Breaking Research: Live Rich or Die Young

You’ve probably already seen the news about this study, but as a behavioral economics researcher by day and a personal finance blogger by night, I can’t ignore it here. Published on Monday in the Journal of the American Medical Association was the paper titled The Association Between Income and Life Expectancy in the United States, 2001-2014. First off, let me address the large scale of this study. It’s amazing. I mean, how does one go about getting “tax records for every individual [with a social security number] for every year from 1999 through 2014”?! That’s crazy! The sample size: 1, 408, 287, 218 person-year observations – no that’s not a typo! They also looked at specific geographic areas and if someone moved after 63, they counted their area as the place they were living at age 61 while working (they’ve seemingly thought of everything!). So let’s get to the findings:

Study Results:

• The median pre-tax household earnings in the U.S. was $61, 175 per year and the mean for household earnings was $97, 725 per year.
• “Men in the bottom 1% of the income distribution at the age of 40 years had an expected age of death of 72.7 years. Men in the top 1% of the income distribution had an expected age of death of 87.3 years, which is 14.6 years (95% CI, 14.4-14.8 years) longer than those in the bottom 1%.”
• “Women in the bottom 1% of the income distribution at the age of 40 years had an expected age of death of 78.8 years. Women in the top 1% had an expected age of death of 88.9 years, which is 10.1 years (95% CI, 9.9-10.3 years) longer than those in the bottom 1%.”
• Also interesting, the gap between the life expectancy of men and women differed between the lowest income holders and the highest: “In the bottom 1% of the income distribution, women lived 6.0 years (95% CI, 5.9-6.2 years) longer than men; in the top 1% of the income distribution, women lived only 1.5 years (95% CI, 1.3-1.8 years) longer than men.”
• The relationship between money and life expectancy was linear above the second lowest income percentile, but then becomes concave in the higher income brackets. “For example, increases in income from $14 000 to $20 000 (the 10th vs the 15th income percentiles), $161 000 to $224 000 (the 90th vs the 95th income percentiles), and $224 000 to $1.95 million (the 95th vs the 100th income percentiles) were all associated with approximately the same difference in life expectancy (ie, an increase of 0.7-0.9 years, averaging men and women).”
  

  figure from the supplementary information attached to the study

• There was substantial variation in life expectancy among the low-income populations based on geography, but very little for the high-income earners. In other words, if you’re poor, where you live matters! If you’re wealthier, it doesn’t as much.
• “Nevada, Indiana, and Oklahoma had the lowest life expectancies (<77.9 years) when men and women in the bottom income quartile were averaged. Of the 10 states with the lowest levels of life expectancy for individuals in the bottom income quartile, 8 formed a geographic belt from Michigan to Kansas (Michigan, Ohio, Indiana, Kentucky, Tennessee, Arkansas, Oklahoma, Kansas). The states with the highest life expectancies for individuals in the bottom income quartile (>80.6 years) were California, New York, and Vermont.”
• “Individuals in the top income quartile had the lowest life expectancies (<85.3 years) in Nevada, Hawaii, and Oklahoma. Individuals in the top income quartile had the highest life expectancies (>87.6 years) in Utah; Washington, DC; and Vermont.”
• Anchorage, Alaska did not appear in their table of top 10 or bottom 10 for either the bottom or top income earners.
• Life expectancies also changed significantly in some areas over time and here’s where Alaska shows up: ” Hawaii, Maine, and Massachusetts had the largest gains in life expectancy (gaining >0.19 years annually) when men and women in the bottom income quartile were averaged. The states in which low-income individuals experienced the largest losses in life expectancy (losing >0.09 years annually) were Alaska, Iowa, and Wyoming.”
• Health also matters: “[T]he majority of the variation in mortality rates across areas among individuals with low socioeconomic status was related to medical causes, such as heart disease and cancer, rather than external causes, such as vehicle crashes, suicide, and homicide”
• Wealth was a larger driver of longer life expectancy than the health factors of the environment: “Theories positing that differences in mortality are driven by the physical environment (eg, exposure to air pollution or a lack of access to healthy food) suggest that the gap in life expectancy between rich and poor individuals should be larger in more residentially segregated cities. Empirically, in areas where rich and poor individuals are more residentially segregated, differences in life expectancy between individuals in the top and bottom income quartile were smaller (r = −0.23, P = .09). Individuals in the bottom income quartile who lived in more segregated commuting zones had higher levels of life expectancy (r = 0.26, P = .04).”
• Labor market conditions also didn’t make much of a difference: “Unemployment rates, changes in population, and changes in the size of the labor force (all measures of local labor market conditions) were not significantly associated with life expectancy among individuals in the bottom income quartile.”
• “Associations between life expectancy for the bottom income quartile and 20 other factors were assessed. The strongest correlates were the local area fraction of immigrants (r = 0.72, P < .001), median home values (r = 0.66, P < .001), local government expenditures per capita (r = 0.57, P < .001), population density (r = 0.48, P < .001), and the fraction of college graduates (r = 0.42, P < .001). Population density and the fraction of college graduates were also significantly positively associated with trends in life expectancy across commuting zones for individuals in the bottom income quartile.”

There’s so much here. And a lot of it is bad news. It’s hard to see a study like this and then argue even us, less-than-average wage-earners have a major leg-up in our financial (and mortal!) journey! What results stick out the most to you?