A Simple Poverty Scorecard for the Philippines

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How accurate are estimates of households’ poverty likelihoods? To measure, thescorecard is applied to 1,000 bootstrap samples of size n = 16,384 from the validationsub-sample. Bootstrapping entails: 17• Score each household in the validation sample• Draw a new bootstrap sample with replacement from the validation sample• For each score, compute the true poverty likelihood in the bootstrap sample, that is,the share of households with the score and income below a poverty line• For each score, record the difference between the estimated poverty likelihood(Figure 4) and the true poverty likelihood in the bootstrap sample• Repeat the previous three steps 1,000 times• For each score, report the average difference between estimated and true povertylikelihoods across the 1,000 bootstrap samples• For each score, report the two-sided interval containing the central 900, 950, or 990differences between estimated and true poverty likelihoodsFor each score range and for n = 16,384, Figure 7 shows the average differencebetween estimated and true poverty likelihoods as well as confidence intervals for thedifferences.For the national line in the validation sample, the average poverty likelihoodacross bootstrap samples for scores of 20–24 in the validation sample is too high by 1.4percentage points (Figure 7). For scores of 25–29, the estimate is too low by 1.3percentage points. 1817Efron and Tibshirani, 1993.18These differences are not zero, in spite of the estimator’s unbiasedness, because thescorecard comes from a single sample. The average difference by score would be zero ifsamples were repeatedly drawn from the population and split into sub-samples beforerepeating the entire scorecard-building process.25
The 90-percent confidence interval for the differences for scores of 20–24 is +/–2.4 percentage points (Figure 7). 19 This means that in 900 of 1,000 bootstraps, thedifference between the estimate and the true value is between –1.0 and 3.8 percentagepoints (because 1.4 – 2.4 = –1.0, and 1.4 + 2.4 = 3.8). In 950 of 1,000 bootstraps (95percent), the difference is 1.4 +/–2.8 percentage points, and in 990 of 1,000 bootstraps(99 percent), the difference is 1.4 +/–3.5 percentage points.For almost all score ranges, Figure 7 shows differences—sometimes large ones—between estimated poverty likelihoods and true values. This is because the validationsub-sample is a single sample that—thanks to sampling variation—differs indistribution from the construction/calibration sub-samples and from the Philippines’population. For targeting, however, what matters is less the difference in all scoreranges and more the difference in score ranges just above and below the targeting cutoff.This mitigates the effects of bias and sampling variation on targeting (Friedman,1997). Section 9 below looks at targeting accuracy in detail.Of course, if estimates of groups’ poverty rates are to be usefully accurate, thenerrors for individual households must largely cancel out. This is generally the case, asdiscussed in the next section.Another possible source of bias is overfitting. By construction, the scorecard hereis unbiased, but it may still be overfit when applied after July 2004 (the end date of the2004 APIS). That is, it may fit the 2004 APIS data so closely that it captures not only19Confidence intervals are a standard, widely understood measure of precision.26
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: 6.2 Accuracy of estimates of househ
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
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Page 47 and 48: To illustrate the use of (8), suppo
Page 49 and 50: the four possible targeting outcome
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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
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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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