Background
Povcalnet uses two methods to estimate poverty and inequality
statistics from grouped data.
* One method is based on fitting a Lorenz Quadratic functional form to
the grouped data
* the other one uses a Beta Lorenz function
This vignette focuses on the Lorenz Quadratic method.
High level example
# Input definition
welfare_mean <- 51.56
ppp <- 3.69
daily_povline <- 1.9
monthly_povline <- daily_povline * 365 / 12
# Create grouped data (Type 1)
# http://iresearch.worldbank.org/povcalnet/PovCalculator.aspx
population <- c(0.0005,
0.0032,
0.014799999999999999,
0.0443,
0.0991,
0.257,
0.4385,
0.5938,
0.7089,
1)
welfare <- c(5.824760527229386e-05,
0.000604029410841011,
0.0037949334793616948,
0.013988878652244477,
0.036992164583098786,
0.12140708906131342,
0.24531391873082081,
0.37446670169288321,
0.48753116241194566,
1)
# Estimate poverty statistics
wbpip:::gd_compute_pip_stats_lb(welfare = welfare,
population = population,
requested_mean = welfare_mean,
povline = monthly_povline,
default_ppp = ppp)
#> $mean
#> [1] 51.56
#>
#> $poverty_line
#> [1] 57.79167
#>
#> $z_min
#> [1] 13.70771
#>
#> $z_max
#> [1] 142.2513
#>
#> $ppp
#> [1] 3.69
#>
#> $gini
#> [1] 0.3123674
#>
#> $median
#> [1] 42.58973
#>
#> $rmhalf
#> [1] 30.01057
#>
#> $polarization
#> [1] 0.2556375
#>
#> $ris
#> [1] 0.2910257
#>
#> $mld
#> [1] 0.1633424
#>
#> $dcm
#> [1] 35.45434
#>
#> $deciles
#> [1] 0.03750486 0.04963605 0.05871314 0.06775231 0.07741935 0.08831018
#> [7] 0.10132911 0.11835493 0.14500187 0.25597820
#>
#> $headcount
#> [1] 0.7184622
#>
#> $poverty_gap
#> [1] 0.2714275
#>
#> $poverty_severity
#> [1] 0.1293701
#>
#> $eh
#> [1] -0.8806334
#>
#> $epg
#> [1] -1.646977
#>
#> $ep
#> [1] -2.196139
#>
#> $gh
#> [1] -0.09495857
#>
#> $gpg
#> [1] 0.7145769
#>
#> $gp
#> [1] 1.547531
#>
#> $watts
#> [1] 0.3788881
#>
#> $dl
#> [1] 1.120948
#>
#> $ddl
#> [1] 1.771549
#>
#> $is_normal
#> [1] TRUE
#>
#> $is_valid
#> [1] TRUE
#>
#> $sse
#> [1] 2.263312e-05
#>
#> $ssez
#> [1] 2.263312e-05
#>
#> $ymean
#> [1] -3.45893
#>
#> $sst
#> [1] 38.99352
#>
#> $coef
#> [1] 0.5780372 0.9420509 0.5257860
#>
#> $sse
#> [1] 0.001335919
#>
#> $r2
#> [1] 0.9999657
#>
#> $mse
#> [1] 0.0002226532
#>
#> $se
#> [1] 0.015403736 0.003191951 0.017792248