for both years are available. This allows us to examine if trends in the predictor variables during the project period are associated with the outcome after the project ended. For example, the analysis for finance has five coefficients: (i) amount of funding from the government during the project period, (ii) amount of funding from non-governmental organizations during the project period, (iii) amount of funding from parents during the project period, (iv) share of annual expenditure for teacher salaries in 2010, and (v) change in the share of annual expenditure for teacher salaries between 2010 and 2013.

For each outcome measure, we perform five logistic regressions (one for each category of predictors). Within each category of predictors, we are interested in the variables that are most predictive of sustainability (controlling for the other variables in the same category).

In the results below, we present coefficient estimates as average marginal effects (instead of logit or odds ratio). Our average marginal effect is the average of the marginal effects over all the possible values of independent variables. With binary or categorical predictors, marginal effects measure the change in predicted probability for a one-unit change in the predictor. With continuous predictors, marginal effects measure the instantaneous rates of change. When the relationship between the outcome and predictor is non-linear, the marginal effects for continuous predictors do not approximate to the change in outcome associated with a one-unit change in the predictor. For ease of interpretation, we also plot the marginal effects for continuous predictors that are statistically significant.

Finally, we examine the relationship between sustainability and the five categories of predictors jointly. We use a two-step process. In the first step, we estimate the predicted probability ( _p_ -hat) from the regression in equation 1. This results in five predicted probabilities ( _p_ -hat) since we estimate one from each of the five categories. Then, we adjust these predicted probabilities by dividing each by its standard deviation. This adjustment ensures that each _p_ -hat is treated on the same scale. The results are standardized indices of the predictors, where the weights have been chosen such as to maximize the predictive power of the index. In the second step, we use the five adjusted _p_ -hat to jointly predict sustainability. We perform the same logistic regression as equation 1 except β� is now a column of _k=5_ coefficients of the adjusted _p_ -hats. We are interested in the relative magnitude of these coefficients because they indicate the degree to which each predictor category predicts sustainability. This two-step process is performed for each of our two outcomes of sustainability: being open in 2016 and being open with two trained teachers in 2016.

# **V. Results**

The results of regressing sustainability on finance characteristics are presented in Table 3. Project playgroups with a higher proportion of annual expenditure on teacher salaries during the project period were significantly more likely to be open in 2016. Moreover, playgroups that increased the share of annual expenditure on teacher salaries over the course of the project period were significantly more likely to remain open (column 1) and remain open with two trained teachers (column 2).<sup>17</sup>

> 17 In alternative specification we reformulate this to include the total amount of funding from all sources and include dummies for whether or not  funding was received from government, non-government or parents. The results are very similar and available upon request.

13