considered – stressing further the importance of adapting the design of the response to the nature of the shock.

The remaining impacts follow a similar ranking as for Albania. If a policy maker would be able to perfectly identify the poor (either pre- or post-shock, columns 2 and 3), it would be the best response; and again, under the baseline shock focusing on the pre-shock poor delivers higher impacts (when measured with the poverty gap squared) because the extreme poor would receive a higher transfer. Using a PMT remains the best response available to a policy maker, while a universal transfer, or selecting beneficiaries through an indicator of losses suffered, deliver worse impacts.

Table 6 shows the results of the same simulations for North Macedonia. Note that there is one additional column (column 1b), which reflects the actual expansion of beneficiaries implemented by the government. To maintain some equity between original and new beneficiaries, under the original spending scenario of 0.09% of GDP we only give the funds to new (15,373) beneficiaries, but under the 1% of GDP spending scenario we give the additional funds to both old and new (33,112) beneficiaries.

In full similarity with Albania and Moldova results are very similar between the two spending scenarios, and the best impacts are delivered by covering all the poor (columns 3 and 4). Again, covering only the pre-shock poor deliver slightly higher impacts because the shock did not affect substantially extreme poverty, and by distributing the budget to less people the average transfer is higher, delivering higher impacts on extreme poverty.

_Table 6: North Macedonia, baseline shock: poverty and inequality impacts_ <!-- Start of picture text --> Baseline Shock<br>Targeting → Household<br>Eligibility ↓<br>PMT Ex-<br>Means PMT Post<br>Means Perfect Imperfect ante or<br>Orginal Testing PMT Ex- PMT Post Shock or<br>Expansion Testing (Ex- Universal Loss Loss Perfect<br>Beneficiaries (Post Shock ante Shock Pefect Loss<br>Ante Poor) Indicator Indicator Loss<br>Poor) Indicator<br>Budget ↓ Indicator<br>(1a) (1b) (2) (3) (4) (5) (6) (7) (8) (9) (10)<br>Poverty Rate Pre-shock: 0.169; Post-shock: 0.241<br>(1) 0.09% GDP -0.028 -0.025 -0.031 -0.034 -0.030 -0.029 -0.035 -0.028 -0.030 -0.028 -0.030<br>(2) 1.0% GDP -0.048 -0.085 -0.102 -0.119 -0.044 -0.068 -0.060 -0.049 -0.047 -0.050 -0.044<br>Poverty Gap Sqr. Pre-shock: 3.523; Post-shock: 4.547<br>(1) 0.09% GDP -1.025 -1.119 -1.202 -1.127 -0.807 -1.029 -1.025 -0.849 -0.783 -0.826 -0.879<br>(2) 1.0% GDP -1.601 -2.200 -3.556 -3.347 -1.413 -2.799 -2.580 -1.422 -1.365 -1.596 -1.550<br>Gini Pre-shock: 0.330; Post-shock: 0.344<br>(1) 0.09% GDP -0.012 -0.013 -0.013 -0.014 -0.012 -0.013 -0.013 -0.012 -0.011 -0.012 -0.013<br>(2) 1.0% GDP -0.022 -0.035 -0.044 -0.045 -0.018 -0.034 -0.031 -0.020 -0.019 -0.021 -0.021<br>Coverage<br>(1) Avera ge Transfer $ 1,415.8 $ 1,640.2 $ 345.8 $ 243.3 $ 44.4 $ 346.9 $ 243.3 $ 63.2 $ 85.4 $ 58.3 $ 56.5<br>(2) Avera ge Transfer $ 15,041.3 $ 8,096.3 $ 3,673.7 $ 2,584.4 $ 471.3 $ 3,685.7 $ 2,584.7 $ 671.4 $ 902.4 $ 619.0 $ 600.1<br> (1) 15,355<br>Beneficiaries 17,790 72,839 103,538 567,743 72,601 103,526 398,575 294,899 432,268 445,932<br>(2) 33,051<br>Ex-ante poor 0.15 0.30 1.00 1.00 1.00 0.59 0.68 0.61 0.46 0.89 0.91<br>Ex-ante non-poor 0.01 0.03 0.00 0.09 1.00 0.08 0.12 0.79 0.58 0.81 0.82<br>Ex-ante Vulnerable 0.02 0.06 0.00 0.22 1.00 0.15 0.22 0.78 0.57 0.82 0.84<br>Post-shock poor 0.11 0.27 0.70 1.00 1.00 0.48 0.57 0.73 0.55 0.92 0.94<br>Note: impacts are color-coded from dark red (highest impacts) to dark blue (lowest impact). Not all households declare<br>labor income.<br><!-- End of picture text --> Interestingly however, and in contrast with Albania, which chose to increase benefits to existing beneficiaries, the government expansion, while small (0.09% of GDP), delivered the 21