analysis does not show a statistically significant increase in the rate of women who received any antenatal care or that of women who received four or more consultations. It is important to stress, though, that the rate of women receiving any antenatal care is practically universal. The impact parameter for four or more ANC visits is positive with a p-value of 0.146. With respect to receiving any postnatal care, however, the program is associated with an increase of nine percentage points. The effect is significant at the 1% level.

Table A1 in the appendix presents results of a different model specification in which controls are added to the regressions. Those are women’s characteristics (age, marital status, education, and birth order), household characteristics (education of the head of the household and number of household members), an indicator for the household being more than four kilometers away from the health center and district dummies. About 10 percent of the sample is lost for having missing information for at least one of the controls. However, the estimated treatment coefficients are almost identical in both magnitude and their statistical significance.

The results presented in Table 3 are from an analysis conducted without using sample weights. While they represent an unbiased estimate of the impacts given the experimental design, the impacts might not be representative given the sampling strategy. Table A2 in the appendix presents results for the same regression specifications as in Table 3 but with weighting observations according to the number of households per sector. The weighting causes negligible changes in the estimated parameters.

# **ii. Analysis of the Rwanda Demographic and Health Survey 2014-15 Data**

Unlike the CPBF impact evaluation survey that interviewed women with the most recent birth in each visited village, the RDHS 2014-15 collected detailed information on pregnancies in the five years preceding the survey. Since the RDHS cluster locations are reported with error, we are uncertain about the sector to which each cluster belongs and therefore about exposure to treatment. In our regressions, we use two classifications of exposure to the demand-side in-kind transfer program. In the first, we regress the outcome indicators on a binary variable for the location of the cluster being misplaced in the area of a treatment sector. For a second approach, we calculated the probabilities that each cluster is in a treatment sector. Using the displacement distances as radiuses, we draw a circle around each misplaced cluster and calculate what percentage of the circle covers a treated sector. We then regress the outcome indicators on treatment probability. The sample sizes for these regressions are larger as we can include all 304 cluster in the 19 districts and not only the 221 clusters misplaced within the study sectors. In both approaches we interact treatment (either the binary variable or treatment probabilities) with year of birth. We control for the age of the woman at the time of birth, her marital status, education level, birth rank, an indicator for a rural DHS cluster and district dummies. The RDHS sample weights are used and standard errors are clustered according to the DHS cluster.<sup>11</sup> The results of this analysis should be taken as suggestive only. First, the treatment coefficients represent underestimates of the actual impacts. Under the assumption that the intervention has a non-negative

> 11 Clustering standard errors at the sector level results in negligible difference in results, as the majority of clusters do not have other clusters displaced in the same sector.

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