electricsheepasia/asia-ilo-emp-stem-sex-cbr-nb-employment-in-stem-occupations-by-sex-and-place-of

Employment in STEM occupations by sex and place of birth (thousands) | Asia (ILOSTAT)

🌏 651 observations · 15 Asia countries · 2000–2024 · Repackaged by Electric Sheep Asia

TL;DR

This dataset contains 651 observations of Workers in STEM occupations data across 15 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: Workers in STEM occupations

Methodology

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

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

Country	Rows	First year	Last year
CYP	126	2011	2024
TUR	126	2000	2024
ISR	117	2012	2024
BRN	81	2014	2024
MNG	34	2010	2024
KHM	33	2009	2019
MDV	30	2014	2019
TLS	29	2010	2022
IRN	24	2006	2011
LAO	12	2017	2022
ARE	9	2022	2022
MMR	8	2014	2014
BGD	8	2017	2017
PAK	8	2021	2021
IRQ	6	2021	2021

Indicators (sample)

• EMP_STEM_SEX_CBR_NB — Employment in STEM occupations by sex and place of birth (thousands)

Schema

Column	Type	Description	Example
ref_area	string	ISO 3166-1 alpha-3 country code	ARE
ref_area.label	string	Country name in English	United Arab Emirates
source	string	ILOSTAT source code (e.g. labour force survey)	BA:716
source.label	string	Source name in English	LFS - Labour Force Survey
indicator	string	ILOSTAT indicator code	EMP_STEM_SEX_CBR_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.)	CBR_BIR_TOTAL
classif1.label	string	—	Place of birth: Total
time	int64	Observation year	2022
obs_value	float64	Observed indicator value (unit varies — see indicator definition)	866.916
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
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-cbr-nb-employment-in-stem-occupations-by-sex-and-place-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"] == "EMP_STEM_SEX_CBR_NB"]
          .sort_values("time"))
sample.plot(x="time", y="obs_value", title="EMP_STEM_SEX_CBR_NB")

Pivot to country × year matrix

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

Citation

@misc{asia_ilo_emp_stem_sex_cbr_nb_employment_in_stem_occupations_by_sex_and_place_of_2024,
  title        = {Employment in STEM occupations by sex and place of birth (thousands) | Asia (ILOSTAT)},
  author       = {International Labour Organization (ILO)},
  year         = {2024},
  url          = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_STEM_SEX_CBR_NB},
  publisher    = {HuggingFace Datasets, repackaged by Electric Sheep Asia},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-emp-stem-sex-cbr-nb-employment-in-stem-occupations-by-sex-and-place-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=EMP_STEM_SEX_CBR_NB