electricsheepasia/asia-ilo-emp-snif-sex-nb-informal-employment-in-stem-occupations-thousands

Informal employment in STEM occupations (thousands) | Asia (ILOSTAT)

🌏 438 observations · 24 Asia countries · 2008–2025 · Repackaged by Electric Sheep Asia

TL;DR

This dataset contains 438 observations of Workers in STEM occupations data across 24 Asia countries, spanning 2008–2025, 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: Workers in STEM occupations

Methodology

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

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

Country	Rows	First year	Last year
MNG	51	2008	2024
PSE	39	2012	2025
TUR	39	2012	2024
VNM	39	2009	2024
KGZ	36	2012	2023
LKA	36	2013	2024
THA	30	2014	2024
BRN	27	2014	2024
PAK	21	2013	2025
GEO	18	2019	2024
JOR	18	2017	2023
MMR	15	2015	2020
BGD	12	2017	2024
IND	12	2022	2025
TLS	9	2010	2021
...	9 more countries

Indicators (sample)

• EMP_SNIF_SEX_NB — Informal employment in STEM occupations (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_SNIF_SEX_NB
indicator.label	string	Indicator name in English	Informal employment in STEM occupatio…
sex	string	Disaggregation by sex (SEX_T = total, SEX_M = male, SEX_F = female)	SEX_T
sex.label	string	—	Total
time	int64	Observation year	2021
obs_value	float64	Observed indicator value (unit varies — see indicator definition)	54.266
obs_status	string	Observation status flag (e.g. provisional, unreliable)	U
obs_status.label	string	—	Unreliable
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 (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-emp-snif-sex-nb-informal-employment-in-stem-occupations-thousands")
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_SNIF_SEX_NB"]
          .sort_values("time"))
sample.plot(x="time", y="obs_value", title="EMP_SNIF_SEX_NB")

Pivot to country × year matrix

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

Citation

@misc{asia_ilo_emp_snif_sex_nb_informal_employment_in_stem_occupations_thousands_2025,
  title        = {Informal employment in STEM occupations (thousands) | Asia (ILOSTAT)},
  author       = {International Labour Organization (ILO)},
  year         = {2025},
  url          = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_SNIF_SEX_NB},
  publisher    = {HuggingFace Datasets, repackaged by Electric Sheep Asia},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-emp-snif-sex-nb-informal-employment-in-stem-occupations-thousands}}
}

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_SNIF_SEX_NB