## **5.7 Placebo tests**

Finally, we perform some placebo tests to ensure that the effect captured is essentially that of the constraints observed in the power sector. To do this, in Table (15), we have dummy variables (1/2 1/2), (1/3 2/3), (2/3 1/3), (1/4 3/4) and (3/4 1/4). When replacing each of these random dummies in our baseline model, we find no statistically significant effect on firm-level revenue-based total factor productivity.

Table 15: Placebo tests – The non-significant effects of random treatment variables on the firm’s revenuebased TFP

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|Random treatment (1/2 1/2)|0.012<br>(0.021)|||||
|Random treatment (1/3 2/3)||-0.002<br>(0.021)||||
|Random treatment (2/3 1/3)|||-0.025<br>(0.022)|||
|Random treatment (1/4 3/4)||||-0.006<br>(0.017)||
|Random treatment (3/4 1/4)|||||0.030<br>(0.019)|
|Observations|31406|31404|31404|31405|31404|

Notes: Table shows the average treatment effects on the treated obtained by weighted least squares regressions (Entropy Balancing). The dependent variables is the logarithm of the firm-level revenue-based total factor productivity for all regressions (1-5). In each regression, the treatment variable represents a random treatment 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

# **6 Conclusion**

The objective of this paper was to analyze the role of power sector constraints on the performance level of firms in developing countries. Theoretically, we have shown that it is relatively complex to conclude the expected overall effect. Indeed, the constraints on the power sector lead to two types of effects in opposite directions on the productivity of firms, namely a direct negative effect and an indirect mitigating effect (output prices adjustment producers). Empirically, our approach based on Entropy Balancing to control endogeneity shows that the overall effect of power outages on revenue-based total factor productivity (TFPR) is negative and statistically significant. Indeed, we observed a 9% lower unconditional average productivity for exposed firms compared to non-exposed firms. Moreover, we establish a robust link between the severity of self-reported power constraints or obstacles by firms (minor, moderate, major, severe, and biggest) and the magnitude of productivity loss for firms. In other words, the greater the level of power constraints self-reported by a firm, the greater the effect of these constraints on its productivity, and vice versa. Our results are robust to changes in the level of clustering, the estimation model, and the measure of our productivity variable.

We also show that the most affected firms operate mainly in Sub-Saharan Africa and in the MENA 26