How long people expect to live could be important for at least two reasons.
First, individuals may know information about their own circumstances that affect their predictions. Maybe they know that they are sick or in frequent danger from gun violence. In that case, their prediction of their own life-expectancy might be a proxy for their social circumstances.
Second, their prediction may change some of their own cost/benefit calculations. There is, for example, no economic or other extrinsic reason to pursue education if you fear that your life is nearly over. Then again, you might procrastinate on getting more education if you think you have a very long time left to live.
Therefore I am interested in who makes optimistic or pessimistic estimates of their own life-expectancies. Of course, younger people will expect to live for more years, on average, than older people, and that doesn’t reflect optimism. So I adjusted for age by looking at the difference between how long people expect to live and how long the Social Security Administration (SSA) predicts that someone of their age and gender will live. If people give a higher number than the SSA, they are optimistic; a lower number reflects pessimism. Pessimism may be entirely warranted and reasonable, but it could still have negative effects on some important behaviors.
The scatterplot shows that individuals’ predictions correlate with what the SSA would say, but there is a lot of variation. One young dude expects to live for another 110 years, whereas the SSA would give him 58 years. Several people expect to die very soon. What accounts for these differences?
Using the Tufts equity research survey, I looked first at the factors that are incorporated in prominent actuarial models–the things that we’re told actually lengthen or shorten our lives. If you go to a “longevity calculator” like this one, it will ask you to enter your own year of birth, gender, race, education level, body mass index, income, whether you are retired, and your habits of exercise, smoking, and drinking alcohol. It will tell you how many years you probably have left to live, based on your answers and a significant body of research.
Some of those factors affect optimism, but some do not. Reporting results from a regression model that are significant at p<.05:
- Younger people are more optimistic, meaning not that they expect to live more years than older people but that their predictions for their lives are higher in comparison to the SSA’s predictions for them.
- White people are more optimistic than people of color, and they have a basis for that.
- Higher body mass index (BMI) correlates with pessimism.
- Regular vigorous exercise predicts more optimism.
- Education, gender, marital status, income, employment status, and drinking and smoking are not related to optimism, even though they are significant predictors of life expectancy in actuarial models.
I also went looking for measures that might predict optimism even though they are not in the actuarial models that I see online. Some of them are quite significant. When added to a regression model with the variables listed above …
- Frequency of attending religious services predicts optimism.
- People with better overall health (per their self-report) are much more optimistic.
- Stress about climate change comes close to predicting pessimism (p=.054).
- Whether you own a gun and whether you planned to vote for Trump or Biden are not related to optimism.
To some extent, people seem to be making accurate predictions based on life circumstances. For instance, they are right to worry about high BMIs. They seem to be missing some important factors, such as smoking and drinking. The correlation with religious participation could reflect the beneficial results of participating in communities, or perhaps religion makes one optimistic about one’s own life, or perhaps people who think they have a long time to live are motivated to attend services.