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Chapter Seven
FAMILY STRUCTURE
7.1. BACKGROUND
As noted in Chapter 1, in addition to promoting work and reducing dependency, PRWORA aimed to reduce unwed childbearing, to promote marriage, and to maintain two-parent families. In this chapter, we turn to the impact of welfare reform on family structure, considering both marriage and childbearing.
PRWORA’s focus on reducing unwed childbearing, promoting marriage, and maintaining two-parent families was partially motivated by concern about trends in those outcomes. Up until about 1970, more than 85 percent of American children were being raised in two-parent families. Over the succeeding three decades, that figure fell to under 70 percent (see Figure 7.1) because of increases in nonmarital childbearing and, to a lesser extent, increases in divorce. Figure 7.2 shows that while in the 1950s less than 5 percent of births were to unmarried women, beginning in the early 1960s, this percentage began to increase sharply. By the early 1990s, one-third of births were to unmarried women. This rise in nonmarital childbearing was an important cause of the decrease in the share of children being raised by two parents.
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[D] |
As seen in both Figures 7.1 and 7.2, some of the trends in family structure and fertility appear to have slowed or stabilized in the latter part of the 1990s, about the time welfare reform was under way. Both the percentage of children living in two-parent families and the percentage of births to unmarried women has been approximately constant since 1994. The overall trend evident in Figure 7.1 is consistent with other recent analyses of family structure, with some evidence that the relative changes in one- versus two-parent families is more pronounced for families with lower income or less education, precisely the groups that are more likely to be affected by welfare reform (Acs and Nelson, 2001; Dupree and Primus, 2001).
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[D] |
In the case of fertility, the leveling-off of the trend for nonmarital childbearing seen in Figure 7.2 has been accompanied by a decline in teen fertility rates during the 1990s (Martin et al., 2001). For example, across all race and ethnic groups, the drop in teen fertility from 1991 to 2000 is 28.9 percent for 15—17-year-olds and 15.8 percent for 18—19-year-olds. Furthermore, the drop is particularly large for blacks (40.3 percent and 23.6 percent for 15—17-year-olds and 17—19-year-olds, respectively). However, some of the decline occurred in the early 1990s, before widespread welfare reform efforts, raising questions about the role that reform played in reducing teen fertility.
These trends are suggestive that welfare reform may have had some impact on fertility and family structure, and a number of provisions implemented by the states initially under section 1115 waivers and then TANF were designed to directly affect these outcomes. As noted in Chapter 2, a number of states instituted family caps with the objective of reducing additional childbearing for mothers already on welfare. Minor residency requirements are another feature designed to make unwed teen childbearing less attractive. In addition, by eliminating differences in eligibility for two-parent versus one-parent families (e.g., the "100-hour rule" and work history requirement), states aimed to diminish any disincentive toward marriage associated with welfare eligibility rules.55
PRWORA’s emphasis on family structure outcomes was partially motivated by an extension of the economic model of the effect of welfare programs developed in the earlier chapters. That extension views women as considering the structure of welfare programs when making choices not only about welfare and work, but also when making choices about family structure–whether to have children, whether to marry the father, and whether to subsequently divorce.
The theory’s implications follow from noting that welfare has primarily been paid to single mothers, but not to childless women, nor (under most circumstances) to married women.56 Welfare therefore lowers the price of raising a child when unmarried relative both to not having a child and relative to having a child and marrying (or not divorcing). Therefore, this model suggests that any policy change that makes welfare relatively more attractive (e.g., higher benefit levels or financial work incentives) will raise fertility (and especially nonmarital fertility) and decrease marriage. Conversely, any policy change that makes welfare relatively less attractive (e.g., a family cap, mandatory work-related activities, or time limits) will lower fertility (and nonmarital fertility) and increase marriage. However, when such reforms are enacted together, the combined effect on marriage and fertility is ambiguous.
These implications of economic theory assume that welfare is not available to married couples. However, welfare was potentially available to married couples under the AFDC Unemployed Parent (AFDC-UP) program and continues to be available under TANF. Making welfare payments to married couples increases the incentive to have children, but lowers the disincentive to marriage (Hu, 2000). As noted above, to further reduce the disincentives to be married, most states have reduced or eliminated the differential treatment of two-parent families under their TANF programs. For two reasons, however, the effects of the provisions of such welfare programs for married women are likely to be small. First, most married couples have income sufficiently high to make them income ineligible for welfare. Second, and perhaps as a consequence, the AFDC-UP program (under TANF, two-parent programs) are quite small in most states.
There are other mechanisms by which welfare reform may affect family structure. For example, if welfare-to-work programs succeed in raising earnings and income, they might make women more attractive spouses and, thus, raise the propensity to marry. At the same time, increased work may limit the time available for searching for a marital partner; then again, interactions at the workplace may ease marital search. As yet another example, low household income may increase the emotional and financial strain on a marriage, so that welfare reforms that raise total income might be expected to increase marriage and, in particular, to help those currently married to stay married.
