Assessing the Field of Post-Adoption Services: Family Needs, Program Models, and Evaluation Issues. Summary Report. 5.3.5 Multivariate Analysis of Payment Changes


Multivariate analyses were based on the subset of 771 children for whom adoption case record data were available. This subset was similar to the larger population in the proportion receiving subsidy changes, the amount and direction of change, and reasons for change. Unlike in North Carolina, most AAP recipients experience periodic payment changes, probably coinciding with recertification.

Family income and maternal education were associated with subsidy increases.

Bivariate relationships between case characteristics and payment changes. Analyses of bivariate associations between changes in subsidy level and adoptive families demographic characteristics focused on positive amount of payment changes because the negative payment changes were often in response to the positive changes. 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. We compared demographic differences in smaller ($0 to $300) and larger ($301 or more) amounts of monthly subsidy increases. Children adopted by a well-educated adopting mother or in higher-income families were significantly more likely to receive large amount of subsidy increase. Associations between payment change and childrens race and age were statistically insignificant. If the association between education and income holds up in the multivariate analysis, this would suggest a need for a more equitable adoption subsidy program.

Multivariate analysis: logistic regression results. We performed 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 childs race, age, and adopting mothers educational level; model 2 includes childs race, age, and adoptive familys income; and model 3 includes childrens race, age, the adopting mothers educational level, and family income. All three logistic models appeared to be significant with acceptable, but not impressive, goodness-of-fit results. However, results should be carefully considered because pseudo R² values are very small across all models, that is, the model did not explain a sizable proportion of the difference in subsidy changes. These models do not include data on child disability, which should be strongly related to subsidy amount.

Event history analysis. These analyses examine the timing of payment changes in order to understand patterns of post-adoptive services need. Many AAP recipients experienced payment change every two years because families must recertify their AAP status every two years. Only 25 percent of AAP recipients have experienced a payment change before the required two-year subsidy change. However, people who have experienced more payment changes are likely to more quickly experience other payment changes before two years. 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.

The probability of payment change varies by family income and race. Confirming the logistic analysis, families with incomes between $26,443 and $36,000 are significantly more likely to experience an payment change within three years after placement. Children who are of other races have a greater likelihood of experiencing a payment change than do white, black, and Hispanic children.

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

Transitions to residential care. Residential treatment has particular policy relevance because the federal government will not reimburse for this, but 19 states will cover some or all of its cost. Only 34 children in this sample entered residential care during the study time frame. 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 small group makes it impossible to estimate medians for individual variables; however, a Cox proportional hazards model could be computed. The model shows a higher likelihood of payment changes associated with residential placement for children adopted when older than three years. The number of payment changes was also significantly related to a payment change for residential treatment. Most children who entered residential treatment had three or more prior payment changes. Families with income between $36,001 and $48,761 were more likely to receive a payment change for residential treatment. Neither race nor the education of the mother was significantly related to the use of subsidies for residential treatment.

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