|Electrification|-0.0124|-0.0254|-0.0289|
|---|---|---|---|
|_% of population_|_(0.0995)_|_(0.0893)_|_(0.0872)_|
|Water access|0.4148*|0.3617*|0.3816*|
|_% of population_|_(0.2201)_|_(0.2158)_|_(0.2067)_|
|Population|0.3123**|0.3006**|0.2649*|
|_in mio_|_(0.1469)_|_(0.1531)_|_(0.1474)_|
|Population density|-0.1246***|-0.1102***|-0.1024***|
|_in 1/km^2_|_(0.0409)_|_(0.0419)_|_(0.0392)_|
|Urbanisation|-0.0759|0.1100|0.1525|
|_% of population_|_(0.3891)_|_(0.3524)_|_(0.3591)_|
|WGI: Government Effectiveness|-4.5237*|-5.2523**|-4.9618**|
|_Index, standard normal_|_(2.5907)_|_(2.5074)_|_(2.4447)_|
|CountryFixed Effects|YES|YES|YES|
|N|43,829|43,829|43,829|
|R<sup>2</sup>|0.31|0.34|0.34|

Estimated with weighted OLS, normalised survey stratification weights applied. Standard errors in parenthesis, clustered within regions. Significance levels: *** 0.01, ** 0.05, * 0.10.

The estimates confirm that power outages have a significant adverse impact on firms. Most notably, the total annual blackout duration reported by firms is estimated to increase sales losses by 0.29 percentage points for each 100 hours of blackout (Table 4, model I). The estimated blackout effect is highly significant and robust, and can be seen to be concave: The squared total blackout time is significantly negative, thus suggesting a diminishing impact of each additional blackout hour. Specifically, the first 100 hours of blackout duration have a much more pronounced impact and reduce sales by 0.73 percentage points (Table 4, model II).

In addition to electricity outages, several control variables are estimated to have significant effects on blackout related sales losses. Most notably, larger firms, i.e. those with higher employee headcounts tend to report smaller sales losses. The country level output gap control variable (constructed based on the Hodrick-Prescott Filter) confirms that blackouts are costlier during booms as they curbing economic upswings. Moreover, higher governance standards are also estimated to reduce disruption impacts significantly.

By introducing an interaction term between blackout duration and the ISIC sectoral classification (Table 4 model III), the estimates also document significant sectoral heterogeneity. For the sake of brevity, the results for 31 economic sectors are reported separately in Annex A.3. These estimates suggest that manufacturing sectors such as oil refineries, producers of basic metals, precision instruments, and motor vehicle industries are particularly hard hit by power outages. By contrast, construction and logistics firms tend to be less vulnerable.

3.2. The cost of back-up generation of electricity due to power outages Facing frequent electricity outages, a common response strategy by firms is to operate their own back-up generators, usually powered by diesel. While these generators enable firms to bridge power outages, they also require firms to purchase, install, maintain, and operate costly machinery. These expenses mean that generators tend to be less affordable for smaller firms with limited cash reserves. As a result, generator ownership is significantly higher among large firms, as shown in Figure 7 for 78,139 firms in 137 countries 16