A Simple Poverty Scorecard for the Philippines

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55.3 percent of the poor while targeting 25.8 percent of all households. For cut-offs of 70and 80 percent, coverage is 83.7 and 91.3 percent, while 46.5 and 55.7 percent of allhouseholds are targeted.For equivalent cut-offs, the scorecard here covers 62, 87, and 93 percent of thepoor, so it is more accurate. The difference more pronounced than this comparisonsuggests, because Reyes tests in-sample (using the same data used in scorecardconstruction), while this paper tests out-of-sample (using data not used in scorecardconstruction). Out-of-sample testing mimics how scoring is used in practice, and itgenerally shows less accuracy than in-sample tests; comparisons for scorecards inCopestake et al. (2005) and Hentschel et al. (2000) suggests reductions of 15–17 percent.Overall, the scorecard in Reyes (2006) is similar to the one here, except it isbased on the 2000 FIES, less user-friendly, and less accurate for targeting. Reyes doesnot address estimating poverty rates nor changes in poverty rates.3.2 Haslett and JonesHaslett and Jones (2005) use “poverty mapping” (Elbers, Lanjouw, and Lanjouw,2003) to estimate poverty rates for Philippines’ municipalities. They first construct apoverty scorecard based on income in the 2000 FIES, using only indicators found bothin the 2000 FIES and in the 2000 Census of Population and Housing. The povertyscorecard is then applied to the census data to estimate poverty rates for smaller areasthan would be possible with only the 2000 FIES. Finally, they make “poverty maps”9
that quickly show how estimated poverty rates vary across areas in a way that makessense to lay people.The poverty mapping in Haslett and Jones has much in common with thepoverty scoring here in that they both:• Build scorecards with nationally representative survey data and then apply them toother data on groups that may not be nationally representative• Use simple, verifiable indicators that are quick and inexpensive to collect• Provide unbiased estimates• Report standard errors for their estimates (or, equivalently, confidence intervals)• Estimate poverty likelihoods for individual households or persons• Estimate poverty rates for groups as averages of individual poverty likelihoods• Seek to be useful in practice and so aim to be understood by non-specialistsPoverty mapping has advantages over poverty scoring in that it:• Has formally established theoretical properties• Can be applied straightforwardly to measures of well-being beyond poverty rates• Requires less data to construct and calibrate a scorecard• Uses only indicators that appear in a censusPoverty scoring has advantages over poverty mapping in that it:• Is simpler in terms of both construction and application• Tests accuracy empirically• Associates poverty likelihoods with scores non-parametrically• Reports sample-size formulas (or equivalently, standard-error formulas)The basic difference between the two approaches is that poverty mapping seeksto help governments design pro-poor policies, while poverty scoring seeks to help small,local pro-poor organizations to manage their outreach when implementing policies. 1212Poverty mapping also appears to differ in that its developers say that it isinappropriate for targeting individual households or persons, while this paper supportstargeting as a legitimate, potentially useful application (Schreiner, 2008a).10
Page 1 and 2: A Simple Poverty Scorecard for the
Page 3 and 4: comparable across organizations nor
Page 5 and 6: Poverty scoring can also be used fo
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: Reyes builds a scorecard using the
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 and 48: To illustrate the use of (8), suppo
Page 49 and 50: the four possible targeting outcome
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
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National Poverty Line Tables(and Ta
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Figure 5 (National poverty line): D
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Figure 7 (National poverty line): B
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Figure 9 (National poverty line): D
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Figure 11 (All poverty lines): Poss
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Figure 13 (National poverty line):
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Figure 4 (National food poverty lin
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Figure 7 (National food poverty lin
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Figure 12 (National food poverty li
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USAID “Extreme” Poverty Line Ta
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Figure 5 (USAID “Extreme” pover
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Figure 9 (USAID “Extreme” pover
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Figure 13 (USAID “Extreme” pove
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Figure 4 (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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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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A Simple Poverty Scorecard for the Philippines

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