Poverty Probability Index (PPI) lookup table for Nigeria
Format
A data frame with 13 columns and 101 rows:
scorePPI score
nlFoodFood poverty line
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)
ppp400Below $4.00 per day purchasing power parity (2005)
ppp500Below $5.00 per day purchasing power parity (2005)
ppp190Below $1.90 per day purchasing power parity (2011)
ppp310Below $3.10 per day purchasing power parity (2011)
Examples
# Access Nigeria PPI table
ppiNGA2015
#> score nlFood nl100 nl150 nl200 half100 ppp125 ppp200 ppp250 ppp400 ppp500
#> 0 0 92.7 100.0 100.0 100.0 96.3 100.0 100.0 100.0 100.0 100.0
#> 1 1 92.7 100.0 100.0 100.0 96.3 100.0 100.0 100.0 100.0 100.0
#> 2 2 92.7 100.0 100.0 100.0 96.3 100.0 100.0 100.0 100.0 100.0
#> 3 3 92.7 100.0 100.0 100.0 96.3 100.0 100.0 100.0 100.0 100.0
#> 4 4 92.7 100.0 100.0 100.0 96.3 100.0 100.0 100.0 100.0 100.0
#> 5 5 92.7 100.0 100.0 100.0 96.3 100.0 100.0 100.0 100.0 100.0
#> 6 6 92.7 100.0 100.0 100.0 96.3 100.0 100.0 100.0 100.0 100.0
#> 7 7 92.7 100.0 100.0 100.0 96.3 100.0 100.0 100.0 100.0 100.0
#> 8 8 92.7 100.0 100.0 100.0 96.3 100.0 100.0 100.0 100.0 100.0
#> 9 9 92.7 100.0 100.0 100.0 96.3 100.0 100.0 100.0 100.0 100.0
#> 10 10 55.5 87.9 98.5 100.0 67.0 81.0 98.5 100.0 100.0 100.0
#> 11 11 55.5 87.9 98.5 100.0 67.0 81.0 98.5 100.0 100.0 100.0
#> 12 12 55.5 87.9 98.5 100.0 67.0 81.0 98.5 100.0 100.0 100.0
#> 13 13 55.5 87.9 98.5 100.0 67.0 81.0 98.5 100.0 100.0 100.0
#> 14 14 55.5 87.9 98.5 100.0 67.0 81.0 98.5 100.0 100.0 100.0
#> 15 15 51.9 82.1 98.5 100.0 60.1 77.7 98.5 100.0 100.0 100.0
#> 16 16 51.9 82.1 98.5 100.0 60.1 77.7 98.5 100.0 100.0 100.0
#> 17 17 51.9 82.1 98.5 100.0 60.1 77.7 98.5 100.0 100.0 100.0
#> 18 18 51.9 82.1 98.5 100.0 60.1 77.7 98.5 100.0 100.0 100.0
#> 19 19 51.9 82.1 98.5 100.0 60.1 77.7 98.5 100.0 100.0 100.0
#> 20 20 44.2 75.9 95.8 97.7 50.4 74.1 96.1 97.5 99.7 99.8
#> 21 21 44.2 75.9 95.8 97.7 50.4 74.1 96.1 97.5 99.7 99.8
#> 22 22 44.2 75.9 95.8 97.7 50.4 74.1 96.1 97.5 99.7 99.8
#> 23 23 44.2 75.9 95.8 97.7 50.4 74.1 96.1 97.5 99.7 99.8
#> 24 24 44.2 75.9 95.8 97.7 50.4 74.1 96.1 97.5 99.7 99.8
#> 25 25 28.8 69.6 92.8 96.8 37.6 63.1 92.9 96.4 99.6 99.8
#> 26 26 28.8 69.6 92.8 96.8 37.6 63.1 92.9 96.4 99.6 99.8
