electricsheepasia/asia-ilo-inj-nftl-inj-eco-nb-cases-of-non-fatal-occupational-injury-by-type-of

Cases of non-fatal occupational injury by type of incapacity and economic activity | Asia (ILOSTAT)

🌏 7,103 observations · 22 Asia countries · 1971–2024 · Repackaged by Electric Sheep Asia

TL;DR

This dataset contains 7,103 observations of Occupational injuries data across 22 Asia countries, spanning 1971–2024, 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=INJ_NFTL_INJ_ECO_NB 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

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

Country	Rows	First year	Last year
PHL	979	1990	2021
SGP	932	1999	2018
TWN	918	1988	2005
THA	872	1999	2024
LKA	723	1999	2024
MYS	563	1994	2022
TUR	485	2008	2024
JOR	360	1990	2006
CYP	302	2009	2023
UZB	192	2021	2024
KAZ	182	1998	2017
PAK	117	2018	2021
GEO	97	2019	2024
IND	84	1971	2007
AZE	82	2013	2022
...	7 more countries

Indicators (sample)

• INJ_NFTL_INJ_ECO_NB — Cases of non-fatal occupational injury by type of incapacity and economic activity

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	INJ_NFTL_INJ_ECO_NB
indicator.label	string	Indicator name in English	Cases of non-fatal occupational injur…
classif1	string	First classification variable (age, education, status, etc.)	INJ_INCAPACITY_TOTAL
classif1.label	string	—	Type of incapacity: Total
classif2	string	Second classification variable where applicable	ECO_AGGREGATE_TOTAL
classif2.label	string	—	Economic activity (Aggregate): Total
time	int64	Observation year	2019
obs_value	float64	Observed indicator value (unit varies — see indicator definition)	24.0
obs_status	string	Observation status flag (e.g. provisional, unreliable)	—
obs_status.label	string	—	—
note_classif	string	—	C5:4107
note_classif.label	string	—	Nonstandard economic activity: Exclud…
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…

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-inj-nftl-inj-eco-nb-cases-of-non-fatal-occupational-injury-by-type-of")
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"] == "INJ_NFTL_INJ_ECO_NB"]
          .sort_values("time"))
sample.plot(x="time", y="obs_value", title="INJ_NFTL_INJ_ECO_NB")

Pivot to country × year matrix

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

Citation

@misc{asia_ilo_inj_nftl_inj_eco_nb_cases_of_non_fatal_occupational_injury_by_type_of_2024,
  title        = {Cases of non-fatal occupational injury by type of incapacity and economic activity | Asia (ILOSTAT)},
  author       = {International Labour Organization (ILO)},
  year         = {2024},
  url          = {https://www.ilo.org/shinyapps/bulkexplorer/?id=INJ_NFTL_INJ_ECO_NB},
  publisher    = {HuggingFace Datasets, repackaged by Electric Sheep Asia},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-inj-nftl-inj-eco-nb-cases-of-non-fatal-occupational-injury-by-type-of}}
}

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 2026-05-28 via the Electric Sheep pipeline. Source URL: https://www.ilo.org/shinyapps/bulkexplorer/?id=INJ_NFTL_INJ_ECO_NB