Skip to contents

Poverty Probability Index (PPI) lookup table for Vietnam

Usage

ppiVNM2009

Format

A data frame with 8 columns and 101 rows:

score

PPI score

nl100

National poverty line (100%)

nlFood

Food poverty line

extreme

USAID extreme poverty line

ppp125

Below $1.25 per day purchasing power parity (2005)

ppp175

Below $1.75 per day purchasing power parity (2005)

ppp250

Below $2.50 per day purchasing power parity (2005)

molisa

MOLISA poverty line

Examples

  # Access Vietnam PPI table
  ppiVNM2009
#>     score nl100 nlFood extreme ppp125 ppp175 ppp250 molisa
#> 0       0  93.0   78.9    78.9   93.0  100.0  100.0   83.1
#> 1       1  93.0   78.9    78.9   93.0  100.0  100.0   83.1
#> 2       2  93.0   78.9    78.9   93.0  100.0  100.0   83.1
#> 3       3  93.0   78.9    78.9   93.0  100.0  100.0   83.1
#> 4       4  93.0   78.9    78.9   93.0  100.0  100.0   83.1
#> 5       5  90.0   78.5    68.1   90.0   96.5  100.0   90.0
#> 6       6  90.0   78.5    68.1   90.0   96.5  100.0   90.0
#> 7       7  90.0   78.5    68.1   90.0   96.5  100.0   90.0
#> 8       8  90.0   78.5    68.1   90.0   96.5  100.0   90.0
#> 9       9  90.0   78.5    68.1   90.0   96.5  100.0   90.0
#> 10     10  74.5   60.9    52.4   84.7   97.7  100.0   67.7
#> 11     11  74.5   60.9    52.4   84.7   97.7  100.0   67.7
#> 12     12  74.5   60.9    52.4   84.7   97.7  100.0   67.7
#> 13     13  74.5   60.9    52.4   84.7   97.7  100.0   67.7
#> 14     14  74.5   60.9    52.4   84.7   97.7  100.0   67.7
#> 15     15  70.9   52.4    37.9   79.2   96.1  100.0   67.8
#> 16     16  70.9   52.4    37.9   79.2   96.1  100.0   67.8
#> 17     17  70.9   52.4    37.9   79.2   96.1  100.0   67.8
#> 18     18  70.9   52.4    37.9   79.2   96.1  100.0   67.8
#> 19     19  70.9   52.4    37.9   79.2   96.1  100.0   67.8
#> 20     20  55.4   30.7    26.8   65.5   89.4   96.7   47.5
#> 21     21  55.4   30.7    26.8   65.5   89.4   96.7   47.5
#> 22     22  55.4   30.7    26.8   65.5   89.4   96.7   47.5
#> 23     23  55.4   30.7    26.8   65.5   89.4   96.7   47.5
#> 24     24  55.4   30.7    26.8   65.5   89.4   96.7   47.5
#> 25     25  35.2   15.4    12.2   44.0   79.5   95.6   26.6
#> 26     26  35.2   15.4    12.2   44.0   79.5   95.6   26.6
#> 27     27  35.2   15.4    12.2   44.0   79.5   95.6   26.6
#> 28     28  35.2   15.4    12.2   44.0   79.5   95.6   26.6
#> 29     29  35.2   15.4    12.2   44.0   79.5   95.6   26.6
#> 30     30  33.0   17.0    13.2   43.4   82.0   95.0   27.1
#> 31     31  33.0   17.0    13.2   43.4   82.0   95.0   27.1
#> 32     32  33.0   17.0    13.2   43.4   82.0   95.0   27.1
#> 33     33  33.0   17.0    13.2   43.4   82.0   95.0   27.1
#> 34     34  33.0   17.0    13.2   43.4   82.0   95.0   27.1
#> 35     35  20.8   10.2     8.3   31.1   70.3   91.2   15.7
#> 36     36  20.8   10.2     8.3   31.1   70.3   91.2   15.7
#> 37     37  20.8   10.2     8.3   31.1   70.3   91.2   15.7
#> 38     38  20.8   10.2     8.3   31.1   70.3   91.2   15.7
#> 39     39  20.8   10.2     8.3   31.1   70.3   91.2   15.7
#> 40     40  10.8    4.7     4.3   17.8   52.0   86.6    8.9
#> 41     41  10.8    4.7     4.3   17.8   52.0   86.6    8.9
#> 42     42  10.8    4.7     4.3   17.8   52.0   86.6    8.9
#> 43     43  10.8    4.7     4.3   17.8   52.0   86.6    8.9
#> 44     44  10.8    4.7     4.3   17.8   52.0   86.6    8.9
#> 45     45   4.9    0.9     0.7   11.7   42.8   75.8    4.5
#> 46     46   4.9    0.9     0.7   11.7   42.8   75.8    4.5
#> 47     47   4.9    0.9     0.7   11.7   42.8   75.8    4.5
#> 48     48   4.9    0.9     0.7   11.7   42.8   75.8    4.5
#> 49     49   4.9    0.9     0.7   11.7   42.8   75.8    4.5
#> 50     50   3.3    0.4     0.8    4.1   25.2   64.9    3.7
#> 51     51   3.3    0.4     0.8    4.1   25.2   64.9    3.7
