A Simple Poverty Scorecard for the Philippines

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The technical approach here is also innovative in how it associates scores withpoverty likelihoods, in the extent of its accuracy tests, and in how it derives sample-sizeformulas. Although these techniques are simple and/or standard, they have rarely ornever been applied to poverty scorecards.The scorecard (Figure 1) is based on the 2004 Annual Poverty Indicators Surveyconducted by the Philippines’ National Statistics Office. Indicators are selected to be:• Inexpensive to collect, easy to answer quickly, and simple to verify• Strongly correlated with poverty• Liable to change over time as poverty status changesAll points in the scorecard are non-negative integers, and total scores range from0 (most likely below a poverty line) to 100 (least likely below a poverty line). Nonspecialistscan collect data and tally scores on paper in the field in five to ten minutes.Poverty scoring can be used to estimate three basic quantities. First, it canestimate a particular household’s “poverty likelihood”, that is, the probability that thehousehold has per-capita income below a given poverty line.Second, poverty scoring can estimate the poverty rate of a group of householdsat a point in time. This is simply the average poverty likelihood among the householdsin the group.Third, poverty scoring can estimate changes in the poverty rate for a group ofhouseholds (or for two independent representative samples of households from the samepopulation) between two points in time. This estimate is the change in the averagepoverty likelihood of the households over time.3
Poverty scoring can also be used for targeting. To help managers choose atargeting cut-off, this paper reports several measures of targeting accuracy for a rangeof possible cut-offs.This paper presents a single scorecard (Figure 1) whose indicators and points arederived from household income data and the Philippines’s national poverty line. Scoresfrom this scorecard are calibrated to poverty likelihoods for eight poverty lines.The scorecard is constructed and calibrated using a sub-sample of the data fromthe 2004 APIS. Its accuracy is validated on a different sub-sample from the 2004 APISas well as on the entire 2002 APIS. While all three scoring estimators are unbiasedwhen applied to the population from which they were derived (that is, they match thetrue value on average in repeated samples from the same population from which thescorecard was built), they are—like all predictive models—biased to some extent whenapplied to a different population. 1Thus, while the indirect scoring approach is less costly than the direct surveyapproach, it is also biased. (The survey approach is unbiased by assumption.) There isbias because scoring must assume that the future relationship between indicators and1In the context of poverty scoring, examples of “different populations” include anationally representative sample at a different point in time or a non-representativesub-group (Tarozzi and Deaton, 2007).4
Page 1 and 2: A Simple Poverty Scorecard for the
Page 3: 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 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
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ReferencesAdams, N.M.; and D.J. Han
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Hentschel, Jesko; Lanjouw, Jean Ols
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Onwujekwe, Obinna; Hanson, Kara; an
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Stillwell, William G.; Barron, F. H
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Figure 1: A simple poverty scorecar
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Figure 3: Poverty indicators by unc
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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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