Poverty Probability Index (PPI) lookup table for Timor Leste
ppiTLS2013
A data frame with 8 columns and 101 rows:
scorePPI score
nl100National lower poverty line (100%)
nu100National upper poverty line (100%)
nu150National upper poverty line (150%)
nu200National upper poverty line (200%)
extremeUSAID extreme poverty
ppp125Below $1.25 per day purchasing power parity (2005)
ppp250Below $2.50 per day purchasing power parity (2005)
# Access Timor Leste PPI table ppiTLS2013#> score nl100 nu100 nu150 nu200 extreme ppp125 ppp250 #> 0 0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 #> 1 1 100.0 100.0 100.0 100.0 100.0 100.0 100.0 #> 2 2 100.0 100.0 100.0 100.0 100.0 100.0 100.0 #> 3 3 100.0 100.0 100.0 100.0 100.0 100.0 100.0 #> 4 4 100.0 100.0 100.0 100.0 100.0 100.0 100.0 #> 5 5 100.0 100.0 100.0 100.0 88.6 100.0 100.0 #> 6 6 100.0 100.0 100.0 100.0 88.6 100.0 100.0 #> 7 7 100.0 100.0 100.0 100.0 88.6 100.0 100.0 #> 8 8 100.0 100.0 100.0 100.0 88.6 100.0 100.0 #> 9 9 100.0 100.0 100.0 100.0 88.6 100.0 100.0 #> 10 10 70.5 93.1 100.0 100.0 64.3 70.5 100.0 #> 11 11 70.5 93.1 100.0 100.0 64.3 70.5 100.0 #> 12 12 70.5 93.1 100.0 100.0 64.3 70.5 100.0 #> 13 13 70.5 93.1 100.0 100.0 64.3 70.5 100.0 #> 14 14 70.5 93.1 100.0 100.0 64.3 70.5 100.0 #> 15 15 78.3 93.1 99.5 100.0 57.7 73.8 100.0 #> 16 16 78.3 93.1 99.5 100.0 57.7 73.8 100.0 #> 17 17 78.3 93.1 99.5 100.0 57.7 73.8 100.0 #> 18 18 78.3 93.1 99.5 100.0 57.7 73.8 100.0 #> 19 19 78.3 93.1 99.5 100.0 57.7 73.8 100.0 #> 20 20 64.7 82.6 98.8 100.0 54.0 61.4 99.9 #> 21 21 64.7 82.6 98.8 100.0 54.0 61.4 99.9 #> 22 22 64.7 82.6 98.8 100.0 54.0 61.4 99.9 #> 23 23 64.7 82.6 98.8 100.0 54.0 61.4 99.9 #> 24 24 64.7 82.6 98.8 100.0 54.0 61.4 99.9 #> 25 25 57.0 78.5 98.2 99.0 41.9 52.4 98.9 #> 26 26 57.0 78.5 98.2 99.0 41.9 52.4 98.9 #> 27 27 57.0 78.5 98.2 99.0 41.9 52.4 98.9 #> 28 28 57.0 78.5 98.2 99.0 41.9 52.4 98.9 #> 29 29 57.0 78.5 98.2 99.0 41.9 52.4 98.9 #> 30 30 37.9 61.1 89.7 96.1 25.5 38.0 92.8 #> 31 31 37.9 61.1 89.7 96.1 25.5 38.0 92.8 #> 32 32 37.9 61.1 89.7 96.1 25.5 38.0 92.8 #> 33 33 37.9 61.1 89.7 96.1 25.5 38.0 92.8 #> 34 34 37.9 61.1 89.7 96.1 25.5 38.0 92.8 #> 35 35 31.1 51.5 84.2 94.2 17.5 25.0 87.2 #> 36 36 31.1 51.5 84.2 94.2 17.5 25.0 87.2 #> 37 37 31.1 51.5 84.2 94.2 17.5 25.0 87.2 #> 38 38 31.1 51.5 84.2 94.2 17.5 25.0 87.2 #> 39 39 31.1 51.5 84.2 94.2 17.5 25.0 87.2 #> 40 40 9.5 27.4 75.0 92.4 3.8 8.8 80.3 #> 41 41 9.5 27.4 75.0 92.4 3.8 8.8 80.3 #> 42 42 9.5 27.4 75.0 92.4 3.8 8.8 80.3 #> 43 43 9.5 27.4 75.0 92.4 3.8 8.8 80.3 #> 44 44 9.5 27.4 75.0 92.4 3.8 8.8 80.3 #> 45 45 9.2 23.4 59.7 84.9 4.1 8.1 69.7 #> 46 46 9.2 23.4 59.7 84.9 4.1 8.1 69.7 #> 47 