Cost estimation models are tools that can assist policy makers and stakeholders to explore the costs and distributional effects of potential policy changes. In this report, the policies examined are those intended to improve access to high-quality early care and education (ECE) opportunities. Cost estimation models address two types of costs: the costs to providers of offering early care and education and the subsidy costs to public and private entities supporting early care and education. Both types of cost are relevant to understanding the impact of policy options.
Different approaches may be appropriate for various categories of audience or user, such as professional policy or budget analysts as opposed to stakeholders, program developers, or administrators. In some cases, it is necessary to have a “general purpose” model that considers all age groups (e.g., infants, toddlers, prekindergartners) and all types of early care and education (center based, prekindergarten, or home based). In other cases, a pressing policy issue may be best addressed by a model specifically designed for a single age group or ECE type (such as promoting access of all 4-year-olds to prekindergarten). An important constraint on cost estimation modeling is the availability of reliable and generalizable data on the costs of certain elements. For example, although facilities are necessary, few data are available on the status of physical facilities and the costs required to rehabilitate or replace them.
This appendix first discusses the key attributes of cost estimation models and considerations for desirable outputs of models. It then briefly describes some of the currently available cost estimation models.
ATTRIBUTES OF COST ESTIMATION MODELS
Cost estimation models vary in their scope and attributes. For example, models may be limited in their geographic area or the range of services included in the estimate; they may also differ in presenting dynamic or static estimates. A static model is likely to underestimate costs as families shift their patterns of utilization when the policies being modeled enhance quality and financial accessibility of ECE options. In other words, families may use more hours and more expensive types of ECE in response to those policies. A dynamic model can reflect the changes in utilization likely to result from the specified quality improvements or changes in prices, facilities, and locations and factor these changes into the estimated costs.
The target geographic areas specified are an important and variable feature of estimation models. Some models refer to a large area, such as a nation or a state; others specify local jurisdictions, such as a city, county, or school district. The focus may even be further narrowed to individual program sites. Consideration of larger areas allows more averaging of data and findings, while assessing smaller areas or individual programs may require more detailed data and analysis. It may also be useful to nest smaller areas within larger ones, such as showing costs for a state, along with costs for each county or school district within the state.
The range of services included is another distinction among models. Some programs or policies require inclusion of ancillary or comprehensive services such as physical health and developmental screening, family supports, and referral to housing or employment services. These services can be covered either by augmenting the staffing specified for ECE programs or treating them as a system-level cost in the ECE estimates.
Finally, an important attribute of cost estimation models is the user-friendliness of the model. Cost estimation model developers balance developing a tool with sufficient complexity to produce accurate results that reflect the realities of service delivery systems against the objective of ensuring the model is accessible to its user-audience. Models may also be developed to allow easy cross-checking and updating of data or key cost inputs (e.g., number of children in a geographic area or salary and benefits levels for specified staffing positions).
OUTPUTS OF COST ESTIMATION MODELS
Outputs can be designed to answer questions such as: what does a program or intervention cost, who benefits from it, and who pays for it? Cost estimation models may also provide direct comparisons of the options being considered—compared both to current costs and to each other.
Two general output-related considerations affect the utility of models: First, can the user of the model vary the outputs to suit the particular policy context? Second, can projected costs be broken down into components that help inform the policy discussion? For example, public agencies may desire models that estimate the numbers of staff who will have to be trained, recruited, retained, and supported to achieve a high-quality ECE delivery system. The relevant measure of workforce numbers may be full-time equivalents (FTEs) or staff slots, or the desired output may be the required number of individuals, depending upon the purpose of the estimate. Furthermore, allowing the user to specify the time unit can increase the value of the model, since some states reimburse providers on the basis of hourly costs and some on weekly or monthly costs, while others write annual service contracts.
A major challenge for model outputs relates to the many possible ways to divide up cost estimates. For some purposes, such as considering fiscal feasibility, the total cost of a combination of quality standards and financial assistance policies is sufficient. For other purposes, such as refining quality standards, partitioning costs into different categories is essential. Models should have the capacity to divide costs into major elements—such as personnel or nonpersonnel, wages and benefits, quality enhancement and workforce support including professional development, and facilities—in order to identify the contribution of each of these components to the cost of high quality. Additionally, providing the costs of offering higher-quality early care and education in a full range of settings is necessary to consider the standards for each setting and the potential implied incentives for families to select among types of ECE services. Similarly, understanding how cost varies among different child-age groups is useful for determining the implications of different staffing standards and the potential costs to different groups of families.
