Complicating the story further is that program costs may also vary substantially within countries. We examine a single cost element, transportation cost per school or facilitation visit (standardized in 2011 USD), for four different programs implemented in Kenya’s Western province; while variation is much lower than that across countries, it is still significant, with transportation for a visit in the Scholarships, Textbooks, or Uniforms programs costing $32 (Kremer, Miguel, & Thornton 2009; Glewwe, Kremer, and Moulin 2009; and Evans, Kremer, and Ngatia 2008, respectively), almost 20 percent more than under the Extra Teachers and Streaming program (Duflo, Dupas, and Kremer 2012), as seen in Figure 7. This could be explained by differences in transport infrastructure even over small distances, by differing levels of school density (which cause variation in the average distance a facilitator has to travel to reach a school), or by the form of transportation used. This variation within country has also been demonstrated in high-income contexts: Levin et al. (2012) compare the same education program implemented in five sites across the United States and find large variation in the cost per student across sites, with the most expensive site costing almost 60% more than the least expensive site.<sup>9</sup> How much does this variation in cost affect estimates of cost-effectiveness? Consider the remedial education program implemented by the non-governmental organization Pratham in India and evaluated by Banerjee et al. (2007). The intervention hired young women (“Balsakhis”) from the community to work with low-achieving children in urban India outside of the classroom for two hours per day. To illustrate the impact of variation in costs on cost-effectiveness estimates, we change just one cost ingredient, updating this cost for a range of different countries to calculate what the cost-effectiveness of the same program would be in these other countries. This is a version of “parameter variation sensitivity testing,” recommended by Boardman et al. (2011). Obviously the variation captured by this exercise significantly underestimates the actual variation that would take place if all cost ingredients were updated across settings: This remedial education program has five other cost elements, making up 40 percent of the program’s cost. Still, the Balsakhi salaries were the single largest cost component, accounting for 60 percent of the program’s cost. We recalculate the cost-effectiveness of the program using community or contract teacher salaries from a sample of seven countries for which these data were available from World Bank reports (World Bank 2012; World Bank 2013; World Bank 2014). All other cost components as well as the impact estimates are held constant.

Table 5 presents the results of this analysis. We observe substantial sensitivity of costeffectiveness to the size of community or contract teacher salaries, with the estimated costeffectiveness in Niger being almost 50 percent higher than that of the original program, for example, while in Nepal it is estimated that the intervention would be only one-tenth as costeffective as the original. Adapting cost-effectiveness estimates using local costs is essential.

> 9 Specifically, the most expensive site for the Talent Search program (intended to reduce high school dropouts) had an annual cost per student of US$670, and the least expensive site had an annual cost of US$420.

8