model. In practice, using probit or logit to determine the weights makes very little difference, as leakage and undercoverage rates change by less than one percentage point.

The final exercise conducts a simple out‐of‐sample test to examine the sensitivity of the targeting performance to the choice of the sample used to estimate the PMT model. We implement this as a two‐ fold cross‐validation exercise in which we split the sample into two parts (say, A and B). We first estimate the PMT model using sample A, predict consumption for sample B, and compute performance measures for sample B. We then conduct the same exercise with sample B. We finally compare the performance of these two scenarios to the one with the full sample. We assume that the bottom 25% of the population is targeted and the 25<sup>th</sup> percentile PMT cutoff score is used. Table A9 presents the results of this exercise. Although we only see small differences in the performance measures across the samples, these differences are likely to be aggravated in smaller samples. This exercise is difficult to evaluate since we do not estimate the standard errors of the performance measures. But if Table A9 gives any indication, out‐of‐sample validation is not a concern if a sample as large as HIES 2016 is used to estimate the PMT model.

# VI. Conclusion

The PMT method has been successfully used to distribute social safety net benefits in many countries. A PMT formula is preferable to a verified means test (e.g. an income criterion) in the Sri Lankan context, because reliable data on income are not available for many workers. The PMT is also attractive because it provides a single index of welfare that can be used for harmonized targeting of several different social welfare programs. Finally, an advantage of the PMT over other targeting methods is its transparency and verifiability. It provides a clear, unbiased determination of a household’s eligibility whereas other approaches, such as simple means tests and community‐level targeting, are more susceptible to reporting errors and imprecise definitions, and may be inconsistently applied in different areas. Despite these arguments for using PMT in Sri Lanka, we recognize that this method of identifying beneficiaries of government transfers may fall short in effectively reducing poverty (Brown et al. 2016).

We have estimated and tested three models that parsimoniously predict household welfare from observable and verifiable information. These models have similar targeting performance, but the model that includes characteristics of the household head slightly outperforms those that exclude these characteristics. Any of these proposed PMT formulas would significantly improve the targeting efficiency and coverage of Samurdhi relative to the existing targeting approach, which is based on self‐declared income. We expect that an examination of other programs, including elderly support and disability benefit programs, would show similar potential improvements in targeting performance from adopting a PMT.

An important issue when considering a PMT in the Sri Lankan context is that Samurdhi eligibility is currently determined separately for each family, whereas the PMT formula is based on a household‐level survey. It is not possible to accurately verify whether the definition of the household unit used in the HIES is comparable to that of the family unit used by the Samurdhi program. This is complicated by the fact that neither does a formal definition of a family exist, nor does there appear to be one applied uniformly by 19