Table 4 reports the results of the spatial DiD regression model specified in equation (2). The dependent variable in all columns is the nighttime radiance in each grid cell measured using data from satellite images. In columns (1) and (2), a simple DiD specification based on only two years (pre and post program) is reported (where the years correspond to those of the household-survey data used for estimating the results in Tables 2 and 3). In columns (3) and (4) of Table 4, the regression model with all available years (2012 to 2020) as specified in equation (2) is reported. For each model, results for two different treatment indicators are reported. The treatment indicator in columns (1) and (3) equals one for the grid cells corresponding to project areas at the most disaggregated level of information about project locations, which is the subdistrict/city level (administrative level 3). The treatment indicator in columns (2) and (4) equals one for all grid cells belonging to the next higher level of administrative division (administrative level 2, i.e. district) of each project location, to capture potential spillover effects on the area surrounding each project location.

According to the results in column (1) in Table 4, project areas (defined at the administrative level 3) experienced an increase in nighttime radiance between the years 2013 and 2019 that was 0.21 units larger than the increase in non-project areas (from the baseline of 1.64 in 2013 in treated areas). When all years between 2012 and 2020 are considered in column (3), the coefficient decreases to 0.048 but remains statistically significant at the 10-percent significance level. In addition, the significant results in columns (2) and (4) suggest that the project also had positive effects on radiance in areas around the cities (or subdivisions) in which the project components were located.

Various studies have shown that remote-sensing data can give rise to useful proxies of energy-related outcomes such as energy production, energy consumption, and carbon emissions (see Section I). The fact that our results from the spatial DiD analysis are consistent with the results obtained from national household survey data suggests that higher nighttime radiance might also serve as a proxy for higher electricity access and, to some extent, even the reliability of electricity (i.e., a reduction in the frequency of blackouts), at least in countries at a similar stage of development as Malawi.

Overall, the results are consistent with the view that the project contributed to improvements in EE on both the supply side and the demand side of electricity in Malawi. The literature has identified several ways in which higher electricity access and quality are linked to improvements in EE. On the supply side, a reduction in blackouts is generally associated with a reduction of electricity losses in the grid (the project’s ICR estimates that these losses were reduced from 25% at baseline to 17% at the end of the project). A similar mechanism applies to the demand side (e.g., a fridge or water boiler interrupted by a blackout must restart its cooling or boiling once electricity has been restored, so that part of the generated energy is lost). In addition, increased access to electricity can help to improve demand-side EE given that households without electricity in their dwelling must rely on alternative, less efficient sources of energy for things like lighting and heating, that could otherwise be performed by using electricity.

The results are also consistent with existing evidence of an (often overlooked) synergy between EE and energy access, according to which improvements in EE constitute a channel to also expand access to electricity (Can et al. 2018; Dagnachew et al. 2018). These studies argue that improvements in EE can help to facilitate the expansion of electricity access. This applies especially to areas where the bottleneck to wider access to electricity is not the cost of connecting more clients to the grid, but limited electricity generation capacity. According to this view, efforts that increase EE among existing clients essentially serve as an energy generation system by increasing the amount of energy that is available for new clients (or offsetting the additional generation capacity that would have otherwise been needed to match increases in the demand of existing clients if these efforts were not implemented). The fact that our findings in Malawi show that a combination of supply-side interventions (including grid expansion) and demand-side measures targeted at increasing EE was associated with a statistically significant increase in electricity access seems to support the existing evidence in this context.

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