#> 27 27 28.8 69.6 92.8 96.8 37.6 63.1 92.9 96.4 99.6 99.8
#> 28 28 28.8 69.6 92.8 96.8 37.6 63.1 92.9 96.4 99.6 99.8
#> 29 29 28.8 69.6 92.8 96.8 37.6 63.1 92.9 96.4 99.6 99.8
#> 30 30 19.2 53.4 84.1 93.8 27.1 48.8 85.0 92.5 99.2 99.8
#> 31 31 19.2 53.4 84.1 93.8 27.1 48.8 85.0 92.5 99.2 99.8
#> 32 32 19.2 53.4 84.1 93.8 27.1 48.8 85.0 92.5 99.2 99.8
#> 33 33 19.2 53.4 84.1 93.8 27.1 48.8 85.0 92.5 99.2 99.8
#> 34 34 19.2 53.4 84.1 93.8 27.1 48.8 85.0 92.5 99.2 99.8
#> 35 35 12.7 40.1 75.4 90.9 18.5 35.8 76.6 87.5 98.5 99.2
#> 36 36 12.7 40.1 75.4 90.9 18.5 35.8 76.6 87.5 98.5 99.2
#> 37 37 12.7 40.1 75.4 90.9 18.5 35.8 76.6 87.5 98.5 99.2
#> 38 38 12.7 40.1 75.4 90.9 18.5 35.8 76.6 87.5 98.5 99.2
#> 39 39 12.7 40.1 75.4 90.9 18.5 35.8 76.6 87.5 98.5 99.2
#> 40 40 6.0 30.6 61.2 81.2 10.2 25.8 62.4 78.5 96.5 98.3
#> 41 41 6.0 30.6 61.2 81.2 10.2 25.8 62.4 78.5 96.5 98.3
#> 42 42 6.0 30.6 61.2 81.2 10.2 25.8 62.4 78.5 96.5 98.3
#> 43 43 6.0 30.6 61.2 81.2 10.2 25.8 62.4 78.5 96.5 98.3
#> 44 44 6.0 30.6 61.2 81.2 10.2 25.8 62.4 78.5 96.5 98.3
#> 45 45 4.4 20.9 55.6 78.8 8.3 16.8 56.7 75.5 95.3 98.1
#> 46 46 4.4 20.9 55.6 78.8 8.3 16.8 56.7 75.5 95.3 98.1
#> 47 47 4.4 20.9 55.6 78.8 8.3 16.8 56.7 75.5 95.3 98.1
#> 48 48 4.4 20.9 55.6 78.8 8.3 16.8 56.7 75.5 95.3 98.1
#> 49 49 4.4 20.9 55.6 78.8 8.3 16.8 56.7 75.5 95.3 98.1
#> 50 50 1.9 13.4 43.1 66.4 5.2 11.1 43.5 63.0 90.2 96.6
#> 51 51 1.9 13.4 43.1 66.4 5.2 11.1 43.5 63.0 90.2 96.6
#> 52 52 1.9 13.4 43.1 66.4 5.2 11.1 43.5 63.0 90.2 96.6
#> 53 53 1.9 13.4 43.1 66.4 5.2 11.1 43.5 63.0 90.2 96.6
#> 54 54 1.9 13.4 43.1 66.4 5.2 11.1 43.5 63.0 90.2 96.6
#> 55 55 1.1 5.0 32.0 54.4 2.0 4.6 32.5 49.2 84.3 91.6
#> 56 56 1.1 5.0 32.0 54.4 2.0 4.6 32.5 49.2 84.3 91.6
#> 57 57 1.1 5.0 32.0 54.4 2.0 4.6 32.5 49.2 84.3 91.6
#> 58 58 1.1 5.0 32.0 54.4 2.0 4.6 32.5 49.2 84.3 91.6
#> 59 59 1.1 5.0 32.0 54.4 2.0 4.6 32.5 49.2 84.3 91.6
#> 60 60 0.2 3.8 25.9 49.4 0.3 2.9 26.5 44.9 82.3 87.7
#> 61 61 0.2 3.8 25.9 49.4 0.3 2.9 26.5 44.9 82.3 87.7
#> 62 62 0.2 3.8 25.9 49.4 0.3 2.9 26.5 44.9 82.3 87.7
#> 63 63 0.2 3.8 25.9 49.4 0.3 2.9 26.5 44.9 82.3 87.7
#> 64 64 0.2 3.8 25.9 49.4 0.3 2.9 26.5 44.9 82.3 87.7
#> 65 65 0.2 2.7 14.2 35.4 0.3 2.5 14.3 32.1 70.0 83.2
