Assessing the Field of Post-Adoption Services:
Family Needs, Program Models, and Evaluation Issues:  Analysis of Secondary Data

3 Adoption Subsidies in California

[ Main page of Report | Contents of Report ]

Contents

• 3.1 Data Sources
• 3.2 CLAS Data on Services and Subsidies
• 3.3 The California AAP Database
  • 3.3.1 AAP Data
  • 3.3.2 Payment Level of the First AAP Payment
  • 3.3.3 Amount and Direction of Payment Changes
  • 3.3.4 Reasons for Payment Changes
  • 3.3.5 Multivariate Analysis of Payment Changes in California

3.1 Data Sources

Analyses of adoption subsidy use in California drew on three key data sources. First we drew on data from the California Long-Range Adoption Study (CLAS), which followed a large cohort of children whose adoptions were finalized in 1988-89, collecting data two, four, and eight years later. From these data we have both quantitative and qualitative indicators relevant to adoption subsidies. Second, we drew on data from the administrative data system that holds subsidy information for children adopted in the State of California during the 1988-89 fiscal year. Third, we had access to data about the adopted children and the adopting families derived from characteristics reports completed by the social workers at the time of the children's placements for adoption (Form 42-[R]elinquishment; see Appendix A). These three data sources were used to examine patterns of adoption subsidy changes, including changes in relation to child and parent characteristics and subsequent residential placement.

[Go to Contents]

3.2 CLAS Data on Services and Subsidies

Participants in CLAS are all adoptive parents in California; thus far they have completed questionnaires at three points in time across roughly an eight-year period following their adoptions (1990, 1992, and 1996). This survey of parents of about 300 former foster youth provides a range of information about their children and the families' use of post-adoption services, some of which has been previously reported (Brooks, Allen, and Barth, 2002). Data from the CLAS study include information on a broad range of psychological, social, economic, and relational characteristics of adoptive families in California. There is a substantial amount of additional information about behavioral problems and children and subsidy changes that have not been described and related analyses that might better explain the use of post-adoption services. Additional analyses were, therefore, conducted for this report.

Youth receiving subsidies are more likely to have severe behavior problems.

The key question in this analysis has to do with whether or not children's behavior is associated with early changes in subsidy payments, also known as adoption assistance. Of the 288 adopted foster children in this sample, exactly equal numbers (144) either received or did not receive Adoption Assistance Program (AAP) funds within two years of their placement in their adoptive homes (Wave 1). Using Wave 1 as a starting point for measuring their trajectories in their placements for the subsequent six years reveal some interesting patterns. First, membership in either group (AAP-Yes or AAP-No) remains stable throughout the placement. For example, approximately 90 percent of those in the AAP-Yes group continued receiving AAP funds at Waves 2 and 3. Close to 80 percent of the original total continued to receive funds at Wave 3. For the AAP-No group, 87 percent of the original total remained in the AAP-No group six years later (see Exhibit 18 for tree diagrams that show these data).

Second, for the AAP-Yes group that consistently received AAP throughout the placement, the percentage of youth with Behavior Problems Index (BPI) scores in the clinical range ranged from 37 percent (at Wave 1) to 43 percent (at Wave 2). In comparison, the percentage of youth with BPI scores in the clinical range (noted as HBPI in the figures below) from families that consistently did not receive AAP was less — ranging from 21 percent (at Wave 1) to 32 percent (at Wave 2). These data suggest that while some families do manage to care for children with high levels of behavior problems without subsidies, the likelihood of having a subsidy and maintaining it is greater for those families with children who score in the problem behavior range.

Exhibit 18.
Subsequent Subsidy Use for Children at the Time of Adoption, Current Age, and Clinical BPI Scores

Perhaps most informative are the families that were not receiving AAP during Wave 1 but began receiving it in later waves. Among those families that switched from no AAP at Wave 1 to AAP atWave 2, the proportion with HBPI were 21 percent at Wave 1 and 73 percent at Wave 2. Most of these families continued to get AAP at Wave 3 — half of them had a child with a HBPI score. Among those that reverted back to not getting AAP by Wave 3, only 33 percent had HBPI scores. Subsidy amounts were available only for Wave 3 and are shown as the average amount for each group receiving subsidy. While not differing greatly among groups, subsidies appear higher for children who initiated them earlier rather than later.

