Divorce and Women’s Risk of Health Insurance Loss

Abstract

This article bridges the literatures on the economic consequences of divorce for women with that on marital transitions and health by focusing on women’s health insurance. Using a monthly calendar of marital status and health insurance coverage from 1,442 women in the Survey of Income and Program Participation, we examine how women’s health insurance changes after divorce. Our estimates suggest that roughly 115,000 American women lose private health insurance annually in the months following divorce and that roughly 65,000 of these women become uninsured. The loss of insurance coverage we observe is not just a short-term disruption. Women’s rates of insurance coverage remain depressed for more than two years after divorce. Insurance loss may compound the economic losses women experience after divorce and contribute to as well as compound previously documented health declines following divorce.

Keywords

health insurance marital status and health uninsured women and divorce women’s health

Social scientists have accumulated a large body of evidence showing that married individuals tend to have better health than their never married and previously married counterparts (e.g., Waite and Gallagher 2000; Liu and Umberson 2008). Marriage is associated with lower mortality rates, better self-rated health, and lower prevalence of specific conditions such as heart disease (Carr and Springer 2010; Koball et al. 2010). One factor contributing to these marital health disparities is selection: Healthier people are more likely to get and stay married (Canady and Broman 2003; Sbarra and Nietert 2009). But several studies have also found that health may deteriorate, at least temporarily, following marital disruption (Prigerson, Maciejewski, and Rosencheck 1999; Williams and Umberson 2004; Lorenz et al. 2006; Hughes and Waite 2009; Liu 2012).

The two primary pathways through which marital disruption is thought to affect health are stress and the loss of health-enhancing resources (Carr and Springer 2010). Our article builds on this body of work by examining changes in health insurance coverage following marital disruption. Health insurance coverage has received surprisingly little attention in discussions regarding the negative effects of marital disruption on health outcomes. The sparse research on marital status and health insurance has been acknowledged in recent reviews addressing the marriage-health link, and multiple scholars have called for more information on the topic (Prigerson et al. 1999; Wood, Goesling, and Avellar 2007; Liu and Umberson 2008; Carr and Springer 2010; Koball et al. 2010).

Loss of health insurance coverage may help to account for the health declines that some individuals experience following marital disruption. Health insurance coverage is a resource more available within than outside of marriage, especially for women, and arguably one that is health enhancing (Bernstein et al. 2008; McWilliams 2009). It facilitates access to medical care, which may in turn promote healthy behaviors (e.g., medical professionals may encourage exercise or discourage smoking). Losing health insurance may also induce additional stress during the process of marital disruption. Its loss signifies a loss of financial protection during a period of already heightened financial vulnerability, especially for women. Past research finds that uninsured women frequently worry about getting sick and about whether they will be able to see a doctor, afford medications, or pay their medical bills if they do (Vuckovic 2000).

To understand the role health insurance may play in the poorer health of the previously married, we must first document the changes to health insurance coverage following marital disruption. At present, the little research available shows that married women are more likely than others to be insured (Bernstein et al. 2008). But little is known about how transitions in marital status are associated with changes in coverage.

Using nationally representative panel data from the Survey of Income and Program Participation (SIPP), we provide the most comprehensive portrait to date of what happens to women’s health insurance coverage upon divorce. We first document women’s rates of health insurance coverage and the sources of this coverage both before and after divorce. We then test whether women tend to lose coverage after divorce, identify characteristics that heighten or buffer this risk, and examine the duration of observed effects. Our analytic approach exploits the strengths of panel data to help account for selection. Observed differences in health insurance coverage between married and divorced women shown in previous studies may result from the greater disadvantages of women who divorce relative to those remain married (see Smock, Manning, and Gupta 1999). Our fixed-effects models control for a set of observed time-varying characteristics, as well as stable unobserved characteristics, which may be correlated with both divorce and insurance coverage.

Background

In the current U.S. health system, nonelderly Americans access and pay for health care through a fragile patchwork of employer-sponsored group insurance policies, individual health plans purchased on the private market, and public programs. As a result, life course events such as job transitions, marital transitions, and the onset of health problems allow individuals to slip through the cracks (Quadagno 2004). Multiple studies have documented that job losses frequently trigger insurance loss (e.g., Gruber and Madrian 1997). In contrast, very little is known about how marital transitions relate to health insurance coverage.

Most current knowledge on marital status and health insurance coverage is based on cross-sectional data (see Short 1998 for an exception). These studies find that unmarried women are between 1.5 and 2 times as likely as married women to be uninsured, and when insured, are more likely to rely on public insurance programs like Medicaid (Meyer and Pavalko 1996; Jovanovic, Lin, and Chang 2003; Bernstein et al. 2008; Montez, Angel, and Angel 2009). Observed differentials in insurance coverage by marital status may result from the higher likelihood of divorce among women with lower levels of socioeconomic resources or other unobserved differences.

But past research indicates that women who divorce not only start out with fewer resources than their counterparts who remain married, but they also experience substantial declines in income after divorce, far greater than their spouses (Smock et al. 1999). This study extends the economic consequences of divorce literature to examine the extent to which women lose another key economic resource—health insurance. As outlined in the next two sections, considering insurance loss can elaborate our understanding of the two primary mechanisms whereby marital disruption is thought to affect health—through the loss of health-enhancing resources and the stress of divorce.

The Loss of Resources Following Divorce

The loss of economic resources after divorce experienced by many women is predicated on their poorer compensation in the labor market relative to men and their continued economic dependence within marriage (Bianchi, Subaiya, and Kahn 1999; Smock et al. 1999). These factors may similarly jeopardize women’s health insurance coverage in the event of marital disruption.

One quarter of women in the United States younger than 65 receive dependent health insurance coverage through the benefits package of a spouse or other family member, substantially higher than the dependent coverage rate for men (Kaiser Family Foundation 2011).¹ Part of this differential comes from married women’s lower rates of labor force participation. Another part is because women tend to work in jobs and occupations with poorer fringe benefits than those of their husbands (Currie 1997). Women also frequently decline health insurance coverage through their own employers and instead elect coverage through a husband’s employer, presumably because they assess this coverage to be more comprehensive or a better value (Buchmueller 1997).

Women may lose health insurance after marital disruption both directly through the loss of dependent coverage or indirectly through their inability to access or afford other forms of coverage. The federal Consolidated Omnibus Budget Reconciliation Act (COBRA) law grants former spouses the option to temporarily purchase an extension of dependent coverage for up to 36 months, but premiums are high (nearly $500 per month for individual coverage in 2011; Claxton et al. 2011). Financial hardship may hinder women’s ability to maintain uninterrupted insurance coverage, making it difficult to purchase COBRA or to continue paying premiums for coverage through their own employers or for private-market policies. But despite women’s substantial economic losses following divorce, few become eligible for public coverage. As of 2012, Medicaid, the state-run health insurance program for low-income people, was limited to parents with incomes (often substantially) below the poverty line in two thirds of states and was available to low-income childless adults in only nine states (Kaiser State Health Facts 2012). Medicare, the federal health insurance program for the elderly and disabled, is rarely available because most divorces occur before retirement age and few meet stringent disability standards.

Like the loss of economic and other health-enhancing resources after divorce, the loss of health insurance may harm women’s health. Compared to those with coverage, uninsured adults experience significantly worse health outcomes and die at younger ages (Institute of Medicine 2002). Recent quasi-experimental and random-assignment studies consistently find positive and often significant effects of health insurance on health outcomes, especially for low-income adults, those with chronic health conditions, and other vulnerable populations (Levy and Meltzer 2008; McWilliams 2009; Finkelstein et al. 2011). Health insurance loss is also associated with subsequent declines in health (Baker et al. 2002).

