Saharan Africa).<sup>1</sup> Due to data availability issues, the analysis is limited to 16 countries. This reduction stems primarily from misreporting in the sales variable but is also due to incomplete data for control variables, and electricity prices; the latter is, for example, not available for Tajikistan. Furthermore, self-reported energy efficiency measures are not available in all countries. Consequently, regressions that include energy efficiency measures are based on data from 13 countries. The sample covers micro, small, medium, and large businesses across all main sectors (agriculture, manufacturing, retail, and other services, including construction).<sup>2</sup> <mark>Table A1 (a-c) in the Annex describes the sample distributon across years, sectors, and frm sizes at the country level. This breakdown is instrumental for categorizing sectors by their energy usage intensity. The sectors are metculously disaggregated to the most granular level the sampling methodology allows. Within the sample, agriculture accounts for 6 percent of the frms, manufacturing for 25 percent, wholesale and retail for 24 percent, and a diverse array of other services for 45 percent. Within the service sector, food services and accommodaton represent 23 percent, transportaton and informaton and communicaton technology (ICT) account for 13 percent, and constructon for 12 percent. The remaining frms are engaged in fnancial actvites, real estate, educaton, health, and other sectors.</mark> Table A2 in the Annex shows the summary statistics of the main variables used in the analysis. The dependent variables used in the estimations are employment, sales, and sales per worker as a measure of labor productivity.<sup>3,4</sup> The median firm has sales of $12,312, 6 workers,<sup>5</sup> with a labor productivity of $1,950 per worker, and has been operating for 15 years. Figure 1 depicts the trends of our outcome variables, labor productivity, sales, and employment.

critical aspects of the data. The first aspect involves classifying sectors based on their energy intensity following the Climate Policy Relevant Sectors (CPRS) framework introduced by Battiston et al. (2017). The CPRS taxonomy identifies energy-intensive sectors through a multifaceted lens, considering (i) the emissions produced by each sector's economic activities, (ii) the sector's contribution to the Greenhouse Gas (GHG) emissions chain, (iii) the sector's engagement in specific policy processes, including its lobbying capacity; and (iv) the transition risk associated with the sector, which inversely relates to the level of fuel substitutability. A sector is deemed energy-intensive if its activities result in emissions, utilizes fuel (or a mix of fuels) as a primary input, and has a low potential for substituting these fuels. The sector classification by energy usage is systematically outlined in Table A3 in the Annex.

1 For each country, the sample frame was based on the statistical data from the National Statistical Committees of each country at the time of the survey's first implementation. For the later survey waves, the list of companies was updated using lists of business associations and internal lists of entrepreneurs from the survey firm.

2 It is also worth acknowledging that firm weights are unavailable, so the sample is not representative. However, the estimations control for firm characteristics, muting to some extent the composition effects.

3 Sales per worker is a widely used proxy for labor productivity, offering a practical measure of output per unit of labor input, especially when value-added data are unavailable. Bloom et al. (2010) emphasize its effectiveness in capturing labor efficiency and management practices, while Syverson (2011) notes that it may reflect other factors, such as market power or capital intensity, which could distort comparisons. Despite these limitations, Bartelsman et al. (2004) highlight its importance in developing economies, where sales data often provide the most accessible alternative for productivity analysis.

4 Sales and sales per worker are winsorized at the 5 and 95 percentiles.

5 5