New Cost Model Analysis Shines Light on the True Cost of Quality Child Care

Over the past year, a number of news articles have drawn attention to the steep cost of child care, which in the District is now more than three times as expensive as in-state college tuition. Despite the amount spent by parents and a sizeable public investment (the District’s child care subsidy reimbursement rates are among the highest in the country), child care providers often struggle to make ends meet financially and the District continues to lack enough high-quality child care slots to accommodate its rapidly growing population of infants and toddlers. If we as a city are spending so much on child care, then why does it seem that we are getting so little in return? The results from a new cost model analysis of child care in the District, released last week, provide the answer.   


What is a cost model analysis?

Cost modeling is a method of early childhood financial analysis that uses data from local providers to estimate the likely cost of delivering child care services at different levels of quality, compared with the likely revenues a program can bring in. A cost model incorporates the impact of a number of important factors in addition to subsidy reimbursement rates, such as program size, enrollment levels, and the age-groups being served, on a provider’s finances (for more information about cost modelling, check out our series of blog series from last summer: Part 1, Part 2, and Part 3).


What does it cost to provide quality child care?

The District’s cost model analysis, which was led by nationally recognized early childhood expert Louis Stoney, provides evidence of what is, for most child care providers, common knowledge – it is extremely difficult to provide high quality care to low income children and break even. In fact, the analysis found that under the current subsidy reimbursement rates, a small child center that serves low-income infants and toddlers would have a predicted budget shortfall of over $200,000 if they were to meet all the requirements necessary to earn a gold rating on the District’s QRIS.



An unintended consequence of universal Pre-K

The chart above also illustrates how much more expensive it is to provide infant and toddler care than it is for older children- a fact that has significant ramifications for the District. In the past, child care providers often relied on the enrollment of older children to offset the higher costs of serving infants and toddlers. However, the District’s universal Pre-K program has drawn many three- and four-year-olds away from the child care sector, leaving infants and toddlers as the age-group in greatest need of child care services. While the District raised its subsidy reimbursement rate for infants and toddlers in 2013, it was not enough to offset these new financial pressures. During a presentation on the new cost model data for the District, lead researcher Louis Stoney explained that, “as the system works now, there is a direct incentive for providers to close infant and toddler classrooms and open Pre-K classrooms.


How did we get to this point?

To further understand why are our subsidy rates are so far below the cost of providing high quality care, it’s helpful to look at how they’ve been set in the past. Traditionally, state and local governments have used market studies to determine subsidy rates. A market study is a survey of local providers to determine the average price parents pay for child care in a given area. However, the current market rate shows what people are willing to pay for child care, NOT what it costs to provide quality services. Louis Stoney explained that, “the number one predictor of the market rate is the incomes of the families served, not the quality of care provided.”


So we just need to raise subsidy rates, right?

Not exactly. A number of different factors contribute significantly to the cost of providing child care, including what is known as the ‘iron triangle’ of child care finance: sufficient revenues to cover the per-child cost, full fee collection, and full enrollment. According to Louis Stoney, “if a program is not close to fully enrolled every day, in every classroom, and is not able to collect tuition and fees, in full and on-time, it will be very difficult to break even.” In fact, the cost model analyses of District child care programs all showed significant financial losses at 85% enrollment, the industry standard. Child care centers were able to break-even, or take in net revenue, only if they could maintain 98% enrollment.


Louis Stoney also explained that program size is another significant factor that impacts a provider’s financial stability, with small programs much more likely to struggle financially. The cost model analysis showed gold-level centers would need to enroll almost 250 children, including two out-of-school time classrooms to break even. The chart below shows the impact of both enrollment levels and program size on the per child cost of care.




What is the solution?

The data from the District’s cost model analysis can be used by both providers and District agencies to strengthen our child care system. For providers, the high cost of providing care means they will likely have to rethink many of their current business practices to “make it financially” viable to run this type of business. Shared service models are one critical way smaller providers, who are unable to take advantage of economies of scale, have been able to achieve financial sustainability.  Shared service models come in many different forms, but the underlying logic is the same: rather than competing against each other, child care providers with similar needs can share resources and costs to expand their business capacity and professional knowledge. This can include providers partnering to share staff members, the cost of professional development trainings, or a centralized back office to cut down on administrative expenses and help ensure sustainable enrollment levels across multiple programs. The District has already begun developing some shared service models. For example, the new Early Childhood Quality Improvement Network (QIN) funds established child care programs to serve as neighborhood “HUBs,” providing professional development and technical assistance to smaller child care centers and homes.


For policy makers, the data from the cost model analysis provides an opportunity to set the tiered subsidy reimbursement rates to cover the true cost of providing care at different levels of quality. OSSE, the agency that commissioned the cost model analysis, has already indicated that it will use the results to inform new reimbursement rates. However, given the steep cost of providing high-quality child care to infants and toddlers, it will take a substantial investment to bring the current subsidy rates in-line with the actual cost of care. Minimizing the burden of these new costs will require strategic thinking to ensure child care programs are able to maintain full enrollment, ensure full fee collection, and maximize the power of scale. Several policy options, including greater investments in contracted child care slots, steering child care subsidy dollars towards larger programs and supporting shared management models between smaller child care programs would help the District to work towards these goals.      


If we did not know this before, the results from the cost model analysis provide strong evidence that our current child care system is not structured to allow providers to sustainably deliver high-quality care, especially for infants and toddlers. Fortunately, the data from the analysis also provide important insights that can help ensure providers have the resources they need to deliver high-quality child care and succeed as small businesses. This kind of system change is only possible if both government agencies and child care providers use the data to inform policy and practice. Finding the necessary funding and reforming a field that has traditionally been slow to evolve will not be easy tasks, but we can no longer afford to maintain the status quo.