Health insurance may preserve health in large part because it facilitates access to more and higher-quality health care services; this is the primary mechanism articulated in the health economics and public health literatures (Hadley 2003; McWilliams 2009; Finkelstein et al. 2011). Uninsured individuals are more likely than those with coverage to postpone or forgo medical checkups, prescribed medications, and other needed medical care and may receive poorer-quality care when they do seek medical attention (Berk, Schur, and Cantor 1995; Ayanian et al. 2000; Baker, Shapiro and Schur 2000; Institute of Medicine 2002; Jovanovic et al. 2003; McWilliams 2009). If some women become uninsured after divorce and subsequently skip medications or forgo visits to the doctor, this could compound existing health problems and lead to deteriorating health over time.

Health insurance may also encourage healthy behaviors. Employer-based health plans are increasingly tied to wellness programs that encourage participants to quit smoking, exercise, and lose weight (Baicker, Cutler, and Song 2010). Losing health insurance may also eliminate the monitoring of one’s health by medical professionals, making it less likely that one will take timely steps, for example, to change diet and exercise in response to high blood pressure.

The Stress That Accompanies Divorce

The second major reason posited to explain why health may decline in the wake of divorce is that the stress of marital disruption depletes both psychological and physical health (Johnson and Wu 2002; Williams and Umberson 2004). Becoming uninsured after divorce may compound the other stresses of the divorce process, potentially further damaging health. For those experiencing financial hardship and medical issues simultaneously, losing insurance coverage after divorce may initiate the need to make stressful tradeoffs such as either paying rent or complying with a doctor’s orders for medication or treatment.

Whether or not one needs immediate access to medical services, health insurance may enhance well-being by providing an increased sense of security. It reassures that one will have access to medical services should the need arise and that in that event, one will be financially protected (Hahn 1993). Uninsured Americans have high levels of financial stress and many carry medical debt, which itself is strongly associated with stress (Drentea 2000; Schoen et al. 2011).

A recent ethnographic study of uninsured women illustrates how the stress of being uninsured can harm women’s health (Vuckovic 2000). Women in the study frequently worried about getting sick and whether they would be able to see a doctor or cope financially if they did. One respondent summed it up: “Worry wears you down and you don’t even realize it” (Vuckovic 2000:199).

The Present Study

Using a recent (1996–2007) longitudinal (up to 48 months per respondent) data set, we provide the most comprehensive portrait to date of what happens to women’s health insurance coverage upon divorce. We focus on divorce rather than marital disruption (separation and divorce) because most separations are informal rather than legal and do not change one’s eligibility for dependent coverage.

We start by documenting the extent to which women who remain married and those who divorce differ on baseline (pre-divorce) characteristics and rates of health insurance coverage. We then examine descriptive estimates of total changes in health insurance coverage after divorce. Next we employ multivariate fixed-effects models to fulfill three additional research goals. Our first goal is to estimate the overall association between divorce and women’s health insurance coverage, net of both measured (time-varying) and unmeasured (time-invariant) characteristics. By using women’s pre-divorce insurance coverage as their own counterfactuals, we control for those factors that are stable but not measured in SIPP (e.g., unmeasured aspects of family background, or taste for insurance coverage). Our second goal is to determine whether the association between divorce and health insurance coverage is stronger for some groups of women than for others (subgroup heterogeneity). We consider factors that may moderate a woman’s risk of insurance loss after divorce such as the source of coverage she has while married and her employment status, job tenure, age, and health status. Our third goal is to determine whether the association between divorce and health insurance wanes over time as women adjust to life after divorce (time heterogeneity).

Data and Method

Data

We analyze pooled data from the three most recent complete panels of the SIPP (1996, 2001, and 2004), a series of longitudinal surveys administered by the Census Bureau. Each panel follows a large, nationally representative clustered sample of civilian noninstitutionalized households and interviews household members every four months for between three and four years. Respondents are asked about core topics including demographic characteristics, labor force participation, income sources, program participation, and health insurance coverage, reporting monthly data for each month since the prior interview.

The SIPP has three key advantages for studying divorce and changes in health insurance coverage. First, it follows individuals over time whether or not they remain in the original sampled household, essential for a study on marital transitions. Second, it allows us to construct a monthly calendar of marital and insurance history for a large nationally representative sample of women for a period of up to 48 months. For women who experience both divorce and insurance loss, this temporal detail allows us to determine precisely which event came first and how much time elapsed between the two. Third, whereas many data sets lack detailed information on sources of health insurance, SIPP data identify whether a private employer-based insurance policy is held in a woman’s own name or in her spouse’s name. This information enables us to examine whether the source of a woman’s insurance prior to divorce affects her risk of insurance loss following divorce.

Together, the three SIPP panels cover the period from 1996 through 2007. The analytic sample includes 54,541 women who remain married and 1,442 women who divorce across the data collection period.² This sample includes original female sample members between the ages of 26 and 64 (no longer eligible for dependent coverage from parents and not yet eligible for Medicare) observed as married in at least one month of the panel. Women enter the analysis sample in the first month in which they are married and in the valid age range (for many, the first month of the panel; we refer to this as the baseline month). Sample members who turn 26 or who turn 65 over the course of the panel are retained in the sample but contribute person-months only while in the valid age range. Those who become widowed (1% of sample members) are censored in the month of husband’s death. Women who divorce and then remarry are censored in the month of remarriage.³ On average, women who divorce contribute 20.5 pre-divorce person-months and 14.0 post-divorce person-months.

As with any longitudinal data set, some attrition of the sample occurs over time. For example, at six months after divorce, we observe 997 of the 1,442 total women who divorce in our analysis sample. A total of 18.4 percent of the original sample is censored before six months after divorce (because divorce occurred fewer than six months before the end of the panel or due to age or remarriage), 8.6 percent attrite before six months after divorce, and 3.9 percent have a missing time point at six months after divorce. To gauge the effect of attrition on estimates, we re-estimated all analyses using final full-panel weights that adjust for attrition, available for slightly fewer than half of our study sample of women who divorce (676). Patterns of findings using the full analysis sample with baseline month weights or the reduced sample with attrition-adjusted panel weights are very similar. Final analyses use the full analysis sample with baseline weights to preserve statistical power.

Measures

Health insurance

The main analyses examine health insurance coverage as the key outcome variable. For each month, respondents report whether they are insured, and if so, by what type of coverage. Based on responses, we code a woman’s health insurance coverage into five categories: (1) job-based coverage through a woman’s own job, (2) job-based coverage through her husband’s job, (3) other private coverage, (4) public insurance (Medicaid or Medicare),⁴ and (5) uninsured. For the sake of simplicity and to minimize measurement error, coverage types are coded hierarchically; a woman who reports more than one type is coded as the first to appear on the above list. Job-based coverage includes insurance policies purchased through or received from a current employer, a former employer, a union, or the military and held in a woman’s own name or that of her husband. Other private coverage consists primarily of individual insurance policies purchased on the private market but also includes a small number of cases in which employer-based coverage is accessed through a nonspouse family member or an individual outside of the household. In many analyses, we collapse job-based and other private coverage into the larger category of private coverage (1, 2, or 3). Some analyses dichotomize insurance status, examining whether women had any coverage (1, 2, 3, or 4) or were uninsured (5).

