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

Submitted by:
Deborah Gibbs and Kristin Siebenaler
RTI International⁽¹⁾

Richard P. Barth
University of North Carolina School of Social Work
Contract No. 100-99-0006

Submitted to:
Laura Feig Radel
U.S. Department of Health and Human Services
Office of the Assistant Secretary for Planning and Evaluation

Funded by:
U.S. Department of Health and Human Services,
Administration for Children and Families
Administration on Children, Youth and Families, Childrenchap146;s Bureau

 

1.  * RTI International is a trade name of Research Triangle Institute.

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Introduction

Most adoptions have positive outcomes for both children and their families. However, many families need supportive services during some part of their child's development. For some, the needs for post-adoption services are quite extensive and may threaten the adoption's survival. In response to these needs, many states have developed post-adoption service (PAS) programs designed to prevent adoption disruptions or dissolutions and to support child and family well-being. As discussed elsewhere (Barth, Gibbs, and Siebenaler, 2001), these services may be provided by public agency adoption workers, by private providers under direct contract with the adoption services program, or by families who receive adoption subsidies and use those to purchase needed services. Services received by adoptive families may be classic "post-adoption services" insofar as they address adoption-related issues, or they may be other educational, vocational, recreational, health, or mental health services that would be little different from those used by other families raising children with special needs.

This report is part of a series that examines these rapidly growing and evolving PAS programs, using a literature review (Barth, Gibbs, and Siebenaler, 2001), case studies of well-regarded programs (Gibbs, Siebenaler, Harris, and Barth, 2002) and, here, the analysis of secondary adoption data. In this report we discuss efforts to learn more about the relationship between adoption subsidies and post-adoption services. We have looked for secondary data for three related purposes: (1) to better understand the use of subsidies for purchase of services; (2) to illuminate post-adoption service use provided byВ  or funded byВ  public adoption agencies; and (3) to describe the disruption, dissolution, and displacement of adoptions. In all of these searches we have been stymied by the highly confidential nature of adoption dataВ  that is, states and agencies are often unable or unwilling to share data about adopted children and their families, given the historically high levels of confidentiality afforded to them.

In this exploratory work, we planned to pursue information about subsidies and the dissolution and disruption of adoptions from administrative foster care and adoption data held by California, North Carolina, and New Jersey. The analyses were expected to illustrate some of the possibilities of this work and to identify barriers. Ultimately, we were only able to obtain relevant data from California and North Carolina.

Before presenting findings from those analyses, we briefly review the available information about the role of adoption subsidies in order to identify some assumptions that this effort attempted to test. We then introduce some additional considerations about data related to adoption disruption, dissolution, and displacement. We finish with some observations about these findings and directions for future research.

This project was funded by the Administration on Children and Families of the U.S. Department of Health and Human Services (DHHS), under contract to the Office of the Assistant Secretary for Planning and Evaluation (ASPE). Research was conducted by RTI and the University of North Carolina at Chapel Hill. We appreciate the participation of the North Carolina Department of Social Services and the California Department of Social Services.

1.1 Adoption Subsidies Data

Our understanding of the relationship between adoption subsidies and other post-adoption services is limited. Administrative data and surveys indicate that adoption subsidies are commonly used. Data from the Adoption and Foster Care Analysis and Reporting System (AFCARS) indicate that 88 percent of children adopted in 2000 were receiving subsidies (AFCARS Report, 2001). Preliminary AFCARS data from 2001 suggest that the number of children receiving subsidies is rising in tandem with the number of adoptions (Penelope Maza, personal communication, August 26, 2002).

Available data on adoption subsidy use are limited and somewhat inconsistent. Little is known about pathways on and off subsidies or the reasons for, or timing of, changes in subsidy levels. There is no analysis that we know of that looks at the transitions associated with receipt of adoption subsidies. These have been treated by scholars as fixed  an assumption that is critical to examine given the many children who are now receiving them. For example, Avery (1998) has looked at the security of adoption subsidies and concluded that, "the life long commitment that accompanies adoption finalization does not necessarily come with the security of continued financial support from the state" (p. 52). Yet her policy analysis does not indicate the proportion of subsidies that decline in amount, over time, or the reasons that they decline. Information about changes in these amounts will help us to better understand the importance of the initial subsidy amount decision.

Many families that could get subsidies do not have them. According to Sedlak and Broadhurst (1993), 84 percent of the children who were adopted between 1983 and 1987 had special needs that would have potentially qualified them for an adoption subsidy (assuming that the adopting family did not have the means to meet those needs without compromising their own well-being), yet only 63 percent were receiving a subsidy at that time. (Much has changed since then, and the coverage of children who are eligible for adoptions has almost certainly increased, as indicated by the AFCARS data.)

Secondary data can also be used to understand the ways that subsidies are a part of the package of post-adoption services and support. A key issue in the use of subsidies is the transition from a deferred (or very low) subsidy to a higher subsidy, suggesting that the family has developed the need for additional services.

States and localities are likely to vary in the assumptions that underlie the design of their subsidy programs. Some consider that subsidies should be set at a rate sufficient to provide general support for needed services. Others set subsidy amounts at a level that can only support the basic care for a child, unless there are time-limited requests for subsidy funds to address specific problems. For example, according to a recent report from the North American Council on Adoptable Children, in four states the subsidy rate slightly exceeds the USDA rate needed to raise a child in a low-income family, yet in three states, the typical state subsidy is just half the USDA estimate (Bower and Laws, 2002). At the same time, some states are far more likely than others to grant a one-time payment or an augmented rate to help families purchase services that they need to better care for their children.

States vary in their subsidy policies and in the organization of subsidy data.

States vary in many other ways that affect the use of subsidies for post-adoption services. Among these are eligibility for Medicaid for state-eligible children, one-time or vendor payments for special services, an augmented "difficulty of care" rate for particularly challenging children, payment for respite care, and support of time-limited placement in residential treatment. In some states there must be quite specific documentation of planned or completed subsidy use. Yet in other states these subsidy expenditures need only be justified in more general terms. In some states, these problems have to be specified at the outset; in other states, there is a mechanism to request the addition of funds to address specific problems that arise later. These different practices certainly have a significant impact on families as well as on efforts to understand subsidy use. The more specific the categories for additional services or requirements for justifying the use of those services, the richer are the potential administrative data.

