The Role of Race and Ethnicity in Children's Health Insurance Coverage
- Presenter:
Chair: Ciaran Phibbs; Discussant: Kosali Simon Wed June 7, 2006 9:45-11:15 Room 213
The Role of Race and Ethnicity in Children’s Health Insurance Coverage: A Decomposition Analysis Using the Medical Expenditure Panel Survey
Large racial and ethnic disparities exist with respect to the insurance of children. In 2002, 77 percent of white (non-Hispanic) children age 0-18 had private coverage during the year, versus only 41% of Hispanic children and 47% of non-Hispanic black children. For non-Hispanic black children, this gap is closed by far higher rates of public coverage: 48% versus 17% among white children. Among Hispanic children public coverage fails to close the gap, and the prevalence of full-year uninsurance at 15% is more than twice the average of all other children.
What explains these large disparities? In this paper, we pool data from the 2000-2002 Medical Expenditure Panel Survey (MEPS) to implement Oaxaca-Blinder decomposition analysis. Pooling yields a sample of nearly 30,000 observations. Factors considered include age, sex, geographic location, family composition, family poverty, parent education, parent employment, the nativity and citizenship of children and their parents, and the language used to administer the survey. Because having a parent who is eligible for employment-related insurance is a key factor in children obtaining private insurance, we also conduct a decomposition analysis of disparities in parent eligibility rates and child take-up rates.
Whereas Oaxaca-Blinder decomposition was developed for the analysis of continuous dependent variables, the dependent variables in our analysis are discrete. This raises issues regarding econometric specification. We implement two solutions. The first is simply to apply Oaxaca-Blinder methods to linear probability regressions. The second is to decompose estimates from a multinomial logit model (MNL). MNL decomposition is a recently-developed technique that requires matching of children across groups. We explore a range of strategies for matching, including a method we develop that more flexibly incorporates sample weighting schemes applicable to most household survey data.
Perhaps not surprisingly, the key determinant of racial and ethnic coverage disparities among children is poverty. Secondary, but nevertheless important, factors include citizenship, language, parent education, and Census division. Together, the factors we examine explain nearly 80 percent of the racial and ethnic coverage disparities in our sample.