Marital transitions

By design, all women in our analysis sample are married at baseline. The marital transition of interest, divorce, occurs when women revise their marital status to divorced. Nearly half of women who divorce report at least one month of marital separation preceding divorce. Although separation rarely initiates a change in eligibility for dependent health insurance coverage, it may be associated with residential and employment changes and decreased sharing of financial resources between spouses. For this reason, all multivariate models control for separation.

Employment

A woman’s labor force attachment also strongly predicts her health insurance status (Montez et al. 2009). As such, multivariate models control for women’s employment status and job tenure. Employment status is tabulated in three categories: full-time (≥ 35 hours per week), part-time (0–34 hours per week), or not working. Because many employers have waiting periods before which new employees are eligible for health insurance, we dichotomize job tenure, indicating whether a woman has worked for the same employer for more than six months (Claxton et al. 2011). In addition to employment status and job tenure, descriptive analyses also examine women’s (annualized) earnings in the baseline survey month, inflation-adjusted to 2010 real dollars using the Consumer Price Index for All Urban Consumers (CPI-U).

Health

We measure health in two ways. First, we code a woman’s self-rated health (1 = good, very good, or excellent; 0 = poor or fair) at her earliest available time point prior to divorce (or censoring, if applicable). Using this variable, we consider how a woman’s changes in health insurance after divorce relate to her pre-divorce health status. Self-rated health serves as a good summary measure of baseline health status because it captures both manifest illnesses and symptoms of conditions not yet diagnosed, and it predicts a range of subsequent health outcomes including physician assessments of health and mortality (Idler and Benyamini 1997; Ferraro and Farmer 1999). Second, we construct a monthly indicator for disability. This variable is equal to 1 if women responded yes to “hav[ing] a physical, mental, or other health condition that limits the kid of work [she] can do,” and 0 otherwise. All multivariate models control explicitly for time-varying disability status and implicitly for baseline health status (captured by fixed effect).

Marital characteristics

Descriptive analyses examine the baseline prevalence of first marriages and the length of marriages for women in the sample.

Demographics

A woman’s overall economic situation is measured by her total family income relative to the poverty threshold for a family of that size and composition. In 2010, for example, the Federal Poverty Line (FPL) for a family with two adults and two children was $22,113. The income-to-poverty ratio is then collapsed into four categories: poor (< 100% FPL), near-poor (100–200% FPL), moderate-income (200–300% FPL), and higher-income (> 300% FPL). Descriptive analyses consider the baseline differences between women who remain married and women who get divorced in age (years), poverty status (< 100% FPL), presence of own children in the household (any children < 18 years old), as well as women’s education (less than high school, high school, some college, college graduate) and race-ethnicity (non-Hispanic white, non-Hispanic black, Hispanic, and Other). Multivariate models include monthly (time-varying) measures of age, children in the household, and income-to-poverty ratio.

Analytic Strategy

We utilize the longitudinal variation in health insurance coverage and marital status to estimate the association between divorce and changes in women’s health insurance coverage. Because women who divorce differ from those who remain married, loss of coverage cannot be inferred from coverage differentials between married and divorced women in cross-sectional data. We use fixed-effects models to remove from our estimates as much selection bias as possible. Women who divorce are more socioeconomically disadvantaged than those who remain married and may also differ on unobserved characteristics such as planning aptitude or taste for health insurance coverage (Smock et al. 1999). Fixed-effect models remove confounding from these and other stable, individual characteristics.

The general model specification is p_it = α_i + βX_it + ε_it, where the dependent variable, p_it, represents the latent probability of individual i’s being insured in month t. This probability is modeled as a linear function of a vector of time-varying covariates including divorced status, X_it, an individual-level fixed effect, α_i, and a person-month random error, ε_it. Additional time-varying covariates include separated status, full-time employment, job tenure, disability, age, any children, income-to-poverty ratio, year, and region.⁵ We choose the linear probability model specification primarily for ease of interpretation; fixed-effect logit analyses generate the same general findings. All analyses are run in Stata and are weighted, and we utilize robust standard errors to account for clustered data. Our approach is consistent with that of Gruber and Madrian (1997), who utilize fixed-effects linear probability models to examine how job loss predicts health insurance loss.

We run three sets of multivariate models, which correspond to our three primary research questions. First, we examine how women’s insurance coverage changes after divorce, holding other factors constant. Next, we add interaction effects to examine how various factors moderate the association between divorce and insurance loss. Last, we examine this association longitudinally by predicting insurance coverage from time since divorce.

One limitation of our study is that the SIPP does not measure overall health status at regular intervals. While controlling for variation in overall health over time would be ideal, we believe its omission is unlikely to substantially bias results for three reasons. First, by controlling for a time-varying indicator of work-limiting disability, major changes in health—those most likely to affect health insurance coverage—are captured in the analysis. Second, fixed-effects models control for stable aspects of overall health. If women who divorce are in generally poorer health (as has been suggested but is not evident in these data), this will be captured by the fixed effect and not bias estimates. Third, while it is plausible that the negative impact of divorce on physical health documented in previous studies may eventually affect health insurance coverage, it is unlikely to happen in the relatively short time frame of the SIPP panels. Furthermore, past studies suggest that divorce has delayed, not immediate, effects on physical health (Lorenz et al. 2006), whereas our results show immediate, not delayed, effects of divorce on insurance coverage.

Results

Baseline Descriptives

Table 1 compares the baseline characteristics of women who remain married with those who divorce. Consistent with past studies, the two groups differ. Women who go on to divorce are less likely to be in a first marriage (64.3 vs. 77.0%); have been married a shorter time, on average (9.7 vs. 15.6 years); and are more likely to have children (60.9 vs. 50.3%) compared to women who remain married. They are also younger (37.8 vs. 42.1 years), have lower levels of education (20.8 vs. 27.7% college graduates), and are more likely to be poor (12.7 vs. 8.2%). There are no statistically significant baseline differences in self-rated overall health status or disability by marital trajectory, although the limited variability in these dummy variables may mask health differences in the two groups found in previous studies (e.g., Canady and Broman 2003). Women who go on to divorce are more likely to work full-time (59.4 vs. 51.1%) compared to women who remain married, yet they exhibit shorter job tenure. While the average earnings for women who divorce is lower than for women who remain married, the difference does not attain statistical significance.

Table 1.

Baseline Descriptives by Marital Trajectory

	Women Who Remain Married	Women Who Divorce	Difference^a
Marital characteristics
First marriage (%)	77.0	64.3	***
Length of marriage (years)	15.6	9.7	***
Employment
Status (%)
Full-Time	51.1	59.4	***
Part-Time	17.0	15.2
Not employed	31.9	25.3	***
Job tenure > six months (%)
Full-Time workers	89.9	85.6	**
Part-Time workers	83.6	76.2	*
Earnings (2010$)
Full-Time workers	42,005	40,398
Part-Time workers	18,584	17,498
Self-Reported health (%)
Good, very good, or excellent^b	89.8	89.8
Disabled	9.2	10.4
Demographics
Age (years)	42.1	37.8	***
Poor (< 100% Federal Poverty Line) (%)	8.2	12.7	***
Any children (%)	50.3	60.9	***
Education (%)
Less than high school	10.9	9.8
High school	29.1	30.3
Some college	32.2	39.0	***
College graduate	27.7	20.8	***
Race-Ethnicity (%)
Non-Hispanic white	75.2	76.3
Non-Hispanic black	7.5	9.0
Hispanic	11.3	10.3
Other	6.0	4.5	**
n (persons)	54,541	1,442

Notes: Data reflect characteristics of sample members at baseline, at which point all women are married. The reported sample size reflects the total sample before dropping missing cases in each analysis. All variables presented have less than 10 percent missing, with the exception of self-reported health (14.4%) and length of marriage (12.6%); neither is used in multivariate models, except interaction models in Table 4.