Although welfare reform was motivated in part by trends in marriage and fertility, these outcomes are less well studied in both the experimental and econometric literatures. Of the random assignment studies we review in this report, WRP, IMPACT, TSMF, FIP, New Hope, SSP Plus, SSP Applicants, VIP/VIEW, PPI, and PIP do not analyze either marriage or fertility. CWPDP, MFIP, and SSP examine only marriage, while AWWDP and FDP consider only fertility. The remaining programs–L.A. Jobs-First GAIN, the 11 NEWWS programs, EMPOWER, ABC, FTP, and Jobs First–analyze both outcomes. Two econometric studies consider either marriage or living arrangements, while there are four econometric analyses of fertility.
Compared with the outcomes examined in Chapters 4, 5, and later in 8, the more limited research on marriage and fertility can be attributed to several factors. First, although PRWORA motivated reform in part by goals related to marriage and childbearing, many of the state programs evaluated under waivers were designed more to influence work and welfare use. Even so, a few of the programs that included family caps, minor residency requirements, and changes in two-parent eligibility requirements do not evaluate either marriage or fertility (e.g., IMPACT and VIP/VIEW).
Second, unlike welfare use, employment and earnings, and some measures of income, marriage and fertility behavior are harder to measure using administrative data (although this is the source of information on fertility for FDP and AWWDP). Thus, those demonstration studies that do not have participant surveys are less likely to consider these outcomes. Third, even when resources are devoted to measuring these outcomes, changes in marital status and additional childbearing while on welfare are relatively rare events and changes in behavior may not be immediate, whether for the recipient generation or for the next generation of daughters of the recipients. As a result, studies with short follow-up periods may be less likely to detect significant changes in these outcomes. In addition, survey data often have smaller samples and are subject to measurement error (e.g., recall bias and differential non-response), leading these analyses to have lower power.57 Consequently, these outcomes may not be included in impact analyses, and when they are, there may be limited statistical power to detect significant changes in behavior.
Fourth, the influence of welfare reform on marriage and fertility behavior is likely to affect women who are not on welfare just as much, if not more, than those who are on welfare. While welfare reform may affect the likelihood that a woman on welfare has additional children or gets married or stays married, it should also affect these decisions for women who are at risk of welfare participation. For these women, welfare reform may affect their likelihood of entering welfare. However, as noted in Chapter 3, conventional demonstration studies are not designed to capture welfare entry effects, so they will miss this pathway by which reforms may affect family structure. This is a significant limitation of the demonstration studies and stresses the need for high-quality econometric studies.
The remainder of this chapter proceeds by considering first the random assignment studies and then the econometric studies of family structure and its two primary components: marriage and fertility. Since there is only one demonstration study with subgroup analyses of marriage and fertility, we discuss these results along with the main results rather than in a separate section (or in Appendix A). After discussing the random assignment and econometric studies in turn, we proceed to a synthesis of the experimental and econometric evidence. The final section offers our conclusions.
7.2. RANDOM ASSIGNMENT STUDIES OF THE EFFECTS OF WELFARE REFORM ON FAMILY STRUCTURE
In this section, we consider the effects of random assignment studies on family structure, namely marriage, household size, and fertility. As noted above, most of the demonstration studies that consider these outcomes use survey data to assess whether a participant in the treatment or control group has had an additional child since random assignment or the participant’s marital status at the time of the follow-up survey. The follow-up interval ranges from 18 months to five years.
In assessing current marital status, studies differ in whether they differentiate between those who are married versus those who are married and living with their spouse. Some studies also report impacts for cohabitation, separate from being married, or combined with those who are married. A few studies also measure whether there was any change in marital status since random assignment, given that the respondent may have married and subsequently become separated, divorced, or widowed by the time of the follow-up. Finally, two studies measure household composition in terms of household size and the number of adults and children. Changes in household size may result from changes in marital status or additional childbearing, but also for other reasons such as "doubling up" with other relatives or nonrelatives, or departures of older children who move out of the household. Where possible, our discussion focuses on marriage with a spouse present, the concept that most closely aligns with PRWORA’s goals, but often only results for other outcomes (e.g., any cohabitation, marital or nonmarital) are available.
Fertility is typically measured for the survey respondent, and the measure is whether the respondent has had any children since random assignment. In one study, EMPOWER, childbearing while on welfare is measured both for case heads and unwed minors in the welfare case unit. That study and ABC also differentiate births since random assignment from conceptions since random assignment (defined as births more than 10 months since random assignment). For the other studies, some of the measured births may have been conceived prior to the time when the program rules becoming effective.
Finally, two studies use information from welfare data systems to measure children born to study participants. However, recording of births in welfare data systems is incomplete. Current welfare recipients have an incentive to report births. A reported birth will enable the child to be enrolled in Medicaid, and, in the absence of a family cap, the family’s welfare payment will increase. Births to mothers not receiving welfare are not recorded in any welfare data system. This is an important limitation because PRWORA’s interest in reducing out-of-wedlock childbearing is not limited to births among welfare recipients.
In the remainder of this section, we focus first on the results for marriage and household size, followed by the results for fertility. In both cases, we organize our discussion by the major reform or reforms considered by the demonstrations.
