Colombian mortality tables — mortality_colombia

A tidy collection of Colombian mortality tables for life-contingency examples. The dataset includes regulatory and pedagogical mortality tables used in Colombian actuarial applications.

Usage

mortality_colombia_tables

Format

A tibble with 12 variables:

table_id: Mortality table identifier.
sex: Sex category, typically "male" or "female".
x: Integer actuarial age.
lx: Number of survivors at exact age x.
dx: Expected number of deaths between ages x and x + 1.
qx: One-year death probability between ages x and x + 1.
px: One-year survival probability between ages x and x + 1.
mu_x: Force of mortality at age x, when available.
ex: Complete life expectancy at age x, when available.
source: Source identifier or reference.
qx_calc: Death probability recalculated from lx and dx, when available.
qx_diff: Difference between reported qx and recalculated qx, when available.

Source

Colombian mortality tables cleaned for tidyactuarial examples. Source identifiers are provided in the source column.

Details

The dataset is intended for actuarial examples involving Colombian mortality tables, survival probabilities, life annuities, life insurance present values, and validation of life-table calculations.

The variable names follow the compact actuarial notation used throughout tidyactuarial: x denotes age, lx the number of lives, dx the number of deaths, qx the one-year death probability, px the one-year survival probability, mu_x the force of mortality, and ex the life expectancy at age x.

The variables qx_calc and qx_diff are included as validation aids. They allow users to compare reported death probabilities against probabilities reconstructed from lx and dx.

Some tables may start at different initial ages, use different terminal ages, or use different radix values. Users should filter the desired table and sex before passing the data to life-contingency functions.

Examples

data(mortality_colombia_tables)

head(mortality_colombia_tables)
#> # A tibble: 6 × 12
#>   table_id        sex       x    lx    dx    qx    px  mu_x    ex source qx_calc
#>   <chr>           <chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>    <dbl>
#> 1 Asegurados_198… Unis…    20   345     0     0     1    NA    NA r0996…       0
#> 2 Asegurados_198… Unis…    21   345     0     0     1    NA    NA r0996…       0
#> 3 Asegurados_198… Unis…    22   346     0     0     1    NA    NA r0996…       0
#> 4 Asegurados_198… Unis…    23   347     0     0     1    NA    NA r0996…       0
#> 5 Asegurados_198… Unis…    24   348     0     0     1    NA    NA r0996…       0
#> 6 Asegurados_198… Unis…    25   348     0     0     1    NA    NA r0996…       0
#> # ℹ 1 more variable: qx_diff <dbl>

mortality_colombia_tables |>
  dplyr::count(table_id, sex)
#> # A tibble: 11 × 3
#>    table_id                       sex        n
#>    <chr>                          <chr>  <int>
#>  1 Asegurados_1984_1988           Unisex    46
#>  2 Asegurados_1998_2003           female    81
#>  3 Asegurados_1998_2003           male      81
#>  4 BEPS_2014                      female    96
#>  5 BEPS_2014                      male      96
#>  6 Mortalidad_invalidos_1980_1989 female    12
#>  7 Mortalidad_invalidos_1980_1989 male       4
#>  8 RV08_Rentistas_2005_2008       female    96
#>  9 RV08_Rentistas_2005_2008       male      96
#> 10 Rentistas_RV89_ISS_1980_1989   female    61
#> 11 Rentistas_RV89_ISS_1980_1989   male      54

rv08_male <- mortality_colombia_tables |>
  dplyr::filter(table_id == "RV08_Rentistas_2005_2008", sex == "male") |>
  dplyr::select(x, lx, dx, qx, px, mu_x, ex)

head(rv08_male)
#> # A tibble: 6 × 7
#>       x      lx    dx       qx    px  mu_x    ex
#>   <int>   <dbl> <dbl>    <dbl> <dbl> <dbl> <dbl>
#> 1    15 1000000   485 0.000485 1.000    NA  64.8
#> 2    16  999515   496 0.000496 1.000    NA  63.9
#> 3    17  999019   509 0.000509 0.999    NA  62.9
#> 4    18  998510   522 0.000523 0.999    NA  61.9
#> 5    19  997988   537 0.000538 0.999    NA  60.9
#> 6    20  997451   553 0.000554 0.999    NA  60