they remain qualitatively unchanged. As Entropy Balancing, the OLS estimator also allows us to see the adverse and progressive effect of power sector constraints on firm productivity in developing countries.

Table 9: Robustness tests – The effect of power outages and constraints on the firm’s revenue-based TFP by using least squares regressions (without entropy balancing re-weighting scheme)

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
|Power Outages|-0.083<sup>_∗∗∗_</sup><br>(0.028)|||||||
|Power is not an obstacle||0.041<br>(0.026)||||||
|Power as minor obstacle|||0.031<br>(0.022)|||||
|Power as moderate obstacle||||-0.000<br>(0.029)||||
|Power as major obstacle|||||-0.028<br>(0.028)|||
|Power as severe obstacle||||||-0.047<sup>_∗∗_</sup><br>(0.024)||
|Power as biggest obstacle|||||||-0.131<sup>_∗∗∗_</sup><br>(0.027)|
|Observations|31403|31406|31406|31406|31406|31406|30491|

Notes: Table shows the coefficients obtained by least squares regressions. The dependent variable is the logarithm of the firm-level revenue-based total factor productivity for all regressions (1-7). In each regression, the treatment variable represents a specific degree of power constraints for firms. 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 In table (10), we opt for alternative measures of firm productivity. Although our main variable (TFPR) is suitable to approximate the level of performance of manufacturing firms in developing countries, it also faces some criticism in the literature. Indeed, the estimation of the TFPR can potentially be problematic for certain reasons such as selection, simultaneity or problems related to the use of monetary measures (as opposed to physical measures) of production and inputs (Levinsohn and Petrin, 2003, Foster et al., 2008, Hsieh and Klenow, 2009). To ensure that our results are not biased or influenced by the choice of this measure, we also test the robustness of our results using alternative measures to approximate the productivity of firms (in the form of factor ratios). These are simple ratios of the corresponding variables. These measures of firm performance based on factor shares also have the advantage of being simple and very informative. In column (1), we repeat the estimation with our main variable (TFPR). In column (2), we estimate the effect of the power constraints on labor costs per USD of sales. The result suggests that the constraints induces an increase in production costs for the treated firms. Finally, in column (3), the constraints lead to a decrease in the amount of total sales per worker (in 2009 USD).

In Table (17) in Appendix, we added additional potentially relevant control variables to minimize potential bias due to unobserved confounding factors. These variables are exports, size of the locality of firms, public ownership versus other actors’ ownership, gender of the top manager, quality certification, informal payment, investment in fixed assets, bank account and access to credit. These variables are potentially relevant variables in our model according to this literature. For each case, our results remain statistically significant. This further demonstrates the stability of our main results.

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