to the LASSO method over common stepwise variable selection methods, which may overfit on in-sample information.

We then use Ordinary Least Squares (OLS) regression of the log of per capita household consumption on the subset of variables identified by the LASSO estimator to develop the PMT. This OLS post-LASSO estimator performs at least as well as the LASSO estimator, but with less bias (Belloni and Chernozhukov 2013) and has been applied in analyses of other topics, such as wage regressions (Böheim and Stöllinger 2021). We have chosen this method, because OLS is common in the PMT literature due to its ease of interpretation and implementation (Brown, Ravallion, and Van de Walle 2018; Sebastian et al. 2018). While additional statistical and machine learning methods may be able to improve prediction accuracy (McBride and Nichols 2018), many of these models are often difficult to implement and interpret, especially as their complexity grows (Petch and Nelson 2021). Furthermore, it is important that the determination of beneficiaries is defensible and transparent or there is a potential for social unrest and erosion of credibility in public administration (Cameron and Shah 2014; Brown, Ravallion, and Van de Walle 2018).<sup>15</sup> The performance of the resulting PMT models is evaluated based on a number of accuracy criteria for PMT cutoff scores set to include the bottom 20%, 40%, and 60% of the observed household consumption distribution. These PMT cutoff scores were chosen to evaluate the PMT’s predictive ability to identify households living in a range of poverty—from approximately extreme poverty to total poverty.<sup>16</sup> The PMT evaluation criteria include total accuracy, poverty accuracy, undercoverage, and leakage commonly found in the PMT literature (McBride and Nichols 2018; Brown, Ravallion, and Van de Walle 2018). Table 1 below presents a confusion matrix for the possible poverty prediction outcomes based on the PMT. These include true positives (TP), where the PMT correctly identifies households living in poverty; false negatives (FN), where the PMT predicts a household to be non-poor when in fact they are; false positives (FP), where households are predicted to be in poverty when in fact they are not; and true negatives (TN), where households are correctly identified as non-poor.

Table 2 presents a list of the PMT evaluation criteria and their definitions. Total accuracy (TA) is the sum of correctly predicted poor (TP) and the correctly predicted non-poor (TN) as a percentage of all households in the sample (TP+FP+FN+TN). Poverty accuracy (PA) is the correctly predicted poor (TP) as a percentage

> 15 The PMT formula and weights may intentionally be kept secret from the public to reduce applicants’ incentives to inaccurately report household information in order to improve their chances of receiving public assistance. In this case, transparency remains important so that independent agencies may audit and verify social program targeting methods.

> 16 The World Bank’s Poverty & Equity Brief (Oct. 2021) reports 2014 poverty rates in Sudan at 12.2% under a $1.90 (2011 PPP) per day per capita poverty line, 44% under a $3.20 (2011 PPP) per day per capita poverty line, and 79.3% under a $5.50 (2011 PPP) per day per capita poverty line (World Bank, 2021).

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