Poverty Probability Index (PPI) lookup table for Ecuador for 2022
Source:R/00_ecuador.R
ppiECU2022.RdPoverty Probability Index (PPI) lookup table for Ecuador for 2022
Format
A data frame with 20 columns and 101 rows:
scorePPI score
nl100National poverty line (100%)
nl_extremeNational poverty line (extreme)
nl150National poverty line (150%)
nl200National poverty line (200%)
ppp215Below $2.15 per day purchasing power parity (2017)
ppp365Below $3.65 per day purchasing power parity (2017)
ppp685Below $6.85 per day purchasing power parity (2017)
ppp100Below $1.00 per day purchasing power parity (2011)
ppp190Below $1.90 per day purchasing power parity (2011)
ppp320Below $3.20 per day purchasing power parity (2011)
ppp550Below $5.50 per day purchasing power parity (2011)
ppp800Below $8.00 per day purchasing power parity (2011)
ppp1100Below $11.00 per day purchasing power parity (2011)
ppp1500Below $15.00 per day purchasing power parity (2011)
ppp2170Below $21.70 per day purchasing power parity (2011)
percentile20Below 20th percentile poverty line
percentile40Below 40th percentile poverty line
percentile60Below 60th percentile poverty line
percentile80Below 80th percentile poverty line
Examples
# Access Ecuador PPI table
ppiECU2015
#> score nlFood nl100 nl150 nl200 half100 ppp125 ppp200 ppp250 ppp500 ppp844
#> 0 0 85.6 99.8 100.0 100.0 92.0 34.7 84.7 96.8 100.0 100.0
#> 1 1 85.6 99.8 100.0 100.0 92.0 34.7 84.7 96.8 100.0 100.0
#> 2 2 85.6 99.8 100.0 100.0 92.0 34.7 84.7 96.8 100.0 100.0
#> 3 3 85.6 99.8 100.0 100.0 92.0 34.7 84.7 96.8 100.0 100.0
#> 4 4 85.6 99.8 100.0 100.0 92.0 34.7 84.7 96.8 100.0 100.0
#> 5 5 68.7 98.3 100.0 100.0 80.3 25.9 67.5 81.2 100.0 100.0
#> 6 6 68.7 98.3 100.0 100.0 80.3 25.9 67.5 81.2 100.0 100.0
#> 7 7 68.7 98.3 100.0 100.0 80.3 25.9 67.5 81.2 100.0 100.0
#> 8 8 68.7 98.3 100.0 100.0 80.3 25.9 67.5 81.2 100.0 100.0
#> 9 9 68.7 98.3 100.0 100.0 80.3 25.9 67.5 81.2 100.0 100.0
#> 10 10 59.2 95.1 100.0 100.0 76.8 17.9 57.3 71.5 99.9 100.0
#> 11 11 59.2 95.1 100.0 100.0 76.8 17.9 57.3 71.5 99.9 100.0
#> 12 12 59.2 95.1 100.0 100.0 76.8 17.9 57.3 71.5 99.9 100.0
#> 13 13 59.2 95.1 100.0 100.0 76.8 17.9 57.3 71.5 99.9 100.0
#> 14 14 59.2 95.1 100.0 100.0 76.8 17.9 57.3 71.5 99.9 100.0
#> 15 15 37.8 93.0 99.7 100.0 70.3 9.0 36.6 64.3 99.0 100.0
#> 16 16 37.8 93.0 99.7 100.0 70.3 9.0 36.6 64.3 99.0 100.0
#> 17 17 37.8 93.0 99.7 100.0 70.3 9.0 36.6 64.3 99.0 100.0
#> 18 18 37.8 93.0 99.7 100.0 70.3 9.0 36.6 64.3 99.0 100.0
#> 19 19 37.8 93.0 99.7 100.0 70.3 9.0 36.6 64.3 99.0 100.0
