according to observable characteristics that are correlated with income. In response 5 we distribute equally the fixed budget to all households below PMT threshold, up to the point where the number of beneficiaries corresponds to the number of pre-shock poor; while in response 6 we do the same, but up to the point where the number of beneficiaries corresponds to the number of post-shock poor (so that there will be more beneficiaries, but the transfer will be lower).<sup>13</sup> Once again payments are in addition to any pre-shock transfer that households may have received but exclude households that may have benefitted from other COVID-19 support programs.

Note that, while the proxy-means indicator may be relatively efficient in identifying pre-shock poor, it may not necessarily be a good indicator of _new_ poor because asset holdings may not change immediately nor predict vulnerability to the shock, and PMT scores are usually not recalculated in the aftermath of a shock. Hence, accurate identification of post-shock beneficiaries using a pre-shock PMT index may vary substantially according to the nature of the shock, depending on whether the shock affected distributional rankings.

At times, however, policy makers may be able to make use of an indicator identifying which households may have suffered a shock. For instance, following a drought one may not be able to perfectly identify the value of the loss, but a good approximation could be obtained by estimating households’ land holdings. Similarly, information of an informal workers’ sector of activity could provide some indication of the extent of the income loss she may have suffered during a crisis.

To reflect the availability of this “imperfect” information, we consider two additional responses (7 and 8). In response 7 we distribute the fixed budget among households that suffered the shock with certainty. We call this a “perfect loss” indicator. In response 8, we introduce an exclusion error of 25% to mimic the case of imperfect information.

The final two responses (9 and 10) combine the PMT indicators with the “perfect loss” indicator. We then distribute the fixed budget to all households ranked low according to each Proxy Means Test _or_ whose indicator shows they suffered a shock. By combining the two indicators we make sure that both the poor and those that suffered a shock receive support. The rationale behind using the perfect loss indicator and not the imperfect one is understanding how access to the best possible information during a crisis can improve proxymeans testing.

# 3. Data and methodology

For the microsimulations, in Albania and North Macedonia we rely on data from the Survey of Income and Living Conditions (SILC – 2018 for Albania and 2019 for North Macedonia). The SILC collects annual information on household incomes, including public and private transfers. It collects information about household’s annual income for the year before the survey, as such incomes for Albania and North Macedonia refer to the years 2017 and 2018, respectively. In Albania the total number of households covered was 772,197 and the number

> 13 This is a scenario where the number of individuals affected is known, but what remains unknown is which individuals were effectively affected or what the value of their loss is.

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