Poverty Probability Index (PPI) lookup table for Tanzania 2022

Poverty Probability Index (PPI) lookup table for Tanzania 2022

Usage

ppiTZA2022

Format

A data frame with 21 columns and 100 rows:

score

PPI score

nl_upper

National upper poverty line

nl_lower

National lower poverty line

extreme

Extreme poverty line

nl150

National poverty line (150%)

nl200

National poverty line (200%)

ppp100

Below $1.00 per day purchasing power parity (2011)

ppp190

Below $1.90 per day purchasing power parity (2011)

ppp320

Below $3.20 per day purchasing power parity (2011)

ppp550

Below $5.50 per day purchasing power parity (2011)

ppp800

Below $8.00 per day purchasing power parity (2011)

ppp1100

Below $11.00 per day purchasing power parity (2011)

ppp1500

Below $15.00 per day purchasing power parity (2011)

ppp2170

Below $21.70 per day purchasing power parity (2011)

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)

percentile20

Below 20th percentile poverty line

percentile40

Below 40th percentile poverty line

percentile60

Below 50th percentile poverty line

percentile80

Below 60th percentile poverty line

Source

https://www.povertyindex.org

Examples

  # Access Tanzania PPI table
  ppiTZA2022
#> # A tibble: 100 × 21
#>    score nl_upper nl_lower extreme nl150 nl200 ppp100 ppp190 ppp320 ppp550
#>    <dbl>    <dbl>    <dbl>   <dbl> <dbl> <dbl>  <dbl>  <dbl>  <dbl>  <dbl>
#>  1     1     87.8     85.3    59.7  97.2  98.7   61.1   95.1   98.7   99.7
#>  2     2     86.9     84.2    57.4  97.0  98.6   58.9   94.7   98.6   99.7
#>  3     3     85.9     83.1    55.1  96.8  98.6   56.8   94.3   98.5   99.7
#>  4     4     85.0     81.8    52.7  96.5  98.4   54.6   93.8   98.4   99.7
#>  5     5     83.9     80.5    50.4  96.3  98.3   52.3   93.4   98.3   99.7
#>  6     6     82.8     79.2    48.1  96.0  98.2   50.1   92.8   98.2   99.6
#>  7     7     81.7     77.8    45.7  95.7  98.1   47.9   92.3   98.0   99.6
#>  8     8     80.4     76.3    43.4  95.3  98.0   45.7   91.7   97.9   99.6
#>  9     9     79.2     74.7    41.1  95.0  97.8   43.5   91.0   97.8   99.6
#> 10    10     77.8     73.1    38.9  94.6  97.7   41.3   90.3   97.6   99.5
#> # ℹ 90 more rows
#> # ℹ 11 more variables: ppp800 <dbl>, ppp1100 <dbl>, ppp1500 <dbl>,
#> #   ppp2170 <dbl>, ppp125 <dbl>, ppp250 <dbl>, ppp500 <dbl>,
#> #   percentile20 <dbl>, percentile40 <dbl>, percentile60 <dbl>,
#> #   percentile80 <dbl>

  # Given a specific PPI score (from 0 - 100), get the row of poverty
  # probabilities from PPI table it corresponds to
  ppiScore <- 50
  ppiTZA2022[ppiTZA2022$score == ppiScore, ]
#> # A tibble: 1 × 21
#>   score nl_upper nl_lower extreme nl150 nl200 ppp100 ppp190 ppp320 ppp550 ppp800
#>   <dbl>    <dbl>    <dbl>   <dbl> <dbl> <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>
#> 1    50     12.8     8.41    1.46  44.6  72.3   1.94   26.2   69.9   92.6   96.4
#> # ℹ 10 more variables: ppp1100 <dbl>, ppp1500 <dbl>, ppp2170 <dbl>,
#> #   ppp125 <dbl>, ppp250 <dbl>, ppp500 <dbl>, percentile20 <dbl>,
#> #   percentile40 <dbl>, percentile60 <dbl>, percentile80 <dbl>

  # Use subset() function to get the row of poverty probabilities corresponding
  # to specific PPI score
  ppiScore <- 50
  subset(ppiTZA2022, score == ppiScore)
#> # A tibble: 1 × 21
#>   score nl_upper nl_lower extreme nl150 nl200 ppp100 ppp190 ppp320 ppp550 ppp800
#>   <dbl>    <dbl>    <dbl>   <dbl> <dbl> <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>
#> 1    50     12.8     8.41    1.46  44.6  72.3   1.94   26.2   69.9   92.6   96.4
#> # ℹ 10 more variables: ppp1100 <dbl>, ppp1500 <dbl>, ppp2170 <dbl>,
#> #   ppp125 <dbl>, ppp250 <dbl>, ppp500 <dbl>, percentile20 <dbl>,
#> #   percentile40 <dbl>, percentile60 <dbl>, percentile80 <dbl>

  # 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
  ppiTZA2022[ppiTZA2022$score == ppiScore, "extreme"]
#> # A tibble: 1 × 1
#>   extreme
#>     <dbl>
#> 1    1.46