Although the opportunity to follow subsidies across time and to merge these patterns with scores on children's behavior is promising, the CLAS analysis is plagued by small numbers of cases. The data do suggest, nonetheless, that families are more likely to transition from no subsidy to subsidy because behavior problems increase, although the reasons that families stop their subsidy use are less clear (i.e., there is less evidence that their subsidies have gone down when problems are reduced). These preliminary findings indicate that families obtain subsidies in order to cope with children who have behavior scores that place them in the clinical range.

These impressions are further supported by their descriptive remarks about the subsidy program and how they understood it and used it. These are provided (in their entirety) in Appendix B and very often show the way that having the subsidy allowed the family to purchase needed services. Four consecutively recorded responses provide a flavor of the role of AAP and the ways that it is used to support families in their efforts to provide compensatory activities for children.

• The foster care grant we originally received really helped. We are not "overly wealthy" and when Fiona was a baby the AAP subsidy helped to pay 2/3 of the cost of her childcare. We continue to need and use the money to provide a better life for our daughter. Some of her medical costs we pay with this money. We also used it to pay costs of preschool and the school that she now attends. During the summer we use the money to send her to Y camp. Without this money we would not be able to afford these extras.
• It costs a lot more than what the AAP gives you to help out when children have problems.
• My insurance does not cover counseling, nor does my husband's. So AAP comes in very handy.
• We greatly desired to adopt children and did so not even knowing AAP was available, but what a wonderful help it has been to us and we are very grateful. It would be a great struggle to provide some of these things without AAP.

[Go to Contents]

3.3 The California AAP Database

3.3.1 AAP Data

The data used in this report are a combined data set of the Adoption Assistance Program — Individual Case Reports (AD 42R) from FY 1988-89 and the matching case records from the state AAP database. The AD 42R data are available for 2,776 children of the 3,113 children (89 percent) placed for adoption in FY 1988- 89. The AD 42R report was completed at the time of the official adoptive placement.

Prior to linking the two data sets, the AAP data set included 1,172 cases. The AAP database is theoretically updated each time a child has a change in what they are receiving from AAP or every two years, whichever comes first. Children participating in AAP typically receive both adoption subsidy payments and Medi-Cal benefits to help their families to support them. Some families only receive either payments or Medi-Cal. As with most administrative databases, some AAP cases are missing from the database and other cases are missing some of their records. Furthermore, children with many changes in rate may be more likely to have information in the database because they have had more opportunities for a worker to enter their records. Therefore, adopted children who spend time in group care may be overrepresented in the database because they often have more rate changes than adopted children who remain in their homes.

When the AAP data set is matched to the AD 42R data, it contains matched records for 771 of the 1,172 cases with AAP records. (Cases were excluded when children were age 18 or more, when cases did not match exactly, and when cases had substantial missing data [a total of 401 cases were excluded].) The AD 42R data will be used to provide additional child and family characteristics in these analyses. This study includes data entered into the AAP database through December of 2000.

The AAP database is generated by "change submissions." Whenever a child who is receiving a subsidy has a change in status — from a change of address, to a change in the subsidy amount, to a termination of the subsidy — a change form is filed. (Address changes were omitted from these analyses.) California has state and county adoption agencies. State agencies operate in branch offices that cover, primarily, rural areas. Based on discussions with state Adoptions Branch staff, we believe that data from state agencies may be more complete than for county agencies.

3.3.2 Payment Level of the First AAP Payment

The first AAP monthly payment was $404, on average. Exhibit 19 shows the payment level of the first AAP monthly payment by demographic characteristics. Families with adopted girls received a slightly higher payment than adoptive families with boys. When children's ages at placement were three or older, they received about $50 more than those who were in the age range of two or less. The payment level of adoptive families with white children was higher than for families adopting children of other races. Adopting mothers who had educational backgrounds with high school or less received less AAP payment than those who had higher educational background. In terms of family income, middle income adoptive families had higher payment levels than low income or high income families. These differences in rates may be attributable to the overlap in child and family characteristics — for example, white families may be more likely to adopt older children and to have higher incomes.

3.3.3 Amount and Direction of Payment Changes

Adoptive families in California are contacted every two years for a required biannual recertification of their subsidy. Payment changes may occur following these recertifications, reflecting routine events, for example, a child gets older and qualifies for a subsidy increase based on age. They may also result from special requests, if a child needs special services that the family cannot afford. Nearly three-fourths (73 percent) of cases had one or two payment changes to recertify or change AAP amounts during the 11-year period covered by the AAP data for these children adopted in 1988- 1989 (see Exhibit 20).