#> 20 20 25.1 84.7 98.2 100.0 58.9 3.7 23.9 51.6 96.3 100.0
#> 21 21 25.1 84.7 98.2 100.0 58.9 3.7 23.9 51.6 96.3 100.0
#> 22 22 25.1 84.7 98.2 100.0 58.9 3.7 23.9 51.6 96.3 100.0
#> 23 23 25.1 84.7 98.2 100.0 58.9 3.7 23.9 51.6 96.3 100.0
#> 24 24 25.1 84.7 98.2 100.0 58.9 3.7 23.9 51.6 96.3 100.0
#> 25 25 16.6 74.2 97.1 99.7 41.7 1.8 16.2 34.3 95.0 99.9
#> 26 26 16.6 74.2 97.1 99.7 41.7 1.8 16.2 34.3 95.0 99.9
#> 27 27 16.6 74.2 97.1 99.7 41.7 1.8 16.2 34.3 95.0 99.9
#> 28 28 16.6 74.2 97.1 99.7 41.7 1.8 16.2 34.3 95.0 99.9
#> 29 29 16.6 74.2 97.1 99.7 41.7 1.8 16.2 34.3 95.0 99.9
#> 30 30 8.8 64.1 93.7 98.9 27.8 0.8 8.0 23.0 89.1 99.5
#> 31 31 8.8 64.1 93.7 98.9 27.8 0.8 8.0 23.0 89.1 99.5
#> 32 32 8.8 64.1 93.7 98.9 27.8 0.8 8.0 23.0 89.1 99.5
#> 33 33 8.8 64.1 93.7 98.9 27.8 0.8 8.0 23.0 89.1 99.5
#> 34 34 8.8 64.1 93.7 98.9 27.8 0.8 8.0 23.0 89.1 99.5
#> 35 35 6.0 50.0 88.9 98.2 20.1 0.4 5.6 15.1 83.5 99.3
#> 36 36 6.0 50.0 88.9 98.2 20.1 0.4 5.6 15.1 83.5 99.3
#> 37 37 6.0 50.0 88.9 98.2 20.1 0.4 5.6 15.1 83.5 99.3
#> 38 38 6.0 50.0 88.9 98.2 20.1 0.4 5.6 15.1 83.5 99.3
#> 39 39 6.0 50.0 88.9 98.2 20.1 0.4 5.6 15.1 83.5 99.3
#> 40 40 4.3 36.6 80.5 95.0 11.5 0.2 3.8 10.3 73.8 98.1
#> 41 41 4.3 36.6 80.5 95.0 11.5 0.2 3.8 10.3 73.8 98.1
#> 42 42 4.3 36.6 80.5 95.0 11.5 0.2 3.8 10.3 73.8 98.1
#> 43 43 4.3 36.6 80.5 95.0 11.5 0.2 3.8 10.3 73.8 98.1
#> 44 44 4.3 36.6 80.5 95.0 11.5 0.2 3.8 10.3 73.8 98.1
#> 45 45 2.1 24.6 65.2 87.8 8.1 0.2 2.0 6.1 57.7 93.7
#> 46 46 2.1 24.6 65.2 87.8 8.1 0.2 2.0 6.1 57.7 93.7
#> 47 47 2.1 24.6 65.2 87.8 8.1 0.2 2.0 6.1 57.7 93.7
#> 48 48 2.1 24.6 65.2 87.8 8.1 0.2 2.0 6.1 57.7 93.7
#> 49 49 2.1 24.6 65.2 87.8 8.1 0.2 2.0 6.1 57.7 93.7
#> 50 50 0.9 12.9 51.9 81.6 3.3 0.0 0.9 2.5 43.2 89.9
#> 51 51 0.9 12.9 51.9 81.6 3.3 0.0 0.9 2.5 43.2 89.9
#> 52 52 0.9 12.9 51.9 81.6 3.3 0.0 0.9 2.5 43.2 89.9
#> 53 53 0.9 12.9 51.9 81.6 3.3 0.0 0.9 2.5 43.2 89.9
#> 54 54 0.9 12.9 51.9 81.6 3.3 0.0 0.9 2.5 43.2 89.9
#> 55 55 0.2 6.5 36.4 67.7 1.2 0.0 0.2 0.9 28.7 81.0
#> 56 56 0.2 6.5 36.4 67.7 1.2 0.0 0.2 0.9 28.7 81.0
#> 57 57 0.2 6.5 36.4 67.7 1.2 0.0 0.2 0.9 28.7 81.0
#> 58 58 0.2 6.5 36.4 67.7 1.2 0.0 0.2 0.9 28.7 81.0
#> 59 59 0.2 6.5 36.4 67.7 1.2 0.0 0.2 0.9 28.7 81.0
#> 60 60 0.0 3.1 24.0 55.1 0.5 0.0 0.0 0.1 17.3 67.9
#> 61 61 0.0 3.1 24.0 55.1 0.5 0.0 0.0 0.1 17.3 67.9
#> 62 62 0.0 3.1 24.0 55.1 0.5 0.0 0.0 0.1 17.3 67.9
#> 63 63 0.0 3.1 24.0 55.1 0.5 0.0 0.0 0.1 17.3 67.9
#> 64 64 0.0 3.1 24.0 55.1 0.5 0.0 0.0 0.1 17.3 67.9
