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Poverty Probability Index (PPI) lookup table for El Salvador

Usage

ppiSLV2010

Format

A data frame with 9 columns and 101 rows:

score

PPI score

nl100

National poverty line (100%)

nlFood

Food poverty line

nl150

National poverty line (150%)

nl200

National poverty line (200%)

extreme

USAID extreme poverty

ppp125

Below $1.25 per day purchasing power parity (2005)

ppp250

Below $2.50 per day purchasing power parity (2005)

ppp375

Below $3.75 per day purchasing power parity (2005)

Examples

  # Access El Salvador PPI table
  ppiSLV2010
#>     score nl100 nlFood nl150 nl200 extreme ppp125 ppp250 ppp375
#> 0       0 100.0   70.3 100.0 100.0    86.8  100.0  100.0  100.0
#> 1       1 100.0   70.3 100.0 100.0    86.8  100.0  100.0  100.0
#> 2       2 100.0   70.3 100.0 100.0    86.8  100.0  100.0  100.0
#> 3       3 100.0   70.3 100.0 100.0    86.8  100.0  100.0  100.0
#> 4       4 100.0   70.3 100.0 100.0    86.8  100.0  100.0  100.0
#> 5       5  88.1   67.9 100.0 100.0    78.8   96.4  100.0  100.0
#> 6       6  88.1   67.9 100.0 100.0    78.8   96.4  100.0  100.0
#> 7       7  88.1   67.9 100.0 100.0    78.8   96.4  100.0  100.0
#> 8       8  88.1   67.9 100.0 100.0    78.8   96.4  100.0  100.0
#> 9       9  88.1   67.9 100.0 100.0    78.8   96.4  100.0  100.0
#> 10     10  93.3   54.0  98.1  99.0    67.9   94.6   99.6  100.0
#> 11     11  93.3   54.0  98.1  99.0    67.9   94.6   99.6  100.0
#> 12     12  93.3   54.0  98.1  99.0    67.9   94.6   99.6  100.0
#> 13     13  93.3   54.0  98.1  99.0    67.9   94.6   99.6  100.0
#> 14     14  93.3   54.0  98.1  99.0    67.9   94.6   99.6  100.0
#> 15     15  85.3   47.9  97.5  99.3    56.5   90.8  100.0  100.0
#> 16     16  85.3   47.9  97.5  99.3    56.5   90.8  100.0  100.0
#> 17     17  85.3   47.9  97.5  99.3    56.5   90.8  100.0  100.0
#> 18     18  85.3   47.9  97.5  99.3    56.5   90.8  100.0  100.0
#> 19     19  85.3   47.9  97.5  99.3    56.5   90.8  100.0  100.0
#> 20     20  80.1   40.2  95.8  97.6    53.8   87.5   99.1  100.0
#> 21     21  80.1   40.2  95.8  97.6    53.8   87.5   99.1  100.0
#> 22     22  80.1   40.2  95.8  97.6    53.8   87.5   99.1  100.0
#> 23     23  80.1   40.2  95.8  97.6    53.8   87.5   99.1  100.0
#> 24     24  80.1   40.2  95.8  97.6    53.8   87.5   99.1  100.0
#> 25     25  75.0   24.6  91.3  95.4    41.3   81.1   97.2   99.9
#> 26     26  75.0   24.6  91.3  95.4    41.3   81.1   97.2   99.9
#> 27     27  75.0   24.6  91.3  95.4    41.3   81.1   97.2   99.9
#> 28     28  75.0   24.6  91.3  95.4    41.3   81.1   97.2   99.9
#> 29     29  75.0   24.6  91.3  95.4    41.3   81.1   97.2   99.9
#> 30     30  69.2   20.3  87.5  94.4    32.1   75.4   96.5   99.3
