Poverty Probability Index (PPI) lookup table for Niger
ppiNER2013
A data frame with 9 columns and 101 rows:
scorePPI score
nlFoodFood poverty line
nl100National poverty line (100%)
nl150National poverty line (150%)
nl200National poverty line (200%)
extremeUSAID extreme poverty
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)
# Access Niger PPI table ppiNER2013#> score nlFood nl100 nl150 nl200 extreme ppp125 ppp200 ppp250 #> 0 0 40.2 90.4 99.1 100.0 72.2 92.0 100.0 100.0 #> 1 1 40.2 90.4 99.1 100.0 72.2 92.0 100.0 100.0 #> 2 2 40.2 90.4 99.1 100.0 72.2 92.0 100.0 100.0 #> 3 3 40.2 90.4 99.1 100.0 72.2 92.0 100.0 100.0 #> 4 4 40.2 90.4 99.1 100.0 72.2 92.0 100.0 100.0 #> 5 5 36.0 89.7 98.6 99.6 54.1 90.9 99.4 99.8 #> 6 6 36.0 89.7 98.6 99.6 54.1 90.9 99.4 99.8 #> 7 7 36.0 89.7 98.6 99.6 54.1 90.9 99.4 99.8 #> 8 8 36.0 89.7 98.6 99.6 54.1 90.9 99.4 99.8 #> 9 9 36.0 89.7 98.6 99.6 54.1 90.9 99.4 99.8 #> 10 10 25.1 78.7 96.6 99.1 41.7 85.6 98.4 99.2 #> 11 11 25.1 78.7 96.6 99.1 41.7 85.6 98.4 99.2 #> 12 12 25.1 78.7 96.6 99.1 41.7 85.6 98.4 99.2 #> 13 13 25.1 78.7 96.6 99.1 41.7 85.6 98.4 99.2 #> 14 14 25.1 78.7 96.6 99.1 41.7 85.6 98.4 99.2 #> 15 15 20.1 76.0 96.6 99.1 35.6 83.8 98.4 99.2 #> 16 16 20.1 76.0 96.6 99.1 35.6 83.8 98.4 99.2 #> 17 17 20.1 76.0 96.6 99.1 35.6 83.8 98.4 99.2 #> 18 18 20.1 76.0 96.6 99.1 35.6 83.8 98.4 99.2 #> 19 19 20.1 76.0 96.6 99.1 35.6 83.8 98.4 99.2 #> 20 20 15.0 67.9 96.6 99.1 33.2 74.6 98.4 99.2 #> 21 21 15.0 67.9 96.6 99.1 33.2 74.6 98.4 99.2 #> 22 22 15.0 67.9 96.6 99.1 33.2 74.6 98.4 99.2 #> 23 23 15.0 67.9 96.6 99.1 33.2 74.6 98.4 99.2 #> 24 24 15.0 67.9 96.6 99.1 33.2 74.6 98.4 99.2 #> 25 25 10.7 53.9 84.3 97.5 19.4 63.4 95.4 98.0 #> 26 26 10.7 53.9 84.3 97.5 19.4 63.4 95.4 98.0 #> 27 27 10.7 53.9 84.3 97.5 19.4 63.4 95.4 98.0 #> 28 28 10.7 53.9 84.3 97.5 19.4 63.4 95.4 98.0 #> 29 29 10.7 53.9 84.3 97.5 19.4 63.4 95.4 98.0 #> 30 30 4.9 40.0 73.0 92.6 12.5 50.2 88.2 94.4 #> 31 31 4.9 40.0 73.0 92.6 12.5 50.2 88.2 94.4 #> 32 32 4.9 40.0 73.0 92.6 12.5 50.2 88.2 94.4 #> 33 33 4.9 40.0 73.0 92.6 12.5 50.2 88.2 94.4 #> 34 34 4.9 40.0 73.0 92.6 12.5 50.2 88.2 94.4 #> 35 35 3.5 32.3 64.6 84.3 10.1 38.8 74.2 89.5 #> 36 36 3.5 32.3 64.6 84.3 10.1 38.8 74.2 89.5 #> 37 37 3.5 32.3 64.6 84.3 10.1 38.8 74.2 89.5 #> 38 38 3.5 32.3 64.6 84.3 10.1 38.8 74.2 89.5 #> 39 39 3.5 32.3 64.6 84.3 10.1 38.8 74.2 89.5 #> 40 40 2.9 32.3 60.7 79.8 10.1 36.4 70.6 83.0 #> 41 41 2.9 32.3 60.7 79.8 10.1 36.4 70.6 83.0 #> 42 42 2.9 32.3 60.7 79.8 10.1 36.4 70.6 83.0 #> 43 43 2.9 32.3 60.7 79.8 10.1 36.4 70.6 83.0 #> 44 44 2.9 32.3 60.7 79.8 10.1 36.4 70.6 83.0 #> 45 45 1.9 25.3 59.9 75.0 6.3 30.0 70.3 77.8 #> 46 46 1.9 25.3 59.9 75.0 6.3 30.0 70.3 77.8 #> 47 47 1.9 25.3 59.9 75.0 6.3 30.0 70.3 77.8 #> 48 48 1.9 25.3 59.9 75.0 6.3 30.0 70.3 77.8 #> 49 49 1.9 25.3 59.9 75.0 6.3 30.0 70.3 77.8 #> 50 50 0.6 11.4 54.4 73.4 