Poverty Probability Index (PPI) lookup table for India using r66 poverty definitions
ppiIND2016_r66
A data frame with 8 columns and 101 rows:
scorePPI score
tendulkarNational tendulkar
tendulkar100National tendulkar (100%)
tendulkar150National tendulkar (150%)
tendulkar200National tendulkar (200%)
ppp125Below $1.25 per day purchasing power parity (2005)
ppp188Below $1.88 per day purchasing power parity (2005)
ppp250Below $2.50 per day purchasing power parity (2005)
# Access India PPI table ppiIND2016_r66#> score tendulkar tendulkar100 tendulkar150 tendulkar200 ppp125 ppp188 ppp250 #> 0 0 74.3 57.7 93.6 99.0 79.5 98.6 99.6 #> 1 1 74.3 57.7 93.6 99.0 79.5 98.6 99.6 #> 2 2 74.3 57.7 93.6 99.0 79.5 98.6 99.6 #> 3 3 74.3 57.7 93.6 99.0 79.5 98.6 99.6 #> 4 4 74.3 57.7 93.6 99.0 79.5 98.6 99.6 #> 5 5 61.5 47.3 90.8 98.3 74.3 97.5 99.4 #> 6 6 61.5 47.3 90.8 98.3 74.3 97.5 99.4 #> 7 7 61.5 47.3 90.8 98.3 74.3 97.5 99.4 #> 8 8 61.5 47.3 90.8 98.3 74.3 97.5 99.4 #> 9 9 61.5 47.3 90.8 98.3 74.3 97.5 99.4 #> 10 10 53.5 38.5 85.8 97.1 64.8 95.5 99.0 #> 11 11 53.5 38.5 85.8 97.1 64.8 95.5 99.0 #> 12 12 53.5 38.5 85.8 97.1 64.8 95.5 99.0 #> 13 13 53.5 38.5 85.8 97.1 64.8 95.5 99.0 #> 14 14 53.5 38.5 85.8 97.1 64.8 95.5 99.0 #> 15 15 42.4 29.0 78.4 94.8 55.7 92.3 98.1 #> 16 16 42.4 29.0 78.4 94.8 55.7 92.3 98.1 #> 17 17 42.4 29.0 78.4 94.8 55.7 92.3 98.1 #> 18 18 42.4 29.0 78.4 94.8 55.7 92.3 98.1 #> 19 19 42.4 29.0 78.4 94.8 55.7 92.3 98.1 #> 20 20 35.8 21.2 71.5 92.0 45.2 87.7 97.0 #> 21 21 35.8 21.2 71.5 92.0 45.2 87.7 97.0 #> 22 22 35.8 21.2 71.5 92.0 45.2 87.7 97.0 #> 23 23 35.8 21.2 71.5 92.0 45.2 87.7 97.0 #> 24 24 35.8 21.2 71.5 92.0 45.2 87.7 97.0 #> 25 25 27.8 17.5 63.8 89.1 38.1 83.4 95.7 #> 26 26 27.8 17.5 63.8 89.1 38.1 83.4 95.7 #> 27 27 27.8 17.5 63.8 89.1 38.1 83.4 95.7 #> 28 28 27.8 17.5 63.8 89.1 38.1 83.4 95.7 #> 29 29 27.8 17.5 63.8 89.1 38.1 83.4 95.7 #> 30 30 19.2 12.6 57.8 85.5 32.5 79.3 93.9 #> 31 31 19.2 12.6 57.8 85.5 32.5 79.3 93.9 #> 32 32 19.2 12.6 57.8 85.5 32.5 79.3 93.9 #> 33 33 19.2 12.6 57.8 85.5 32.5 79.3 93.9 #> 34 34 19.2 12.6 57.8 85.5 32.5 79.3 93.9 #> 35 35 13.1 7.4 46.3 77.7 21.9 70.6 89.4 #> 36 36 13.1 7.4 46.3 77.7 21.9 70.6 89.4 #> 37 37 13.1 7.4 46.3 77.7 21.9 70.6 89.4 #> 38 38 13.1 7.4 46.3 77.7 21.9 70.6 89.4 #> 39 39 13.1 7.4 46.3 77.7 21.9 70.6 89.4 #> 40 40 9.9 5.7 37.1 68.6 16.6 60.9 84.9 #> 41 41 9.9 5.7 37.1 68.6 16.6 60.9 84.9 #> 42 42 9.9 5.7 37.1 68.6 16.6 60.9 84.9 #> 43 43 9.9 5.7 37.1 68.6 16.6 60.9 84.9 #> 44 44 9.9 5.7 37.1 68.6 16.6 60.9 84.9 #> 45 45 7.1 3.9 24.8 55.9 11.2 46.4 75.1 #> 46 46 7.1 3.9 24.8 55.9 11.2 46.4 75.1 #> 47 47 7.1 