This PPI was created in April 2019 using Mozambique’s 2014/15 Inquérito Sobre Orçamento Familiar Survey and was released in May 2019.
Format
A data frame with 15 columns and 101 rows:
scorePPI score
nl100National poverty line (100)
nl150National poverty line (150)
nl200National poverty line (200)
ppp190Below $1.90 per day purchasing power parity (2011)
ppp320Below $3.20 per day purchasing power parity (2011)
ppp550Below $5.50 per day purchasing power parity (2011)
ppp800Below $8.00 per day purchasing power parity (2011)
ppp1100Below $11.00 per day purchasing power parity (2011)
ppp1500Below $15.00 per day purchasing power parity (2011)
ppp2170Below $21.70 per day purchasing power parity (2011)
percentile20Below 20th percentile poverty line
percentile40Below 40th percentile poverty line
percentile60Below 50th percentile poverty line
percentile80Below 60th percentile poverty line
Examples
# Access Mozambique PPI table
ppiMOZ2019
#> # A tibble: 101 × 15
#> score nl100 nl150 nl200 ppp190 ppp320 ppp550 ppp800 ppp1100 ppp1500 ppp2170
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 0 91 96.9 98.6 95.4 98.6 99.8 99.9 100 100 100
#> 2 1 90.3 96.6 98.5 95 98.5 99.8 99.9 100 100 100
#> 3 2 89.5 96.3 98.4 94.6 98.4 99.7 99.9 100 100 100
#> 4 3 88.7 96 98.2 94.1 98.2 99.7 99.9 100 100 100
#> 5 4 87.8 95.6 98 93.6 98.1 99.7 99.9 100 100 100
#> 6 5 86.9 95.2 97.8 93.1 97.9 99.6 99.9 100 100 100
#> 7 6 85.9 94.8 97.7 92.5 97.7 99.6 99.9 100 100 100
#> 8 7 84.8 94.3 97.4 91.8 97.5 99.6 99.9 100 100 100
#> 9 8 83.6 93.8 97.2 91.2 97.3 99.5 99.9 100 100 100
#> 10 9 82.4 93.3 96.9 90.4 97 99.5 99.9 100 100 100
#> # ℹ 91 more rows
#> # ℹ 4 more variables: percentile20 <dbl>, percentile40 <dbl>,
#> # percentile60 <dbl>, percentile80 <dbl>
# Given a specific PPI score (from 0 - 100), get the row of poverty
# probabilities from PPI table it corresponds to
ppiScore <- 50
ppiMOZ2019[ppiMOZ2019$score == ppiScore, ]
#> # A tibble: 1 × 15
#> score nl100 nl150 nl200 ppp190 ppp320 ppp550 ppp800 ppp1100 ppp1500 ppp2170
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 50 12.2 26.2 43.2 20.3 46 81.1 92 97.6 98.2 99.3
#> # ℹ 4 more variables: 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(ppiMOZ2019, score == ppiScore)
#> # A tibble: 1 × 15
#> score nl100 nl150 nl200 ppp190 ppp320 ppp550 ppp800 ppp1100 ppp1500 ppp2170
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 50 12.2 26.2 43.2 20.3 46 81.1 92 97.6 98.2 99.3
#> # ℹ 4 more variables: 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 national
# poverty line is used
ppiScore <- 50
ppiMOZ2019[ppiMOZ2019$score == ppiScore, "nl100"]
#> # A tibble: 1 × 1
#> nl100
#> <dbl>
#> 1 12.2