Adoption subsidy data may be maintained at the county level (in states that have state-supervised but county-administered adoption programs) or the state level (for state-administered systems), or both. In some cases, these data are integrated into the financial system used to make foster care payments, and in other cases the adoption subsidy data are maintained in a stand-alone system. In some states, we learned there is good documentation of the reasons for subsidy changes, and in other states these are not well documented.

1.2 Adoption Disruption, Dissolution, and Displacement Data

Most studies of adoption disruption, dissolution, or displacement have not relied solely on administrative data (cf. Goerge, Howard, and Yu, 1996). These studies have relied on case record reviews and interviews  labor intensive and costly approaches that are difficult to replicate in subsequent years, to determine if rates are varying. Yet the relative success of AFCARS, and the Multi-State Data Archive at Chapin Hall argue that there are substantial efficiencies to be had in the development of data capacity that does allow for the analysis of such events.

Adoptions can end in several ways. Adoptions that disrupt are foster care placements with the intent of ending in adoption that end before parental rights and responsibilities are transferred to the adoptive parents. Dissolutions occur when a legally finalized adoption is legally ended. This may occur either by a vacation of the adoption order by the court, by the adoptive parents relinquishing their rights or consenting to another's adoption of the child, or by the court terminating the adoptive parents' rights and responsibilities. Displacement occurs when a child leaves the parental home, although the parents maintain their legal relationship with, and responsibilities for, the child. Examples of displacements are children who leave home against parental permission, go into residential care, are incarcerated, or enter mental health hospitalization. Conceptually, there are two types of displacements  those that include continued parental involvement with the child and those in which the parents have given up efforts to be involved as parents, even though the adoption still legally exists on paper (J. Magruder, personal communication, August 4, 2002). We did not find data that expressly address disruption, dissolution, or displacement (of either type) of adoption  we have had to infer this from manipulating administrative data.

The single study that has used administrative data to study disruptions and dissolutions (Goerge, Howard, and Yu, 1996) was set in Illinois. In the course of examining multiple spells of children in foster care, the investigators determined that some of the children who were entering foster care had case information that matched  to a great extent, but not perfectly  children who had previously exited foster care to adoption. The authors interpreted these adoptions as having ended, although it is also possible that these replacements into foster care were displacements rather than disruptions or dissolutions. The authors determined which children had previously been adopted and were now experiencing a dissolution (about 4 percent) and which children had been placed for adoption and had reentered foster care without ever having completed the adoption (about 14 percent). Because states may sharply differ in the way that they define an adoptive placement, these figures may or may not be meaningful in cross-state comparisons. Nonetheless, this effort provided a prototype for the work with North Carolina data, described in the following section.

Adoption Dissolution, Disruption, and Supports in North Carolina

These analyses of administrative foster care and adoption data address adoption disruption and dissolution as well as subsidies. Their purpose is, in part, to gain some insight into the adoption and subsidy dynamics in North Carolina. More fundamentally, however, they are intended to demonstrate the strengths and limitations of research methods in states whose approach to storing administrative data is similar to that of North Carolina and to determine what additional information might need to be collected and stored in order to refine these analyses. The data files used for this work are maintained by the North Carolina Department of Social Services and cover the last 10 years.

2.1 North Carolina Administrative Data

Four different administrative data files maintained by the North Carolina Department of Social Services provided data for the analysis files used to examine adoption dissolution, adoption disruption, and adoption assistance in North Carolina. To define the population of children to be included in this study, three data files were merged: (1) summary information on each child receiving adoption assistance, (2) vendor payments made to other post-adoption service providers in the name of adopted children, and (3)records of adoption subsidy checks. The summary child-level file was first merged with the adoption subsidy check record file, and then information on payments to vendors was added into this merged file. The total number of unique ID numbers in this merged file was 12,067. Exhibit 1 summarizes the number of unique ID numbers in each data file.

Since there are no records in the payment files prior to January 1990, children with a final order of adoption before January 1, 1990, or who had no information on the date of the final order were identified and deleted from the final analysis file, leaving a possible 8,647 children in this file. Our next step was to assess the quality of this data merge based upon DSS-assigned ID numbers.

Assignment of multiple ID numbers complicates analysis of adoptions.

In North Carolina, children adopted from the public child welfare agency may have up to three different ID numbers. The first ID number, the foster care ID number, is assigned when a child initially enters out-of-home placement. After the adoption decree is final and if the child is to receive adoption assistance, a second ID number is assigned to track these payments. Finally, should an adoption dissolve and a child reenter out-of-home placement, a third ID number may be assigned. To further complicate these analyses, if a child receives adoption assistance in the form of reimbursement for nonrecurring costs prior to the final decree of adoption, the payments are recorded under the foster care ID number. After the adoption is final, the child may receive cash assistance and/or vendor payments recorded under the second ID number. Given the confidentiality of the adoption process, multiple ID numbers for the same child are not linked, making it impossible to assess the experiences of children across the continuum of adoption events.

To address these issues, even though it is possible that adopted children received additional assistance under the foster care ID number, we limit our analyses of adoption assistance to cash assistance and vendor payments that occur after the final decree. Additionally, we developed an algorithm to assess whether there were children with multiple ID numbers in this study population. We identified 182 pairs of ID numbers for children with the same gender, birth date, and date of final adoption decree. Since this number was so small, less than 4 percent of the study population, it appears that our restriction algorithm for study inclusion effectively resulted in a mostly unduplicated count of children. Exhibit 2 summarizes the source of information for the 8,647 children in the final study population. All but 340 children in the final data set had a record of either receiving a cash assistance payment or a vendor payment made in their name. Over half of the children received both.

After using the vendor payment and adoption assistance data files to identify the study population and to analyze patterns of adoption subsidy, we accessed one additional source of information, the North Carolina longitudinal foster care placement data files. These files contain information on all children placed in out-of-home placement in North Carolina since the mid-1980s. The placement data files when linked to the cash assistance data provided the data necessary for studying adoption dissolution. Additionally, the placement data files provided data for the adoption disruption analyses. Exhibit 3 summarizes the source of data for each topical area under study. The results of the analyses are described in the sections that follow.

2.2 Study Population

There were 8,647 children in the study population. All children had a final decree of adoption between January 1, 1990, and July 15, 2001. Exhibit 4 summarizes the demographic characteristics of the group. Only 12 percent of the children were older than 11 at the time of the final decree of adoption. There were slightly more boys than girls, and more than half of the adopted children were members of minority groups. A large percentage of the children, 90 percent, were in open cases and currently receiving some form of adoption assistance. Virtually all of the children (99.8 percent) were identified as emotionally disturbed, which meets the adoption assistance eligibility requirement for "special needs." It is unclear whether these children were considered emotionally disturbed on the basis of having been in foster care, or whether there were particular criteria that children met in order to be given this classification.

