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These analyses demonstrate possible uses of administrative data for adoption research. |
Information relevant to understanding post-adoption dynamics, post-adoption services, and subsidy use is routinely collected and underused. Because there has been so little attention to these data, we have found substantial confusion about them. This is indicative of how foster care data were kept prior to SACWIS and other innovations in foster care data use. In some states, we believe that adoption subsidy data continue to be written over, so that only the current subsidy shows — there is no history, therefore, of subsidy changes. These kinds of procedures greatly weaken our chances of showing how the pattern of subsidy changes is related to adoption outcomes. Demonstrating possible uses of subsidy data is important to motivating states to do a better job of collection, storage, retrieval, and analysis.
Taken together, the analyses in this document serve several purposes. They offer a sample of the kinds of administrative data that are available to better understand post-adoption services and supports. They offer some ideas about the kinds of analyses that can be done to bring meaning to these data. They offer some substantive findings about adoption subsidies and how they are used. Finally, they offer some ideas about modifications to administrative data systems that could improve their usefulness in understanding adoption.
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.)
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.
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