Iraqi households through its targeted cash transfers, was the best placed institution to meet the needs of conflict-affected households vulnerable to future shocks or poverty.<sup>4</sup> One of the main outcomes of the workshop was the agreement to undertake a joint desk review by members of the CLCI, CWG, and the WB to identify the potential for closer alignment between humanitarian cash assistance and MoLSA cash transfer program, as well as possible transitional pathways from the former to the latter. To do so, one of the first tasks was to gauge what share of humanitarian caseloads would be eligible for MoLSA’s cash transfer program were they to be referred to the MoLSA program. Although the proxies used in the models were different, both the national and humanitarian CT programs used proxy-means-test (PMT) method to assess eligibility. Had all the information been available to apply the national PMT on the CLCI’s database, this step would have been straightforward. But it was not possible to evaluate MoLSA’s PMT on the CLCI’s current caseload because not all the indicators necessary for the national PMT were available in the CLCI database.

To overcome the impediment, we develop an analytical tool called pseudo-PMT (PPMT) that provides a bridge between the targeting models of the two CT programs for potential beneficiaries. Like a regular PMT, the PPMT relies on proxies and their weights to calculate potential beneficiaries’ well-being score (PPMT-score) and uses it to identify program eligibility. Unlike in a regular PMT where an optimum set of covariates that correlates best with the household welfare variable (consumption, income, etc.) are selected from an extensive list, the PPMT uses only proxies that are common to both the humanitarian’s and the government’s CTP targeting formulas.

By design the PPMT cannot reproduce the score that would be produced by the full PMT model. Therefore, we can only assess the probability that a case with a certain PPMT score will be eligible for the MoLSA’s program determined using the full PMT model. Since both the PPMT and the full PMT for MoLSA’s program are devised using the same national household survey, the 2017/18 Survey of WellBeing via Instant and Frequent Tracking (SWIFT), we use the data to calculate the probability of qualification under the full PMT given a household’s PPMT score. Households with lower PPMT score will have higher probability of being eligible under the full PMT and vice-versa. One can be conservative in making the referral recommendation, i.e., referring a caseload to MoLSA only if its PPMT score is very low so that its probability of selection for the MoLSA program is very high. The benefit of doing so is a high “hit” rate, but it may be costly in that many cases that would be eligible are not referred. On the other hand, one could be liberal in making the referral recommendation by increasing the PPMT score threshold below which cases will be recommended to MoLSA’s review. This reduces the chances that potentially eligible cases do not get an opportunity for MoLSA’s screening because they are not referred. However, it will add significant administrative burden on MoLSA as many cases will be referred for verification.

Results from applying the pseudo-PMT models to the CLCI’s legacy database of current and past beneficiaries (37,457 households corresponding to 224,736 individual) suggest potential for both significant referral numbers and a sequenced referral strategy.<sup>5</sup> The analysis suggests that referring only those in the bottom 18 percent nationally (the national poverty rate according to SWIFT 2017/18 survey

> 4 Throughout the paper, Social safety net (SSN), Social Protection Network (SPN), national program, MoLSA’s cash transfer program etc. are used loosely and interchangeably to refer to the government’s cash transfer program delivered by the Ministry of Social and Labour Affairs (MoLSA).

> 5 Due to the CLCI’s CT program coverage, the database is limited to the Northern or ‘conflict-affected’ governorates: Anbar, Diyala, Kirkuk, Ninewa, Salah al-Din.

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