Although there are some children in the study who were adopted in the early 1990s, almost three-quarters of the children in this study, 70 percent, were adopted in the past five years. Exhibit 5 provides a summary of the number and percentage of children by the year of final adoption decree.

2.3 Adoption Displacement or Dissolution

2.3.1 Data for Studying Dissolution

North Carolina enters information on adoption assistance payments into its payment databases but does not identify adopted children within its foster care files. However, by combining adoption subsidy and foster care placement data, we can establish a cohort of adopted children, who can then be studied like other cohorts to determine if they subsequently have a new placement. A voluntary placement may indicate a post-adoption services displacement episode rather than a dissolution. An involuntary placement would be more likely to be a dissolution than a displacement, although these involuntary placements could also be made  depending on the locally administered rules  for children of parents who retain legal custody of them. Termination of parental rights (TPR) of the adoptive parents would represent a less ambiguous indicator of dissolution, but this event was not available from the files available for analysis. Without integrating data on the legal relationship between parent and child, the differences between dissolutions and displacements are not possible to determine. Although UNC currently has been given limited access to use North Carolina foster care data for this exploratory effort, we have so far only requested permission from the state to use the foster care data and the subsidy data. We have not requested the use of the data collected during the processing of adoption cases, which includes substantially more information about the adoption circumstances. Exhibit 6 shows the key dissolution and displacement questions and the possibility of answering them with the data that we are likely to be able to access.

Exhibit 6.
Key Questions for Dissolution Analysis and Likely Data Access

Questions	Comments
What percentage of adoptions has a subsequent spell in care?	Although case ID numbers changed when an adoption was finalized, by linking these new ID numbers with the placement data files, we were able to determine whether children have entered care from an adoption  especially since the inception of the related AFCARS reporting requirements.
How many have a subsequent spell that is voluntary?	A code in the NC data file can determine if new admissions are court mandated or voluntary, help determine the characteristics of a child's replacement into care following adoption, and distinguish between displacements and dissolutions.
What is the duration of elapsed time before this subsequent spell begins?	If we can identify children who reenter care, then we can identify all the elements needed to conduct event history analysis.
What case characteristics (e.g., race and age at the time of adoption) are associated with a subsequent spell?
How do subsequent spells end (e.g., in reunification to the adoptive family, in no reunification, in a subsequent adoption)?

2.3.2 Adoption Dissolution and Displacement in North Carolina

Older children and minority children are the ones most likely to experience dissolution.

We used two lines of analyses to examine adoption dissolution in North Carolina. First, we tried to track our cohort of adopted children to see if they experienced an out-of-home placement after the final decree. Second, we looked at all children who had entered out-of-home placement since July 1, 1998, to determine whether a child was previously adopted. Although neither line of analysis was entirely satisfactory, both provided information about possibilities for further research. Each is described below.

Cohort analysis. Because names of children were not included in our analysis files, we had to rely on the ID numbers to determine whether a child experienced a subsequent out-of-home placement after adoption. For a reentry to placement by a child receiving cash assistance payments to be considered an adoption dissolution, three conditions had to be met: (1) the date of entry to out-of-home placement occurs at least 90 days after final adoption decree date, (2) adoption assistance was no longer being received after this placement, and (3) if permanency was achieved at end of this placement, it was achieved with someone other than the primary caregiver at the time of the previous adoptive placement. This is the closest semblance we have to being able to define a dissolution, without having court records to establish the final outcome of the adoption. Of the 8,647 children in the adoption assistance data file, 2,217 were also in the placement data file, indicating that these children could have experienced a dissolution. Further analyses identified 70 children who met the adoption dissolution criteria that we established for these analyses. The remaining 2,147 placements, which did not meet all three criteria, represent out-of-home placements that did not result in dissolutions.

Delays and inconsistencies in assigning new client identification numbers for adoptive children who are receiving cash assistance resulted in the original overestimate of potential dissolutions. This is an indicator of the challenges of using administrative data for studying this issue. There were 2,217 children whose child welfare system ID numbers were in the placement data file but the identified child did not meet one of the three criteria that we set out for an adoption dissolution  i.e., placement date after the adoption, no longer receiving adoption assistance after the placement, or exit reason suggests reunification with primary caregiver at time of placement. These cases are difficult to interpret. They underscore the importance of treating these analyses as preliminary work that demonstrates the use of data to study post-adoption experiences and services rather than presenting firm conclusions about the number of dissolutions/disruptions in North Carolina.

Exhibit 7 summarizes age at adoption, race, gender, and year of adoption for the approximately 1 percent of children who experienced a dissolution  by the aforementioned criteria  compared to those who did not. Even these criteria are not airtight, however, as a child who is still adopted could  in very rare instances  be placed into guardianship with another member of their family.

Exhibit 7.
Characteristics of Children Who Experienced an Adoption Dissolution and Likelihood of Dissolution

	No dissolution	Dissolution	RR
Age at adoption
Birth to 5	4,381 (99.6%)	17 (0.4%)	1.0
6 - 11	3,231 (98.8%)	38 (1.2%)	3.2(1)
12 - 15	809 (98.2%)	15 (1.8%)	6.2(1)
16 - 17	156 (100%)	0 (0.0%)	.1
White	3,815 (99.4%)	24 (0.6%)	1.0
Black	3,870 (99.1%)	37 (0.9%)	2.0(2)
Other	892 (99%)	9 (1.0%)	2.2(3)
Male	4,402 (99.4%)	28 (0.6%	1.0
Female	4,175 (99.0%)	42 (1.0%)	1.5
Year of adoption
1990 - 1995	2,124 (98.5%)	33 (1.6%)	1.0
1996 - 1998	2,639 (98.8%)	31 (1.2%)	.65
1999 - 2000	3,809 (99.8%)	6 (.2%)	.35
1p < .001 2 .001 < p < .01 3 .01 < p < .05

Older children (current age) and minority children appear to be slightly more likely to experience an adoption dissolution. While statistically significant, these analyses are based on a small number of cases and should be viewed with caution.

