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Poverty Probability Index (PPI) lookup table for Kyrgyzstan

Usage

ppiKGZ2015

Format

A data frame with 9 columns and 101 rows:

score

PPI score

nl100

National poverty line (100%)

nl150

National poverty line (150%)

nl200

National poverty line (200%)

median

Poorest half below 100% national

ppp125

Below $1.25 per day purchasing power parity (2005)

ppp200

Below $2.00 per day purchasing power parity (2005)

ppp250

Below $2.50 per day purchasing power parity (2005)

ppp500

Below $5.00 per day purchasing power parity (2005)

Examples

  # Access Kyrgyzstan PPI table
  ppiKGZ2015
#>     score nl100 nl150 nl200 median ppp125 ppp200 ppp250 ppp500
#> 0       0 100.0 100.0 100.0   91.5    5.8   88.9  100.0  100.0
#> 1       1 100.0 100.0 100.0   91.5    5.8   88.9  100.0  100.0
#> 2       2 100.0 100.0 100.0   91.5    5.8   88.9  100.0  100.0
#> 3       3 100.0 100.0 100.0   91.5    5.8   88.9  100.0  100.0
#> 4       4 100.0 100.0 100.0   91.5    5.8   88.9  100.0  100.0
#> 5       5  98.0 100.0 100.0   88.6    1.6   71.4   92.0  100.0
#> 6       6  98.0 100.0 100.0   88.6    1.6   71.4   92.0  100.0
#> 7       7  98.0 100.0 100.0   88.6    1.6   71.4   92.0  100.0
#> 8       8  98.0 100.0 100.0   88.6    1.6   71.4   92.0  100.0
#> 9       9  98.0 100.0 100.0   88.6    1.6   71.4   92.0  100.0
#> 10     10  96.6 100.0 100.0   59.9    1.4   51.3   78.7  100.0
#> 11     11  96.6 100.0 100.0   59.9    1.4   51.3   78.7  100.0
#> 12     12  96.6 100.0 100.0   59.9    1.4   51.3   78.7  100.0
#> 13     13  96.6 100.0 100.0   59.9    1.4   51.3   78.7  100.0
#> 14     14  96.6 100.0 100.0   59.9    1.4   51.3   78.7  100.0
#> 15     15  89.9 100.0 100.0   55.3    1.4   38.7   74.5  100.0
#> 16     16  89.9 100.0 100.0   55.3    1.4   38.7   74.5  100.0
#> 17     17  89.9 100.0 100.0   55.3    1.4   38.7   74.5  100.0
#> 18     18  89.9 100.0 100.0   55.3    1.4   38.7   74.5  100.0
#> 19     19  89.9 100.0 100.0   55.3    1.4   38.7   74.5  100.0
#> 20     20  77.3  99.8 100.0   39.5    1.4   24.3   58.4  100.0
#> 21     21  77.3  99.8 100.0   39.5    1.4   24.3   58.4  100.0
#> 22     22  77.3  99.8 100.0   39.5    1.4   24.3   58.4  100.0
#> 23     23  77.3  99.8 100.0   39.5    1.4   24.3   58.4  100.0
#> 24     24  77.3  99.8 100.0   39.5    1.4   24.3   58.4  100.0
#> 25     25  68.0  99.1 100.0   30.5    1.1   15.8   45.7  100.0
#> 26     26  68.0  99.1 100.0   30.5    1.1   15.8   45.7  100.0
#> 27     27  68.0  99.1 100.0   30.5    1.1   15.8   45.7  100.0
#> 28     28  68.0  99.1 100.0   30.5    1.1   15.8   45.7  100.0
#> 29     29  68.0  99.1 100.0   30.5    1.1   15.8   45.7  100.0
#> 30     30  46.5  96.1 100.0   20.2    0.9    9.3   27.1   99.7
#> 31     31  46.5  96.1 100.0   20.2    0.9    9.3   27.1   99.7
#> 32     32  46.5  96.1 100.0   20.2    0.9    9.3   27.1   99.7
#> 33     33  46.5  96.1 100.0   20.2    0.9    9.3   27.1   99.7
#> 34     34  46.5  96.1 100.0   20.2    0.9    9.3   27.1   99.7
#> 35     35  40.7  89.4  99.9   12.6    0.3    4.5   19.2   96.0
#> 36     36  40.7  89.4  99.9   12.6    0.3    4.5   19.2   96.0
#> 37     37  40.7  89.4  99.9   12.6    0.3    4.5   19.2   96.0
#> 38     38  40.7  89.4  99.9   12.6    0.3    4.5   19.2   96.0
#> 39     39  40.7  89.4  99.9   12.6    0.3    4.5   19.2   96.0
#> 40     40  15.8  76.6  95.6    6.2    0.1    3.9   10.0   92.1
#> 41     41  15.8  76.6  95.6    6.2    0.1    3.9   10.0   92.1
#> 42     42  15.8  76.6  95.6    6.2    0.1    3.9   10.0   92.1
#> 43     43  15.8  76.6  95.6    6.2    0.1    3.9   10.0   92.1
#> 44     44  15.8  76.6  95.6    6.2    0.1    3.9   10.0   92.1
#> 45     45  11.7  65.2  90.5    5.4    0.1    2.5    7.3   85.2
#> 46     46  11.7  65.2  90.5    5.4    0.1    2.5    7.3   85.2
#> 47     47  11.7  65.2  90.5    5.4    0.1    2.5    7.3   85.2
#> 48     48  11.7  65.2  90.5    5.4    0.1    2.5    7.3   85.2
#> 49     49  11.7  65.2  90.5    5.4    0.1    2.5    7.3   85.2
#> 50     50   4.1  43.9  80.5    1.2    0.0    0.2    3.1   69.5