1.4 16.8 67.4 76.9 #> 51 51 0.6 11.4 54.4 73.4 1.4 16.8 67.4 76.9 #> 52 52 0.6 11.4 54.4 73.4 1.4 16.8 67.4 76.9 #> 53 53 0.6 11.4 54.4 73.4 1.4 16.8 67.4 76.9 #> 54 54 0.6 11.4 54.4 73.4 1.4 16.8 67.4 76.9 #> 55 55 0.2 5.3 24.2 44.4 0.3 9.0 36.3 51.2 #> 56 56 0.2 5.3 24.2 44.4 0.3 9.0 36.3 51.2 #> 57 57 0.2 5.3 24.2 44.4 0.3 9.0 36.3 51.2 #> 58 58 0.2 5.3 24.2 44.4 0.3 9.0 36.3 51.2 #> 59 59 0.2 5.3 24.2 44.4 0.3 9.0 36.3 51.2 #> 60 60 0.0 1.7 17.2 39.6 0.2 5.4 33.1 47.5 #> 61 61 0.0 1.7 17.2 39.6 0.2 5.4 33.1 47.5 #> 62 62 0.0 1.7 17.2 39.6 0.2 5.4 33.1 47.5 #> 63 63 0.0 1.7 17.2 39.6 0.2 5.4 33.1 47.5 #> 64 64 0.0 1.7 17.2 39.6 0.2 5.4 33.1 47.5 #> 65 65 0.0 1.7 9.9 32.3 0.0 2.9 21.6 37.2 #> 66 66 0.0 1.7 9.9 32.3 0.0 2.9 21.6 37.2 #> 67 67 0.0 1.7 9.9 32.3 0.0 2.9 21.6 37.2 #> 68 68 0.0 1.7 9.9 32.3 0.0 2.9 21.6 37.2 #> 69 69 0.0 1.7 9.9 32.3 0.0 2.9 21.6 37.2 #> 70 70 0.0 0.0 5.7 14.0 0.0 0.7 7.8 19.7 #> 71 71 0.0 0.0 5.7 14.0 0.0 0.7 7.8 19.7 #> 72 72 0.0 0.0 5.7 14.0 0.0 0.7 7.8 19.7 #> 73 73 0.0 0.0 5.7 14.0 0.0 0.7 7.8 19.7 #> 74 74 0.0 0.0 5.7 14.0 0.0 0.7 7.8 19.7 #> 75 75 0.0 0.0 4.4 11.0 0.0 0.7 6.7 18.1 #> 76 76 0.0 0.0 4.4 11.0 0.0 0.7 6.7 18.1 #> 77 77 0.0 0.0 4.4 11.0 0.0 0.7 6.7 18.1 #> 78 78 0.0 0.0 4.4 11.0 0.0 0.7 6.7 18.1 #> 79 79 0.0 0.0 4.4 11.0 0.0 0.7 6.7 18.1 #> 80 80 0.0 0.0 4.4 11.0 0.0 0.7 6.7 16.6 #> 81 81 0.0 0.0 4.4 11.0 0.0 0.7 6.7 16.6 #> 82 82 0.0 0.0 4.4 11.0 0.0 0.7 6.7 16.6 #> 83 83 0.0 0.0 4.4 11.0 0.0 0.7 6.7 16.6 #> 84 84 0.0 0.0 4.4 11.0 0.0 0.7 6.7 16.6 #> 85 85 0.0 0.0 4.4 11.0 0.0 0.0 6.7 15.7 #> 86 86 0.0 0.0 4.4 11.0 0.0 0.0 6.7 15.7 #> 87 87 0.0 0.0 4.4 11.0 0.0 0.0 6.7 15.7 #> 88 88 0.0 0.0 4.4 11.0 0.0 0.0 6.7 15.7 #> 89 89 0.0 0.0 4.4 11.0 0.0 0.0 6.7 15.7 #> 90 90 0.0 0.0 0.0 3.5 0.0 0.0 0.0 8.4 #> 91 91 0.0 0.0 0.0 3.5 0.0 0.0 0.0 8.4 #> 92 92 0.0 0.0 0.0 3.5 0.0 0.0 0.0 8.4 #> 93 93 0.0 0.0 0.0 3.5 0.0 0.0 0.0 8.4 #> 94 94 0.0 0.0 0.0 3.5 0.0 0.0 0.0 8.4 #> 95 95 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 #> 96 96 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 #> 97 97 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 #> 98 98 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 #> 99 99 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 #> 100 100 0.0 0.0 0.0 0.0 0.0 0.0 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 ppiNER2013[ppiNER2013$score == ppiScore, ]#> score nlFood nl100 nl150 nl200 extreme ppp125 ppp200 ppp250 #> 50 50 0.6 11.4 54.4 73.4 1.4 16.8 67.4 76.9# Use subset() function to get the row of poverty probabilities corresponding # to specific PPI score ppiScore <- 50 subset(ppiNER2013, score == ppiScore)#> score nlFood nl100 nl150 nl200 extreme ppp125 ppp200 ppp250 #> 50 50 0.6 11.4 54.4 73.4 1.4 16.8 67.4 76.9# 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 ppiNER2013[ppiNER2013$score == ppiScore, "nl100"]#> [1] 11.4