Using Cox Proportional Hazards Models, we estimated the risk of adoption dissolution, shown in the final column in Exhibit 7, by age at adoption, race, gender, and year of adoption. Older children (current age) are significantly more likely to experience dissolution than younger children. Children who are 6 to 11 years old at adoption are three times as likely to experience dissolution than infants while young teenagers are over six times as likely. The risk of adoption dissolution in North Carolina is low for all children. However, compared to white children, black children are twice as likely as white children to return to placement after an adoption.

The following chart (Exhibit 8) provides a summary of the number of days between the final adoption decree and subsequent placement into out-of-home care for the 70 children that we identified. It shows that about 50 percent of these dissolutions occur within three years of adoption.

Exhibit 8.
Length of Time Until Dissolution

These merged data may or may not provide valid estimates of children who return to placement after being adopted. The findings of a less than 1 percent dissolution rate in North Carolina must be viewed cautiously. Since the population of adopted children who we are able to include in these analyses is limited to those receiving cash assistance payments, it is possible that the dissolution rate is low because these are among the most stable adoptive relationships. This would suggest that the study population did not include a substantial group of adopted children who are the most likely to disrupt. However, this seems unlikely for a few reasons. First, conversations with state officials in North Carolina indicated that they believe most adopted children in the state receive cash assistance payments and, thus, would be in our study population. In addition, comparisons of the number of children adopted in North Carolina over the past several years and the number of children receiving adoption subsidy payments during the same time period supports the conclusion that these analyses represent the appropriate universe of adopted children.

It is also possible that these data and our linking algorithms do not validly identify all adoption dissolutions. It is likely that most children who reentered placement subsequent to adoption did so under a different ID number, either the foster care number or a newly assigned number. Since the policy in North Carolina is not specific in terms of which ID number to use for a reentry following an adoption, it is not surprising that these data are not definitive, at this point in time, for these analyses.

A third possibility is that these data actually represent what is going on in North Carolina. The state's rate of reentry to foster care is far better than the national average, suggesting that a low rate of adoption dissolution may also be plausible.

Even though this line of analysis did not produce the results that we expected, if new ID numbers were systematically and consistently assigned to all children in the state who were adopted, this approach could be useful in understanding the course of an adoption that ultimately fails.

Older children and white children are more likely than others to reenter care after adoption

Entry into foster care. The North Carolina longitudinal placement data files provided the source of data for the second line of adoption dissolution analysis. In July 1997, as part of the AFCARS enhancement, North Carolina added a data element to the placement data files that recorded whether a child who was entering out-of-home placement had been previously adopted. Newly updated longitudinal data files created in April 2002 contained information on placement experiences of children who had entered placement through December 2001, providing a minimum of four and a half years of data for the new AFCARS data elements.

Of the children entering placement between July 1997 and December 2001, 318 had been previously adopted. Exhibit 9 summarizes the characteristics of legally adopted children who entered placement. Over half were teenagers; 58 percent were white; and 51 percent were female. Compared to the characteristics of children not previously adopted who initially entered placement during the last 10 years, previously adopted children entering placement were more likely to be white (56 percent versus 47 percent) and teenagers (66 percent versus 26 percent).

Exhibit 9.
Characteristics of Children Entering Placement Authority after July 1997 by Previous Adoption

	Children with previous adoption entering care		Children not previously adopted entering care
	#	%	#	%
Age (yrs) at entry to placement following adoption
Birth to 5	25	9	10,943	46
6 - 11	83	26	6,487	27
12 - 17	210	66	6,253	26
White	185	58	11,158	47
Black	111	35	9,669	41
Other minority	22	7	2,888	12
Male	155	49	11,870	50
Female	163	51	11,842	50

Not all legally adopted children who entered placement authority experienced adoption dissolution, as shown in Exhibit 10. About one-third of the children were reunified with their primary caretaker or exited placement to a parent other than the parent who originally lost custody of the child, a guardian, or a court-appointed caretaker; 17 percent left for unknown reasons or miscellaneous other reasons; 16 percent were adopted; and 10 percent were emancipated; leaving slightly over one-fourth still in placement in April 2002.

Although these analyses do not provide sufficient data to calculate a dissolution rate, they suggest a higher level of dissolution than seen in the cohort analysis. The analyses provide some insight into the number of adoption dissolutions that occur per year and the characteristics of children who are reentering placement following an adoption.

2.4 Adoption Disruption

Using the foster care placement files, we next examined the subject of how many children experienced an adoption disruption, that is, had placements coded as an adoptive home but ultimately were not adopted. There were 54,747 children who initially entered placement in North Carolina from July 1, 1989, through June 30, 2001. The North Carolina longitudinal placement data files track all placements experienced by these children. There were 463 children who had a first placement recorded as an adoptive home. A full 77 percent of these children subsequently exited placement to adoption. Only 5 percent of these first placements were still in the placement authority of the state when the files were created. The remaining 18 percent of children might have experienced disruptions in adoptive placement or had changes in their adoption plans for other reasons, including reunification, emancipation, running away, or a conversion to a guardianship. At this time, we cannot determine the ultimate case status of these children who had an adoption plan. North Carolina did not record all of these exit reasons until July of 1997, when they added this feature in order to meet their AFCARS requirement. Thus in future years, this information about children who had an adoption plan, but were not adopted and no longer have such a plan, will be available.

A larger group of children (2,657) entered foster care for reasons other than adoption but were subsequently in a home that was identified as an adoption placement. The majority of these children (59 percent) have subsequently exited placement to adoption; another 10 percent were still in the placement authority of the state, very possibly still en route to adoption. This leaves 31 percent of children who had an adoption plan  at one time  but no longer have one. Again, this group could include some adoption disruptions, but is also likely to include many more children who left the placement authority of North Carolina for other reasons. We cannot determine the difference.

The apparent imprecision in recording of the pathway to adoption is also demonstrated by the finding that the majority of children (65 percent) who achieve permanency through adoption are never placed in an identified "adoptive home." These are most likely foster children who are adopted by foster parents without ever having been identified in the data system as changing status from foster to adoptive homes. This suggests that case plan goals are often not updated in a complete or timely fashion  this is a common finding in research using administrative foster care data systems. Taken together, then, the data do not support an effort to precisely estimate adoption disruption rates in North Carolina. They do indicate, however, that this will become more possible in future years.

2.5 Adoption Subsidy and Vendor Payments for Adoptive Families

These analyses use data that record payments to adopted children (subsidies) or payment for services received by adopted children (vendor payments). These vendor payments may be used to purchase such services as respite care, therapeutic summer camp, and specialty mental health or educational services. Since the information is tracked by the adopted child's client ID number, the quality and completeness of the data are dependent on this data element. In North Carolina, adopted children receive a new client ID number after the adoption decree is final, making it impossible to track total expenses for children across their entire child welfare career. Thus, these analyses focus solely on the amount of assistance that a child receives after the final adoption decree.

