|Real exchange rate growth(t-1)(%)|11,677|5.17|10.73|-20.65|31.42|
|---|---|---|---|---|---|
|Trade openness(t-1)(% GDP)|13,025|43.62|13.19|15.32|79.46|
|**_Covariates_**||||||
|Small-sized firms (labor<20)|13,025|0.54|0.50|0|1|
|Medium-sized firms (20<labor<99)|13,025|0.31|0.46|0|1|
|Large-sized firms (labor>99)|13,025|0.15|0.35|0|1|
|Generator use (dummy, 0-1)|12,818|0.50|0.50|0.00|1.00|
|Electricity losses(t-1)(% total power<br>production)|8,574|17.07|9.61|5.37|71.03|
|GDP per capita(t-1)(US$)|11,617|1693.08|1478.72|319.40|6938.85|
|GDP growth(t-1)(%)|11,617|5.58|4.11|-20.60|15.17|
|Rule of lawgrowth(t-1) (%)|8,204|-3.59|26.51|-82.63|91.82|

Source: WBES, WDI, KOF data, PWT.

# 4. Empirical strategy

Using a firm-level treatment variable, we carry out an impact assessment method to understand how access to electricity leads manufacturing firms to become more export-oriented. We consider a binary variable that takes the value 1 if the company did not experience power outages in the last fiscal year, and 0 otherwise. To investigate the exposure of domestic firms to the treatment, we construct the interaction variable between the treatment and the dummy, which is 1 if the company is domestic and 0 otherwise (Treatment#Domestic_Firm). For foreign firms, we construct another dummy that is 1 if the company is foreign and 0 otherwise and we determine the interactive variable between the dummy and the treatment variable (Treatment#Foreign_Firm).

The initial idea of our impact assessment is to compare the export or domestic sales’ performance of manufacturing firms before ( ) and after ( ) receiving the treatment. Thus, the effect of the treatment would be determined by the difference between the outcomes and . Since we do not have YY<sup>0</sup> YY<sup>1</sup> information on the same firms before and after the treatment, we look for a counterfactual of our treated YY<sup>1</sup> YY<sup>0</sup> firms before the treatment. This counterfactual is therefore made up of firms that were not assigned to the treatment, i.e., that have experienced power outages during the last fiscal year. Hence, if the treatment was randomly assigned, then its effect would be determined by the difference between the outcomes of treated and untreated firms. Nevertheless, electricity access is not random, it is linked to certain characteristics and performances at the country and firm-level. The macroeconomic performances, the economic development, institutions, and the quality of electricity service provided by the country, can affect the occurrence of power outages. For instance, countries with good quality of institutions and high level of economic development may have good electricity infrastructure, which can limit power shortages. At the firm level, some features such as firms' size and the use of generators can explain why some companies experience power shortages while others do not. Given that these macroeconomic and microeconomic variables are also associated to the export and domestic sales’ performance, the occurrence of power outages is not random.

To deal with the selection bias linked to the treatment, we apply a matching method that consists in making the counterfactual as comparable as possible to the treated firms. Hence, treated units are matched to the untreated ones based on their observable pre-treatment characteristics. The average 9