thresholds for these targeting rates are presented in Table A 2. Once the pseudo-PMT is implemented in the CLCI beneficiary database, one can use the information provided in this subsection to: (i) calculate the chances of being eligible for the MoLSA program for each beneficiary; and (ii) choose the optimal level of referral threshold, by referring to the graphs and tables presented in this subsection. MM _Table 5: PPMT cut-off thresholds for referral to MoLSA program targeting the poorest 18 percent_ WW _Table 5: PPMT cut-off thresholds for referral to MoLSA program targeting the poorest 18 percent_

|**Referral confidence**|**level =**|**90%**|**80%**|**70%**|**60%**|**50%**|
|---|---|---|---|---|---|---|
|**North**|VM|4.2|4.5|4.6|4.7|4.7|
||SEVAT|4.3|4.4|4.6|4.6|4.8|
|**Center-South**|VM|4.0|4.5|4.6|4.7|4.8|
||SEVAT|4.0|4.0|4.5|4.7|4.8|
|**Kurdistan**|VM|--|--|--|--|4.6|
||SEVAT|--|--|--|--|--|

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

As with any targeting methodology, we also propose performance criteria for the PPMT models. Figure 7 illustrates the ways to calculate these referral statistics or the performance criteria for PPMT. While the performance of the PMT (inclusion and exclusion errors) is evaluated with respect to the “true” poverty status, the performance of the PPMT is evaluated with respect to the PMT, i.e., how well does PPMT reproduce the eligibility decisions of the PMT? In Figure 7, pseudo-PMT score, , is represented on the vertical axis, the full PMT score, , is on the horizontal axis. The vertical dotted line represents the full MM PMT targeting threshold, , which changes only when the overall targeting (e.g. poorest 18, 25, 30 yy FF percent nationally) is changed. The horizontal dotted line is a probabilistic yy and, in contrast, changes FF with the chosen confidence level of referral. Lower the threshold, higher is the referral accuracy, but WW MM higher may also be exclusion error as many who would have been eligible under the full PMT will not be WW referred.

Cases in quadrant A in the figure are those that are eligible for the MoLSA program under the full PMT but would not be referred by the pseudo-PMT formula (e.g. this would be a failure to refer). Cases in quadrant B are correctly not referred (as they would not be eligible under the full PMT model); those in C are incorrectly referred (they are also not eligible under the full PMT model but the pseudo-PMT would indicate that they are); and those in D are successfully referred to the MoLSA program (both pseudo-PMT and full PMT indicate they are eligible). Based on these case types, one can calculate various referral statistics as shown in the figure. Statistic related to the “referral of ineligible cases” and the “non-referral of eligible cases” are comparable to the “inclusion error” and the “exclusion error” under the full-PMT.

Since both the MoLSA PMT and pseudo-PMT are devised on the SWIFT data, one can estimate these statistics using the SWIFT data. However, given the differences between the survey data (nationally representative) and the database, these statistics would be more indicative and “theoretical” in nature. To assess the referral statistics in the humanitarian database, one would have to apply the PPMT and PMT on the database, perhaps by implementing a light survey allowing the implementation of PPMT and PMT formula on a small sample of humanitarian cases.

12