Nearly all adopted children receive subsidies, which are generally stable over time.

Because 94 percent of the children in this analysis file received cash assistance payments at some point in time and these payments can only begin after the final adoption decree is entered, it is our assumption that the ID numbers represent payments to unique children and that a single child will not have two ID numbers while receiving cash assistance. To the extent that this assumption is incorrect, we may be over-counting the number of children receiving adoption assistance payment. A comparison of the number of foster children adopted in North Carolina in the past six years, 6,122, to the number of children with a final adoption decree in the last six years who are included in these assistance files, 6,182, suggests that there may be a few children with multiple ID numbers in the data files. These are probably not enough, however, to change the result of these findings in any major way.

Exhibit 11 summarizes the timing and amount of assistance received by adopted children in North Carolina since 1990. Almost all children with adoption assistance received cash payments (94%), and close to two-thirds (61 percent) also received additional assistance in the form of payments to vendors for therapeutic or medical services or nonrecurring costs of adoption. Half of the children started receiving cash payments almost immediately after the final decree. Within six months of the decree 96 percent had received their first cash assistance check. The average cash payment amount during this time period was $346 per month received for an average of 42 months. However, because most of these cases are still open, these averages may change over time since there are some increases in payments as children age. Very young children received average cash assistance payments equal to $315; the average payment for children between 6 and 12 years old was $364; for children older than 12 the average payment increased to $409.

Slightly over half (51 percent) of children had no change in their subsidy amounts over the course of their assistance period. For the remaining children, many of them having been adopted for relatively short times, the increases were not substantial. This resulted in an average difference between the initial and last or most recent payment equal to $32. Exhibit 12 provides more detailed information about the length of time children receive subsidy payments in North Carolina in relation to subsidy increases. These analyses suggest that there are relatively few increases during the first few years of the subsidy and that the subsidy changes peak after four years.

Exhibit 12.
Patterns of Increases in Subsidy

The average time between the first cash payment and the initial increase is almost two years; however, this varies by the age and race of the adopted child, as shown in Exhibit 13. This exhibit suggests that older children are less likely to receive subsidy increases and that the initial increase does not occur as quickly as it does for younger children. This is not surprising since the most frequent type of increase in subsidy amount in North Carolina seems to be tied to the age of the child and appears to parallel the increases that other foster children receive  increases that become less frequent as the child ages. These analyses, however, are perhaps somewhat misleading, since older children actually have less time in which they are eligible to receive assistance. To account for differences in eligibility time, using survival analysis we calculate the probability that a child will receive an increase.

Exhibit 13.
Patterns of Subsidy Increases by Child Characteristics

	Average no. of months between 1st payment and initial increase	Percentage with at least 1 increase	Avg. no. of increases for those with increase
Age (yrs) at 1st payment
Birth - 5	19.4	59%	2
6 - 12	24.5	44%	2
13 - 17	26.9	18%	2
Male	22.1	46%	2
Female	22.6	45%	2
White	22.6	48%	2
Black	21.9	46%	2
Other minority	23.7	35%	2

Survival analysis techniques make maximum use of available data by including all eligible children in the analyses whether they have already received a subsidy increase or not. Since most of the study population are active cases, it is possible, even likely, that many of the children who have not yet received a subsidy increase will eventually receive one. Survival analyses estimate this likelihood by calculating the cumulative probability that the event of interest, in this case, increase in subsidy amount, occurs by specific time points. Exhibit 14 estimates the overall probability that an increase will occur at a given time after first subsidy receipt. Exhibits 15 and 16 provide this probability estimate for children by age and race, respectively.

Exhibit 14.
Probability of an Increase in Cash Assistance

Exhibit 15.
Probability of Increase in Cash Assistance, by Age

Exhibit 16.
Probability of Increase in Cash Assistance by Race

Children who receive subsidies at young ages are most likely to receive increases

The overall cumulative probability of an increase is depicted in Exhibit 14. During the first year of assistance about 20 percent of children are likely to receive an increase; by the two-and-a-half-year mark the probability increases to about 50 percent. The probability of an increase varies by both age and race, as shown in the following two exhibits. Children under five years of age were the most likely to have subsidy increases and to incur them more quickly as indicated by the slope of the curve. By the one-year mark there is an increase in subsidy amount for an estimated 30 percent of young children compared with 20 percent of 6- to 12-year-olds and 10 percent of teenagers. Four years after the initial subsidy payment approximately 10 percent of children adopted before age 5, 40 percent of adopted 6- to 12-year-olds, and almost 70 percent of those adopted as teenagers are receiving the same subsidy payment. Because teenagers are aging out of the adoption assistance programs, these results are not surprising.

Because many factors are related to the length of time until an increase occurs, survival analysis was used to examine the relationships. Exhibit 17 presents the results of a Cox proportional hazards model that analyzes the likelihood that a subsidy increase will occur while controlling for characteristics of adopted children and length of eligibility time. Race and age at initial payment are significantly related to the likelihood of a subsidy increase. Even though the model controls for the number of months of assistance, children who begin receiving adoption assistance before age five (the reference group) are much more likely to receive increased subsidy payments than older children. Other minority children are less likely to receive an increased subsidy than either white (the reference group) or black children.

Exhibit 17.
Likelihood of Increased Subsidy Amount

	B	Sig.	Exp (B)	95.0% CI Exp (B)
				Lower	Upper
Gender	-.014	.660	.986	.926	1.050
Race		.038
White (reference group)					1.000
Black	-.054	.115	.948	.887	1.013
Other minority	-.146	.018	.864	.766	.975
Age 1st payment		.000
0 - 5 yrs (reference group)					1.000
6 - 11 yrs	-.614	.000	.541	.506	.579
12 - 17 yrs	-1.100	.000	.333	.277	.399
Months of payments		.000
0 - 36 (reference group)					1.000
37 - 72	.401	.000	1.493	1.366	1.632
73 - 108	1.406	.000	4.082	3.711	4.490
Over 108	1.568	.000	4.799	4.195	5.490
# Vendor checks		.000
0 (reference group)					1.000
1	.061	.137	1.063	.981	1.152
2 - 10	.252	.000	1.287	1.188	1.395
Over 10	.158	.009	1.171	1.040	1.319

Analysis of vendor payments indicated that half of the children with a vendor payment had the first payment within two months of the adoption decree and 74 percent had first payment within six months of the decree. The average number of vendor payments per child was four, with amounts ranging up to $2,000. The analysis of these payments is complicated by the fact that children could receive these payments before and after the final decree, and thus payments for one child could be recorded under different ID numbers. Thus, it is likely that these numbers actually underestimate the amount of vendor payments incurred by an individual child.

