The key takeaway from these results is that changing just one cost parameter in an evaluation creates substantial variation in the resulting cost-effectiveness estimates. Figure 8 illustrates this point graphically by plotting the simulated cost-effectiveness estimates alongside those of the learning programs evaluated earlier (see Figure 1), to see how the relative cost-effectiveness ranking of the Balsakhi program would change if exported to these other contexts. The change is dramatic: Whereas the original program was the seventh most cost-effective program, these transplanted programs range from among the most costeffective to the least cost-effective.

This is of course only a partial analysis, varying one parameter, and even this singleparameter comparison relies on strong assumptions. Most notably, this analysis relies on the assumption that in all contexts there would be an adequate supply of volunteers with sufficient education (completed high school) to take advantage of the provided pedagogical materials to deliver out-of-school tutoring to local children (as highlighted in Dhaliwal et al. (2013). This was the case in the area of India were the original Balsakhi remedial tutoring program was tested, but may not necessarily extrapolate to other contexts.

Of course, data on the precise type of teacher from the program of interest may not be available in the target country, perhaps because that kind of teacher has not been contracted previously. An alternative, for example, would be to use data on high school graduate salaries as a proxy for remedial tutor salaries, as most community teachers, such as those in the Balskahi program, are high school graduates. We use data from the Living Standards Measurement Study (World Bank 2014) to calculate average monthly salaries for salaried workers who completed high school (and no further education) and use this to re-compute cost-effectiveness estimates for seven countries across four categories: (i) neighboring countries, (ii) countries with similar GDP per capita, (iii) countries with lower GDP per capita, and (iv) countries with higher GDP per capita. The results are in Table 6. In every case, the cost-effectiveness of the remedial tutoring program is much lower, never more than 30 percent of the cost-effectiveness in India. This consistency across dramatically different environments suggests that back-of-the-envelope cost contextualization may provide very imprecise results. Again, policy makers and advisors would need to calculate the likely local costs with great care in order to infer locally meaningful estimates.

# **Cost Extrapolation as a Function of Program Complexity**

If a policy maker were considering a program for implementation, then the above type of analysis would be most plausible if a program has few cost ingredients. Across the sample of 14 education programs targeted at improving learning outcomes, the number of measured cost ingredients per program varies from 1 to 11, with a median of seven cost ingredients (see Figure 9). Figure 10 demonstrates for the same sample that, as expected, the percentage of cost explained by the largest cost ingredient decreases with the total number of cost 9