Poverty Probability Index (PPI) lookup table for Angola
Format
A data frame with 9 columns and 101 rows:
scorePPI score
nl100National poverty line (100%)
nl150National poverty line (150%)
nl200National poverty line (200%)
half100Poorest half below 100% national
ppp125Below $1.25 per day purchasing power parity (2005)
ppp200Below $2.00 per day purchasing power parity (2005)
ppp250Below $2.50 per day purchasing power parity (2005)
ppp500Below $5.00 per day purchasing power parity (2005)
Examples
# Access Angola PPI table
ppiAGO2015
#> score nl100 nl150 nl200 half100 ppp125 ppp200 ppp250 ppp500
#> 0 0 100.0 100.0 100.0 86.5 100.0 100.0 100.0 100.0
#> 1 1 100.0 100.0 100.0 86.5 100.0 100.0 100.0 100.0
#> 2 2 100.0 100.0 100.0 86.5 100.0 100.0 100.0 100.0
#> 3 3 100.0 100.0 100.0 86.5 100.0 100.0 100.0 100.0
#> 4 4 100.0 100.0 100.0 86.5 100.0 100.0 100.0 100.0
#> 5 5 100.0 100.0 100.0 80.9 100.0 100.0 100.0 100.0
#> 6 6 100.0 100.0 100.0 80.9 100.0 100.0 100.0 100.0
#> 7 7 100.0 100.0 100.0 80.9 100.0 100.0 100.0 100.0
#> 8 8 100.0 100.0 100.0 80.9 100.0 100.0 100.0 100.0
#> 9 9 100.0 100.0 100.0 80.9 100.0 100.0 100.0 100.0
#> 10 10 98.9 99.4 100.0 79.1 99.3 100.0 100.0 100.0
#> 11 11 98.9 99.4 100.0 79.1 99.3 100.0 100.0 100.0
#> 12 12 98.9 99.4 100.0 79.1 99.3 100.0 100.0 100.0
#> 13 13 98.9 99.4 100.0 79.1 99.3 100.0 100.0 100.0
#> 14 14 98.9 99.4 100.0 79.1 99.3 100.0 100.0 100.0
#> 15 15 97.9 98.8 100.0 75.7 98.8 100.0 100.0 100.0
#> 16 16 97.9 98.8 100.0 75.7 98.8 100.0 100.0 100.0
#> 17 17 97.9 98.8 100.0 75.7 98.8 100.0 100.0 100.0
#> 18 18 97.9 98.8 100.0 75.7 98.8 100.0 100.0 100.0
#> 19 19 97.9 98.8 100.0 75.7 98.8 100.0 100.0 100.0
#> 20 20 86.1 97.8 99.9 56.0 94.1 99.9 100.0 100.0
#> 21 21 86.1 97.8 99.9 56.0 94.1 99.9 100.0 100.0
#> 22 22 86.1 97.8 99.9 56.0 94.1 99.9 100.0 100.0
#> 23 23 86.1 97.8 99.9 56.0 94.1 99.9 100.0 100.0
#> 24 24 86.1 97.8 99.9 56.0 94.1 99.9 100.0 100.0
#> 25 25 78.8 95.7 99.0 44.1 87.7 99.0 99.5 100.0
#> 26 26 78.8 95.7 99.0 44.1 87.7 99.0 99.5 100.0
#> 27 27 78.8 95.7 99.0 44.1 87.7 99.0 99.5 100.0
#> 28 28 78.8 95.7 99.0 44.1 87.7 99.0 99.5 100.0
#> 29 29 78.8 95.7 99.0 44.1 87.7 99.0 99.5 100.0
#> 30 30 68.0 92.2 97.8 29.1 78.7 97.0 98.4 100.0
#> 31 31 68.0 92.2 97.8 29.1 78.7 97.0 98.4 100.0
#> 32 32 68.0 92.2 97.8 29.1 78.7 97.0 98.4 100.0
#> 33 33 68.0 92.2 97.8 29.1 78.7 97.0 98.4 100.0
#> 34 34 68.0 92.2 97.8 29.1 78.7 97.0 98.4 100.0
#> 35 35 59.3 87.7 96.0 16.5 70.1 93.8 96.6 100.0
#> 36 36 59.3 87.7 96.0 16.5 70.1 93.8 96.6 100.0
#> 37 37 59.3 87.7 96.0 16.5 70.1 93.8 96.6 100.0
#> 38 38 59.3 87.7 96.0 16.5 70.1 93.8 96.6 100.0
#> 39 39 59.3 87.7 96.0 16.5 70.1 93.8 96.6 100.0
#> 40 40 40.0 76.1 88.2 13.0 52.0 86.4 92.7 99.8
#> 41 41 40.0 76.1 88.2 13.0 52.0 86.4 92.7 99.8
#> 42 42 40.0 76.1 88.2 13.0 52.0 86.4 92.7 99.8
#> 43 43 40.0 76.1 88.2 13.0 52.0 86.4 92.7 99.8
#> 44 44 40.0 76.1 88.2 13.0 52.0 86.4 92.7 99.8
#> 45 45 29.5 62.1 81.6 6.2 39.8 79.7 89.8 98.4
#> 46 46 29.5 62.1 81.6 6.2 39.8 79.7 89.8 98.4
#> 47 47 29.5 62.1 81.6 6.2 39.8 79.7 89.8 98.4
#> 48 48 29.5 62.1 81.6 6.2 39.8 79.7 89.8 98.4
#> 49 49 29.5 62.1 81.6 6.2 39.8 79.7 89.8 98.4
