Adding OEWS Wage and Employment Context • onet2r

O*NET tells you what occupations do: their tasks, abilities, skills, knowledge, and work contexts. The Bureau of Labor Statistics Occupational Employment and Wage Statistics (OEWS) program tells you how large those occupations are in the labor market and what they pay. Joining the two lets you move from an occupation-level descriptor table to a labor-market-weighted question.

This walkthrough asks: among occupations in a small O*NET ability panel, what does a national employment-weighted ability score look like, and what changes if we use a custom PUMS-style population instead?

Read OEWS Estimates

onet_oews_national() normalizes BLS OEWS files into snake_case columns and parses formatted employment and wage fields into numeric values.

BLS sometimes rejects automated ZIP downloads even when the same OEWS URL works in a browser. In interactive sessions, onet_oews_national() opens the official BLS URL in your browser, waits for the matching ZIP to appear in your Downloads folder, validates it, and copies it into the package cache. You can set options(onet2r.oews_download_dir = "path/to/downloads") for a different folder or pass the file explicitly with path, which is what this article does so it can build without network access. Passing path is also the fallback if BLS changes the OEWS URL or file name.

oews <- onet_oews_national(year = 2023, path = sample_oews)

oews |>
  select(occ_code, occ_title, tot_emp, a_median, h_median, a_median_topcoded) |>
  knitr::kable(digits = 3, align = "l")

occ_code	occ_title	tot_emp	a_median	h_median	a_median_topcoded
00-0000	All Occupations	151853870	48060	23.11	FALSE
15-0000	Computer and Mathematical Occupations	5199210	104420	50.20	FALSE
15-1252	Software Developers	1847900	133080	63.98	FALSE
29-1141	Registered Nurses	3175400	93070	44.75	FALSE
11-1011	Chief Executives	211230	NA	NA	TRUE

The sample includes BLS hierarchy rows and a top-coded wage. The parser keeps the o_group column, parses numeric fields, and adds flag columns when a wage is top-coded or suppressed.

Build a Reference-SOC Weight Panel

OEWS uses 6-digit SOC codes. O*NET archive tables use 8-digit O*NET-SOC detail codes. The weight-panel helper records the source and reference taxonomy so the join is auditable.

oews_weights <- onet_weight_panel_oews(oews, year = 2024)
#> Dropped 2 OEWS aggregate rows; keeping "detailed" occupations.

oews_weights |>
  knitr::kable(digits = 3, align = "l")

reference_soc_code	year	employment	weight_share	source	source_taxonomy	reference_taxonomy
11-1011	2024	211230	0.040	OEWS	2018 SOC	2018 SOC
15-1252	2024	1847900	0.353	OEWS	2018 SOC	2018 SOC
29-1141	2024	3175400	0.607	OEWS	2018 SOC	2018 SOC

Get O*NET Occupation Scores from an Archive

The occupation scores below come from onet_archive_read(), not a hand-built example table. We use Oral Comprehension as a concrete descriptor because it is available in the bundled fixture.

abilities <- onet_archive_read(
  "30.3",
  "Abilities",
  path = archive_303,
  release_date = "2026-05-01"
)

oral_scores <- abilities |>
  filter(element_id == "1.A.1.a.1") |>
  transmute(
    onet_soc_code,
    title,
    measure_score = data_value
  )

oral_scores |>
  knitr::kable(digits = 3, align = "l")

onet_soc_code	title	measure_score
15-1252.00	NA	4.35
29-1141.00	NA	4.71
11-1011.00	NA	4.50
41-1011.00	NA	4.15

Aggregate with OEWS Employment

oral_oews <- onet_measure_aggregate(
  oral_scores,
  oews_weights,
  measure_id = "oral_comprehension_fixture"
)

oral_oews |>
  select(-coverage, -provenance) |>
  knitr::kable(digits = 3, align = "l")

measure_id	aggregate	total_employment	covered_employment	employment_coverage_share	n_occupations	n_reference_soc
oral_comprehension_fixture	4.574	5234530	5234530	1	4	4


onet_provenance(oral_oews) |>
  knitr::kable(digits = 3, align = "l")

measure_id	measure_release	weight_source	weight_year	source_taxonomy	reference_taxonomy	bridge_used	crosswalk_path
oral_comprehension_fixture	NA	OEWS	2024	2018 SOC	2018 SOC	FALSE	2018 SOC -> 2018 SOC

