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Poverty Probability Index (PPI) lookup table for Philippines using legacy poverty definitions

Usage

ppiPHL2014

Format

A data frame with 6 columns and 101 rows:

score

PPI score

nl100

National poverty line (100%)

ppp125

Below $1.25 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)

ppp432

Below $4.32 per day purchasing power parity (1993)

Examples

  # Access Philippines PPI table
  ppiPHL2014
#>     score nl100 ppp125 ppp250 ppp500 ppp432
#> 0       0 100.0  100.0  100.0  100.0  100.0
#> 1       1 100.0  100.0  100.0  100.0  100.0
#> 2       2 100.0  100.0  100.0  100.0  100.0
#> 3       3 100.0  100.0  100.0  100.0  100.0
#> 4       4 100.0  100.0  100.0  100.0  100.0
#> 5       5 100.0  100.0  100.0  100.0  100.0
#> 6       6 100.0  100.0  100.0  100.0  100.0
#> 7       7 100.0  100.0  100.0  100.0  100.0
#> 8       8 100.0  100.0  100.0  100.0  100.0
#> 9       9 100.0  100.0  100.0  100.0  100.0
#> 10     10  95.0   85.1   98.8  100.0   97.8
#> 11     11  95.0   85.1   98.8  100.0   97.8
#> 12     12  95.0   85.1   98.8  100.0   97.8
#> 13     13  95.0   85.1   98.8  100.0   97.8
#> 14     14  95.0   85.1   98.8  100.0   97.8
#> 15     15  90.1   72.6   98.0  100.0   96.8
#> 16     16  90.1   72.6   98.0  100.0   96.8
#> 17     17  90.1   72.6   98.0  100.0   96.8
#> 18     18  90.1   72.6   98.0  100.0   96.8
#> 19     19  90.1   72.6   98.0  100.0   96.8
#> 20     20  80.1   58.0   93.6   99.6   91.7
#> 21     21  80.1   58.0   93.6   99.6   91.7
#> 22     22  80.1   58.0   93.6   99.6   91.7
#> 23     23  80.1   58.0   93.6   99.6   91.7
#> 24     24  80.1   58.0   93.6   99.6   91.7
#> 25     25  71.8   42.7   90.6   99.3   88.2
#> 26     26  71.8   42.7   90.6   99.3   88.2
#> 27     27  71.8   42.7   90.6   99.3   88.2
#> 28     28  71.8   42.7   90.6   99.3   88.2
#> 29     29  71.8   42.7   90.6   99.3   88.2
#> 30     30  57.2   29.0   82.7   98.6   77.3
#> 31     31  57.2   29.0   82.7   98.6   77.3
#> 32     32  57.2   29.0   82.7   98.6   77.3
#> 33     33  57.2   29.0   82.7   98.6   77.3
#> 34     34  57.2   29.0   82.7   98.6   77.3
#> 35     35  41.6   17.6   71.4   97.7   64.5
#> 36     36  41.6   17.6   71.4   97.7   64.5
#> 37     37  41.6   17.6   71.4   97.7   64.5
#> 38     38  41.6   17.6   71.4   97.7   64.5
#> 39     39  41.6   17.6   71.4   97.7   64.5
#> 40     40  26.7   10.2   56.4   94.6   49.8
#> 41     41  26.7   10.2   56.4   94.6   49.8
#> 42     42  26.7   10.2   56.4   94.6   49.8
#> 43     43  26.7   10.2   56.4   94.6   49.8
#> 44     44  26.7   10.2   56.4   94.6   49.8
#> 45     45  18.5    6.4   41.8   86.7   35.1
#> 46     46  18.5    6.4   41.8   86.7   35.1
#> 47     47  18.5    6.4   41.8   86.7   35.1
#> 48     48  18.5    6.4   41.8   86.7   35.1
#> 49     49  18.5    6.4   41.8   86.7   35.1
#> 50     50   7.7    2.6   26.3   76.0   19.5
