**Table 1.** Pre-program differences between households residing in project areas and others, 2013

||M<br>|ean<br>|Differenc<br>|e in Means<br>|
|---|---|---|---|---|
|Variable|Control|Treatment|T-Statistic|P-value|
||(1)|(2)|(3)|(4)|
|Access to electricity|0.032|0.186|-9.417|0.000|
|ESCOM present in village|0.218|0.387|-6.738|0.000|
|Amount paid for electricity|10.749|14.964|-0.981|0.328|
|Regular blackouts|0.833|0.945|-1.797|0.074|
|Use electricity for lighting|0.030|0.186|-9.541|0.000|
|Use electricity for cooking|0.000|0.043|-5.409|0.000|
|Urban|0.005|0.418|-21.350|0.000|
|Distance to road|10.252|5.860|9.093|0.000|
|Non-permanent dwelling|0.503|0.271|9.419|0.000|
|Grass roof|0.725|0.454|10.888|0.000|
|Dwelling owned by household|0.892|0.740|7.526|0.000|
|Household size|5.163|5.254|-0.760|0.447|
|Male head|0.723|0.777|-2.383|0.017|
|Secondary education (head)|0.150|0.328|-8.016|0.000|
|Age (head)|44.065|43.681|0.468|0.640|
|Observations (households)|658|822|||

_Notes:_ Numbers in columns (1) and (2) are raw means. The means in the rows “Amount paid for electricity”, and “Regular blackouts” are based on households that have electricity in their dwelling.

_Source_ : Authors’ analysis using data from the Living Standards Measurement Study (LSMS).

Table 2 presents the results of the DiD regressions specified in equation (1) for three different outcome variables capturing access to electricity, amount paid for electricity, and regular blackouts. In columns (1), (4), and (7), the respective outcome variable is regressed on the time indicator (pre or post program), treatment indicator, and the interaction term between the time and treatment indicators. The specifications reported in columns (2), (5), and (8) include additional control variables. The regressions in columns (3), (6), and (9) further add district fixed effects (FE).

The interaction term ( _Post × Treat)_ is statistically significant in most of the specifications in columns (1) to (3) and (7) to (9) in Table 2, indicating that the project was associated with an increase in electricity access and a decrease in the reported occurrence of regular blackouts. At the same time, the insignificant coefficients of the interaction term in columns (4) to (6) suggest that the project did not cause households who were living in project areas to pay more for their electricity.

According to the results in columns (1) to (3), the change in the share of households with access to electricity increased by 5.7 to 5.9 percentage points more in areas with project components than in areas without project components. The coefficient (0.057) of the interaction term ( _Post × Treat)_ in column (1) is statistically significant at the 1-percent significance level. Once the control variables are added in column (2), the coefficient increases to 0.059 and remains highly significant. The result is also robust to the inclusion of district fixed effects (as reported in column 3) and to clustering standard errors at the district or stratum level (not reported here).<sup>12</sup>

> 12 In the LSMS dataset, stratum is defined based on region and a dummy for rural areas. There are three regions in Malawi (North, Center, South) so that stratum takes six different values.

10