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

Average monthly earnings of employees by sex, economic activity and currency | Europe (ILOSTAT) 🇪🇺 191,287 observations · 41 Europe countries · 1969–2025 · Repackaged by Electric Sheep Europe TL;DR This dataset contains 191,287 observations of Earnings data across 41 Europe countries, spanning 1969–2025, covering 1 distinct indicators. About the source ILOSTAT is the ILO's central statistics database, the leading global source for labour… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepeurope/europe-ilo-ear-emta-sex-eco-cur-nb-average-monthly-earnings-of-employees-by-sex-econo.

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Dataset Card

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

🇪🇺 191,287 observations · 41 Europe countries · 1969–2025 · Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)

rows countries years indicators license

TL;DR

This dataset contains 191,287 observations of Earnings data across 41 Europe countries, spanning 1969–2025, 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_ECO_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

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

CountryRowsFirst yearLast year
CHE9,12419692025
GBR8,20819692025
PRT7,64019952025
FRA7,40719972024
HUN6,92019702024
LVA6,87319902024
NLD6,50719692024
SWE6,42319902024
MDA6,10819812025
HRV5,85919812024
DNK5,81419692024
BGR5,81019692024
SVN5,51319832024
POL5,43219692024
NOR5,38519972024
...26 more countries

Indicators (sample)

  • —EAR_EMTA_SEX_ECO_CUR_NB — Average monthly earnings of employees by sex, economic activity and currency

Schema

ColumnTypeDescriptionExample
ref_areastringISO 3166-1 alpha-3 country codeALB
ref_area.labelstringCountry name in EnglishAlbania
sourcestringILOSTAT source code (e.g. labour force survey)FA:13364
source.labelstringSource name in EnglishADM-IR - General Directorate of Taxation
indicatorstringILOSTAT indicator codeEAR_EMTA_SEX_ECO_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.)ECO_SECTOR_TOTAL
classif1.labelstring—Economic activity (Broad sector): Total
classif2stringSecond classification variable where applicableCUR_TYPE_LCU
classif2.labelstring—Currency: Local currency
timeint64Observation year2022
obs_valuefloat64Observed indicator value (unit varies — see indicator definition)61898.0
obs_statusstringObservation status flag (e.g. provisional, unreliable)U
obs_status.labelstring—Unreliable
note_classifstring—C5:1010
note_classif.labelstring—Nonstandard economic activity: Includ…
note_indicatorstring—T12:145_T30:159
note_indicator.labelstring—Working time arrangement coverage: Fu…
note_sourcestring—S9:259
note_source.labelstring—Reference group coverage: Insured per…

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-eco-cur-nb-average-monthly-earnings-of-employees-by-sex-econo")
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_ECO_CUR_NB"]
          .sort_values("time"))
sample.plot(x="time", y="obs_value", title="EAR_EMTA_SEX_ECO_CUR_NB")

Pivot to country × year matrix

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

Citation

bibtex
@misc{europe_ilo_ear_emta_sex_eco_cur_nb_average_monthly_earnings_of_employees_by_sex_econo_2025,
  title        = {Average monthly earnings of employees by sex, economic activity and currency | Europe (ILOSTAT)},
  author       = {International Labour Organization (ILO)},
  year         = {2025},
  url          = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EAR_EMTA_SEX_ECO_CUR_NB},
  publisher    = {HuggingFace Datasets, repackaged by Electric Sheep Europe},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-ear-emta-sex-eco-cur-nb-average-monthly-earnings-of-employees-by-sex-econo}}
}

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=EAREMTASEXECOCURNB_