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electricsheepeurope/europe-ilo-ear-emta-sex-age-cur-nb-average-monthly-earnings-of-employees-by-sex-age-a

Average monthly earnings of employees by sex, age and currency | Europe (ILOSTAT) 🇪🇺 45,496 observations · 35 Europe countries · 1991–2025 · Repackaged by Electric Sheep Europe TL;DR This dataset contains 45,496 observations of Earnings data across 35 Europe countries, spanning 1991–2025, covering 1 distinct indicators. About the source ILOSTAT is the ILO's central statistics database, the leading global source for labour statistics. It… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepeurope/europe-ilo-ear-emta-sex-age-cur-nb-average-monthly-earnings-of-employees-by-sex-age-a.

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Dataset Card

Average monthly earnings of employees by sex, age and currency | Europe (ILOSTAT)

🇪🇺 45,496 observations · 35 Europe countries · 1991–2025 · Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)

rows countries years indicators license

TL;DR

This dataset contains 45,496 observations of Earnings data across 35 Europe countries, spanning 1991–2025, 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_EMTA_SEX_AGE_CUR_NB and filtered to Europe 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

35 Europe countries · top rows shown below, sorted by row count:

CountryRowsFirst yearLast year
CHE2,11219912025
PRT1,84819982025
EST1,51220042024
IRL1,51220042024
SWE1,51220042024
NOR1,51220042024
FIN1,51220042024
DNK1,51220042024
FRA1,50620042024
CZE1,47819922024
POL1,47319922024
SVK1,47219922024
LUX1,43420042024
HUN1,43420052024
NLD1,43120052024
...20 more countries

Indicators (sample)

  • —EAR_EMTA_SEX_AGE_CUR_NB — Average monthly earnings of employees by sex, age and currency

Schema

ColumnTypeDescriptionExample
ref_areastringISO 3166-1 alpha-3 country codeAUT
ref_area.labelstringCountry name in EnglishAustria
sourcestringILOSTAT source code (e.g. labour force survey)BB:275
source.labelstringSource name in EnglishHIES - EU Statistics on Income and Li…
indicatorstringILOSTAT indicator codeEAR_EMTA_SEX_AGE_CUR_NB
indicator.labelstringIndicator name in EnglishAverage monthly 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.)AGE_YTHADULT_YGE15
classif1.labelstring—Age (Youth, adults): 15+
classif2stringSecond classification variable where applicableCUR_TYPE_LCU
classif2.labelstring—Currency: Local currency
timeint64Observation year2024
obs_valuefloat64Observed indicator value (unit varies — see indicator definition)4922.564
obs_statusstringObservation status flag (e.g. provisional, unreliable)U
obs_status.labelstring—Unreliable
note_classifstring—C6:1058
note_classif.labelstring—Nonstandard age group: Excluding age 15
note_indicatorstring—T30:161
note_indicator.labelstring—Currency: AUT - Euro (EUR)
note_sourcestring—R1:3513_T2:85
note_source.labelstring—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

python
from datasets import load_dataset

ds = load_dataset("electricsheepeurope/europe-ilo-ear-emta-sex-age-cur-nb-average-monthly-earnings-of-employees-by-sex-age-a")
df = ds["train"].to_pandas()
print(df.head())

Filter to one country

python
germany = df[df["ref_area"] == "DEU"]

Time-series for a single indicator

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

Pivot to country × year matrix

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

Citation

bibtex
@misc{europe_ilo_ear_emta_sex_age_cur_nb_average_monthly_earnings_of_employees_by_sex_age_a_2025,
  title        = {Average monthly earnings of employees by sex, age and currency | Europe (ILOSTAT)},
  author       = {International Labour Organization (ILO)},
  year         = {2025},
  url          = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EAR_EMTA_SEX_AGE_CUR_NB},
  publisher    = {HuggingFace Datasets, repackaged by Electric Sheep Europe},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-ear-emta-sex-age-cur-nb-average-monthly-earnings-of-employees-by-sex-age-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 Europe repackaging.

About Electric Sheep

Electric Sheep Europe is part of the Electric Sheep mission: a unified, ML-ready data layer for Europe 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/electricsheepeurope


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