#> 51     51   4.1  43.9  80.5    1.2    0.0    0.2    3.1   69.5
#> 52     52   4.1  43.9  80.5    1.2    0.0    0.2    3.1   69.5
#> 53     53   4.1  43.9  80.5    1.2    0.0    0.2    3.1   69.5
#> 54     54   4.1  43.9  80.5    1.2    0.0    0.2    3.1   69.5
#> 55     55   3.9  27.3  60.2    0.7    0.0    0.2    2.2   50.3
#> 56     56   3.9  27.3  60.2    0.7    0.0    0.2    2.2   50.3
#> 57     57   3.9  27.3  60.2    0.7    0.0    0.2    2.2   50.3
#> 58     58   3.9  27.3  60.2    0.7    0.0    0.2    2.2   50.3
#> 59     59   3.9  27.3  60.2    0.7    0.0    0.2    2.2   50.3
#> 60     60   2.9  21.4  51.0    0.4    0.0    0.2    1.6   39.8
#> 61     61   2.9  21.4  51.0    0.4    0.0    0.2    1.6   39.8
#> 62     62   2.9  21.4  51.0    0.4    0.0    0.2    1.6   39.8
#> 63     63   2.9  21.4  51.0    0.4    0.0    0.2    1.6   39.8
#> 64     64   2.9  21.4  51.0    0.4    0.0    0.2    1.6   39.8
#> 65     65   1.3   6.9  38.8    0.4    0.0    0.2    0.9   25.0
#> 66     66   1.3   6.9  38.8    0.4    0.0    0.2    0.9   25.0
#> 67     67   1.3   6.9  38.8    0.4    0.0    0.2    0.9   25.0
#> 68     68   1.3   6.9  38.8    0.4    0.0    0.2    0.9   25.0
#> 69     69   1.3   6.9  38.8    0.4    0.0    0.2    0.9   25.0
#> 70     70   1.2   4.5  31.6    0.4    0.0    0.2    0.9   19.6
#> 71     71   1.2   4.5  31.6    0.4    0.0    0.2    0.9   19.6
#> 72     72   1.2   4.5  31.6    0.4    0.0    0.2    0.9   19.6
#> 73     73   1.2   4.5  31.6    0.4    0.0    0.2    0.9   19.6
#> 74     74   1.2   4.5  31.6    0.4    0.0    0.2    0.9   19.6
#> 75     75   1.2   4.5  18.9    0.4    0.0    0.2    0.9   12.1
#> 76     76   1.2   4.5  18.9    0.4    0.0    0.2    0.9   12.1
#> 77     77   1.2   4.5  18.9    0.4    0.0    0.2    0.9   12.1
#> 78     78   1.2   4.5  18.9    0.4    0.0    0.2    0.9   12.1
#> 79     79   1.2   4.5  18.9    0.4    0.0    0.2    0.9   12.1
#> 80     80   1.2   4.5  18.7    0.4    0.0    0.2    0.9   12.1
#> 81     81   1.2   4.5  18.7    0.4    0.0    0.2    0.9   12.1
#> 82     82   1.2   4.5  18.7    0.4    0.0    0.2    0.9   12.1
#> 83     83   1.2   4.5  18.7    0.4    0.0    0.2    0.9   12.1
#> 84     84   1.2   4.5  18.7    0.4    0.0    0.2    0.9   12.1
#> 85     85   1.2   4.5  18.7    0.4    0.0    0.2    0.9   12.1
#> 86     86   1.2   4.5  18.7    0.4    0.0    0.2    0.9   12.1
#> 87     87   1.2   4.5  18.7    0.4    0.0    0.2    0.9   12.1
#> 88     88   1.2   4.5  18.7    0.4    0.0    0.2    0.9   12.1
#> 89     89   1.2   4.5  18.7    0.4    0.0    0.2    0.9   12.1
#> 90     90   1.2   4.5  18.7    0.4    0.0    0.2    0.9   12.1
#> 91     91   1.2   4.5  18.7    0.4    0.0    0.2    0.9   12.1
#> 92     92   1.2   4.5  18.7    0.4    0.0    0.2    0.9   12.1
#> 93     93   1.2   4.5  18.7    0.4    0.0    0.2    0.9   12.1
#> 94     94   1.2   4.5  18.7    0.4    0.0    0.2    0.9   12.1
#> 95     95   1.2   4.5  18.7    0.4    0.0    0.2    0.9   12.1
#> 96     96   1.2   4.5  18.7    0.4    0.0    0.2    0.9   12.1
#> 97     97   1.2   4.5  18.7    0.4    0.0    0.2    0.9   12.1
#> 98     98   1.2   4.5  18.7    0.4    0.0    0.2    0.9   12.1
#> 99     99   1.2   4.5  18.7    0.4    0.0    0.2    0.9   12.1
#> 100   100   1.2   4.5  18.7    0.4    0.0    0.2    0.9   12.1

  # Given a specific PPI score (from 0 - 100), get the row of poverty
  # probabilities from PPI table it corresponds to
  ppiScore <- 50
  ppiKGZ2015[ppiKGZ2015$score == ppiScore, ]
#>    score nl100 nl150 nl200 median ppp125 ppp200 ppp250 ppp500
#> 50    50   4.1  43.9  80.5    1.2      0    0.2    3.1   69.5

  # Use subset() function to get the row of poverty probabilities corresponding
  # to specific PPI score
  ppiScore <- 50
  subset(ppiKGZ2015, score == ppiScore)
#>    score nl100 nl150 nl200 median ppp125 ppp200 ppp250 ppp500
#> 50    50   4.1  43.9  80.5    1.2      0    0.2    3.1   69.5

  # 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
  ppiKGZ2015[ppiKGZ2015$score == ppiScore, "nl100"]
#> [1] 4.1