1. Does this study show that income transfers to low-income individuals will increase their life expectancies?
No, our findings do not necessarily imply that income has a causal effect on life expectancy. That is, giving someone more money may not increase their lifespan. It is equally plausible that the association between income and life expectancy is driven by other unmeasured factors correlated with both health and income, such as differences in education or health behavior.
2. How is life expectancy measured in each calendar year?
We construct measures of period life expectancy, defined as the expected length of life for a hypothetical individual who experiences mortality rates at each subsequent age (starting at age 40) that match those observed for the population in a given year. These period life expectancy estimates are based on a “snapshot” of mortality rates in a given year; because they do not incorporate changes in mortality rates over time, they do not directly predict how long a given individual will live.
3. What are "race- and ethnicity-adjusted” estimates of life expectancy?
Life expectancy is known to differ across racial groups. Race-and-ethnicity adjusted estimates remove the differences in life expectancy across areas and income groups that are due to differences in the racial composition of those areas. Specifically, our race- and ethnicity- adjusted estimates represent the mean life expectancy that would prevail if each area and income group had the same proportions of black, Hispanic, and Asian individuals as the full national population.
4. What is a commuting zone?
We divide the U.S. into 741 Commuting Zones (CZs). CZs are groups of adjacent counties that are defined based on commuting patterns. For example, if people in neighboring counties work in the same city, then those counties are likely to belong to the same CZ. CZs are similar to metro areas, but have the advantage of covering rural areas as well. Note that each CZ is typically named after the biggest city in that zone. Hence, our statistics reflect average outcomes in a broad area around that city and not just that one city itself.
5. Would moving to an area with higher average life expectancy increase a given individual’s life expectancy?
Our research does not answer this question; we characterize the outcomes of individuals living in a given place, but have not analyzed whether moving to a different area affects longevity. The geographic variation we document could potentially be driven by differences in the characteristics of the residents of each area, in which case moving would not affect a given individual’s health outcomes. However, research based on the Moving to Opportunity Experiment has shown that moving to a better neighborhood can improve health outcomes significantly (e.g., reducing rates of obesity).
6. Does the absence of a strong correlation between health care access and life expectancy across areas mean health care does not affect life expectancy?
No it does not, for two reasons. First, the variation in medical care across areas may be small relative to the variation in other factors that affect life expectancy: e.g. differences in healthcare may be swamped by differences in smoking and obesity. Second, health care utilization may be higher in areas with sicker populations (reverse causality), in which case we might observe no differences in longevity in areas with more health care use even if health care matters for longevity. More broadly, the correlational analysis in our study does not uncover causal mechanisms. While we identify the local area characteristics associated with greater longevity, we do not provide direct evidence that changing these characteristics would (or would not) affect longevity.
7. What can policy makers and health care practicioners do to reduce socioeconomic gaps in longevity given these new findings?
The geographic variation in longevity gaps we identify suggest that reducing disparities in longevity will require targeted local efforts in specific communities. Any such efforts will likely have to change the health behaviors of the poor, as the strongest predictors of differences in life expectancy across areas are health behaviors (such as smoking and exercise). The study does not offer specific recommendations about what changes in clinical practice or policy are necessary to improve health and longevity. However, in future research, we and others will use the local area data we have constructed to monitor local progress and identify which approaches are most effective.
8. Whom can I contact if I have other questions about the study or the data?
Please email us at firstname.lastname@example.org