#> 50 50 10.0 44.5 69.9 3.5 19.1 64.1 80.4 97.4
#> 51 51 10.0 44.5 69.9 3.5 19.1 64.1 80.4 97.4
#> 52 52 10.0 44.5 69.9 3.5 19.1 64.1 80.4 97.4
#> 53 53 10.0 44.5 69.9 3.5 19.1 64.1 80.4 97.4
#> 54 54 10.0 44.5 69.9 3.5 19.1 64.1 80.4 97.4
#> 55 55 5.6 32.3 62.5 1.2 12.4 53.8 73.4 96.9
#> 56 56 5.6 32.3 62.5 1.2 12.4 53.8 73.4 96.9
#> 57 57 5.6 32.3 62.5 1.2 12.4 53.8 73.4 96.9
#> 58 58 5.6 32.3 62.5 1.2 12.4 53.8 73.4 96.9
#> 59 59 5.6 32.3 62.5 1.2 12.4 53.8 73.4 96.9
#> 60 60 4.6 30.9 55.9 1.1 10.4 49.8 65.9 94.8
#> 61 61 4.6 30.9 55.9 1.1 10.4 49.8 65.9 94.8
#> 62 62 4.6 30.9 55.9 1.1 10.4 49.8 65.9 94.8
#> 63 63 4.6 30.9 55.9 1.1 10.4 49.8 65.9 94.8
#> 64 64 4.6 30.9 55.9 1.1 10.4 49.8 65.9 94.8
#> 65 65 4.4 18.5 55.9 1.1 7.5 45.0 62.5 94.8
#> 66 66 4.4 18.5 55.9 1.1 7.5 45.0 62.5 94.8
#> 67 67 4.4 18.5 55.9 1.1 7.5 45.0 62.5 94.8
#> 68 68 4.4 18.5 55.9 1.1 7.5 45.0 62.5 94.8
#> 69 69 4.4 18.5 55.9 1.1 7.5 45.0 62.5 94.8
#> 70 70 4.4 18.5 33.4 1.1 7.5 28.1 42.1 93.9
#> 71 71 4.4 18.5 33.4 1.1 7.5 28.1 42.1 93.9
#> 72 72 4.4 18.5 33.4 1.1 7.5 28.1 42.1 93.9
#> 73 73 4.4 18.5 33.4 1.1 7.5 28.1 42.1 93.9
#> 74 74 4.4 18.5 33.4 1.1 7.5 28.1 42.1 93.9
#> 75 75 4.4 18.5 33.4 1.1 7.5 28.1 42.1 84.7
#> 76 76 4.4 18.5 33.4 1.1 7.5 28.1 42.1 84.7
#> 77 77 4.4 18.5 33.4 1.1 7.5 28.1 42.1 84.7
#> 78 78 4.4 18.5 33.4 1.1 7.5 28.1 42.1 84.7
#> 79 79 4.4 18.5 33.4 1.1 7.5 28.1 42.1 84.7
#> 80 80 4.4 18.5 33.4 1.1 7.5 28.1 42.1 80.9
#> 81 81 4.4 18.5 33.4 1.1 7.5 28.1 42.1 80.9
#> 82 82 4.4 18.5 33.4 1.1 7.5 28.1 42.1 80.9
#> 83 83 4.4 18.5 33.4 1.1 7.5 28.1 42.1 80.9
#> 84 84 4.4 18.5 33.4 1.1 7.5 28.1 42.1 80.9
#> 85 85 4.4 18.5 33.4 1.1 7.5 28.1 42.1 80.9
#> 86 86 4.4 18.5 33.4 1.1 7.5 28.1 42.1 80.9
#> 87 87 4.4 18.5 33.4 1.1 7.5 28.1 42.1 80.9
#> 88 88 4.4 18.5 33.4 1.1 7.5 28.1 42.1 80.9
#> 89 89 4.4 18.5 33.4 1.1 7.5 28.1 42.1 80.9
#> 90 90 4.4 18.5 33.4 1.1 7.5 28.1 42.1 80.9
#> 91 91 4.4 18.5 33.4 1.1 7.5 28.1 42.1 80.9
#> 92 92 4.4 18.5 33.4 1.1 7.5 28.1 42.1 80.9
#> 93 93 4.4 18.5 33.4 1.1 7.5 28.1 42.1 80.9
#> 94 94 4.4 18.5 33.4 1.1 7.5 28.1 42.1 80.9
#> 95 95 4.4 18.5 33.4 1.1 7.5 28.1 42.1 80.9
#> 96 96 4.4 18.5 33.4 1.1 7.5 28.1 42.1 80.9
#> 97 97 4.4 18.5 33.4 1.1 7.5 28.1 42.1 80.9
#> 98 98 4.4 18.5 33.4 1.1 7.5 28.1 42.1 80.9
#> 99 99 4.4 18.5 33.4 1.1 7.5 28.1 42.1 80.9
#> 100 100 4.4 18.5 33.4 1.1 7.5 28.1 42.1 80.9
# Given a specific PPI score (from 0 - 100), get the row of poverty
# probabilities from PPI table it corresponds to
ppiScore <- 50
ppiAGO2015[ppiAGO2015$score == ppiScore, ]
#> score nl100 nl150 nl200 half100 ppp125 ppp200 ppp250 ppp500
#> 50 50 10 44.5 69.9 3.5 19.1 64.1 80.4 97.4
# Use subset() function to get the row of poverty probabilities corresponding
# to specific PPI score
ppiScore <- 50
subset(ppiAGO2015, score == ppiScore)
#> score nl100 nl150 nl200 half100 ppp125 ppp200 ppp250 ppp500
#> 50 50 10 44.5 69.9 3.5 19.1 64.1 80.4 97.4
# Given a specific PPI score (from 0 - 100), get a poverty probability
# based on a specific poverty definition. In this example, the national
# poverty line definition
ppiScore <- 50
ppiAGO2015[ppiAGO2015$score == ppiScore, "extreme"]
#> NULL