2.6 Summary

These analyses serve multiple purposes. First, the analyses provide substantial instruction about the way that adoption data may be organized and some possible strategies for gaining information from them. In North Carolina adoption data are maintained in the foster care system (which indicates whether or not children placed into foster care had ever been adopted and whether children leave to adoption), in a payment system (which indicates whether there have been subsidies or vendor payments), in special manualized systems (for particular programs like those for HIV-affected children), and in the adoption information system (which contains information about the adoptive family and the circumstances of the adoption). The finding that the adoption data are in several places may be more typical than unusual, although work with more states will be required before determining whether any state has a typical system. North Carolina's data does not allow for a precise estimation of adoption dissolutions or displacements. To accomplish this, several adjustments would need to be made in the way data about reentries into foster care from adoptive families are collected.

Analysis of disruptions could also be improved if more specific information were collected about the reasons that adoption plans were terminated. The utility of this improvement would depend, ultimately, on how comprehensively adoption plans were identified in the data. If adoption plans are not recorded in a consistent and timely way, then timely information about disruptions of plans that do occur cannot tell the total story. We believe that many states currently lack the capacity to accurately record case plans.

These analyses suggest methodologies that could be useful in other states.

These analyses also demonstrate analytic methodologies that could be used to explore these issues in other states. Moreover, they highlight the critical need to consistently assign identification numbers at various points in time across the placement-adoption time continuum. It is possible to link multiple data files only when there is confidence in this process.

Equally important, the analyses provide substantive information about children who receive adoption assistance and describe the continuity of support provided by these important programs to adopted children in North Carolina. The analyses suggest that most adopted children in North Carolina receive some form of cash assistance support. Cash payments begin very soon after the final decree is entered and usually continue until the child reaches the age of 18. Although this report does not provide analyses of other sources of support for families with adopted children in North Carolina, it is important to acknowledge these here. In addition to cash assistance payments, North Carolina provides special supplements for adopted children with HIV and for children with substantial functional impairments. Because these programs have special requirements and payment structures, their accounting is not automated. Thus, they are not included in these analyses. North Carolina also makes all children who are adopted (except those with their own income) Medicaid eligible. In addition to the cash assistance payments, the state provides support to adoptive children in the form of vendor payments to cover the cost of special services, especially counseling and medical services when Medicaid coverage is exhausted.

Another area of analyses examines the likelihood that an adoption in the state will dissolve or disrupt. Because these analyses try to adapt data historically collected in legacy systems for administrative purposes, the data for these analyses are more tenuous than the adoption subsidy data. North Carolina has a low reentry to out-of-home placement rate so it perhaps should not be surprising that the adoption dissolution rate is also extremely low. Problems with the assignment of new client identification numbers and the inconsistency of these data across the state's 100 counties suggest that the state should undertake additional training and analyses to better understand adoption dissolution in North Carolina.

Adoption Subsidies in California

3.1 Data Sources

California analyses combined administrative data with a survey of adopted families.

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.

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.

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

Subsidy changes tend to occur in conjunction with required recertifications.

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.

Discussion

4.1 Substantive Findings

Differences in data availability and structure between North Carolina and California limit our ability to assess the generalizability of our findings. Yet some clear similarities and differences have emerged. Almost all (94 percent) of the children adopted from foster care in North Carolina received cash assistance subsidy payments. This is consistent with the findings from Adoption and Foster Care Analysis and Reporting System (AFCARS) that 88 percent of children who have been adopted in recent years are receiving subsidies. About half of them received the initial payment within one month of the final decree and almost all of the rest within six months of the decree. The amount of the subsidy payment remained unchanged for slightly over half of the children (51 percent)  some of them had the same subsidy amount for the full 10 years. For the rest there were gradual increases in the amount of cash payment that appear to occur as the child grows older.

These stable subsidy amounts appear to differ from those in California, although this cannot be confirmed because we do not have the population of all children who have ever had a subsidy in that state. Among the children for whom we have data, only 17 percent never had a payment change. Most children then, have had payment changes. In California, like North Carolina, many of these payment changes are routine subsidy increases  resulting from biannual recertification requirements  but there also appear to be fewer cases in which there are no changes. The probability of a payment change is associated with the prior number of payment changes. As prior payment changes occur, the rapidity of subsequent changes increases. Thus the number of payment changes provided could be used as a marker for outreach to families who may need additional guidance or assistance.

In North Carolina, 39 percent of children had a vendor payment made to purchase services on their behalf. Data on vendor payments are not available in the data that we have from California, as this is not common practice there. Instead, families purchase additional services following increases in subsidies. Thus, we can presume that more families in North Carolina would have had a subsidy increase if they had not had these additional vendor payments made on their behalf.

Relatively large subsidy increases in California are also associated with a few family characteristics  specifically, the child's age at the time of adoption and family income. Families at middle income levels  that is, not in the lowest quartile or the highest quartile  are the most likely to obtain larger subsidy increases. Also, families that have more-educated mothers obtain larger subsidies. Although we have some evidence from California Long-range Adoption Study (CLAS) data that subsidy increases are associated with the worsening of children's behavior, we also see that they are strongly associated with parental characteristics. The finding that relatively more educated and affluent families are likely to get larger subsidy increases could be a function of their taking on more difficult children, but this holds true after controlling for the ages of children. The equitability of adoption subsidy adjustments needs to be better understood. There appear to be no inequities in subsidies that are associated with the race of the children.

Children in the North Carolina data had received adoption assistance for an average of 3.5 years. However, since 90 percent of the children in the data were still receiving adoption assistance when the data files were created, it is expected that this estimate will increase with time. Only small proportions of the children in North Carolina and California had "aged out" of adoption assistance at the time of these analyses  our results suggest that many children continue to receive adoption assistance until they reach 18 years old.