#> 65 65 0.0 1.1 12.1 33.0 0.2 0.0 0.0 0.1 10.3 52.9
#> 66 66 0.0 1.1 12.1 33.0 0.2 0.0 0.0 0.1 10.3 52.9
#> 67 67 0.0 1.1 12.1 33.0 0.2 0.0 0.0 0.1 10.3 52.9
#> 68 68 0.0 1.1 12.1 33.0 0.2 0.0 0.0 0.1 10.3 52.9
#> 69 69 0.0 1.1 12.1 33.0 0.2 0.0 0.0 0.1 10.3 52.9
#> 70 70 0.0 0.9 6.4 21.8 0.2 0.0 0.0 0.1 4.6 38.8
#> 71 71 0.0 0.9 6.4 21.8 0.2 0.0 0.0 0.1 4.6 38.8
#> 72 72 0.0 0.9 6.4 21.8 0.2 0.0 0.0 0.1 4.6 38.8
#> 73 73 0.0 0.9 6.4 21.8 0.2 0.0 0.0 0.1 4.6 38.8
#> 74 74 0.0 0.9 6.4 21.8 0.2 0.0 0.0 0.1 4.6 38.8
#> 75 75 0.0 0.9 6.4 16.4 0.2 0.0 0.0 0.1 4.6 26.6
#> 76 76 0.0 0.9 6.4 16.4 0.2 0.0 0.0 0.1 4.6 26.6
#> 77 77 0.0 0.9 6.4 16.4 0.2 0.0 0.0 0.1 4.6 26.6
#> 78 78 0.0 0.9 6.4 16.4 0.2 0.0 0.0 0.1 4.6 26.6
#> 79 79 0.0 0.9 6.4 16.4 0.2 0.0 0.0 0.1 4.6 26.6
#> 80 80 0.0 0.9 6.4 16.4 0.2 0.0 0.0 0.1 4.6 24.2
#> 81 81 0.0 0.9 6.4 16.4 0.2 0.0 0.0 0.1 4.6 24.2
#> 82 82 0.0 0.9 6.4 16.4 0.2 0.0 0.0 0.1 4.6 24.2
#> 83 83 0.0 0.9 6.4 16.4 0.2 0.0 0.0 0.1 4.6 24.2
#> 84 84 0.0 0.9 6.4 16.4 0.2 0.0 0.0 0.1 4.6 24.2
#> 85 85 0.0 0.9 6.4 16.4 0.2 0.0 0.0 0.1 4.6 24.2
#> 86 86 0.0 0.9 6.4 16.4 0.2 0.0 0.0 0.1 4.6 24.2
#> 87 87 0.0 0.9 6.4 16.4 0.2 0.0 0.0 0.1 4.6 24.2
#> 88 88 0.0 0.9 6.4 16.4 0.2 0.0 0.0 0.1 4.6 24.2
#> 89 89 0.0 0.9 6.4 16.4 0.2 0.0 0.0 0.1 4.6 24.2
#> 90 90 0.0 0.9 6.4 16.4 0.2 0.0 0.0 0.1 4.6 24.2
#> 91 91 0.0 0.9 6.4 16.4 0.2 0.0 0.0 0.1 4.6 24.2
#> 92 92 0.0 0.9 6.4 16.4 0.2 0.0 0.0 0.1 4.6 24.2
#> 93 93 0.0 0.9 6.4 16.4 0.2 0.0 0.0 0.1 4.6 24.2
#> 94 94 0.0 0.9 6.4 16.4 0.2 0.0 0.0 0.1 4.6 24.2
#> 95 95 0.0 0.9 6.4 16.4 0.2 0.0 0.0 0.1 4.6 24.2
#> 96 96 0.0 0.9 6.4 16.4 0.2 0.0 0.0 0.1 4.6 24.2
#> 97 97 0.0 0.9 6.4 16.4 0.2 0.0 0.0 0.1 4.6 24.2
#> 98 98 0.0 0.9 6.4 16.4 0.2 0.0 0.0 0.1 4.6 24.2
#> 99 99 0.0 0.9 6.4 16.4 0.2 0.0 0.0 0.1 4.6 24.2
#> 100 100 0.0 0.9 6.4 16.4 0.2 0.0 0.0 0.1 4.6 24.2
# Given a specific PPI score (from 0 - 100), get the row of poverty
# probabilities from PPI table it corresponds to
ppiScore <- 50
ppiECU2015[ppiECU2015$score == ppiScore, ]
#> score nlFood nl100 nl150 nl200 half100 ppp125 ppp200 ppp250 ppp500 ppp844
#> 50 50 0.9 12.9 51.9 81.6 3.3 0 0.9 2.5 43.2 89.9
# Use subset() function to get the row of poverty probabilities corresponding
# to specific PPI score
ppiScore <- 50
subset(ppiECU2015, score == ppiScore)
#> score nlFood nl100 nl150 nl200 half100 ppp125 ppp200 ppp250 ppp500 ppp844
#> 50 50 0.9 12.9 51.9 81.6 3.3 0 0.9 2.5 43.2 89.9
# 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
ppiECU2015[ppiECU2015$score == ppiScore, "nl100"]
#> [1] 12.9