**Table A-3. RUSMOD uprating factors, 2017**

||2016|2017||
|---|---|---|---|
|C Pi Id Db|||Source:|
|onsumer rce nex, ecemer to<br>December (2007=100%)|213.9|219.2|http://www.gks.ru/free_doc/new_site/prices/potr/tab-<br>potr1.htm.|
|Federal Subsistence Minimum Level (IV Q), rubles<br>|9691|9786|Source: https://fedstat.ru/indicator/30957.|
|Accrued average monthly nominal wages per<br>employee (IV Q), rubles|47554|51197|Source: Short-term indicators digest by Rosstat.|
|Average awarded pension (IV Q), rubles|12428.6|12949.5|Source: Short-term indicators digest by Rosstat.|
|Average per capita money income (IV Q), rubles|45938.2|48674.9|Source: Short-term indicators digest by Rosstat.|
|Average per capita consumption expenditures (IV<br>Q), rubles|17735.8|17908|Source: Income, expenditures and consumption (HBS<br>data).|
|Average per capita expenditures on housing and<br>utilities (IV Q), rubles|2087|1896.2|Source: Income, expenditures and consumption (HBS<br>data).|
|Expenditures on social benefits and assistance, mln<br>rubles|2552247|2552247|Source: Russian Statistical Annual Digest 2017.|
|Expenditures on unemployment benefits and<br>material aid to the unemployed, mln rubles|37590|37590|Source: Russian Statistical Annual Digest 2017.|
|Expenditures on childcare allowances up to 1.5<br>years, mln rubles|157361|157361|Source: Russian Statistical Annual Digest 2017.|
|Expenditures on child benefits up to 16(18) years,<br>mln rubles|51709|51709|Source: Russian Statistical Annual Digest 2017.|
|Expenditures on cash payments to some categories<br>of citizens, mln rubles|931251|931251|Source: Russian Statistical Annual Digest 2017.|
|Expenditures on housing subsidies and privileges,<br>mln rubles|359001|359001|Source: Russian Statistical Annual Digest 2017.|
|Expenditures on scholarships,mln rubles|82400|82400|Source: Russian Statistical Annual Digest 2017.|

Source: RUSMOD for 2017 policy year and 2016 RLMS-HSE data

# **Annex 2. Main assumptions and model validation**

Validating simulation output against official statistics is a necessary step in assessing the robustness of a microsimulation model. To ensure comparability with external sources, such as national accounts or administrative data, some assumptions were used in the model. Three main assumptions/adjustments are discussed below. In general, although our modeling reflects as close as possible the existing statutory rules, in the baseline scenario we tried computing economic rather than statutory incidence of taxes and benefits.

First, RUSMOD excludes informal sector workers from simulations of PIT and SIC. The RLMS attempts to capture informal sector jobs, which appear to be quite widespread, especially for additional jobs. According to RLMS data, as of 2016, the share of informally employed amounted to 19.8 percent at the main job and 44.2 percent at the second job, among employees. Among the selfemployed, 63 percent were classified as informal workers at the main job.<sup>36</sup> Among those who reported occasional self-employment, 89 percent were informal. To account for this, in simulations of SIC, PIT and earnings-related benefits the earnings of informal sector workers were set to zero.

Second, we impute unreported income. A large share of earnings and other types of income might remain unaccounted for by the survey due to non-response or underreporting. In this respect, the availability of information about both household income and consumption is one of the big advantages of the RLMS data. Assuming that expenditures are a more reliable and less volatile welfare indicator than income, we have imputed _unreported income_ .<sup>37</sup> To arrive at this estimate, households’ disposable income reported in the survey (before any simulations are done) was

> 36 A similar question for the self-employed at a second job was not included in the questionnaire. Hence, they were classified as informal.

> 37 This is done in a separate policy rule, which is switched on by default, but can be switched off by the user if necessary.

39