Heteroscedasticity-robust t-tests are weighted and adjusted for the Survey of Income and Program Participation’s clustered sampling design.

Measured at the earliest available timepoint prior to divorce or censoring.

p < .05. **p < .01. ***p < .001 (two-tailed tests).

Women who go on to divorce may be more likely to work full-time at baseline for multiple reasons. Some scholars have suggested that women’s economic independence reduces their incentive to remain married (Becker 1981), but this theory has received little empirical support (Oppenheimer 1997). Although wives who contribute a larger percentage to family income are more likely to divorce, the association becomes insignificant after accounting for gender ideology (Sayer and Bianchi 2000). On the other hand, women who anticipate divorce may prepare for economic independence by increasing their labor supply (Johnson and Skinner 1986). Rogers (1999) documented that increasing marital discord raises the odds that nonworking women will enter the labor market. Although we do not observe a substantial increase in women’s employment until the month of divorce in our data, we control for monthly employment status in all multivariate models to take account of changing employment situations.

Overall, Table 1 suggests that women who divorce are more socioeconomically disadvantaged than women who remain married. This is consistent with prior studies (Amato 2010; Smock et al. 1999) and implies that selectivity underlies some of the observed differences in health insurance between married and divorced women.

Insurance Coverage by Marital Status

Women who get divorced are less likely to have health insurance coverage compared to those who remain married, even while married. But their probability of being uninsured climbs even higher following divorce.

Table 2 displays health insurance profiles by marital trajectory. The first two columns compare the baseline insurance coverage for women who remain married and women who divorce. At baseline, 11.6 percent of women who remain married are uninsured, compared to 15.9 percent of women who go on to divorce. Women who divorce are also less likely to be covered by private insurance at baseline (78.5 vs. 84.4%) and more likely to have public insurance such as Medicaid (5.6 vs. 3.9%). Additionally, those with private coverage are less likely to rely on husbands’ health plans at baseline and more likely to receive coverage through their own jobs.

Table 2.

Insurance Coverage by Marital Status (in Percentages)

	Women Who Remain Married	Women Who Divorce		Women Who Divorce Insured at Baseline
	Baseline	Baseline	Six Months after Divorce	Baseline	Six Months after Divorce
Insured
Job-Based, own job	36.0	42.4	55.0	50.5	60.7
Job-Based, husband’s job	40.8	28.9	0.0	34.4	.0
Other private	7.6	7.2	12.2	8.5	13.8
Public	3.9	5.6	10.3	6.6	8.7
Uninsured	11.6	15.9	22.5	.0	16.8
Net loss of coverage			6.6		16.8

Notes: Using heteroscedasticity-robust two-sample t-tests, all baseline differences between women who remain married and women who divorce were found to be significant (p < .05) except for other private coverage. All pre-post divorce differences are significant (p < .05) using paired-sample t-tests on the reduced sample that included women with nonmissing data at both timepoints (n = 997; percentages shown for this sample).

Compounding divorced women’s lower baseline probabilities of insurance coverage, some women lose insurance coverage after divorce. Women are 6.6 percent less likely to be insured six months after divorce than they had been before divorce. If we consider only the subgroup of women insured at baseline, nearly 17 percent are uninsured six months after divorce.

The proportion losing coverage would be greater if some women were not able to switch from private to public coverage. Six months after divorce, 10.3 percent of women hold public insurance coverage, compared to 5.6 percent at baseline. More women also acquire their own employer-based coverage after divorce. The majority of women insured through their own jobs six months after divorce were employed at the same job prior to divorce, although some increased their hours from part- to full-time employment (analyses not shown).

Partitioning the sample by pre-divorce source of insurance coverage demonstrates substantial heterogeneity in the loss of coverage following divorce (not in table). In particular, nearly one quarter (23.2%) of women insured as dependents on their husbands’ insurance policies at baseline become uninsured by six months after divorce, more than double the rate of women with their own employer-based health insurance at baseline (10.6%).

The risk of insurance loss also differs by a woman’s family income at baseline and the magnitude of economic loss she experiences after divorce (not in table). Women who experience the greatest economic losses after divorce (in terms of income-poverty ratio) are most likely to lose health insurance coverage. Significant declines in the probability of holding health insurance occur only for women from moderate- or higher-income families (> 200% FPL). The loss of private coverage experienced by low-income women is largely offset by their take-up of public coverage. In contrast, few moderate-income and higher-income women who lose private coverage can access public coverage because most states restrict Medicaid to families with incomes less than twice the poverty line (Kaiser State Health Facts 2012).

Changes in Insurance Coverage after Divorce

Women are less likely to be insured after divorce than before. Net of changes in employment, economic resources, and other factors, divorce leads to an approximately 8 percentage point decline in women’s private health insurance coverage and an increase of 3 percentage points in women’s public health insurance coverage, for a total drop of 5 percentage points.

Table 3 presents a series of six multivariate fixed-effects models examining the relationship between divorced status and the likelihood of holding any insurance coverage (Models 1 and 2), private coverage (Models 3 and 4), and public coverage (Models 5 and 6), net of other factors. These models compare each woman’s health insurance coverage in the months before divorce with the same woman’s coverage in the following months.

Table 3.

Changes in Insurance Coverage after Divorce: Evidence from Fixed-Effects Models

	Any Insurance		Private Insurance		Public Insurance
	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6
Divorced	−.063***	−.046***	−.091***	−.076***	.028***	.031***
Covariates
Separated		−.008		−.035*		.026**
Full-Time work		.017		.042**		−.024**
Job tenure > six months		.079***		.091***		−.012
Disabled		.068*		.009		.059*
Age		.003		−.002		.004
Any children		.058**		.024		.033*
Income-to-Poverty ratio
Poor (< 100% FPL)		−.113***		−.149***		.035*
Near-Poor (100–200% FPL)		−.072***		−.065***		−.007
Moderate income (200– 300% FPL)		−.023*		−.013		−.009*
Higher income (> 300% FPL)		—		—		—
Constant	.855***	.617***	.781***	.715***	.074***	−.106
n (persons)	1,410	1,410	1,410	1,410	1,410	1,410
n (person-months)	45,209	45,209	45,209	45,209	45,209	45,209

Notes: Sample includes person-months from women who divorce in 1996, 2001, and 2004 Survey of Income and Program Participation. Results are based on weighted linear probability models with robust standard errors. Coefficients for survey year and region fixed effects in Models 2, 4, and 6 are not shown. FPL = Federal Poverty Line.

p < .05. **p < .01. ***p < .001 (two-tailed tests).

Model 1 estimates the gross change in health insurance coverage after divorce. Compared to when they were married, divorced women are 6 percentage points less likely to have any type of health insurance. The addition of time-varying covariates in Model 2 (including year and region fixed effects) attenuates the main effect by less than 2 percentage points. Findings show that when women have children in the household, when they are employed in stable jobs, and when they are disabled, they are more likely to be insured. Household financial resources are also important. When women are living in poverty, they are 11 percentage points less likely to have health insurance relative to when their incomes are more than three times the FPL, holding other factors constant.

