Income Inequality and Population Health: A Global Gradient?

SSM - Population Health

Ore

et al. 2019

Although studies have shown that air pollution can be devastating to population health, little is known about the health implications of the intersection of air pollution and income inequality. We investigate if air pollution is especially detrimental to the health of US state populations characterized by more inequitable distributions of income. In other words, are the populations of states with higher levels of income inequality especially vulnerable to similar levels of air pollution? We use two-way fixed-effects panel regression techniques to analyze longitudinal data for 49 US states and the District of Columbia (2000–2010) to model state-level life expectancy as a function of fine particulate matter, income inequality, and other state-level factors. We estimate models with interaction terms to formally assess whether the association between fine particulate matter and life expectancy varies by level of state income inequality. Across multiple life expectancy outcomes and additive models, states with higher PM 2.5 levels tend to exhibit lower average life expectancy. This general pattern is observed with our specifications for raw and weighted PM 2.5 and with adjustments for income share of the top 10%, total population, GDP per capita, median household income, median age, percent college degree or higher, percent black, and percent Hispanic/Latino. We also find that the association between state PM 2.5 levels and average life expectancy intensifies in states with higher levels of income inequality. More specifically, PM 2.5 levels are more detrimental to population life expectancy in states where a higher percentage of income is concentrated in the top 10% of the state income distribution. We discuss the implications of our results for future research in social epidemiology and environmental justice.

Section: Introductionmentioning

confidence: 94%

Air quality and life expectancy in the United States: An analysis of the moderating effect of income inequality

Hill

SSM - Population Health

Ore

et al. 2019

“…More specifically, PM 2.5 levels are more detrimental to population life expectancy in states where a higher percentage of income is concentrated in the top ten percent. They sketch out and apply the theoretical principles of power, proximity, and physiology to help explain how income inequality can exacerbate the population health impacts of air pollution (see also Charafeddine and Boden 2008, Curran and Mahutga 2018.…”

Section: Introductionmentioning

confidence: 99%

Power, proximity, and physiology: does income inequality and racial composition amplify the impacts of air pollution on life expectancy in the United States?

Hill

Clark

et al. 2020

Environ. Res. Lett.

This study advances research at the intersection of environmental degradation, social stratification, and population health in the United States. Expanding the theoretical principles of power, proximity, and physiology, we hypothesize that the harmful effect of fine particulate matter on life expectancy is greater in states with higher levels of income inequality and larger black populations. To test our hypothesis, we use two-way fixed effects regression analysis to estimate the effect of a three-way interaction between fine particulate matter, income share of the top ten percent, and the percent of the population that is black on state-level average life expectancy for all US states and the District of Columbia (2000Columbia ( -2014. The findings support our hypothesis: the estimated effect of the three-way interaction on average life expectancy is negative and statistically significant, net of various socioeconomic and demographic controls. Using post-estimation techniques, we visually illustrate that the harmful effect of fine particulate matter on life expectancy is especially pronounced in states with both very high levels of income inequality and very large black populations. We conclude by summarizing the theoretical and substantive implications of our findings, the limitations of the study, and potential next steps in this evolving area of interdisciplinary research. Recently, Hill and colleagues (2019) conducted a longitudinal analysis of US states and the District of Columbia

“…A large body of research in the social sciences suggests that inequality is a key driver of a range of health-related outcomes (see Pickett and Wilkinson 2015; Wilkinson and Pickett 2010, 2019). Recent studies that used relatively more sophisticated statistical modeling techniques have provided empirical evidence of the detrimental health impacts of macro levels of income inequality, especially reductions in country-level and U.S. state-level average life expectancy (e.g., Curran and Mahutga 2018; Hill and Jorgenson 2018; Jorgenson et al 2020). A number of studies have also observed inequality to be associated with adult and infant mortality, obesity, HIV infections, mental illness, and homicides (Buot et al 2014; Daly 2016; Ribeiro et al 2017; Torre and Myrskylä 2014; Wilkinson and Pickett 2010, 2019).…”

Section: Introductionmentioning

confidence: 99%

What Is Driving the Drug Overdose Epidemic in the United States?

Thombs

et al. 2020

J Health Soc Behav

The demand-side perspective argues that the drug overdose epidemic is a consequence of changes in the economy that leave behind working-class people who lack a college education. In contrast, the supply-side perspective maintains that the epidemic is primarily due to changes in the licit and illicit drug environment, whereas a third, distinct perspective argues that income inequality is likely a key driver of the epidemic. To evaluate these competing perspectives, we use a two-level random intercept model and U.S. state-level data from 2006 to 2017. Contrary to the demand-side approach, we find that educational attainment is not associated with drug-related mortality. In support of the supply-side approach, we provide evidence indicating that opioid prescription rates are positively associated with drug-related mortality. We also find that income inequality is a key driver of the epidemic, particularly the lack of resources going to the bottom 20% of earners. We conclude by arguing that considerations of income inequality are an important way to link the arguments made by the demand-side and the supply-side perspectives.