subtracted from the sum of household consumption expenditures. If the result was positive it was treated as untaxed unreported income.<sup>38</sup> For almost a third of the sample the unreported income was above zero. The share of unreported income in the simulated disposable income ranged from less than 10 percent in the bottom decile to almost 30 percent in the top deciles. This fits well with the a-priori assumptions about the distribution of non-response. Unreported income was then included in the means test for two allowances<sup>39</sup> : the _state social assistance_ and the _housing subsidy_ , as proxy for unobserved household assets.<sup>40</sup> Third, we assign non-take up probabilities for the three (partially) means-tested benefits and for maternity capital. The share of households eligible for means-tested allowances in the survey data is higher than administrative records suggest. Unfortunately, Rosstat and other external sources do not collect any information about non-take up of benefits, therefore we had to apply a random non-take up correction, per EUROMOD guidelines.<sup>41</sup> The take up probabilities were estimated as the ratio between the caseload recipients reported in administrative statistics and those simulated to be eligible by RUSMOD. Those probabilities (41.3 percent for child allowances up to 16 (18) years, 26.9 percent for state social assistance, 29.8 percent for housing subsidies and 94.3 percent for maternity capital, respectively) were applied at the benefit unit level.<sup>42</sup> This method is rather rough and does not fully reflect the actual take-up issues at play (discretionary power of local social protection offices, detailed assets test), but in the absence of any take-up adjustment, the numbers of recipients of benefits would be inflated leading to an underestimation of poverty.

The remainder of this section compares RUSMOD aggregates with that from official statistics for 2017. For example, the number of employed people in the data appears to be underestimated by 18 percent compared with Rosstat (Table A-4), while the number of pensioners is overestimated by 19 percent and the number of the unemployment benefit recipients by almost 67 percent (Table A-4). This could be due to the sample bias typical for all longitudinal surveys such as RLMS-HSE. At the same time, the number of the unemployed by ILO definition is under-predicted by 9 percent.

**Table A-4. Number of employed and unemployed (in thousands), 2017**

||EUROMOD|External|Ratio|
|---|---|---|---|
|number of employed|59,282|72,324|0.82|
|number of unemployed(ILO)|3,867|4,264|0.91|

Source: Rosstat and RUSMOD for 2017 policy year and 2016 RLMS-HSE data Results for numbers of benefit recipients and taxpayers are presented in the tables below. Receipt of the _monthly cash payment_ and _categorical benefits for housing/utilities_ is underreported by about 50 percent (Table A-5). For the former, the reason might be that the payment is provided from the

> 38 This is, of course, a very rough treatment that disregards factors such as household saving behavior, life-cycle effects and other reasons why reported income may be lower than reported consumption. Nevertheless, the imputation needs to be done for income figures to be comparable with the official statistics. Furthermore, similar adjustments are done regularly for macro-statistics.

> 39 The means test for child allowances does not account for the household income. Only earnings or replacement incomes (pensions, maternity allowances, etc.) of parents have to be reported.

> 40 Given that information on assets reported in the survey is limited, compared with that available to local social protection offices, and given the positive correlation between household assets and unreported income, the inclusion of the latter enables us to simulate the means test more precisely.

> 41 Non-take up here means that some of those who qualify for a benefit do not receive it for any reason. Non-take up rate is defined as the ratio between those who are eligible, but not receiving the benefit, and all those who are eligible. 42 Take-up probabilities are defined in each relevant policy rule at the end of the simulation. A user can change these probabilities or switch off the take-up correction for each benefit separately by modifying the relevant parameters in each policy rule.

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