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Understanding the costs of an intervention is of course a prerequisite for performing CE and BC analyses. In itself, cost analysis is also valuable. It allows us to perform a cost study and a cost feasibility analysis (we provide examples of these in subsequent chapters). Even before that, however, cost analysis helps the analyst learn about the intervention in two key respects. These are the main motives for performing a cost analysis per se.

First, cost analysis is a way of describing an intervention. Many educational interventions are complex or multifaceted; by providing details on the resources, we are explaining what is required for the intervention. Information on costs can indicate the quality or intensity of a program. For example, the number of counselors per student may indicate the quality of counseling programs, as would data on the number of days of training required for counselors before the program begins operation. As well, the cost per student for a program will reflect the dosage of the treatment each student receives. For example, the annual cost of a mentoring program might be $500, but the cost per student will depend on how many years each student participates.

Information on costs sheds light on the types of resources needed to implement a given program. Some interventions may require a substantial commitment of in-kind resources; these include volunteer-intensive youth supports such as Big Brothers Big Sisters of America and elementary school literacy programs such as Time to Read (Grossman & Tierney, 1998; Miller & Connolly, 2012). Other interventions may require significant capital investments; for example, massive open online courses (MOOCs) may necessitate investment in an expensive technology infrastructure before any students have enrolled (Bowen, Chingos, Lack, & Nygren, 2014). For some interventions, there may be reorganizational costs that are not obvious. For example, implementation of Success for All requires cross-grade grouping—that is, putting students from different grades in the same classroom; this grouping may have implications for how the school day is organized (Quint, Zhu, Balu, Rappaport, & DeLaurentis, 2015). Finally, some programs may have substantial induced costs such that the biggest cost item is not the intervention itself but the extra resources that flow from implementing the program. A series of examples, showing how costs change as students progress through community college at different rates, is modeled by Belfield, Crosta, and Jenkins (2014).

Relatedly, cost analysis can be applied to check for fidelity of implementation of an intervention. For example, high-quality preschool programs are expected to have a trained lead teacher; the cost analyst can verify if sufficient funds were allocated to ensure that this is affordable. Subject to the caveats just noted, the analyst can compare actual resource use with an ex ante budget to see whether the program resources match the intended design.

The second intrinsic value of cost analysis is that it helps the researcher with prediction and modeling. It is important to recognize that costs are estimated, just as effects are in an impact evaluation. The researcher is estimating an expectation based on the sample units of the intervention. In some cases, the analyst will want to estimate the actual costs of the program as it was delivered in a particular setting for a specific group of students. It is more likely that the analyst will prefer to estimate expected costs—that is, the best prediction of what the program will cost if implemented again. These two estimates will be different: For expected costs, the analyst will use actual ingredients but general prices; for actual costs, the analyst will apply the actual (or as close as possible) prices each site paid for a particular ingredient. This latter approach is similar to a budget approach and so must be applied cautiously.

The use of expected costs allows the analyst to more accurately model costs. One concern for education research is how to take a successful small or pilot intervention and implement it at scale with the same level of success (Quint, Bloom, Black, Stephens, & Akey, 2005). For instance, the policymaker might mandate a class size reduction policy such that all classes must be 10% smaller. What resources would be required to implement such a mandate? It may require proportionately (10% extra) teachers and classroom space but only a smaller proportion (less than 10% extra) of administrative faculty and school facilities. With information on the costs of reducing class size by various proportions, it is possible to predict average cost as the intervention expands (i.e., economies of scale). For example, in their analysis of a schoolwide positive behavioral support intervention, Blonigen and colleagues (2008) estimated significantly lower costs for districts with 10 schools relative to districts with just one school. Deming, Goldin, Katz, and Yuchtman (2016) showed the use of online learning in higher education can shift the average cost function downward—that is, lower the cost per student.

Cost information can also be used to model different versions of the intervention. With data on how programs vary in their resource use, the analyst can see if there are alternative mixes that yield a similar cost per participant. For example, a preschool program might be able to hire four different levels of instructor (e.g., master teacher, lead teacher, adjunct, or teacher aide). Cost analysis can help determine how these instructor types can be combined in a way that would yield a given cost per preschooler. For a thorough example using cost functions for child care, see Blau and Mocan (2006).

Finally, information on the distribution of financing of resources across stakeholder groups is useful for policy modeling. Typically, costs are distributed among various social stakeholders: These might include the federal government, school districts, schools, parents, local businesses, philanthropic agencies, and so on. By understanding the distribution of the financing of costs, we are in a better position to analyze how stakeholders might support or oppose an educational program. For example, the economic value of college is typically found to be very high, prompting questions as to why students do not invest more (Avery & Turner, 2012). But if this value is shared across the student and society, then the student may not have an incentive to stay in college for longer (Trostel, 2010). Alternatively, an intervention may make economic sense to a school district because it does not have to fully fund the intervention: With matched federal funding, a program need only be modestly effective to be worthwhile to the school district.