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.

The _third_ caveat relates to our decision to work with income, rather than consumption data. The advantage of using income is that it allows us to link welfare impact on households directly with potential channels of impact, which are employment, labor earnings and remittances. There are two primary caveats to working with income data: (i) income data often tends to be of lower quality than consumption data, which introduces an element of noise into the analysis due to the unobserved presence of measurement error; (ii) certain assumptions, which can be challenged on the grounds of realism, are needed to convert predicted income levels into consumption and consumption‐based welfare measures. It is important to note, however, that (ii) would not be necessary for countries that use an income‐based measure of poverty, which is the case in most Latin American countries.

The approach we have adopted to convert income into consumption assumes that the ratio of consumption to income is unchanged for every household between the baseline and prediction years. The constant savings rate that this assumption implies is probably more realistic for poor households than for better‐off households. This also implies that our approach may _overestimate_ the consumption impact of the crisis on better‐off households, since such households may compensate for an income loss by reducing savings (or dis‐saving), resulting in a smaller impact on consumption.

_Fourthly_ , our model does not explicitly account for labor demand at the sectoral level and instead assumes that the labor market conditions mirror (or are proxied by) the macroeconomic projections. The simple approach we adopt implicitly assumes stable relationships between output, demand for labor and labor earnings, which may not hold due to the distortions (such as segmentation and downward stickiness of nominal wages) that typically exist in the labor market and are likely to affect adjustments during a crisis.

Related to the above point, the simplifying assumptions for the labor market also do not account for the possibility of structural shifts in labor demand due to the crisis. Sectoral movements of labor are modeled as depending only on individual/household characteristics (through the multinomial logit model) and population growth. This cannot take into account the kind of structural shifts that have apparently been observed in some countries, such as a reduction in the relative demand for skilled labor. That said, structural changes (for example, in relative demand for skills) can be incorporated into our model if there exists prior analytical work that provides parametric estimates of how these changes may have affected labor earnings.

_Fifthly_ , our model is 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, by using 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 Bangladesh, as the recent food crisis has shown. At the same time, there are other potential 12