coverage is quite limited relative to the size of the poor population,<sup>31</sup> there is little justification for using the traditional programs to specifically target crisis‐affected households, who appear to be better‐ placed than the structurally poor households in terms of their endowments and access to economic opportunities. As the impacts of the crisis take hold, there may be a case though for extending or deepening certain types of existing programs (e.g. public works programs) in specific areas, regions or sectors. Extending coverage among the urban poor, for instance, may address an important need, given that the urban poor (10<sup>th</sup> to 40<sup>th</sup> percentiles) is likely to be one of the key affected groups and coverage of existing safety net programs is extremely low in urban areas.

**Implications for simulations in other countries** . Clearly, our model is applicable to a number of country contexts and for addressing a range of questions related to distributional impacts of an economic shock. In terms of _where_ this approach can be applied, the minimum data requirements include up‐to‐date macroeconomic projections by sector, with and without crisis, and a fairly recent household survey with income data that can be classified by sector. In addition to Bangladesh, this approach is currently being attempted to assess crisis impacts in Philippines, Mexico and Egypt. While these countries share some of the conditions with Bangladesh in terms of the potential channels of impact, the specific country and data circumstances will also require the approach to be tailored somewhat to fit the relevant context.

Given the choice of models available to simulate the distributional impact of the crisis, it is also useful to revisit the comparison between existing approaches and the model we have proposed here. The comparison between our model and PovStat is the most relevant, given that both models have similar data requirements. In our view, the choice of which model to use should be guided by the kind of projections one needs to generate. In a situation where the impact on poverty and inequality measures at the national and regional levels are all that is required and/or resources in terms of personnel and time is limited, PovStat is likely to be the better and more practical choice. On the other hand, if more detailed distributional impacts – including estimates on how the impact will be distributed across groups or types of households and the sources of these impacts – are a concern, our model provides a feasible approach. Even in that case, the robustness of the results can be tested by comparing the poverty and inequality estimates generated from our model with those using PovStat, as it was done for Bangladesh.

Finally, decisions to extend or nuance the type of modeling approach used here would depend on the availability of data and the specific country context. For example, the availability of more disaggregated macroeconomic projections would allow for a more nuanced distributional analysis that is able to capture movements within sectors and/or types of employment. The availability of better migration and remittance data, particularly in the household survey, could allow for explicit modeling of remittances and migration rather than the somewhat arbitrary assignment rule used here. Availability of surveys conducted in 2009 (e.g. any labor force or rapid monitoring survey) that hint at some of the impacts of the ongoing crisis would be useful in fine‐tuning some of our analysis, particularly to take into account any sudden price movements or structural changes in the labor market.

> 31 Around 13% of households, including around 5% of urban households, benefited from at least one safety net program in 2005; 8% of all households benefited from targeted programs (Chapter 6, World Bank 2008).

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