For 10 children, this first payment change was to end their adoption, due to turning 18. These children had not, then, had any changes in subsidy amounts. This differs from what would have occurred had these children stayed in foster care and received automatic payment increases based on age. (Since January 1, 2000, California has provided automatic increases of AAP payments when foster care payments are increased.) Only 21 children were identified as having their payment started or restored during this time. These numbers are similar to those that we obtained from the CLAS survey, described above. We have limited the majority of our analyses to cases that had recertification changes.

These proportions are displayed graphically in Exhibit 21. The graph shows, more clearly, that the vast majority of changes filed were a result of recertifications. Further, the greatest proportion of those who had any changes had only one change during this 10-year period. Clearly, adoption subsidy payments in California are, on the whole, quite stable.

Exhibit 21.
Payment Changes with Each Case Action

Additional analyses addressed the direction and size of subsidy changes. We first eliminated change forms that were not related to payment changes. Then we grouped the payment submission changes according to whether they indicated an increase or decrease in payments and the size of those increases or decreases.

We examined how much each payment increased or decreased when payment changes occurred. We excluded payment changes solely due to termination of the case (because the child aged out of the adoption subsidy program at age 18), because these payment changes were, apparently, not related to changes in demand for services. We divided the amounts of payment changes into payment increases and payment decreases, and also looked into the total average amount of each payment change. The average size of the payment changes grew from the first payment change to the fifth payment change. Total average amount of each monthly payment change was just $89, a meaningful change when viewed in comparison with the average initial monthly payment of $404 noted earlier.

Exhibit 22 shows that a sizable proportion of changes are payment decreases. Of all payment changes, 26 percent were reductions in payments. We have no direct way to quantify the reason for these payment decreases, but they appear to have been made to correct payment increases that were too high or meant to be temporary. The increase in the size of the payment decreases and increases is consistent, although the magnitude of the change is far greater for payment decreases. Taken together, then, the average payment change increases in size as the number of payment changes grows.

We then examined how the size of the monthly AAP payment changes with each payment change (excluding payment changes identified as terminations). To do this, the amount of AAP payment changes were split into six categories: loss of $501 or more, loss of $101- $500, loss of $1.00-$ 100, gain of $1.00-$ 100, gain of $101- $500, and gain of $501 or more. The typical change in amount was small, although there was a gradual movement toward larger increases with payment changes. So, among all first payment changes, 68 percent were gains or losses of less than 100 — this had dropped only slightly (to 64 percent by the third payment change). But by the fifth payment change, only 38 percent of payment changes were of that size. During the first payment change, 22 percent of changes were greater than $100, but by the fifth payment change this had grown to 32 percent. The number of payment changes and the size of those payment changes are clearly associated at the extremes (see Exhibit 23).

In future analyses we will need to better understand the negative payment changes and when these occur. We have identified those that occur because a child turns 18 or 21 and is coded as a termination. We believe that the other negative payment changes generally follow a high payment for some special, time-limited services. In any event, these negative payment changes are not independent from the positive events. This suggests advantages of omitting the negative events for some analyses, and creating a dependent measure of payment changes that only includes the positive changes. This focus on positive increases is necessary to isolate changes obtained for new post-adoption services. If only the net change in subsidy is used for analysis, then evidence of temporarily increased funding to address service needs is lost.

3.3.4 Reasons for Payment Changes

When an AAP payment is started, restored, recertified, or changed, the reasons for the AAP payment change must be submitted. Multiple choices are allowed at any time. Reasons for AAP changes were recoded into four categories of rate changes: (1) basic care, (2)basic and special care, (3) special care, and (4) residential care, which account for 98 percent of all rate changes. The remainder were excluded from the analyses. We examined reasons for each payment change. The major reasons for payment changes were basic care changes (70 percent) or basic and special care changes (23 percent). The percentage of AAP recipients needing special care and residential care changes consistently increased from the first payment change to the fifth payment change. For instance, the portion (2 percent) of AAP recipients seeking residential care increased from 2 percent at the first payment change to 16 percent by the fifth payment change (see Exhibits 24 and 25 ).

Exhibit 25.
Percentage of Reasons for Payment Changes

Transition to residential care is often preceded by multiple subsidy increases.

