A Simple Poverty Scorecard for the Philippines - About the Philippines

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9. TargetingWhen a program uses poverty scoring for targeting, households with scores at orbelow a cut-off are labeled targeted and treated—for program purposes—as if they arebelow a given poverty line. Households with scores above a cut-off are labeled nontargetedand treated—for program purposes—as if they are above a given poverty line.There is a distinction between targeting status (scoring at or below a targetingcut-off) and poverty status (income below a poverty line). Poverty status is a fact thatdepends on whether income is below a poverty line as directly measured by a survey. Incontrast, targeting status is a program’s policy choice that depends on a cut-off and onan indirect estimate from a scorecard.Targeting is successful when households truly below a poverty line are targeted(inclusion) and when households truly above a poverty line are not targeted (exclusion).Of course, no scorecard is perfect, and targeting is unsuccessful when households trulybelow a poverty line are not targeted (undercoverage) or when households truly above apoverty line are targeted (leakage). Figure 11 depicts these four possible targetingoutcomes. Targeting accuracy varies by cut-off; a higher cut-off has better inclusion(but greater leakage), while a lower cut-off has better exclusion (but higherundercoverage).A program should weigh these trade-offs when setting a cut-off. A formal way todo this is to assign net benefits—based on a program’s values and mission—to each of41
the four possible targeting outcomes and then to choose the cut-off that maximizes totalnet benefits (Adams and Hand, 2000; Hoadley and Oliver, 1998).Figure 12 shows the distribution of households by targeting outcome for thescorecard applied to the validation sample and the 2002 APIS. For an example cut-offof 35–39, outcomes for the national line are:• Inclusion: 23.8 percent are below the line and correctly targeted• Undercoverage: 7.9 percent are below the line and mistakenly not targeted• Leakage: 10.8 percent are above the line and mistakenly targeted• Exclusion: 57.6 percent are above the line and correctly not targetedIncreasing the cut-off to 40–44 improves inclusion and undercoverage butworsens leakage and exclusion:• Inclusion: 26.9 percent are below the line and correctly targeted• Undercoverage: 4.8 percent are below the line and mistakenly not targeted• Leakage: 16.5 percent are above the line and mistakenly targeted• Exclusion: 51.8 percent are above the line and correctly not targetedWhich cut-off is preferred depends on total net benefit. If each targeting outcomehas a per-household benefit or cost, then total net benefit for a given cut-off is:Benefit per household correctly included x Households correctly included –Cost per household mistakenly not covered x Households mistakenly not covered –Cost per household mistakenly leaked x Households mistakenly leaked +Benefit per household correctly excluded x Households correctly excluded.To set an optimal cut-off, a program would:• Assign benefits and costs to possible outcomes, based on its values and mission• Tally total net benefits for each cut-off using Figure 12 for a given poverty line• Select the cut-off with the highest total net benefitThe most difficult step is assigning benefits and costs to targeting outcomes. Anyprogram that uses targeting—with or without scoring—should thoughtfully consider42
Page 1 and 2: A Simple Poverty Scorecard for the
Page 3 and 4: comparable across organizations nor
Page 7 and 8: points or less for estimates of cha
Page 9 and 10: counted as if it had only one perso
Page 11 and 12: derived from the 2000 FIES by the N
Page 13 and 14: Given this, the USD1.25/day 2005 PP
Page 15 and 16: Reyes builds a scorecard using the
Page 17 and 18: that quickly show how estimated pov
Page 19 and 20: 3.3 Gwatkin et al.Gwatkin et al. (2
Page 21 and 22: includes income, the PCA index cann
Page 23 and 24: One of these one-indicator scorecar
Page 25 and 26: The scorecard in Figure 1 is ready
Page 27 and 28: If not determined by other factors,
Page 29 and 30: 6.1 Calibrating scores with poverty
Page 31 and 32: 6.2 Accuracy of estimates of househ
Page 33 and 34: The 90-percent confidence interval
Page 35 and 36: 7. Estimates of a group’s poverty
Page 37 and 38: To derive a sample-size formula (or
Page 39 and 40: 21.64n = 0. 55 ⋅⎛ ⎞⎜ ⎟
Page 41 and 42: 8. Estimates of changes in group po
Page 43 and 44: 2002 in the Philippines is the diff
Page 45 and 46: from 0.63 to 0.77 (Figure 10), sugg
Page 47: To illustrate the use of (8), suppo
Page 51 and 52: As an alternative to assigning bene
Page 53 and 54: at a point in time is always less t
Page 55 and 56: ReferencesAdams, N.M.; and D.J. Han
Page 57 and 58: Hentschel, Jesko; Lanjouw, Jean Ols
Page 59 and 60: Onwujekwe, Obinna; Hanson, Kara; an
Page 61 and 62: Stillwell, William G.; Barron, F. H
Page 63 and 64: Figure 1: A simple poverty scorecar
Page 65 and 66: Figure 3: Poverty indicators by unc
Page 67 and 68: Figure 3 (cont.): Poverty indicator
Page 69 and 70: National Poverty Line Tables(and Ta
Page 71 and 72: Figure 5 (National poverty line): D
Page 73 and 74: Figure 7 (National poverty line): B
Page 75 and 76: Figure 9 (National poverty line): D
Page 77 and 78: Figure 11 (All poverty lines): Poss
Page 79 and 80: Figure 13 (National poverty line):
Page 81 and 82: Figure 4 (National food poverty lin
Page 83 and 84: Figure 7 (National food poverty lin
Page 85 and 86: Figure 12 (National food poverty li
