Most Welfare k. Welfare Recipients By State Show Sources. Rates rose slightly, as the figure indicates. The major conclusion from Table 7—4 and Figure 7—2 is that relative race-ethnic differentials in welfare participation have been fairly stable over the last decade. Although a somewhat greater increase in Medicaid and Food Stamp participation by Hispanics than by non-Hispanic Blacks and by Whites led to a somewhat higher rate of growth of overall welfare-program participation over the decade, the three race-ethnic groups did not change relative position.
In both and , participation rates among Hispanics and non-Hispanic Blacks were in the same ballpark, with Hispanic rates somewhat or slightly below those of non-Hispanic Blacks, and rates among non-Hispanic Whites far below that. Thus, it is reasonable to conclude that race and ethnic differentials in welfare-program participation have been relatively stable over time. Although relative welfare-receipt rates were stable from to , the AFDC-TANF caseload began to decrease precipitously for all races around ; and the decrease accelerated in In part, this decrease is a result of the improvement in the economy, and in part it is a result of the PRWORA legislation and the state waiver reforms that began prior to that.
There have been no significant changes in the racial-ethnic distribution of the caseload, however; hence, the relative participation rates shown in Tables 7—1 and 7—4 are still accurate.
Finally, Figure 7—3 shows related trends, namely in the percent of different race-ethnic groups on AFDC similar figures for the other welfare programs are not available. The percents of the AFDC caseload composed of White and Black families have been very close to one another over the period, but both have slowly decreased relative to that of Hispanics. But the growth of the Hispanic representation on AFDC is not, as Table 7—4 indicates, reflective of an increase in the propensity of the His-.
The Medicaid question changed in ; hence, there is some noncomparability between the figures at the two dates. In , White parents constituted Department of Health and Human Services, Table 3. As of mid the most recent data available , the respective percents for TANF adults were Department of Health and Human Services, Table This serves to illustrate the more general point that the percentages of different race-ethnic groups among welfare recipients are not very reliable indicators of the propensity of different groups to receive welfare, because those percentages reflect, in part, differences in relative population size.
The participation rates shown in Tables 7—1 through 7—4 are more reliable indicators of the propensities that are the more important subjects of policy interest.
An important question is why the differences in welfare-participation rates across race and ethnic groups are so large. A number of factors are known to be associated with welfare-program participation in general for reviews, see Blank, ; Moffitt, Factors include low income and poverty, most obviously, but also family structure—in particular, whether the household is headed by an unmarried woman with children—as well as labor-force participation and earnings, urban-rural loca-.
The most conventional conceptual model of welfare participation presumes eligible women with children choosing between going onto welfare or not based on relative income and other circumstances on and off the rolls. The level of the benefit, the greater level of Medicaid coverage, possibly greater child-care support, and more free time to care for children are among the attracting forces of going onto welfare.
The level of potential earnings and the availability of income from other sources family, friends, etc. Many studies of welfare participation have examined whether racial differences in participation exist after these and similar variables measuring the risk factors for receipt and the relative incomes on and off the rolls are controlled for.
The evidence to date is mixed. For example, of the studies of welfare participation through reviewed by Moffitt Tables 6 and 7 , approximately two-thirds found no significant differences in participation across race groups after accounting for measurable variables. However, these studies usually did not examine race and ethnic differences fully; and in those studies that more fully explored race and ethnic differences, significant differences were found even after accounting for the measured variables e.
The risk factors we use to explain welfare receipt are listed in Table 7—5 , which shows the association of several risk factors with welfare-program participation by households, and also the composition of the population of each race and ethnic group relative to each risk factor. For example, the first four rows of the first column of the table show that household type is highly correlated with welfare participation, for almost 54 percent of all female heads of households with children— not restricted by income or any other characteristic—received either AFDC, Food Stamps, Medicaid, or housing assistance in the mids.
This high rate reflects primarily the extremely low income of such households. Not surprising is the fact that households headed by unmarried. The distinction being referred to here is the regression specification difference between allowing only race dummies in the participation equation, versus stratifying the equation by race and thereby allowing all coefficients to differ. Notes: Welfare participation is defined as receipt during the year of benefits from any of the four programs shown in Table 7—1.