These data show a pattern of usage that may indicate that it is unusual for children to have high payment changes ($500 or more) as their first payment change. Among all the first payment changes, only 20 of 642 (3 percent) were greater than $500; by the fourth and fifth payment changes, however, this percentage had increased to 7 percent and 11 percent, respectively. This is confirmed by the pattern of residential expenditures. Most children who are entering residential care do so after several payment changes requested by families to help them provide services to their children (n's not shown in table). So, among those families that obtain residential care for their children during the fifth payment change, they have already become users of substantial subsidies — this is why the fifth payment change related to residential care ($1,193) is only one-third the earlier changes. Knowing that many families have already had increases in their subsidy rates makes the provision of residential care seem somewhat less costly than it would be if this were a common first payment change.

3.3.5 Multivariate Analysis of Payment Changes in California

To better understand the family, child, and adoption characteristics associated with payment changes, we drew on the information about adoptive families contained in the California AD 42-R. The sample of cases in which the AD 42R data match with the AAP data includes 771 children under 18 years old. Unlike in North Carolina, most AAP recipients experience periodic payment changes, probably coinciding with recertification.

Exhibit 26 shows basic demographic information on adoptive families. Approximately half of the children were female (53 percent). Over 70 percent of children had been removed from their previous home before they were two years old, and 59 percent had come to live with the adoptive families by the age of two. The majority of both birth mothers (61 percent) and adopting mothers (70 percent) were white not Hispanic. While the percentage of birth mothers with Hispanic origin (23 percent) in the sample was higher than that of African-American birth mothers (14 percent), the percentage of African-American adopting mothers was the same as the percentage of Hispanic adopting mothers (14 percent). In terms of transracial adoption, three-quarters (77 percent) of adopting mothers had the same race as the birth mother. Most of the adopting mothers were high school graduates (29 percent) or had some college or trade school (35 percent). About four-fifths (79 percent) of the adopting parent(s) were not related to the adopted children prior to the adoption. Almost half (48 percent) of the adopting families had two or three minor children, and about half (52 percent) of the adopting mothers were in the third decade of their lives. About half (52 percent) of the adopting mothers were in their thirties, and roughly one-quarter of the mothers (24 percent) were in their forties. Four-fifths (81 percent) of adoptive families had two parents, and just over half (51 percent) of adopting mothers worked outside the home prior to the adoption.

Studying monthly payment changes with the AAP database. Before conducting multivariate analyses we further described the sample's involvement with payment changes. We tracked up to five payment changes per case. Exhibit 27 shows how many AAP recipients had experienced each payment change. AAP recipients changed their subsidy levels twice on average with most experiencing one or two payment changes. These data are consistent with the larger population of AAP cases described above, and of which this sample is a subset.

We examined the timing of AAP recipients' payment changes. Change forms without subsidy payment changes, e.g., address changes were excluded from the analysis. Exhibit 28 graphically shows the distribution of monthly payment change amount by the duration of time from the first AAP record. Most AAP recipients have experienced AAP payment changes periodically, about every two years — probably coinciding with recertifications. Few of these payment changes were more than $300 per month in either direction.

Exhibit 28.
Scatterplot of Monthly Payment Change Amounts and Durations

Exhibit 29 shows average subsidy levels of each payment change by the duration of time since the first AAP record. The average amounts of the first payment change generally decreased except at the fourth year since the first AAP record. On the other hand, the amounts of second payment changes increased up to three years since the first case action and went down again after the fifth year.

Bivariate relationships between case characteristics and payment changes. With cross tabulations and chi-square tests, we examined bivariate associations between changes in subsidy level and adoptive families' demographic characteristics (Exhibit 30). This analysis focused on positive amount of payment changes because the negative payment changes were often in response to the positive changes (i.e., they were subsequent corrections or readjustments) and were not independent of them. These analyses examine payment changes as events that signal needs (of varying magnitude) within the adoptive family, rather than focusing on the amount of subsidies received over time. Therefore, the negative amount of each payment change was recoded to zero, and the sum of increased changes from the first payment change to the fifth payment change was considered as positive amounts of changes in subsidy level.

We compared demographic differences in smaller amounts ($0 to $300) of overall increases and larger ($301 or more) amounts of monthly subsidy increases. Children aged 3 or older were more likely to receive larger subsidy increases over time, but this finding was not statistically significant. If adopted children lived with a well-educated adopting mother, they were more likely to experience high amounts of subsidy payment changes, X²;(1, 642) = 4.16, p < .05. Adopting mothers working outside the home prior to adoption were more likely to receive larger subsidy changes over time than mothers who were at home, although not at a statistically significant level (p = .065). In terms of family income, the families in the upper 50th percentile of family income were more likely to receive large amounts of subsidy changes over time than relatively low-income families, X²;(3, 642) = 9.39, p < .05. The association between the amount of payment change and children's race was very small and statistically insignificant (Exhibit 30).

