and 2005. This is understandable, given that the poorest groups experienced unusually high consumption growth during this period – per capita real consumption grew annually at 2.9% for the bottom decile compared to 2.4% for the population.

Thus the validation based on historical data provides some confidence in the ability of the simulation model to project future outcomes, linking macro statistics with econometric models built on household data. However, important caveats apply to these results. _Firstly_ , the validation has the benefit of using _actual_ macro data, whereas the 2009‐2010 projections rely on macro projections. The ability of the model to predict the future would then critically depend on the accuracy of the macro projections. _Secondly_ , the output‐employment elasticities used for the validation are derived from the actual employment data from HIES 2000 and 2005 and macro output statistics. The same elasticities are used for our simulations for future years, but whether these will _continue_ to be valid – particularly in a crisis scenario that may lead to structural changes in the economy – is an important question that will matter for the accuracy of our projections. Taken together, these caveats basically imply that with reliable macroeconomic inputs and elasticities our model does well with household data for Bangladesh. While this does not necessarily validate our future projections of poverty and distributional impact, a full validation will only become possible when household data from 2009 or beyond becomes available.

# **5. Conclusion: implications of our results and extensions**

Even with the caveats and limitations discussed in Section 3, our model appears to yield reasonable and intuitively appealing results, based on a transparent and flexible approach using macro projections and household survey data that are typically available in a developing country. The limited validation exercise we conduct on pre‐crisis data appears to suggest that the simulation results on poverty and inequality from the model, based on real‐world macroeconomic data for relevant years, are close to the actual results. The impacts of the crisis on aggregate poverty and inequality estimates projected by the model also appear to be consistent with those obtained using alternate, simpler approaches commonly used by the Bank.

The value that our model _adds_ to those simpler models is the ability to analyze potential distributional impacts in detail, linking these changes to the channels through which the impact of the crisis is likely to flow. The application of our model to Bangladesh also yields some useful insights relevant to the country specifically, in two main areas discussed below: monitoring of impacts of the crisis and possible policies to address these impacts.

**Implications for Bangladesh in the context of the crisis.** _Firstly_ , our results suggest that in Bangladesh, a select few indicators are prime candidates to monitor as real‐time or “early‐warning” indicators for the poverty and distributional impact of the crisis. To be useful for rapid monitoring of poverty and distributional impacts, the indicators must be easily obtained and measurable, sensitive to changes in economic conditions, and correlated with changes in poverty and distribution. By these criteria, changes in _remittance flows from abroad_ , the movement of _prices (including food prices)_ and movement of _wages by sector_ can serve as useful indicators for rapid monitoring. The extent of losses in foreign remittances and labor income, and the price of food relative to other prices emerge from our

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