Welfare share by quantile in group data — pipgd_welfare_share

pipgd_welfare_share_at returns the share of welfare held by the specified share of the population in the parameter popshare. Alternatively, you can select the number of quantiles (10 be default), to estimate the corresponding share of welfare in each.

Usage

pipgd_welfare_share_at(
  params = NULL,
  welfare = NULL,
  weight = NULL,
  complete = getOption("pipster.return_complete"),
  lorenz = NULL,
  n = 10,
  popshare = seq(from = 1/n, to = 1, by = 1/n)
)

Arguments

params: list of parameters from pipgd_validate_lorenz()
welfare: numeric vector of cumulative share of welfare (income/consumption)
weight: numeric vector of cumulative share of the population
complete: logical: If TRUE, returns a list a cumulative returns from previously used get_gd functions. Default is FALSE
lorenz: character or NULL. Lorenz curve selected. It could be "lq" for Lorenz Quadratic or "lb" for Lorenz Beta
n: numeric scalar for the number of quantiles to be used in popshare
popshare: numeric: vector of share of population. Default is seq(from = 1/n, to = 1, by = 1/n)

Value

Returns a nested list containing distribution statistics: $dist_stats$pop_share is a numeric vector containing the share of the population. $dist_stats$welfare_share_at is a numeric vector showing the corresponding share of welfare. If complete = TRUE, it returns a pipgd_params object with additional details and intermediate calculations.

Examples

# Example 1: Basic usage with default parameters
pipgd_welfare_share_at(welfare = pip_gd$L,
                       weight = pip_gd$P,
                       complete = FALSE)
#> $dist_stats
#> $dist_stats$popshare
#>  [1] 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
#> 
#> $dist_stats$welfare_share_at
#>  [1] 0.03911772 0.09067747 0.15263425 0.22412849 0.30507843 0.39614586
#>  [7] 0.49903558 0.61752824 0.76157408 1.00000000
#> 
#> 

# Example 2: Specifying a custom number of quantiles (n = 5)
pipgd_welfare_share_at(welfare = pip_gd$L,
                       weight = pip_gd$P,
                       complete = FALSE,
                       n = 5)
#> $dist_stats
#> $dist_stats$popshare
#> [1] 0.2 0.4 0.6 0.8 1.0
#> 
#> $dist_stats$welfare_share_at
#> [1] 0.09067747 0.22412849 0.39614586 0.61752824 1.00000000
#> 
#> 

# Example 3: Using a custom population share vector
custom_popshare_vector <- seq(from = 1/13, to = 1, length.out = 13)
pipgd_welfare_share_at(welfare = pip_gd$L,
                       weight = pip_gd$P,
                       complete = FALSE,
                       popshare = custom_popshare_vector)
#> $dist_stats
#> $dist_stats$popshare
#>  [1] 0.07692308 0.15384615 0.23076923 0.30769231 0.38461538 0.46153846
#>  [7] 0.53846154 0.61538462 0.69230769 0.76923077 0.84615385 0.92307692
#> [13] 1.00000000
#> 
#> $dist_stats$welfare_share_at
#>  [1] 0.02887655 0.06550084 0.10869119 0.15780056 0.21251996 0.27280064
#>  [7] 0.33884270 0.41114336 0.49063514 0.57902253 0.67970615 0.80128755
#> [13] 1.00000000
#> 
#> 


# Example 4: Using a specified Lorenz curve (e.g., Lorenz Beta)
pipgd_welfare_share_at(welfare = pip_gd$L,
                       weight = pip_gd$P,
                       complete = FALSE,
                       lorenz = "lb")
#> $dist_stats
#> $dist_stats$popshare
#>  [1] 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
#> 
#> $dist_stats$welfare_share_at
#>  [1] 0.03809217 0.08976495 0.15119586 0.22213280 0.30303589 0.39493547
#>  [7] 0.49966955 0.62069307 0.76615187 1.00000000
#> 
#> 

