lead to downward movements) are more narrowly concentrated among the upper middle class, likely because the losses occur in the sectors (primarily industry) that produce relatively high incomes. The fact that movements are less frequent when households are grouped by per capita labor income suggests that a significant part of the movement in terms of total income is due to loss of remittances. While there are some movements, more than 90% of all households remain in the same decile because the aggregate income shock due to the crisis is quite low, due to the limited macroeconomic impact projected for Bangladesh.

# **_4.7 A limited validation of the model using pre‐crisis data for Bangladesh_**

How well does our model perform in estimating the impact of macroeconomic changes on poverty and distribution? Addressing this question to our satisfaction will require a validation exercise that compares our projections with actual post‐ (or during‐) crisis estimates from a household survey, which must await the availability of a survey conducted during or after the crisis. That said, a limited validation exercise can be undertaken with available pre‐crisis data.

In case of Bangladesh, such a validation involves using household data from an earlier year (HIES round of 2000) to “project” poverty and other distributional estimates for 2005, and comparing these projections to the actual estimates from HIES 2005. In practical terms this implies replicating the exact same simulation method described in this paper on HIES 2000 data, using _actual_ macroeconomic statistics (growth rates of sectoral and aggregate GDP and remittance, and general and food CPI figures) for the period 2000‐2005, which are analogous to the macro projections used for the forward‐looking projections described above.

Table 5 shows the results of this simulation and compares them with the actual estimates for 2005. The simulated poverty headcount rates are within 1 percentage point of the actual estimates for that year, with the difference between actual and simulated rates being 0.7 and 0.3 percentage points for moderate and extreme poverty rates, respectively. The simulated results are also close to the actual indices for depth (poverty gap) and severity (squared gap) of moderate and extreme poverty, as well as for the Gini index of inequality in per capita expenditure.

The results suggest that the model does quite well in simulating the distribution of expenditures and poverty and inequality indices. Interestingly, all the simulated indices, with the sole exception of

**Table 5: Poverty and inequality estimates for 2005 – simulated and actual**

||2000|200<br>|5<br>|
|---|---|---|---|
|||Simulated|Actual|
|**Moderate poverty**||||
|‐Headcount rate|48.9|39.3|40.0|
|‐Poverty gap|12.8|9.2|9.0|
|‐Squared poverty gap|4.6|3.1|2.9|
|**Extreme poverty**||||
|‐Headcount rate|34.3|25.4|25.1|
|‐Poverty gap/depth|7.5|5.1|4.7|
|‐Squared poverty gap|2.4|1.5|1.3|
|**Inequality per‐cap. Exp**||||
|‐Gini|0.31|0.33|0.31|
|‐Theil|0.18|0.22|0.19|

_Source_ : HIES 2005; micro‐simulations using macro statistics and HIES 2000 the (moderate) poverty headcount rate, are slightly above the actual values. This seems to indicate that the simulation model underestimates the consumption growth for the poorest groups between 2000 28