Table 1 shows the number and percentage of people living in households with hardships in 1995. The most common of the hardships reported here were related to paying the bills -- not meeting essential expenses, not paying the full rent or mortgage or not paying the full amount of a gas, electric or oil bill. It was somewhat less common to reside in a household that didn't get food, medical care or dental care. Least common were situations where the household got far enough behind in paying bills that they had their utilities or phone service cut off, or were evicted from their apartment or home. The results from the other two panels (not shown) agree closely with those from the 1993 panel, with exceptions noted below.
Table 1 Percent and Number of People in Households Experiencing Material Harship, 1995 (1k)
Edin and Lein examined the "survival strategies" of families with limited budgets and noted that they often play one type of hardship off against another (1997). They might scrimp on food to buy Christmas presents, or forestall paying one bill in order to pay another. This implies that, over the course of a year, those who have limited resources would experience more than one type of hardship. Table 2 shows that this relationship is evident in the data collected in the SIPP. Fifty-four percent of those experiencing hardship experienced more than one hardship. (This excludes those who reported their household "didn't meet essential expenses" and reported only one of the specific types of expenses about which they were subsequently asked.) Of people in households that didn't meet essential expenses, 64.2 percent experienced more than one type of hardship. For each of the other types of hardship, at least 70 percent of people lived in households with two or more types of hardship.
Table 2 Percent Reporting Two or More Hardships Of Those with at Least One Hardship (1k)
Table 3 shows the relationship between income and hardship by household. A little over 40 percent of those in the lowest income category experience hardship, and around one-quarter experience more than one type of hardship. At the other extreme, less than 5 percent of those in the highest income category experience hardship and 2 percent experience more than one. Thus, there is a strong relationship between income and hardship. On the other hand, these figures indicate that hardship is not a clear indicator of what one might call "true need." One in five households with income between $30,000 and $40,000 reported hardship. Ten percent of households with income between $50,000 and $100,000 reported hardship.
Table 3 Percent experiencing hardships by household income (1k)
One of the original motivations for turning to non-income based measures of poverty was to avoid misunderstanding the situation of the poor (and non poor) who under-report income. However, judging from the proportion of high income households reporting hardship, these hardship indicators also do not capture material distress in an unambiguous way.
Compared to reports of any hardship, reports of multiple hardship are slightly more concentrated among those with low income. The odds of reporting multiple hardships is around half the odds of reporting a single hardship among those in the lowest income categories. At the highest categories, the odds of reporting multiple hardships falls to around 35 percent of the odds of reporting a single hardship.
To further evaluate the hardship measures, it is useful to examine how they are related to other measures of economic well-being that have proven their validity in previous research. Households headed by people with characteristics often associated with poverty -- low education, minority group membership, single parenthood -- should also be households with need. At the same time, a somewhat anomalous finding from previous research needs to be explored. Mayer and Jencks found age to be negatively correlated with hardship, despite the prevalence of low income levels among the aged population. It is of interest to see if this relationship also turns up in the current data.
Table 4 shows the results of a set of regressions designed to examine these relationships. The dependent variable is a binary variable coded as one if the household reported multiple hardships, zero otherwise. Shows are the results of a logistic regression of hardship on the income-poverty ratio, poverty, age, several additional characteristics, and for panel response patterns (these variables will be explained later). The poverty ratio has a strong and significant effect on hardship, as might be expected from the income-hardship relationship just explored. (The main difference between the income measure and the poverty ratio is that the latter is adjusted to account for family size, while the income level is not.) Households whose heads are male, Hispanic or other race, married, employed, well educated and not disabled have lower levels hardship than those without these characteristics. Higher levels of hardship are found among households with children, receiving government transfers, in rental housing and lacking health insurance for one or more members. Many of these influences are related to poverty, and adding these controls decreases the effect of poverty by nearly one-half (the zero order effect of poverty ratio is -.46, compared to the value of -.27 when controls are added).
