In summarizing results from several welfare-to-work programs from the 1980s, Friedlander found that earnings gains were concentrated neither among groups of long-term recipients, who are expected to have a hard time finding work and leaving welfare, nor among groups such as new welfare applicants, who are most likely to work without assistance from a welfare-to-work program. Instead, Friedlander found the largest earnings gains among a middle group of welfare applicants who had spent some but not a great deal of prior time on welfare. In contrast, welfare savings came primarily from long-term recipients, especially those without a high school diploma or with little recent work experience.(5)
Do the NEWWS sites provide evidence that their approach changed these patterns by making the impacts for long-term recipients closer to those for short-term recipients? Results already presented in this chapter differ from Friedlander's, in that impacts on earnings were generally larger for the more disadvantaged group than for the less disadvantaged group. Table 7.3 shows impacts for three groups defined by three barriers to work. (As noted above, the most disadvantaged did not have a high school diploma or GED prior to random assignment, did not work in the year prior to random assignment, and were on welfare for two or more years prior to random assignment. The moderately disadvantaged faced only one or two of the three barriers, while the least disadvantaged faced none.)
Site and Program
|Sample Size||Average Total Earningsin Years1 to 5 ($)||Average Welfare Payments in Years1 to 5 ($)||Combined Income in Years 1 to 5 ($)|
|Atlanta Labor Force Attachment||698||1,946*||-766**||1,285|
|Atlanta Human Capital Development||734||-290||-243||-452|
|Grand Rapids Labor Force Attachment||458||3,994***||-4,028***||-828|
|Grand Rapids Human Capital Development||453||1,566||-2,886||-2,136|
|Riverside Labor Force Attachment||1,362||2,938***||-3,592***||-1,395|
|Riverside Human Capital Development||1,362||3,279***||-2,973***||-445|
|Atlanta Labor Force Attachment||1,887||3,406***||-1,020***||1,772*|
|Atlanta Human Capital Development||1,911||2,617**||-904***||1,489|
|Grand Rapids Labor Force Attachment||2,123||270||-2,396***||-2,510**|
|Grand Rapids Human Capital Development||2,078||934||-1,749***||-1,187|
|Riverside Labor Force Attachment||4,298||3,035***||-2,547***||-204|
|Riverside Human Capital Development||3,049||-1,307||-2,185***||-3,902***|
|Atlanta Labor Force Attachment||353||-1,121||-119||-1,347|
|Atlanta Human Capital Development||347||709||-514||161|
|Grand Rapids Labor Force Attachment||431||1,998||-1,145*||665|
|Grand Rapids Human Capital Development||466||1,869||-408||1,664|
|Riverside Labor Force Attachment||1,066||516||-1,871***||-2,162|
|SOURCES: MDRC calculations from unemployment insurance (UI) earnings records and AFDC records.
NOTES: Impacts on earnings were significantly different across subgroups in Grand Rapids LFA, Riverside LFA, Riverside HCD, and Columbus Integrated.
Impacts on AFDC benefits were significantly different across subgroups in Grand Rapids LFA and HCD and Portland. Impacts on total income were significantly different across subgroups in Riverside HCD, Columbus Traditional, Columbus Integrated, and Detroit.
N/a = not applicable.
This method of defining the most and the least disadvantaged does a good job of finding groups that would fare well and poorly on their own, in terms of their earnings levels. In the five years after random assignment, average earnings for the most disadvantaged control group members ranged from less than $4,000 in Oklahoma City to about $11,500 in Detroit (not shown in Table 7.3). In contrast, earnings levels for the least disadvantaged control group members were at least three times higher in all sites, ranging from about $20,000 in Oklahoma City to nearly $45,000 in Columbus (and more than $40,000 in Detroit).
Examining subgroups by level of disadvantage may also help find more precisely defined groups who are helped most and least by the programs. This may help policymakers and welfare administrators decide whether the more disadvantaged or the less disadvantaged sample members benefit more from employment-focused or education-focused activities. It may also help them determine whether new services are needed for the more disadvantaged or the less disadvantaged sample members. In this sense, results by level of disadvantage are in the spirit of recent work on "profiling."(6)
Impacts for the most disadvantaged. These welfare-to-work programs generally increased earnings for the most disadvantaged, but their effects for this group were not as strong as for either long-term welfare recipients or those who had not worked in the year prior to random assignment. In 10 of the 11 programs, the most disadvantaged program group members earned more than their control group counterparts. Because there were relatively few people in this group, the impacts on earnings were statistically significant in only five of the programs. While several programs were moderately successful, increasing earnings by about $3,000 or more, just as many were not very effective, with impacts on earnings close to zero.
Impacts on welfare benefits were more systematic, with significant reductions in 9 of the 10 programs for which they could be measured. This same pattern was seen for the other subgroups discussed above: more systematic changes in welfare payments than in earnings levels. As a result, the most disadvantaged program group members had lower income than their control group counterparts in 8 of the 10 programs (though the impact was not statistically significant in any of the programs).
These results are somewhat different than the results for the two barriers described above, where earnings impacts were quite strong for those who had not worked in the year prior to random assignment and for long-term welfare recipients. This could imply that it is particularly difficult to assist people with three barriers to work rather than possibly just one. On the other hand, it could indicate an important drawback to this method of counting barriers to work: Not all barriers are equal. In particular, Michalopoulos and Schwartz indicate that earnings gains were generally larger for people with a high school credential than for those who lacked one.(7) Thus, two of the barriers appear to be related to greater effects of the welfare-to-work programs, while a third barrier appears to be related to smaller effects of the programs. In such circumstances, understanding the effects of multiple barriers requires more sophisticated methods than the categorization shown in Table 7.3
Comparing impacts by level of disadvantage. If Friedlander's 1988 findings on pre-FSA programs hold for programs in the NEWWS Evaluation, then Table 7.3should indicate that impacts are larger for the moderately disadvantaged than for the most disadvantaged. It does not. The impacts of the NEWWS programs on earnings were generally more broadly distributed than the impacts of the programs studied by Friedlander. Seven of the 11 programs had significant impacts on earnings for the moderately disadvantaged group, but 5 programs also had significant impacts on earnings for the most disadvantaged group. In addition, in both Grand Rapids programs and the Riverside HCD program earnings impacts were much larger for the most disadvantaged than for the moderately disadvantaged, while in the two Atlanta programs and the Portland program the opposite was true.
In one way, however, results in Table 7.3 are similar to results from the pre-FSA programs studied by Friedlander: The programs had little effect on earnings for the least disadvantaged. Impacts on cumulative earnings were not statistically significant for any of the programs. Moreover, just as many programs left the least disadvantaged program group members with higher earnings than their control group counterparts as left them with lower earnings. Despite the modest effects on earnings, each of the programs reduced cash assistance amounts four of them significantly so although the programs did not have a consistently negative effect on income.