Table 10: Robustness tests – The effect of power outages on the firm’s revenue-based TFP and factor share based estimates of productivity

||(1)|(2)|(3)|
|---|---|---|---|
||revenue-based TFP|Cost of inputsper unit of sales|Salesper labor cost|
|Power Outages|-0.102<sup>_∗∗∗_</sup>|0.010<sup>_∗∗_</sup>|-1.193<sup>_∗∗∗_</sup>|
||(0.032)|(0.005)|(0.439)|
|Observations|31403|31403|31403|

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 in regression (1), Cost of inputs per unit of sales in regression (2) and Sales per labor cost in regression (3). 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

# **5.4 Heterogeneity check**

We also tested the evolution of our results following the income group of the countries in which the firms operate and following the geographical regions. In Table (11), we have the effect of power constraints on TFPR following the income level of countries. We see that the negative effect found in Lower Income countries far exceeds those found in Middle Income countries. Indeed, the magnitude found in these Lower Income countries is 2.5 times that found in Upper Middle Income countries and 4.5 times that found in Lower Middle Income countries. For the Lower Income countries, this negative effect could be explained by the low level of resilience for firms in these fragile environments. For the Upper Middle Income countries, part of the explanation could lie in the fact that firms in these countries are relatively more energy-intensive, and therefore more exposed to energy constraints. The absence of a significant effect in the Middle Income countries would reflect the fact that firms in these countries are relatively more resilient than those in the Lower Income countries, and energy intensity is lower than in the Upper Middle Income countries.

Table 11: Heterogeneity check – The effect of power outages on the firm’s revenue-based TFP by countries income group

||(1)|(2)|(3)|
|---|---|---|---|
||Low Income|Lower Middle Income|Upper Middle Income|
|Power Outages|-0.214<sup>_∗∗_</sup>|-0.047|-0.085<sup>_∗_</sup>|
||(0.082)|(0.031)|(0.044)|
|Observations|6848|14215|10340|

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 (1-3). 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 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 In the Table (12), we then provide further evidence that the overall negative effect is particularly driven by Sub-Saharan African (column 1) and MENA countries (column 5). This highlights to some extent the 23