Public policy analysis requires that the model estimate the costs to public or private entities of assisting families to afford higher-quality ECE options by distinguishing between direct assistance to families through subsidies and indirect assistance through financial support of provider entities. In addition to estimating the likely fiscal feasibility of different standards, such cost estimates shed light on how different financing mechanisms and assistance policies affect ECE affordability for families. Further, to compare different financing mechanisms, it is desirable that the cost of subsidies provided to families or providers be partitioned into such policy-relevant categories as different family income categories, geographic areas, or family characteristics (e.g., family structure, employment status).
Different agencies within and across federal, state, and local jurisdictions use various budgeting categories. In general, models that derive costs from detailed components and allow aggregation into an array of
user-specified categories are most useful. Such models allow the greatest flexibility for components to be added in variable ways to match diverse budget structures and to provide outputs categorized by desired components.
This wide range of output requirements on cost estimates may overwhelm the user of the model with details. One approach is to “unfold” different levels of detail, depending upon the needs of the user. Thus, one tab in a worksheet may display total dollar costs and personnel requirements. A second tab might break these totals into components such as child-age groups, ECE types, and personnel versus nonpersonnel costs. A third tab might show detailed costs by staff category, salaries versus benefits, and different types of nonpersonnel costs.
COST ESTIMATOR MODELS
This section briefly describes the following examples of currently available cost estimator models:
- Human Services Policy Center Cost Simulation
- Department of Defense Education Activity (DoDEA) Cost and Staffing Calculator
- Provider Cost of Quality Calculator (PCQC)
- Center for Benefit-Cost Studies in Education (CBCSE) Cost Tool Kit
- Center on Enhancing Early Learning Outcomes (CEELO) Cost of Preschool Quality (CPQ) Tool
- Quality Rating & Improvement System (QRIS) Cost Estimation Model
- The Standardized Early Childhood Development Costing Tool (SECT)
- Professional Development System Cost Analysis Tool
Human Services Policy Center Cost Simulation
The Human Services Policy Center Cost Simulation is a model that estimates the cost of making high-quality early care and education affordable for families of all children from birth to 5 years of age. A database developed from a representative household survey of ECE utilization is used for the calculations. The model allows the user to stipulate a range of parameters based on policy specifications for high-quality early care and education, such as staff qualifications and compensation, ECE type (center; home-based; or friends, family, neighbors), educator-to-child ratio, duration of programs (e.g., hours per day), utilization rates adjusted for
a defined geographical area, and infrastructure elements. The estimates provide hourly cost differentiated by child’s age and ECE type. A cost of high-quality early care and education for each child is then calculated by applying the hourly cost to the number of hours of each ECE type used by that child. The tool also provides an estimate of the subsidy available for each child, based on specified policies and family characteristics (Brandon, 2004; Brandon, Kagan, and Joesch, 2000).
DoDEA Cost and Staffing Calculator
The DoDEA Cost and Staffing Calculator is an Excel-based tool developed to enable comparisons among various policy components of early childhood education for 4-year-olds at different military installations or regions. The quality components used in the calculator are drawn from a comprehensive search of the scientific literature and include educator and child interactions, educator qualifications, educator professional development, class size and educator-to-child ratio, curriculum, child assessment, family engagement, and administrator qualifications and support. Policy parameters influencing access to prekindergarten, operating hours and days, and quality of service are entered into the calculator. Outcomes consist of staffing requirements and gross estimates of the costs associated with providing early care and education. These outcomes are reported both as total cost and cost per student for both DoDEA schools and Child Development Centers.1 The tool enables the user to account for policy inputs and cost and staffing outputs at a broad level, or to study each of those in more detail. Personnel requirements are the main focus of the analysis (Elicker, Brandon, and MacDermid, 2016).
The PCQC is a tool that estimates the cost of high-quality ECE programs based on data supplied by providers. The PCQC can be accessed through the website of the National Center on Child Care Quality Improvement.2 With this tool, the costs of delivering high-quality ECE services can be compared to the funding available for programs. This comparison can help to plan for the resources needed for the ECE system.
The tool allows flexibility in the quality of the program and the provider category (centers, schools, or family childcare homes) examined to estimate the costs of specific types of programs. The PCQC can be tied to components of a state’s QRIS. Specific policy requirements, such as
1 Child Development Centers are ECE centers on military installations.
staff-to-child ratios, class size, and subsidy and tuition rates, can be entered into the calculations. Information from the national and state levels is also incorporated in the tool; for example, Child and Adult Care Food Program rates and Bureau of Labor Statistics state wage estimates by occupation are included. Customizing the tool enables profiles to be developed for all provider types with varying combinations of child ages, family incomes, and other features.