Data in North Carolina support previous findings of low dissolution rates. We utilized two lines of analyses to estimate the rate of adoption dissolutions in North Carolina. First, we identified a cohort of children that had been adopted and were receiving adoption assistance and looked to see whether these children had entered placement authority after the final adoption decree. Second, we looked at children entering placement authority since July 1997 to determine the reunification rate for children who had been previously adopted. Although the results suggest that the risk of adoption dissolution in North Carolina is lower than that seen elsewhere, further analyses show that the risk is greater for older children and for minority children compared with infants and white children in the state. We were unable to definitively quantify disruption or displacement rates in North Carolina.

In California, we could study the transition from home to residential/group treatment for the relatively small proportion of children who used this option. Event history analysis indicates that age at placement, the number of prior payment changes, and  to a lesser extent  family income are associated with the use of state-funded residential care. (It is worth noting that some additional residential care could be provided for these children, many of whom are not yet adolescents.)

4.2 Data System Issues

Confidentiality concerns, incompatible data systems, and incomplete data limit analysis.

Adoption data are highly confidential and fragmented. Data about foster care histories and foster care payment amounts, adoption home studies (or their electronic summaries), adoption subsidy amounts, payments for special services (i.e., vendor payments), and disruptions, dissolutions, or displacements are often collected and stored in unrelated data systems, if at all. Record matching is often required because common identifiers do not exist. The importance of confidentiality around adoption issues impeded efforts to link the data in different files. Adopted children in North Carolina can have as many as three identification numbers in the data, making it problematic to link data between data files. Inconsistencies in the assignment of ID numbers across counties can further complicate the use of these data to estimate dissolutions.

Data on adoption assistance in North Carolina provide a clear estimate of the payment amount and length of time that children receive cash subsidy payments. The picture of vendor payments is less clear because the overall summary data maintain year-to-date estimates rather than career estimates of payments for each child. No reasons for subsidy changes or vendor payments are included in the data that we used. Nevertheless, even with these identified data constraints, these analyses do provide an important first look at these critical issues and begin to identify ways in which administrative data files might be modified to support future analyses.

The California analyses also provide important information about data issues. First, the subsidy data are not as complete as could be hoped  some children who have subsidy changes are not included in the database, as this information does not always get sent from the counties to the state. Second, there is no field in the AAP database that indicates the starting subsidy amount, all that can be gleaned from these data are the subsidy amounts upon the first payment change. Third, these data cannot be readily linked back to the foster care data, so critical information about foster care histories is not available for explaining subsequent subsidy use.

References

Allphin, S. (2000). Receipt of residential services for children receiving adoption subsidies in California. Unpublished draft report available from the author. Berkeley, CA: University of California at Berkeley, School of Social Welfare, Center for Social Services Research.

Avery, R. J. (1998). Adoption assistance under PL 96-272: A policy analysis. Children and Youth Services Review, 20, 29- 55.

Barth, R. P., Gibbs, D. A, and Siebenaler, K. (2001). Literature Review. Assessing the field of post-adoption service: Family needs, program models and evaluation issues. Research Triangle Park: Research Triangle Institute.

Bower, J. W., and Laws, R. (2002). A policy analysis of adoption subsidy programs in the United States: Support for families of children with special needs. North American Council on Adoptable Children.

Brooks, D., Allen, J., and Barth, R. P. (2002). Adoption services use, helpfulness, and need: A comparison of public and private agency and independent adoptive families. Children and Youth Services Review, 24, 213- 238.

Gibbs, D. A., Siebenaler, K., Harris, S., and Barth, R. P. (2002). Case study report: Assessing the field of post-adoption services: Family needs, program models, and evaluation issues. Research Triangle Park: RTI International.

Goerge, R. M., Howard, E. C., and Yu, D. (1996). Adoption, disruption, and dissolution in the Illinois child welfare system, 1976- 94. Chicago: Chapin Hall Center for Children.

Magruder, J. August 4, 2002. Personal communication.

Sedlak, A., and Broadhurst, D. D. (1993). Study of adoption assistance impact and outcomes: Final report. Rockville, MD: Westat.

U.S. Department of Health and Human Services, Adoption and Foster Care Analysis and Reporting System (AFCARS) (2001). Interim FY1999 Estimates as of June 2001(6). The AFCARS Report. http://www.acf.dhhs.gov/programs/cb/publications/afcars/june2001.htm.

Appendices

Data Elements from the California Relinquishment/Independent Adoption Program Individual Case Report -AAP

[ See PDF file of Appendix A. ]

CLAS Participants' Responses to Open-Ended Questions about Adoption Costs and Subsidies

My daughter has been in specialized schools, $3400/month (8/1990-6/1992). Ran away 7/92. Foster care 9/92-5/96. Ran away numerous times.

My adoption daughter is a teen now and has many needs nearing adulthood. The AAP really makes the difference in setting some of the standards of living she enjoys, thus giving her a stronger feeling of well being and confidence in moving forward as an adult. Her wishes are to be a social worker for CPS or adoptions, or such like fields. (We did good HA!)

Right now, we are looking into a school for Adrienne, which will get her out of our home, at least temporarily. Ideally we want something that will address her attachment problem and give us some respite, but even if we can't find anything for her specifically, we do intend to send her to boarding school. She is making life miserable here.

It seems if you do not make a good living it is hard to adopt. I feel this is very unfair, the more income you have the easier it is to adopt.

The families are being stressed by agencies in our local. They are being denied placements if they need AAP and payments are being reduced against the family wishes. This is causing some kids to remain in the long-term foster care instead of adoption. Also, some families in our area are breaking up. And our county has the highest rate of failed adoption in the state.

The foster care grant we originally received really helped. We are not "overly wealthy" and when Karina 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 husbands. 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.

I feel I should be receiving AAP, feel distressed not. My daughter is diagnosed with FAS/ADHD and I can no longer work full time and maintain the family. The money would help but apparently we do not qualify due to it being a private adoption. We do not feel this is fair.

Adoption assistance was granted to us because Jeff has a neurological disorder that requires monitoring on a regular basis.

The AAP helped us tremendously.

The aid to adopt helps a lot. With more we could get many new clothes instead of thrift clothes, and good counseling.

The aid to adopt helps tremendously for educational materials. If it were higher he could have his own room, have some privacy to do homework.

The AAP subsidy has been invaluable to us. Without it, we could never have provided the needed counseling. It has also come in handy recently to pay legal fees and juvenile hall bills. I would never ask someone to adopt a special needs child without these funds being available.