7.2.1 Marriage and Household SizeTable 7.1 records the results for the random assignment studies that examine marriage and household size. With the exception of Panel E, at least one study in each of the other policies or groups of policies in Panels A to F examines a measure of marriage.
Programs That Focus on Financial Work Incentives
As seen in Panel A of Table 7.1, results for two programs that focus on financial work incentives provide some evidence for an increase in marriage. Hu (2000) estimates the effects of the CWPDP on marital status. While he finds no effect for AFDC-Basic (i.e., single parent) cases, he finds that the experiment increased marriage for AFDC-UP cases. The effect appears to be the result of less divorce. The interpretation of these results is, however, complicated by policy bundling. The CWPDP waiver included a financial incentive; it also included a cut in the AFDC benefit level (at zero earnings) and removed some of the restrictions on eligibility of two-parent families (similar to those in MFIP discussed below). It is not clear which of the components of the bundle caused the marital status effect.
| Marital Status | Change in Marital Status | Household Size | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Name | Cases served | Data | Measure | Control mean | Impact | % | Measure | Control mean | Impact | % | Measure | Control mean | Impact | % |
| A. Programs that focus on financial work incentives | ||||||||||||||
| CWPDP | Single-parent recipients | S | R is married at 29-41 mo FU (%) | 13.7 | 2.1 | 15.3% | ||||||||
| Two-parent recipients | S | R is married at 29-41 mo FU (%) | 71.2 | 7.6** | 10.7% | |||||||||
| MFIP-IO | Urban single-parent recipients | S | R is married at the 36-mo FU (%) | 5.8 | 5.2** | 89.7% | ||||||||
| S | R is married or living with partner at the 36-moFU (%) | 20.8 | 2.7 | 13.0% | ||||||||||
| Urban single-parent applicants | S | R is married at the 36-mo FU (%) | 15.1 | -2.2 | -14.6% | |||||||||
| S | R is married or living with partner at the 36-moFU (%) | 29.6 | -2.6 | -8.8% | ||||||||||
| B. Programs that focus on financial work incentives tied to hours of work | ||||||||||||||
| SSP (a) | Single-parent recipients | S | R is married at 36-mo FU (%) | 9.5 | -0.6 | -6.3% | ||||||||
| S | R is married or in common law relationship at 36-mo FU (%) | 17.3 | 0.1 | 0.6% | R ever married or in common law relationship as of 36-mo FU (%) | 19.2 | 0.3 | 1.6% | ||||||
| C. Programs that focus on mandatory work-related activities | ||||||||||||||
| LA Jobs-1st GAIN | Single-parent recipients and applicants | S | R is married and living with spouse at 2-yr FU (%) | 6.9 | 2.2 | 31.9% | ||||||||
| S | R is living with partner at 2-yr FU (%) | 8.5 | -1.1 | -12.9% | ||||||||||
| Atlanta LFA | Recipients and applicants | S | R is married and living with spouse at 2-yr FU (%) | 4.0 | -0.3 | -7.5% | ||||||||
| S | R is married and living with spouse at 5-yr FU (%) | 8.4 | 1.3 | 15.5% | ||||||||||
| Grand Rapids LFA | Recipients and applicants | S | R is married and living with spouse at 2-yr FU (%) | 11.8 | 1.3 | 11.0% | ||||||||
| S | R is married and living with spouse at 5-yr FU (%) | 20.5 | 2.3 | 11.2% | ||||||||||
| Riverside LFA | Recipients and applicants | S | R is married and living with spouse at 2-yr FU (%) | 13.4 | -2.7* | -20.1% | ||||||||
| S | R is married and living with spouse at 5-yr FU (%) | 22.0 | -1.4 | -6.4% | ||||||||||
| Portland | Recipients and applicants; no cases with substantial barriers | S | R is married and living with spouse at 2-yr FU (%) | 9.0 | -0.2 | -2.2% | ||||||||
| S | R is married and living with spouse at 5-yr FU (%) | 23.6 | -6.2 | -26.3% | ||||||||||
| Atlanta HCD | Recipients and applicants | S | R is married and living with spouse at 2-yr FU (%) | 4.0 | -1.2 | -30.0% | ||||||||
| S | R is married and living with spouse at 5-yr FU (%) | 8.4 | -1.5 | -17.9% | ||||||||||
| Grand Rapids HCD | Recipients and applicants | S | R is married and living with spouse at 2-yr FU (%) | 11.8 | 0.3 | 2.5% | ||||||||
| S | R is married and living with spouse at 5-yr FU (%) | 20.5 | -0.2 | -1.0% | ||||||||||
| Riverside HCD | Recipients and applicants | S | R is married and living with spouse at 2-yr FU (%) | 10.9 | 1.6 | 14.7% | ||||||||
| S | R is married and living with spouse at 5-yr FU (%) | 18.1 | 3.7 | 20.4% | ||||||||||
| Columbus Integrated | Recipients and applicants | S | R is married and living with spouse at 2-yr FU (%) | 9.0 | 1.1 | 12.2% | ||||||||
| Columbus Traditional | Recipients and applicants | S | R is married and living with spouse at 2-yr FU (%) | 9.0 | 0.9 | 10.0% | ||||||||
| Detroit | Recipients and applicants | S | R is married and living with spouse at 2-yr FU (%) | 7.6 | -3.4 | -44.7% | ||||||||
| Oklahoma City | Applicants | S | R is married and living with spouse at 2-yr FU (%) | 19.1 | -3.4 | -17.8% | ||||||||
| D. Programs that focus on financial work incentives and mandatory work-related activities | ||||||||||||||