The findings that families with mothers with higher education and families with greater affluence were more likely to receive higher amounts of subsidy increases are consistent with research suggesting that these parents have higher expectations for their children (Barth and Berry, 1991). In addition, these parents may be more able to advocate for subsidy increases.

If these findings hold up in the multivariate analysis — that is, after controlling for the age of the child at the time of adoption (a proxy for child behavior problems) — this would need to be considered in the evolution of a more equitable adoption subsidy program.

Multivariate analysis: logistic regression results. The bivariate results suggest that there are associations between children's and adoptive families' demographic characteristics and the amount of payment changes. We performed a logistic regression analysis in order to test associations between individual demographic characteristics — after controlling for their association with other case characteristics — and the amount of payment changes. We ran three slightly different models, each one including a somewhat different combination of variables, because all variables could not be tested simultaneously and because we wanted to see whether removing education or income — which are highly correlated — affected the results. Model 1 includes the child's race, age, and adopting mother's educational level; model 2 includes child's race, age, and adoptive family's income; and model 3 includes children's race, age, the adopting mother's educational level, and family income. The number of minor children, gender, single parent, and employment status were tried in the models, but did not improve the model fit and showed no significant relationship to the dependent variable.

A limitation of these models is the lack of data representing child characteristics such as disability and behavior problems, which should be strongly related to subsidy amount. Children's age serves to some extent as a proxy for these measures, since problems typically manifest as children grow older. The strongest predictor in these models is family income, although not entirely in the direction that would be expected if subsidies were being used to help families meet children's service needs.

As Exhibit 31 shows, all three logistic models appeared to be significant with acceptable, but not impressive, goodness-of-fit results. However, other results should be carefully considered because pseudo R&sup2; values are very small across all models (i.e., the models do not explain a sizable proportion of the differences in subsidy changes). In addition, of the independent variables, none of constituent item-level dummy variables — -except family incomes less than $26,443 and family income of between $26,443 and $36,000 (at the time of placement in 1988- 89) — were significant.

Exhibit 31.
Logistic Regression Results for Likelihood of Large Payment Changes

Dependent Variable		Model 1	Model 2	Model 3
		Sum of positive change in any payment change ($0 -$ 300, $301+)
Goodness-of-fit (Hosmer and Lemeshow Test) Pseudo R2		X2 = 7.77, p =.457 .027	X2 = 15.13, p =.057 .034	X2 = 8.38, p =.397 .042
Parameter Estimates		Odds ratios
Child's age	0 - 2	1.0	1.0	1.0
	3 or older	1.33	1.35	1.35
Child's race	White	1.00	1.00	1.00
	Hispanic	.70	.76	.70
	Black	.73	.85	.79
	Others	.96	.88	.94
Family income	< $26,442	N/A	1.00*	1.00
	$26,443-$36,000	N/A	.63	.58^
	$36,001-$48,761	N/A	1.37	1.10
	> $48,761	N/A	1.38	1.12
Mother's education	High school or less	1.00	N/A	1.00
	Some college or trade school	1.27	N/A	1.23
	Four year college or more	1.72*	N/A	1.57^
^p < .10 *p < .05

Event history analysis. We endeavored to understand the timing of payment changes in California in order to understand patterns of post-adoptive services need. Exhibit 32 shows the overall cumulative probability of the first payment change following placement. For this first payment change, and all subsequent payment changes, a large portion of AAP recipients has experienced payment change every two years because families must recertify their AAP status every two years. The portion of people with standard two-year payment changes who are receiving a fourth payment change or a fifth payment change is much smaller than the portion receiving routine payment changes at the first payment change or second payment change. That is, people who have experienced more payment changes are likely to more quickly experience other payment changes before two years.

Exhibit 32.
Length of Time from Placement to First Payment

Between the time of placement and the first payment change, only 25 percent of AAP recipients have experienced a payment change before the required two-year recertification. Yet about 41 percent of AAP recipients who have experienced a fifth payment change experienced their fifth payment change before two years from the date of fourth payment change. Exhibit 33 shows the quartiles for payment changes (estimated with Kaplan-Meier) and the proportion of the payment changes that occurred prior to the first year and prior to the routine second-year payment change.

