probabilistically. That is, given a pseudo-PMT score, , of a household we can calculate its probability of qualifying for SSN under the full PMT.<sup>10</sup> To measure the probabilistic eligibility, we fit the following MM yy ii logistic model: Pr(P i 0 1 ) (2) MM where, P i is household ’s qualification statues (= 1 if eligible, 0 otherwise) under the full PMT PP p pprr = 1) = FF(ββ + ββ yy for a specific MoLSA targeting rate ( ), is household’s PPMT score and ) . Figures PP p pprr ii A1 to A6 in Annex A present these probabilities for the national targeting rate of the bottom 18 percent MM rr yy FF(zz) = ee<sup>zz</sup> /(1 −ee<sup>zz</sup> ( ) – the national poverty rate without adjusting for the SWIFT non-coverage of the most conflict affected districts.<sup>11</sup> As expected, a clear negative relationship between the probability of accurate referral rr = 18% and PPMT scores is observed, i.e., households that receive a low score using only the common set of variables (PPMT score) have a higher probability of having a low score according to the full PMT model and thus qualifying for MoLSA’s cash transfer program. For instance, households in the North with a PPMT score of 4.2 or less have a 90 percent probability of qualifying for the SSN program using the full MoLSA PMT to screen potential recipients (Table 5). Likewise, the referral threshold with 90 percent confidence of eligibility in Central-Southern region is 4.0 (Table 5). Given relatively low level of poverty in the Kurdistan region, almost no one from the region will qualify for the government program when targeting for the MoLSA CT is set at bottom 18 percent nationally (Table 5). As a consequence, there are no PPMT referral thresholds for the region at high confidence levels.<sup>12</sup> For practical purpose, these cutoff values can be interpreted as the referral threshold ( ), below which humanitarian caseloads would be referred to MoLSA for screening. For a given program size i.e. targeting mm WW bottom 18 percent nationally, changes with the level of referral confidence. However, there is a tradeoff between the confidence level and the number of referrals – for each program size number of mm WW household that would be referred to the government program decreased with confidence level. For example, if the PPMT score referral threshold is increased to 4.7 (VM-based score) or 4.8 (SEVAT-based score) in the North, it will entail screening more households but the referred households will have a 50 percent chance of being eligible for the government’s cash transfer program.

For a given targeting rate, the PPMT referral thresholds corresponding to different confidence levels can be calculated for households in different regions of the country. Figures A1 to A6 in Annex A show the probability of referral to the MoLSA program (targeting the bottom 18 percent nationally) based on the VM- and SEVAT-based PPMT scores for the North, Central-South, and Kurdistan. These thresholds can be adjusted according to the capacity of the national SP system to screen referred households and absorb them into the system. We repeat the exercise for several other targeting rates namely: poorest 15 percent, poorest 25 percent, poorest 30 percent, and poorest 35 percent nationally. The PPMT cut-off World Bank (2019) for more. Hence, it is possible that, in addition to the smaller set, pseudo-PMT may be employing inferior proxies.

> 10 Refer to Box A 1, which presents a simple example for a quick understanding of how PPMT model and its referral probabilities can be calculated.

> 11 The continuous security issues prevented data collection from 14 qhadas (districts) and the final SWIFT sample covers only 106 of the 120 districts in the country. See <mark>Sharma & Wai-Poi (2019).</mark>

> 12 Currently there is no MoLSA’s CT program in Kurdistan region. However, a full PMT model was devised just like in the other three regions using the SWIFT 2017/18 data and same methodology for an emergency CT pilot that is currently being prepared by the Kurdistan Regional Government (KRG) MoLSA and the World Bank.

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