Models 3 through 6 examine private and public insurance separately. As Model 4 shows, after divorce, women are 8 percentage points less likely to have private insurance coverage than before, net of other factors. Results suggest that employment status, job tenure, and financial resources are more strongly associated with private insurance coverage than with the overall probability of any coverage. The table also shows that separated status has a negative impact on women’s private health insurance coverage. Rather than a single event, divorce may be best thought of as a longer process during which partners may prepare for and adjust to the impending divorce in the months prior to its occurrence (Amato 2000). For example, during this pre-divorce period, partners may move out of the house and/or change jobs, both of which could be associated with instability in health insurance coverage. Individuals may also remove estranged spouses from employer-based health plans during annual open enrollment periods, even prior to any legal termination of eligibility for dependent coverage.

In contrast to private insurance, women’s probability of holding public health insurance increases after divorce. Women are 3 percentage points more likely to have public health insurance coverage after divorce than before, net of other factors. When women have children, are disabled, are poor, or are not working full-time they are more likely to hold public insurance coverage, consistent with eligibility requirements.

Changes in Insurance Coverage after Divorce: Subgroup Heterogeneity

To examine heterogeneity in the relationship between divorce and health insurance changes, we estimate a second series of models in which the divorced status dummy variable is fully interacted with time-invariant characteristics of interest (implicitly, the omitted comparison group is “still married”). Table 4 presents these results. Each model includes the same covariates as the previous set of models.

Table 4.

Heterogeneity in Changes in Insurance Coverage after Divorce: Subgroup Interactions with Divorced Status

	Any Insurance	Private Insurance	Public Insurance
Insurance status at baseline
Job-Based, own job	−.035**	−.052***	.017
Job-Based, husband’s job	−.136***	−.194***	.058***
Other private	−.167***	−.249***	.082**
Public	−.129**	.004	−.133**
Uninsured	.160***	.087**	.073**
Education at baseline
Less than high school	−.041	−.132***	.091**
High school	−.049*	−.083***	.035**
Some college	−.068***	−.092***	.023*
College graduate	−.004	−.015	.010
Employment status at baseline
Full-Time	−.014	−.026*	.012
Part-Time	−.076*	−.141***	.065**
Not employed	−.110***	−.168***	.059**
Job tenure at baseline
More than six months	−.016	−.033*	.018*
Less than six months	−.067*	−.110***	.042*
Not employed	−.109***	−.168***	.059**
Race-Ethnicity
Non-Hispanic white	−.060***	−.082***	.023**
Hispanic or nonwhite	−.001	−.056**	.056**
Poverty status at baseline
Poor (< 100% FPL)	−.042	−.062*	.020
Near-Poor (100–200% FPL)	−.061*	−.128***	.067**
Moderate income (200–300% FPL)	−.071**	−.110***	.039**
Higher income (> 300% FPL)	−.032*	−.050***	.019*
Age at divorce
26–34 years	−.053**	−.083***	.029*
35–49 years	−.029*	−.063***	.034**
50–64 years	−.082**	−.107***	.024
Self-Reported health before divorce^a
Poor or fair	−.029	−.113**	.085*
Good, very good, or excellent	−.054***	−.074***	.020*
Any children at time of divorce
At least one child	−.061***	−.090***	.029**
No children	−.029	−.062***	.033**

Notes: Sample includes person-months from women who divorce in 1996, 2001, and 2004 Survey of Income and Program Participation. Results are based on weighted linear probability models with robust standard errors. Only coefficients for divorced status interacted with subgroups of interest are shown. Covariates not shown include those in Table 3 models, including year and region fixed effects. FPL = Federal Poverty Line.

Self-reported health status is measured at the earliest available timepoint in the sample prior to divorce or censoring.

p < .05. **p < .01. ***p < .001 (two-tailed tests).

Results indicate that some types of insurance coverage are more vulnerable to loss than others. Women with employer-based insurance in their own names before divorce are largely, although not fully, protected. In contrast, women insured as dependents through a husband’s employer at baseline face a 14 percentage point decrease in overall probability of coverage, net of other factors, four times as large as the drop experienced by women with employer-based insurance in their own names. There is also a considerable risk of loss associated with other forms of private coverage (17 percentage point decrease; may be difficult to afford after divorce) and with public insurance coverage as well (13 percentage point decrease).

This magnitude of loss of public coverage is surprisingly high, given that women generally experience economic declines after divorce. Several factors could explain this finding. First, because divorce is a turbulent time of transition, some women may fail to recertify their eligibility. Second, some women may lose eligibility for Medicaid if they enter the labor market or increase their earnings levels after divorce, as documented by a recent study (Tamborini, Iams, and Reznik 2011), or if they move to states with less-generous Medicaid programs. Third, our finding may reflect the generally high instability associated with this form of coverage—what other researchers have termed “churning” (Summer and Mann 2006; Saunders and Alexander 2009)—rather than the impact of divorce on public insurance per se. Indeed, when we repeat the same analysis among the group of women who remain married using a randomly generated month in the survey as a cut point (rather than month of divorce), we still observe that many women with public insurance lose public coverage after the random cut point (18% for public coverage vs. 2% for job-based coverage) and become uninsured (9% for public coverage vs. 2% for job-based coverage). This supports the churning hypothesis.

The more education a woman has and the greater her attachment to the labor force at baseline, the less likely she is to lose insurance coverage. College graduates appear to be immune to the effect altogether. Women with less education tend to lose private coverage. However, for women with less than high school education the net effect is zero, as the loss of private coverage is fully offset by an uptake in public insurance after divorce.

Women working full-time and those in jobs for longer than six months appear to be largely protected from insurance loss. The importance of job tenure is not surprising: Three quarters of workers whose firms provide health insurance face a short waiting period during which they are ineligible for coverage, meaning that newer employees more often lack coverage (Claxton et al. 2011). Additionally, workers in low-wage, benefit-poor occupations typically have high rates of turnover. Minorities are more likely to work in these occupations and are less likely in general to have employer-based coverage than whites (Hall, Collins, and Glied 1999). Our analyses indicate that only white women show a significant drop in net health insurance coverage after divorce due to both a larger drop in private coverage and lower acquisition of public coverage. The smaller drop in private insurance that minorities experience is largely offset by public insurance coverage.

Financial resources at baseline, as measured by the income-to-poverty ratio, moderate the linkage between divorce and insurance coverage. Women in moderate-income families before divorce are most vulnerable to insurance loss, with an estimated drop of 7 percentage points. These women face an 11 percentage point decline in the probability of private insurance and a 4 percentage point increase in the probability of public insurance, net of other factors. Many lower-middle-class women may fall into the ranks of the near-poor after divorce, leaving them with too much money to qualify for Medicaid and other public insurance programs (as their poorer counterparts) but not enough to purchase private insurance policies (as their higher-income counterparts).

Table 4 also shows that women who divorce between ages 50 and 64 are more vulnerable to insurance loss than younger women. This result is consistent with recent findings suggesting that insurance coverage in later middle age may be particularly tenuous. Angel, Montez, and Angel (2011) document greater reliance on dependent coverage among women in this age group relative to those at other ages, and Kirby and Kaneda (2010) find that adults in later middle age (55–64) have the second highest rates of uninsurance of any age group (highest is young adults 18–25). Women are most likely to lose health insurance coverage when they report themselves in good, very good, or excellent health before divorce (as opposed to poor or fair), perhaps because women in poorer health may place a greater priority on maintaining continuous coverage. Women in poor or fair health do lose private insurance coverage more than others, but this is offset by their acquisition of public insurance coverage. Some of these women may have chronic illnesses or disabilities that qualify them for Medicare.