| MFIP | Urban single-parent recipients | S | R is married at the 36-mo FU (%) | 5.8 | 2.8 | 48.3% | ||||||||
| S | R is married or living with partner at the 36-mo FU (%) | 20.8 | 3.2 | 15.4% | ||||||||||
| Urban single-parent applicants | S | R is married at the 36-mo FU (%) | 15.1 | 1.7 | 11.3% | |||||||||
| S | R is married or living with partner at the 36-mo FU (%) | 29.6 | 4.1 | 13.9% | ||||||||||
| E. Programs that focus on other individual reforms | ||||||||||||||
| F. Programs that focus on TANF-like bundle of reforms (time limits with financial incentives, work-related activities, or both) | ||||||||||||||
| EMPOWER (b) | Recipients | S | R is married at 3-yr FU(%) | 28.9 | -0.9 | -3.1% | R changed marital status since RA as of 3-yr FU (%) | 7.7 | 0.0 | 0.0% | ||||
| ABC | Single parent recipients and applicants | S | R is married and living with spouse at 4-19-mo FU (%) | 7.6 | 1.4* | 18.4% | ||||||||
| FTP | Recipients and applicants | S | R is married and living with spouse at 4-yr FU (%) | 19.1 | -1.9 | -9.9% | Total number of HH members (including R) at 18-mo FU | 3.9 | 0.0 | 0.0% | ||||
| Jobs First | Recipients and applicants | S | R is married and living with spouse at 18-mo FU (%) | 7.0 | -1.2 | -17.1% | R changed marital status since RA as of 18-mo FU (%) | 19.9 | -1.7 | -8.5% | Total number of HH members at 18-mo FU | 3.3 | 0.2*** | 7.6% |
| S | R is married and living with spouse at 3-yr FU (%) | 10.8 | -1.6 | -14.8% | Total number of HH members at 3-yr FU | 3.4 | 0.1 | 2.9% | ||||||
| NOTES: For full program names and citations,
see Table 3.4. Abbreviations: A=administrative data; S=survey data;
FU=follow-up; HH=household; R=respondent; RA=random assignment. * = statistically significant at the 10 percent level; ** = statistically significant at the 5 percent level; *** = statistically significant at the 1 percent level. (a) New Brunswick and British Columbia combined. (b) Phoenix site only, cash assistance. |
MFIP-IO is a pure financial incentive program, and its financial work incentives were deliberately designed to encourage marriage. Some restrictions on eligibility for two-parent families were eliminated, and the treatment of stepparent earnings was liberalized. Consistent with this intention, the experimental evaluation of the financial work incentives alone (i.e., MFIP-IO) suggests that marriage increases. For single parent recipients, the fraction married at the time of the 36-month follow-up interview is 11.0 percent in the treatment group versus 5.8 percent in the control group, a statistically significant difference of 5.2 percent. The impact is also positive on the combined status of married or cohabiting, but the difference is not significant. For single parent applicants, treatment group members are less likely to be married–or married or cohabiting–but again the difference is not significant.
Programs That Focus on Financial Work Incentives Tied to Hours of Work
Among the programs that tied financial work incentives to hours of work, SSP is the only one that assesses the impact on marriage behavior (Panel B of Table 7.1). The structure of SSP’s incentives was specifically designed to lower disincentives to marry. Canada’s Income Assistance program counts a husband’s income when calculating the welfare benefit. If the husband works, this will usually result in a lower benefit and thus would be expected to discourage marriage. In contrast, SSP disregards income contributed by a husband or common-law spouse when calculating the earnings supplement, thereby removing the disincentive and encouraging marriage. However, the higher household income under SSP (discussed further in Chapter 8) might have been expected to induce some women to choose to live on their own, thus decreasing marriage.
SSP includes a measure of marital status as well as a broader measure that includes both formal marriage and Canadian common-law relationships.58 Using this combined marriage and common-law relationship concept, SSP has insignificant impacts. Marriage is slightly less common; common-law relationships are slightly more common; neither effect is statistically different from zero.
The interpretation of the SSP results is, however, complicated by considering the two provinces–British Columbia and New Brunswick–separately. For almost all outcomes considered in the SSP evaluation, impacts do not differ significantly across the two provinces. Marriage is the exception. Using the broad SSP definition, marriage significantly decreases in British Columbia (by 3.1 percentage points, or 18 percent of the value for the control group); while marriage significantly increases in New Brunswick (by 4.1 percentage points; or 20 percent). Using a narrow definition of marriage that excludes common law relationships, the effect in British Columbia is still negative, but at p < 0.10 (but not at p < 0.05). The effect in New Brunswick is still positive, but not statistically different from zero. The difference is significant at p < 0.10, but not at p < 0.05.
