Income, Poverty, and Material Hardship Among Older Americans

Basic Descriptive Statistics

Table 1 presents basic descriptive statistics for the full sample and also by family income relative to poverty (less than 100 percent of the federal poverty level, 100 to 199 percent, 200 to 299 percent, 300 to 399 percent, 400 to 499 percent, and 500 percent or higher). Overall, 21 percent of the elderly reported some hardship; considering only the four hardships other than financial dissatisfaction (food insecurity, skipped meals, medication cutbacks, or difficulty paying bills), this figure is 13 percent. Seven percent reported medication cutbacks, 4 percent reported food cutbacks, and 2 percent reported skipping meals; 5 percent had difficulty paying bills, and 14 percent were dissatisfied with their financial situation. Among those who did experience some hardship, lower-income households were more likely to experience more than one hardship. Seven percent of the sample lived in poverty, and nearly one-quarter lived in a family with income less than 175 percent of the federal poverty level. Ten percent of the elderly reported using Medicaid, food stamps, SSI, or some combination of the three programs, with a much higher prevalence, not surprisingly, among lower-income households; half of all households in poverty reported some program use, while fewer than 2 percent of households with income higher than five times the poverty level (corresponding to approximately the top quartile of the income distribution) did. Most (84 percent) of the sample members were no longer working, and 26 percent reported that their health was only fair or poor.

How Well Does Income Explain Hardship Among the Elderly?

The prevalence of hardship declines as income increases, as shown in figure 1 (the data used to create this figure are in appendix table A1). There is a strong relationship between income and hardship: in the lowest income category—households with income below 75 percent of the poverty threshold, which account for about 3.4 percent of older Americans—38.5 percent experienced hardship. The prevalence of hardship increases slightly in the next income category, then declines slowly as income increases. At a certain point, which varies depending on which hardship is being analyzed, the relationship flattens out; additional income above this level does nothing to reduce hardship.

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Figure 1.

Hardship Among Americans Age Sixty-Five and Older by Income

The nonlinear relationship between income and hardship is not particularly surprising; what is surprising is how high income must be before additional income is no longer associated with lower hardship. Income must be about five times the poverty level before its effect on the probability of reporting any hardship fades out. This persistence of hardship is driven mostly by medication cutbacks, difficulty paying bills, and financial dissatisfaction; the income gradients associated with food insecurity and skipped meals fade out earlier, at about twice the poverty level. Also surprising is the fact that even at the highest levels of income, the prevalence of hardship is not zero. In families with incomes greater than five times the poverty level, about 5 percent of elderly individuals experienced some hardship. Hardship at higher levels of income consists mostly of medication cutbacks and dissatisfaction; only 1 or 2 percent of these individuals reported food insecurity or difficulty paying bills, and skipping meals was essentially unknown among families with income greater than three times the poverty level.

Would Alternative Measures of Resources Predict Hardship Among the Elderly More Accurately than Income Does?

As already noted, other resource measures, such as consumption and disposable income, may target hardship more accurately than income does, particularly for the elderly. To test this possibility, I explore two other measures of resources: (1) total spending, using the subset of respondents for whom CAMS data are available, and (2) income minus household out-of-pocket medical spending. Figures 2 and 3 replicate figure 1 using these two alternative measures of resources. Figure 2 shows that total spending does not seem to do a better job than income of discriminating between those households that are likely to experience hardship and those that are not. This echoes the conclusion reached by Kerwin Kofi Charles and his colleagues (2006), who use somewhat different methods. In contrast, repeating the same tabulation using a measure of household income net of out-of-pocket medical spending (figure 3) does a somewhat better job than does income alone of identifying households at risk of hardship. That is, there is more mass in the left tail in figure 3 than in figures 1 or 2, and the probability of hardship declines more smoothly.

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Figure 2.

Hardship Among Americans Age Sixty-Five and Older by Spending

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Figure 3.

Hardship Among Americans Age Sixty-Five and Older by Income Minus Out-of-Pocket Medical Spending

While this eyeball test makes sense, it is not obvious how we could quantify the accuracy with which a resource measure identifies hardship. One way of doing this is to calculate how high up the resource distribution it is necessary to go before we have identified half (or some other chosen fraction) of those in hardship. Table 2 operationalizes this concept by calculating the median value of different resource measures for individuals experiencing each kind of hardship. A resource measure that more accurately identifies hardship will have lower values in table 2; that is, more of the population with hardship will be clustered at low resource levels. Table 2 confirms the intuition of figures 1, 2, and 3: total spending is inferior to income for targeting hardship, while income net of out-of-pocket medical spending is superior. There is still a lot of hardship, however, that is not explained by subtracting out-of-pocket medical spending from income; half of individuals reporting hardship were in households with income net of out-of-pocket medical spending greater than 170 percent of their federal poverty threshold.

Is Hardship Related to Failure to Take Up Transfer Programs?

