grouped into homogeneous groups, and simple random samples are selected from each group. Individual observations are then weighed using sampling weights that consider the selection probabilities in strata (firm size, business sector, and geographic region), enabling companies to be representative within countries.

The paper uses the most recent standardized data set for the period 2006-2022, which includes 67 surveys. After processing the database, 13,025 manufacturing firms in 39 SSA countries remained. The database provides information on companies' perception regarding the business environment and on their characteristics as well as their performances. The distribution of surveyed firms is unbalanced among industries. The food industry accounts for 26.46% of the surveyed firms (Table A.1), followed by the wearing apparel industry (13.79%), the furniture industry (11.14%) and the metal industry (9.00%). These four industries represent more than half of the surveyed manufacturing firms (60.39%). In addition, some countries have many more companies surveyed than others (Table B.1 & Table B.2). Nigeria, Kenya, South Africa, and Zambia are countries whose firms are the most represented in the database. The survey waves in Nigeria (2017 and 2014) alone represent 16.82% of interviewed firms. Surveys conducted in Kenya (2007, 2013 and 2018) represent 9.39% of the total sample. In South Africa (2007) and Zambia (2007 and 2013), firms interviewed represent 5.03% and 4.98% of the total sample, respectively.

# 3.2. Variables

Six groups of variables are used in this paper: ISSC’s measures, electricity access measure, firm-level, and country-level control variables, as well as covariates (at country- and firm-level).

## **1.** **<u>Intra-Sectoral Structural Change Measures</u>**

- _Intra-sectoral allocation of firms:_ The ISSC is measured in this paper by the ability of existing firms to move across export and domestic markets. It is thus measured by two variables: the share of exports and that of domestic sales in total sales.

## **2.** **<u>Electricity Access Measure</u>**

- _Treatment=Not experiencing power outages:_ Our treatment is a dummy variable that indicates whether or not a company has experienced power outages in the last fiscal year. It is therefore 1 if the company declares not suffering from power outages, and 0 otherwise. It is thus the electricity access measure at the firm-level.

## **3.** **<u>Firm-level control variables</u>**

- _Firms’ age:_ Age is measured in this paper by the gap between the year of survey and the year the firm began its activity _._ Since age is associated with the firm's experience in its field, it is often considered as a key determinant of firms’ performances. According to the literature on learning by doing, the longer a firm operates in a market, the more it learns about that environment. However, some studies shed light on the adverse effects of age on firms’ performances (Hannan and Freeman, 1984; Leonard-Barton, 1992).

- _Legal status and gender:_ The performance gap between firms is often attributed to differences in management quality. Because of the managerial expertise of the parent from which they benefit, foreign firms would be well managed compared to domestic ones (Bellak, 2004). In the absence

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