electricsheepeurope/europe-ilo-pop-3wap-sex-geo-tra-nb-youth-working-age-population-by-sex-rural-urban-ar

Youth working-age population by sex, rural / urban areas and forms of transition (thousand | Europe (ILOSTAT)

🇪🇺 58,063 observations · 36 Europe countries · 1992–2025 · Repackaged by Electric Sheep Europe

TL;DR

This dataset contains 58,063 observations of Population data across 36 Europe countries, spanning 1992–2025, 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_3WAP_SEX_GEO_TRA_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

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

Country	Rows	First year	Last year
FRA	2,367	1998	2024
GRC	2,349	1992	2020
FIN	2,246	1998	2024
ESP	2,187	1998	2024
ITA	2,187	1998	2024
PRT	2,185	1998	2024
DEU	2,172	1998	2024
DNK	2,172	1998	2024
BEL	2,152	1998	2024
NLD	2,107	1998	2024
LUX	2,065	1998	2024
AUT	2,061	1999	2025
POL	2,025	2001	2025
SWE	1,950	1999	2024
HUN	1,935	2001	2024
...	21 more countries

Indicators (sample)

• POP_3WAP_SEX_GEO_TRA_NB — Youth working-age population by sex, rural / urban areas and forms of transition (thousands)

Schema

Column	Type	Description	Example
ref_area	string	ISO 3166-1 alpha-3 country code	AUT
ref_area.label	string	Country name in English	Austria
source	string	ILOSTAT source code (e.g. labour force survey)	BA:103
source.label	string	Source name in English	LFS - Labour Force Survey
indicator	string	ILOSTAT indicator code	POP_3WAP_SEX_GEO_TRA_NB
indicator.label	string	Indicator name in English	Youth working-age population by sex, …
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.)	GEO_COV_NAT
classif1.label	string	—	Area type: National
classif2	string	Second classification variable where applicable	TRA_FORMS_TOTAL
classif2.label	string	—	Transition forms: Total
time	int64	Observation year	2025
obs_value	float64	Observed indicator value (unit varies — see indicator definition)	1486.315
obs_status	string	Observation status flag (e.g. provisional, unreliable)	U
obs_status.label	string	—	Unreliable
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-3wap-sex-geo-tra-nb-youth-working-age-population-by-sex-rural-urban-ar")
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_3WAP_SEX_GEO_TRA_NB"]
          .sort_values("time"))
sample.plot(x="time", y="obs_value", title="POP_3WAP_SEX_GEO_TRA_NB")

Pivot to country × year matrix

matrix = (df[df["indicator"] == "POP_3WAP_SEX_GEO_TRA_NB"]
          .pivot_table(index="time", columns="ref_area", values="obs_value"))
print(matrix.tail())

Citation

@misc{europe_ilo_pop_3wap_sex_geo_tra_nb_youth_working_age_population_by_sex_rural_urban_ar_2025,
  title        = {Youth working-age population by sex, rural / urban areas and forms of transition (thousand | Europe (ILOSTAT)},
  author       = {International Labour Organization (ILO)},
  year         = {2025},
  url          = {https://www.ilo.org/shinyapps/bulkexplorer/?id=POP_3WAP_SEX_GEO_TRA_NB},
  publisher    = {HuggingFace Datasets, repackaged by Electric Sheep Europe},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-pop-3wap-sex-geo-tra-nb-youth-working-age-population-by-sex-rural-urban-ar}}
}

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 2026-05-27 via the Electric Sheep pipeline. Source URL: https://www.ilo.org/shinyapps/bulkexplorer/?id=POP_3WAP_SEX_GEO_TRA_NB