Poverty Probability Index (PPI) lookup table for India using r66 poverty definitions
Source:R/04_data.R
ppiIND2016_r66.RdPoverty Probability Index (PPI) lookup table for India using r66 poverty definitions
Format
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)
Examples
# 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