electricsheepeurope/europe-ilo-pop-xwap-sex-age-geo-nb-working-age-population-by-sex-age-and-rural-urban

Working-age population by sex, age and rural / urban areas (thousands) | Europe (ILOSTAT)

🇪🇺 136,370 observations · 41 Europe countries · 1987–2025 · Repackaged by Electric Sheep Europe

TL;DR

This dataset contains 136,370 observations of Population data across 41 Europe countries, spanning 1987–2025, 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: Population

Methodology

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

Country	Rows	First year	Last year
GRC	5,616	1987	2025
DEU	4,896	1992	2025
NLD	4,896	1992	2025
PRT	4,896	1992	2025
ITA	4,896	1992	2025
ESP	4,896	1992	2025
FRA	4,896	1992	2025
DNK	4,896	1992	2025
BEL	4,896	1992	2025
SWE	4,779	1995	2025
IRL	4,752	1993	2025
FIN	4,637	1995	2025
AUT	4,464	1995	2025
GBR	4,416	1992	2019
LUX	4,176	1997	2025
...	26 more countries

Indicators (sample)

• POP_XWAP_SEX_AGE_GEO_NB — Working-age population by sex, age and rural / urban areas (thousands)

Schema

Column	Type	Description	Example
ref_area	string	ISO 3166-1 alpha-3 country code	ALB
ref_area.label	string	Country name in English	Albania
source	string	ILOSTAT source code (e.g. labour force survey)	BB:7401
source.label	string	Source name in English	HIES - Living Standards Survey
indicator	string	ILOSTAT indicator code	POP_XWAP_SEX_AGE_GEO_NB
indicator.label	string	Indicator name in English	Working-age population by sex, age an…
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.)	AGE_YTHADULT_YGE15
classif1.label	string	—	Age (Youth, adults): 15+
classif2	string	Second classification variable where applicable	GEO_COV_NAT
classif2.label	string	—	Area type: National
time	int64	Observation year	2012
obs_value	float64	Observed indicator value (unit varies — see indicator definition)	2311.688
obs_status	string	Observation status flag (e.g. provisional, unreliable)	B
obs_status.label	string	—	Break in series
note_classif	string	—	C12:2951
note_classif.label	string	—	Rural / urban areas coverage definiti…
note_indicator	string	—	I11:264
note_indicator.label	string	—	Break in series: Methodology revised
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("electricsheepeurope/europe-ilo-pop-xwap-sex-age-geo-nb-working-age-population-by-sex-age-and-rural-urban")
df = ds["train"].to_pandas()
print(df.head())

Filter to one country

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

Time-series for a single indicator

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

Pivot to country × year matrix

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

Citation

@misc{europe_ilo_pop_xwap_sex_age_geo_nb_working_age_population_by_sex_age_and_rural_urban_2025,
  title        = {Working-age population by sex, age and rural / urban areas (thousands) | Europe (ILOSTAT)},
  author       = {International Labour Organization (ILO)},
  year         = {2025},
  url          = {https://www.ilo.org/shinyapps/bulkexplorer/?id=POP_XWAP_SEX_AGE_GEO_NB},
  publisher    = {HuggingFace Datasets, repackaged by Electric Sheep Europe},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-pop-xwap-sex-age-geo-nb-working-age-population-by-sex-age-and-rural-urban}}
}

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

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Provenance: ingested 2026-05-25 via the Electric Sheep pipeline. Source URL: https://www.ilo.org/shinyapps/bulkexplorer/?id=POP_XWAP_SEX_AGE_GEO_NB