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

Average hourly earnings of employees by sex, occupation and currency | Asia (ILOSTAT) 🌏 30,895 observations · 31 Asia countries · 1997–2025 · Repackaged by Electric Sheep Asia TL;DR This dataset contains 30,895 observations of Earnings data across 31 Asia countries, spanning 1997–2025, 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-ocu-cur-nb-average-hourly-earnings-of-employees-by-sex-occupa.

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Dataset Card

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

🌏 30,895 observations · 31 Asia countries · 1997–2025 · Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)

rows countries years indicators license

TL;DR

This dataset contains 30,895 observations of Earnings data across 31 Asia countries, spanning 1997–2025, 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_OCU_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

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

CountryRowsFirst yearLast year
KHM2,71219972023
PHL2,42420072023
VNM2,33720072024
TUR2,18420052024
IDN2,17820072023
MNG2,12420082024
LKA1,99920102024
PAK1,80320062025
THA1,61120142024
KOR1,38620092022
MYS1,08020112020
JOR98420172024
IND94820182025
BTN72920182022
BGD72920132024
...16 more countries

Indicators (sample)

  • —EAR_EHRA_SEX_OCU_CUR_NB — Average hourly earnings of employees by sex, occupation 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_OCU_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.)OCU_SKILL_TOTAL
classif1.labelstring—Occupation (Skill level): 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—C4:1002
note_classif.labelstring—Nonstandard occupation: Including 3
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-ocu-cur-nb-average-hourly-earnings-of-employees-by-sex-occupa")
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_OCU_CUR_NB"]
          .sort_values("time"))
sample.plot(x="time", y="obs_value", title="EAR_EHRA_SEX_OCU_CUR_NB")

Pivot to country × year matrix

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

Citation

bibtex
@misc{asia_ilo_ear_ehra_sex_ocu_cur_nb_average_hourly_earnings_of_employees_by_sex_occupa_2025,
  title        = {Average hourly earnings of employees by sex, occupation and currency | Asia (ILOSTAT)},
  author       = {International Labour Organization (ILO)},
  year         = {2025},
  url          = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EAR_EHRA_SEX_OCU_CUR_NB},
  publisher    = {HuggingFace Datasets, repackaged by Electric Sheep Asia},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-ear-ehra-sex-ocu-cur-nb-average-hourly-earnings-of-employees-by-sex-occupa}}
}

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=EAREHRASEXOCUCURNB_