CBCSE Cost Tool Kit
The CBCSE Cost Tool Kit, known as CostOut, is a tool used to estimate the cost or cost-effectiveness of education or social programs. It is based on the “ingredients method” developed by CBCSE’s director, Henry Levin. Included in the tool kit is a worksheet that allows users to list the program ingredients required for an intervention and allocate costs to each ingredient. Prices of frequently used components can be found in the “Database of Educational Resource Prices,” which is provided with the kit. Adjustments for inflation, geographical location, and, for multiyear programs, the time of investment are provided by the tool when needed. CostOut estimates full costs (total costs) and per-participant costs of an intervention and can also provide cost-effectiveness comparisons if alternative interventions are being considered (Caronongan et al., 2016; Teachers College Newsroom, 2015).
CEELO CPQ Tool
The CPQ tool, which is available through the CEELO website, uses an Excel platform and is designed for states or districts to project the cost of expanding high-quality prekindergarten specifically for 3- and 4-year olds.3 The quality settings used in the tool are based on the 10 National Institute for Early Education Research quality standards4 and the requirements of the Preschool Development Grant program. However, the CPQ tool provides the ability to modify these settings so that states can estimate the cost of different approaches to delivering services. It also has the flexibility to change information entered into the tool based on state and local data, including the population being served, program components (e.g., length of day, class size), and expenses (primarily educator salary). In addition, the tool can specify different combinations of providers among public school, private providers, and Head Start programs. With support from CEELO, the CPQ tool can inform states about the extent to which a current program
4 See http://nieer.org/wp-content/uploads/2017/05/Benchmarks.jpg [October 2017].
could be expanded using existing standards, the amount of funding necessary to raise standards, and the estimated costs of proposed state policies (Rickus, Barnett, and Nores, 2016).
QRIS Cost Estimation Model
The QRIS Cost Estimation Model is available on the National Center on Child Care Quality Improvement website.5 The user enters as input available data, including the costs associated with quality assessment, monitoring, and administration; professional development; technical assistance; financial incentives; communication for public awareness; facility improvements; and system evaluation. The outcome of the calculations is a determination of the potential costs of implementing a QRIS (Caronongan et al., 2016).
SECT is an Excel-based costing tool designed to provide methodological consistency when estimating costs associated with ECE programs. The Center for Universal Education at the Brookings Institution partnered with the Strategic Impact Evaluation Fund at the World Bank to develop SECT.
Cost data entered into the tool can be sorted into three main categories: overhead costs, direct costs, and imputed costs. Although SECT includes a list of common ECE components, it can be modified to incorporate interventions used in the ECE programs of the user. Key components to consider when doing the analysis include services provided, program frequency and duration, staff-to-student ratios and staff compensation, staff supervision and professional development, geography, delivery setting, and size of the program. The data can be analyzed for ECE-specified line items, giving the user the capability to track types of spending, such as staff, training, and equipment costs, across a variety of programs. The flexibility of SECT allows for input of data from multiple service providers and for inclusion of publicly and privately funded elements. Both scale-up costs and unit costs can be estimated using SECT (Gustafsson-Wright, Boggild-Jones, and Gardiner, 2017).
Professional Development System Cost Analysis Tool
The Professional Development System Cost Analysis Tool was developed by the Office of Child Care and the Office of Head Start (both within the Administration for Children and Families) to assist states and territories
5 See https://cemocc.icfwebservices.com/index.cfm?do=viewlogin [June 2018].
in comprehending the current costs and target resources associated with professional development systems and other initiatives that contribute to highly qualified ECE professionals.6 It produces “data analyses related to workforce qualifications and professional development investments, defines and categorizes workforce investments, and estimates annual costs to advance the workforce” competence and skills (Office of Child Care, 2016c).
The user must gather and enter the number of ECE practitioners, by type, in the workforce, which is used to generate various estimates of public and private shares of annual costs of professional development. Further information on the sectors (e.g., childcare, Head Start, public prekindergarten), type of early care and education provided, roles of the practitioners in the workforce, ages of the children served, and educational credentials will produce more detailed results and additional reports. There are four steps involved in the use of the tool:
- Enter demographics of the workforce, including baseline estimates of practitioners’ qualifications.
- Identify the qualifications or educational milestones of the workforce desired by the states or territories.
- Enter and categorize specifics of current professional development programs and investments.
- Examine various estimates of public and private shares of annual costs that have been developed by the tool to move toward the educational milestones desired by the state or territory.
The Professional Development System Cost Analysis Tool can provide system leaders with information on the present status of their workforce and estimates of the resources required to increase the quality of the current professional development system (Reidt-Parker, 2015).