Poverty Probability Index (PPI) lookup table for India using r62 poverty definitions
Source:R/04_data.R
ppiIND2016_r62.RdPoverty Probability Index (PPI) lookup table for India using r62 poverty definitions
Format
A data frame with 7 columns and 101 rows:
scorePPI score
saxenaNational saxena
ppp108Below $1.08 per day purchasing power parity (1993)
ppp81Below $0.81 per day purchasing power parity (1993)
ppp135Below $1.35 per day purchasing power parity (1993)
ppp162Below $1.62 per day purchasing power parity (1993)
ppp216Below $2.16 per day purchasing power parity (1993)
Examples
# Access India PPI table
ppiIND2016_r62
#> score saxena ppp108 ppp81 ppp135 ppp162 ppp216
#> 0 0 50.2 66.4 25.9 86.0 94.5 98.8
#> 1 1 50.2 66.4 25.9 86.0 94.5 98.8
#> 2 2 50.2 66.4 25.9 86.0 94.5 98.8
#> 3 3 50.2 66.4 25.9 86.0 94.5 98.8
#> 4 4 50.2 66.4 25.9 86.0 94.5 98.8
#> 5 5 37.6 52.9 21.8 76.7 89.9 98.0
#> 6 6 37.6 52.9 21.8 76.7 89.9 98.0
#> 7 7 37.6 52.9 21.8 76.7 89.9 98.0
#> 8 8 37.6 52.9 21.8 76.7 89.9 98.0
#> 9 9 37.6 52.9 21.8 76.7 89.9 98.0
#> 10 10 28.7 44.2 12.9 70.9 86.9 97.7
#> 11 11 28.7 44.2 12.9 70.9 86.9 97.7
#> 12 12 28.7 44.2 12.9 70.9 86.9 97.7
#> 13 13 28.7 44.2 12.9 70.9 86.9 97.7
#> 14 14 28.7 44.2 12.9 70.9 86.9 97.7
#> 15 15 18.7 31.9 8.9 61.9 80.7 95.9
#> 16 16 18.7 31.9 8.9 61.9 80.7 95.9
#> 17 17 18.7 31.9 8.9 61.9 80.7 95.9
#> 18 18 18.7 31.9 8.9 61.9 80.7 95.9
#> 19 19 18.7 31.9 8.9 61.9 80.7 95.9
#> 20 20 15.0 26.7 6.2 53.5 75.9 94.1
#> 21 21 15.0 26.7 6.2 53.5 75.9 94.1
#> 22 22 15.0 26.7 6.2 53.5 75.9 94.1
#> 23 23 15.0 26.7 6.2 53.5 75.9 94.1
#> 24 24 15.0 26.7 6.2 53.5 75.9 94.1
#> 25 25 11.5 19.6 3.7 45.3 66.3 88.8
#> 26 26 11.5 19.6 3.7 45.3 66.3 88.8
#> 27 27 11.5 19.6 3.7 45.3 66.3 88.8
#> 28 28 11.5 19.6 3.7 45.3 66.3 88.8
#> 29 29 11.5 19.6 3.7 45.3 66.3 88.8
#> 30 30 7.2 12.8 2.3 34.7 58.9 83.7
#> 31 31 7.2 12.8 2.3 34.7 58.9 83.7
#> 32 32 7.2 12.8 2.3 34.7 58.9 83.7
#> 33 33 7.2 12.8 2.3 34.7 58.9 83.7
#> 34 34 7.2 12.8 2.3 34.7 58.9 83.7
#> 35 35 5.1 9.0 1.6 25.4 45.5 76.2
#> 36 36 5.1 9.0 1.6 25.4 45.5 76.2
#> 37 37 5.1 9.0 1.6 25.4 45.5 76.2
#> 38 38 5.1 9.0 1.6 25.4 45.5 76.2
#> 39 39 5.1 9.0 1.6 25.4 45.5 76.2
#> 40 40 3.8 5.8 1.0 18.5 35.3 68.3
#> 41 41 3.8 5.8 1.0 18.5 35.3 68.3
#> 42 42 3.8 5.8 1.0 18.5 35.3 68.3
#> 43 43 3.8 5.8 1.0 18.5 35.3 68.3
#> 44 44 3.8 5.8 1.0 18.5 35.3 68.3
#> 45 45 2.8 3.6 0.5 12.6 23.9 53.8
#> 46 46 2.8 3.6 0.5 12.6 23.9 53.8
#> 47 47 2.8 3.6 0.5 12.6 23.9 53.8
#> 48 48 2.8 3.6 0.5 12.6 23.9 53.8
