work activities that generate income for the owners of production means in each economy. Its other unique advantage is the possibility of tying the individual income of the entire working population to the characteristics of the workplaces where such income is generated.

These strengths come with limitations. First, the unit of observation is not the business but the individual, and employers are not identified. Therefore, we can only characterize the employmentweighted business size distribution, not its unweighted version. Second, we are constrained to a reduced number of size bins, already limited in the original data sources and further reduced in our analysis for comparability across countries.<sup>3</sup> Third, because the data are at the level of individual workers, we have no information on capital stock or other business characteristics. Our only measurable business characteristics are size, sector, and labor productivity, measured from the earnings of employees and owners, which yield proxy measures of productivity distorted by the presence of labor market power and labor regulations.

We summarize here the basic characteristics of the data sources used for the different countries in our sample. Details are provided in the Appendix. For Latin America, we use the 2019 national household surveys, with the exception of Chile (2017) and Mexico (2018). The microdata for most countries are publicly available, and we use harmonized versions to ensure comparability across countries. We have data for 11 LATAM countries: Argentina, Bolivia, Brazil, Chile, Colombia, Costa Rica, the Dominican Republic, Mexico, Paraguay, Peru, and Uruguay.<sup>4</sup> We also have data for three LDCs in Asia (India, Pakistan, and Nepal) for which we use information from their 2019 labor force surveys (2018 in the case of Nepal).<sup>5</sup> Our advanced economies include all countries in the European Union, the U.S., the Republic of Korea, Japan, and Australia. We use the 2019 microdata from the EU-SILC (Statistics on Income and Living Conditions) database for the European Union (EUROSTAT, 2019). We split E.U. countries between higher and lower income countries in the region.<sup>6</sup> For the remaining countries; our datasets are the U.S.’ Current Population Survey Annual Social and Economic Supplement (CPS-ASEC; U.S. Census Bureau, 2018; Ruggles et al., 2023), Korea’s Labour and Income Panel Study (KLIPS; Korea Labor Institute, 2019), Australia’s Household Income and Labour Dynamics dataset (HILDA, Department of Social Services, 2019), and Japan’s Labour Force Survey (Official

> 3 In all countries, workers are asked whether they work on their own or in a business with more or fewer than 10 employees, but higher levels of disaggregation are captured using thresholds that are not always comparable across countries. We define comparable groups.

> 4 The specific sources are: Argentina (INDEC, 2019), Bolivia (Bolivia, 2017), Brazil (IBGE, 2019), Chile (Ministerio de Desarrollo Social y Familia, 2017), Colombia (DANE, 2019), Costa Rica (INEC, 2019), the Dominican Republic (de la Rep ́ublica Dominicana], 2019), Mexico (INEGI, 2018), Paraguay (INE Paraguay, 2019), Peru (INEI, 2019), and Uruguay (INE Uruguay, 2019)

> 5 India’s Periodic Labour Force Survey (National Statistical Office, 2018-2019), Pakistan’s Labour Force Survey (Pakistan Bureau of Statistics, 2018-2019), and Nepal’s Labour Force Survey (Central Bureau of Statistics, 2017-2018).

> 6 Countries in each group are listed in the Appendix.

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