Michalopoulos et al. (2000) discuss the possible reasons for the difference in results across provinces. They note that the results within each province are robust across subgroups, so that the small differences in baseline characteristics between the two provinces do not explain the differences in impact. They also note that the impacts on income and full-time employment were similar across the two provinces and the policy changes removing the marriage penalty were identical.
They suggest two other plausible reasons for the divergence across provinces. A first reason relates to the marriage market. During the period of the experiment, the unemployment rate for men was considerably higher in New Brunswick than in British Columbia. They speculate that these poor job prospects for men made the additional employment, earnings, and income provided by SSP more attractive. It should be noted that this argument–that poor economic prospects for men encourage them to marry–is the opposite of the standard argument that marriage among American black women is low because there are few marriageable men (Wilson and Neckerman, 1987). A second reason concerns cultural differences. New Brunswick is more rural, and the majority is Catholic; British Columbia is more urban, and there are fewer Catholics. With only two sites and nominally identical programs, more definite conclusions are not possible. They conclude: "The opposite direction of impacts by province underscores the importance of geographic and cultural context in translating employment and earnings impacts into effects on family structure."
Programs That Focus on Mandatory Work-Related Activities
With one exception, the programs that evaluate mandatory work-related activities show no significant impacts on the fraction married and living with their spouse as of the two-year follow-up survey. As seen in Panel C of Table 7.1, the 12 insignificant impacts are evenly divided in sign and most involve a small percentage point change. Only Riverside LFA has a marginally (p < 0.10) statistically significant negative impact on the likelihood of being married. For seven of the NEWSS sites, there are also five-year follow-up results. In none of them (including Riverside LFA) can we reject the hypothesis of no effect. Again, the sites are divided in sign and the point estimates are small.
Programs That Focus on Financial Work Incentives and Mandatory Work-Related Activities
Among programs that combine financial work incentives with mandatory work-related activities, only MFIP assesses the impact on marriage defined as marriage alone and a broader measure that includes cohabitation (see Panel D of Table 7.1). For both urban single parent recipients and applicants, the MFIP impacts on the narrow (marriage) and broad (cohabitation) measures are positive, but none are statistically significant.59
In addition, the MFIP evaluation considered the impact of the full program on marriage for the sample of two-parent families (results not shown). For that study sample, the MFIP intervention increased the fraction remaining married by nearly 40 percent, from 48.3 percent for the control group to 67.4 percent for the treatment group, and the result is statistically significant. Analyses of other outcomes suggest that the effect is concentrated among those married (rather than cohabiting) at random assignment and works partially through a drop in the divorce rate (about 6.5 percentage points). The balance of the effect appears to be higher rates of married couples living together. Furthermore, these results are confirmed and strengthened by an analysis of official divorce records. Five years post-randomization, the control group had a 20 percent divorce rate, while the experimental families had an 11 percent divorce rate.
Finally, we note that these results are consistent with the MFIP-IO results that also find an effect on marriage. Since the studies of mandatory work-related activities alone find no effect on marriage, it seems reasonable to interpret the main MFIP results (including mandatory work-related activities) as a financial incentive effect, lending more support to the inference that financial work incentives increase marriage.
Programs That Focus on TANF-Like Bundles of Reforms
Finally, four of the programs that involve TANF-like bundles of reforms assess marriage and, in two cases each, changes in marital status (EMPOWER and Jobs First) and household size (FTP and Jobs First). The follow-up periods range from as little as four months (ABC, for those entering latest) to four years (FTP). EMPOWER, FTP, and Jobs First have negative but insignificant impacts on the likelihood of being married (or married and living with their spouse). Changes in marital status in EMPOWER and Jobs First and are also insignificant. The impact on household size is zero for FTP but small, positive, and significant for Jobs First, where household size increases by 0.2 persons. Disaggregation by adults and children (not shown) shows the increase is evenly split between the two types of household members.
ABC is the only study to show a statistically significant (p < 0.10) increase in marriage, and this occurs even though the follow-up period averages 12 months, with a range from 4 to 19 months. Analyses for subgroups show a significant positive impact on marriage for women under 25, those who are capable of having additional children, those never married, and those with less than 12 years of schooling. The differences between age and education groups are also statistically significant. There are no significant impacts for subgroups defined by length of prior welfare receipt. A broader measure that includes living with a spouse or the respondent expects to marry shows no significant impact overall. For this broader measure of marriage and marriage expectations, the only significant difference for subgroups is by education, again with the least educated having the largest impact.
7.2.2. Fertility
Like marriage, with a few exceptions, the results for births since random assignment summarized in Table 7.2 are small and insignificant. For this outcome, results are available only for programs that focus on mandatory work-related activities, on family caps, and on TANF-like bundles of reforms.
