the incidence of dismissals, which is endogenous to labor regulation. The same concerns hold for another available indicator, the number of detected violations per thousand employees. We do not use the other available measures (“all cases filed” and “pay cases filed”), because they are not specifically about hiring and firing disputes.

# **3.2 Firm‐level data**

Our regressions use panel data from the RUSLANA database (from Bureau van Djik, BvD). RUSLANA is an extensive data set that provides up to 10 years of financial, administrative, locational and managerial information on about 65,000 registered firms in Russia. The data are based on firms’ reporting to various administrative databases such as the tax directorate and social security.

According to BvD, every effort is made to ensure comprehensive coverage of these official databases, so that RUSLANA may include all registered and active firms. The fact remains that RUSLANA coverage is dependent on the extent to which firms report to such official databases. As we understand, a specific issue with coverage is that some firms ‘enter’ and ‘exit’ RUSLANA with a lag relative to their actual dates of market entry and exit, possibly due to lagged updating of official databases. The lag in entry into the database, in particular, indicates that RUSLANA has more selective coverage of newly established firms. The selection process is unobserved, in the sense that we lack information on why some new firms enter the database with a lag, while others do not. It could be a concern for our estimation if it varies systematically across provinces, and if the employment adjustment behavior of such selectively excluded 11 firms is systematically different from that of other firms.

The data set we use is the 2012 ‘version’ of RUSLANA, meaning that it was last updated by BvD in 2012, and spans the 1999‐2011 period. Due to relatively poorer availability of employment data in years prior 12 to 2003 and in 2011, we limit our analysis to 2004‐2010. In addition, all observations with negative or missing values for any of the following variables used on our analysis—age, operating revenue, tangible fixed assets and number of employees—were dropped; observations with missing profit value were also dropped. Outlier values (outside the 1‐99 percentile range) of key variables (such as the level and change in employment and revenue) were Winsorized (top‐coding and bottom‐coding the 1 percentile tails).

# **3.3 Measuring shocks to industrial sectors**

We classify large deviations from the trend annual growth rate of a manufacturing sector as a shock episode. For each 4‐digit level sector, a global trend growth rate was measured using long‐term, cross‐ country data on industrial output.<sup>13</sup> A sector is defined as being in ‘surge’ (resp. ‘slump’) when the difference between its actual sales growth rate and the trend growth rate is larger (resp., smaller) than a pre‐specified cutoff value (resp., the 75<sup>th</sup> and 25<sup>th</sup> percentile values of the distribution of the trend growth

> 11 Concerns around exit and panel definition are addressed in Section 4.1.

> 12 The gaps in year 2011 data are likely due to updating lags in the official databases which BvD uses to compile RUSLANA.

> 13 Only countries in the same per capita income quartile as Russia were included, on the assumption that they would share similar technological levels and global markets with Russia.

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