The official US welfare fraud statistics show that the state agencies opened and completed , welfare fraud investigations in This figure is by Among SNAP recipients, there were 5, prosecution convictions and acquittals. A total of 55, disqualifications from SNAP were recorded that year. US government welfare statistics show that in October , about 2. California recorded, by far, the biggest number of TANF welfare recipients by state, i. Ohio , and Washington , rank fourth and fifth.
All the other states have less than , recipients. A total of 2,, of TANF recipients are children, and this is one of the most worrying welfare facts. This presents a clear picture that American children are the most vulnerable group of citizens in the country. California , and New York , remain the states with the highest number of TANF recipients in this category, as well. When it comes to adult users, the situation is also the same, with California , and New York 97, taking the top spots.
In a January December study by the US Census, the government collected data on the average participation period on welfare programs. These welfare dependency statistics showed that only 9.
Housing assistance and SNAP noted that respective Welfare facts and statistics also show that respective When it comes to Medicaid, TANF assisted 5. Welfare dependency statistics put this figure as an all-time high in the history of the United States. Ever since the number of TANF welfare recipients by year has been declining. In June , this figure dropped to 1.
Most of the participants live in Florida , , Texas 97, , and California 94, , according to US welfare statistics. SNAP is critical to supplying struggling veterans with an adequate diet.
Namely, around 38, US veterans were homeless in January The biggest number of people on welfare from this group, i. Around 8.
The official US child welfare statistics highlight the importance of SNAP towards providing an adequate diet to millions of children. Namely, a typical family with children that participated in SNAP in was represented by an adult and two kids. In the United States, most people who receive public assistance are aged The Census welfare statistics by race show that non-Hispanic white Americans used only The racial breakdown of welfare recipients covers the period from and places Asians next on the list with Hispanic and African American citizens received respective Among those, 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. Nevertheless, this is still a sizable degree of explanation and implies that the majority of the differences are so-explained. The remaining differences in welfare receipt, even though small, can be interpreted as a measure of the differences resulting from cultural and social norms toward welfare across the different groups.
These variables are not without problems, if interpreted solely as taste shifters, however. This issue has been discussed extensively in the research literature among studies able to use data sets that measure these variables. Also omitted from the list of risk factors are those that would enable a more accurate accounting for job availability and options in the labor market, including residential location and distance from jobs; variables measuring health and disability status; and variables measuring capital market constraints and constraints on ability to borrow.
There is a sense that estimates of percentage explained in Figures 7—4 and 7—5 are too high because the risk factors used for the adjustment are themselves, to some degree, a result of individual and household choices.
This raises questions about the direction of causality in the relationship between welfare participation and the risk factors. If single mothers withdraw from the labor force when they go onto welfare, or if they have a child prior to marriage and simultaneously go onto welfare, it is not clear which event is causing which, or the degree to which the decisions are jointly made rather than one causing another.
Although this issue is important for some purposes, the direction of causality is not a major issue here. The question addressed by the calculations shown in Figures 7—4 and 7—5 is whether, given the other decisions made by the different racial and ethnic groups, there is any remaining difference in their welfare-participation decisions, even if the other decisions are jointly made with the welfare decision.
The policy implications to be drawn from the calculations are not necessarily that policies be implemented that directly alter income or female heading of households it is difficult to imagine policies that would alter the latter, in any case. Rather, policy implications are that the underlying determinate of low income and earnings, and of females heading households—such as education, job skills, wage rates; and policy variables such as benefit levels, tax rates, and public programs for training— should be the subjects of policy attention.
The results of the calculations here imply that if these underlying determinants of both welfare participation and low income and female heading of households were altered by policy, welfare-participation decisions would necessarily, and perforce, change as well.
Thus, race and ethnic differences in welfare dependency could be greatly reduced by reducing the differentials in the underlying determinants of the risk factors. We have found in this study racial and ethnic disparities in welfare-program participation that are disturbingly large. American Indians and Alaska Natives have the highest probabilities of receiving benefits; more than one-half receive one of four major types of benefit.
Non-Hispanic Black households and Hispanic households also have very high rates of receipt. For the populations of these three groups as a whole, long-term dependence on benefits is not extensive in either a participation or a monetary sense i. These racial differences have been quite stable over the last decade, which is at least favorable if one were expecting them to widen as some other racial differences have , but is still discouraging because it is desirable to see those differences reduced in magnitude.
We have found little evidence for an important role for differences in social norms, cultural attitudes, or differences in the stigma of welfare receipt across race-ethnic groups in explaining differentials in welfare dependency.
Most of these risk factors for welfare participation are addressable by public policies. Although it does not seem likely that race-specific welfare policies are either likely in the near future or desirable, reducing the disparities in the underlying risk factors, or in their underlying causes, should have the beneficial by-product of reducing disparities in welfare receipt as well.
The data used for the analysis are the pooled March Current Population Surveys for , , and The unit of observation is the household, and the survey universe is all U. The dependent variable in the regressions is either receipt of AFDC income by at least one member of the household in the prior calendar year, or receipt of income from one of the four programs shown in Table 7—1.
Separate ordinary least-squares regressions are estimated for each of the five race-ethnic. The adjusted participation rates were obtained by inserting the non-Hispanic White means for these regressors into the estimated equation for each of the four minority groups. The estimated regression coefficients are available upon request.
Blank, R. New York: Russell Sage Foundation. Bobo, L. Smith Antipoverty policy, affirmative action, and racial attitudes. Questionnaire Guidelines Guidelines 2 Annual Inequality.
No regular date exists. Questionnaire Guidelines Annual wellbeing oecd. When available, dates and timeliness requirements are only indicative and may be subject to change. As often as possible, the most up-to-date version of the questionnaire has been included in this list, however, there can be slight variations from version to version. In these cases, a list of data series requirements or classifications are made available.
Related Documents. Benefits and Wages. Data and methodology. Transmission date. Submission date. Questionnaire 2. Data frequency. Pre-filled tables updated by the respondent country Metadata standard template. ISIC Rev.
Family database.
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