A Simple Poverty Scorecard for the Philippines

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concepts in the scorecard is essential. For the example of Nigeria, Onwujekwe, Hanson,and Fox-Rushby (2006) find distressingly low inter-rater and test-retest correlations forindicators as seemingly simple and obvious as whether the household owns anautomobile. In Mexico, however, Martinelli and Parker (2007) find that errors byinterviewers and lies by respondents have negligible effects on targeting accuracy. Fornow, it is unknown whether these results are universal or country-specific.In terms of sampling design, an organization must make choices about:• Who will do the scoring• How scores will be recorded• What participants will be scored• How many participants will be scored• How frequently participants will be scored• Whether scoring will be applied at more than one point in time• Whether the same participants will be scored at more than one point in timeThe non-specialists who apply the scorecard with participants in the field can be:• Employees of the organization• Third-party contractorsResponses, scores, and poverty likelihoods can be recorded:• On paper in the field and then filed at an office• On paper in the field and then keyed into a database or spreadsheet at an office• On portable electronic devices in the field and downloaded to a databaseThe subjects to be scored can be:• All participants (or all new participants)• A representative sample of all participants (or of all new participants)• All participants (or all new participants) in a representative sample of branches• A representative sample of all participants (or of all new participants) in arepresentative sample of branches19
If not determined by other factors, the number of participants to be scored canbe derived from sample-size formulas (presented later) for a desired level of confidenceand a desired confidence interval.Frequency of application can be:• At in-take of new clients only (precluding measuring change in poverty rates)• As a once-off project for current participants (precluding measuring change)• Once a year (or at some other fixed time interval, allowing measuring change)• Each time a field worker visits a participant at home (allowing measuring change)When the scorecard is applied more than once in order to measure change inpoverty rates, it can be applied:• With a different set of participants• With the same set of participantsAn example set of choices were made by BRAC and ASA, two microlenders inBangladesh (each with 7 million participants) who are applying a poverty scorecardsimilar to the one here (Schreiner, 2006b). Their design is that loan officers in a randomsample of branches will score all participants each time they visit a homestead (aboutonce a year) as part of their standard due diligence prior to loan disbursement.Responses are recorded on paper in the field before being sent to a central office to beentered into a database. ASA’s and BRAC’s sampling plans cover 50,000–100,000participants each.20
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 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: The scorecard in Figure 1 is ready
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
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
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Figure 11 (All poverty lines): Poss
Page 79 and 80:
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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