3.9 24.8 55.9 11.2 46.4 75.1 #> 48 48 7.1 3.9 24.8 55.9 11.2 46.4 75.1 #> 49 49 7.1 3.9 24.8 55.9 11.2 46.4 75.1 #> 50 50 4.5 2.2 18.4 45.9 6.4 36.6 66.4 #> 51 51 4.5 2.2 18.4 45.9 6.4 36.6 66.4 #> 52 52 4.5 2.2 18.4 45.9 6.4 36.6 66.4 #> 53 53 4.5 2.2 18.4 45.9 6.4 36.6 66.4 #> 54 54 4.5 2.2 18.4 45.9 6.4 36.6 66.4 #> 55 55 1.7 1.0 10.8 33.3 2.9 25.1 51.5 #> 56 56 1.7 1.0 10.8 33.3 2.9 25.1 51.5 #> 57 57 1.7 1.0 10.8 33.3 2.9 25.1 51.5 #> 58 58 1.7 1.0 10.8 33.3 2.9 25.1 51.5 #> 59 59 1.7 1.0 10.8 33.3 2.9 25.1 51.5 #> 60 60 0.5 0.7 7.2 26.9 1.7 19.5 44.7 #> 61 61 0.5 0.7 7.2 26.9 1.7 19.5 44.7 #> 62 62 0.5 0.7 7.2 26.9 1.7 19.5 44.7 #> 63 63 0.5 0.7 7.2 26.9 1.7 19.5 44.7 #> 64 64 0.5 0.7 7.2 26.9 1.7 19.5 44.7 #> 65 65 0.5 0.3 4.8 21.6 0.6 14.3 39.4 #> 66 66 0.5 0.3 4.8 21.6 0.6 14.3 39.4 #> 67 67 0.5 0.3 4.8 21.6 0.6 14.3 39.4 #> 68 68 0.5 0.3 4.8 21.6 0.6 14.3 39.4 #> 69 69 0.5 0.3 4.8 21.6 0.6 14.3 39.4 #> 70 70 0.2 0.1 2.2 13.1 0.4 7.7 28.2 #> 71 71 0.2 0.1 2.2 13.1 0.4 7.7 28.2 #> 72 72 0.2 0.1 2.2 13.1 0.4 7.7 28.2 #> 73 73 0.2 0.1 2.2 13.1 0.4 7.7 28.2 #> 74 74 0.2 0.1 2.2 13.1 0.4 7.7 28.2 #> 75 75 0.1 0.0 1.3 8.6 0.1 4.5 18.5 #> 76 76 0.1 0.0 1.3 8.6 0.1 4.5 18.5 #> 77 77 0.1 0.0 1.3 8.6 0.1 4.5 18.5 #> 78 78 0.1 0.0 1.3 8.6 0.1 4.5 18.5 #> 79 79 0.1 0.0 1.3 8.6 0.1 4.5 18.5 #> 80 80 0.1 0.0 0.7 5.1 0.0 2.9 13.2 #> 81 81 0.1 0.0 0.7 5.1 0.0 2.9 13.2 #> 82 82 0.1 0.0 0.7 5.1 0.0 2.9 13.2 #> 83 83 0.1 0.0 0.7 5.1 0.0 2.9 13.2 #> 84 84 0.1 0.0 0.7 5.1 0.0 2.9 13.2 #> 85 85 0.0 0.0 0.1 3.0 0.0 0.8 7.9 #> 86 86 0.0 0.0 0.1 3.0 0.0 0.8 7.9 #> 87 87 0.0 0.0 0.1 3.0 0.0 0.8 7.9 #> 88 88 0.0 0.0 0.1 3.0 0.0 0.8 7.9 #> 89 89 0.0 0.0 0.1 3.0 0.0 0.8 7.9 #> 90 90 0.0 0.0 0.0 0.8 0.0 0.0 1.7 #> 91 91 0.0 0.0 0.0 0.8 0.0 0.0 1.7 #> 92 92 0.0 0.0 0.0 0.8 0.0 0.0 1.7 #> 93 93 0.0 0.0 0.0 0.8 0.0 0.0 1.7 #> 94 94 0.0 0.0 0.0 0.8 0.0 0.0 1.7 #> 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 ppiIND2016_r66[ppiIND2016_r66$score == ppiScore, ]#> score tendulkar tendulkar100 tendulkar150 tendulkar200 ppp125 ppp188 ppp250 #> 50 50 4.5 2.2 18.4 45.9 6.4 36.6 66.4# Use subset() function to get the row of poverty probabilities corresponding # to specific PPI score ppiScore <- 50 subset(ppiIND2016_r66, score == ppiScore)#> score tendulkar tendulkar100 tendulkar150 tendulkar200 ppp125 ppp188 ppp250 #> 50 50 4.5 2.2 18.4 45.9 6.4 36.6 66.4# Given a specific PPI score (from 0 - 100), get a poverty probability # based on a specific poverty definition. In this example, the national # tendulkar poverty definition ppiScore <- 50 ppiIND2016_r66[ppiIND2016_r66$score == ppiScore, "tendulkar"]#> [1] 4.5