in the economy. For each firm, ORBIS also provides a large set of indicators including information on the firms’ ownership structure as well as their income statements and balance sheet data.

One of the key obstacles of identifying SOEs in ORBIS is the lack of a specific variable for this identification. The closest proxy variable provided by ORBIS is denoted as the Global Ultimate Owner (GUO), defined as the individual or entity at the top of the corporate ownership structure (majority owner). In theory, one could obtain a list of SOEs by selecting firms whose GUOs are government public authorities. However, the information provided by the GUO is limited to entities of which the state or public authority owns 25% or more (GUO25), or 50% or more (GUO50), and state ownership in SOEs could be lower and still provide the ability of the governments to influence over firms (e.g., golden shares). Moreover, the GUO variable does not include every shareholder at 25% or 50% or more level of ownership; it includes only the largest shareholder at the 25% or 50% threshold level, which may not be a state or a government. As a result, utilizing the GUO variable will yield an incomplete list of SOEs and firms with state participation – and therefore underestimate the businesses of the state. Furthermore, the GUO variable tends to misclassify some firms as private even if they are owned by the government.

The Global BOS database leverages a large set of alternative data sources to compensate for ORBIS’s substantial coverage variation across countries and regions. In general, ORBIS representativeness in ECA countries is better compared to emerging economies in EAP and LAC (Gal, 2013) (Bajgar, Berlingieri, Calligaris, Criscuolo, & Timmis, 2020).<sup>6</sup> Furthermore, the ORBIS raw database suffers from several issues, including duplicates, omission, and measurement error. Several cleaning steps had to be implemented before the raw data in ORBIS could be processed and analyzed, as detailed in the following sections of the methodology. In addition, to complement and improve upon ORBIS’s coverage of firm-level indicators, the Global BOS database leverages alternative data sources (EMIS, Factiva, Pitchbook, among others), World Bank proprietary data sets, country-level reports, and project information. For most countries, firm-level data collection has been also performed which allowed not only to produce a comprehensive data set of SOEs,<sup>7</sup> but also to complete the data initially available for each SOE in terms of employment, revenues, and other financial and firm level characteristics. World Bank country teams also reviewed and validated the country-level data to ensure completeness and accuracy of the information. The country-specific databases were also subject to technical exchanges with government counterparts.

The BOS database identifies an unprecedented number of firms with state participation across countries and economic activities, as well as providing novel insights on financial performance, economic performance, and governance of SOEs. A deep-dive analysis of 36 countries within the BOS database shows that 69% of SOEs operate in competitive activities (low efficiency-rationale for state participation), 16% are in partially contestable industries (moderate efficiency-rationale), and 15% are natural monopolies (strong efficiency-rationale). Furthermore, this analysis suggests that performance-based productivity of SOEs (revenues per worker) is negatively correlated with government control variables, such as government shareholding percentage and direct vs. indirect government ownership.

The remainder of this paper is organized as follows. Section 2 introduces the rationale for the analysis and describes the conceptual framework that defines and provides the structure of the Global BOS database. Section 3 presents the main data sources used. Section 4 describes the methodology to identify SOEs and create the Global BOS data set using ORBIS and additional data sources. Section 5 describes the structure

> 6 Some empirical efforts to estimate models using firm-level data in ORBIS have documented the low number of observations in ORBIS in Australia, Chile, Iceland, Mexico, New Zealand, Israel, among others.

> 7 A number of SOEs in the Global BOS database would not have been identified if some of these data sources had not been used.

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