Page 87 and 88: USAID “Extreme” Poverty Line Ta
Page 89 and 90: Figure 5 (USAID “Extreme” pover
Page 91 and 92: Figure 9 (USAID “Extreme” pover
Page 93 and 94: Figure 13 (USAID “Extreme” pove
Page 95 and 96: Figure 4 (USD1.25/day 2005 PPP line
Page 97 and 98: Figure 7 (USD1.25/day 2005 PPP line
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Figure 12 (USD1.25/day 2005 PPP lin
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USD2.50/Day 2005 PPP Poverty Line T
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Figure 5 (USD2.50/day 2005 PPP line
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USD5.00/Day 2005 PPP Poverty Line T
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National Poverty Line Tablesfor 200
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Figure 9 (National line): Differenc
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Figure 13 (National line): For a gi
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Figure 7 (Food line): Bootstrapped
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Figure 12 (Food line): Households b
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USAID “Extreme” Poverty Line Ta
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Figure 9 (USAID “extreme” line)
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Figure 13 (USAID “extreme” line
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USD2.50/day 2005 PPP Poverty Line T
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USD5.00/day 2005 PPP Poverty Line T
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Poverty Lines and Poverty Rates,by
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Figure A2: All-urban Philippines an
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Figure A4: Agusan del Norte, povert
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Figure A6: Aklan, poverty lines and
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Figure A8: Antique, poverty lines a
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Figure A10: Bataan, poverty lines a
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Figure A12: Batangas, poverty lines
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Figure A14: Bohol, poverty lines an
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Figure A16: Bulacan, poverty lines
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Figure A18: Camarines Norte, povert
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Figure A20: Camiguin, poverty lines
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Figure A22: Catanduanes, poverty li
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Figure A24: Cebu, poverty lines and
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Figure A26: Davao del Sur, poverty
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Figure A28: Eastern Samar, poverty
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Figure A30: Ilocos Norte, poverty l
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Figure A32: Iloilo, poverty lines a
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Figure A34: Kalinga, poverty lines
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Figure A36: Laguna, poverty lines a
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Figure A38: Lanao del Sur, poverty
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Figure A40: Maguindanao, poverty li
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Figure A42: Marinduque, poverty lin
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Figure A44: Misamis Occidental, pov
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Figure A46: Mountain Province, pove
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Figure A48: Negros Oriental, povert
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Figure A50: Northern Samar, poverty
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Figure A52: Nueva Vizcaya, poverty
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Figure A54: Oriental Mindoro, pover
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Figure A56: Pampanga, poverty lines
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Figure A58: Quezon, poverty lines a
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Figure A60: Rizal, poverty lines an
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Figure A62: Western Samar, poverty
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Figure A64: Sorsogon, poverty lines
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Figure A66: Southern Leyte, poverty
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Figure A68: Sulu, poverty lines and
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Figure A70: Surigao del Sur, povert
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Figure A72: Tawi-tawi, poverty line
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Figure A74: Zamboanga del Norte, po
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Figure A76: 2nd District, poverty l
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Figure A78: 4th District, poverty l
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Figure A80: Biliran, poverty lines
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Figure A82: Saranggani, poverty lin
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Figure A84: Compostela Valley, pove
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Figure A86: Shariff Kabunsuan, pove
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Figure A88: Isabela City, poverty l
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