Disability Shocks Near Retirement Age and Financial Well-Being

Introduction

The increased incidence of disablement near retirement age is of policy concern because of potentially nontrivial negative consequences on financial security and overall well-being. This is reflected in an increasing body of literature addressing various aspects of disability shocks (Smith 1999, 2005, 2007; Ward-Batts 2001; Coile 2004; Johnson, Mermin, and Murphy 2007; Johnson, Mermin, and Uccello 2005; Johnson, Favreault, and Mommaerts 2010; McGarry and Skinner 2009). More specifically, those previous studies have looked at the effect of health and disability shocks on various indicators such as wealth, income, and consumption. Their findings suggest that reductions in labor income and increased medical out-of-pocket (MOOP) expenditures following health and disability shocks are the major sources of mean reductions in financial well-being in the near retirement-age population.

The goal of this article is to analyze the differential effects of health and disability shocks on financial well-being prior to retirement age. We focus on a representative sample of people in their early fifties (aged 51–56 in 1992) and follow them as they approach the Social Security full retirement age (FRA). Our motivation is rooted in the interaction of two factors: (1) the dramatically increasing incidence of disability among people in their fifties and early sixties, and (2) gaps in safety nets near retirement age. In contrast to previous studies, we classify our sample into three separate groups by different patterns of disability experiences over an 8-year follow-up period. The first group consists of people who were nondisabled in 1992 and stayed nondisabled by 2000. They exhibit a relatively simple pattern of stable financial security despite labor force withdrawal. The second group—comprising people with no disabilities at the baseline who experience at least one disability shock between the baseline and the followup 8 years later—exhibits increasing economic vulnerability by income measures, but a slight reduction in the proportion of people without any health insurance. The third group—consisting of people who were already disabled at the baseline and stayed disabled 8 years later—exhibits a high degree of economic vulnerability at the baseline, has fallen further behind on income measures, but displays a substantial reduction in the proportion of people without any health insurance. The contrast between the second and third groups is attributable to the fact that public health insurance coverage is conditioned on disablement.¹

There are several distinct features of our study.

1. We follow up people at an age well before the early Social Security retirement age at the baseline (aged 51–56 in 1992) who do not reach age 65 at the followup (at the year-2000 wave). Thus, we focus on a segment of the life cycle when the risk of disablement dramatically rises, yet people are subject to much weaker safety nets than would be available past age 65. At age 65 Supplemental Security Income (SSI), Medicaid, and Medicare become available without a tight disability screen and without any waiting period (relevant for Medicare). Under Social Security, age 65 is the applicable FRA for the oldest members of our 1936–1941 birth cohorts (those born in 1936 or 1937). For people born in 1938–1941, the FRA increases by 2 months per year and reaches 65 years and 8 months for the 1941 birth cohort.
2. We compare people who experienced a disability shock with two, rather than one, comparison groups. Nondisabled individuals who did not experience a disability shock are commonly used as a counterfactual group, but in this study, we also include a second comparison group of people who were disabled at the baseline and stayed disabled over the observation period. Chronically disabled people comprise a somewhat neglected group in longitudinal studies because the focus in those analyses is often on the effect of disability shocks, which needs a comparison group consisting of nondisabled people who did not experience a disability shock. However, the group of people who started as disabled and remained disabled provides a useful comparison basis consisting of individuals who experienced chronic disablement, in some cases going back to youth or even childhood.
3. We use a complex array of outcome measures including poverty status, various household income measures, several measures of wealth, household liabilities, public and private health insurance coverage, and reported MOOP expenditures.
4. We construct our measure of “disability shock” by combining three separate survey measures of disability—instead of one of them commonly used in the literature—to indicate whether a person experienced a disability shock.

In sum, we focus on three major groups of people defined by their disability experiences over time and describe what changes occur between 1992 and 2000 in their financial well-being. The three groups differ in their baseline characteristics. We think that describing the experience of these groups of people is of substantial policy interest in its own right. Adjusting for differences in characteristics would also be a worthwhile exercise, but at the expense of shifting the focus from assessing the real-life experiences of actual groups of people to variables explaining those differences. In this article, we do make some descriptive subgroup comparisons, but defer causal analyses for future studies. In the following section, we discuss the data and methodology used in this analysis. The next section presents the empirical results, and then we conclude.

