# **5.2 Treatment effects**

Based on the treatment effects for endogenous treatments described in Wooldridge (2010), we performed an endogeneity test to ensure that the conditional independence assumption (CIA) is respected before running the regressions based on entropy balancing. The results shows that conditional on our covariates, the treatment and outcome unobservables are uncorrelated (H0).<sup>14</sup> We can be sure that our estimates below from entropy balancing represent the consistent treatment effects on the treated (ATT).

The results of the Table (6) indicate that power constraints characterized by outages negatively affect exposed firms, since we observe a negative and significant coefficient associated with power outages dummy variable of 0.102, i.e., 1.1 percentage points or 9% of the unconditional average productivity (column 1). This first result supports those found by a large part of this literature (Cole et al., 2018; Abdisa, 2018; Elliott et al., 2021; Xiao et al., 2022). However, we note that the effects of the constraints are not statistically significant when the levels of constraints are not severe (columns 2 to 5), even if they are gradually increasing. As soon as the constraints become very important (severe), the effect becomes negative and statistically significant (column 6). In column (7), the adverse effect is greatest when power proves to be the firm’s biggest obstacle (ahead of factors such as access to finance, problems with the tax administration, transport problems or problems related to corruption). The visualization of these coefficients are shown in Figure (6).

Table 6: The effect of power outages and constraints on the firm’s revenue-based TFP

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
|Power Outages|-0.102<sup>_∗∗∗_</sup><br>(0.032)|||||||
|Power is not an obstacle||0.041<br>(0.027)||||||
|Power as minor obstacle|||0.040<br>(0.026)|||||
|Power as moderate obstacle||||0.004<br>(0.031)||||
|Power as major obstacle|||||-0.032<br>(0.028)|||
|Power as severe obstacle||||||-0.044<sup>_∗_</sup><br>(0.025)||
|Power as biggest obstacle|||||||-0.148<sup>_∗∗∗_</sup><br>(0.025)|
|Observations|31403|31406|31406|31406|31406|31406|30491|

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 (1-7). 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. The variation in the number of observations is related to the fact that the treatment variables are derived from three separate survey questions. 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 (7), we consider the interaction of the outages treatment variable with each specific level of obstacles encountered by firms in the power sector (the other treatment variables in our analysis). In column

> (1), when we consider the outages treatment variable and no constraints or obstacles, we have no effect of

> 14Test of endogeneity. H0: Treatment and outcome unobservables are uncorrelated. chi2(2) = 0.40 ; Prob _>_ chi2 = 0.8191). We cannot reject H0.

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