A Simple Poverty Scorecard for the Philippines

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A Simple Poverty Scorecard for the Philippines1. IntroductionThis paper presents an easy-to-use poverty scorecard that pro-poor programs inthe Philippines can use to estimate the likelihood that a household has income below agiven poverty line, to monitor groups’ poverty rates at a point in time, to track changesin groups’ poverty rates between two points in time, and to target services tohouseholds.The direct approach to poverty measurement via surveys is difficult and costly,asking households about a lengthy list of income categories. In contrast, the indirectapproach via poverty scoring is simple, quick, and inexpensive. It uses ten verifiableindicators (such as “What is the house’s roof made out of?” or “What type of toiletfacility does the family have?”) to get a score that is highly correlated with povertystatus as measured by the exhaustive survey.The poverty scorecard here differs from “proxy means tests” (Coady, Grosh, andHoddinott, 2002) in that it is tailored to the capabilities and purposes not of nationalgovernments but rather of local, pro-poor organizations. The feasible povertymeasurementoptions for these organizations are typically subjective and relative (suchas participatory wealth ranking by skilled field workers) or blunt (such as rules basedon land-ownership or housing quality). Results from these approaches are not1
comparable across organizations nor across countries, they may be costly, and theiraccuracy and precision are unknown.If an organization wants to know what share of its participants are below apoverty line (say, USD1.25/day at 2005 purchase-power parity for the MillenniumDevelopment Goals, or the poorest half of people below the national poverty line asrequired of USAID microenterprise partners), or if it wants to measure movementacross a poverty line (for example, to report to the Microcredit Summit Campaign),then it needs an income-based, objective tool with known accuracy. While incomesurveys are costly even for governments, many small, local organizations can implementan inexpensive scorecard that can serve for monitoring, management, and targeting.The statistical approach here aims to be understood by non-specialists. After all,if managers are to adopt poverty scoring on their own and apply it to inform theirdecisions, they must first trust that it works. Transparency and simplicity build trust.Getting “buy-in” matters; proxy means tests and regressions on the “determinants ofpoverty” have been around for three decades, but they are rarely used to informdecisions, not because they do not work, but because they are presented (when they arepresented at all) as tables of regression coefficients incomprehensible to lay people (withcryptic indicator names such as “HHSIZE_2”, negative values, many decimal places, andstandard errors). Thanks to the predictive-modeling phenomenon known as the “flatmax”, simple scorecards are about accurate as complex ones.2
Page 1: A Simple Poverty Scorecard for the
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 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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at a point in time is always less t
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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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