Data and Methodology

The data for this study come from the Health and Retirement Study (HRS), a longitudinal, nationally representative survey of Americans over the age of 50 and their spouses of any age. The sample for this analysis is comprised of respondents aged 51–56 at the first wave of interviews conducted in 1992. We follow up those working-aged adults until 2000 (8 years later) when they are aged 59–64, just before they reach the Social Security FRA. We focus on this segment of the life cycle when the risk of disablement dramatically rises, yet people are subject to much weaker safety nets than would be available to them past age 65. At age 65, SSI, Medicaid, and Medicare become available without any disability screen and without any waiting period (relevant for Medicare). Most of our analysis is based on comparisons between the 1992 and 2000 waves of the HRS. Nevertheless, we also conduct supplementary analyses using more detailed information from the 1994, 1996, and 1998 waves. The analysis is limited to a subsample of the 1992 cross section of people who survived until the 2000 interview. Thus, an important caveat is that people who died in the interim are excluded from the analysis. The excluded members of the 1992 sample are likely to have relatively low socioeconomic status (SES) at the baseline; be disproportionately disabled at the baseline; experience a disability shock prior to death; be eligible for Disability Insurance (DI), SSI, Medicaid, or Medicare prior to death; and perhaps to experience financial distress sometime between disability onset and death. (These estimates are not shown, but are available upon request from the authors.) The financial well-being of working-age people who die prior to reaching the FRA under Social Security is of obvious interest in its own right and would be a fruitful subject for future study.

We use a combination of three HRS variables to identify the disability status and a disability shock. Those variables include a doctor-diagnosed major health condition as defined by Smith (1999), self-reported work-limiting health condition, and self-reported health status as “poor” or “fair.” More specifically, in this analysis a respondent is defined as being disabled if at least one of the three conditions is satisfied: the respondent reported a doctor-diagnosed major health condition, self-reported having a work-limiting health condition, or self-reported being in poor or fair health. Thus, our definition of disability status is a broader definition than the Social Security Administration's (SSA's) programmatic definition of categorical disability. We use all three variables to identify disability status because they represent different aspects of disablement with different strengths and weaknesses. Of the three measures, having a doctor-diagnosed major health condition is the most objective one.² However, it is less helpful for identifying disabilities among people without access to health insurance coverage, and in some cases, it reflects a curable health condition that does not lead to disablement. Self-reported work-limiting disabilities are relevant in that they are directly related to the capacity to work, but have been often criticized as being subject to reporting bias based on labor force status. Self-reported poor or fair health is admittedly subjective, but it is based on the interview subject's knowledge of his or her own health limitations and has substantial long-term predictive strength (Rupp and Davies 2004). This is the only measure that is truly meaningful for people who have limited access to health care and have been out of the labor force for most of their lives, often because of severe disablement.

We distinguish three groups of people: (1) those who were not disabled both at the baseline and followup (ND→ND); (2) those who were nondisabled at the baseline, but were disabled at the followup (ND→D); and (3) those who were disabled both at the baseline and the followup (D→D). The ND→ND group is defined as nondisabled on all three measures at both the baseline and followup. That is a strong counterfactual compared with alternative definitions based on a single measure. The ND→D group is defined by a transition from nondisabled status on all three measures at the baseline to disabled status on at least one of the three measures at the followup. In this article, we refer to this transition from a nondisabled to a disabled status as a disability shock. The D→D group is defined based on the presence of a disability in at least one of the three indicators (not necessarily the same indicator) both at the baseline and at the followup. Thus, the D→D group includes not only people who were severely disabled at the baseline, but also others with self-reported disabilities that may have been less severe. Therefore, this group is broader in scope than the group of disability beneficiaries that satisfies SSA's stringent categorically disabled definition. For simplicity, in the rest of this article we will refer to the three groups as “nondisabled” (ND→ND), “newly disabled” (ND→D), and “disabled” (D→D).

