_Table 2: Social Assistance responses considered in the simulations_

||**Social Assistance Response**<br>**Distributing an additional fixed budget to:**|
|---|---|
|1a.|Pre-shock beneficiaries of Social Assistanceprograms|
|1b.|Actualpost-shock beneficiaryexpansion(if any)|
|2|All households identified aspoor before the shock hit|
|3.|All households identified aspoor after the shock hit|
|4.|All households(i.e. universal,flat transfer)|
|5.|All households ranked according to a Proxy Means Test, up to the point where the number of<br>beneficiaries corresponds to the number ofpre-shockpoor|
|6.|All households ranked according to a Proxy Means Test, up to the point where the number of<br>beneficiaries corresponds to the number ofpost-shockpoor|
|7.|All households identified as havingsuffered a shock(Perfect Indicator)|
|8.|All households identified as havingsuffered a shock(Imperfect Indicator)|
|9.|All households in(5)and households havingfaced a shock accordingto aperfect indicator|
|10.|All households in(6)and households havingfaced a shock accordingto aperfect indicator|

Responses 1a and 1b simulate the policies deployed as a response to the COVID-19 shock, hence we assess the welfare impact of actual program responses in the three countries. Specifically, we model response 1 of Table 2 by distributing a fixed budget to all the pre-shock beneficiaries of Social Assistance programs. The payment is _in addition_ to all the payments that these beneficiaries were receiving before the shock occurred. In response 1b we do the same but consider the actual expansion of beneficiaries that occurred in North Macedonia - the only country among the three that enacted a major expansion of the beneficiary base to respond to the crisis.

Responses 2 and 3 are hypothetical benchmarks as they assume perfect identification of the poor – something that is only possible under perfect means testing – an ideal that we simulate to establish a “first-best” response. In response 2 we identify in the survey all poor households _before_ the shock and distribute the same fixed budget to all of them; and in response 3 we do the same but consider instead all households identified as poor _after_ the shock hit. The payments simulated here are added to any pre-shock transfer that households may have reported. It is important to note that due to data limitations this simulation does not include other types of transfers that were implemented in response to the COVID-19 shock, such as wage subsidies or new unemployment benefits. Doing this would have required extreme assumptions about how transfers to firms would eventually be channeled to households and about the households affected by shock-related unemployment. It is also important to note that unemployment insurance programs in these countries have very low coverage and thus are not well captured in household surveys.

Response 4 reflects a pragmatic choice – i.e., a universal, flat transfer that divides the fixed budget equally among all households, independently from their income and from whether they suffered an income shock. This scenario captures the absence of any information on which to target resources during an emergency.

Responses 5 and 6 are more realistic versions of 2 and 3. Specifically, pre-shock poverty status is estimated using a proxy-means test indicator (PMT scores) that ranks households’ welfare 12