Abstract
We study subjective life satisfaction inequality in the United States using panel data from the 2005 to 2010 Behavioral Risk Factor Surveillance System. We aggregate individual level data to the state level and study how the average self-reported life satisfaction of various income, education, and life satisfaction groups changes with the average self-reported life satisfaction of the state. We find that the subjective life satisfaction of the least satisfied does not increase in equal proportion with the average life satisfaction of society, suggesting that increasing satisfaction levels are likely to lead to greater life satisfaction inequality. However, the life satisfaction of the poorest and least educated does increase in equal proportions with average life satisfaction.
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Notes
Throughout, our life satisfaction measure is self-reported. Therefore, “life satisfaction” refers to self-reported measures of subjective life satisfaction.
See Graham (2012) for a discussion of the policy relevance of Gross National Happiness versus Gross Domestic Product.
While this paper makes a contribution to the understanding of the distribution of life satisfaction by using a new methodological approach, it is also important to note what we do not do in this paper: it is not our goal to investigate the determinants of individual life satisfaction.
Ott (2005) hypothesizes that the positive correlation between income inequality and average levels of life satisfaction in poor countries may be due to an omitted variable, GDP growth.
A Hausman test rejects a random effects specification at the 5 percent significance level.
Calculating the mean of ordinal data can also be problematic if the increments in the scale do not correspond to equal increments in the underlying latent variable of life satisfaction. In that case, then our responses need to be carefully interpreted as reports of life satisfaction rather than actual life satisfaction. Nonetheless, assuming that the scale is cardinal and calculating average levels of self-reported life satisfaction is common in the literature (see, for example, Ott 2005; Clark et al. 2008; or Blanchflower et al. 2011). Blanchflower et al. (2011) justify this approach by noting that treating life satisfaction data as cardinal yields similar results compared to when it is treated as ordinal.
We also experimented with groupings based on race, however, in the survey data, there was a low response rate for the question that elicited an individual’s race. Therefore we are not confident that the average values calculated from this data would be reliable indicators of the true averages of those racial groups within each state so we do not include these results in our analysis.
Although we call this group “high income,” $50,000 might more accurately be described as middle to high income. The median household income in the U.S. during the time period we studied was approximately $54,000. Unfortunately, the BRFSS does not provide a more detailed income grouping that would allow us to identify top earners more selectively.
The BRFSS data can be obtained from http://www.cdc.gov/brfss/.
While we include these additional variables as controls to guard against omitted variable bias, including them when they are not significant should not affect the interpretation of our results. We also experimented with additional state level controls such as state-level long-term unemployment rates, percent of people receiving unemployment compensation, wage distribution within the state, and the percent of people on a government-sponsored income maintenance program. None of these variables were statistically significant in any of the estimations so we present the more parsimonious specification.
For example, if the rich invest more in either human or physical capital than do the poor, the distribution of income can affect future levels of income by affecting the stock of physical or human capital.
Note that the omitted year is 2010 so the interpretation of these year fixed effects is relative to the effect for 2010.
That said, not all of the variables have an obvious corollary at the group level (e.g., poverty rates among the high income should be zero).
Furthermore, it is the case that unemployment rates and poverty rates are strongly negatively correlated with average life satisfaction within each state. So, our results do not imply that policy makers should not be concerned with unemployment rates and poverty rates. They suggest only that unemployment and poverty rates do not have a disproportionate effect on the average life satisfaction of various income groups, once state fixed effects and the average level of life satisfaction are taken into account.
Note that our use of the fixed effects technique allows us to draw a conclusion about how the life satisfaction of the least satisfied will respond to changes in average life satisfaction. Because the technique essentially allows for a state-specific constant in the estimation, the coefficient on average life satisfaction can literally be interpreted as indicating how the life satisfaction of the least satisfied deviates from its average when average life satisfaction deviates from its average in that state. In a cross-section estimation that does not allow for the estimation of a state-specific constant, the only interpretation would be a less interesting one: that the life satisfaction of the least satisfied is less than the average life satisfaction overall.
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Acknowledgments
We are grateful for helpful comments from Jeffrey Pliskin, Stephen Wu, and two anonymous referees.
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Owen, A.L., Phillips, A. How Does the Life Satisfaction of the Poor, Least Educated, and Least Satisfied Change as Average Life Satisfaction Increases?. J Happiness Stud 17, 2389–2406 (2016). https://doi.org/10.1007/s10902-015-9699-4
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DOI: https://doi.org/10.1007/s10902-015-9699-4