Examining the Relationship between Race/Ethnicity and Health Status: Do Assets Matter?

Purpose: Health disparities are often linked to differences in socioeconomic status (SES), often referred to as the SES-health gradient. SES is a multifaceted concept capturing a broad range of social circumstances including human capital (education level) and financial capital (household income and assets). The SES-health gradient is especially relevant in light of the continuing and expanding economic inequalities among racial and ethnic groups in the U.S. Despite the vast research that exists on the SES-health gradient, few studies have examined how multiple indicators of SES shape health status across different racial and ethnic groups in the U.S., leaving the effects of SES indicators on self-rated health for each major racial/ethnic group largely undetermined. Another limitation of current research is that inferences about the racial and ethnic differences in health status have only employed socioeconomic factors such as income and education, thereby yielding limited results. We employ Grossman's (1972) economic model of health framework to help us better understand the relationship between SES and health status by expanding the analyses to include household assets. The purpose of this study was to address this knowledge gap by examining the relationships between household assets and health status among African Americans, Latinos, Asians, and non-Hispanic Whites.

Methods: This study utilized cross-sectional data from the 2004 Immigration and Intergenerational Mobility in Metropolitan Los Angeles Survey, which included responses from self-identified African Americans (n=445), Asians (n=1,698), Latinos (n=1,808) and non-Hispanic whites (n=704) respondents. In addition to controlling for age and martial status, socioeconomic variables included: education, income, savings or checking account status, homeownership, and ownership of mutual funds, stocks, or 401ks. The outcome measure, self-rated health, is considered a quality global evaluation of a person's health and is also considered a more influential predictor of mortality compared to other objective health measures. Multinomial logistic regression techniques estimated how each factor predicts the probability of being in one category of health status versus another for every racial and ethnic group. In this study, the “poor” category served as the reference group.

Results: Results of the multinomial regression equation suggest that racial and ethnic differences exist in determining how SES shapes health. Among African Americans, income, and age were significant, but homeownership played a much more significant role. Among Asians, only income and age significantly predicted health status. Among Latinos, income and age were significant predictors, however, owning a checking account had the strongest association with health status. Among non-Hispanic whites, age and education were significant, but homeownership predicted the greatest odds of significantly predicting better health.

Conclusion: Our findings lend support to the social policy initiatives that created asset development programs since assets have a positive influence on health, independent of income. Continuing to fund asset development programs could be a viable way of reducing health inequities among America's various racial and ethnic groups, in particular African Americans and Latinos. Our findings suggest that although education and income are highly relevant aspects of the health/SES gradient, assets also share an equally significant association.