Men as Dependents? Marriage and Changes in Health Insurance Coverage among Working-age Adults in the United States, 1988 to 2008

Abstract

Changes in marriage and employment patterns may have affected health insurance coverage rates differently for women and men. The author investigates changes in health insurance coverage between 1988 and 2008, focusing on employersponsored insurance (ESI) dependent and employee coverage. Using Oaxaca-Blinder decompositions and Current Population Survey data, the author finds that married men's coverage as dependents increased from 1988 to 2008, but a smaller share of men were married in 2008. Coupled with declines in ESI employee coverage, changes in marriage increased men's uninsurance rate. In contrast, marital changes for women were offset by their increased employment and insurance coverage as employees.

The Change in Living Kidney Donation in Women and Men in the United States (2005–2015): A Population-Based Analysis

“…Ensuring adequate medical follow-up for living donors postdonation is increasingly recognized a priority for transplantation programs, 22,23 and therefore lack of health insurance may be a significant barrier to donation. Because a larger proportion of women in the United States have their health insurance benefits covered as a dependent, 24 the threat of job loss may not necessarily affect their ability to access health care in the future. Conversely, the prospect of a male donor losing his job may more frequently result in loss of health insurance for him and his dependents.…”

Margins that Matter: Exploring the Association Between Academic Match and Bachelor’s Degree Completion Over Time

“…For example, if I find that overmatched students from the 2011 cohort have a higher graduation rate than their 1995 counterparts, regression decomposition can quantify the extent to which this can be explained by between-cohort differences in overmatched students' observed characteristics. Regression decomposition methods have typically been used to analyze differences in outcomes by race, ethnicity, and gender (see Bielby et al, 2014;Blinder, 1973;Fairlie & Robb, 2007;Oaxaca, 1973), but they have also been used to examine change over time (see Fortin et al, 2010;Percheski, 2017). It is important to note that regression decomposition is not designed to address concerns related to selection bias; to the extent that the cross-cohort regression models are affected by selection bias, these results will also be affected.…”