Programs That Focus on Mandatory Work-Related Activities
Of the 12 studies that focus on mandatory work-related activities, only Columbus Traditional has a borderline statistically significant negative impact on births in the two years following random assignment. Against the prediction of the theory, the signs of the impacts in the other sites are more often positive than negative. For seven of the sites (but not Columbus), there are also five-year follow-up results. For none of these sites can we reject the hypothesis of no effect. Again, the signs are mixed, with more positive point estimates than negative point estimates.
| Fertility | ||||||
|---|---|---|---|---|---|---|
| Name | Cases served | Data | Measure | Control mean | Impact | % |
| A. Programs that focus on financial work incentives | ||||||
| B. Programs that focus on financial work incentives tied to hours of work | ||||||
| C. Programs that focus on mandatory work-related activities | ||||||
| LA Jobs-1st GAIN | Single-parent recipients and applicants | S | R had child since RA as of 2-yr FU (%) | 9.3 | -0.2 | -2.2% |
| Atlanta LFA | Recipients and applicants | S | R had child since RA as of 2-yr FU (%) | 6.4 | 0.5 | 7.8% |
| S | R had new baby present in HH as of 5-yr FU (%) | 12.4 | -0.8 | -6.5% | ||
| Grand Rapids LFA | Recipients and applicants | S | R had child since RA as of 2-yr FU (%) | 11.1 | 1.9 | 17.1% |
| S | R had new baby present in HH as of 5-yr FU (%) | 21.7 | 0.9 | 4.1% | ||
| Riverside LFA | Recipients and applicants | S | R had child since RA as of 2-yr FU (%) | 12.7 | -0.2 | -1.6% |
| S | R had new baby present in HH as of 5-yr FU (%) | 22.1 | 3.4 | 15.4% | ||
| Portland | Recipients and applicants; no cases with substantial barriers | S | R had child since RA as of 2-yr FU (%) | 10.7 | -1.2 | -11.2% |
| S | R had new baby present in HH as of 5-yr FU (%) | 22.7 | -5.3 | -23.3% | ||
| Atlanta HCD | Recipients and applicants | S | R had child since RA as of 2-yr FU (%) | 6.4 | 1.4 | 21.9% |
| S | R had new baby present in HH as of 5-yr FU (%) | 12.4 | 0.1 | 0.8% | ||
| Grand Rapids HCD | Recipients and applicants | S | R had child since RA as of 2-yr FU (%) | 11.1 | 2.4 | 21.6% |
| S | R had new baby present in HH as of 5-yr FU (%) | 21.7 | 0.5 | 2.3% | ||
| Riverside HCD | Recipients and applicants | S | R had child since RA as of 2-yr FU (%) | 13.6 | 0.7 | 5.1% |
| S | R had new baby present in HH as of 5-yr FU (%) | 23.1 | 1.0 | 4.3% | ||
| Columbus Integrated | Recipients and applicants | S | R had child since RA as of 2-yr FU (%) | 7.9 | 1.7 | 21.5% |
| Columbus Traditional | Recipients and applicants | S | R had child since RA as of 2-yr FU (%) | 7.9 | -3.2* | -40.5% |
| Detroit | Recipients and applicants | S | R had child since RA as of 2-yr FU (%) | 12.3 | -2.6 | -21.1% |
| Oklahoma City | Applicants | S | R had child since RA as of 2-yr FU (%) | 14.9 | 0.7 | 4.7% |
| D. Programs that focus on financial work incentives and mandatory work-related activities | ||||||
| E. Programs that focus on other individual reforms | ||||||
| AWWDP | Recipients and applicants | A | Avg. number of births since RA as of 5-yr FU | 0.16 | 0.0 | -12.5% |
| FDP | Recipients | A | Regression-projected likelihood of R having a child since RA as of 17-Q FU (%) | 34.9 | -3.2** | -9.2% |
| Applicants | A | Regression-projected likelihood of R having a child since RA as of 17-Q FU (%) | 30.3 | -3.7** | -12.2% | |
| F. Programs that focus on TANF-like bundle of reforms (time limits with financial incentives, work-related activities, or both) | ||||||
| EMPOWER (a) | Recipients | S | Case head had child since RA as of 3-yr FU (%) | 18.0 | -1.0 | -5.6% |
| S | Case head conceived a child since RA as of 3-yr FU (%) | 11.3 | 0.1 | 0.9% | ||
| S | Unwed minor had child since RA as of 3-yr FU (%) | 4.0 | -2.4** | -60.0% | ||
| S | Unwed minor conceived a child since RA as of 3-yr FU (%) | 2.9 | -1.8* | -62.1% | ||
| ABC | Single parent recipients and applicants | S | R conceived a child since RA as of 4-19-mo FU (%) | 13.8 | -0.3 | -2.2% |
| FTP | Recipients and applicants | S | R had child since RA as of 4-yr FU (%) | 22.7 | 1.2 | 5.3% |
| Jobs First | Recipients and applicants | S | R had child since RA as of 18-mo FU (%) | 24.3 | -0.2 | -0.8% |
| S | R had child since RA as of 3-yr FU (%) | 20.7 | 0.1 | 0.5% | ||
| NOTES: For full program names and citations, see
Table 3.4. Abbreviations: A=administrative data; S=survey data; FU=follow-up;
HH=household; R=respondent; RA=random assignment. * = statistically significant at the 10 percent level; ** = statistically significant at the 5 percent level; *** = statistically significant at the 1 percent level. (a) Phoenix site only, cash assistance. |
Programs That Focus on Family Caps
Two experiments, FDP and AWWDP, evaluated a family cap. Both studies rely on administrative data from the welfare system to identify births after random assignment. They therefore analyze only the effect of the experiment on births while on welfare. This is a different concept from that analyzed by the other studies of fertility effects.