The presence of children in the household is associated with a greater risk of insurance loss after divorce. Women with children typically face greater financial troubles after divorce, as they frequently become the sole provider, which could help to explain this finding (Smock 1994). Surprisingly, we find that mothers and women without children are about equally likely to acquire public coverage. We would have expected that only mothers would experience a significant take-up of public coverage, due to eligibility restrictions in many states. One possible explanation is that a greater proportion of women without children at baseline are disabled, so the take-up of public insurance may in part stem from this subgroup’s enrolling in Medicare.

Changes in Insurance Coverage after Divorce: Heterogeneity across Time

We now turn to the temporal pattern of insurance loss and recovery relative to time of divorce. For this investigation, we use a simple form of nonparametric regression called piecewise linear regression. Piecewise linear regression, or spline regression, breaks a continuous variable into intervals and fits a separate linear slope for each interval (Marsh 2001). In our application, we group person-month observations into intervals based on time to divorce such that a sufficient number of observations in each group results (at least 500) to ensure adequate precision of the estimates.⁶ Because the mass of data points lies at or near the time of divorce, months close to divorce form their own groups; months further from divorce are grouped together.

Our three piecewise linear regressions—predicting any coverage, private coverage, and public coverage—are estimated using the same multivariate fixed-effects models (including controls) as in Table 3, but we replace our key predictor, divorced status, with the series of spline terms representing intervals of months relative to divorce. Predicted probabilities are generated from model estimates and plotted in Figure 1. We calculate predicted probabilities holding constant all covariates at their average values (except separated status, held constant at its pre-divorce value, .188, before divorce, and at 0 post-divorce).

Figure 1.

The Changing Distribution of Women’s Health Insurance Coverage across Time since Divorce

Figure 1 demonstrates a striking shift in the distribution of women’s insurance coverage at the time of divorce. The height of the dark grey area in the bottom of the graph represents the estimated probability of women’s holding any form of private insurance coverage across time relative to time of divorce. The height of the light band across the middle of the graph represents the estimated probability of public insurance across time. The black area at the top of the graph represents the estimated probability of women’s being uninsured across time. The vertical line above zero on the x-axis represents the month of divorce; to its left are pre-divorce months, and to its right are post-divorce months.

As the graph indicates, there is a significant decrease in the probability of being insured in the month of divorce. This net loss of insurance (widening of black area) is the cumulative effect of an even larger decline in the probability of private insurance coverage (dark grey), which is only partially mitigated by an increase in public insurance coverage (light grey). At 12 months before divorce, 14.4 percent of women are estimated to be uninsured, increasing to 20.9 percent by 24 months after divorce. The loss of insurance coverage is statistically significant for as long as our data extend, more than two years after divorce.

Discussion

Using recent longitudinal data from the SIPP, we examine how women’s health insurance coverage changes after divorce. We find that even while married, women who later divorce are more likely to be uninsured than women who remain married. Adding to this baseline disparity, a substantial number of women lose health insurance after divorce. Considering that approximately 1 million divorces occur in the United States every year (U.S. Census Bureau 2011), our descriptive estimates (see Table 2) suggest that roughly 115,000 women lose private health insurance annually in the months following divorce and that roughly 65,000 of these women become uninsured. The loss is not just a temporary disruption to women’s health insurance coverage; rather, women’s overall rates of health insurance coverage remain depressed for more than two years after divorce, as long as our data allow us to test.

Not all women are equally likely to lose health insurance after divorce. Those insured as dependents on husbands’ employer-based insurance plans are most vulnerable to insurance loss, while stable, full-time employment buffers against it. Women from moderate-income (200–300% FPL) families are particularly vulnerable. Many of these women fall into the ranks of the near-poor after divorce, with too much money to qualify for Medicaid but not enough to purchase private health insurance coverage.

Moreover, the loss of health insurance coverage after divorce documented here may underestimate the true latent risk of health insurance loss for two reasons. First, the potential loss of economic resources, including health insurance, may deter divorce. Indeed, some couples choose to separate rather than divorce to maintain insurance coverage for both spouses. Second, divorce is not an exogenous shock. Spouses (or at least one spouse) generally anticipate it and may prepare ahead of time for financial independence. Thus, women who lose health insurance may represent a subset of women, those who despite their efforts were unable to secure insurance coverage for themselves in advance of divorce.

Admittedly, our modeling strategy has some limitations. Fixed-effects models can control for observed and stable unobserved characteristics of women who divorce. But we cannot conclude that divorce causes women to lose health insurance coverage because in this context unobserved time-varying covariates are important. For example, one recent study suggests that women who divorce have greater risk tolerance than those who stay married (Light and Ahn 2010). If this is so, then some women may become uninsured after divorce not because they lack affordable insurance options but because they do not place a high value on maintaining coverage. (This represents an omitted time-varying interaction between changing context and stable trait.) Additionally, we do not (nor do we have the data to) explicitly model the decision-making process behind divorce, but we do know from past studies that women are more likely than their husbands to initiate divorce (Wang and Amato 2000). Given this, we cannot claim that loss of health insurance coverage is something that happens to women. Rather, in many cases, a woman’s decision to divorce may involve tradeoffs between loss of material resources and anticipated gains in well-being in other dimensions.

Regardless of the causes, the loss of health insurance following divorce has important implications for women’s financial well-being and health. This study broadens the economic consequences of divorce literature by showing that not only income but also women’s health insurance coverage is at risk—an important instrument of financial protection in the event of major injuries or illnesses and a means to pay for routine medical care. Women who experience the greatest economic losses also are most likely to lose insurance coverage. Future studies should assess whether, in parallel to their smaller economic losses after divorce, men are also less likely to lose health insurance coverage.

Women’s loss of health insurance may contribute to our understanding of the health declines sometimes experienced following marital disruption (Lorenz et al. 2006; Hughes and Waite 2009). Losing health insurance may compound the other stresses of the divorce process, and stress has negative ramifications for health (Pearlin et al. 2005). Being uninsured also means women are less likely to go to the doctor or to get medical care when they do get sick, potentially exacerbating conditions and illnesses (McWilliams 2009). Future studies using longitudinal data on health as well as health insurance should formally test whether loss of insurance coverage helps to account for some of the health declines women experience after marital disruption (see Lavelle, Lorenz, and Wickrama forthcoming for preliminary evidence).

Our findings also add to the body of evidence that the current health care and insurance system in the United States is inadequate for a population in which multiple family and job changes over the life course are not uncommon. It remains to be seen how effectively the Affordable Care Act (ACA) of 2010—expected to be fully implanted by 2014—will remedy the problem of insurance loss after divorce. In spite of the fact that this issue was all but absent from discussions of insurance system inadequacies leading up to the ACA’s passage, the law has provisions that may help substantially. The ACA is slated to expand the availability of insurance to women through their own jobs, make insurance more affordable to women on the private market, and expand eligibility for Medicaid. Moving forward, policy makers should be aware that a system that induces a de facto linkage between marital status and health insurance may have unintentional adverse consequences.

