between the year of the data and the policy year. The household and personal characteristics, such as housing type, employment status or demographic attributes, are constant in relation to the data year. Table A-3 in the Annex contains more information on the uprating factors.

# **Limitations of the model**

As every model, RUSMOD is not without its limitations, of which users should be aware. The following three main warnings are summarized below, along with the remedies typically applied: (i) RUSMOD is a static, partial equilibrium model; (ii) RUSMOD can never fully replicate the official statistics; and (iii) not all the programs could be fully simulated.

Being a static, partial equilibrium model, RUSMOD estimates only first-order effects of policy reforms on income distribution and fiscal outcomes, and generally does _not_ take into account actions by economic agents taken in response, in order to adapt to changes. This problem, for example, can be acute if the _intention_ of policy action is to actually influence behavior (for example, reduce the labor tax wedge in order to encourage formal employment). A solution could be to link the microsimulation model with a behavioral model, or assume some elasticity between variables of interest (Creedy & Duncan, 2002). One example is presented in section IV below, where we estimate the share of formalized workers after a tax reform using a behavioral model.

Models, such as RUSMOD, cannot fully replicate the official statistics. Comparing (“validating”) model output, such as the total amounts of tax receipts and spending on social transfers and inequality and poverty indices, with external data is a standard robustness check. In that regard, for example, the total amounts of public pensions are _over-reported_ in RUSMOD by about 25 percent, expenditures on monthly cash payments are _under-reported_ by 65 percent, while simulated PIT, SIC and VAT receipts are _under-estimated_ by the model by 30-35 percent—vis-à-vis the official/external statistics. Gini index is estimated at 34.8 versus 41.0, the official (Rosstat) number. On the other hand, for several programs (e.g. unemployment benefit, maternity capital, child allowances up to 16 (18) years, housing privileges and subsidies) the match is quite accurate.

There are numerous reasons for the mismatch. First, a large share of incomes may remain unaccounted for in the survey due to non-response or underreporting. Also, some benefits (such as a monthly cash payment) may be paid together with other benefits (pensions from the Pension Fund, in this case), and not distinguished separately by recipients. On the other hand, official/external statistics may use other sources to compile the data—for example, in the case of the Gini index and national poverty headcount, Rosstat adjusts the survey number by imputing a top-end of the income distribution, which is believed to be missing from the data.

The mismatch with official data is not a fatal flaw. First, in general, surveys are used precisely to enrich and triangulate other statistics, and may not necessarily be a less accurate reflection of the real world. Second, the discrepancies are not large for EUROMOD standards. Nevertheless, the modeling approach in this paper attempts to reflect as close as possible the existing statutory rules, while at the same trying to compute economic rather than statutory incidence of taxes and benefits. In particular, we (i) excluded informal sector workers from simulations of PIT and SIC, (ii) imputed unreported income, and (iii) assigned non-take up probabilities for means-tested benefits.<sup>4</sup> Moreover, where appropriate, _changes_ produced by the model, which are presumed to be more accurate, are used to

> 4 This model behavior can, of course, be switched off if the statutory incidence needs to be obtained. For example, in the ‘tax maneuver’ scenario some share of workers was formalized and then PIT and SIC were included in the model.

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