Table 13: Main channels – The effect of power outages on the firm’s Capacity Utilization (%) and Losses due to Outages (% of annual Sales)

||(1)|(2)|
|---|---|---|
||CapacityUtilization(%)|Losses(% Sales)|
|Power Outages|-1.390<sup>_∗∗_</sup>|6.680<sup>_∗∗∗_</sup>|
||(0.692)|(0.700)|
|Observations|30711|14397|

Notes: Table shows the average treatment effects on the treated obtained by weighted least squares regressions (Entropy Balancing). The dependent variable is the logarithm of the firm-level revenue-based total factor productivity for all regressions (1-2). In each regression, the treatment variable represents power outages dummy variable. Firm-level controls variables are: Firm size, Firm’s longevity, Own website, Foreign private participation. Country-level controls variables are: GDP growth (annual %), Inflation (annual %), log(Bank concentration in %), log(FD index), log(GDP per capita), log(Net ODA in % of GNI), log(Regulatory Quality). Each estimate includes Year FE, Country FE, Region FE, Income group FE and a Constant. Standard errors in parentheses are clustered at country level.<sup>_∗_</sup> _p <_ 0 _._ 10,<sup>_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01 generators really a solution to the constraints in the power sector in developing countries? In column (3) and (4) of the Table (14), we can see that having a back-up generator reduces the negative effect of power constraints by half, even if it does not fully correct the shock (Abdisa, 2020).

Given the prominent role of research and development (R&D) in the operation of firms in general, we also tested a potential mitigating role for the R&D investments of the firms in our analysis. Through R&D, firms can mitigate the effect of unreliable energy supply by switching to less energy-intensive technologies (Alam, 2013), or can replace power with other types of fuels (Allcott et al., 2016) or materials (FisherVanden et al., 2015). R&D can also instruct firms on the production of energy-intensive intermediates to be externalized instead of producing them internally. R&D also allows firms to effectively modify their production strategy. This ability to re-optimize decisions can therefore limit the negative effects of poor quality of power service for the affected firms (Alam, 2013). This is why we were also keen to test the mitigating role of R&D in column (1) and (2). The results suggest that investments in R&D allow firms to mitigate the negative effects of the constraints encountered in the power sector.

Table 14: Mitigating factors – The effect of power outages on the firm’s revenue-based TFP

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||No R&D|R&D|No Generator|Generator|
|Power Outages|-0.151<sup>_∗∗∗_</sup>|-0.093|-0.179<sup>_∗∗∗_</sup>|-0.098<sup>_∗∗_</sup>|
||(0.034)|(0.093)|(0.035)|(0.042)|
|Observations|16152|5683|20229|10210|

Notes: Table shows the average treatment effects on the treated obtained by weighted least squares regressions (Entropy Balancing). The dependent variable is the logarithm of the firm-level revenue-based total factor productivity for all regressions (1-4). In each regression, the treatment variable represents power outages dummy variable. Firm-level controls variables are: Firm size, Firm’s longevity, Own website, Foreign private participation. Country-level controls variables are: GDP growth (annual %), Inflation (annual %), log(Bank concentration in %), log(FD index), log(GDP per capita), log(Net ODA in % of GNI), log(Regulatory Quality). Each estimate includes Year FE, Country FE, Region FE, Income group FE and a Constant. Standard errors in parentheses are clustered at country level.<sup>_∗_</sup> _p <_ 0 _._ 10,<sup>_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01 25