for both years, but slightly higher for 2010 than 2009. The impact of the crisis on aggregate measures of inequality is negligible (see Annex 2, Table D.1 for detailed results).

The poverty rate in the absence of the crisis would have declined by 15.4 percentage points between 2005 and 2010. With the crisis affecting GDP and remittance growth in 2009 and 2010, the poverty rate is projected to decline by 14.2 percentage points between 2005 and 2010.<sup>21</sup> The difference of 1.2 percentage points between the two scenarios translate to around 2 million _additional_ poor individuals in 2010 due to the impact of the crisis.

**Comparison with other simulation methods.** Our results on the poverty impact of the crisis are comparable to results obtained using other commonly used methods, for the same macroeconomic projections (Table 4). Using Povstat and the aggregate elasticity of poverty to growth, the poverty impacts are 0.2 and 0.6 percentage points for 2009, respectively, compared to a 0.4 point impact using our micro‐macro simulation method. For 2010, both the alternate approaches yield a poverty impact of 1.2 percentage points, identical to the impact estimated by our approach.

For every year and scenario, the poverty rates projected by the three approaches are within 2 percentage points of each other (for **Table 4: Comparing poverty headcount rate (%) using different** more detailed results, see Annex 2, **<u>projection methods</u>** Impact of Crisis Table D‐2). Our method consistently Baseline With Crisis _<u>(Crisis – Benchmark)</u>_ yields poverty rates that are <mark>2005 2009 2010 2009 2010</mark> somewhere in‐between those Povstat 40.0 28.9 26.3 0.2 1.2 generated by Povstat and the Elasticity of elasticity method (Table 4). The 40.0 28.0 25.6 0.6 1.2 poverty to growth reasons why these small differences in Our simulation 40.0 28.6 25.8 0.4 1.2 predictions occur are hard to pinpoint _Source_ : Simulations/computations using macro projections and HIES (2005) since the other two approaches are substantively different from ours, as well as from each other. A quick comparison between Povstat and our approach, however, is useful since both approaches involve simulations linking micro data with macro changes. One key difference between Povstat and our approach is the treatment of remittances. Povstat implicitly assumes that remittances grow at the same rate as other incomes, whereas (actual and projected) growth in remittances has been much higher than that of GDP after 2005. Our model explicitly takes into account actual and projected growth in remittances since 2005 and as a consequence, generates larger improvements in household income and lower poverty for all scenarios compared to Povstat.

**Price adjustments underlying the poverty projections.** All projected expenditures are at 2005 prices, adjusted for spatial differences.<sup>22</sup> This would normally imply that no adjustments are necessary for the

> 21 The decline in poverty rate for both benchmark and crisis scenarios would be an improvement upon past performance – poverty fell by 9 percentage points between 2000 and 2005, a period when annual average growth in GDP and remittances was lower than the 2005‐2010 annual average growth for both scenarios.

> 22 All expenditures and the poverty line for the baseline year (2005) are adjusted to 2005 rural Dhaka division prices, which is one of the strata in the sample of HIES 2005.

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