Perhaps as for your next survey you might want to include gas or travel expenses. As to having adopted a drug-affected child, there are many hidden costs in terms to traveling to doctors and other professionals to deal with the needs of your child. Our son's physicians to deal with his emotional problems are 2hrs and 100 miles from where we live. We have spent traveling back and forth to doctors' appointments.

We used the AAP for some dental work that insurance doesn't cover. Also for 4th grade he went to a special reading clinic everyday because of a learning disability. It's not covered through the school so we had to pay for it. We know there will be plenty more dental work but hope our insurance covers most of it. We will continue to keep the Medi-Cal card for eyeglasses and counseling as needed.

I have received AAP; I am not sure what it is. Other than when the children were in foster care with me waiting to adopt, we haven't received any outside money or help.

I consider my AAP as my son's child support had I been married to his father and divorced. I asked for an additional $50.00 per month for his braces and $50.00 per month for tutoring. I was denied help for the braces (I did it on my own) but was granted $50.00 for his tutoring. However, when I went to get him help it was going to cost around $200-250 per month and I couldn't afford it. My son's teeth were pretty bad (he had to have two oral surgeries). Even though I had a strain on my budget, I made the sacrifice. My insurance covered 50% I had to cover the rest. I work in the teaching profession and have been helped by many wonderful teachers. My son and I are very blessed.

Very satisfied, I do hate the annual reviews, however necessary they are.

The adoption subsidy has been very helpful because he has required more participation in activities (sports, etc.) to help with behavior that seems linked to his drug exposure prior to birth.

AAP was not enough to cover all tutoring needed. We had to limit tutoring.

Julie was considered hard to adopt because of having been placed in previous foster care placements, and possible speech problems. We were given $500 at the time of adoption. Considering that all of the "problems" were unfounded. Julie is in a gifted program at school, and her ITAS scores are in the high 90's, I have no idea why we were given the money.

Dental care was difficult to obtain; we just paid out of pocket.

I do not feel adoptive families should be treated negatively. Although we are getting by, AAP would be very welcome and allow us to do more for our child. I know that some families get large amounts for "hard to place" healthy children. We got $294 a month when we did get AAP. Piano lessons are costly, but she so wanted them that we are cutting corners in other ways. Our guardianship son is costing us $111.10 a month for orthodontic work and we have no aid for him. We are hoping Stephanie won't need work; however, the doctor is watching because there is some indication it might be needed. Fortunately, she has been a healthy child. And has had lots of nice "hand me downs," so shoes, underwear, and coats have been are largest costs to date for clothing.

It breaks my heart that Brandi requires $0 for social activities and fun things in life. She is a medical/psychological catastrophe at this point requiring more money in help than we can afford to give her. She no longer wants/needs parents, she requires a staff of professionals.

I appreciated the county help, but we did not demand it.

AAP was an unexpected source of money when we adopted our first child, but it can mean the difference of having an enriching childhood for the children or "just getting by." We have set some of the AAP aside to help with college costs or a car purchase when they grow up.

The AAP really helps us with the extras.

The cost of this child's adoption was very expensive because we needed to retain an attorney to represent us in litigation regarding the child.

It's very important especially if you adopt older children or siblings. I couldn't have afforded the five kids we adopted even if I hadn't wanted to split them up. I think if it weren't available most siblings would get split up only because people couldn't afford them. The mediCal is vital.

Counseling is required and it is expensive. Private insurance does an inadequate job at providing enough coverage of this expense.

I do not think our relationships would be so great without the AAP subsidy. The ability for me to sty home and devote myself as a full time mother and the many challenges has been a Godsend. My children are fabulous, bright brilliant, and happy. But it has taken a lot of effort, time, energy, money, and commitment. Without the subsidy we couldn't provide a quality home life.

For what adoptive parents go through, emotionally, physically, and psychologically as a result of adopting an older child from an abusive background, the money that is given is hardly enough. But, when you look at it as a commitment to simply helping someone, of taking a child into and giving them another chance at life, then the money isn't even an issue. I do wish there were a program that could help the "adult" adoptees with therapy and counseling. Our adopted son still needs help with issues that are coming up. And pushing old buttons for him. He still has problems with relationships now that he is dating and with taking responsibilities for his own life and making decisions. He still has problems with bonding, with communication, with changes, with self-esteem and even sexual dysfunction. I try to counsel him as much as I know how, and give him books to read and direct him to others that may be able to relate but he still needs real therapy and we can't afford it. And neither can he. If the Marines knew he needed psychological help they would not let him re-enlist.

We ate $250 worth of counseling bills and had to stop for lack of funding in 94-95. Medical denied payment, as did HMO. Eventually we applied through adoption assistance to get increased funding. It came through.

By accepting and receiving AAP has enabled me to be a full time Mom of the beginning and even now, I only work part-time. I feel this is very important for the child to feel secure.

We tried to get some help with psychiatrist bills for psychotherapy for Donald, but were turned down. Our insurance pays 50%.

I changed jobs in 1995. My new insurance would not cover any pre existing illnesses. My son has been receiving care for attention def. And hyperactivity and depression since age seven. He was hospitalized under my prior insurance the beginning of June 95. This child is very destructive and lacks control over his impulses.

Medi-Cal should cover early orthodontics when recommended by dentists or orthodontist. Would save more money in the long run by reducing more orthodontists and dental work a few years later.

Qualified psychologists who have experience dealing with adopted (late) children are rare and very expensive i.e. $125 per hour. Reasonable resources need to be available to adopted parents.

I am glad we receive the $400 a month because Michael receives counseling on and of special services not covered by insurance and this really helps us out.

I think funding for counseling should be provided b/c there are not a lot of good medical psychologists and my insurance only covers $20.00 per visit. These kids really need "outside" help and adoptive parents need that support too. The adoption did not cost us any money since we took special need siblings.

The subsidies for health problems should go up with age as medical covers less and less.

They have been a blessing.

Always need more money.

La county reimbursed me the agency fee and the legal fee I paid to adopt my son. I started a savings account with that small amount of money for him.

Our daughter's residential care would not be possible without AAP county mental health funds. It is running over $45,000 a year. The residential care saved our daughter's life.

Other than the counseling expenses I do not believe we need any more favor expenses for our children than any other family does for natural children.

We spent $8,000 for two years (pre-school and kindergarten) for a special school to work with Matthew's ADHD until he was old enough for appropriate medicine. Until he could be medically treated, mainstream schools were very unsuccessful.

I need the subsidy because Ricky's psychiatric problems are not coverable by regular insurance.

Without AAP I would not have been able to provide special activity.