covered by the supplemental survey. At the community level, information was also available from the 2000 census, and this was an important source of pre-determined variables for use in estimating the propensity score equation.

# **Results 1: The Example of Potable Water**

To illustrate the approach that we take to measuring the impacts of SFD programs, we first present in some detail the results for potable water projects, comparing villages that have both SFD and other (“nonSFD”) potable water interventions with villages that only have non-SFD interventions; this is the C vs. E comparison, to use our terminology from the previous section. This allows us to discuss both the methodological and practical issues involved in our estimations. A complete summary of the results for the other SFD programs and comparisons is given in the subsequent section of the paper.

**_Propensity Score Matching._** The first step here is to estimate a propensity score equation that satisfies the balancing property.  Our procedure was to estimate a probit  assignment model, check for balance, and then adjust the model until balance was achieved.  The model whose estimates are shown in Table 4 is “balanced”, and it is also broadly reasonable: SFD potable water projects were more likely to occur in areas where families were larger, the poverty rate higher, and access to sewers was lower.  The region of common support stretches from 0.012 to 0.725, which includes all the SFD project areas and 1,044 of the 1,089 non-SFD areas, and should provide an adequate basis for subsequent matching.

**Table 4.  Estimate of Propensity Score Equation, Potable Water Projects, Comparing “Mixed” SFD and** **<u>Other Interventions with Non-SFD Interventions</u>**

||Coefficient|Standard Error|
|---|---|---|
|Dependent variable: =1 if village has SFD piped water project, =0 otherwise|||
|Households with access to a sewage system in 2004/05 (%)|-0.169|0.013|
|Average household size in 2004/05|0.251|0.007|
|Female-headed households in 2004/05 (% of all households)|0.577|0.051|
|PSU had one or more training programs (yes=1) in 2004/05|-0.304|0.014|
|Illiteracy rate in 2004/05 (%)|-0.243|0.007|
|Headcount poverty rate in 2000 (%)|0.0015|0.0003|
|Female-headed households in 2000 (% of all households)|0.033|0.001|
|Intercept|-2.548|0.040|

_Notes._ Probit equation; pseudo R<sup>~~2~~</sup> = 0.082. Data come from 1,291 PSUs (villages and wards), of which 202 have SFD and other interventions, and 1,089 have non-SFD interventions only. The regression was weighted by population size. The region of common support is [0.012, 0.725] and includes all the PSUs with SFD and other interventions and 1,044 of the PSUs with nonSFD interventions. _Source:_ Authors’ calculations based on HIECS and community survey 2004/2005.

In Tables 5-7 we present a number of estimates of the effect of SFD interventions on the headcount poverty rate, which is one of the long-term impacts of most interest. The first measure is based on a regression equation in which the headcount poverty rate in a village is the dependent variable, and the _Impact Evaluation: Egyptian Social Fund for Development_ _Version of June 29, 2009_ _Page 14 of 36_