The other columns in Table 7—5 show that race-ethnic groups differ markedly in their relative numbers comprising the different household types. More than 24 percent of non-Hispanic Black households and almost 19 percent of American Indian and Alaska Native families were headed by unmarried women with children, as compared to less than 6 percent for non-Hispanic White households.
Interesting to note is that Hispanic households, despite their relatively heavy welfare-participation rates, as shown in prior tables, are not as likely to be headed by unmarried females, and are much more likely to be married with children, relative to non-Hispanic Blacks and American Indians. Marriage rates for Hispanics are, with those of Asians, the highest among the groups. Thus, household type is a less powerful indicator of welfare participation for Hispanics than it is for some of the other race-ethnic groups.
The other major risk factors are income and earnings. Table 7—5 shows the distribution, across nationwide quartiles, of household nonwelfare income and earnings of the different race-ethnic groups as well as how welfare-participation rates vary with such income. At the same time, the different groups have significantly different distributions of income and earnings.
For example, about 20 percent of the former groups are in the lower quartile of the nonwelfare income distribution, whereas approximately 35 to 40 percent of the latter groups are. It is interesting to note that the differences are not nearly so large for household earnings, where, for example, there are more non-Hispanic Whites than Hispanics in the lowest quartile. The earnings differences, however, show up primarily in the second lowest quartile between the 25th and 50th quartile points , where non-Hispanic Blacks, Hispanics, and American Indians and Alaska Natives have the greatest concentration.
Still, because the differences in welfare-participation rates between the second-lowest earnings quartile interval The quartile points are defined from the income and earnings distributions of all races pooled together. Consequently, the percentages across each row must necessarily center about 25 percent.
The other risk factors listed in Table 7—5 show the importance of the other factors in explaining the race-ethnic differences. There are differences in employment status of household heads across the groups, although not as large as one might have expected. Welfare participation rates do, however, correlate strongly with such status, with working heads of households having much lower rates Heads of households who have attained higher education levels also have much lower welfare receipt rates.
At the same time, education levels are much lower among non-Hispanic Blacks and American Indians—especially among Hispanics—as compared to non-Hispanic Whites and Asians. Thus, education may prove to be a factor that is more important in explaining welfare-participation rates for Hispanics which may also counter the lesser importance of family structure mentioned above. Age differences across the groups are not dramatic, although they are not minor either. Combined with the strong correlation of age with welfare participation, age difference explains some of the variance in rates across the groups; Hispanics and American Indians are the youngest, for example.
On the other hand, urban-rural residential status, while differing strongly across the race-ethnic groups, is not correlated with welfare participation. The degree to which these risk factors can explain welfare receipt across the various race and ethnic groups can be quantified using wellknown statistical methods.
Working with a fixed set of measurable risk factors—those in Table 7—5 , for example—one can determine how those risk factors correlate with welfare-participation rates for a particular race-ethnic group, say, Hispanics. The second step is to estimate welfare-participation rates for any specific group—Hispanics, for example—and what the rates would be if the levels of their risk factors were the same as those of the majority White population.
Table 7—5 shows the difference in those levels. The importance of the risk factors themselves, as opposed to differences in propensities to be on welfare across groups for the same levels of risk factors, is measurable quantitatively by how close the adjusted participation rates of each are to those of the majority White population.
Figures 7—4 and 7—5 show the results of such calculations. Also shown are adjusted rates—i. The result immediately apparent from Figure 7—4 is that the vast majority of the differences are explainable by the risk factors; very little remains after the adjustment. Approximately 89 percent of the gap between non-Hispanic Blacks and non-Hispanic Whites is so-explained, and more than 95 percent is explained for Hispanics, American Indians and Alaska Natives, and Asians and Pacific Islanders.
Thus, the differences across groups in factors that can be identified and measured—income, family structure, and related variables—provide the explanation for the higher welfare-participation rates of the four minority groups. This is, to some extent, a favorable result for policy because at least these variables provide mechanisms through which policy levers might be able to reduce the disparity in race-ethnic welfare-participation rates.
Although the adjusted differences are still considerably smaller than those for AFDC alone, the amount of reduction is not nearly so large. For most of the groups, the adjustments explain approximately 60 percent of the unadjusted gap.
