A Simple Poverty Scorecard for the Philippines

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parents, it is less likely to own a refrigerator, one or more televisions, or have a salariedjob. Because scoring must assume that the future is exactly like the past, it cannotcapture such rapid change well.Under direct measurement, the sample-size formula for the estimate of change inpoverty rates between two points in time with two independent samples. is:n2z= 2 ⋅⎛ ⎞⎜ ⎟ ⋅ pˆ⋅ ( 1 − pˆ), (3)⎝ c ⎠where z, c, and pˆ are defined as in (1). Before measurement, pˆ is assumed equal atboth baseline and follow-up. n is the sample size at both baseline and follow-up. 26The method developed in the previous section can be used again to derive asample-size formula for indirect measurement via poverty scoring:n2z= α ⋅ 2 ⋅⎛ ⎞⎜ ⎟ ⋅ pˆ⋅ ( 1 − pˆ). (4)⎝ c ⎠The corresponding formula for the standard error σ of scoring’s estimate of2 ⋅ α ⋅ pˆ⋅ ( 1 − pˆ)change in two independent group’s poverty rate is σ =.nAs before, α is the average across sample sizes ≥ 256 of the ratio between theempirical sample size required by scoring for a given precision and the theoreticalsample size required under direct measurement. For the Philippines’ scorecard, α ranges26This means that, for a given precision and with direct measurement, estimating thechange in a poverty rate between two points in time requires four times as manymeasurements (not twice as many) as does estimating a poverty rate at a point in time.37
from 0.63 to 0.77 (Figure 10), suggesting that the indirect measurement of change withscoring is more precise than direct measurement with full-blown income surveys.To illustrate the use of (4) to determine sample size for estimating changes inpoverty rates across two independent samples, suppose the desired confidence level is 90percent (z = 1.64), the desired confidence interval is 2 percentage points (c = 0.02), thepoverty line is the national line, the baseline is 2004 and the follow-up is 2002, α = 0.63(from Figure 10), and pˆ = 0.313 (from Figure 2). Then the baseline sample size is21.64n = 0. 63 ⋅ 2 ⋅⎛ ⎞⎜ ⎟ ⋅ 0.313 ⋅ ( 1 − 0.313)= 1,822, and the follow-up sample size is also⎝ 0.02 ⎠1,822.8.4 Accuracy for estimated change for one sample, scored twiceIn general, the direct-measurement sample-size formula for this case is: 27n2[ pˆ⋅ ( 1 − pˆ) + pˆ⋅ ( 1 − pˆ) + 2 ⋅ pˆ⋅ pˆ]z=⎛ ⎞⎜ ⎟ ⋅1212 212112 21, (5)⎝ c ⎠where z and c are defined as in (1), ˆp12is the expected (before measurement) share ofall sampled cases that move from below the poverty line to above it, and ˆp21is theexpected share of all sampled cases that move from above the line to below it.How can a user set ˆp 12and ˆp 21? Before measurement, a reasonable assumption isthat the change in the poverty rate is zero. Then ˆp12= ˆp 21= ˆp *and (5) becomes:27See McNemar (1947) and Johnson (2007). John Pezzullo helped find this formula.38
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 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: 2002 in the Philippines is the diff
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
Page 77 and 78: Figure 11 (All poverty lines): Poss
Page 79 and 80: Figure 13 (National poverty line):
Page 81 and 82: Figure 4 (National food poverty lin
Page 83 and 84: Figure 7 (National food poverty lin
Page 85 and 86: Figure 12 (National food poverty li
Page 87 and 88: USAID “Extreme” Poverty Line Ta
Page 89 and 90: Figure 5 (USAID “Extreme” pover
Page 91 and 92: Figure 9 (USAID “Extreme” pover
Page 93 and 94: 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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