#> 52     52   3.3    0.4     0.8    4.1   25.2   64.9    3.7
#> 53     53   3.3    0.4     0.8    4.1   25.2   64.9    3.7
#> 54     54   3.3    0.4     0.8    4.1   25.2   64.9    3.7
#> 55     55   1.2    0.9     0.9    1.9   16.4   57.1    0.9
#> 56     56   1.2    0.9     0.9    1.9   16.4   57.1    0.9
#> 57     57   1.2    0.9     0.9    1.9   16.4   57.1    0.9
#> 58     58   1.2    0.9     0.9    1.9   16.4   57.1    0.9
#> 59     59   1.2    0.9     0.9    1.9   16.4   57.1    0.9
#> 60     60   1.2    0.0     0.0    2.2   14.1   49.4    2.3
#> 61     61   1.2    0.0     0.0    2.2   14.1   49.4    2.3
#> 62     62   1.2    0.0     0.0    2.2   14.1   49.4    2.3
#> 63     63   1.2    0.0     0.0    2.2   14.1   49.4    2.3
#> 64     64   1.2    0.0     0.0    2.2   14.1   49.4    2.3
#> 65     65   0.5    0.5     0.0    0.5   10.8   48.5    1.0
#> 66     66   0.5    0.5     0.0    0.5   10.8   48.5    1.0
#> 67     67   0.5    0.5     0.0    0.5   10.8   48.5    1.0
#> 68     68   0.5    0.5     0.0    0.5   10.8   48.5    1.0
#> 69     69   0.5    0.5     0.0    0.5   10.8   48.5    1.0
#> 70     70   0.5    0.5     0.5    0.5    5.2   32.7    0.5
#> 71     71   0.5    0.5     0.5    0.5    5.2   32.7    0.5
#> 72     72   0.5    0.5     0.5    0.5    5.2   32.7    0.5
#> 73     73   0.5    0.5     0.5    0.5    5.2   32.7    0.5
#> 74     74   0.5    0.5     0.5    0.5    5.2   32.7    0.5
#> 75     75   0.0    0.0     0.0    0.0    3.6   19.0    1.2
#> 76     76   0.0    0.0     0.0    0.0    3.6   19.0    1.2
#> 77     77   0.0    0.0     0.0    0.0    3.6   19.0    1.2
#> 78     78   0.0    0.0     0.0    0.0    3.6   19.0    1.2
#> 79     79   0.0    0.0     0.0    0.0    3.6   19.0    1.2
#> 80     80   0.0    0.0     0.0    0.0    0.5    8.1    0.0
#> 81     81   0.0    0.0     0.0    0.0    0.5    8.1    0.0
#> 82     82   0.0    0.0     0.0    0.0    0.5    8.1    0.0
#> 83     83   0.0    0.0     0.0    0.0    0.5    8.1    0.0
#> 84     84   0.0    0.0     0.0    0.0    0.5    8.1    0.0
#> 85     85   0.0    0.0     0.0    0.0    0.0    7.2    0.0
#> 86     86   0.0    0.0     0.0    0.0    0.0    7.2    0.0
#> 87     87   0.0    0.0     0.0    0.0    0.0    7.2    0.0
#> 88     88   0.0    0.0     0.0    0.0    0.0    7.2    0.0
#> 89     89   0.0    0.0     0.0    0.0    0.0    7.2    0.0
#> 90     90   0.0    0.0     0.0    0.0    0.0    0.8    0.0
#> 91     91   0.0    0.0     0.0    0.0    0.0    0.8    0.0
#> 92     92   0.0    0.0     0.0    0.0    0.0    0.8    0.0
#> 93     93   0.0    0.0     0.0    0.0    0.0    0.8    0.0
#> 94     94   0.0    0.0     0.0    0.0    0.0    0.8    0.0
#> 95     95   0.0    0.0     0.0    0.0    0.0    1.4    0.0
#> 96     96   0.0    0.0     0.0    0.0    0.0    1.4    0.0
#> 97     97   0.0    0.0     0.0    0.0    0.0    1.4    0.0
#> 98     98   0.0    0.0     0.0    0.0    0.0    1.4    0.0
#> 99     99   0.0    0.0     0.0    0.0    0.0    1.4    0.0
#> 100   100   0.0    0.0     0.0    0.0    0.0    1.4    0.0

  # Given a specific PPI score (from 0 - 100), get the row of poverty
  # probabilities from PPI table it corresponds to
  ppiScore <- 50
  ppiVNM2009[ppiVNM2009$score == ppiScore, ]
#>    score nl100 nlFood extreme ppp125 ppp175 ppp250 molisa
#> 50    50   3.3    0.4     0.8    4.1   25.2   64.9    3.7

  # Use subset() function to get the row of poverty probabilities corresponding
  # to specific PPI score
  ppiScore <- 50
  subset(ppiVNM2009, score == ppiScore)
#>    score nl100 nlFood extreme ppp125 ppp175 ppp250 molisa
#> 50    50   3.3    0.4     0.8    4.1   25.2   64.9    3.7

  # 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
  ppiVNM2009[ppiVNM2009$score == ppiScore, "nl100"]
#> [1] 3.3