Exhibit 33.
Time to Quartiles (in Days) and Proportion with Payment Changes Prior to 1 and 2 Years

	25th %	Median	75th %	< 1 year	< 2 years
Placement to 1st payment change	728 days	731 days	1035 days	11%	25%
1st payment change to 2nd payment change	609 days	731 days	N/A	17%	31%
2nd payment change to 3rd payment change	565 days	756 days	N/A	18%	31%
3rd payment change to 4th payment change	516 days	N/A	N/A	20%	34%
4th payment change to 5th payment change	245 days	730 days	N/A	32%	41%
Note: The 25th percentile indicates that 25 percent of families had a payment change at these times. The 75th percentile indicates that 75 percent of families had a payment change by this time. N/A indicates that the median and quartiles could not be estimated.

We next examined the probability of a payment change by the recipient's characteristics. Whereas there is little difference by child's age at adoption placement or educational level of adopting mother, the probability of payment change varies by family income and race. Confirming the logistic analysis, families with family incomes of $26,443 to $36,000 are significantly more likely to experience a payment change three years from placement. White, black, and Hispanic groups have similar "risk" of experiencing a payment change but children who are of "Other" races have a greater likelihood of experiencing a payment change.

We next endeavored to understand the likelihood of a payment change, simultaneously controlling for other case characteristics. Exhibit 34 shows median durations and risk ratios of a Cox proportional hazards model that analyzes the likelihood that a payment change will occur from placement and to fifth payment change while controlling for characteristics of adopted children and adoptive families. The median duration was two years (731 days) for each payment change.

These data clearly show that the timing of most payment changes was right at the two-year recertification point. Yet there were case characteristics that made the timing to payment changes vary significantly. Parents with at least some college, children who were three or older at the time of placement, and black children were the only groups whose risk ratios were significantly different from others, although this did not occur for all payment changes.

Transitions to residential care. Because of the particular policy relevance of time-limited placements in residential treatment for children receiving subsidies — insofar as the federal government will not reimburse for this, but 19 states will cover the costs (at least in part) — we completed a model for those who had a payment change with a reason of "residential care." In an earlier report (Barth, Gibbs, and Siebenhaler, 2001), we had indicated that older children, white children, children in nonkinship adoptions, and children who were not in the deferred adoption agreement program were all more likely to receive residential care. In this analysis we examine some of these factors, and we also consider family income, mother's education, and the history of payment changes. We also learn about timing of those transitions to residential treatment.

Only 34 children in this sample entered residential care during the study time frame. This makes it impossible to estimate medians for individual variables. Yet a Cox proportional hazards model could be computed, and is shown in Exhibit 35. This model is consistent with earlier work showing that children adopted when older than three years have a higher likelihood of entering residential placement that is paid for by a payment change. (California does not pay for for-profit residential treatment, so some children may have entered residential treatment but not be included in these data.) The number of payment changes was also significantly related to a payment change for residential treatment. Although 11 children, about one-third of all entries to residential care, obtained a payment change for residential treatment as their first payment change, this was not typical. Most children who entered residential treatment had three or more prior payment changes. Parental income has a tendency to be related to a payment change for residential treatment, with the group of families earning between $36,001 and $48,761 at the time of adoption having the highest risk ratios. Neither race nor the education of the mother was significantly related to the use of subsidies for residential treatment.

Exhibit 35.
Risk Ratios from Cox Regression Analysis of Payment Change for Residential Treatment

		Risk ratio	95% CI Exp(B)
			Lower	Upper
Education of adopting mother	High school or less	1.00
	Some college or more	1.12	.52	2.41
Child's age at placement	0 -2	1.00
	3 or older	2.07*	1.03	4.17
Family income	$26,442	1.00
	$26,443 -$36,000	1.32	.37	4.68
	$36,001 - $48,761	2.67^	.86	8.26
	> $48,761	1.52	.47	4.91
Child's race	White	1.00
	Hispanic	.91	.39	2.12
	Black	.00	.00	1.38
	Others	1.45	.34	6.19
Number of payment change	1 -3 of payment changes	1.00
	3 or more payment changes	4.86*	2.25	10.50
^ p < 0.10; * p < 0.05

The time to use a subsidy payment for residential care varied, in our study, from a little more than 2 years to 10 years, with the midpoint of those changes at about 7 years. This suggests that the likelihood of placement into AAP-funded residential care is accelerating.

Where to?

Top of Page | Contents

Main Page of Report | Contents of Report

Home Pages:
Human Services Policy (HSP)
Assistant Secretary for Planning and Evaluation (ASPE)
U.S. Department of Health and Human Services (HHS)