A Simple Poverty Scorecard for the Philippines

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4. Scorecard constructionAbout 60 potential indicators are initially prepared in the areas of:• Family composition (such as household size and female headship)• Education (such as school attendance of children)• Housing (such as the type of roofing material)• Ownership of durable goods (such as televisions and refrigerators)Each indicator is first screened with the entropy-based “uncertainty coefficient”(Goodman and Kruskal, 1979) that measures how well the indicator predicts poverty onits own. Figure 3 lists the candidate indicators, ranked by uncertainty coefficient.Responses for each indicator in Figure 3 are ordered starting with those most stronglyassociated with poverty.The scorecard also aims to measure changes in poverty through time. This meansthat, when selecting indicators and holding other considerations constant, preference isgiven to more sensitive indicators. For example, ownership of a television set isprobably more likely to change in response to changes in poverty than is the educationof the male head/spouse.The scorecard itself is built using the national poverty line and Logit regressionon the construction sub-sample (Figure 2). Indicator selection uses both judgment andstatistics (forward stepwise, based on “c”). The first step is to use Logit to build onescorecard for each candidate indicator. Each scorecard’s accuracy is taken as “c”, ameasure of ability to rank by poverty status (SAS Institute Inc., 2004).15
One of these one-indicator scorecards is then selected based on several factors(Schreiner et al., 2004; Zeller, 2004), including improvement in accuracy, likelihood ofacceptance by users (determined by simplicity, cost of collection, and “face validity” interms of experience, theory, and common sense), sensitivity to changes in povertystatus, variety among indicators, and verifiability.A series of two-indicator scorecards are then built, each based on the oneindicatorscorecard selected from the first step, with a second candidate indicatoradded. The best two-indicator scorecard is then selected, again based on “c” andjudgment. These steps are repeated until the scorecard has 10 indicators.The final step is to transform the Logit coefficients into non-negative integerssuch that total scores range from 0 (most likely below a poverty line) to 100 (leastlikely below a poverty line).This algorithm is the Logit analogue to the familiar R 2 -based stepwise with leastsquaresregression. It differs from naïve stepwise in that the criteria for selectingindicators include not only statistical accuracy but also judgment and non-statisticalfactors. The use of non-statistical criteria can improve robustness through time andhelps ensure that indicators are simple and make sense to users.The single poverty scorecard here applies to all of the Philippines. Evidence fromIndia and Mexico (Schreiner, 2006a and 2005a), Sri Lanka (Narayan and Yoshida,2005), and Jamaica (Grosh and Baker, 1995) suggests that segmenting scorecards byurban/rural does not improve accuracy much.16
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: includes income, the PCA index cann
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
Page 69 and 70: National Poverty Line Tables(and Ta
Page 71 and 72: 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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