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electricsheepasia/asia-ilo-ear-ehra-sex-edu-cur-nb-average-hourly-earnings-of-employees-by-sex-educat

Average hourly earnings of employees by sex, education and currency | Asia (ILOSTAT) 🌏 22,960 observations · 25 Asia countries · 1996–2025 · Repackaged by Electric Sheep Asia TL;DR This dataset contains 22,960 observations of Earnings data across 25 Asia countries, spanning 1996–2025, covering 1 distinct indicators. About the source ILOSTAT is the ILO's central statistics database, the leading global source for labour statistics. It compiles… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepasia/asia-ilo-ear-ehra-sex-edu-cur-nb-average-hourly-earnings-of-employees-by-sex-educat.

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Dataset Card

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

🌏 22,960 observations · 25 Asia countries · 1996–2025 · Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)

rows countries years indicators license

TL;DR

This dataset contains 22,960 observations of Earnings data across 25 Asia countries, spanning 1996–2025, 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: Earnings

Methodology

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

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

CountryRowsFirst yearLast year
IDN3,20419962023
KHM2,63419972023
VNM1,88120112024
LKA1,74520102024
THA1,58420142024
PAK1,53620062025
TUR1,10420052024
JOR1,00820172024
IND99020182025
ARM86120072017
MNG75320192024
BGD75020132024
BTN75020182022
TLS65320012021
PHL61220032023
...10 more countries

Indicators (sample)

  • —EAR_EHRA_SEX_EDU_CUR_NB — Average hourly earnings of employees by sex, education and currency

Schema

ColumnTypeDescriptionExample
ref_areastringISO 3166-1 alpha-3 country codeAFG
ref_area.labelstringCountry name in EnglishAfghanistan
sourcestringILOSTAT source code (e.g. labour force survey)BA:15715
source.labelstringSource name in EnglishLFS - Labour Force Survey
indicatorstringILOSTAT indicator codeEAR_EHRA_SEX_EDU_CUR_NB
indicator.labelstringIndicator name in EnglishAverage hourly earnings of employees …
sexstringDisaggregation by sex (SEXT = total, SEXM = male, SEX_F = female)SEX_T
sex.labelstring—Total
classif1stringFirst classification variable (age, education, status, etc.)EDU_AGGREGATE_TOTAL
classif1.labelstring—Education (Aggregate levels): Total
classif2stringSecond classification variable where applicableCUR_TYPE_LCU
classif2.labelstring—Currency: Local currency
timeint64Observation year2020
obs_valuefloat64Observed indicator value (unit varies — see indicator definition)90.929
obs_statusstringObservation status flag (e.g. provisional, unreliable)B
obs_status.labelstring—Break in series
note_classifstring—C3:2620
note_classif.labelstring—Nonstandard education level: Includin…
note_indicatorstring—T30:110_I11:264
note_indicator.labelstring—`Currency: AFG - Afghani (AFN)Break…`
note_sourcestring—R1:3513
note_source.labelstring—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

python
from datasets import load_dataset

ds = load_dataset("electricsheepasia/asia-ilo-ear-ehra-sex-edu-cur-nb-average-hourly-earnings-of-employees-by-sex-educat")
df = ds["train"].to_pandas()
print(df.head())

Filter to one country

python
indonesia = df[df["ref_area"] == "IDN"]

Time-series for a single indicator

python
sample = (df[df["indicator"] == "EAR_EHRA_SEX_EDU_CUR_NB"]
          .sort_values("time"))
sample.plot(x="time", y="obs_value", title="EAR_EHRA_SEX_EDU_CUR_NB")

Pivot to country × year matrix

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

Citation

bibtex
@misc{asia_ilo_ear_ehra_sex_edu_cur_nb_average_hourly_earnings_of_employees_by_sex_educat_2025,
  title        = {Average hourly earnings of employees by sex, education and currency | Asia (ILOSTAT)},
  author       = {International Labour Organization (ILO)},
  year         = {2025},
  url          = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EAR_EHRA_SEX_EDU_CUR_NB},
  publisher    = {HuggingFace Datasets, repackaged by Electric Sheep Asia},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-ear-ehra-sex-edu-cur-nb-average-hourly-earnings-of-employees-by-sex-educat}}
}

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 2026-05-27 via the Electric Sheep pipeline. Source URL: https://www.ilo.org/shinyapps/bulkexplorer/?id=EAREHRASEXEDUCURNB_