report results, rather than absolute values.<sup>5</sup> Other options that may potentially improve accuracy are: (i) using the official Rosstat household income survey; and (ii) using administrative data for microsimulation (Scandinavian countries do that). A more detailed discussion on RUSMOD validation can be found in Annex 2. Main assumptions and model validation.

Not all the programs could be (fully) simulated. First, some policies are beyond the scope of the model entirely—these include, for example, corporate taxes or in-kind benefits (such as free or subsidized health care, education and other social services).<sup>6</sup> Second, due to lack of information in the survey, a number of programs could not be simulated. For example, the simulation of contributory pension benefits requires information about the contribution record and wages over a person’s lifetime. Other such programs include: a _monthly cash payment_ , _cash and near-cash categorical benefits_ (such as discounts for housing, utilities, transportation or medication), _unemployment benefits_ , and _scholarships_ . Third, some benefits cannot be separated from other payments. For example, a _social supplement to pensions_ is paid together with pensions and recipients are not able to tell them apart. Finally, there are some programs targeted at smaller groups of the population that can neither be simulated due to the lack of information in the survey, nor are available in the data set (for example, payments to families with foster/adopted children). Amounts for these programs are taken directly from the survey data, if available. The main drawback here is that the impact of parametric changes to these programs (for example, an increase in the statutory retirement age) cannot be simulated without strong assumptions.

# III. Performance of the Russian tax-benefit system

## **Baseline scenario: Actual incidence of the Russian tax-benefit system as of 2017**

**The fiscal system in Russia reduces poverty by half and inequality by a third.** The poverty rate under the original income would be 34 percent and the Gini index 50 — this is reduced to 15 percent and 35 respectively (Table 1). The bulk of redistribution happens between the _original_ and _disposable income_ —that is, at the point of direct taxes/benefits, and before indirect taxes/subsidies. The inequality of _consumable income_ (that is, after indirect taxes/benefits), increases somewhat (by 0.7 percentage points), reflecting the slight regressivity of the VAT.

**Table 1. The overall redistribution effect of the tax-benefit system in Russia**

||Original<br>income|Disposable Income|Consumable<br>Income|
|---|---|---|---|
|**Poverty rate, percent**|34.0|14.7|21.1|
|<br>absolute change w.r.t original income, p.p.||-19.2|-12.9|
|relative change w.r.t. original income, percent||-56.7|-38.0|
|**Gini index**|49.6|34.8|35.5|

> 5 For example, a _change_ in tax revenues as derived from the model can be applied to the actual budgetary data on revenues to obtain impact in absolute terms (such as trillions of rubles, or percent of GDP).

6 In fact, in-kind programs typically play an important role in reducing inequality. In theory, these could be simulated using auxiliary models and/or strong assumptions. This is however not attempted here for reasons of tractability and simplicity, as well as in order to maintain the policy narrative in terms of monetary instruments. The role of in-kind programs in Russia was explored in Lopez-Calva et al. (2017) and Popova et al. (2018).

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