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metadata
license: cc-by-4.0
language:
  - en
task_categories:
  - tabular-classification
  - tabular-regression
  - time-series-forecasting
multilinguality: monolingual
size_categories:
  - 1K<n<10K
tags:
  - tabular
  - asia
  - ilostat
  - workers-in-stem-occupations
  - ilo
  - labour
  - employment
pretty_name: Average hourly earnings of STEM employees by sex and currency | Asia (ILOSTAT)

Average hourly earnings of STEM employees by sex and currency | Asia (ILOSTAT)

🌏 1,110 observations · 21 Asia countries · 2007–2025 · Repackaged by Electric Sheep Asia

rows countries years indicators license

TL;DR

This dataset contains 1,110 observations of Workers in STEM occupations data across 21 Asia countries, spanning 2007–2025, 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=EAR_SHRA_SEX_CUR_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

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

Country Rows First year Last year
VNM 135 2009 2024
KHM 126 2007 2023
TUR 117 2012 2024
LKA 105 2013 2024
THA 99 2014 2024
PHL 63 2017 2023
PAK 60 2013 2025
JOR 54 2017 2023
MNG 54 2019 2024
BTN 45 2018 2022
MMR 45 2015 2020
TLS 36 2010 2021
BGD 36 2017 2024
IND 33 2022 2025
PSE 30 2015 2025
... 6 more countries

Indicators (sample)

  • EAR_SHRA_SEX_CUR_NB — Average hourly earnings of STEM employees by sex and currency

Schema

Column Type Description Example
ref_area string ISO 3166-1 alpha-3 country code BGD
ref_area.label string Country name in English Bangladesh
source string ILOSTAT source code (e.g. labour force survey) BA:673
source.label string Source name in English LFS - Labour Force Survey
indicator string ILOSTAT indicator code EAR_SHRA_SEX_CUR_NB
indicator.label string Indicator name in English Average hourly earnings of STEM emplo…
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.) CUR_TYPE_LCU
classif1.label string Currency: Local currency
time int64 Observation year 2024
obs_value float64 Observed indicator value (unit varies — see indicator definition) 126.37
obs_status string Observation status flag (e.g. provisional, unreliable) B
obs_status.label string Break in series
note_indicator string T30:114
note_indicator.label string Currency: BGD - Taka (BDT)
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-ear-shra-sex-cur-nb-average-hourly-earnings-of-stem-employees-by-sex-a")
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"] == "EAR_SHRA_SEX_CUR_NB"]
          .sort_values("time"))
sample.plot(x="time", y="obs_value", title="EAR_SHRA_SEX_CUR_NB")

Pivot to country × year matrix

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

Citation

@misc{asia_ilo_ear_shra_sex_cur_nb_average_hourly_earnings_of_stem_employees_by_sex_a_2025,
  title        = {Average hourly earnings of STEM employees by sex and currency | Asia (ILOSTAT)},
  author       = {International Labour Organization (ILO)},
  year         = {2025},
  url          = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EAR_SHRA_SEX_CUR_NB},
  publisher    = {HuggingFace Datasets, repackaged by Electric Sheep Asia},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-ear-shra-sex-cur-nb-average-hourly-earnings-of-stem-employees-by-sex-a}}
}

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


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