and profits grow at same rate within a sector. How realistic this assumption is would depend on the country and sectoral context.

- The ability to directly model and predict changes in the relative shares of formal and informal employment within sectors is limited by the lack of projections on the growth of each type of employment. Instead, formal and informal status are assigned in proportion to what is observed in 2006 for each sector and consequently all compositional changes in sectoral employment are driven by population growth and individual employment transition, rather than more general labor market cyclical dynamics.

- The ability to model remittances depends on the quality of the available data on migrants and remittances, particularly for countries with rapid and/or volatile growth of remittances.

_Secondly_ , the simulations implicitly assume that the structural relationships estimated as part of the calibration process on the baseline data continue to be valid in the future years for which the projections are made. The more distant in the past the baseline year is, the more questionable this assumption is likely to be. In the case of Philippines, for example, the baseline year is 2006, which is a full 3 to 4 years away from the prediction years (2009 and 2010). This particular caveat, however, links directly back to the constraints imposed by availability of data. In most countries, household survey data that is available for analysis and processed to the extent necessary for the analysis is likely to pre‐date the prediction years (usually 2010) by at least 3 to 4 years.<sup>10</sup> _Thirdly,_ there is no mobility of factors (labor or capital) across regions and across urban and rural areas under the current model formulation. The same is true about international migration. Consequently all individuals are assumed to remain in their 2006 place of residence, irrespective of whether they experience a change in labor force status or not.

_Fourthly_ , our model is also limited in its ability to account for shifts in relative prices between different sectors of the economy as a result of the shock. The model _does_ take into account the impact of shifts in the price of food relative to general inflation on poverty measures, via the simple device of adjusting poverty lines that are anchored to a fixed food basket. It can be argued that changes in the relative price of food represent the most significant source of price impact on poor or near‐poor households in a country like Philippines, as the recent food crisis has shown. At the same time, there are other potential sources of price impacts – for example, the effect of a change in the terms of trade between agriculture and other sectors on real household incomes in all sectors – our approach does not take into account. Unfortunately, in the absence of a CGE model to link up to, it is nearly impossible to explicitly model for changes in terms of trade between sectors.

The _final_ limitation, related to validation of hypothesis, applies to _all_ ex‐ante approaches including ours. The only validation or test for our simulation model is to combine ex‐ante and ex‐post analysis (see Bourguignon and Ferreira, 2003). Since ex post data will not be available for some time, some

> 10 An additional caveat, for some countries, relates to our decision to work with income rather than consumption data. This caveat however only applies to countries where poverty is measured against a poverty line defined in terms of consumption, rather than income. In the case of Philippines, since poverty is measured as a function of household income, our approach is entirely appropriate.

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