The aggregate is the employment-weighted score for occupations covered by both the O*NET fixture and the OEWS sample. Coverage tells you how much of the weight panel was included.

onet_coverage(oral_oews) |>
  knitr::kable(digits = 3, align = "l")

measure_id	total_employment	covered_employment	employment_coverage_share	n_occupations	n_reference_soc
oral_comprehension_fixture	5234530	5234530	1	4	4

Inspect Occupation Contributions

contributions <- oral_scores |>
  mutate(reference_soc_code = sub("\\.\\d{2}$", "", onet_soc_code)) |>
  summarise(
    title = paste(sort(unique(title)), collapse = "; "),
    measure_score = mean(measure_score),
    .by = "reference_soc_code"
  ) |>
  inner_join(oews_weights, by = join_by(reference_soc_code), relationship = "many-to-one") |>
  mutate(weighted_score = measure_score * employment) |>
  arrange(desc(weighted_score)) |>
  select(title, reference_soc_code, measure_score, employment, weight_share, weighted_score)

contributions |>
  knitr::kable(digits = 3, align = "l")

reference_soc_code	measure_score	employment	weight_share	weighted_score
29-1141	4.71	3175400	0.607	14956134
15-1252	4.35	1847900	0.353	8038365
11-1011	4.50	211230	0.040	950535

The contribution is already the weighted_score column: occupation score times employment. In this fixture a separate chart would only redraw the table.

Compare OEWS with a PUMS-Style Weight Panel

OEWS is the right default for official occupation employment and wage context. PUMS-derived weights are useful when the target population is a custom sample, geography, or demographic cell.

pums <- tibble::tibble(
  SOCP = c("151252", "151252", "291141", "291141", "111011"),
  PWGTP = c(120, 80, 90, 110, 20),
  sex = c("F", "M", "F", "M", "F")
)

pums_weights <- onet_weight_panel_pums(
  pums,
  year = 2022,
  group = "sex"
)

pums_weights |>
  knitr::kable(digits = 3, align = "l")

reference_soc_code	sex	year	employment	weight_share	source	source_taxonomy	reference_taxonomy
11-1011	F	2022	20	0.087	PUMS	2018 SOC	2018 SOC
15-1252	F	2022	120	0.522	PUMS	2018 SOC	2018 SOC
15-1252	M	2022	80	0.421	PUMS	2018 SOC	2018 SOC
29-1141	F	2022	90	0.391	PUMS	2018 SOC	2018 SOC
29-1141	M	2022	110	0.579	PUMS	2018 SOC	2018 SOC

Because this PUMS-style panel has cells, pick one cell before aggregating.

For real ACS PUMS, filter to the employment universe before building weights. The common starting point is employed civilians, often ESR %in% c(1, 2) and age 16 or older. SOCP is the SOC field. OCCP is a Census occupation recode and needs a separate crosswalk before it can be used with O*NET or OEWS.

pums <- tidycensus::get_pums(
  variables = c("SOCP", "PWGTP", "ESR", "AGEP"),
  state = "WA",
  year = 2023,
  survey = "acs1"
) |>
  filter(ESR %in% c("1", "2"), AGEP >= 16)

pums_weights <- onet_weight_panel_pums(pums, year = 2023)

oral_pums_f <- onet_measure_aggregate(
  oral_scores,
  pums_weights,
  measure_id = "oral_comprehension_fixture",
  cell = list(sex = "F")
)

tibble::tibble(
  weight_source = c("OEWS national", "PUMS-style F cell"),
  aggregate = c(oral_oews$aggregate, oral_pums_f$aggregate),
  coverage = c(
    oral_oews$employment_coverage_share,
    oral_pums_f$employment_coverage_share
  )
) |>
  knitr::kable(digits = 3, align = "l")

weight_source	aggregate	coverage
OEWS national	4.574	1
PUMS-style F cell	4.504	1

Neither source is always better. OEWS gives official labor market estimates; PUMS gives custom cells. The package records the weight source, year, taxonomy, and coverage so a reader can see what changed.