Key Elements for PMT Methods: Traditional Models, Processes, and Machine Learning

380 | Revisiting Targeting in Social Assistance

BOX 6.4 (continued)

workers, but they were less reliable for the level of income. By triangulating data from survey and administrative sources, CRES and World Bank (2018) estimate that there is potential underreporting of onequarter of the wage bill, especially among nonwage contributors. Informal agricultural and nonagricultural incomes, as well as data on interest, capital income, and tax records, were confirmed as hard to verify. Finally, the report recommended steps to improve the accessibility and quality of data (removal of legal barriers in accessing administrative data while respecting the privacy of the provider, improvement in wage reporting, and, in some cases, moving from paper-based to electronic records). With these improvements, HMT could be implemented in the future for selecting beneficiaries for social programs in Tunisia. The country’s ongoing efforts include interoperability and a new household survey, which collected income data for the first time in Tunisia.

Source: CRES and World Bank (2018).

Key Elements for PMT Methods: Traditional Models, Processes, and Machine Learning

PMT is an inference-based assessment of household income or consumption, which is used when means testing or HMT is not available. The technical details require greater elaboration compared with other methods. PMT uses statistical methods to estimate a household’s level of income or consumption or its eligibility on a monetary welfare metric. Given that PMT estimates welfare indirectly based on other observable household characteristics, it is more complex and opaque than other means-testing assessments. At the same time, with many developing countries having large informal populations and no direct data on their income or consumption, PMT is a widespread targeting mechanism. Much of the remainder of this chapter is devoted to the technical details on how best to implement this imperfect but commonly used alternative.

PMT was initially developed in Latin American countries, beginning with Chile’s Ficha CAS (Comité de Asistencia Social [Social Assistance Committee]) in the early 1980s. Other early and iconic examples are Colombia’s System for the Selection of Beneficiaries for Social Programs (SISBEN), which was launched in 1994; Costa Rica’s Sistema de Información de la Población Objetivo (SIPO), which was inaugurated in 1999; and the registry for

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Mexico’s PROGRESA-Oportunidades-Prospera program, which operated from 1997 to 2019. PMT was developed to identify poor households in the context of high levels of informality, inequality, and poverty in the region. The method spread to many other countries that desired to focus their programs on the poor or vulnerable, mostly countries with significant rates of informality but often much lower levels of inequality.

A PMT is a model for translating readily observable household, community, and regional characteristics (explanatory variables) into an estimate of household consumption or income based on any of several forms of statistical modeling. The predicted weights obtained from the model are then applied to the information for program recipients to estimate household welfare and thus determine program eligibility. Household-specific information is usually obtained from households self-reporting in an office interview or home visit. Often the information is on characteristics that are easily observable, to prevent error and fraud. Location-specific characteristics obtained from administrative data or other systems—such as early warning systems, census data, poverty/vulnerability maps, or even for other sensing data (see the subsection on data-related limitations and considerations)—may be included to improve the estimations. Consumption or income can be estimated in PMT, but it is advisable to use the metric that is used by the country to measure and determine official poverty. The modeling is predictive only, with no pretense of providing causation (box 6.5).33

A handful of programs use scoring formulae that are not derived based on statistical modeling but on the expert opinion of program designers or the experience of social workers and their perception of the factors that are associated with poverty. Some programs start like this and then migrate to a statistically derived formula once the relevant microdata are collected and analyzed. The Kenyan Cash Transfer to Orphans and Vulnerable Children program started by using a poverty test that was based on 17 binary questions (yes/no questions recorded during application) in addition to three other methods: categorical targeting (to determine whether a child was an orphan, CBT for preselection of potential beneficiaries, and geographical targeting due to budgetary constraints. Any household exhibiting eight or more “yes” answers for these questions was classified as poor. After the evaluations34 showed limited efficiency in finding the poor, the program migrated to a full-fledged PMT scoring formula to replace the poverty test. The Moroccan Medical Assistance Plan is a health insurance waiver program that serves the poorest quintile of the population. It operated for a decade based on two ad hoc scoring formulae, for rural and urban areas, respectively, and will be replaced by a full-fledged PMT and social registry in the near future.35 In all these cases, the factor that triggered the change from an ad hoc formula to an analytically derived scoring model was a comparison between