Table 4 Effect of Panel, Poverty, Age and Other Economic and Demographic Factors on Experience of Multiple Hardships (2k)
That some of these variables have significant effects, above and beyond the effect of poverty, may reflect several factors. First, it may simply be that error in income reporting results in measurable effects of other aspects of poverty. Second, some of these effects may serve as indicators of lack of access to resources, such as savings or ability to borrow funds. This may explain the significance of home ownership status, and may be part of the reason that blacks have higher levels of hardship even with controls. Since lack of assets is generally a condition for participation in government support programs, this may explain the positive effect of transfers on hardship.
Third, the ability to appropriately manage resources might have an impact on hardship, independent of the level of resources available. This may help explain the effect of education. It may also contribute to the large effect of work disability on hardship, since those with disabilities must overcome barriers to successfully manage their affairs. It should be evident, though, that a combination of factors is probably responsible for these effects, since those with less access to resources often have less ability to manage resources for other reasons, and vice-versa.
A fourth potential influence on the patterns observed here is the willingness to report hardship. This may be the reason households with children appear to have significantly greater levels of hardship with these controls in place. Those with children may perceive these issues more acutely than those without children, and may feel more justified in not paying bills in order to meet other expenses when the latter are for the benefit of others within the household. However, this hypothesis is quite speculative at this point.
The effect of age may be partially explained by some of the factors just listed, but it is clearly not explained by poverty (or any of the other control variables). Moving from zero order effects (not shown) to the regression in table 4, the effect of age barely shifts, except for the youngest age category (age 15 to 25). When age is interacted with any of a number of other variables, the same pattern remains. One might, for example, imagine that the effect of age would have to do with the lower costs of owning a home. Older people may pay off mortgages based on a pre-inflationary home purchase price, or own a home free and clear. However, as shown in figure 1, the effect of age is almost exactly the same for renters as for homeowners. More than any of the other variables found significant in these regressions, the effect of age indicates that hardship, as measured here, is not a simple function of material need.
Figure 1 Effect of Age on Multiple Hardship Among Renters and Homeowners (14k)
Table 5 shows nine separate regressions of material hardship measures regressed on the full set of variables just considered. Each of these nine hardship measures are coded to 1 if the person experienced hardship, 0 if they did not. The hardships are: (1) inability to meet essential expenses, (2) non-payment of rent or mortgage, (3) eviction, (4) failure to pay utility bills, (5) utilities being cut off, (6) phone service disconnected, (7) foregoing needed medical care, (8) foregoing needed dental care, and (9) not enough food to eat in the household.
Table 5 Effect of Demographic and Economic Factors on Nine Types of Hardship (7k)
What stands out from the table is the near equality of regression coefficients across all nine hardship measures. This provides an indication that all these measures tap into a single basic phenomenon, justifying the summary measures used earlier.
A few variables are somewhat different in their effects on some outcomes than their effects on other outcomes. Visiting the doctor, visiting the dentist and lack of food are dependent variables characterized by a number of coefficients differing from those in other regressions. So there is some evidence that being unable to visit a health professional is a slightly different sort of hardship than the others.3 The effect of having moved is much greater for eviction than for other hardships. This is probably due to the effect of eviction on moving rather than the other way around. The effect of being in the 1993 panel raises the probability of reporting lack of food, while it decreases the probability of reporting other kinds of hardship. The reason for the change in effect on food shortages was a change in question wording between the two surveys.
From the evidence in this section, it is clear that these material hardship measures hang together reasonably well, and are closely related to traditional measures of income and poverty. On the other hand, material hardship does not function exactly in the same way poverty does. Many high income households report material hardship, and one of the most basic variables that predicts poverty -- age -- predicts lower, rather than higher levels of hardship. Together, these bits of evidence seem to show that there are aspects of material hardship that are not related to poverty as it is traditionally understood. Although income may provide an imperfect basis for measuring of "poverty," material hardship may not be any better. This leaves the analyst with a hanging question -- which provides the more reliable indicator? A way to answer this is to look at how well hardship measures an outcome traditionally associated with poverty -- high school dropout.