In some of the analysis, we also use a measure based on how many of the three disability status variables are met. Meeting a single variable forms the weakest disability indicator, and meeting all three conditions forms the strongest indication of a disability. The bulk of the analysis uses a simplification of defining membership in one of the three groups exclusively based on status in the beginning of our study period (1992) and the end (2000). That approach misses some nuances arising from transitions that may involve the 1994, 1996, and 1998 waves of the HRS. Thus, we also use all five waves in auxiliary analyses (and even those might miss some details because of possible unobserved transitions between the five waves). In addition, we also conduct a supplementary analysis by type of disability measure for respondents reporting a single disability defined by only one of our three disability indicator measures. Finally, most of our analysis ignores the important pattern of the “recovered” group (D→ND) because of the small sample size, but we do report some key results for this group as well within the limits allowed by the sample size.

Measures of financial well-being include poverty status, household income, various indicators of household wealth, household liabilities, private and public health insurance coverage, and MOOP expenditures. We also derive variables to characterize important aspects of financial well-being. First we create a household annuitized nonhousing wealth variable based on a simple 4 percent withdrawal rule.³ That allows comparisons of household wealth and household income (with or without MOOP expenditures on an equal footing. Then we create a wealth-adjusted income indicator, which adds annuitized nonhousing wealth to our income measure, designed to identify three broad categories of people by overall financial well-being. The three mutually exclusive groups include people who are potentially eligible for SSI disability, others with low wealth-adjusted income, and a residual group of people with high wealth-adjusted income.⁴ As previously noted, all of our analysis is descriptive; no causal interpretation is intended. Categorical variables are characterized by percent distributions and numeric variables by means, medians, percentiles, and percent measures. Estimated standard errors account for complex survey design.

Results

Sample characteristics by longitudinal patterns of disability are provided in Table 1. Nondisabled people (ND→ND) generally exhibit characteristics that are associated with favorable indicators of financial well-being. Only 5 percent are poor, about half have at least some college education, and about half are in the middle or upper tercile by income and household wealth. Over 80 percent are married, and a similar proportion of nondisabled people are non-Hispanic white. In contrast, disabled people (D→D) are generally the most disadvantaged, with a poverty rate around 19 percent and pluralities in the lowest income and asset levels; about 30 percent of them are high school dropouts. Newly disabled people (ND→D) are generally in-between those who are nondisabled and those who are disabled and are clearly worse off than those who stay nondisabled with respect to some of their socioeconomic characteristics. This is consistent with the fact that they have baseline socioeconomic characteristics associated with less favorable outcomes regardless of the disability shock they have yet to experience.

Overall Outcomes by Longitudinal Pattern of Disability

Information on poverty status and household income is provided in Table 2. Compared with the other two groups, the nondisabled (ND→ND) group showed the smallest change, with its poverty rate almost unchanged (0.2 percentage points), and some decline in household income at the median (-11 percent), but an increase at the mean (6 percent), suggesting relatively favorable outcomes at the higher end of the income distribution. The second column shows that the newly disabled (ND→D) group experienced a substantial increase in poverty rate (76 percent) and a sharp decline in median household income (-25 percent), although a much smaller decline at the mean, most likely affected by those individuals at the higher end of the income distribution. The disabled (D→D) group experienced about the same decline in median and mean income as the group that became newly disabled, but with a much smaller increase in poverty in relative terms.

Table 3 shows how various components of household income contribute to the financial well-being, separately for each of the three groups, at the baseline and followup, and it highlights their changes. The sources include own earnings; public benefits; own pensions; and other sources, which are largely spousal earnings.⁵ One of the main conclusions from this table is that, on average, own earnings substantially decreased for all three groups. Butrica, Toder, and Toohey (2008) highlight the potentially powerful role of delaying retirement (that is, labor force withdrawal) on financial security. However, the causes and consequences of early labor force withdrawal are profoundly different by disability status. Most importantly, delaying retirement is less of an option for those who experience a disability shock (the ND→D group). For the nondisabled (ND→ND) group, labor force withdrawal is obviously unrelated to major adverse health or disability shocks. For that group, “other sources” almost completely replace loss of own earnings. Thus, it is not surprising that despite the low rate at which own pensions and public benefits replace the average lost earnings (44 percent; authors' calculation), mean household income increases for this group mainly because of the increase in other sources of income.