Like the results for other outcomes (e.g., welfare use and earnings), the evaluation of the AWWDP in Arkansas finds no effect on fertility. In addition, there was no statistically significant effect on participation in family planning or use of birth control. However, several methodological issues suggest caution in interpreting these findings. First, the sample size used for the analysis of fertility is very small: the researchers use a 5 percent random subsample of the population available for study. Thus, for their analysis of births, the samples sizes in the treatment and control groups are 86 and 88, respectively. Such small samples make it difficult to detect even moderate sized effects.
Second, the AWWDP evaluators report that "a substantial portion of workers explained the cap on benefits to clients in both the experimental and control groups" (Turturro, Benda, and Turney, 1997, p. 2). It is therefore not surprising that the family cap appears to have been only poorly understood. In a small survey of study participants (N = 102), about half did not know how their benefits would change with an additional child (45.7 in the experimental group versus 51.8 percent in the control group). Inasmuch as members of the control group believed that they were subject to the family cap, the experiment will underestimate its true effect.
Results for New Jersey are quite different. In New Jersey, the family cap was instituted as part of FDP, a wide-ranging waiver package including enhanced welfare-to-work services, financial work incentives, transitional Medicaid, and elimination of some marriage penalties. Comparisons of the experimental and control groups imply that for recipients the entire package of reforms led to a statistically significant decline in fertility of 9 percent, but there was no effect on abortion (not shown). For applicants, FDP resulted in a statistically significant 12 percent decline in fertility. In addition, abortions increased 14 percent, but this effect appears to be concentrated in the early months of the experiment, with convergence by the end of the analysis period (1996, four years later).
The experimental analysis of FDP also found effects on family planning. Survey questions indicate that, compared to those in the control group, those in the treatment group were 4 percentage points more likely to use family planning in the last year (30.9 percent versus 26.6 percent). Regression analyses of sterilization and family planning visits from Medicaid files are also consistent with a moderate to large effect on fertility practices, and the timing of these effects is also plausible.
Like AWWDP, however, methodological issues suggest concern in interpreting the findings. First, randomization does not appear to have been performed properly. More than one-quarter of case workers admitted to evaluators that they used discretion when making assignments to the treatment and control groups (Camasso et al., 1996). Second, like the Arkansas demonstration, the FDP client survey suggests that understanding of the program was very poor.60 Combining the groups that reported either that their cash benefits would not increase or that none of their benefits (including food stamps and Medicaid) would increase, survey results suggest that only 3.5 percent more of the experimental group believed that the cash benefit would not increase with the birth of a new child.
If understanding of the program was truly this weak, then the large fertility and abortion effects that were found are surprising. Poor recipient understanding of the family cap would be expected to bias the effects of the program downward relative to more complete understanding. These results would then imply even larger effects when the program was understood. Another interpretation is possible. FDP was broader than the family cap. It also involved an enhanced earnings disregard, enhanced case management, and relaxation of the marriage penalty. Thus, even if recipients did not understand the family cap, fertility effects might have resulted from these other program components.
Nevertheless, less than perfect understanding by the treatment and control groups of the policies that applied to them would still lead to a downward bias in the estimated program impact. Partially to address this concern, the New Jersey evaluation also conducted a before-and-after econometric analysis. In particular, again using the administrative data, Camasso et al. (1999) estimated a standard regression model for fertility with controls for demographic characteristics (e.g., age, marital status, education, and number of children), earnings, history of AFDC use, the unemployment rate, the FDP participation rate, county dummies, and a linear time trend. The effect of FDP was estimated as the deviation from the time trend implied by this regression model. Again, large negative effects of FDP on fertility were detected, as were moderate positive effects on abortions. Note, however, that by our standards for judging observational studies, this is a weak design. If fertility began to decline (or the decline accelerated) nationally for welfare recipients (as Figure 7.1 suggests), this approach would have attributed that decline to FDP. A stronger design would have included some form of control for trends in other states (which did not implement a family cap); however, as a New Jersey-specific evaluation, the evaluators did not have easy access to such data.
