Subjective life expectancies, time preference heterogeneity, and wealth inequality
What is this research about and why did you do it?
This paper examines how objective and subjective heterogeneity in life expectancy affects savings behavior of healthy and unhealthy people. Standard consumption/savings theory predicts that people who place a larger weight on future states will be wealthier than people who are more impatient, all else equal. We asked ourselves: how much of the observed health-wealth gap can be explained by the fact that unhealthy individuals expect to live a shorter time, thus put a smaller weight on future states, and are therefore not willing to save as much?
How did you answer this question?
Using data from the Health and Retirement Study, we first investigated what individuals actually think about their probability of surviving to older ages, and then compared those beliefs to a statistical measure of their life expectancy, conditional on a rich set of covariates. To gauge the effect of the subjective beliefs on savings behavior and wealth accumulation, we used an overlapping-generations model where survival probabilities and beliefs evolve according to a health and survival process estimated to capture the elicited beliefs from the data.
What did you find?
First, we found systematic biases in survival beliefs across self-reported health: those in poor health not only have a shorter actual lifespan but also underestimate their remaining life time. Second, using the structural OLG model, we were able to quantify the importance of these belief biases for wealth accumulation. We concluded that differences in life expectancy are important to understand savings behavior, and that the belief biases, especially among the unhealthy, can explain up to a fifth of the observed health-wealth gap.
Elicited beliefs about survival versus estimated objective (statistical) survival probabilities for nonblack men. Each bubble represents the average for an age/health group. The x-axis shows the model-predicted (objective) survival probability to the age of 75. The y-axis shows the average self-reported survival probability for that group and age. Colors indicate the health state: dark green is excellent while red is poor health. The size indicates the number of observations in each cell.
What implications does this have for the study (research and teaching) of wealth concentration or economic inequality?
This paper ties into a strand of current research investigating preference heterogeneity and its importance for individual choices and aggregate outcomes. We provide an intuitively plausible and micro-founded source of heterogeneity: the perceived probability of surviving to future states of the world. Our quantification of this channel shows that life expectancy heterogeneity is important and should be included in the list of potential sources of heterogeneity that we need to consider in our analyses.
What are the next steps in your agenda?
We continue to investigate how life expectancy heterogeneity shape economic outcomes, in particular how this heterogeneity contribute to wealth inequality. For instance, how does life expectancy inequality contribute to differences in retirement wealth?
Citation and related resources
Foltyn, R. and Olsson, J. Subjective life expectancies, time preference heterogeneity, and wealth inequality. Quantitative Economics 15, no. 3(2024): 699-736.
