Summary Of Health Inequality

Though this research, I seek to identify forces driving health inequality. Throughout history, vulnerable members of the population have been more susceptible to both illnesses and poor health. Health condition inequality and health care accessibility are not only structural injustices in society, but also a matter of life or death for lower income individuals. Especially in a country where federal funding for healthcare is constantly debated, it is essential to study if income disparities predict both health quality and healthcare inequalities. Additionally, it is essential to study if the feminization of poverty could potentially put women at risk for poor health conditions.
Through General Social Survey Data from 2016, I will study if

income is a predictor of perceived health conditions. I will explore if higher income leads to improved perceived health conditions and if the relationship with income changes when controlling for years of education and if the respondent is female.

Literature Review
In order to review previous research on the relationship between income and health, I reviewed similar studies by Herd, Goesling, and House and Mirowsky and Hu.

Herd, Goesling, and House specifically focused on the onset and progression of health problems and hypothesized that “while education may work primarily to delay the onset of health problems, income seems likely to primarily slow the course or progression of health problems” (Herd et al. 2007:226). They utilized data from the Americans’ Changing Lives study to “examine how education and income vary in importance in predicting (1) the onset of poor health and (2) the progression of poor health to worse health or death” (Herd et al. 2007:226). The Americans’ Changing Lives study, a longitudinal panel survey conducted at University of Michigan, surveys a “probability sample of noninstitutionalized civilian adults 25 years or older living in the contiguous United States” (Herd et al. 2007:227). They found that education alone does not predict health, but that income is “significantly associated with all stages of health decline” (Herd et al.

2007:230).
On the other hand, Mirowsky and Hu specifically focused on physical impairments and hypothesized that higher income is a predictor of lower impairment and that “adjusting for sex, race, marriage, and education substantially and significantly reduces the nonlinear association between income and impairment” (Mirowsky and Hu 1996:1077). They utilized the 1990 U.S. Survey of Work, Family, and Well-Being, a telephone survey that yielding “2,031 respondents ranging in age from 18 to 90” as well as the National Survey of Personal Health Practices and Consequences (Mirowsky and Hu 1996:1078). They found that “higher income predicts a lower rate of increase in impairment” (Mirowsky and Hu 1996:1090).
Based on the findings of these previous studies, I hypothesize that income is a predictor of perceived health conditions.

More specifically, I hypothesize that higher income is a predictor of improved perceived health, increased years of education strengthens perceived health, and that females have weakened perceptions of health quality. My study will differ from these previous studies by focusing on the respondent’s perceived health conditions rather than their specific physical impairments or the onset and progression of health problems. I will explore this research question through General Social Survey 2016 data, using the recoded variable INCOME16_5 to assess median total family incomes and R_HEALTH to assess the respondents’ perceived quality of health. Similarly to prior studies, I also control for if the respondent is female and the amount of years of education that the respondent has

obtained.

Data & Methods
In order to test if income is a predictor of perceived quality of health, I will utilize the variables R_HEALTH as the dependent variable and INCOME16_5 as the independent variable. In addition to a univariate analysis of each variable, I will conduct both a crosstabulation and a regression. In the regression analysis, I will also control for if the respondent is female and years of education obtained.