47 9.2 23.4 59.7 84.9 4.1 8.1 69.7 #> 48 48 9.2 23.4 59.7 84.9 4.1 8.1 69.7 #> 49 49 9.2 23.4 59.7 84.9 4.1 8.1 69.7 #> 50 50 2.9 8.6 52.4 78.0 1.4 2.7 61.6 #> 51 51 2.9 8.6 52.4 78.0 1.4 2.7 61.6 #> 52 52 2.9 8.6 52.4 78.0 1.4 2.7 61.6 #> 53 53 2.9 8.6 52.4 78.0 1.4 2.7 61.6 #> 54 54 2.9 8.6 52.4 78.0 1.4 2.7 61.6 #> 55 55 5.3 9.5 33.9 57.2 1.9 4.7 43.5 #> 56 56 5.3 9.5 33.9 57.2 1.9 4.7 43.5 #> 57 57 5.3 9.5 33.9 57.2 1.9 4.7 43.5 #> 58 58 5.3 9.5 33.9 57.2 1.9 4.7 43.5 #> 59 59 5.3 9.5 33.9 57.2 1.9 4.7 43.5 #> 60 60 2.2 4.4 20.6 54.7 1.7 3.9 24.4 #> 61 61 2.2 4.4 20.6 54.7 1.7 3.9 24.4 #> 62 62 2.2 4.4 20.6 54.7 1.7 3.9 24.4 #> 63 63 2.2 4.4 20.6 54.7 1.7 3.9 24.4 #> 64 64 2.2 4.4 20.6 54.7 1.7 3.9 24.4 #> 65 65 0.0 0.1 17.4 56.2 0.0 0.0 31.6 #> 66 66 0.0 0.1 17.4 56.2 0.0 0.0 31.6 #> 67 67 0.0 0.1 17.4 56.2 0.0 0.0 31.6 #> 68 68 0.0 0.1 17.4 56.2 0.0 0.0 31.6 #> 69 69 0.0 0.1 17.4 56.2 0.0 0.0 31.6 #> 70 70 0.0 0.0 0.0 19.5 0.0 0.0 0.0 #> 71 71 0.0 0.0 0.0 19.5 0.0 0.0 0.0 #> 72 72 0.0 0.0 0.0 19.5 0.0 0.0 0.0 #> 73 73 0.0 0.0 0.0 19.5 0.0 0.0 0.0 #> 74 74 0.0 0.0 0.0 19.5 0.0 0.0 0.0 #> 75 75 0.0 0.0 0.0 52.9 0.0 0.0 0.0 #> 76 76 0.0 0.0 0.0 52.9 0.0 0.0 0.0 #> 77 77 0.0 0.0 0.0 52.9 0.0 0.0 0.0 #> 78 78 0.0 0.0 0.0 52.9 0.0 0.0 0.0 #> 79 79 0.0 0.0 0.0 52.9 0.0 0.0 0.0 #> 80 80 0.0 0.0 0.0 0.0 0.0 0.0 0.0 #> 81 81 0.0 0.0 0.0 0.0 0.0 0.0 0.0 #> 82 82 0.0 0.0 0.0 0.0 0.0 0.0 0.0 #> 83 83 0.0 0.0 0.0 0.0 0.0 0.0 0.0 #> 84 84 0.0 0.0 0.0 0.0 0.0 0.0 0.0 #> 85 85 0.0 0.0 0.0 0.0 0.0 0.0 0.0 #> 86 86 0.0 0.0 0.0 0.0 0.0 0.0 0.0 #> 87 87 0.0 0.0 0.0 0.0 0.0 0.0 0.0 #> 88 88 0.0 0.0 0.0 0.0 0.0 0.0 0.0 #> 89 89 0.0 0.0 0.0 0.0 0.0 0.0 0.0 #> 90 90 0.0 0.0 0.0 0.0 0.0 0.0 0.0 #> 91 91 0.0 0.0 0.0 0.0 0.0 0.0 0.0 #> 92 92 0.0 0.0 0.0 0.0 0.0 0.0 0.0 #> 93 93 0.0 0.0 0.0 0.0 0.0 0.0 0.0 #> 94 94 0.0 0.0 0.0 0.0 0.0 0.0 0.0 #> 95 95 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 #> 97 97 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 #> 99 99 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# Given a specific PPI score (from 0 - 100), get the row of poverty # probabilities from PPI table it corresponds to ppiScore <- 50 ppiTLS2013[ppiTLS2013$score == ppiScore, ]#> score nl100 nu100 nu150 nu200 extreme ppp125 ppp250 #> 50 50 2.9 8.6 52.4 78 1.4 2.7 61.6# Use subset() function to get the row of poverty probabilities corresponding # to specific PPI score ppiScore <- 50 subset(ppiTLS2013, score == ppiScore)#> score nl100 nu100 nu150 nu200 extreme ppp125 ppp250 #> 50 50 2.9 8.6 52.4 78 1.4 2.7 61.6# 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 ppiTLS2013[ppiTLS2013$score == ppiScore, "nl100"]#> [1] 2.9