included for sampling purposes.<sup>11</sup> Of a total of 7,732,018 active firms recorded in Ruslana, there are a total of 28,370 firms that are within the identified sector, federal district and size parameters. The sample is further restricted to firms with non-missing information on sales, employment, capital, and cost from 2012-2016 if the firm is established prior to 2012. Firms born after 2012 are included if this information is non-missing for all years within this duration. This leaves us with a total of 14,105 firms in the sampling frame.

# _2.5 Response rate and selection bias_

Bloom et al. (2016) note that for these types of surveys private sector firms often only have response rates of 5 to 10 percent which raises an obvious concern about selection on unobservables. To reach a target of 997 manufacturing firms in the selected sector and size categories, we contacted a total of 12,912 firms during July-October 2018. Of these firms, only 59.6 percent could be contacted, with 17 percent of the firms making hard refusals to participate, while another 15 percent of the firms did not fit the actual parameters of the sampling frame perhaps due to changes in firm operations or due to incorrect recording in Ruslana. Lastly, of the 997 successful interviews, 19 firm had to be dropped out of the sample because of reliability issues, which brought the final survey sample to 978 firms. Overall, the response rate among firms that could be contacted and those that were within the identified parameters is close to 17 percent.

It is worth noting that an average firm in our survey is _larger_ in terms of employment (within the SME category) and _older_ than the sampling frame. The average size in the sampling frame is 62 employees, whilst in our survey it is 83 employees (See Table 1). Likewise, the average age in the sampling frame is 12.3 years, whilst in our survey it is 21.7 years. Our survey has 28 percent of firms reporting to have exported in 2017 as compared to the observed 9.5 percent in the sampling frame when matching Ruslana information with customs data for 2016.<sup>12</sup> However, part of the difference could also be indicative of a selection bias.<sup>13</sup> To understand this, Annex Table A.1 presents a regression of respondent dummy on a host of firm characteristics to show that relative to all firms in the sample frame, respondents are more likely older, larger and exporters. Given our finding that exporters have better managerial capabilities (see section 3), we use this information to infer that the overall management score in Russia may be over-

> 11 Small firms are defined as those with no more than 100 employees and/or 800 million RUB in sales; medium firms are defined as those with no more than 250 employees and/or 2,000 million RUB in sales.

> 12 Customs data are another source of trade data that record each transaction by all exporting firms. These data are then matched to the Ruslana sample frame. The share of exporters in these matched data could be biased downward partly due to mismatches between customs data and survey responses and upward because the sample frame is not the full universe of firms. Moreover, it should also be noted that the percentage of surveyed firms that reported to be exporting in 2016 is 15%, which is 13 percentage points lower than that reported by respondents in 2017.

> 13 Annex table A.2 presents the results of an OLS regression of the probability of conducting a successful interview against a series of firm characteristics. Size, age and having exported in 2016 according to customs data come out as being positively and significantly associated to a higher probability of being interviewed. Thus, based on these statistics one may get an impression that the management score in Russia is overstated. However, it should also be remembered that larger cities like Moscow and St. Petersburg are not covered, which are more likely to have firms exposed to structured management practices. Finally, given that Ruslana is not a representative sample, it is difficult to establish the direction and extent of the bias.

7