Poverty Probability Index (PPI) lookup table for Timor Leste
Format
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)
Examples
# 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