variables should be well correlated with poverty and have the following three characteristics (Coady et al., 2004):

- The set of variables should be small enough that it is feasible and cost‐effective to collect the required data from a significant share of the population.

- Each selected variable must be easy to measure and observe.

- Each selected variable should be difficult for the household to manipulate.

A broad range of candidate variables is typically drawn from a detailed survey data set—for example, a household budget survey or a multi‐topic survey—that includes detailed information on consumption, employment, education, health, housing, and family structure for a population representative sample. In this paper, we use HIES 2016, the most recent detailed household survey of this type available.

We first reduce the set of candidate variables through analysis of variance (ANOVA), in which we try to model the determinants of the log of per capita household consumption. We use ANOVA to discard variables with low partial sum of squares (SS), in other words, variables that are poor at explaining variation in consumption. This yields three plausible models of the determinants of the log of per capita household consumption. The first is a typical PMT model that includes characteristics of the household head but does not include any variables with an SS below 2. The second is restricted to variables with SS above 3.5, and excludes characteristics of the household head. The third model is much more selective and drops any variables with an SS below 10.<sup>6</sup> This exercise was initially conducted using the HIES 2012/13 and feedback was obtained from various Sri Lankan government agencies during 2015‐17 before finalizing the model specifications.

An OLS regression of the log of per capita household consumption on the three model specifications is then conducted to simplify interpretation of the magnitude and significance of coefficients for predicting poverty. We then measure the extent to which the model correctly identifies the poor. Following Narayan and Yoshida (2005), individuals are categorized into four groups according to whether their true and predicted welfare levels fall above or below the defined cutoff score. Those whose true welfare falls below the cutoff score constitute the _target group_ , while those with predicted welfare below the cutoff score constitute the _eligible group_ . Individuals whose true and predicted welfare measures put them on the same side of the cutoff are considered _targeting successes_ .

Prediction errors in the model may lead some eligible households to be excluded from the program (exclusion errors) while other ineligible households are included (inclusion errors). These are also known respectively as Type I and Type II errors, and are summarized in Figure 2. The _undercoverage rate_ is calculated by dividing the number of cases of Type I error by the total number of individuals who should get benefits (e1/n1). Increased undercoverage reduces the impact of the program on the welfare level of the intended beneficiaries, but carries no budgetary cost. The _leakage rate_ , on the other hand, is calculated by dividing the number in the Type II error category by the number of persons served by the program

> 6 In addition to some characteristics of head of household, the only exception is the variable “drinking water source inside”, which does not satisfy the SS cutoff criteria but is left in the model because it is considered to be an important predictor of poverty.

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