A Simple Poverty Scorecard for the Philippines

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5. Practical guidelines for scorecard useThe main challenge of scorecard design is not to squeeze out the last drops ofaccuracy but rather to improve the chances that scoring is actually used (Schreiner,2005b). When scoring projects fail, the reason is not usually technical inaccuracy butrather the failure of an organization to decide to do what is needed to integrate scoringin its processes and to learn to use it properly (Schreiner, 2002). After all, mostreasonable scorecards predict tolerably well, thanks to the empirical phenomenon knownas the “flat max” (Hand, 2006; Baesens et al., 2003; Lovie and Lovie, 1986; Kolesar andShowers, 1985; Stillwell, Hutton, and Edwards, 1983; Dawes, 1979; Wainer, 1976; Myersand Forgy, 1963). The bottleneck is less technical and more human, not statistics butorganizational change management. Accuracy is easier to achieve than adoption.The scorecard here is designed to encourage understanding and trust so thatusers will adopt it and use it properly. Of course, accuracy matters, but it is balancedagainst simplicity, ease-of-use, and “face validity”. Programs are more likely to collectdata, compute scores, and pay attention to the results if, in their view, scoring does notmake a lot of “extra” work and if the whole process generally seems to make sense.To this end, the scorecard here fits on one page (Figure 1). The constructionprocess, indicators, and points are simple and transparent. “Extra” work is minimized;non-specialists can compute scores by hand in the field because the scorecard has:• Only 10 indicators• Only categorical indicators• Simple weights (non-negative integers, no arithmetic beyond addition)17
The scorecard in Figure 1 is ready to be photocopied and can be used with asimple spreadsheet database (Microfinance Risk Management, L.L.C., 2008) thatrecords identifying information for the participant, dates, indicator values, scores, andpoverty likelihoods.A field worker using the paper scorecard would:• Record participant identifiers• Read each question from the scorecard• Circle the response and its points• Write the points in the far-right column• Add up the points to get the total score• Implement targeting policy (if any)• Deliver the paper scorecard to a central office for filing or data entryOf course, field workers must be trained. Quality outputs depend on qualityinputs. If organizations or field workers gather their own data and have an incentive toexaggerate poverty rates (for example, if funders reward them for higher poverty rates),then it is wise to do on-going quality control via data review and random audits (Matuland Kline, 2003). 14 IRIS Center (2007a) and Toohig (2007) are useful nuts-and-boltsguides for budgeting, training field workers and supervisors, logistics, sampling,interviewing, piloting, recording data, and controlling quality.In particular, while collecting scorecard indicators is relatively easier thanalternatives, it is still absolutely difficult. Training and explicit definitions of terms and14If an organization does not want field workers to know the points associated withindicators, then they can use the version of Figure 1 without points and apply thepoints later in a spreadsheet or database at the central office.18
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: One of these one-indicator scorecar
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
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
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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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