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www.prenatal5fiscal.org 11 scenarios to understand the impact of varying characterisDcs on the cost of care. For example, scenarios can demonstrate the difference in cost of care based on • Size of program (number of children served) • Ages of children served • Demographics of children and families served • Type of care (full day, full year, school day, part-year, and so on; may relate to funding source and regulaDons associated with the funding source) • Hours of operaDon (nontradiDonal hours in parDcular) • Facility type (center, home, or school-based) • Geographic locaDon • The compensaDon level selected in the model.

In this way, the model can demonstrate the variaDons in cost of care which can help inform policy decisions when senng subsidy rates for different program types. Outputs from these scenarios in the cost model represent the actual cost of care, which leaders can then compare to the current subsidy rates, to understand the difference between the market-driven rates and the actual cost of care that programs experience. O1enDmes this exercise highlights dispariDes in the current system, with certain provider types, or care for certain age groups, having the largest gap between current subsidy rates and the true cost of care.

Using these results to inform rate senng requires aligning scenarios in the model with the policy objecDves for subsidy rate senng. For example, states may consider changing the geographic boundaries used in their subsidy system, based on the results of the cost analysis as opposed to the current market analysis. The model could run mulDple scenarios to show the opDons for grouping counDes into regions, informed by the cost data, or could decide to have one statewide rate if the cost model demonstrated limited variability in costs across the state.

Depending on the model inputs, states may or may not be able to set rates at 100 percent of the cost of the care, depending on budget limitaDons. Therefore, decisions must be made regarding what porDon of the true cost of care subsidy rates will cover and how that may vary across program types. For example, a state may recognize a decline in family child care homes and want to help reverse this decline by reimbursing home-based providers at a higher percentage of the cost of care, in comparison to center- based providers. As a result, a state may cover 100 percent of the cost of care for family child care homes, where the gap between market price and true cost is o1en greatest, but only 85 percent for other programs. The data produced by the cost model can help states make these decisions and provide transparency into how those decisions are made. For instance, during the process in 2021, New Mexico’s Lead Agency found that family child care senngs were faring far worse than centers when comparing subsidy rates to cost of care. In senng rates informed by cost of care, the rates for family child care achieved 100 percent of the program cost of care, and rates for center-based senngs were set at an average of 94 percent of the cost of care.

Similarly, Virginia’s cost esDmaDon model showed that the gap between the cost of care and the current subsidy rates varied significantly by age of child and provider type. Therefore, when senng rates informed by cost, the Virginia Department of EducaDon decided to use their limited resources to cover a larger share of the cost of care for those programs for whom the gap between subsidy rates based on what families can afford to pay, and the true cost, were greatest. Figure 2 illustrates what percentage of the esDmated cost of care cost the new cost-informed rates cover for centers, and for each age group within a family child care home.