SDG 8.8.1: fatal occupational injuries by sex & migrant status (annual)
Fatal occupational injuries per 100,000 workers by sex and migrant status, annual, from the official SDG 8.8.1 series — dataflow DF_SDG_F881_SEX_MIG_RT. Method: cases of fatal occupational injury reported to national compensation or notification systems, divided by the reference worker population, scaled to 100,000 workers. Three series per country x sex: all workers, migrants and non-migrants. Units: injuries per 100,000 workers. Spot values (verified 2026-09-29): USA 2023 total 3.5 (male 5.7, female 0.7), Canada 2023 total 5.198, Australia 2023 total 1.416, Argentina 2024 migrants 3.793 vs non-migrants 3.12 (2023). Caveats: the migrant-status breakdown is sparse — most countries publish only the total; reference populations and reporting coverage differ between countries (e.g. Argentina covers insured persons and includes occupational disease), so cross-country levels need care — the migrant/non-migrant gap within a country-year is the comparable cut; figures are revised with each ILO vintage.
- Source
- ILO ILOSTAT
- Rows
- 3,185
- Columns
- 4
- Source cadence
- Yearly
- Last refreshed
- Sep 29, 2026
- Theme
- labor
| Column | Type | Description |
|---|---|---|
| date | date | Observation year from the ILO SDMX TIME_PERIOD dimension (annual frequency, FREQ=A), stored as January 1 of that year. |
| series_id | string | ILOSTAT dimension codes from the SDMX response as REF_AREA.SEX.MIG, e.g. 'ARG.SEX_T.MIG_STATUS_MIGRANT' (Argentina, Total, Migrants). REF_AREA codes are ISO alpha-3 country codes; SEX is the ILO codelist CL_SEX; MIG is the migrant-status slice of the ILO codelist CL_MIG (Total, Migrants, Non-migrants). |
| series_label | string | Country name from the ILO codelist CL_AREA, sex label from CL_SEX and migrant-status label from CL_MIG, joined as 'Country — Sex — Migrant status', e.g. 'Argentina — Total — Migrants'. |
| value | float | OBS_VALUE from the ILO SDMX response for DF_SDG_F881_SEX_MIG_RT, migrant status: all workers; migrants; non-migrants. Unit of measure reported by the ILO as RT (injuries per 100,000 workers) — injuries per 100,000 workers. (unit: injuries per 100,000 workers) |
First 10 sample rows — a preview, not the complete dataset.
| date | series_id | series_label | value |
|---|---|---|---|
| 2019-01-01 | ARG.SEX_F.MIG_STATUS_MIGRANT | Argentina — Female — Migrants | 0.92 |
| 2020-01-01 | ARG.SEX_F.MIG_STATUS_MIGRANT | Argentina — Female — Migrants | 0.46 |
| 2017-01-01 | ARG.SEX_F.MIG_STATUS_NONMIG | Argentina — Female — Non-migrants | 0.297 |
| 2018-01-01 | ARG.SEX_F.MIG_STATUS_NONMIG | Argentina — Female — Non-migrants | 0.265 |
| 2019-01-01 | ARG.SEX_F.MIG_STATUS_NONMIG | Argentina — Female — Non-migrants | 0.18 |
| 2020-01-01 | ARG.SEX_F.MIG_STATUS_NONMIG | Argentina — Female — Non-migrants | 0.23 |
| 2021-01-01 | ARG.SEX_F.MIG_STATUS_NONMIG | Argentina — Female — Non-migrants | 0.32 |
| 2022-01-01 | ARG.SEX_F.MIG_STATUS_NONMIG | Argentina — Female — Non-migrants | 0.152 |
| 2023-01-01 | ARG.SEX_F.MIG_STATUS_NONMIG | Argentina — Female — Non-migrants | 0.24 |
| 2024-01-01 | ARG.SEX_F.MIG_STATUS_NONMIG | Argentina — Female — Non-migrants | 0.217 |
Profiled Sep 29, 2026 from snapshot 20260929T101108Z-f8ea397247f6
Measured- Completeness
- 100%
- Rows
- 3,185
- Columns
- 4
- Columns with gaps
- 1
| Column | Missing | Distinct | Range | Distribution |
|---|---|---|---|---|
| datedate | 0% | 27 | Jan 1, 2000 → Jan 1, 2024 | — |
| series_idvarchar | 0% | 417 | — |
|
| series_labelvarchar | 0% | 402 | — |
|
| valuedouble | 0.03% | 1,162 | 0 → 556.99median 3 | 32 outside 1st–99th percentile |
- Current
20260929T101108Z-f8ea397247f6 · sha256 f8ea397247f6…
3,185 rows · first snapshot
Point any LLM at the metadata endpoint — the documentation above is machine-readable too (JSON-LD + Croissant).
curl "https://datazimuts.com/v1/datasets/ilostat/ilo_fatal_occupational_injuries" | jq '{title, rows, columns_count, license}'import requests
ds = requests.get("https://datazimuts.com/v1/datasets/ilostat/ilo_fatal_occupational_injuries").json()
print(ds["title"], ds["rows"], "rows")
# Sample rows for an LLM context window
for row in ds.get("sample_rows", [])[:5]:
print(row)API endpoint: https://datazimuts.com/v1/datasets/ilostat/ilo_fatal_occupational_injuries
Tip: fetch /llms.txt for the full machine-readable catalog.
Where this data comes from and what was made from it. Other people's work shows as counts; only shared projects are named.
Cite this snapshot
Pinned to snapshot 20260929T101108Z-f8ea397247f6 and its content hash, so readers get exactly the data you used.
ILO ILOSTAT. (2026). SDG 8.8.1: fatal occupational injuries by sex & migrant status (annual) [Data set, snapshot 20260929T101108Z-f8ea397247f6, sha256 f8ea397247f6]. Datazimuts. Retrieved 2026-09-30, from https://datazimuts.com/en/datasets/ilostat/ilo_fatal_occupational_injuries?snapshot=20260929T101108Z-f8ea397247f6
@misc{dz_ilostat_ilo_fatal_occupational_injuries_f8ea3972,
title = {{SDG 8.8.1: fatal occupational injuries by sex \& migrant status (annual)}},
author = {{ILO ILOSTAT}},
year = {2026},
publisher = {Datazimuts},
howpublished = {\url{https://datazimuts.com/en/datasets/ilostat/ilo_fatal_occupational_injuries?snapshot=20260929T101108Z-f8ea397247f6}},
note = {Snapshot 20260929T101108Z-f8ea397247f6, sha256 f8ea397247f659300cd2e61ecd54691274a81b6722d773c9e79d1e1b72a47280; accessed 2026-09-30}
}Embed a table or a chart
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