weighted regressions of the Raven score on age is presented in Figure 18, while Figure 19 presents the linear fit for both the treatment and control villages. These figures suggest three facts. First, the scores seem to be on average one point higher at each age in the endline compared to the baseline. Second, the slope of progress of Raven scores seems identical between the baseline and the endline. There is on average an increase of 0.4 points per year, until aged 12, where the increase slows. Third, there are no striking visual differences in the age pattern of Raven scores between control and treated villages, nor in their evolution.

In this section, we analyze whether the program had heterogeneous effects by child ability. To test whether high-ability children benefitted more from the program than the low ability, we add an interaction term between the treatment and the continuous Raven score variable, measured at baseline. If the interaction term is positive, then it would indicate that higher ability children would have benefitted more from the program. We analyze whether this is the case for the three variables of interest: enrollment, literacy, and mathematics. We present the results in Table 8.

The regressions presented in Table 8 show that the program did not have any heterogeneous effect. Two main lessons can be drawn. First, higher ability children are more likely to be enrolled in school and have higher test scores. The inclusion of the child’s ability is however not affecting the magnitude of treatment effect and the interaction term (treatment*ability) is not significant on any of the three outcomes of interest. It seems that the treatment did not have a heterogeneous effect by children.

Finally, we analyze whether ever-been-to-school children have higher test scores in mathematics, and whether this is due to the selection of high ability children in school. In Figure 20, we show that at the baseline, schooled children have higher score levels and progression over their age compared to not schooled children. We see no differences between treated and control villages. This relationship holds in the endline. However, at the time of the endline, we observe that the unschooled children in the treated group have slightly lower levels of mathematics compared to control group. Visually, this suggests that perhaps it is the lowest ability children which were kept unschooled in the treated areas, or put another way, that the treatment effects on increasing enrollment with cash transfers were more focused on higher ability children. The regression results presented in Table 8 do not, however, confirm this suggestion.

# **IV. Results on Vaccines**

One of the program’s objectives was to increase vaccination rates. Vaccinations are free and can be done at primary health centers when there is supply. Data on vaccination of infants shows that in 2012, 78 percent of children aged 12 months in Guinea had a vaccination card. This percentage had been increasing steadily. We used the 2012 ELEP household survey that is representative of the population, and we find that vaccination rates increased from 70 to 78 percent of children between 2007 and 2012. This is shown in Figure 21, which we created from the 2012 ELEP household survey.

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