|SEVAT<br>||||||
|---|---|---|---|---|---|
|Non-referral failure|23.4|22.0|20.8|19.8|16.9|
|Non-referral success|76.6|78.0|79.2|80.2|83.1|
|Referral failure|45.8|36.2|35.7|31.2|28.3|
|Referral success|54.2|63.8|64.3|68.8|71.7|
|Non-referral of eligible cases|92.8|84.7|78.1|73.2|59.7|
|Referral of non-eligible cases|2.0|2.8|3.9|3.9|5.2|

_Source_ : Authors’ calculation using the Rapid Welfare Monitoring Survey (SWIFT) 2017-18.

# 4. Results

This section presents the results from applying the pseudo-PMT models within the legacy database of current humanitarian cash assistance beneficiaries. The total revised legacy caseload at the time of the analysis was 37,457 households corresponding to 224,736 individuals. Initial analysis suggests strong potential for a sequenced referral process. CLCI’s CT program only operates in the districts affected by the conflict in the Northern governorates of Iraq. The CLCI’s legacy database and, hence, the results presented in this section are focused on the Northern region only.<sup>13</sup> As discussed under the methodology section, there are two dimensions of analytical variation. First, the likely size of the MoLSA budgets will determine how many new households can enter the program. Second, the degree of confidence of a successful referral is taken into account. Referring more people will increase the absolute number of households being determined eligible after MoLSA assessment but will also increase the absolute number of households being referred and not being determined eligible, potentially resulting in resentment and mismanaged expectations. Thus, thresholds were set for differing degrees of confidence that a given pseudo-PMT score would result in eligibility when the full MoLSA PMT is applied. To simplify the exposition, we present the results for targeting poorest 18 percent of the population under low (50 percent), medium (70 percent), and high (90 percent) referral confidence. The results for other targeting thresholds – 25 percent, 30 percent, and 35 percent – are presented in the Annex.

## 4.1 Likely Eligibility of Humanitarian Beneficiaries under MoLSA Targeting

Initial aggregate results highlight the potential for both significant referral numbers and a sequenced strategy. Table 7 presents a first scenario with a smaller MoLSA budget for varying level of referral confidence. For a MoLSA program targeting the poorest 18 percent of the population and requiring a 90 percent confidence in the referral based on the pseudo-PMT, 24.3 percent of the existing humanitarian database would be eligible for referral. For the same level of targeting, 44.2 and 54.1 percent of the

> 13 At the time of the analysis, the new MCNA models were yet to be implemented for the humanitarian assistance eligibility. Besides it being limited to the Norther governorates, the beneficiary database was entirely based on the 2016 VM targeting methodology. Hence, the analysis in this section focuses on the 2016 VM pseudo-PMT model for the North only. Similar analytical exercise using the database resulting from employing the SEVAT targeting criteria and for other regions using other CWG partner’s database remains part of the next steps.

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