Table 3: Summary statistics on our main categorical variables

||**No Outages**|**Outages**|**Total**|**No Outages**|<br>**Outages**|**Total**|**No Outages**|**Outages**|**Total**|
|---|---|---|---|---|---|---|---|---|---|
|||Count||Ro|w percent|ages|Colu|mn percen|tages|
|**Firm size**||||||||||
|Small(_<_20)|7,974|8,639|16,613|48.0%|52.0%|100.0%|54.7%|51.4%|52.9%|
|Medium(20-99)|4,984|6,087|11,071|45.0%|55.0%|100.0%|34.2%|36.2%|35.3%|
|Large(100 And Over)|1,629|2,091|3,719|43.8%|56.2%|100.0%|11.2%|12.4%|11.8%|
|**Total**|14,587|16,816|31,403|46.4%|53.6%|100.0%|100.0%|100.0%|100.0%|
|**R&D**||||||||||
|No|8,726|9,492|18,219|47.9%|52.1%|100.0%|84.4%|78.0%|80.9%|
|Yes|1,617|2,672|4,289|37.7%|62.3%|100.0%|15.6%|22.0%|19.1%|
|**Total**|10,344|12,164|22,508|46.0%|54.0%|100.0%|100.0%|100.0%|100.0%|
|**Generator**||||||||||
|No|13,016|11,596|24,612|52.9%|47.1%|100.0%|89.5%|69.0%|78.5%|
|Yes|1,533|5,202|6,735|22.8%|77.2%|100.0%|10.5%|31.0%|21.5%|
|**Total**|14,549|16,798|31,347|46.4%|53.6%|100.0%|100.0%|100.0%|100.0%|
|**Own website**||||||||||
|No|9,327|10,983|20,309|45.9%|54.1%|100.0%|63.9%|65.3%|64.7%|
|Yes|5,260|5,834|11,094|47.4%|52.6%|100.0%|36.1%|34.7%|35.3%|
|**Total**|14,587|16,816|31,403|46.4%|53.6%|100.0%|100.0%|100.0%|100.0%|
|**WB income group**||||||||||
|Low Income|281|1,386|1,666|16.8%|83.2%|100.0%|1.9%|8.2%|5.3%|
|Lower Middle Income|7,634|10,411|18,045|42.3%|57.7%|100.0%|52.3%|61.9%|57.5%|
|Upper Middle Income|6,672|5,020|11,692|57.1%|42.9%|100.0%|45.7%|29.9%|37.2%|
|**Total**|14,587|16,816|31,403|46.4%|53.6%|100.0%|100.0%|100.0%|100.0%|
|**Region**||||||||||
|AFR|2,542|3,075|5,616|45.3%|54.7%|100.0%|17.4%|18.3%|17.9%|
|EAP|3,790|4,315|8,105|46.8%|53.2%|100.0%|26.0%|25.7%|25.8%|
|ECA|1,605|967|2,573|62.4%|37.6%|100.0%|11.0%|5.8%|8.2%|
|LAC|2,993|2,747|5,740|52.1%|47.9%|100.0%|20.5%|16.3%|18.3%|
|MNA|2,076|2,487|4,563|45.5%|54.5%|100.0%|14.2%|14.8%|14.5%|
|SAR|1,581|3,225|4,806|32.9%|67.1%|100.0%|10.8%|19.2%|15.3%|
|**Total**|14,587|16,816|31,403|46.4%|53.6%|100.0%|100.0%|100.0%|100.0%|
|**N**|12,317|19,086|31,403|||||||

indicate the respective medians. After a quasi-constant evolution within minor and moderate obstacles, we observe a drop in median total factor productivity as soon as the constraints become major for the firms (box plots 1). It can be seen that this deterioration in the power sector lowers the median total factor productivity of firms. Similarly, the box plots 2 shows a decrease in median firms’ capacity utilization with the level of power constraints. We can see that this deterioration in the power sector reduces the median capacity utilization. The box plots 3 shows that the median losses incurred by firms increases with the level of constraints encountered in the power sector. The deterioration in the power sector increases median losses.

Figure (2) shows the effects of constraints on losses with a breakdown by geographical region. We can see that the negative effects of power constraints on losses by region are reinforced when the constraints become major or severe, especially for the Sub-Saharan Africa, MENA and South Asia regions.

Figure (3) highlights the combined effect of outages and other constraints. When the outages are not associated with the highest level of constraints, we can see the decrease in productivity only for two regions (EAP and LAC). However, when the outages are associated with the highest level of constraints, then we find the decrease in productivity in four regions (AFR, EAP, ECA, SAR). Moreover, intra-regional volatility in total factor productivity becomes more important between firms within the same region.

Table (4) shows the sample means of all matching covariates, divided into two groups: observations of firms facing power constraints (outages) or the treatment group (column 1) and observations of firms not facing power constraints or the potential control group (column 2).<sup>13</sup> The last column shows the standard13As Asiedu et al. (2021), we log some of our macroeconomic variables to mitigate the effect of outliers.

13