of heightened economic activity. In this paper, I examine mortality by individual characteristic during the 1980s and 1990s using the U.S. National Longitudinal Mortality Study. I find scant evidence that disadvantaged groups are significantly more exposed to procyclical mortality. Rather, working-age men with more education appear to bear a heavier burden, while those with little education experience countercyclical mortality.

Figure 1

Age-adjusted Mortality in the NLMS and HMD Notes: Data are drawn from the National Longitudinal Mortality Study (NLMS) and the Human Mortality Database (HMD). Each series consists of age-adjusted mortality rates for both sexes combined at ages 10 and over calculated by the author using age-specific mortality rates from each source and the age distribution for both sexes combined in 1990 as provided by the HMD.

Figure 2

Changes in log mortality versus changes in unemployment 1980–1998 Notes: This figure plots changes in log age-adjusted mortality rates for both sexes combined at ages 10 and over against changes in the civilian unemployment rate. The mortality data are taken from the two series depicted in Figure 1; the unemployment data are provided by the U.S. Bureau of Labor Statistics. Trend lines are ordinary least squares fits of the each data series to a constant and the change in unemployment. In the NLMS data, the slope is estimated at γ̂ = − 0.0067 with a standard error of 0.0003, while in the HMD data, it is γ̂= −0.0019 with a standard error of 0.0036.