first best response for a policy maker who is not able to perfectly identify the poor (column 1b). For the actual budget of 0.09% of GDP the response was in fact slightly more effective than covering the poor by relying on a PMT score (columns 5 and 6). Relying on the PMT score keeps delivering however higher impacts than a universal transfer (column 4) or including an indicator of who suffered a shock in the process of identifying beneficiaries (columns 7-10). Moreover, relying on a PMT score also delivers higher impacts for the 1% of GDP scenario – highlighting the fact that response designs also need to take into consideration the available budgetary envelope. Finally, note again that ranking of impacts for inequality remain very similar than ranking of impacts for the poverty gap squared.

# Proportional loss shock

Next, we analyze best responses when the shock affects all workers’ labor incomes proportionally, so that the shape of the income distribution and households’ welfare rankings (insofar labor income is concerned) remain the same. Overall, the ranking of responses remains similar to the one for the baseline COVID-19 shock, which reflects in part the fact that the way the baseline shock was simulated had limited impacts on the poor’s welfare rankings, making it very similar to a proportional loss shock.

_Table 7 : Albania – Proportional loss shock_

|**Targeting →**<br>**Budget↓**|**Original**<br>**Beneficiaries**|**Means**<br>**Testing (Ex-**<br>**Ante Poor)**|**Means**<br>**Testing**<br>**(Post Shock**<br>**Poor)**|**Universal **<br>**Pro**|**PMT Ex-ante **<br>**Eligibility ↓**<br>**portional Loss**<br>**Household**|<sup>**PMT Post**</sup><br>**Shock**<br><br>**Shock**|**Perfect**<br>**Loss**<br>**Indicator**|**Imperfect**<br>**Loss**<br>**Indicator**|**PMT Ex-**<br>**ante or**<br>**Perfect**<br>**Loss**<br>**Indicator**|**PMT Post**<br>**Shock or**<br>**Perfect**<br>**Loss**<br>**Indicator**|
|---|---|---|---|---|---|---|---|---|---|---|
|**Poverty Rate**|**(1a)**|**(2)**|**(3)**|**(4)**<br>**Pre-shock**|**(5)**<br>**: 0.320; Post-**|**(6)**<br>**shock: 0.361**|**(7)**<br>|**(8)**|**(9)**|**(10)**|
|(1)0.05% GDP|-0.008|-0.007|-0.010|-0.006|-0.007|-0.007|-0.007|-0.007|-0.006|-0.006|
|(2)1.0% GDP|-0.029|-0.045|-0.071|-0.025|-0.034|-0.033|-0.027|-0.027|-0.026|-0.026|
|**Poverty Gap Sqr.**||||**Pre-shock**|**: 6.721; Post-**|**shock: 7.493**|||||
|(1)0.05% GDP|-0.196|-0.314|-0.293|-0.164|-0.235|-0.227|-0.160|-0.158|-0.167|-0.168|
|(2)1.0% GDP|-1.336|-3.243|-2.973|-1.086|-2.091|-2.004|-1.012|-0.982|-1.149|-1.153|
|**Gini**<br>||||**Pre-shock**|**: 0.371; Post-**|**shock: 0.367**|||||
|(1)0.05% GDP|0.000|-0.001|-0.001|0.000|-0.001|-0.001|0.000|0.000|0.000|0.000|
|(2)1.0% GDP|-0.011|-0.029|-0.028|-0.008|-0.019|-0.018|-0.007|-0.007|-0.008|-0.008|
|**Coverage**|||||||||||
|(1)Average Transfer|$105.1|$70.3|$62.5|$18.7|$70.3|$62.6|$23.1|$30.5|$21.1|$20.9|
|(2)Average Transfer|$1,946.5|$1,301.7|$1,158.1|$345.5|$1,302.4|$1,159.3|$428.8|$562.6|$391.4|$388.0|
|Beneficiaries|137,071|204,973|230,390|772,197|204,856|230,155|622,231|471,915|681,636|687,644|
|Ex-antepoor|0.28|1.00|1.00|1.00|0.60|0.63|0.83|0.63|0.96|0.97|
|Ex-ante non-poor|0.13|0.00|0.06|1.00|0.17|0.21|0.90|0.69|0.93|0.94|
|Ex-ante Vulnerable|0.17|0.00|0.10|1.00|0.25|0.30|0.88|0.66|0.93|0.93|
|Post-shockpoor|0.26|0.89|1.00|1.00|0.57|0.61|0.85|0.63|0.96|0.97|

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

For Albania (Table 7), ranking of impacts as captured by the poverty gap squared remain similar across the two spending scenarios. Again, impacts that remain the closest from being able to perfectly identify the pre- or post-shock poor (columns 2 and 3) are achieved using a PMT score (columns 5 and 6). Just distributing the additional budget to existing beneficiaries (column 1a) remains a second-best response. However, it delivers still higher impacts than a 22