benefitting households living in (or nearby) project areas relatively more than households residing in nonproject areas. Given that the allocation of project locations was not determined randomly, a simple (ordinary least squares) regression of household outcomes on the corresponding treatment indicator would clearly suffer from endogeneity, since the treatment indicator would be correlated with (unobservable) factors that also tend to affect the considered outcomes. The DiD approach helps to circumvent this issue, based on additional assumptions which we discuss in detail below.<sup>4</sup> The analysis shows that a combination of demand-side and supply-side interventions was associated with a statistically significant increase in electricity access, a decrease in the reported frequency of blackouts, and a switch from traditional fuels to electricity as the main source of energy for lighting (albeit not for cooking).<sup>5</sup> These results are based both on evidence obtained from national household survey data and from the fact that nighttime radiance (as measured by satellite images) increased relatively more in grid cells belonging to areas with project components than in non-project areas. There is no evidence that the project caused households to pay more for electricity (e.g., due to excessive rebound effects).

The paper contributes to several strands of literature. First, we add to the literature on the impacts of development interventions aimed at improving EE and facilitating the adoption of modern technologies to mitigate climate change. Due to limited data availability on EE outcomes, especially at a nationally representative level, existing EE project evaluations are often based on information collected during the project’s implementation phase or on expert ratings and stakeholder analysis at the end of the project (e.g., SENER and World Bank 2015; Agyarko et al. 2020). The methodology and data sources used in this study offer several benefits over traditional approaches to project evaluation. Specifically, our evidence is obtained from data that are (a) nationally representative, (b) collected independently of the studied project, and (c) able to control for improvements in the considered outcomes that might have occurred even without the project. The second aspect (b) matters, because using data that are unrelated to the evaluated projects minimizes the risk of researcher demand bias (e.g., the risk that beneficiaries of a project provide more favorable responses when they are interviewed by someone who is associated with the project). In addition, the employed DiD method can separate observed changes in the outcomes of beneficiaries that were due to the project from changes that would have also occurred in the absence of the project. Moreover, existing research on EE interventions tends to focus on supply-side solutions (Creutzig et al. 2018) whereas the project studied here represents a combination of demand- and supply-side measures.

Our findings also relate to studies that argue that there exists a link between EE and energy access (Can et al. 2018; Dagnachew et al. 2018). Specifically, these studies argue that improvements in EE can act like an increase in energy generation capacity which helps to facilitate the expansion of electricity access, especially in areas where the bottleneck to wider electricity access is not the cost of connecting more clients to the grid, but limited electricity generation capacity. The results we obtain from the project in Malawi are consistent with this view and the existence of synergies between EE and energy access.

Finally, the paper contributes to the growing literature that uses remote-sensing data from satellites to measure economic and energy-related outcomes (Henderson et al 2012; <mark>Shi et al. 2018; Falcheta et al 2019)</mark> . Such data have been shown to give rise to valid indicators of energy production, energy consumption, and carbon emissions (see Jasiński 2019 and the survey in Zhu et al. 2019). Our findings are consistent with the view that remote-sensing data may also be useful as a proxy for electricity access and, to some extent, even the reliability of electricity (i.e., a reduction in the frequency of blackouts) in developing countries such as Malawi.

> 4 The DiD estimator also requires that (a) the intervention’s design permits the identification of both a treatment and a control group (i.e., households that benefited from the program and those that did not benefit), and (b) there is sufficient data available on the outcomes for both groups, and both before and after the implementation of the project. For the project studied in this paper, (a) is fulfilled based on the variation in the geographical locations of project components, and (b) is achieved by using existing, nationally representative household surveys as well as data from satellite images. Both types of data are available for years before and after the implementation of the project.

> 5 Our data only allow us to study the impact of the project as a whole. Thus, we are unable to disentangle the effects of individual project components (including of components focused on the supply side and those focused on the demand side).

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