electricsheepasia/asia-ilo-emp-stem-sex-edu-nb-employment-in-stem-occupations-by-sex-and-educatio

Employment in STEM occupations by sex and education (thousands) | Asia (ILOSTAT)

🌏 3,225 observations · 30 Asia countries · 2000–2025 · Repackaged by Electric Sheep Asia

TL;DR

This dataset contains 3,225 observations of Workers in STEM occupations data across 30 Asia countries, spanning 2000–2025, 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_STEM_SEX_EDU_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

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

Country	Rows	First year	Last year
IRN	275	2008	2024
KHM	231	2007	2023
THA	216	2013	2024
VNM	214	2010	2024
ISR	200	2012	2024
TUR	198	2000	2024
MNG	185	2008	2024
LKA	167	2013	2024
CYP	163	2011	2024
PAK	158	2012	2025
PSE	157	2012	2025
BRN	129	2014	2024
BTN	104	2018	2024
JOR	88	2017	2023
MMR	88	2014	2020
...	15 more countries

Indicators (sample)

• EMP_STEM_SEX_EDU_NB — Employment in STEM occupations by sex and education (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_STEM_SEX_EDU_NB
indicator.label	string	Indicator name in English	Employment in STEM occupations by sex…
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.)	EDU_AGGREGATE_TOTAL
classif1.label	string	—	Education (Aggregate levels): Total
time	int64	Observation year	2021
obs_value	float64	Observed indicator value (unit varies — see indicator definition)	96.457
obs_status	string	Observation status flag (e.g. provisional, unreliable)	U
obs_status.label	string	—	Unreliable
note_classif	string	—	—
note_classif.label	string	—	—
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-stem-sex-edu-nb-employment-in-stem-occupations-by-sex-and-educatio")
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_STEM_SEX_EDU_NB"]
          .sort_values("time"))
sample.plot(x="time", y="obs_value", title="EMP_STEM_SEX_EDU_NB")

Pivot to country × year matrix

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

Citation

@misc{asia_ilo_emp_stem_sex_edu_nb_employment_in_stem_occupations_by_sex_and_educatio_2025,
  title        = {Employment in STEM occupations by sex and education (thousands) | Asia (ILOSTAT)},
  author       = {International Labour Organization (ILO)},
  year         = {2025},
  url          = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_STEM_SEX_EDU_NB},
  publisher    = {HuggingFace Datasets, repackaged by Electric Sheep Asia},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-emp-stem-sex-edu-nb-employment-in-stem-occupations-by-sex-and-educatio}}
}

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_STEM_SEX_EDU_NB