defined according to nuclear family relationships.<sup>7</sup> It is therefore possible that more than one family may reside in a single household. Partly because of this, we present three models of the PMT: Model 1 includes 36 predictors of consumption, inclusive of 9 household head variables. Model 2 excludes head characteristics, and therefore includes 27 predictors, while a more parsimonious Model 3 has only 18 predictors.<sup>8</sup> Models 2 and 3 exclude head characteristics because current targeting of the Samurdhi program is done at the family level, and there may be significant differences between household and family head information. Although including more variables predicts welfare slightly better, including more variables in the model makes data collection more costly and increases the chance of errors. PMT models used in other countries are usually similar to Model 1 and include characteristics of the household head. In fact, the results of the three models are not substantially different from each other, and all three lead to considerably better targeting than the existing selection method.

While income typically fluctuates as a result of both permanent and transitory income shocks, households typically smooth consumption over time, so we use the log of per capita household consumption as the measure of welfare. Consumption comprises monthly expenditure on both durables and non‐durables, as well as the reported rental value of owner‐occupied housing. The DCS has published official poverty lines for each district based on the 2016 HIES. We use these district poverty lines to construct a district price index, and deflate the consumption measure by this index.<sup>9</sup> This enables us to set a single cutoff score for households across the country. The PMT score for a household is created by multiplying its predicted log real per capita consumption by 100.

The household head characteristics included in Model 1 are age, gender, marital status, and employment status (government, semi‐government, private, or self‐employment). Household demographics include categorical household size variables, the dependency ratio (the share of all household members that are older than 64 or younger than 15), and the highest education level of household members not currently enrolled in school. We include several household assets, including ownership of a computer, cooker, electric fan, refrigerator, land phone, washing machine, water pump, motorcycle, car or van, three‐ wheeler, and four‐wheel tractor. Housing quality and facilities variables included are bedrooms per person, an inside drinking water source, the use of electricity for lighting, the presence of floor tiles/terrazzo, walls made of brick/cabok/cement, and the presence of an inside toilet. Due to the household/family distinction, instead of considering the number of rooms within a household, we use rooms per capita.<sup>10</sup> Further, we consider the highest education level of all household members.

> 7 However, the precise definition of a family used by the Samurdhi program does not seem to exist, possibly to allow for context‐ specific interpretations of a family.

> 8See Appendix Table A1 for a comparison across models used in other papers developing a PMT in South Asia including Narayan and Yoshida (2005), Sharif (2009) and Hou (2008).

> 9 The spatial price index for a district is computed as the ratio of district poverty line to national poverty line.  Spatially price adjusted consumption is computed by deflating the nominal consumption by relevant spatial price indices of the districts. DCS computed the district poverty lines by using a spatial price index, considering the price variations among districts for the selected basket of food items consumed as per HIES 2016 survey.

> 10 We expect rooms per capita will be more similar for families and households than total rooms in the household.

7