In general, different kinds of information can be used to calculate shadow prices. One is the market price of various resources in the early childhood context, such as wages and benefits for teachers. If the market is distorted, as, for example, when a preschool program does not pay market price for the use of school buildings, an adjustment might be made by calculating opportunity costs. Economists might also use indirect methods to calculate values for which there are no clear market prices (missing markets). For example, they might calculate the statistical value of a life year, or infer the contingent value—the amount people say (usually in response to survey questions) they would be willing to pay for a resource that does not have a market value. In the context of early childhood, that might mean calculating opportunity costs for volunteer time or benefits of improved educational outcomes or a reduction in crime.

Theoretically, the ideal way to conduct a benefit-cost analysis for early childhood interventions would be to use a long-term random assignment experiment, much like the Perry Preschool Project, “except bigger and perhaps more geographically representative,” Weimer explained. These data would make it possible to predict the impact of other similar programs, using shadow prices to estimate earnings changes, quality of life changes, willingness to pay for various benefits, and so forth. However, Weimer suggested that this model is not actually ideal from a public policy perspective. Long-term studies are expensive, so sample sizes tend to be small, and such studies are relatively rare. Researchers often encounter problems with attrition and have difficulty accurately taking into account long-term shifts in the context—such as changes in the sorts of alternatives that are available to the program being studied. And, of course, results are often delayed.

Weimer offered several alternative approaches. First, work could be done to develop better shadow prices for the early childhood context. He pointed out, for example, that the RAND Health Insurance Experiment (Manning et al., 1987) provided a way of developing estimates of the price elasticity of demand for health care. Existing early childhood studies provide observational data that could be used in a similar fashion to link program effects to outcomes, such as school completion, for which shadow prices may be more readily available. More work is also needed in the development of shadow prices for willingness to pay for societal benefits, such as reducing poverty, using contingent valuation techniques.

Another promising approach is to improve strategies for linking observable outcomes to a wider array of social benefits (Weimer and Vining, 2009). Decades ago, Haveman and Wolfe (1984) used a household utility approach to estimate the nonlabor market benefits of schooling (such as reduction in crime, efficiency of consumption). They calculated a monetary value for such outcomes as children’s cognitive develop-



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