Consequently, leaving welfare has been particularly disadvantageous for these women and their children. The existence of such a group shows that there is great diversity in the experiences of welfare leavers, for while some have fared reasonably well, others have not. Not surprisingly, employment rates of less educated leavers are considerably below those of more educated leavers, and poverty rates are higher, as are the employment and poverty rates of those leavers who are in relatively poor health.
Random assignment studies of time-limited pre welfare reforms show some evidence that welfare reform results in a larger fraction of families ending up with below average incomes. The presence of a group of women who have left welfare and are not doing well is consistent with broader trend studies indicating that the poorest single mother families have experienced declines in income in the post-reform period.
As noted previously, women who were once welfare recipients and have left welfare are not the only ones affected by welfare reform. Some women have chosen not to apply for welfare subsequent to reform, possibly discouraged by the work requirements and other new mandates that come with being on welfare, and possibly encouraged enough by the good economy to stay off welfare and work. Other women have applied for welfare but have been rejected. Over twenty states have formal diversion programs, which encourage women through financial inducements and other means to not come onto the welfare rolls.
More than thirty states have either diversion policies or have imposed work requirements that must be fulfilled prior to eligibility for benefits. The decline in the number of women joining the TANF rolls has been very large in the post-reform era. In some states, the decline in entry onto welfare has been more important quantitatively than the increase in exit rates in accounting for the caseload decline.
This finding casts a different light on the caseload decline and demonstrates that there is an important group of women other than leavers whose employment, earnings, and income should be of interest to policymakers. Unfortunately, no studies have been conducted to date that examine this group, so their employment status and well-being remains unknown.
However, the studies which have showed large post-reform increases in employment rates of single mothers as a whole, and which necessarily combine both those who have left welfare and those who have not come onto the rolls, strongly suggest that employment rates of women who choose not to enter the welfare system are high.
The overall picture of employment among single mothers in the wake of welfare reform is a favorable one, indicating widespread work among former welfare recipients and among low-income single mothers as a whole. With this accomplishment a given, reauthorization should focus on policies that address the remaining problems. There are two major problems that deserve attention.
One is the broad issue of how to improve the income gains of women who have left welfare for work. Income gains are too modest for too many families, with earnings gains insufficient to counter reductions in benefits and with poverty rates-though lower than for families staying on welfare-remaining high.
Aside from the need to increase the income of former welfare families for its own sake, income gains from leaving welfare will be necessary, in the long term, to provide financial incentives for women to leave welfare for work.
While sanctions and work requirements can continue to be used to push women into the work force, they will operate much more successfully if the financial incentives operate in the same direction. More supports for working families in the form of increased child care assistance, assistance with transportation, and other work-related services can substantially increase the incentive to work.
Moving more women from part-time work to full-time work would be another direction to pursue, but this approach has limits if adequate child care and transportation are not available. Major improvements beyond this are likely to come only from increased earnings. This calls for expanding policies aimed at job retention, skills enhancement, and job training.
States are only now beginning to think about these types of policies and have a long way to go before such policies are widespread and have a major impact on incomes.
The second major issue is how to develop policies to assist families that have special difficulties in establishing employment. One important result of the studies reviewed here is that many of these families are found not to be on TANF or on any other major welfare program. Rather, they are already on their own, off welfare, and have very low incomes.
Any set of services that is directed mainly to TANF recipients alone on the presumption that the most disadvantaged families are still on the rolls, will not reach these families.
This fact requires a major expansion of assistance to the non-TANF population. Some states, notably Wisconsin, have made such an expansion a major goal, but most states are far from having penetrated this population deeply with services and programs. Most observers already recognize that designing successful policies to move non-employed families into stable work will be very difficult, given the severity of the difficulties these families face.
Lilly was married with a home and a thriving Avon business. After only a few years of marriage, she realized that if she stayed with the physically and emotionally abusive man she had married, she might not survive. She escaped, only to find herself in a new town with no money, no home, no family and no job. Her dog may seem like an unnecessary expense, but he provides crucial comfort for Lilly as she moves toward self-sufficiency.
Many people told us stories that illuminated one of the problems they found most frustrating with the current welfare system: An increase in income can result in a corresponding reduction in benefits. Rather than climbing a ladder to success with each promotion, they remain on a treadmill.
Less care means less money for Louise to pay her rent and feed her children. Every time when I start another job, I know I got to report that income.
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