Samurdhi allocation rule, increasing the poorest quintile’s share of benefits from 39.5 percent to 55.2 percent (at the 30% cutoff).

Another important goal is to minimize selection of richer households. Table 2 shows that the PMT far outperforms existing Samurdhi targeting in this respect as well. At the 30 percent cutoff, 0.8 and 0.2 percent of households in the top quintile and decile would be eligible for benefits, compared to an estimated 4.1 and 1.2 percent of those in the top quintile and decile that currently benefit from Samurdhi. The inclusion error, or the leakage rate, also increases as the cutoff score increases (Table 3). In setting the cutoff, therefore, policy makers face a trade‐off between undercoverage and leakage given a fixed program size and budget.

Table 3. Undercoverage, leakage and eligible share by cutoff score and model for the target population of Bottom 25%

||<br>|Model 1<br>||<br>|Model 2<br>||<br>|Model 3<br>||
|---|---|---|---|---|---|---|---|---|---|
|Cutoff score|Under‐<br>coverage|Leak<br>age|Eligible<br>share|Under‐<br>coverage|Leaka<br>ge|Eligible<br>share|Under‐<br>coverage|Leaka<br>ge|Eligible<br>share|
|Score: 856 (10th percentile)|89.6|11.5|2.9|89.9|12.4|2.9|90.8|11.8|2.6|
|Score: 868 (15th percentile)|78.1|19.5|6.8|77.9|17.9|6.7|78.2|19.8|6.8|
|Score: 878 (20th percentile)|64.6|25.5|11.9|65.1|24.7|11.6|66.7|25.2|11.1|
|Score: 887 (25th percentile)|51.3|30.5|17.5|52.5|30.6|17.1|52.3|31.0|17.3|
|Score: 895 (30th percentile)|39.3|36.0|23.7|40.0|36.0|23.5|41.0|36.6|23.2|
|Score: 902 (35th percentile)|29.2|41.3|30.2|30.1|41.1|29.7|30.6|41.7|29.8|
|Score: 909 (40th percentile)|21.0|46.1|36.6|21.8|45.9|36.1|22.0|46.4|36.4|

* The PMT score cutoffs represent different percentiles of the true per capita consumption distribution. For example, the score 887 is derived by multiplying the 25<sup>th</sup> percentile of the log of the true consumption distribution by 100.

The results from Table 2 and Table A2 collectively present a clear argument for the consideration of the proposed PMT model for targeting Samurdhi. A significantly higher share of the poor can be reached, and inclusion errors from the program can be simultaneously minimized relative to the program’s current targeting method. Models 2 and 3 perform a few percentage points worse than Model 1 in undercoverage and leakage at all cutoffs (Table 3). The advantage of Model 3 is its use of fewer predictors, making the model simpler and cheaper to implement, but at the cost of slightly higher undercoverage and leakage.

# _Choice of Target Population_

Sri Lanka’s official poverty headcount, based on the HIES 2016 data, was 4.1 percent. That survey reveals 18.8 percent of the population to be Samurdhi beneficiaries, but the true proportion is believed to be 20‐ 25 percent due to underreporting among survey respondents. For this reason, we recommend setting the target population for Samurdhi as the bottom 25 percent.

Table 4 presents the shares of beneficiaries identified by PMT, by (actual) consumption deciles under different cutoff rates and PMT models. The eligibility rates for all three models are more or less similar, with overall coverage of 17.5, 23.7 and 36.6 percent for target populations of 25, 30, and 40 percent 11