The role of transfer programs is particularly interesting because the existing literature mostly shows that hardships are more likely among recipients than among eligible nonrecipients. In particular, a number of studies find that food insecurity is more prevalent among food stamp recipients than among nonrecipients (Haider, Jacknowitz, and Schoeni 2003; Wilde and Nord 2005), presumably because the timing of enrollment, conditional on having income low enough to be eligible, is not random: people enroll in these programs when they have fallen on tough times. As a result, the partial correlation between program use and hardship reflects both self-selection into program use and any causal effect of program use on hardship, two factors that are likely to go in different directions (Haider, Jacknowitz, and Schoeni 2003; Wilde and Nord 2005). More recent studies relying on plausibly exogenous variation in program use to identify the effect of food stamps find mixed effects, which is probably related to variation in the validity of the instrumental variables strategy (Greenhalgh-Stanley and Fitzpatrick 2013; Schmidt, Shore-Sheppard, and Watson 2013).

Because I have no exogenous variation in program use, and also because public program use is not well measured in surveys (Meyer, Mok, and Sullivan 2009), I do not attempt to draw any causal inference about the relationship between take-up and hardship. Rather, my goal is to document how many of those experiencing hardship are not taking up public programs for which they may be eligible, in order to understand how much scope there is for reducing hardship by, for example, improving program outreach. Figure 4 (based on the data presented in appendix table A2) explores the relationship between hardship and transfer program use by categorizing the elderly experiencing hardship by income (collapsed into two categories: greater than or less than 175 percent of poverty) and the use of transfer programs (food stamps, Medicaid, and SSI). As already noted, about half of those experiencing hardship fell into the higher income category; about 10 percent of these (or 5 percent of the total) were programs users. In the lower-income group, most but by no means all of the elderly who experienced food insecurity or skipped meals were already using public programs. About half of low-income elders who had difficulty paying bills used public programs. For the other hardships—medication cutbacks and financial dissatisfaction—most of the elderly who experienced them did not use SSI, Medicaid, or food stamps. The fact that, overall, only 23 percent of those experiencing hardship were low-income individuals failing to take up any public programs suggests that improving outreach alone, without also expanding eligibility or increasing benefits, would have a limited effect in mitigating hardship.

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Figure 4.

Distribution of Americans Age Sixty-Five and Older Experiencing Hardship by Family Income and Use of Transfer Programs

Multivariate Results

The main result from this descriptive analysis is that while hardship is more likely among those with lower incomes, most of the elderly experiencing hardship are not poor. Why are so many nonpoor elderly experiencing hardship? To understand the determinants of hardship in a multivariate context, I estimate linear probability models predicting hardship as a function of economic resources, demographic characteristics, health, and cognition. I estimate the model separately for five hardships—(1) food insecurity, (2) skipped meals, (3) medication cutbacks, (4) difficulty paying bills, and (5) dissatisfaction with one's financial situation—and for two summary outcomes: (6) any of the above five hardships, and (7) any of the above five hardships except dissatisfaction with one's financial situation.

All models include controls for the natural log of real income net of out-of-pocket medical spending; the natural log of total real wealth; age; an indicator for fair or poor self-rated health; the number of Nagi limitations; the number of medications the respondent takes regularly (0 to 12); memory score (0 to 20); Serial Sevens score (0 to 5); gender and marital status dummies; an indicator for education less than high school; and race and ethnicity dummies. Regressions also include indicators of labor force status at the time of the interview (working, unemployed, or disabled), with the omitted group consisting mostly of respondents who were out of the labor force (homemakers, retirees) and a small number of respondents who reported “other” labor force status. The models reported here are parsimonious ones in which most variables (for example, education, cognition, and health status) are entered linearly or as a single dummy variable. More flexible models that include these variables as vectors of categorical dummies yield results that are very similar to the parsimonious models. Additional specification checks are discussed later.

Table 3 presents the basic multivariate results. Income and assets both significantly reduce the probability of all kinds of hardship. The coefficients imply that a 1 percent increase in income or assets translates, on average, into about a one-percentage-point reduction in the probability of food insecurity and about a two-percentage-point reduction in the probability of any of the hardships.

Other than income and assets, the most striking predictors of hardship are measures of poor health, consistent with earlier studies (Lee and Frongillo 2001; She and Livermore 2007). Being in fair or poor health, having a greater number of Nagi limitations, or reporting one's work status as “disabled” all increase the probability of hardship substantially. Why is health so important? There are three plausible mechanisms through which poor health or disability may lead to hardship. First, poor health may reduce income directly by making it harder to work. Second, poor health may increase out-of-pocket medical spending, tying up income that could be used to meet other basic needs such as food. Third, poor health may affect one's ability to make do, as suggested by earlier qualitative work (Wolfe, Frongillo, and Valois 2003).

