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High-quality early education is expensive—low child-to-teacher ratios mean high personnel costs, and rent, facilities, and supplies add up quickly. Tuition alone often can’t cover what programs need, and states subsidize the difference. Historically, states have set those subsidies using market rate surveys, which measure what programs currently charge rather than what high-quality care actually costs. Because tuition is effectively capped by what families can afford to pay, . States typically anchor subsidies to market rates—and —leaving programs to close the gap by keeping wages low or deciding they can’t afford to serve subsidy-eligible children at all.

Many states are now turning to cost estimation models that ask a different question: How much does it actually cost to provide high-quality care?

What Does the Research Say?

High-quality early education and gives families the stability they need to and community life—but . Educator compensation : research consistently links higher compensation to , which in turn help support more stable caregiving relationships and better outcomes for children. When don’t reflect the true cost of quality, programs face difficult tradeoffs——any of which can undermine quality and limit their ability to provide the stable, enriching environments that children and families need.

Virginia Child Care Cost Estimation Model

In Virginia, data showed that early education programs accepting subsidies paid their teachers $2 less per hour on average than sites that only accepted private pay, exacerbating teacher turnover at subsidized sites and keeping children from families with low incomes from accessing high-quality care. Inadequate compensation within publicly funded sites clashed with Virginia’s quality rating system, the Unified Virginia Quality Birth to Five System (VQB5), built on the principle that strong teacher-child interactions are key drivers of quality in the classroom. Leaders decided a cost estimation model would better capture what it would take for providers to meet VQB5 standards than the market (price-based) model used previously.

How Leaders Made It Happen

Structured subsidy rates to support, not just reward, high quality. In the past, most states tied subsidy eligibility to measurements of a program’s quality. Virginia leaders took a different approach: after defining quality through VQB5, they built a model that explicitly considered what it would cost for providers to achieve high quality. They structured rates to help providers reach higher quality, not just to reward them after they got there. As one leader said, the traditional approach is “a little backwards”—asking providers to achieve higher quality before giving them the resources to do it.¹ Virginia flipped that logic: “We measure quality this way, we expect everyone to meet it, and we pay at a level designed for you to get there, with supports if you’re not there yet.”²

Anchored model design in competitive workforce compensation. A key assumption of Virginia’s Child Care Cost Estimation Model is that “well-compensated, qualified educators are the primary driver of quality.”³ Leaders rejected using status quo low wages as the basis for subsidy rates, instead pegging early educator wages to K-12 salaries. They kept adequately compensated personnel as a central, non-negotiable assumption of the cost model.

Invested analytic effort only where it could change results. Leaders explicitly resisted the urge to “account for every permutation of every provider’s unique expenses” ��when creating an effective model.⁴ They focused on the biggest cost drivers, including salaries and teacher-child ratios, and applied flat inflation rates to update some more minor non-personnel costs instead of conducting data updates and granular recalculations for those less influential cost drivers.

Built buy-in around methodology rather than outputs. When walking providers and policymakers through the model, leaders focused on the rigor and rationale behind the inputs rather than the dollar amounts produced. As one leader explained, “you’re never going to generate a number from these models that makes everybody happy”—but if stakeholders agree the approach is sound and the inputs reflect providers’ real costs, the outputs become less contentious.⁵ Leaders held briefings at varying levels of depth, incorporated feedback where it made sense, and offered clear rationales when they couldn’t incorporate feedback.

Invested in internal capacity and treated the model as core infrastructure. Leaders at Virginia’s Department of Education viewed their team’s operational and analytic capacity as key to the cost model’s success, allowing them to update the model regularly. One leader said the team “understands [the model] deeply, … [they] are really good with numbers and data, and are committed to building the use of these tools and the outputs of them into our everyday work.”⁶ Virginia has already embedded pre-K rates in a biannual re-benchmarking process, incorporating changes in staff salaries, benefits, and inflation into the state’s school funding formula. The same expectation for rebenchmarking has not been established for child care rates, but this has been discussed by the General Assembly at several junctures. Should a rebenchmarking expectation be set, the agency team already has the analytic capacity to enact the policy.

