of the countries and survey waves included in our analysis, while Figure 1 shows their geographical distribution.

As noted by Besley and Mueller (2018), there is a trade-off in using the WBES. While in some countries the information in the WBES dataset is less detailed than that found in national-level manufacturing surveys, the WBES offers several clear advantages. It provides harmonized information across a large and diverse set of countries, covers multiple sectors, and includes data on specific obstacles and market characteristics—such as informal competition—that are typically unavailable in other surveys. Moreover, given the focus of our analysis and the empirical strategy employed, which requires longitudinal firm-level information, the WBES offers better coverage than any other global dataset, including ORBIS Bureau van Dijk.<sup>8</sup> A typical firm in our sample is domestically owned and operates in the private sector. As shown in Table A2, the median firm has 22 employees and has been operating for 16 years. It mainly serves the domestic market—only 16% of firms are exporters—and generates $334,700 in annual sales and $271,800 in annual profits (in 2000 constant USD).<sup>9</sup> All variables are defined in Table C1.

**Selection, attrition, and missing values** As discussed in Section 3, our estimating strategy relies on the subset of firms with multiple observations over time, i.e., those included in the panel component of the WBES. This may raise concerns about potential selection issues affecting our estimating sample. In particular, one might worry about the selfselection of firms that are re-interviewed in subsequent survey waves, vis `a vis firms that, for various reasons, are interviewed only once. If such selection is correlated with our main

waves: 42 have two waves, 45 have three, and two have four. Across these country-waves, the sample includes 40,884 firm-year observations, corresponding to 16,608 firms observed twice and 2,556 observed three times. Dropping firms without sales data yields a final sample of 36,087 firm-year observations across 89 countries.

> 8The ORBIS dataset lacks meaningful coverage for 63 countries in our sample. Most notably, geolocalization data—a central component of our analysis—is sparsely populated in ORBIS across the majority of countries, significantly limiting the usable sample and raising concerns about sample selection. When we require the availability of both sales, costs, and profits (as needed for the baseline regression) and geolocated information, WBES provides superior coverage in all cases except Kazakhstan, where ORBIS still includes a comparable number of firms relative to WBES. In addition, ORBIS does not collect data on several key variables necessary to investigate the mechanisms underlying our results—such as informality, access to imported inputs, power outages, and other operational constraints. See Appendix B for further discussion.

> 9Profits are not directly provided in the WBES. We construct _Profits_ as _Sales_ minus _Total costs_ , defined as the sum of labor costs and raw materials and intermediate inputs costs, including energy expenditures (for details on the variable definitions, see Table C1). For input costs, the WBES questionnaire administered to service firms only collects information on electricity expenses. As a result, for service firms, the _Total costs_ variable may underestimate overall production expenditures (e.g., by excluding costs related to equipment, telecommunication services, etc.), potentially affecting the comparability of _Profits_ across sectors. To address this, we exclude from the sample all service firms with a _Total costs_ -to- _Sales_ ratio below the fifth percentile of the distribution. This restriction improves the reliability of economic performance comparisons across sectors. In any case, our results are virtually unchanged when focusing only on manufacturing firms (see Section 4.1, Table A15).

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