Poverty Probability Index (PPI) lookup table for India using r62 poverty definitions
ppiIND2016_r62
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)
# 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