#> 66 66 0.2 2.7 14.2 35.4 0.3 2.5 14.3 32.1 70.0 83.2
#> 67 67 0.2 2.7 14.2 35.4 0.3 2.5 14.3 32.1 70.0 83.2
#> 68 68 0.2 2.7 14.2 35.4 0.3 2.5 14.3 32.1 70.0 83.2
#> 69 69 0.2 2.7 14.2 35.4 0.3 2.5 14.3 32.1 70.0 83.2
#> 70 70 0.2 2.6 9.3 22.4 0.3 2.5 9.5 19.1 55.3 73.6
#> 71 71 0.2 2.6 9.3 22.4 0.3 2.5 9.5 19.1 55.3 73.6
#> 72 72 0.2 2.6 9.3 22.4 0.3 2.5 9.5 19.1 55.3 73.6
#> 73 73 0.2 2.6 9.3 22.4 0.3 2.5 9.5 19.1 55.3 73.6
#> 74 74 0.2 2.6 9.3 22.4 0.3 2.5 9.5 19.1 55.3 73.6
#> 75 75 0.0 0.0 2.7 7.9 0.0 0.0 2.7 7.1 43.2 59.9
#> 76 76 0.0 0.0 2.7 7.9 0.0 0.0 2.7 7.1 43.2 59.9
#> 77 77 0.0 0.0 2.7 7.9 0.0 0.0 2.7 7.1 43.2 59.9
#> 78 78 0.0 0.0 2.7 7.9 0.0 0.0 2.7 7.1 43.2 59.9
#> 79 79 0.0 0.0 2.7 7.9 0.0 0.0 2.7 7.1 43.2 59.9
#> 80 80 0.0 0.0 0.0 4.5 0.0 0.0 0.0 3.5 22.5 43.1
#> 81 81 0.0 0.0 0.0 4.5 0.0 0.0 0.0 3.5 22.5 43.1
#> 82 82 0.0 0.0 0.0 4.5 0.0 0.0 0.0 3.5 22.5 43.1
#> 83 83 0.0 0.0 0.0 4.5 0.0 0.0 0.0 3.5 22.5 43.1
#> 84 84 0.0 0.0 0.0 4.5 0.0 0.0 0.0 3.5 22.5 43.1
#> 85 85 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 11.7 26.9
#> 86 86 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 11.7 26.9
#> 87 87 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 11.7 26.9
#> 88 88 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 11.7 26.9
#> 89 89 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 11.7 26.9
#> 90 90 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.5 19.0
#> 91 91 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.5 19.0
#> 92 92 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.5 19.0
#> 93 93 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.5 19.0
#> 94 94 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.5 19.0
#> 95 95 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 13.4
#> 96 96 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 13.4
#> 97 97 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 13.4
#> 98 98 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 13.4
#> 99 99 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 13.4
#> 100 100 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 13.4
#> ppp190 ppp310
#> 0 96.3 100.0
#> 1 96.3 100.0
#> 2 96.3 100.0
#> 3 96.3 100.0