Disability shocks are associated with a greater decline in average earnings. The rate of replacement of lost own earnings by own public benefits and private pensions is somewhat lower for the newly disabled (ND→D) group (41 percent; authors' calculation) than for the nondisabled (ND→ND) group. The main reason for declining average household income for the former group is that the increase in other sources of income replaces a much lower share of lost earnings compared with the nondisabled group. Note that private pensions are more important than public benefits in buffering the effects of income loss associated with disability shocks in our sample. Previous studies have amply demonstrated the secular decline in overall defined benefit pension coverage in the last two decades and the increasing prevalence and importance of defined contribution (DC) plans.⁶ Although this shift may lead to changes in the role of public and private pension income for future cohorts, it is less likely to have had a substantial impact for the cohort in our sample because DC plans were less prevalent for this cohort.⁷ Finally, for the disabled (D→D) group, the most salient fact is that the increase in other sources of income when compared with the decrease in own earnings plays a relatively smaller role than for the other two groups (17 percent versus 96 percent for the ND→ND group and 34 percent for the ND→D group; authors' calculations). Appendix Table A-1 provides more distributional detail. It shows that the percentage of respondents with own earnings declined, while the percentage with public benefits and pensions combined increased between the baseline and followup across the board. Detailed distributional statistics on changes in income from various sources show more complex patterns.

Table 4 shows that marital status (measured at the baseline) is indeed an important factor affecting poverty outcomes associated with disablement.⁸ The poverty rate among the nondisabled (ND→ND) group, compared with the other two groups, is relatively lower at both the baseline and followup, and there is little percentage point change in poverty rates between the baseline and followup. While the rate of poverty for people in the nonmarried category actually decreases at the followup, those in that category continue to experience much higher poverty rates than their married peers.

For both newly disabled people and those who start and stay disabled, the differences in poverty rate by marital status are large at the baseline. While the poverty rate increases for both married and nonmarried respondents, the percentage point increase is much larger for those in the nonmarried group than for their married peers. The poverty rate increase for the people in the nonmarried category relative to those who are married is 5.5 percentage points higher among the newly disabled (ND→D) group and 8.2 percentage points higher for those in the disabled (D→D) group (authors' calculations). Being married clearly provides an important buffer against impoverishment associated with a disability shock or continued disability. Importantly, nonmarried people already disabled at the baseline are not only the most likely to be poor both at the baseline and followup, but their poverty rate increases from essentially one-third to almost half between the baseline and followup. Thus, the combination of being single and having a continued disability is associated with substantial economic vulnerability.

Table 5 shows indicators of net household wealth and liabilities. Baseline differences in wealth measures among the three groups are substantial, in the expected direction: Both the percent with any positive amount from a given source and mean/median 1992 levels are highest among nondisabled people and lowest among those who start and stay disabled. Between 1992 and 2000 there were increases in all measures of wealth for all three disability status groups, but the magnitudes of change substantially differed by wealth measure and group; the medians clearly suggest a negative effect of disability shocks.⁹ To give some perspective on these findings, we make two observations. First, this was a period of substantial rise in the stock market (the S&P 500 index increased by 258 percent), low inflation (23 percent increase), and appreciation in the housing market (37 percent increase in Case-Schiller index) that outpaced inflation.¹⁰ Second, household annuitized nonhousing wealth (Table 5) is small compared with household income (Table 2) for all three groups.

Median net household financial wealth (Table 5) at the baseline ranged from a high of $15,000 for nondisabled respondents to a low of $2,000 for those already disabled. The percent increase between the baseline and followup showed a similar pattern for the three groups; it stayed essentially flat for those already disabled at the baseline.¹¹ Because median net household financial wealth was only 17 percent of median household annual income at the baseline for the newly disabled (ND→D) group and only 7 percent for the already disabled at the baseline (the D→D group) (authors' calculations using figures in Tables 2 and 5), financial wealth hardly provided any cushion against the income loss associated with a disability shock among respondents newly disabled; and, there was a continued high rate of poverty among those already disabled.

The percent with household liabilities was slightly higher for the nondisabled (ND→ND) group than for the other two groups, but the median of household liabilities was only roughly 10 percent of the median net household wealth for both the nondisabled and the newly disabled (ND→D) groups, and even less so for the disabled (D→D) group. Respondents who stayed disabled had the lowest level of median household liabilities at the baseline. While median household liabilities substantially declined for all three groups, overall, liabilities do not seem to have played a major role in differences in financial well-being among the three longitudinal pattern groups.

