money and death

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).”

    income life expectancy

    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?

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21 Comments

  1. With situations like the water crisis in Flint (and pockets of Chicago), it’s easy to see why this holds true. Environment dictates so much about a person’s life and health. I won’t even get on my soap box about wealth and education 😉

    • MaggieBanks

      Interestingly, the environmental aspects didn’t impact things as much. That was a surprising finding to me because I’ve been a big believer in the fact that zones exist where it is nearly impossible to get healthy food. I’m sure those still impact life…. but not the life expectancy/wealth things as much. And obviously you need to write ten more posts on education and wealth… everyone needs to hear it again and again!

  2. The data about a decreased gap between male and female life expectancies in the highest income is interesting. I sometimes think about the future and how, according to statistics, Mr. Smith will die before me. It makes me somewhat hopeful that with the advantages that money provides, we might be able to spend more time on this earth together. Because here’s the thing – it’s not the large paycheck that’s causing these effects, it’s everything that goes along with it. If we’re able to reach a financial position where we’re not stressed out about money, I believe we will gain some of the same benefits.

    • MaggieBanks

      That bullet point was the most interesting to me as well. More for the emotional aspect… oh good! I only have to live alone for 1.5 years if we’re rich! 🙂

  3. Tawcan

    Interesting stats and study for sure. I wonder how much of an affect does health insurance contribute to the study. Presumably the bottom 1% ppl might have difficulties with health insurance where the top 1% probably won’t.

    • MaggieBanks

      “Measures of health insurance coverage and spending (the fraction of uninsured and risk-adjusted Medicare spending per enrollee) were not significantly associated with life expectancy for individuals in the bottom income quartile. Life expectancy was negatively correlated with hospital mortality rates (r = −0.31, P < .001), but was not significantly associated with the quality of primary care (r = 0.05; 95% CI, −0.19 to 0.29)."

  4. I’d love to see if there was any correlation with access to healthcare and health insurance. Could there be a point in the income levels that makes it more likely a family has access to company provided health insurance? If so, will this trend change with the introduction of Obamacare? I also wonder if the same trends are true in other countries – the UK or Canada for example, which have had nationalized healthcare systems for many years?

    • MaggieBanks

      Surprisingly: “Measures of health insurance coverage and spending (the fraction of uninsured and risk-adjusted Medicare spending per enrollee) were not significantly associated with life expectancy for individuals in the bottom income quartile. Life expectancy was negatively correlated with hospital mortality rates (r = −0.31, P < .001), but was not significantly associated with the quality of primary care (r = 0.05; 95% CI, −0.19 to 0.29)."

      • Hmmm, not that then!

        • MaggieBanks

          But I would be interested in seeing the difference in different countries. I bet the social insurances as well as the health insurance have an impact.

  5. Oh Maggie, I love it when you have correlation co-efficients and confidence intervals in your posts!
    Fascinating stuff. Surprised about the unemployment/employment stats.
    Agree with Penny re education, something I’m really passionate about closing the gap on … In Australia indigenous background is also a key factor in life expectancy.
    I’ve used one of those life expectancy calculators which uses all of these variables and it predicts I will live till 93! I apparently have all the good predictors; female, married, post grad education, breastfed (who knew?), OK weight range etc. but do I want to live that long? I could ‘give back’ by volunteering to help underprivileged kids learn to read.
    And will our money last that long? Makes me think about housing options as I certainly dont want to be mowing the lawn at aged 93 …. First world problems I know!

    • MaggieBanks

      Ha ha ha! I’m glad you enjoy the geeky stuff! And I really hope to not live until 93! That seems like an awfully long life. Though my 91-year-old great aunt serves “meals to the old people” as she says a few times a week. The “old people” are the age of her children!

  6. I should look up the actual article when I have time, but can you explain the first bullet point? (I see an asterisk but it doesn’t seem to refer to anything.) I can’t believe that the mean is that much higher than the median — is that correct? If so, talk about the 1 percent!!

    • MaggieBanks

      The asterisk was a footnote saying that I can officially say I’m not average! 🙂 But I removed the comment, so thanks for catching the asterisk. And yes, that’s all true. The 1% is actually skewing it THAT much.

  7. A very interesting study, they had some serious data to look over.

    I think the bottom % of men is probably a reflection of the manual labour they have done during their lives (as well as a multitude of other similar things; working conditions, alcohol etc). The top % would only have the alcohol problem 🙂

    I imagine with time the men and women ages will get steadily the same as on average less men will be doing manual labour.

    The healthcare is also an important thing. In countries without universal health coverage (Europe, Canada, Australia, NZ), I imagine that the people who can’t afford the expensive health options sadly won’t live as long as they could have. But in the universal healthcare countries, it’s probably more equal.

    I bet cities that didn’t have industrial factories etc fare better than the ones they did/do. LA & San Francisco v Cleveland, Detroit etc (which shows up on the states that have high and low life age).

    It also strikes me that the warmer states will do better than cold ones (particularly ones that get really cold) for many obvious reasons. Old bodies don’t work well in cold temperatures.

    Tristan

    • MaggieBanks

      Good observations on the men. I’m sure there is a big factor on manual labor vs. office job in this scale for both men and women. And based on your bets, we better leave Alaska! 🙂

  8. Does that mean that wealth can buy additional years of life?
    That is a scary thought about inequality…
    Even if access to health care doesn’t have much impact, I wonder how much the stress of not having easy access to something (good food, good heathcare, stable job…) would have.
    It’s interesting to note too that more socially oriented economies like Japan and most of Europe have longer life expectancies than the US, even though the median wage is lower.

    Does the study make suggestions on how life expectancy could be improved?

    • MaggieBanks

      Definitely a scary thought! And I do wonder how better healthcare systems do overall (the U.S. has really poor outcomes in a lot of things compared to a lot of countries when it comes to healthcare, as you point out). And no, the study has no advice on the matter. Be rich, obviously. 🙂

  9. This stuff is so sobering. I want everyone who blathers on about anyone being able to achieve the American dream to read this and really let it sink in. And this doesn’t even address the lack of income mobility! Thanks for sharing this — I always love your research posts!

    • MaggieBanks

      I know! I completely agree on all levels! LIFE ISN’T FAIR, PEOPLE. See?

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