enough to have an overview of management practices but not so senior as to be detached from day-today operations.

# _2.3 RUSLANA as Sampling frame_

The data on firm performance in Russia are basic, substantially limiting the ability of researchers to apply the latest techniques for measuring and understanding firm productivity (see Kilinc, 2019). Consultations with the authorities and academics confirmed that nearly all analysis is done with firms’ financial/accounting data, which are available from Rosstat via third-party aggregators like Ruslana, Spark, and Integrum. These data sets allow for very basic analysis of productivity (e.g., output per worker) or require analysts to impose fairly strict assumptions/theoretical structure on the data. Although Rosstat collects very detailed data on firm inputs and outputs, as well as quantities and prices, these data are confidential by Russian law and hence they are difficult to access (even in anonymized form). Therefore, researchers generally shy away from using them – creating a perception that data quality may be uneven and disabling positive feedback loops from data users to data producers.

Ruslana is a commercial data set, which contains administrative data covering more than 9 million companies in Russia (Ruslana is a subset of Bureau van Dijk’s global Orbis data set, comprising of Russia, Kazakhstan, and Ukraine). Cusolito and Winkler (2015) use this data set for the period 2005-2013, comprising of 30,000 to 40,000 firms per year, amounting to a total of more than 300,000 firm-year observations. Since Ruslana is not a census nor a representative survey, the results based on an analysis of these data could be biased. For example, it represents only about 10 percent of total wage employment in the Russian economy and small firms are particularly under-represented. Likewise, Cusolito and Winkler (2015) show that while the share of firms in many sectors covered in RUSLANA is similar to Rosstat, there are important differences. In particular, wholesale and retail trade firms are over-represented in Ruslana, while real estate, education and other services are under-represented. However, given that Ruslana is the only reliable source of firm-level information and that it does a good job at matching the evolution of employment and value added over time (Cusolito and Winkler, 2015), we use Ruslana as the reference population for our work. Moreover, given that we study the impact of managerial capabilities on firm performance in manufacturing, the lower coverage of small firms, over-representativity in certain services does not affect the usefulness of our approach in using Ruslana as the reference population.

# _2.4 Sample Stratification_

Taking the reference population from Ruslana, the sample was stratified by sector and federal districts. Within sectors, only manufacturing was considered and the following six aggregate groupings were used in stratification: Food and beverage (ISIC Revision 4 codes: 10 and 11); Textile, apparel and leather (ISIC Revision 4 codes: 13-15); Chemical, rubber and non-metallic products (ISIC Revision 4 codes: 19-23); Basic metals and metal products (ISIC Revision 4 codes: 24 and 25); Electronics and electrical equipment (ISIC Revision 4 codes: 26 and 27); and Machinery and motor vehicles, including transport (ISIC Revision 4 codes: 28-31). The following five Federal districts were used in stratification: Central (excluding Moscow oblast and Moscow city); Northwestern (excluding Leningrad oblast and Saint Petersburg city); Southern; Ural; and Volga. In all sectors and federal districts only small and medium firms (25-250 employees) were 6