Next, we turn to health insurance coverage and MOOP expenditures (Table 6). These are important aspects of financial well-being in the context of disablement because disabilities tend to be associated with high medical expenditures. During the period under study, there was no mechanism of universal access to health insurance among the working-age population in the United States, and access to affordable private insurance among people with preexisting conditions has been limited. Overall, the percent of our sample universe with health insurance coverage at the baseline was lowest among people who were disabled (D→D) and highest among those who were nondisabled (ND→ND). However, that pattern reversed between the baseline and followup because of the increase in public health insurance coverage among the two groups of people with disability experience. Still, given the fact that disabled respondents may be in greater need for health insurance protection, at our followup, over 15 percent of respondents in both disability groups were without any health insurance coverage.

These overall patterns reflect opposite dynamics with respect to private and public health insurance coverage. At the baseline, access to private health insurance was the highest among nondisabled (ND→ND) respondents, and it changed very little. In contrast, access to public health insurance was the lowest for respondents in that group, and increased only slightly. Thus, it is not surprising that the nondisabled group experienced little overall change in health insurance coverage.

Regarding access to private health insurance, there is no statistically significant difference at the baseline between the nondisabled (ND→ND) group and the newly disabled (ND→D) group, but private coverage is significantly lower among the continuously disabled (D→D) group. Over the period, the two groups with disability involvement (ND→D and D→D) experienced a greater decline in private coverage than did the nondisabled (ND→ND) group. This is not surprising given the fact that the private insurance market over the observed period (1992 to 2000) did not address the needs of people with increasing medical needs and decreasing earnings potential associated with disabilities. More specifically, it was possible for private health insurance companies during that time to deny coverage to high utilizers or people with preexisting conditions, whereas employer-provided insurance often was not available to part-time workers, and COBRA coverage for those who lost their jobs was relatively expensive. In contrast, public health insurance—primarily Medicaid and Medicare—increased substantially for the two groups with disability involvement, more than compensating for the loss of private health insurance on the average. This is expected, given the heavy conditioning of access to public health insurance among the working-age population in the United States on the presence of severe disabilities, largely in combination with poverty. Nevertheless, only 18.5 percent of newly disabled (ND→D) respondents and only slightly more than a third (36.7 percent) of those who were already disabled (D→D) at the baseline had public insurance coverage at the followup. The 15–16 percent of disabled people without any health insurance at the followup may reflect either limited access to private coverage among those who were severely disabled and may not have been eligible for Medicaid or Medicare for a variety of reasons¹² or others whose disabilities were not sufficiently severe to qualify for public health insurance coverage, but were still limiting in a variety of ways. The vast majority of all three groups reported some MOOP expenditures at the baseline. We observe some increase in the proportion with any MOOP expenditures for all three groups between the baseline and follow-up period; the percentage point increase is largest for the nondisabled (ND→ND) group and lowest for the disabled (D→D) group. The mean of MOOP expenditures (including those with zero values) at the baseline and the followup is higher for the two groups with disability involvement than for the nondisabled (ND→ND) group. The differences are larger when compared with mean household income. Nevertheless, the largest percentage of MOOP expenditures relative to household income (2.7 percent at followup for those who stayed disabled) is still a small fraction (authors' calculation using figures from Tables 2 and 6). Based on the median MOOP expenditures at the followup, our qualitative conclusion is that while MOOP expenditures were clearly higher for disabled people compared with those nondisabled, those costs do not appear to have been a big issue for at least half of the respondents (below the median) who were either already disabled or newly disabled. Nevertheless, for some people, disabilities are associated with relatively large MOOP expenditures, as the 90^th percentile MOOP expenditure figures indicate, especially at the followup.

Variation of Outcomes by Pattern of Disability Indicators

In the analyses above, we focused on overall comparisons among three groups of people defined by longitudinal patterns of disability based on a measure composed of three indicators of disability. Here, our analysis of detailed patterns is motivated by a desire to characterize the nature, intensity, and persistence of disablement in ways that are similar to the approach followed by Meyer and Mok (2006), although the operational measures we use here are different. Table 7 adds some detail on poverty outcomes for the newly disabled (ND→D) group in addition to providing data on a fourth group that has not been previously used or discussed in our analysis: the D→ND group, comprised of people who recovered between the baseline and followup. In order to provide a full picture, we also report the poverty outcome information for the other two groups: (1) the nondisabled (ND→ND) group, and (2) the disabled (D→D) group. Several interesting findings emerge from this analysis.