Footnotes

Acknowledgements

We gratefully acknowledge use of the services and facilities of the Population Studies Center at the University of Michigan, funded by NICHD Center Grant R24 HD041028, and the National Science Foundation’s support of Lavelle through a Graduate Research Fellowship. We thank Barbara Anderson, Jacqueline Angel, Sarah Burgard, Charles Brown, Deborah Carr, Sheldon Danziger, Andrew Goodman-Bacon, Jim House, Helen Levy, Katherine Lin, Harold Pollack, and Yu Xie for helpful comments on earlier versions of this article.

Authors’ Note

The opinions and conclusions expressed herein are solely those of the authors and should not be construed as representing the opinions or policy of the National Poverty Center, the Population Studies Center, or any agency of the federal government.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by the National Poverty Center using funds received from the U.S. Census Bureau, Housing and Household Economics Statistics Division, through Contract 50YABC266059/TO002.

Notes

Bios

Bridget Lavelle is a doctoral candidate in public policy and sociology at the University of Michigan-Ann Arbor. Her broad research interests relate to economic stresses on families and the moderating effects of social and health policies. Supported by a Graduate Research Fellowship from the National Science Foundation, her dissertation focuses on the impact of life course transitions on health insurance coverage in the United States.

Pamela J. Smock is a professor of sociology at the University of Michigan-Ann Arbor and a research professor at the Population Studies Center of the Institute for Social Research. Her research focuses on changing family patterns in the United States and the implications of social class and gender inequality for families. She has published on an array of topics including unmarried cohabitation, the economic consequences of divorce and marriage for women and men, nonresident fatherhood, and the motherhood wage penalty.

References

Amato

Paul R.

2000. “The Consequences of Divorce for Adults and Children.” Journal of Marriage and Family 62:1269–87.

Amato

Paul R.

2010. “Research on Divorce: Continuing Trends and New Developments.” Journal of Marriage and Family 72:650–66.

Angel

Jacqueline L.

Montez

Jennifer K.

Angel

Ronald J.

2011. “A Window of Vulnerability: Health Insurance Coverage among Women 55 to 64 Years of Age.” Women’s Health Issues 21:6–11.

Ayanian

John Z.

Weissman

Joel S.

Schneider

Eric C.

Ginsburg

Jack A.

Zaslavsky

Alan M.

2000. “Unmet Health Needs of Uninsured Adults in the United States.” Journal of the American Medical Association 284:2061–69.

Baicker

Katherine

Cutler

David

Song

Zirui

. 2010. “Workplace Wellness Programs Can Generate Savings.” Health Affairs 29:304–11.

Baker

David W.

Shapiro

Martin F.

Schur

Claudia L.

2000. “Health Insurance and Access to Care for Symptomatic Conditions.” Archives of Internal Medicine 160:1269–74.

Baker

David W.

Sudano

Joseph J.

Albert

Jeffrey M.

Borawski

Elaine A.

Dor

Avi

. 2002. “Loss of Health Insurance and the Risk for a Decline in Self-Reported Health and Physical Functioning.” Medical Care 40:1126–31.

Becker

Gary

. 1981. A Treatise on the Family. Cambridge, MA: Harvard University Press.

Berk

Marc L.

Schur

Claudia L.

Cantor

Joel C.

1995. “Ability to Obtain Health Care: Recent Estimates from the Robert Wood Johnson Foundation National Access to Care Survey.” Health Affairs 14:139–46.

10.

Bernstein

Amy B.

Cohen

Robin A.

Brett

Kate M.

Bush

Mary A.

2008. “Marital Status Is Associated with Health Insurance Coverage for Working-Age Women at All Income Levels, 2007.” NCHS Data Brief, No. 11. Hyattsville, MD: National Center for Health Statistics.

11.

Bianchi

Suzanne M.

Subaiya

Lekha

Kahn

Joan R.

1999. “The Gender Gap in the Economic Well-Being of Nonresident Fathers and Custodial Mothers.” Demography 35:195–203.

12.

Buchmueller

Thomas C.

1997. “Marital Status, Spousal Coverage, and the Gender Gap in Employer-Sponsored Health Insurance.” Inquiry 33:308–16.

13.

Canady

Renee B.

Broman

Clifford

. 2003. “Marital Disruption and Health: Investigating the Role of Divorce in Differential Outcomes.” Sociological Focus 36:241–55.

14.

Carr

Deborah

Springer

Kristen W.

2010. “Advances in Families and Health Research in the 21st Century.” Journal of Marriage and Family 72:743–61.

15.

Claxton

Gary

Rae

Matthew

Panchal

Nirmita

Lundy

Janet

Damico

Anthony

Osei-Anto

Awo

Kenward

Kevin

Whitmore

Heidi

Pickreign

Jeremy

. 2011. “Employer Health Benefits: 2011 Annual Survey.” Menlo Park, CA: Kaiser Family Foundation and Chicago: Health Research and Educational Trust. Retrieved August 16, 2012 (http://ehbs.kff.org/pdf/2011/8225.pdf).

16.

Currie

Janet

. 1997. “Gender Gaps in Benefits Coverage.” Pp. 175–98 in The Handbook of Human Resource Management, edited by Lewin

Mitchell

Zaidi

Greenwich, CT: JAI Press.

17.

Drentea

Patricia

. 2000. “Age, Debt and Anxiety.” Journal of Health and Social Behavior 41:437–50.

18.

Ferraro

Kenneth F.

Farmer

Melissa M.

1999. “Utility of Health Data from Social Surveys: Is There a Gold Standard for Measuring Morbidity?” American Sociological Review 64:303–15.

19.

Finkelstein

Amy

Taubman

Sarah

Wright

Bill

Bernstein

Mira

Gruber

Johnathan

Newhouse

Joseph P.

Allen

Heidi

Baicker

Katherine

. 2011. “The Oregon Health Insurance Experiment: Evidence from the First Year.” NBER Working Paper Series No. 17190. New York: National Bureau of Economic Research.

20.

Gruber

Jonathan

Madrian

Brigitte

. 1997. “Employment Separation and Health Insurance Coverage.” Journal of Public Economics 66:349–82.

21.

Hadley

Jack

. 2003. “Sicker and Poorer—The Consequences of Being Uninsured: A Review of the Research on the Relationship between Health Insurance, Medical Care Use, Health, Work, and Income.” Medical Care Research and Review 60:3S–75S.

22.

Hahn

Beth A.

1993. “Marital Status and Women’s Health: The Effect of Economic Marital Acquisitions.” Journal of Marriage and Family 55:495–504.

23.

Hall

Allyson G.

Collins

Karen S.

Glied

Sherry

. 1999. Employer-Sponsored Health Insurance: Implications for Minority Workers. New York: Commonwealth Fund.

24.

Hughes

Mary E.

Waite

Linda J.

2009. “Marital Biography and Health at Midlife.” Journal of Health and Social Behavior 50:344–58.

25.

Idler

Ellen L.

Benyamini

Yael

. 1997. “Self-Rated Health and Mortality: A Review of Twenty-Seven Community Studies.” Journal of Health and Social Behavior 38:21–37.

26.

Institute of Medicine. 2002. Care without Coverage: Too Little, Too Late. Washington, DC: National Academies Press.

27.

Johnson

David R.

Jian

. 2002. “An Empirical Test of Crisis, Social Selection, and Role Explanations of the Relationship between Marital Disruption and Psychological Distress: A Pooled Time-Series Analysis of Four-Wave Panel Data.” Journal of Marriage and Family 64:211–24.

28.

Johnson

William R.

Skinner

Jonathan

. 1986. “Labor Supply and Marital Separation.” American Economic Review 76:455–69.

