The Effect of Income on Mortality: Evidence from the Social Security Notch

Abstract

Legislation in the 1970s created a Notch in social security payments, with those born after January 1, 1917, receiving sharply lower benefits. Using restricted-use versions of the National Mortality Detail File combined with Census data, we use this quasi experiment to examine the income mortality link in an elderly population. Estimates from difference-in-difference and regression discontinuity models show the higher-income group has a statistically significantly higher mortality rate, contradicting the previous literature. We also found that younger cohorts responded to lower incomes by increasing postretirement work effort, suggesting that moderate employment has beneficial health effects for the elderly. Copyright by the President and Fellows of Harvard College and the Massachusetts Institute of Technology.

Understanding the effect of retirement on health: Mechanisms and heterogeneity

“…Kuhn et al (2010) report increased mortality upon retirement. Snyder and Evans (2006) conclude that employment past retirement age decreases mortality. However, Hernaes et al (2013) find no significant effect of retirement on mortality, while Blake and Garrouste (2013), Bloemen et al (2013) and Hallberg et al (2014) even find that retirement leads to a decrease in mortality.…”

Health responses to a wealth shock: evidence from a Swedish tax reform

“…They also find that the likelihood of dying within 2 years of the crisis increased by 5%. Similarly, Snyder and Evans (2006) use a legislative change in the US Social Security system that unexpectedly lowered the benefits for people born after January 1, 1917-the so-called Notch generation. A comparison of 5-year mortality rates after the age of 65 for males born in the first quarter of 1917 and the last quarter of 1916 shows that the "Notch" generation had slightly lower 5-year mortality rates than the previous cohort.…”

“…3 Given the practical constraints involved in randomizing people to receive different amounts of wealth, researchers have tried to solve these methodological challenges with quasi-experimental designs, in particular by exploiting exogenous variation generated by individual wealth or income shocks. Important examples include lottery winnings (Lindahl 2005;Gardner and Oswald 2007;Apouey and Clark 2015;Cesarini et al 2016), stock market fluctuations (Schwandt 2014), inheritances (Meer et al 2003;Kim and Ruhm 2012;Carman 2013), and unanticipated policy changes (Jensen and Richter 2004;Case 2004;Frijters et al 2005;Snyder and Evans 2006). 4 The general finding is that wealth and income have a limited impact on adult health in the short to medium run.…”

An empirical analysis of the importance of controlling for unobserved heterogeneity when estimating the income-mortality gradient

“…Allied to this research are correlated and shared frailty models (not discussed here) that often use data on twins to examine the role of genes in longevity (Yashin, Iachine, and Harris 1999). 4 A related issue, one beyond the scope of this paper, is that of a causal effect (as opposed to an association or a correlation) of income on mortality (Lindahl 2005;Snyder and Evans 2006). Kenya. In sum, although unobserved heterogeneity is significantly present in all four studies, not one suggests that failing to model unobserved heterogeneity yields a large attenuation bias in the estimated income-mortality gradient.…”