Grama Niladharis to select beneficiaries. The household head characteristics used in Model 1 are unlikely to match those of the family head in all cases. Further, even the use of household assets across models may be inaccurate to the extent that household and family assets differ, and family size will be systematically lower than household size. It may therefore be worth considering using the household‐level model and adjusting program rules as needed to account for multiple families in a single (eligible) household.

Two further considerations are important. First, the choice of eligibility cutoff is key, since it influences both the number of eligible households and the PMT’s targeting performance. A higher cutoff simultaneously increases coverage among the target group and increases leakage of benefits to those outside the target group. The choice of cutoff is ultimately dependent on policy priorities, the size of the budget, and the desired average benefit. Second, it is recommended that the formula should be updated every 6 years to reflect structural changes in living standards and patterns of poverty. Such an update would be in line with the triennial timetable for the HIES. Finally, the basic household level information used to determine household eligibility should be collected and updated more frequently for existing beneficiaries or new applicants, taking into account budgetary and practical considerations, but ideally every two to three years.

We recommend a few steps for validating and refining the models presented in this paper. A pilot survey to collect additional data on household characteristics, assets and household consumption could be used to validate and refine the PMT formula. The data could also be used to verify the extent of measurement error in the PMT formula due to potential differences in definitions of household and family. This pilot could also be used to validate the PMT model by checking if particular types of households are systematically included or excluded (inclusion and exclusion errors). Additional criteria can be proposed for selecting beneficiaries in these scenarios. An important aspect of this pilot should be to verify the definition of the family that is used as the unit of program eligibility. This can be done by collecting information on the family structure within households. In addition to this, Grama Niladharis, who are responsible for selecting beneficiaries, can be enquired for the definition of family they use.

In addition to these issues, there are practical aspects of program implementation that we do not address here but may be critical in determining program success. A deep assessment of the current implementation process may help in identifying and addressing sources of implementation errors. Designing a proper grievance or appeals mechanism can help reduce potential exclusion errors. For example, smaller households are less likely to be poor and therefore more likely to receive a higher PMT score. However, this may miss smaller vulnerable households like those headed by single parents or widows with small children. A redress process can help address specific cases of exclusion like these. An effective outreach strategy to enroll the intended population can help in reducing exclusion errors. Similarly, designing an effective exit strategy to remove relevant beneficiaries from the program is crucial in order to reduce inclusion errors and to make the program budget‐friendly. Future extensions to our work could also examine the concept of vulnerability threshold, which is set above the targeting threshold and defines a set of households that can be monitored and assisted if needed ‐ i.e. in the event of a shock, the incidence of which can be monitored with rapid phone surveys.

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