29.

Jovanovic

Zorana

Lin

Chyongchiou J.

Chang

Chung-Chou H.

2003. “Uninsured vs. Insured Population: Variations among Nonelderly Americans.” Journal of Health and Social Behavior 17:71–85.

30.

Kaiser Family Foundation. 2011. “Women’s Health Insurance Coverage.” Fact Sheet, Women’s Health Policy. Menlo Park, CA: Kaiser Family Foundation.

31.

Kaiser State Health Facts. 2012. “Medicaid Income Eligibility Limits for Adults as a Percent of Federal Poverty Level, July 2012.” Retrieved October 3, 2012 (http://www.statehealthfacts.org/comparereport.jsp?rep=130&cat=4) .

32.

Kirby

James B.

Kaneda

Toshiko

. 2010. “Unhealthy and Uninsured: Exploring Racial Differences in Health and Health Insurance Coverage Using a Life Table Approach.” Demography 47:1035–52.

33.

Koball

Heather L.

Moiduddin

Emily

Henderson

Jamila

Goesling

Brian

Besculides

Melanie

. 2010. “What Do We Know about the Link between Marriage and Health?” Journal of Family Issues 31:1019–40.

34.

Lavelle

Bridget

Lorenz

Frederick O.

Wickrama

K. A. S.

Forthcoming. “What Explains Divorced Women’s Poorer Health? The Mediating Role of Health Insurance and Access to Health Care in a Rural Iowan Sample.” Rural Sociology.

35.

Levy

Helen

Meltzer

David

. 2008. “The Impact of Health Insurance on Health.” Annual Review of Public Health 29:399–409.

36.

Light

Audrey

Ahn

Taehyun

. 2010. “Divorce as Risky Behavior.” Demography 47:895–921.

37.

Liu

Hui

. 2012. “Marital Dissolution and Self-Rated Health: Age Trajectories and Birth Cohort Variations.” Social Science & Medicine 74:1107–16.

38.

Liu

Hui

Umberson

Debra J.

2008. “The Times They Are a Changin’: Marital Status and Health Differentials from 1972 to 2003.” Journal of Health and Social Behavior 49:239–53.

39.

Lorenz

Frederick O.

Wickrama

K. A. S.

Conger

Rand D.

Elder

Glen H.

Jr.

2006. “The Short-Term and Decade-Long Effects of Divorce on Women’s Midlife Health.” Journal of Health and Social Behavior 47:111–25.

40.

Marsh

Lawrence

. 2001. Spline Regression Models. Thousand Oaks, CA: Sage.

41.

McWilliams

J. Michael

. 2009. “Health Consequences of Uninsurance among Adults in the United States: Recent Evidence and Implications.” Milbank Quarterly 87:443–94.

42.

Meyer

Bruce D.

Mok

Wallace K.

Sullivan

James X.

2009. “The Under-Reporting of Transfers in Household Surveys: Its Nature and Consequences.” NBER Working Paper Series No. 15181. New York: National Bureau of Economic Research.

43.

Meyer

Madonna H.

Pavalko

Eliza K.

1996. “Family, Work, and Access to Health Insurance among Mature Women.” Journal of Health and Social Behavior 37:311–25.

44.

Montez

Jennifer K.

Angel

Jacqueline L.

Angel

Ronald J.

2009. “Employment, Marriage, and Inequality in Health Insurance for Mexican-Origin Women.” Journal of Health and Social Behavior 50:132–48.

45.

Oppenheimer

Valerie K.

1997. “Women’s Employment and the Gain to Marriage: The Specialization and Trading Model.” Annual Review of Sociology 23:431–53.

46.

Pearlin

Leonard I.

Schieman

Scott

Fazio

Elena M.

Meersman

Stephen C.

2005. “Stress, Health, and the Life Course: Some Conceptual Perspectives.” Journal of Health and Social Behavior 46:205–19.

47.

Prigerson

Holly G.

Maciejewski

Paul K.

Rosencheck

Robert A.

1999. “The Effects of Marital Dissolution and Marital Quality on Health and Health Service Use among Women.” Medical Care 37:858–73.

48.

Quadagno

Jill

. 2004. “Why the United States Has No National Health Insurance: Stakeholder Mobilization against the Welfare State.” Journal of Health and Social Behavior 45:25–44.

49.

Rogers

Stacy J.

1999. “Wives’ Income and Marital Quality: Are There Reciprocal Effects?” Journal of Marriage and Family 61:123–32.

50.

Saunders

Milda R.

Caleb

Alexander G.

2009. “Turning and Churning: Loss of Health Insurance among Adults in Medicaid.” Journal of General Internal Medicine 24:133–34.

51.

Sayer

Liana C.

Bianchi

Suzanne M.

2000. “Women’s Economic Independence and the Probability of Divorce.” Journal of Family Issues 21:906–43.

52.

Sbarra

David A.

Nietert

Paul J.

2009. “Divorce and Death: Forty Years of the Charleston Heart Study.” Psychological Science 20:107–13.

53.

Schoen

Cathy

Doty

Michelle M.

Robertson

Ruth H.

Collins

Sara R.

2011. “Affordable Care Act Reforms Could Reduce the Number of Underinsured U.S. Adults by 70 Percent.” Health Affairs 30:1762–71.

54.

Short

Pamela F.

1998. “Gaps and Transitions in Health Insurance: What Are the Concerns of Women?” Journal of Women’s Health 7:725–37.

55.

Smock

Pamela J.

1994. “Gender and the Short-Run Economic Consequences of Marital Disruption.” Social Forces 73:243–62.

56.

Smock

Pamela J.

Manning

Wendy D.

Gupta

Sanjiv

. 1999. “The Effect of Marriage and Divorce on Women’s Economic Well-Being.” American Sociological Review 64:794–812.

57.

Summer

Laura

Mann

Cindy

. 2006. “Instability of Public Health Insurance Coverage for Children and Their Families: Causes, Consequences, and Remedies.” Washington, DC: Georgetown University Health Policy Institute.

58.

Tamborini

Christopher R.

Iams

Howard M.

Reznik

Gayle L.

2011. “Women’s Earnings before and after Marital Dissolution: Evidence from Longitudinal Earnings Records Matched to Survey Data.” Journal of Family and Economic Issues 33:69–82.

59.

U.S. Census Bureau. 2011. “Table 78. Live Births, Deaths, Marriages, and Divorces: 1960 to 2008.” In Statistical Abstract of the United States: 2012. 131st ed. Washington, DC: U.S. Census Bureau.

60.

Vuckovic

Nancy

. 2000. “Self-Care among the Uninsured: ‘You Do What You Can Do.’” Health Affairs 19:197–99.

61.

Waite

Linda J.

Gallagher

Maggie

. 2000. The Case for Marriage: Why Married People Are Happier, Healthier, and Better off Financially. New York: Doubleday.

62.

Wang

Hongyu

Amato

Paul R.

2000. “Predictors of Divorce Adjustment: Stressors, Resources, and Definitions.” Journal of Marriage and Family 62:655–68.

63.

Williams

Kristi

Umberson

Debra

. 2004. “Marital Status, Marital Transitions, and Health: A Gendered Life Course Perspective.” Journal of Health and Social Behavior 45:81–98.

64.

Wood

Robert G.

Goesling

Brian

Avellar

Sarah

. 2007. The Effects of Marriage on Health: A Synthesis of Recent Research Evidence. Washington, DC: Mathematica Policy Research.

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