universal, flat transfer (column 4). Finally, under a proportional loss shock, using an indicator of which households suffered a shock (columns 7-10) does not deliver any additional impacts as all households are affected: when income ranks do not change because of a shock, the best responses continue to be to support households according to their pre-shock poverty status.<sup>22</sup> _Table 8 : Moldova – Proportional loss shock_

|**Targeting →**||||**Pr**|**oportiona**<br>**Hous**<br>**Eligibi**|**l Loss Sho**<br>**ehold**<br>**lity ↓**|**ck**||||
|---|---|---|---|---|---|---|---|---|---|---|
|||**Means**|**Means**||||||**PMT Ex-**|**PMT Post**|
|**Budget ↓**|**Original**<br>**Beneficiaries**|**Testing**<br>**(Ex-**<br>**Ante**<br>**Poor)**|**Testing**<br>**(Post**<br>**Shock**<br>**Poor)**|**Universal**|**PMT Ex-**<br>**ante**|**PMT**<br>**Post**<br>**Shock**|**Perfect**<br>**Loss**<br>**Indicator**|**Imperfect**<br>**Loss**<br>**Indicator**|**ante or**<br>**Perfect**<br>**Loss**<br>**Indicator**|**Shock or**<br>**Perfect**<br>**Loss**<br>**Indicator**|
||**(1a)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|**(8)**|**(9)**|**(10)**|
|**Poverty Rate**||||**Pre-sho**|**ck: 0.120;**|**Post-shoc**|**k: 0.128**||||
|(1) 0.05% GDP|0.000|-0.001|-0.002|0.000|0.000|0.000|0.000|0.000|0.000|0.000|
|(2) 1.0% GDP|-0.014|-0.034|-0.039|-0.005|-0.010|-0.010|-0.004|-0.003|-0.005|-0.005|
|**Poverty Gap Sqr.**||||**Pre-sho**|**ck: 1.344;**|**Post-shoc**|**k: 1.402**||||
|(1) 0.05% GDP|-0.073|-0.040|-0.038|-0.005|-0.017|-0.015|-0.002|-0.002|-0.004|-0.004|
|(2) 1.0% GDP|-0.787|-0.616|-0.587|-0.097|-0.286|-0.268|-0.042|-0.044|-0.075|-0.074|
|**Gini**<br>||||**Pre-sho**|**ck: 0.320;**|**Post-shoc**|**k: 0.316**||||
|(1) 0.05% GDP|-0.001|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|
|(2) 1.0% GDP|-0.009|-0.008|-0.008|-0.002|-0.005|-0.005|-0.001|-0.001|-0.001|-0.001|
|**Coverage**|||||||||||
|(1) Average Transfer|$ 124.95|$ 41.71|$ 39.11|$ 5.09|$ 42.15|$ 39.45|$ 7.58|$ 10.26|$ 6.80|$ 6.84|
|(2) Average Transfer|$ 2,499.04|$ 834.28|$ 782.12|$ 101.73|$ 842.91|$ 789.01|$ 151.51|$ 204.31|$ 136.03|$ 136.71|
|Beneficiaries|109,169|327,009|348,817|2,681,733|323,663|345,773|1,800,685|1,329,764|2,005,571|1,995,577|
|Ex-ante poor|0.29|1.00|1.00|1.00|0.49|0.48|0.43|0.33|0.74|0.71|
|Ex-ante non-poor|0.00|0.00|0.01|1.00|0.07|0.08|0.70|0.51|0.74|0.74|
|Ex-ante Vulnerable|0.01|0.00|0.02|1.00|0.13|0.14|0.58|0.42|0.66|0.66|
|Post-shock poor|0.28|0.94|1.00|1.00|0.47|0.46|0.46|0.36|0.75|0.73|

Note: impacts are color-coded from dark red (highest impacts) to dark blue (lowest impact). Not all households declare labor income.

Results for Moldova (Table 8) also remain very similar to the ones for the baseline, COVID-19 shock, suggesting again that the nature of the COVID-19 shock was very similar to a proportional loss shock. In terms of ranking of impacts, they remain basically the same than for the rankings of the COVID-19 shock discussed in Table 5.

Finally, results for North Macedonia (Table 9) show again that using pre-shock indicators of poverty status to determine coverage, such as a PMT (columns 1b-3, 5, 6), is an effective response when the shock does not affect substantially the poverty rankings; and again, going universal (column 4) or focusing the response only on households affected by the shock (columns 7 or 8) delivers worse impacts.

In contrast to the baseline shock, however, the actual expansion of beneficiaries does not deliver much higher impacts than relying on a PMT score; for the 1% of GDP scenario, it delivers actually lower impacts. This, on the one hand, shows the importance of tailoring the

> 22 Note that, because some households do not declare any labor income in the survey, coverage of the transfer under a proportional shock is not universal even when selection is based on an indicator of households who suffered a shock (i.e., column 7).

23