presence of an SFD intervention is captured as a binary covariate. The regression is confined to the sample of PSUs (villages and wards) in which there are non-SFD projects to supply potable water, and so the measure of impact here should pick up the incremental effect of SFD interventions, given that there are already other interventions.

The regression results in Table 5 use only those observations that are in the region of common support, and they weight the observations by the inverse of the propensity scores, as suggested by Imbens (2004, p.16). By this measure, the presence of an SFD piped water project reduces the poverty rate by 0.8 percentage points, an estimate that is significant at the 1% level.

**Table 5.  Estimates of Regression Showing Impact of SFD projects on the Headcount Poverty Rate, Comparing PSUs Having “Mixed” SFD and Other Interventions with PSUs Having only Non-SFD Interventions**

||Coefficient|p-value|
|---|---|---|
|Dependent variable: Headcount poverty rate (%)|||
|**SFD piped water project in place (1=yes, 0=no)**|**-0.81**|**0.00**|
|Households with access to a sewage system in 2004/05 (%)|-1.53|0.00|
|Average household size in 2004/05|0.26|0.07|
|Female-headed households in 2004/05 (% of all households)|1.65|0.08|
|PSU had one or more training programs (yes=1) in 2004/05|-0.16|0.64|
|Illiteracyrate in 2004/05(% illiterate amongthose older than 10)|-0.45|0.01|
|Headcount poverty rate in 2000 (%)|0.07|0.00|
|Female-headed households in 2000 (% of all households)|0.06|0.02|
|Intercept|1.08|0.17|

_Notes._ Observations are weighted by the square root of the inverse of the propensity score (if SFD project is in place) or by square root of the inverse of 1-propensity score (if SFD project is not in place). Adjusted R<sup>2</sup> = 0.168. Data come from 1,290 PSUs (villages and wards) for which data were available and fall within the region of common support (as determined by the propensity score equation – see Table 4). _Source:_ Authors’ calculations based on HIECS and community survey 2004/2005.

The results of the propensity score matching are shown in Table 6. The upper panel uses nearestneighbor matching, and also finds that the poverty rate is 0.8 percentage points lower in villages with SFD projects than in the comparison villages, although the difference is not quite statistically significant. Kernel matching finds that the under-5 mortality rate is about 0.4 percentage points lower in the treatment villages, but here again the difference is not quite statistically significant.

Table 7 displays the results of estimating straightforward impact equations. These use observations from the full data set, and not just for villages in the area of common support, and they include a large number of other variables (which reduces the sample size somewhat, because some values are missing). The results of the common-impact model – given in the middle columns of Table 7 – show that the presence of a SFD piped water project is associated with a 0.5 percentage point reduction in the poverty rate, after controlling for other influences, but the result is not statistically significant. The general-impact model is based on the same variables as used in the common-impact model, but also includes interactions between _Impact Evaluation: Egyptian Social Fund for Development_ _Version of June 29, 2009_ _Page 15 of 36_