A Simple Poverty Scorecard for the Philippines

As an alternative to assigning benefits and costs to targeting outcomes and thenchoosing a cut-off to maximize total net benefit, a program could set a cut-off toachieve a desired poverty rate among targeted households. The third column of Figure13 (“% targeted who are poor”) shows, for the Philippines’ scorecard applied to thevalidation sample, the expected poverty rate among households who score at or below agiven cut-off. For the example of the national line, targeting households who score 35–39 or less would target 34.5 percent of all Filipino households and produce a povertyrate among those targeted of 68.8 percent.Figure 13 also reports two other measures of targeting accuracy. The first is aversion of coverage (“% of poor who are targeted”). For the example of the national lineand a cut-off of 35–39, 75.0 percent of all poor households are covered.The final targeting measure in Figure 13 is the number of successfully targetedpoor households for each non-poor household mistakenly targeted (right-most column).For the national line and a cut-off of 35–39, covering 2.2 poor households means leakingto 1 non-poor household.Although inclusion (and therefore targeting accuracy) appears in the BPAC formula,BPAC is in fact maximized (for a given poverty line and a single-step scorecard) whenthe difference between the estimated poverty rate and its true value is minimized,regardless of inclusion. Thus, selecting a scorecard on the basis of BPAC is equivalentto selecting on the basis of the difference between the estimated poverty rate and itstrue value (what IRIS calls “PIE”). It would therefore be clearer to drop the BPACnomenclature and simply discuss directly the accuracy and precision of the estimatedpoverty rate.44