# Example 5: Detailed output with complete = TRUE
pipgd_welfare_share_at(welfare = pip_gd$L,
                       weight = pip_gd$P,
                       complete = TRUE)
#> $gd_params
#> $gd_params$lq
#> $gd_params$lq$reg_results
#> $gd_params$lq$reg_results$ymean
#> [1] 0.1219752
#> 
#> $gd_params$lq$reg_results$sst
#> [1] 0.08456216
#> 
#> $gd_params$lq$reg_results$coef
#>          A          B          C 
#>  0.8877478 -1.4514459  0.2026400 
#> 
#> $gd_params$lq$reg_results$sse
#> [1] 3.418058e-06
#> 
#> $gd_params$lq$reg_results$r2
#> [1] 0.9999596
#> 
#> $gd_params$lq$reg_results$mse
#> [1] 3.797842e-07
#> 
#> $gd_params$lq$reg_results$se
#> [1] 0.006673127 0.019034521 0.012827923
#> 
#> 
#> $gd_params$lq$key_values
#> $gd_params$lq$key_values$e
#> [1] -0.638942
#> 
#> $gd_params$lq$key_values$m
#> [1] -1.444296
#> 
#> $gd_params$lq$key_values$n
#> [1] 1.044219
#> 
#> $gd_params$lq$key_values$r
#> [1] 1.857124
#> 
#> $gd_params$lq$key_values$s1
#> [1] -0.2814192
#> 
#> $gd_params$lq$key_values$s2
#> [1] 1.004414
#> 
#> 
#> $gd_params$lq$validity
#> $gd_params$lq$validity$is_normal
#> [1] TRUE
#> 
#> $gd_params$lq$validity$is_valid
#> [1] TRUE
#> 
#> $gd_params$lq$validity$headcount
#> [1] 0.6284604
#> 
#> 
#> 
#> $gd_params$lb
#> $gd_params$lb$reg_results
#> $gd_params$lb$reg_results$ymean
#> [1] -2.496791
#> 
#> $gd_params$lb$reg_results$sst
#> [1] 10.98072
#> 
#> $gd_params$lb$reg_results$coef
#>         A         B         C 
#> 0.5613532 0.9309501 0.5800259 
#> 
#> $gd_params$lb$reg_results$sse
#> [1] 0.003204989
#> 
#> $gd_params$lb$reg_results$r2
#> [1] 0.9997081
#> 
#> $gd_params$lb$reg_results$mse
#> [1] 0.0003561098
#> 
#> $gd_params$lb$reg_results$se
#> [1] 0.014871578 0.005505620 0.006407669
#> 
#> 
#> $gd_params$lb$key_values
#> [1] NA
#> 
#> $gd_params$lb$validity
#> $gd_params$lb$validity$is_valid
#> [1] TRUE
#> 
#> $gd_params$lb$validity$is_normal
#> [1] TRUE
#> attr(,"label")
#> [1] "Normality with a mean of 1 and a poverty line of 1;1 times the mean."
#> 
#> $gd_params$lb$validity$headcount
#> [1] 0.6161877
#> 
#> 
#> 
#> 
#> $data
#> $data$welfare
#>  [1] 0.00208 0.01013 0.03122 0.07083 0.12808 0.23498 0.34887 0.51994 0.64270
#> [10] 0.79201 0.86966 0.91277 1.00000
#> attr(,"label")
#> [1] "Cumulative share of welfare"
#> 
#> $data$weight
#>  [1] 0.0092 0.0339 0.0850 0.1640 0.2609 0.4133 0.5497 0.7196 0.8196 0.9174
#> [11] 0.9570 0.9751 1.0000
#> attr(,"label")
#> [1] "Cumulative share of population"
#> 
#> 
#> $selected_lorenz
#> $selected_lorenz$for_dist
#> [1] "lq"
#> 
#> $selected_lorenz$for_pov
#> [1] "lb"
#> 
#> $selected_lorenz$use_lq_for_dist
#> [1] TRUE
#> 
#> $selected_lorenz$use_lq_for_pov
#> [1] FALSE
#> 
#> 
#> $dist_stats
#> $dist_stats$popshare
#>  [1] 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
#> 
#> $dist_stats$welfare_share_at
#>  [1] 0.03911772 0.09067747 0.15263425 0.22412849 0.30507843 0.39614586
#>  [7] 0.49903558 0.61752824 0.76157408 1.00000000
#> 
#> 
#> attr(,"class")
#> [1] "pipgd_params"