Main Treatment 1: PMT.—In the PMT treatment, the government created for- mulas that mapped easily observable household characteristics into a single index using regression techniques. Specifically, it created a list of 49 indicators similar to those used in Indonesia’s 2008 registration of poor households, encompassing the household’s home attributes (wall type, roof type, etc.), assets (TV, motorbike, etc.), household composition, and household head’s education and occupation. Using pre- existing survey data, the government estimated the relationship between these vari- ables and household per capita consumption.11 While it collected the same set of indicators in all regions, the government estimated district-specific formulas due to the high variance in the best predictors of poverty across districts. On average, these regressions had an R2 of 0.48 (online Appendix Table 1).12
Government enumerators from BPS collected these indicators from all house- holds in the PMT subvillages by conducting a door-to-door survey. These data were then used to calculate a computer-generated poverty score for each household using the district-specific PMT formula. A list of beneficiaries was generated by selecting the predetermined number of households with the lowest PMT scores in each subvillage.