electricsheepasia/asia-ilo-emp-5emp-sex-oc2-nb-employment-by-sex-and-occupation-isco-level-2-19th

Employment by sex and occupation - ISCO level 2 -- 19th ICLS (thousands) | Asia (ILOSTAT)

🌏 10,229 observations · 20 Asia countries · 2014–2025 · Repackaged by Electric Sheep Asia

TL;DR

This dataset contains 10,229 observations of Employment data across 20 Asia countries, spanning 2014–2025, 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: Employment

Methodology

Data pulled directly from the ILOSTAT REST API at https://rplumber.ilo.org/data/indicator?id=EMP_5EMP_SEX_OC2_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

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

Country	Rows	First year	Last year
ISR	1,350	2014	2024
CYP	1,340	2014	2024
MNG	741	2019	2024
JPN	720	2014	2023
JOR	655	2017	2023
VNM	650	2020	2024
ARE	631	2017	2022
GEO	617	2020	2024
SGP	583	2021	2025
KHM	498	2019	2023
MMR	494	2017	2020
BRN	470	2014	2024
BGD	397	2022	2024
MDV	240	2016	2019
TLS	219	2021	2022
...	5 more countries

Indicators (sample)

• EMP_5EMP_SEX_OC2_NB — Employment by sex and occupation - ISCO level 2 -- 19th ICLS (thousands)

Schema

Column	Type	Description	Example
ref_area	string	ISO 3166-1 alpha-3 country code	AFG
ref_area.label	string	Country name in English	Afghanistan
source	string	ILOSTAT source code (e.g. labour force survey)	BA:15715
source.label	string	Source name in English	LFS - Labour Force Survey
indicator	string	ILOSTAT indicator code	EMP_5EMP_SEX_OC2_NB
indicator.label	string	Indicator name in English	Employment by sex and occupation - IS…
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.)	OC2_ISCO08_TOTAL
classif1.label	string	—	Occupation (ISCO-08), 2 digit level: …
time	int64	Observation year	2021
obs_value	float64	Observed indicator value (unit varies — see indicator definition)	5042.173
obs_status	string	Observation status flag (e.g. provisional, unreliable)	U
obs_status.label	string	—	Unreliable
note_classif	string	—	C4:6305
note_classif.label	string	—	Nonstandard occupation: Including ass…
note_indicator	string	—	I11:264
note_indicator.label	string	—	Break in series: Methodology revised
note_source	string	—	R1:3513_S3:8
note_source.label	string	—	Repository: ILO-STATISTICS - Micro da…

Disaggregation dimensions

The following columns provide disaggregation dimensions:

• sex (4 unique values): SEX_T, SEX_M, SEX_F, SEX_O

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-emp-5emp-sex-oc2-nb-employment-by-sex-and-occupation-isco-level-2-19th")
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"] == "EMP_5EMP_SEX_OC2_NB"]
          .sort_values("time"))
sample.plot(x="time", y="obs_value", title="EMP_5EMP_SEX_OC2_NB")

Pivot to country × year matrix

matrix = (df[df["indicator"] == "EMP_5EMP_SEX_OC2_NB"]
          .pivot_table(index="time", columns="ref_area", values="obs_value"))
print(matrix.tail())

Citation

@misc{asia_ilo_emp_5emp_sex_oc2_nb_employment_by_sex_and_occupation_isco_level_2_19th_2025,
  title        = {Employment by sex and occupation - ISCO level 2 -- 19th ICLS (thousands) | Asia (ILOSTAT)},
  author       = {International Labour Organization (ILO)},
  year         = {2025},
  url          = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_5EMP_SEX_OC2_NB},
  publisher    = {HuggingFace Datasets, repackaged by Electric Sheep Asia},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-emp-5emp-sex-oc2-nb-employment-by-sex-and-occupation-isco-level-2-19th}}
}

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=EMP_5EMP_SEX_OC2_NB