Programs That Focus on TANF-Like Bundles of Reforms
The four programs that focus on TANF-like bundles of reforms all find small and insignificant impacts on births or conceptions for the recipient for a follow-up interval ranging from four months (ABC) to four years (FTP). Three of the six impact estimates are negative. ABC also included an analysis of fertility desires (results not shown) by asking whether the respondent wants to have more children. Overall the impact estimate is negative but insignificant. Subgroup analyses for ABC showed a significant reduction in conceptions only for those on welfare between one and two years in the past five years. There was also a significant negative impact on fertility desires for this subgroup. In addition, the impact on fertility desires was significantly negative for women age 25 and above and for those ever married.
EMPOWER also measures births and conceptions for unwed minors and finds statistically significant negative impacts for both measures. As seen in Table 3.5, EMPOWER’s reforms included a family cap, as well as a minor residency requirement and a provision removing the exemption from JOBS participation for teens under age 16 (those age 13 and above must now participate). Because these three reforms are bundled with the program’s other reforms; it is not possible to ascribe the reduction in unwed teen fertility to these specific policies. It is also worth noting that the control group in EMPOWER became subject to the treatment group provisions two years into the three-year follow-up period. Thus, some of the measured impact of the EMPOWER reforms on adult and teen fertility may have been diluted by the control-group crossover.
7.3. ECONOMETRIC ANALYSES OF THE EFFECTS OF WELFARE REFORM ON FAMILY STRUCTURE
The effects of waivers and TANF on family structure have also been explored using econometric methods. As noted earlier, since welfare reform’s effect on family structure may be expected to operate primarily through entry effects that are not captured by random assignment studies, econometric approaches are likely to be more appropriate.
Table 7.3 summarizes the results of the two econometric studies that consider marriage and living arrangements using CPS data, and all but one of the studies considering fertility and abortion. Table 7.4 provides additional results from another study of a fertility outcome–the nonmarital fertility ratio. We begin by discussing results for marriage and living arrangements, followed by those for fertility.
7.3.1. Marriage and Living Arrangements
Schoeni and Blank (2000) consider the propensity to be married and the propensity to be a female head of household using the March CPS. (See the discussion of their analyses of other outcomes in earlier chapters.) As seen in Section A of Table 7.3, their DoD specification suggests that for high school dropouts, any implemented waiver increases marriage (by about 2 percentage points) and depresses female headship (also by about 2 percentage points). For those with exactly 12 years of schooling, waivers have a significant negative effect on marriage (not what would be expected) and a positive (but not statistically significant) effect on female headship. For those with more than 12 years of schooling, waivers again have a significant positive effect on marriage, but not female headship.
| Other controls | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Study | Data | Sample population | Begin | End | Outcome | Dep. var. | Policy var. | Coeff. (s.e.) | % effect | Economy | Demogr. and Geogr. | Fixed Effects | Policy |
| A. Marriage and Headship | |||||||||||||
| Schoeni and Blank (2000) | CPS aggregated | women 16-54, educ<12 | 76 | 98 | Percent married | Level | Any waiver | 0.0229 (0.0073) | 5.4 | U, U-1, EG, each *E |
A, E, A*E, R | S, Y, state time trends, Y*E |
B, B*E |
| women 16-54, educ=12 | Any waiver | -0.0144 (0.0060) | -2.2 | ||||||||||
| women 16-54, educ>12 | Any waiver | 0.0075 (0.0049) | 1.3 | ||||||||||
| women 16-54, educ<12 | TANF | -0.0004 (0.0171) | -0.1 | ||||||||||
| women 16-54, educ=12 | TANF | -0.0161 (0.0150) | -2.5 | ||||||||||
| women 16-54, educ>12 | TANF | 0.0034 (0.0114) | 0.6 | ||||||||||
| Schoeni and Blank (2000) | CPS aggregated | women 16-54, educ<12 | 76 | 98 | Percent head of household | Level | Any waiver | -0.0171 (0.0070) | -8.2 | U, U-1, EG, each *E |
A, E, A*E, R | S, Y, state time trends, Y*E |
B, B*E |
| women 16-54, educ=12 | Any waiver | 0.0052 (0.0058) | 2.3 | ||||||||||
| women 16-54, educ>12 | Any waiver | -0.0014 (0.0047) | -0.5 | ||||||||||
| women 16-54, educ<12 | TANF | -0.0133 (0.0165) | -6.4 | ||||||||||
| women 16-54, educ=12 | TANF | -0.0025 (0.0144) | -1.1 | ||||||||||
| women 16-54, educ>12 | TANF | 0.0239 (0.0110) | 8.5 | ||||||||||
| B. Living Arrangements | |||||||||||||
| Bitler, Gelbach and Hoynes (2001) | CPS micro data | women 16-54 | 84 | 98 | Number of persons in household | Level | Any waiver | 0.055 (0.020) | 1.2 | U, U-1, EG |
R, MSA, CC | S, Y | B |
| TANF and ever had waiver | 0.100 (0.037) | 2.2 | |||||||||||
| TANF and never had waiver | 0.042 (0.038) | 0.9 | |||||||||||
| Bitler, Gelbach and Hoynes (2001) | CPS micro data | women 16-54 | 84 | 98 | Number of children in household | Level | Any waiver | 0.030 (0.017) | 1.3 | U, U-1, EG |
R, MSA, CC | ||