#> 4 96.3 100.0
#> 5 96.3 100.0
#> 6 96.3 100.0
#> 7 96.3 100.0
#> 8 96.3 100.0
#> 9 96.3 100.0
#> 10 75.7 95.4
#> 11 75.7 95.4
#> 12 75.7 95.4
#> 13 75.7 95.4
#> 14 75.7 95.4
#> 15 71.4 95.3
#> 16 71.4 95.3
#> 17 71.4 95.3
#> 18 71.4 95.3
#> 19 71.4 95.3
#> 20 62.5 92.0
#> 21 62.5 92.0
#> 22 62.5 92.0
#> 23 62.5 92.0
#> 24 62.5 92.0
#> 25 48.0 87.5
#> 26 48.0 87.5
#> 27 48.0 87.5
#> 28 48.0 87.5
#> 29 48.0 87.5
#> 30 36.8 76.4
#> 31 36.8 76.4
#> 32 36.8 76.4
#> 33 36.8 76.4
#> 34 36.8 76.4
#> 35 25.9 65.8
#> 36 25.9 65.8
#> 37 25.9 65.8
#> 38 25.9 65.8
#> 39 25.9 65.8
#> 40 15.4 50.7
#> 41 15.4 50.7
#> 42 15.4 50.7
#> 43 15.4 50.7
#> 44 15.4 50.7
#> 45 10.6 42.5
#> 46 10.6 42.5
#> 47 10.6 42.5
#> 48 10.6 42.5
#> 49 10.6 42.5
#> 50 7.9 32.0
#> 51 7.9 32.0
#> 52 7.9 32.0
#> 53 7.9 32.0
#> 54 7.9 32.0
#> 55 2.9 20.4
#> 56 2.9 20.4
#> 57 2.9 20.4
#> 58 2.9 20.4
#> 59 2.9 20.4
#> 60 0.5 15.4
#> 61 0.5 15.4
#> 62 0.5 15.4
#> 63 0.5 15.4
#> 64 0.5 15.4
#> 65 0.5 7.8
#> 66 0.5 7.8
#> 67 0.5 7.8
#> 68 0.5 7.8
#> 69 0.5 7.8
#> 70 0.5 4.8
#> 71 0.5 4.8
#> 72 0.5 4.8
#> 73 0.5 4.8
#> 74 0.5 4.8
#> 75 0.0 1.8
#> 76 0.0 1.8
#> 77 0.0 1.8
#> 78 0.0 1.8
#> 79 0.0 1.8
#> 80 0.0 0.0
#> 81 0.0 0.0
#> 82 0.0 0.0
#> 83 0.0 0.0
#> 84 0.0 0.0
#> 85 0.0 0.0
#> 86 0.0 0.0
#> 87 0.0 0.0
#> 88 0.0 0.0
#> 89 0.0 0.0
#> 90 0.0 0.0
#> 91 0.0 0.0
#> 92 0.0 0.0
#> 93 0.0 0.0
#> 94 0.0 0.0
#> 95 0.0 0.0
#> 96 0.0 0.0
#> 97 0.0 0.0
#> 98 0.0 0.0
#> 99 0.0 0.0
#> 100 0.0 0.0
# Given a specific PPI score (from 0 - 100), get the row of poverty
# probabilities from PPI table it corresponds to
ppiScore <- 50
ppiNGA2015[ppiNGA2015$score == ppiScore, ]
#> score nlFood nl100 nl150 nl200 half100 ppp125 ppp200 ppp250 ppp400 ppp500
#> 50 50 1.9 13.4 43.1 66.4 5.2 11.1 43.5 63 90.2 96.6
#> ppp190 ppp310
#> 50 7.9 32
# Use subset() function to get the row of poverty probabilities corresponding
# to specific PPI score
ppiScore <- 50
subset(ppiNGA2015, score == ppiScore)
#> score nlFood nl100 nl150 nl200 half100 ppp125 ppp200 ppp250 ppp400 ppp500
#> 50 50 1.9 13.4 43.1 66.4 5.2 11.1 43.5 63 90.2 96.6
#> ppp190 ppp310
#> 50 7.9 32
# 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
ppiNGA2015[ppiNGA2015$score == ppiScore, "nl100"]
#> [1] 13.4