#> 51     51   7.7    2.6   26.3   76.0   19.5
#> 52     52   7.7    2.6   26.3   76.0   19.5
#> 53     53   7.7    2.6   26.3   76.0   19.5
#> 54     54   7.7    2.6   26.3   76.0   19.5
#> 55     55   3.4    0.9   12.1   60.9    8.6
#> 56     56   3.4    0.9   12.1   60.9    8.6
#> 57     57   3.4    0.9   12.1   60.9    8.6
#> 58     58   3.4    0.9   12.1   60.9    8.6
#> 59     59   3.4    0.9   12.1   60.9    8.6
#> 60     60   1.0    0.3    6.0   46.4    4.0
#> 61     61   1.0    0.3    6.0   46.4    4.0
#> 62     62   1.0    0.3    6.0   46.4    4.0
#> 63     63   1.0    0.3    6.0   46.4    4.0
#> 64     64   1.0    0.3    6.0   46.4    4.0
#> 65     65   0.7    0.2    2.9   30.8    2.0
#> 66     66   0.7    0.2    2.9   30.8    2.0
#> 67     67   0.7    0.2    2.9   30.8    2.0
#> 68     68   0.7    0.2    2.9   30.8    2.0
#> 69     69   0.7    0.2    2.9   30.8    2.0
#> 70     70   0.2    0.0    1.1   19.7    0.9
#> 71     71   0.2    0.0    1.1   19.7    0.9
#> 72     72   0.2    0.0    1.1   19.7    0.9
#> 73     73   0.2    0.0    1.1   19.7    0.9
#> 74     74   0.2    0.0    1.1   19.7    0.9
#> 75     75   0.1    0.0    0.7    7.6    0.6
#> 76     76   0.1    0.0    0.7    7.6    0.6
#> 77     77   0.1    0.0    0.7    7.6    0.6
#> 78     78   0.1    0.0    0.7    7.6    0.6
#> 79     79   0.1    0.0    0.7    7.6    0.6
#> 80     80   0.0    0.0    0.3    3.2    0.0
#> 81     81   0.0    0.0    0.3    3.2    0.0
#> 82     82   0.0    0.0    0.3    3.2    0.0
#> 83     83   0.0    0.0    0.3    3.2    0.0
#> 84     84   0.0    0.0    0.3    3.2    0.0
#> 85     85   0.0    0.0    0.0    1.1    0.0
#> 86     86   0.0    0.0    0.0    1.1    0.0
#> 87     87   0.0    0.0    0.0    1.1    0.0
#> 88     88   0.0    0.0    0.0    1.1    0.0
#> 89     89   0.0    0.0    0.0    1.1    0.0
#> 90     90   0.0    0.0    0.0    0.0    0.0
#> 91     91   0.0    0.0    0.0    0.0    0.0
#> 92     92   0.0    0.0    0.0    0.0    0.0
#> 93     93   0.0    0.0    0.0    0.0    0.0
#> 94     94   0.0    0.0    0.0    0.0    0.0
#> 95     95   0.0    0.0    0.0    0.0    0.0
#> 96     96   0.0    0.0    0.0    0.0    0.0
#> 97     97   0.0    0.0    0.0    0.0    0.0
#> 98     98   0.0    0.0    0.0    0.0    0.0
#> 99     99   0.0    0.0    0.0    0.0    0.0
#> 100   100   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
  ppiPHL2014[ppiPHL2014$score == ppiScore, ]
#>    score nl100 ppp125 ppp250 ppp500 ppp432
#> 50    50   7.7    2.6   26.3     76   19.5

  # Use subset() function to get the row of poverty probabilities corresponding
  # to specific PPI score
  ppiScore <- 50
  subset(ppiPHL2014, score == ppiScore)
#>    score nl100 ppp125 ppp250 ppp500 ppp432
#> 50    50   7.7    2.6   26.3     76   19.5

  # 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
  ppiPHL2014[ppiPHL2014$score == ppiScore, "nl100"]
#> [1] 7.7