electricsheepasia/asia-ilo-sdg-f881-sex-mig-rt-sdg-indicator-8-8-1-fatal-occupational-injuries-pe

SDG indicator 8.8.1 - Fatal occupational injuries per 100'000 workers by sex and migrant s | Asia (ILOSTAT)

🌏 691 observations · 25 Asia countries · 2000–2024 · Repackaged by Electric Sheep Asia

TL;DR

This dataset contains 691 observations of Occupational injuries data across 25 Asia countries, spanning 2000–2024, covering 1 distinct indicators.

About the source

ILOSTAT is the ILO's central statistics database, the leading global source for labour statistics. It compiles indicators across employment, unemployment, wages, working time, child labour, informal economy, social protection, occupational injuries, and SDG decent work targets — drawing on national labour force surveys, household income surveys, establishment surveys, and administrative records. Coverage spans 200+ economies, with the ILO's Department of Statistics responsible for harmonisation.

• Source: ILOSTAT
• Publisher: International Labour Organization (ILO)
• License: cc-by-4.0
• Topic: Occupational injuries

Methodology

Data pulled directly from the ILOSTAT REST API at https://rplumber.ilo.org/data/indicator?id=SDG_F881_SEX_MIG_RT and filtered to Asia ISO3 country codes. ILOSTAT harmonises raw survey microdata using ICLS (International Conference of Labour Statisticians) definitions; sources are flagged in the source.label column for traceability.

Geographic coverage

25 Asia countries · top rows shown below, sorted by row count:

Country	Rows	First year	Last year
TUR	138	2000	2024
UZB	72	2009	2024
MMR	46	2000	2019
KAZ	42	2000	2017
LKA	41	2000	2024
AZE	41	2000	2023
CYP	38	2000	2023
ISR	33	2000	2023
MYS	28	2000	2022
KOR	24	2000	2023
SGP	24	2000	2023
MNG	21	2016	2024
THA	21	2000	2024
KGZ	18	2000	2015
BHR	18	2000	2020
...	10 more countries

Indicators (sample)

• SDG_F881_SEX_MIG_RT — SDG indicator 8.8.1 - Fatal occupational injuries per 100'000 workers by sex and migrant status

Schema

Column	Type	Description	Example
ref_area	string	ISO 3166-1 alpha-3 country code	ARM
ref_area.label	string	Country name in English	Armenia
source	string	ILOSTAT source code (e.g. labour force survey)	EB:176
source.label	string	Source name in English	Other - Statistical Report on Occupat…
indicator	string	ILOSTAT indicator code	SDG_F881_SEX_MIG_RT
indicator.label	string	Indicator name in English	SDG indicator 8.8.1 - Fatal occupatio…
sex	string	Disaggregation by sex (SEX_T = total, SEX_M = male, SEX_F = female)	SEX_T
sex.label	string	—	Total
classif1	string	First classification variable (age, education, status, etc.)	MIG_STATUS_TOTAL
classif1.label	string	—	Migrant status: Total
time	int64	Observation year	2020
obs_value	float64	Observed indicator value (unit varies — see indicator definition)	4.19
obs_status	string	Observation status flag (e.g. provisional, unreliable)	—
obs_status.label	string	—	—
note_indicator	string	—	I20:4077_T13:149_T14:156
note_indicator.label	string	—	Employment definition: Excluding own-…
note_source	string	—	S8:1619_S9:66_S10:73
note_source.label	string	—	Economic activity coverage: Excluding…

Disaggregation dimensions

The following columns provide disaggregation dimensions:

• sex (3 unique values): SEX_T, SEX_M, SEX_F

Data quality & caveats

• Data is annual frequency. Some indicators also publish monthly or quarterly series — those are not included here.
• When an indicator has multiple sources for the same country×year, the ILO-selected 'best source' is used.
• Disaggregation columns (sex, classif1, classif2) are non-null only when the indicator publishes that breakdown.

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepasia/asia-ilo-sdg-f881-sex-mig-rt-sdg-indicator-8-8-1-fatal-occupational-injuries-pe")
df = ds["train"].to_pandas()
print(df.head())

Filter to one country

indonesia = df[df["ref_area"] == "IDN"]

Time-series for a single indicator

sample = (df[df["indicator"] == "SDG_F881_SEX_MIG_RT"]
          .sort_values("time"))
sample.plot(x="time", y="obs_value", title="SDG_F881_SEX_MIG_RT")

Pivot to country × year matrix

matrix = (df[df["indicator"] == "SDG_F881_SEX_MIG_RT"]
          .pivot_table(index="time", columns="ref_area", values="obs_value"))
print(matrix.tail())

Citation

@misc{asia_ilo_sdg_f881_sex_mig_rt_sdg_indicator_8_8_1_fatal_occupational_injuries_pe_2024,
  title        = {SDG indicator 8.8.1 - Fatal occupational injuries per 100'000 workers by sex and migrant s | Asia (ILOSTAT)},
  author       = {International Labour Organization (ILO)},
  year         = {2024},
  url          = {https://www.ilo.org/shinyapps/bulkexplorer/?id=SDG_F881_SEX_MIG_RT},
  publisher    = {HuggingFace Datasets, repackaged by Electric Sheep Asia},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-sdg-f881-sex-mig-rt-sdg-indicator-8-8-1-fatal-occupational-injuries-pe}}
}

License

Released under cc-by-4.0.

Original data © International Labour Organization (ILO). When using this dataset, please cite both the original source above and the Electric Sheep Asia repackaging.

About Electric Sheep

Electric Sheep Asia is part of the Electric Sheep mission: a unified, ML-ready data layer for Asia on HuggingFace. We pull data from authoritative open sources, normalize the schemas, package as Parquet, and publish with consistent dataset cards so researchers and developers can use load_dataset() to start working in seconds.

Browse the full collection: huggingface.co/electricsheepasia

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Provenance: ingested 2026-05-25 via the Electric Sheep pipeline. Source URL: https://www.ilo.org/shinyapps/bulkexplorer/?id=SDG_F881_SEX_MIG_RT