#> 49 49 2.8 3.6 0.5 12.6 23.9 53.8
#> 50 50 1.4 1.8 0.2 7.7 16.5 42.5
#> 51 51 1.4 1.8 0.2 7.7 16.5 42.5
#> 52 52 1.4 1.8 0.2 7.7 16.5 42.5
#> 53 53 1.4 1.8 0.2 7.7 16.5 42.5
#> 54 54 1.4 1.8 0.2 7.7 16.5 42.5
#> 55 55 0.9 0.6 0.1 4.0 10.0 29.4
#> 56 56 0.9 0.6 0.1 4.0 10.0 29.4
#> 57 57 0.9 0.6 0.1 4.0 10.0 29.4
#> 58 58 0.9 0.6 0.1 4.0 10.0 29.4
#> 59 59 0.9 0.6 0.1 4.0 10.0 29.4
#> 60 60 0.3 0.2 0.0 1.3 5.6 22.5
#> 61 61 0.3 0.2 0.0 1.3 5.6 22.5
#> 62 62 0.3 0.2 0.0 1.3 5.6 22.5
#> 63 63 0.3 0.2 0.0 1.3 5.6 22.5
#> 64 64 0.3 0.2 0.0 1.3 5.6 22.5
#> 65 65 0.2 0.1 0.0 1.0 3.4 15.5
#> 66 66 0.2 0.1 0.0 1.0 3.4 15.5
#> 67 67 0.2 0.1 0.0 1.0 3.4 15.5
#> 68 68 0.2 0.1 0.0 1.0 3.4 15.5
#> 69 69 0.2 0.1 0.0 1.0 3.4 15.5
#> 70 70 0.1 0.0 0.0 0.3 1.4 10.2
#> 71 71 0.1 0.0 0.0 0.3 1.4 10.2
#> 72 72 0.1 0.0 0.0 0.3 1.4 10.2
#> 73 73 0.1 0.0 0.0 0.3 1.4 10.2
#> 74 74 0.1 0.0 0.0 0.3 1.4 10.2
#> 75 75 0.0 0.0 0.0 0.1 0.5 4.9
#> 76 76 0.0 0.0 0.0 0.1 0.5 4.9
#> 77 77 0.0 0.0 0.0 0.1 0.5 4.9
#> 78 78 0.0 0.0 0.0 0.1 0.5 4.9
#> 79 79 0.0 0.0 0.0 0.1 0.5 4.9
#> 80 80 0.0 0.0 0.0 0.1 0.4 3.7
#> 81 81 0.0 0.0 0.0 0.1 0.4 3.7
#> 82 82 0.0 0.0 0.0 0.1 0.4 3.7
#> 83 83 0.0 0.0 0.0 0.1 0.4 3.7
#> 84 84 0.0 0.0 0.0 0.1 0.4 3.7
#> 85 85 0.0 0.0 0.0 0.0 0.2 1.0
#> 86 86 0.0 0.0 0.0 0.0 0.2 1.0
#> 87 87 0.0 0.0 0.0 0.0 0.2 1.0
#> 88 88 0.0 0.0 0.0 0.0 0.2 1.0
#> 89 89 0.0 0.0 0.0 0.0 0.2 1.0
#> 90 90 0.0 0.0 0.0 0.0 0.0 0.1
#> 91 91 0.0 0.0 0.0 0.0 0.0 0.1
#> 92 92 0.0 0.0 0.0 0.0 0.0 0.1
#> 93 93 0.0 0.0 0.0 0.0 0.0 0.1
#> 94 94 0.0 0.0 0.0 0.0 0.0 0.1
#> 95 95 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
#> 97 97 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
#> 99 99 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
# Given a specific PPI score (from 0 - 100), get the row of poverty
# probabilities from PPI table it corresponds to
ppiScore <- 50
ppiIND2016_r62[ppiIND2016_r62$score == ppiScore, ]
#> score saxena ppp108 ppp81 ppp135 ppp162 ppp216
#> 50 50 1.4 1.8 0.2 7.7 16.5 42.5
# Use subset() function to get the row of poverty probabilities corresponding
# to specific PPI score
ppiScore <- 50
subset(ppiIND2016_r62, score == ppiScore)
#> score saxena ppp108 ppp81 ppp135 ppp162 ppp216
#> 50 50 1.4 1.8 0.2 7.7 16.5 42.5
# Given a specific PPI score (from 0 - 100), get a poverty probability
# based on a specific poverty definition. In this example, the national
# saxena poverty definition
ppiScore <- 50
ppiIND2016_r62[ppiIND2016_r62$score == ppiScore, "saxena"]
#> [1] 1.4