Compute Relative Poverty Rate

Usage

run_weighted_relative_poverty(
  data_list,
  var_name,
  wgt_name = NULL,
  times_median = 0.5,
  type = c("type_4", "type_2"),
  na.rm = TRUE
)

Arguments

data_list: A named list of data frames.
var_name: A string specifying the variable name (e.g., "dhi", "pilabour").
wgt_name: A string (optional). The name of the weight variable. If NULL, equal weights are assumed.
times_median: A numeric scalar. The multiple of the median used to define the poverty threshold (default is 0.5).
type: A character vector indicating the percentile estimation type (passed to compute_weighted_percentiles). Default is "type_4".
na.rm: Logical. Should missing values be removed before computation? Default is TRUE.

Value

A named list. Each list element is named by country and contains a named numeric vector, where the names are years and the values are the computed statistics.

Examples

if (FALSE) { # \dontrun{
library(lissyrtools)
library(purrr)

datasets <- lissyrtools::lissyuse(data = c("de", "es", "uk"), vars = c("dhi"), from = 2016)

# Poverty line is defined at 50%  of the median value by default. 

rel_pvt_rate_50 <- datasets %>% 
 map(~ .x %>% filter(!is.na(dhi))) %>%
 map(~ .x %>% mutate(new_wgt = hwgt * nhhmem)) %>%
 apply_iqr_top_bottom_coding("dhi", "hwgt", type = "type_2") %>%
 apply_sqrt_equivalisation("dhi") %>% 
 run_weighted_relative_poverty("dhi", "new_wgt")
 
print(rel_pvt_rate_50)  
 
 # It can be defined at other values by specifying the argument `times_median`
 
rel_pvt_rate_40 <- datasets %>% 
 map(~ .x %>% filter(!is.na(dhi))) %>%
 map(~ .x %>% mutate(new_wgt = hwgt * nhhmem)) %>%
 apply_iqr_top_bottom_coding("dhi", "hwgt", type = "type_2") %>%
 apply_sqrt_equivalisation("dhi") %>% 
 run_weighted_relative_poverty("dhi", "new_wgt", times_median = 0.4)
 
print(rel_pvt_rate_40)   
} # }