Massachusetts Cost Estimation Model

Early education programs in lower-income regions of Massachusetts couldn’t charge as much in tuition as programs in higher-income areas. This meant that when market prices—that is, what parents were paying for care—determined subsidy rates in Massachusetts, reimbursement in those regions was much lower. This exposed geographic inequities across subsidy rates driven by what families were able to pay. State leaders used existing data infrastructure to develop a cost estimation model that accurately reflects the true cost of care for providers across the state.

How Leaders Made It Happen

Balanced fiscal reality and long-term vision. Massachusetts leaders maintain two cost models—one based on current costs and one based on what it would cost with higher wages and greater staffing levels. Using both models, they balance existing funding with a potential future vision of what’s possible. One leader said: “Having a realistic picture is really important, because the dollars that we have are the dollars that we have…we need to make sure that we are benchmarking ourselves against the resources that we have, and the degree to which those [resources] can support programs as they operate right now.”⁷

Treated data infrastructure as foundational. Developing robust cost estimation models depends on having reliable data such as wages, enrollment, tuition, and other program features. Massachusetts was in a strong position: through the Commonwealth Cares for Children (C3) Program, leaders had access to that data for roughly 90 percent of the state’s licensed and funded programs. One leader said that data “is the biggest challenge to these cost models.”⁸ Existing programs with high provider participation are a natural foundation for data collection.

Addressed geographic inequity. Through their examination of program costs across regions, leaders were able to better understand where geographic inequities in reimbursement existed. When they looked more closely, they found costs didn’t vary nearly as much as subsidies did. As one leader explained, programs in lower-income regions “can only charge so much because the market won’t bear a higher price.”⁹ Under the market rate system, that translated directly into lower public reimbursement, even when operating costs were similar. Leaders kept the six geographic regions but collapsed them into three rate tiers, directing a significant share of new investment toward the regions that had been systematically underfunded.

What These Stories Tell Us and a Glimpse at Other Examples

A couple of patterns stand out across these examples:

• Leaders used cost estimation models to envision what it would take to adequately compensate early educators. They put personnel costs first because they knew that a qualified, well-compensated workforce drives quality.
• Both cost models became living tools, regularly updated. The greater challenge has been translating updated cost estimates into actual subsidy increases.

Other Promising Approaches

Centering early educator voice in cost model design. South Carolina to ensure providers heard about engagement opportunities from sources they already trusted.

Building on a statewide cost model at the city and county level. In 2023, King County Best Starts for Kids and the Seattle Department of Education and Early Learning collected data from local providers to specific to their localities, recognizing that providers and families need support beyond what the state offers.

Updating a cost model to ensure equity across the mixed-delivery system. In 2024, Boston Public Schools (BPS) and the Children’s Funding Project to incorporate the Boston Teachers Union salary scale for Universal Pre-K lead teachers in community-based programs, making their wages equal to those of BPS employees.

Cross-Cutting Implementation Challenges

• Aspiration versus fiscal reality – Cost models often identify needs, including adequate compensation, that exceed available resources, demanding sustained legislative advocacy
• Data infrastructure – Accurate models require reliable wage, enrollment, and tuition data from across a locality
• Maintenance – Models become less accurate if not regularly updated

Measures of Progress

• Number of programs participating in the subsidy program, and, if possible, their motivation for doing so
• Educator wages
• Distance between subsidy reimbursement rates and the true cost of care
• Number of children participating in the subsidy program

Tools for Action

• Toolkit for translating this brief’s findings into concrete next steps
• Questions to guide your work
• —a user-friendly, interactive digital tool that helps policymakers, advocates, and leaders explore how cities and states across the country are tackling challenges related to early education