# **Annex 1: Measurement of ‘Surges’ and ‘Slumps’**

18 Global sector‐level trend growth rates that were estimated using the INDSTAT 4 2009 Revision 2 and 19 INDSTAT 4 2012 Revision 3 data sets from the United Nations Industrial Development Organization (UNIDO) are used. The two UNIDO data sets were combined to create a database representing 84 sectors 20 (4‐digit NACE) from 134 countries for the time‐period 1993 to 2009. Outlier observations – identified as growth greater than 3 standard deviations above or below the mean for each sector in each country– were removed. This results in dropping about 45 percent of the observations in the data set.

In order to identify surges and slumps, first, a trend output growth rate for each sector is estimated. In order to account for life product cycle effects, only countries in the same GDP per capita quartile as the Russian Federation are included, and an average growth trend of each 4‐digit NACE sector in this group 21 calculated by OLS regression of log output on time. To increase the robustness of the results sectors with fewer than 60 observations from the trend regressions are dropped.

Shocks are defined in terms of “extreme” deviations from this global trend, relative to the distribution of the deviations during 1993‐2009. Since the distribution of deviations could vary by sector, the next step is to calculate the sector‐wise distribution of deviations from trend in the UNIDO data. For each country, sector and year in UNIDO, the percent deviation of actual output from trend output is calculated. For each sector, the 75<sup>th</sup> and 25<sup>th</sup> percentiles of the distribution of this deviation (across countries and during 1993‐ 2009) are calculated. Let D<sup>75</sup> s and D<sup>25</sup> s be the 75<sup>th</sup> and 25<sup>th</sup> percentiles of the deviations from trend output in sector s.

Next, the global trend for each sector is used to calculate predicted Russian output for that sector using RUSLANA data. The percent deviation of actual output from this predicted output is calculated for each sector‐year. A sector s is defined to be in slump in year _t_ if its deviation from trend in year _t_ is above D<sup>75</sup> s, and to be in slump if its deviation from trend is below D<sup>25</sup> s. The sector is in a “normal” year if its deviation is between D<sup>75</sup> s and D<sup>25</sup> s.

> 18 <u>http://unstats.un.org/unsd/cr/registry/regcst.asp?Cl=8&Lg=1</u>

> 19 <u>http://unstats.un.org/unsd/cr/registry/regcst.asp?Cl=2&Lg=1</u>

> 20 NACE is the acronym used to designate the various statistical classifications of economic activities developed since 1970 in the European Union (EU). NACE provides the framework for collecting and presenting a large range of statistical data according to economic activity in the fields of economic statistics (e.g. production, employment, national accounts) and in other statistical domains. This classification was designed to delineate broad economic categories, into large economic classes of commodities, distinguishing food, industrial supplies, capital equipment, consumer durables and consumer non‐durables.  It is broadly used to stand for sectors.  The higher the number of digits for the NACE, the more detailed the sector; from the most aggregate to the least, the classifications are organized by Section, Division, Group and finally Class.  The analysis here is at the 4‐digit NACE level; namely at the Group level.  For more information, see <u>http://unstats.un.org/unsd/cr/registry/regcst.asp?Cl=27&Lg=1.</u>

> 21 It is reasonable to assume that sectors that are booming in poorer countries may be shrinking in richer ones. To take this into account, countries with different income levels are allowed to have different sectoral growth trends.

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