#> 31     31  69.2   20.3  87.5  94.4    32.1   75.4   96.5   99.3
#> 32     32  69.2   20.3  87.5  94.4    32.1   75.4   96.5   99.3
#> 33     33  69.2   20.3  87.5  94.4    32.1   75.4   96.5   99.3
#> 34     34  69.2   20.3  87.5  94.4    32.1   75.4   96.5   99.3
#> 35     35  56.0   12.1  80.3  92.3    23.1   62.9   94.4   98.9
#> 36     36  56.0   12.1  80.3  92.3    23.1   62.9   94.4   98.9
#> 37     37  56.0   12.1  80.3  92.3    23.1   62.9   94.4   98.9
#> 38     38  56.0   12.1  80.3  92.3    23.1   62.9   94.4   98.9
#> 39     39  56.0   12.1  80.3  92.3    23.1   62.9   94.4   98.9
#> 40     40  43.5    8.4  67.3  86.7    16.5   51.6   88.6   94.4
#> 41     41  43.5    8.4  67.3  86.7    16.5   51.6   88.6   94.4
#> 42     42  43.5    8.4  67.3  86.7    16.5   51.6   88.6   94.4
#> 43     43  43.5    8.4  67.3  86.7    16.5   51.6   88.6   94.4
#> 44     44  43.5    8.4  67.3  86.7    16.5   51.6   88.6   94.4
#> 45     45  40.4    5.8  69.3  81.9    13.2   47.6   85.0   93.3
#> 46     46  40.4    5.8  69.3  81.9    13.2   47.6   85.0   93.3
#> 47     47  40.4    5.8  69.3  81.9    13.2   47.6   85.0   93.3
#> 48     48  40.4    5.8  69.3  81.9    13.2   47.6   85.0   93.3
#> 49     49  40.4    5.8  69.3  81.9    13.2   47.6   85.0   93.3
#> 50     50  27.5    2.9  55.1  70.0     9.2   37.6   77.4   90.0
#> 51     51  27.5    2.9  55.1  70.0     9.2   37.6   77.4   90.0
#> 52     52  27.5    2.9  55.1  70.0     9.2   37.6   77.4   90.0
#> 53     53  27.5    2.9  55.1  70.0     9.2   37.6   77.4   90.0
#> 54     54  27.5    2.9  55.1  70.0     9.2   37.6   77.4   90.0
#> 55     55  19.3    2.2  49.5  63.6     5.7   28.2   73.3   89.5
#> 56     56  19.3    2.2  49.5  63.6     5.7   28.2   73.3   89.5
#> 57     57  19.3    2.2  49.5  63.6     5.7   28.2   73.3   89.5
#> 58     58  19.3    2.2  49.5  63.6     5.7   28.2   73.3   89.5
#> 59     59  19.3    2.2  49.5  63.6     5.7   28.2   73.3   89.5
#> 60     60  11.8    0.3  32.5  52.8     0.7   18.3   62.3   82.5
#> 61     61  11.8    0.3  32.5  52.8     0.7   18.3   62.3   82.5
#> 62     62  11.8    0.3  32.5  52.8     0.7   18.3   62.3   82.5
#> 63     63  11.8    0.3  32.5  52.8     0.7   18.3   62.3   82.5
#> 64     64  11.8    0.3  32.5  52.8     0.7   18.3   62.3   82.5
#> 65     65  13.0    0.5  26.3  44.6     0.9   16.1   55.0   73.4
#> 66     66  13.0    0.5  26.3  44.6     0.9   16.1   55.0   73.4
#> 67     67  13.0    0.5  26.3  44.6     0.9   16.1   55.0   73.4
#> 68     68  13.0    0.5  26.3  44.6     0.9   16.1   55.0   73.4
#> 69     69  13.0    0.5  26.3  44.6     0.9   16.1   55.0   73.4
#> 70     70   6.4    0.5  23.8  44.1     0.5   12.9   49.2   75.8
#> 71     71   6.4    0.5  23.8  44.1     0.5   12.9   49.2   75.8
#> 72     72   6.4    0.5  23.8  44.1     0.5   12.9   49.2   75.8