First, we look at the “intensity” of disablement by creating three subgroups within the newly disabled (ND→D) group: a subgroup consisting of respondents who experienced a disability shock on a single indicator alone, a second subgroup of those who experienced a disability shock on any two indicators, and a third subgroup consisting of those who experienced a disability shock on all three of our indicators. Using those subgroups together with the nondisabled (ND→ND) group that in effect transitioned from a zero disability indicator at the baseline to a zero disability indicator at the followup, we have a scale of the severity of the disability shock ranging from zero to three. Consistent with the results of Meyer and Mok (2006), we find that the severity of the shock is strongly related to increased economic vulnerability as measured by the percentage point change in the proportion poor, ranging from 0.2 percentage point among those with no disability shock to 20.2 percentage points among those who experienced a disability shock on all three indicators (Table 7).

Second, we provide additional analysis by type of disability indicator. Because of sample size constraints, we analyze this level of detail only for those people who experienced a disability shock on only one of the following three indicators: (1) doctor-diagnosed major health condition, (2) self-reported poor or fair health, and (3) self-reported work-limiting condition. We find substantial heterogeneity in financial vulnerability between the three measures at the baseline and in the magnitude and pattern of change. Consistent with the overall results, there is a clear increase in percentage poor for both the doctor-diagnosed major health condition and self-reported work-limiting condition indicators. The results for self-reported poor or fair health are dramatically different: There is a relatively high poverty rate at the baseline and a decline in poverty between the baseline and followup.

To improve our understanding of differences among respondents reporting a disability shock on a single indicator only, we analyze detailed characteristics of the three disability indicator subgroups defined by the type of indicator and observe some salient patterns (data not shown). In general, respondents reporting only doctor-diagnosed major health conditions are consistently better off at the baseline compared with members of the other two subgroups on a number of SES indicators. The fact that the mean household income of those in the group reporting a doctor-diagnosed major health condition actually increases between the baseline and followup is consistent with the lowest poverty rate increase for that group. The main distinguishing characteristics of the respondents in the group that self-reported poor or fair health are the vast overrepresentation of high school dropouts and minorities (Hispanic and non-Hispanic blacks) among them. The differences between respondents in this group and those in the group self-reporting only work-limiting conditions are complex. Compared with those in the group self-reporting only work-limiting conditions, those self-reporting only poor or fair health have comparable average earnings and lower mean household income at the baseline, but experience more muted declines in average earnings and household income (estimates are available from the authors upon request).

The decrease in poverty rate among those in the group that self-reported poor or fair health runs counter to the expectation that disability shocks should lead to increased financial vulnerability. However, a more careful analysis considering the SES characteristics of this group explains the seeming anomaly. Heterogeneity is clearly very important here. First, the poverty rate decreases, even though the average household income decreases by 16 percent between the baseline and followup. Second, a closer look at what happens at the lower tail of the income distribution provides an explanation for the decrease in the poverty rate. In general, the loss of earnings is the major mechanism creating a relationship between disability shocks and deterioration in financial well-being. However, the loss of earnings is less relevant than changes in other income items as determinants of poverty status for this group. For respondents who did not work at the baseline, the poverty rate is essentially bound to decrease because the loss of earnings cannot contribute to increased poverty, while increased public benefits and pensions and positive earnings for some at the followup, however small, pushes household income above the poverty line in some cases. More importantly, among respondents self-reporting poor or fair health who worked at the baseline—in contrast to those in the other two subgroups—the poverty rate goes down, particularly among the working poor. Earnings loss among the working poor cannot result in poverty because those people are already poor, whereas the increase in both the proportion of those receiving income and the average amount received (while small) from public benefits and pensions pushes household income above the poverty line. Although the sample size is small, data tabulations (available upon request from the authors) indicate that the sample of respondents classified as working poor at the baseline who self-reported poor or fair health are much more likely to be Hispanic and without a high school degree. While this discussion highlighted some of the factors explaining the poverty-related differences among the three disability indicator subgroups, much remains for future analyses. In particular, the results from Table 7 provide clear motivation for follow-up studies focusing on differences by SES indicators.

