Summarise O*NET Values with Employment or Wage Weights — onet_weighted

Computes employment-weighted summaries for O*NET task, skill, ability, or work-activity rows after joining occupation-level employment and wage estimates. This is useful for moving from occupation-level O*NET outputs to labor-market-weighted measures.

Usage

onet_weighted_summary(
  data,
  group,
  value,
  occupation_code = "code",
  oews = NULL,
  weight = "tot_emp",
  wage = "a_median"
)

Arguments

data: A data frame containing one row per occupation-value pair.
group: Character vector of columns to group by, such as task or skill identifiers.
value: Name of the numeric O*NET value column to average.
occupation_code: Name of the occupation code column in data.
oews: Optional OEWS tibble. If supplied and weight is not already in data, it is joined with onet_join_oews().
weight: Name of the employment weight column. Defaults to tot_emp, the OEWS total-employment column.
wage: Name of the wage column used for wage-weighted summaries.

Value

A tibble with group columns plus n_records, n_occupations, total_weight, weighted_mean, and wage_weighted_mean.

Details

Lifecycle

This helper is soft-deprecated for measure work. Prefer onet_weight_panel_oews(), onet_weight_panel_pums(), and onet_measure_aggregate() for vintage-aware weighting.

Examples

skills <- tibble::tibble(
  code = c("15-1252.00", "15-1252.00", "29-1141.00", "29-1141.00"),
  element_id = c("2.A.1.a", "2.A.1.b", "2.A.1.a", "2.A.1.b"),
  element_name = c(
    "Reading Comprehension", "Active Listening",
    "Reading Comprehension", "Active Listening"
  ),
  data_value = c(4.12, 4.00, 3.88, 4.25)
)

oews <- tibble::tibble(
  occ_code = c("15-1252", "29-1141"),
  tot_emp = c(1847900, 3175400),
  a_median = c(133080, 93070)
)

suppressWarnings(
  onet_weighted_summary(
    skills,
    group = c("element_id", "element_name"),
    value = "data_value",
    oews = oews
  )
)
#> # A tibble: 2 × 7
#>   element_id element_name     n_records n_occupations total_weight weighted_mean
#>   <chr>      <chr>                <int>         <int>        <dbl>         <dbl>
#> 1 2.A.1.a    Reading Compreh…         2             2      5023300          3.97
#> 2 2.A.1.b    Active Listening         2             2      5023300          4.16
#> # ℹ 1 more variable: wage_weighted_mean <dbl>