resulting poverty predictions for 2009 and 2010 are reported in Table 9. Notably, the poverty projections become closer to the actual poverty estimates for 2009 and 2010. The 2009 projection is within the confidence interval of the observed poverty headcount while 2010 projections are largely unaffected, given the marginal increase in the SP system between 2009 and 2010, and remain statistically indistinguishable from the actual 2010 poverty estimate.<sup>25</sup>

## _5.4 Point estimates projections based on the elasticity approach_

For comparison, we also forecast the poverty incidence for 2009 and 2010 based on the elasticity approach (Table 10). The results suggest that the full pass-through model predicts a poverty incidence for 2009 that is statistically indistinguishable from the actual poverty headcount in 2009, whereas the partial pass-through model forecasts a poverty incidence of 6.2 percent in 2009, lower than the actual estimate.

Given positive GDP growth between 2009 and 2010, the full path-through simulation suggests a decrease in the poverty headcount from 6.9 in 2009 to 6.6 in 2010, whereas the partial pass-through model leaves the poverty headcount estimate unchanged. This is in sharp contrast with the considerable increase in poverty between 2009 and 2010, as recorded in the HBS data.

The key reason for the poor forecast for 2010 of the elasticity approach is the divergence in the dynamics of GDP and household consumption between 2009 and 2010, noted above (see figure 7). Recall that GDP growth was marginally positive during 2009-2010. Changes in consumption during 2009-2010 do not mirror changes in GDP. According to HBS data, total household consumption decreased by 5.9 percent in real terms during 2009-2010. Similarly, during 2008-2009 total household consumption declined by 1.7 percent, less than half the contraction of GDP during the same period.

One possible improvement is to rely, instead of GDP, on the dynamic of household final consumption expenditure (HFCE). According to Eurostat data, in countries like Romania and Bulgaria HFCE accounts for 85 percent or more of actual household expenditures, such that HFCE may constitute a decent approximation for actual consumption dynamics. Figure 8 suggests that HFCE fell in real terms between 2009 and 2010, whereas GDP growth was positive.<sup>26</sup> Assuming a full pass-through from HFCE to household consumption, the forecast for 2009 slightly underestimates the poverty headcount, although the true estimate is within the 95% confidence interval. For 2010 the predicted poverty estimate is lower than the true poverty headcount of 9.2%, but it is still a considerable improvement over the simulations relying on GDP dynamics, which would imply a declining poverty rate in 2010 as the result of GDP growth.

These results suggest that simple poverty forecasts based on GDP dynamics can fall rather far from the true poverty estimates, and can even imply poverty dynamics of opposite sign from the true poverty changes between two points in time. Furthermore, using HFCE data instead of GDP data appear to offer an improvement, especially when the poverty measure is based on expenditure data, which need not follow GDP dynamics. HFCE data are not available, however, for 2011, such that it is not possible to simulate a poverty headcount for that year.

# **6. Concluding remarks**

Faced with the absence of HBS data for 2011, the purpose of this paper was to forecast the prevalence of poverty in Serbia for that year in a way that captures the complex macroeconomic dynamics during the 20102011 period, including positive GDP growth accompanied with continually deteriorating labor market

> 25 These results are broadly in line with those of Tiongson and Sulla (2013), who find that the accuracy of microsimulation-based projections improves when social protection projections are available and are incorporated into the microsimulation model. 26 HFCE data only available through 2010.

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