First, we account for the fact that between 2006 and 2009‐2010, food prices have increased at a much higher rate than other prices. To do this we adjust the 2006 poverty line by the expected food and non‐ food CPI in 2009 (or 2010), using the food and non‐food shares in the NCR poverty line as weights (approximately 57% and 43%, respectively). Since these weights are different from those in the CPI, the adjustment reflects the poverty line required for an individual to be able to afford the same food basket despite the disproportionate rise in food prices. This method yields a 2009 (or 2010) poverty line, which we then readjust to 2006 prices using the appropriate general CPI (see Annex 2, Table C).

Second, we calculate total household income by aggregating labor income across all employed individuals and adding non‐labor income, and then use information on household size to construct a measure of per‐capita household income, as in (3).

Finally, we use information on household and individual income levels to evaluate the poverty and distributional impacts of the crisis by comparing poverty and other outcomes between the benchmark (without crisis) and “with crisis” scenarios. It is important to note that the Philippines poverty line is defined in terms of per capita income, which implies that per capita household income derived from (3) above can be directly compared with the poverty line to obtain poverty measures.

# **_Limitations and caveats of the simulation exercise_**

The proposed approach has some appealing features, the primary one being its capacity to generate income counterfactuals at the individual and household levels that can then be used to assess impacts across the entire distribution. However, it also has some important limitations that must be taken into account when interpreting the results presented below. We discuss these below.

_Firstly_ , the quality and accuracy of the simulation output is a function of the nature and quality of data underpinning the exercise. The results would depend not only on the micro‐models, but also on the macro projections of the crisis _and_ the benchmark or no‐crisis scenarios. In a typical _ex‐ante_ assessment of this type, building the counterfactual to evaluate impact is especially tricky because the comparison between the situations “with” and “without” the “treatment” (the macro shock) is purely virtual or notional. This is particularly important with regard to the output and employment projections since they are key drivers of the results in the absence of a CGE or similar model. In addition:

- The ability to account for heterogeneity across sectors, groups, and others depends on the level of disaggregation of the available macro projection. For instance, the behavior of the tradeable and non‐tradable sectors within industry can only be modeled separately if output growth projections are available for each sector.

- The ability to accurately predict employment and earning changes depends on the available information and on the assumptions needed to correct for information gaps. For instance, in the absence of projections on total and sectoral earnings growth, we need to assume that earnings

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