#> 73     73   6.4    0.5  23.8  44.1     0.5   12.9   49.2   75.8
#> 74     74   6.4    0.5  23.8  44.1     0.5   12.9   49.2   75.8
#> 75     75   3.9    0.0  11.5  32.1     0.5    4.3   39.2   64.0
#> 76     76   3.9    0.0  11.5  32.1     0.5    4.3   39.2   64.0
#> 77     77   3.9    0.0  11.5  32.1     0.5    4.3   39.2   64.0
#> 78     78   3.9    0.0  11.5  32.1     0.5    4.3   39.2   64.0
#> 79     79   3.9    0.0  11.5  32.1     0.5    4.3   39.2   64.0
#> 80     80   1.0    0.0   8.4  24.8     0.0    1.0   29.8   54.4
#> 81     81   1.0    0.0   8.4  24.8     0.0    1.0   29.8   54.4
#> 82     82   1.0    0.0   8.4  24.8     0.0    1.0   29.8   54.4
#> 83     83   1.0    0.0   8.4  24.8     0.0    1.0   29.8   54.4
#> 84     84   1.0    0.0   8.4  24.8     0.0    1.0   29.8   54.4
#> 85     85   0.5    0.0  11.0  25.2     0.0    0.5   28.3   42.9
#> 86     86   0.5    0.0  11.0  25.2     0.0    0.5   28.3   42.9
#> 87     87   0.5    0.0  11.0  25.2     0.0    0.5   28.3   42.9
#> 88     88   0.5    0.0  11.0  25.2     0.0    0.5   28.3   42.9
#> 89     89   0.5    0.0  11.0  25.2     0.0    0.5   28.3   42.9
#> 90     90   0.0    0.0   0.0   6.2     0.0    0.0    6.2   20.3
#> 91     91   0.0    0.0   0.0   6.2     0.0    0.0    6.2   20.3
#> 92     92   0.0    0.0   0.0   6.2     0.0    0.0    6.2   20.3
#> 93     93   0.0    0.0   0.0   6.2     0.0    0.0    6.2   20.3
#> 94     94   0.0    0.0   0.0   6.2     0.0    0.0    6.2   20.3
#> 95     95   0.0    0.0   0.0   0.0     0.0    0.0    0.0   24.4
#> 96     96   0.0    0.0   0.0   0.0     0.0    0.0    0.0   24.4
#> 97     97   0.0    0.0   0.0   0.0     0.0    0.0    0.0   24.4
#> 98     98   0.0    0.0   0.0   0.0     0.0    0.0    0.0   24.4
#> 99     99   0.0    0.0   0.0   0.0     0.0    0.0    0.0   24.4
#> 100   100   0.0    0.0   0.0   0.0     0.0    0.0    0.0   24.4

  # Given a specific PPI score (from 0 - 100), get the row of poverty
  # probabilities from PPI table it corresponds to
  ppiScore <- 50
  ppiSLV2010[ppiSLV2010$score == ppiScore, ]
#>    score nl100 nlFood nl150 nl200 extreme ppp125 ppp250 ppp375
#> 50    50  27.5    2.9  55.1    70     9.2   37.6   77.4     90

  # Use subset() function to get the row of poverty probabilities corresponding
  # to specific PPI score
  ppiScore <- 50
  subset(ppiSLV2010, score == ppiScore)
#>    score nl100 nlFood nl150 nl200 extreme ppp125 ppp250 ppp375
#> 50    50  27.5    2.9  55.1    70     9.2   37.6   77.4     90

  # Given a specific PPI score (from 0 - 100), get a poverty probability
  # based on a specific poverty definition. In this example, the USAID
  # extreme poverty definition
  ppiScore <- 50
  ppiSLV2010[ppiSLV2010$score == ppiScore, "extreme"]
#> [1] 9.2