To test the importance of the first hypothesis, I reestimated the regressions both without any income controls and with more extensive controls for income (a set of fifteen dummies reflecting categories of income-to-needs). If income is an important channel through which poor health affects material hardship, dropping the income controls ought to increase the coefficient on the measures of poor health (self-reported health, Nagi limitations, number of medications taken regularly, and disability status). In fact, these coefficients are essentially unchanged by the exclusion of income controls or the inclusion of more detailed income controls. I conclude that, among the elderly at least, income is not the primary channel through which poor health translates into hardship.

The second channel through which health may affect hardship is through out-of-pocket medical spending. As already discussed in the context of figures 1 through 3 and table 2, out-of-pocket spending does seem to explain at least partly why apparently high-income individuals experience hardship. But reestimating the models in table 1 using income minus out-of-pocket spending rather than income changes the coefficients on the health variables very little. Thus, the regression results suggest that out-of-pocket medical spending explains relatively little of the effect of poor health on hardship; health has a strong independent effect on hardship regardless of income or out-of-pocket medical spending.

This leaves the third reason for why poor health could mean more hardship: it is simply harder to get by for someone who is in poor health, and it is especially difficult to make do with less. For example, an elderly person with activity limitations might have to purchase (more expensive) prepared meals, rather than cook from scratch, or have to shop at expensive convenience stores, being unable to shop around for lower prices.

Two supplemental analyses lend support to this story. The first explores the role of spouses and their physical limitations. It is evident from the results of the multivariate models in table 3 that being married may actually increase the risk of some hardships for men, while being married reduces hardship across the board for women. Table 4 presents coefficients from multivariate models that are the same as those presented in table 3 except that (1) the models are estimated separately for men and women, and (2) marital status is now measured using a complete set of dummies representing the spouse's level of physical limitations using the Nagi measures described earlier. The results for men in the top panel of table 4 show that having an able-bodied wife reduces the risk of hardship somewhat, but that having a disabled wife significantly increases the risk of food insecurity. For women, having a husband reduces the risk of hardship across the board, while having a husband with no physical limitations reduces hardship the most.

The second supplemental analysis focuses on children. The top row of table 5 reports the coefficients from models similar to those in table 3 but augmented with a dummy for whether or not the respondent had children. Having children does not, on average, affect hardship, but this lack of an effect masks heterogeneity in the nature of the respondent's relationship with his or her children. The psychosocial leave-behind questionnaire asks respondents with children: “How much can you rely on them if you have a serious problem?” Possible responses are “a lot,” “some,” “a little,” and “not at all”; a small fraction of respondents with children (1 percent) did not respond to this question, and these nonresponses are treated as a separate category. When I estimate models similar to those in table 3 but augmented with a set of variables distinguishing between reliable and unreliable children, it is clear that the lack of an effect on average masks a significant increase of hardship among those with very unreliable children. (Fortunately, this type of children were quite rare: only 3 percent of parents reported having them. This explains the lack of any significant effect in the models with a simple indicator for whether or not the respondent had children.)3

Turning back to table 3 to look at other predictors of hardship, we see that better memory actually increases the probability of hardship slightly, possibly because these respondents were more likely to know or more likely to report that they had a problem. Respondents with higher scores on the Serial Sevens test, in contrast, were significantly less likely to report difficulty paying bills or financial dissatisfaction. Education is not a significant predictor of hardship in these models, since it is highly correlated with age, income, education, and cognition. In all models, older individuals were less likely to report hardship. Steven Haider, Alison Jacknowitz, and Robert Schoeni (2003) report a similar finding for food-related hardship (skipped meals) and speculate that the well-established fact that caloric needs decline with age may be responsible. On the other hand, a similar story cannot explain why medication cutbacks also decline with age, since medication use increases with age.

African Americans had significantly higher rates of food insecurity and medication cutbacks; rates for other nonwhites and Hispanics were similar to those for white non-Hispanics (the omitted group). Unmarried women, three-quarters of whom are widows, reported significantly higher rates of hardship than did single men, married men, or married women; much of this effect is due to their lower incomes. As already noted, the effect of marriage is different for men and women and depends, for men, on how healthy their wife is.

Individual Fixed-Effects Estimates

To address at least partially the concern that unmeasured individual-level factors correlated with the explanatory variables may actually be causing hardship, I also estimate models with an individual fixed effect (FE) using data from the 2008 and 2010 surveys. I cannot use fixed-effects models to analyze difficulty paying bills or financial dissatisfaction, since questions about these hardships are not asked every wave. Therefore, the FE models focus on food insecurity, skipped meals, and medication cutbacks. The FE models reported in table 6 confirm the central finding of the ordinary least squares (OLS) models reported in table 3: health measured using either Nagi limitations or self-reported disability status is an important predictor of hardship. Poor self-reported health status has a small and insignificant negative effect on hardship in the FE models. Income and assets remain significant predictors for some outcomes but not for others, and with smaller magnitudes than in the OLS models. Other explanatory variables that were significant in the OLS models, such as cognition, are no longer significant in the FE models, although it is difficult to say with certainty whether this is because these variables truly do not matter in determining hardship or because there was not much change in these characteristics over the two-year window from 2008 to 2010.