Import the Felten, Raj, and Seamans AIOE Scores as a Task-Grain Measure

Reads the occupation-level AI Occupational Exposure (AIOE) scores from Felten, Raj, and Seamans (2021), "Occupational, Industry, and Geographic Exposure to Artificial Intelligence", and broadcasts them onto the tasks of a Task Ratings style panel, returning a task-grain onet_measure() keyed on (occupation, task). Every task inherits its occupation's published AIOE score. This is a thin adapter: it selects the score column, standardizes the SOC code, joins to the panel, and records provenance without transforming the published values.

Usage

onet_import_felten_aioe(
  panel,
  path = NULL,
  url = onet_felten_aioe_url,
  score = "AIOE",
  key = NULL,
  sheet = "Appendix A",
  occupation_code = "onet_soc_code",
  task_id = "task_id",
  measure_id = "felten_aioe",
  measure_name = "Felten, Raj, and Seamans (2021) AIOE",
  force = FALSE,
  ...
)

Arguments

panel: A Task Ratings style panel with an occupation column (occupation_code, default "onet_soc_code") and a task column (task_id, default "task_id"). Its distinct occupation-task pairs set the grain of the returned measure.
path: Optional path to a local copy of the AIOE workbook or a comma or tab separated export. When supplied, no download is attempted.
url: Download URL used when path is NULL. Defaults to the pinned AIOE_DataAppendix.xlsx in the authors' public repository.
score: Name of the exposure column to use as the measure score. Defaults to "AIOE".
key: Optional name of the SOC code column in the workbook. When NULL, common column names such as "SOC Code" are detected automatically.
sheet: Worksheet holding the occupation scores. Defaults to "Appendix A", the occupation sheet of the published workbook.
occupation_code, task_id: Names of the occupation and task columns in panel.
measure_id, measure_name: Identifiers recorded on the returned measure.
force: Logical; re-download even when a cached copy exists.
...: Additional arguments passed to onet_measure(), such as universe or weight_panel.

Value

A task-grain onet_measure object (key_type = "task") keyed on task_id and scored on the selected AIOE column, with the occupation code retained. It is ready for onet_task_to_occupation().

Details

AIOE scores are indexed by 6-digit SOC code, not by 8-digit O*NET-SOC code. The adapter derives a 6-digit SOC code from the panel's occupation code and joins on it, then broadcasts each occupation's score to its tasks. Tasks whose occupation has no published score are dropped with a warning. Pass a single-release panel so each task id is unique.

onet2r never bundles or ships the workbook; you must supply path or download it from url. Downloads are cached under tools::R_user_dir("onet2r", "cache") in the reference section and can be cleared with onet_cache_clear(what = "reference"). The AIOE workbook is provided for research use; cite Felten, Raj, and Seamans (2021) when you use the scores.

Examples

# Offline: broadcast a small local extract onto a tiny panel.
extract <- tempfile(fileext = ".csv")
utils::write.csv(
  data.frame(
    `SOC Code` = c("15-1252", "29-1141"),
    `Occupation Title` = c("Software Developers", "Registered Nurses"),
    AIOE = c(1.08, -0.32),
    check.names = FALSE
  ),
  extract,
  row.names = FALSE
)
panel <- tibble::tibble(
  onet_soc_code = rep(c("15-1252.00", "29-1141.00"), each = 2),
  task_id = c("1", "2", "3", "4")
)
measure <- onet_import_felten_aioe(panel, path = extract)
measure$data
#> # A tibble: 4 × 6
#>   onet_soc_code task_id soc_code AIOE  measure_key measure_score
#>   <chr>         <chr>   <chr>    <chr> <chr>               <dbl>
#> 1 15-1252.00    1       15-1252  1.08  1                    1.08
#> 2 15-1252.00    2       15-1252  1.08  2                    1.08
#> 3 29-1141.00    3       29-1141  -0.32 3                   -0.32
#> 4 29-1141.00    4       29-1141  -0.32 4                   -0.32

# Online: download the published AIOE workbook.
if (interactive()) {
  aioe <- onet_import_felten_aioe(panel)
  head(aioe$data)
}