Finally, Table 7 shows that recovery (D→ND) is associated with a reduction in the poverty rate, producing an improvement on this indicator of economic vulnerability (4.4 percentage point reduction in the poverty rate) that is roughly comparable to the absolute magnitude of increase in poverty (5.7 percentage point increase) associated with disability shocks among the newly disabled (ND→D).

So far, in our analysis, we have looked at changes in disability status between two points in time. This of course provides a simplified picture because people may move in and out of a disability state. Hence, in Table 8, we focus on another dimension of the longitudinal pattern of disability: the “persistence” of disablement as measured by the number of waves with at least one reported disability indicator from 1992 to 2000. As expected, the poverty rate is generally positively associated with disability persistence, as revealed by the increase in poverty rate (both in 1992 and in 2000) and the number of waves with reported disability. Furthermore, the percentage point change in poverty rate between the baseline and followup increases among respondents with reported disability in two or more waves, although the pattern shows a nonlinear relationship.

Our overall conclusion from this brief analysis of variation on two dimensions of the pattern of disablement is that both severity and persistence are important determinants of the economic vulnerability associated with disability shocks. These results are generally in agreement with earlier findings by Meyer and Mok (2006). In addition, we conclude that the overall findings reported in the main body of this study, while instructive, may mask some heterogeneity by SES, and therefore more disaggregated analyses focusing on the interactions between SES and disablement in the future could provide additional insights on the role of social safety nets in affecting the financial security of people experiencing disability shocks near the retirement age. Another direction for future research involves looking at the temporal patterns of disability before and after the full retirement age, as well as differential effects of disability on financial well-being across cohorts.

Conclusion

In this article, we followed three groups of adults aged 51–56 in 1992 with different disability experiences during an 8-year period and found distinct patterns of changes in financial security. First, people who started as nondisabled (in 1992) and were also nondisabled 8 years later (in 2000) have experienced little change in their income and poverty status despite substantial loss of earnings associated with labor force withdrawal; they started with private health insurance that was largely retained over time; and they experienced substantial increases in wealth. Second, newly disabled people (those who started nondisabled and experienced a disability shock later) experienced substantial increases in poverty and sharp declines in median income. The average earnings loss was greatest for people in this group, and on average, public benefits and private pensions replaced less than half of their lost earnings. The net increase in poverty was somewhat muted because of spousal income among the married, while the poverty increase was substantially larger among the nonmarried. Among newly disabled people, median household wealth increased, but at a much lower rate than among those who started and stayed nondisabled. While the private health insurance coverage of those newly disabled declined, this was more than compensated for by the increase in public health insurance. Third, people who were already disabled at the baseline and stayed disabled at the followup experienced a decline in their income security, but public cash benefits muted the drop. While those people started out with the lowest level of household wealth, they experienced growth in median net household wealth. Nevertheless, their liquid resources—financial wealth—remained meager. Importantly, their overall health insurance status improved because of a substantial increase in public health insurance coverage. Finally, greater severity of disability results in a greater increase in poverty for the affected disability group. Based on limited data, we observed some improvement in the poverty status of a small group that shifted from disabled to nondisabled between the baseline and followup.

Much remains for future research. Most importantly, a major source of heterogeneity is due to the conditional nature of the social safety net system in the United States (based on age, low-income levels, and severe disability) with defined benefit and defined contribution pensions (Dushi, Iams, and Tamborini 2011) and health insurance playing a more prominent role at higher SES levels; public cash benefits and health insurance programs play a larger role at the low end of the SES spectrum. However, among the working-age population, public health benefits are also heavily conditioned on severe disabilities. Thus, safety nets dampen the effect of disability shocks in ways that may interact with SES. Of particular interest to policymakers is how subgroups defined by educational attainment fare in the event of a disability shock near retirement age. This issue calls for a study of the relationship between disability shocks and financial well-being at a more disaggregated level. Finally, more analytically oriented studies focusing on the effect of disability shocks net of other factors are also needed for a better understanding of financial security as people move closer to the Social Security FRA.

Appendix