We examine how per capita consumption changes are distributed across households with different baseline consumption levels. For this purpose we use the Growth Incidence Curves (GIC) and perform three estimates: one for all households and for two specific groups (i.e. urban and rural areas). Each GIC allows us to compare percentage per capita consumption gains or losses across households within the group. Comparisons across groups (e.g. rural versus urban households) are not straightforward. For instance, given that on average consumption levels are higher in urban than in rural areas, a household in the 30<sup>th</sup> percentile of the urban consumption distribution is quite likely to be significantly better off than that of the rural distribution.

Actual economic growth over time is expected to translate into higher consumption mainly in the upper half of the entire distribution as well as in both rural and urban areas (Figures 3 and 4). Per capita consumption gains are largest among those at the top of the distribution (80<sup>th</sup> to 100<sup>th</sup> percentile), with an average gain of 4% annual growth rate between 2010 and 2011. Households at the bottom 1<sup>st</sup> to 30<sup>th</sup> percentile have been less fortunate than the rest. They are projected to experience an average -5% annual decrease in per capita consumption (Figure 3).

Consumption losses are higher in urban than in rural areas and are mainly concentrated in the lower part of the distribution (Figure 4). This difference is likely due to the fact that the deterioration of labor market conditions was mostly felt in urban than in rural areas. However, the gains of the recovery process that Serbia has experienced during 2011 were distributed in the upper part of both distributions.

# **5. Validation exercises**

One important limitation of any ex-ante approach is related to the validation of the hypothesis. The only validation or test for the micro-simulation model is to combine ex-ante and ex-post analysis.<sup>17</sup> Since HBS 2011 data is not available, some uncertainty about the simulation results is bound to remain. However, it is possible to conduct some validation exercises using historical data and assess how well the model performs in predicting poverty and distributional changes over time in Serbia.

## _5.1 Projected point estimates_

In order to evaluate the probable accuracy of these estimates, we use the same model to predict poverty incidence in 2009 (2010) by applying 2008-2009 (2009-2010) GDP dynamics to 2008 (2009) baseline data and then compare these predictions to the actual poverty incidence in 2009 (2010). The model performs well in making a prediction for 2010 (Table 9). The observed national poverty estimate increases from 6.9% in 2009 to 9.2% in 2010, and the predicted estimate is statistically indistinguishable from the actual poverty rate during 2010. On the other hand, applying the model to 2008 data provides a poor prediction of the 2009 poverty rate. The model overestimates the poverty impact of the financial crisis, predicting an increase in the poverty rate from 6.1% in 2008 percent to 8.9% in 2009, instead of the actual increase to 6.9% according to HBS data.<sup>18</sup>

## _5.2 Distributional projections_

Figure 5 presents actual and simulated GICs for 2009 and 2010 using the GDP as macro inputs. The first graph for 2009 shows a very good fit of the projections from the 30<sup>th</sup> percentile up. However, the lower part of the distribution is underestimated which relates with an overestimation not only of the poverty incidence but also for those indices which capture the distribution among the poor such as gap and severity.

> 17 See Bourguignon and Ferreira (2003)

> 18 This is similar to Tiongson and Sulla (2013) who also find that microsimulation-based forecasts for a set of ECA countries are more accurate between 2009-2010 compared to 2008-2009.

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