Computes poverty and inequality statistics (grouped) — gd_compute_pip

Compute poverty statistics for grouped data by selecting the best functional fit for the Lorenz curve (either beta or quadratic).

Usage

gd_compute_pip_stats(
  welfare,
  povline,
  population,
  requested_mean,
  popshare = NULL,
  default_ppp = 1,
  ppp = NULL,
  p0 = 0.5
)

Arguments

welfare: numeric: Cumulative proportion of welfare held by that proportion of the population (Lorenz Curve).
povline: numeric: Poverty line.
population: numeric: Cumulative proportion of population.
requested_mean: numeric: Welfare mean.
popshare: numeric: Share of population living below the poverty line. Optional.
default_ppp: numeric: Default purchasing power parity.
ppp: numeric: PPP request by user.
p0: numeric: TO BE DOCUMENTED.

Value

list

Examples

# Compute PIP stats
res <- wbpip:::gd_compute_pip_stats(
         grouped_data_ex2$welfare,
         grouped_data_ex2$weight,
         requested_mean = 2.911786,
         povline = 1.9,
         default_ppp = 1)