are limited to formal firms with five or more employees and with over 1% private ownership or participation. In this analysis, we start with the year 2006 because most surveys from this year onwards use stratified sampling and contain weights based on this information. Earlier surveys may not contain any information on weights. All monetary variables are adjusted to 2009 levels using a World Bank GDP deflator and then transformed to US dollars using the International Monetary Fund’s purchasing power parity (PPP) index.

Our treatment variables (dummy variables taking 0 or 1) characterize the different levels of power constraints that firms report being exposed to. One advantage of this measure is that it reflects firms’ perceptions of the extent to which power constrains their operations. This is important because the firm’s perception is one of the most important factors influencing their operational and investment decisions (Asiedu and Freeman, 2009).

To approximate the level of performance of each firm, we opt for total factor productivity (TFP), the part of output that is not explained by the quantity of inputs used.<sup>9</sup> The productivity of firms, i.e. the ability to generate greater output with fewer inputs, is one of the key elements of economic growth. As a reminder, productivity is estimated from a Cobb-Douglas VA (value added) function of the following form: where value added at the firm-level _VAi_ is a function of the inputs of capital ( _Ki_ ) and labour ( _Li_ ).<sup>10</sup> The efficiency of firms’ production is measured by the term _Ai_ which is the part of output that cannot be directly attributed to inputs used. Equation (14) could be rewritten as: log(TFP _i_ ) is estimated as a sum of the constant and the residual, i.e., log(TFP _i_ )= _α_ ˆ0 + _ε_ ˆ _i_ .<sup>11</sup> Therefore, the TFP is the part of output that is not explained by the quantity of inputs used.

In equation (14), TFP _Ai_ is estimated separately for each industry. This avoids the assumption of a common production technology (i.e. _αk_ and _αl_ are the same within the sample). In addition, wherever possible, the elasticities of output with respect to capital and labor (i.e. _αk_ and _αl_ ) can vary according to the income level group of the corresponding economy. Finally, as in Halvorsen et al. (1980), the country and year effects are controlled via dummy variables for each country and year. The table (1) shows an average log(TFP) of 2.46 for our sample of firms. However, we have a large heterogeneity between our firms as it varies from -3.23 for the least productive firms to 8.83 for the best performing firms.

At the firm-level, we control for the age of the firm, the size of the firm, whether or not the firm has a website, and most importantly, the ownership of the firm.<sup>12</sup> Indeed, over time, firms tend to find ways to solve or mitigate the problem of power constraints. In addition, older firms are found to be more productive (Majumdar, 1997). Moreover, a positive relationship between firm size and TFP is found in the manufacturing sector in general (Leung et al., 2008; Tovar et al., 2011). Also, we use the possession of a website by a firm as a good signal of the quality of the firm’s marketing, it can influence its productivity. Finally,

> 9World Bank Group, Enterprise Analysis Unit. 2017. “firm-level Productivity Estimates”.

> 10 _VA_ is represented by the difference between the establishment’s total annual sales and the total annual cost of inputs, _K_ is represented by the replacement value of machinery, vehicles and equipment; _L_ is represented by the total annual labor cost. 11 Where log(A _i_ )=log(TFP _i_ )

> 12We include both age and size because, contrary to popular belief, St-Pierre et al. (2010) have shown that firm size and age are not substitutes in an exploratory study of 288 Quebec manufacturing firms.

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