electricsheepeurope/europe-ilo-pop-3wap-sex-edu-tra-nb-youth-working-age-population-by-sex-education-and

Youth working-age population by sex, education and forms of transition (thousands) | Europe (ILOSTAT)

🇪🇺 100,388 observations · 38 Europe countries · 1991–2025 · Repackaged by Electric Sheep Europe

TL;DR

This dataset contains 100,388 observations of Population data across 38 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: Population

Methodology

Data pulled directly from the ILOSTAT REST API at https://rplumber.ilo.org/data/indicator?id=POP_3WAP_SEX_EDU_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

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

Country	Rows	First year	Last year
GBR	4,276	1999	2025
CHE	4,164	1991	2025
FRA	4,012	1998	2024
GRC	4,004	1992	2020
NLD	3,598	1998	2024
ITA	3,405	1998	2024
ESP	3,373	1998	2025
PRT	3,328	1998	2024
BEL	3,293	1998	2024
DNK	3,229	1998	2024
DEU	3,188	1999	2024
ROU	3,160	1998	2024
IRL	3,142	2002	2024
SWE	3,087	1999	2024
HUN	3,015	1998	2024
...	23 more countries

Indicators (sample)

• POP_3WAP_SEX_EDU_TRA_NB — Youth working-age population by sex, education and forms of transition (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)	BA:480
source.label	string	Source name in English	LFS - Labour Force Survey
indicator	string	ILOSTAT indicator code	POP_3WAP_SEX_EDU_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.)	EDU_AGGREGATE_TOTAL
classif1.label	string	—	Education (Aggregate levels): Total
classif2	string	Second classification variable where applicable	TRA_FORMS_TOTAL
classif2.label	string	—	Transition forms: Total
time	int64	Observation year	2024
obs_value	float64	Observed indicator value (unit varies — see indicator definition)	424.517
obs_status	string	Observation status flag (e.g. provisional, unreliable)	U
obs_status.label	string	—	Unreliable
note_classif	string	—	C3:5578
note_classif.label	string	—	Nonstandard education level: Includin…
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-edu-tra-nb-youth-working-age-population-by-sex-education-and")
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_EDU_TRA_NB"]
          .sort_values("time"))
sample.plot(x="time", y="obs_value", title="POP_3WAP_SEX_EDU_TRA_NB")

Pivot to country × year matrix

matrix = (df[df["indicator"] == "POP_3WAP_SEX_EDU_TRA_NB"]
          .pivot_table(index="time", columns="ref_area", values="obs_value"))
print(matrix.tail())

Citation

@misc{europe_ilo_pop_3wap_sex_edu_tra_nb_youth_working_age_population_by_sex_education_and_2025,
  title        = {Youth working-age population by sex, education and forms of transition (thousands) | Europe (ILOSTAT)},
  author       = {International Labour Organization (ILO)},
  year         = {2025},
  url          = {https://www.ilo.org/shinyapps/bulkexplorer/?id=POP_3WAP_SEX_EDU_TRA_NB},
  publisher    = {HuggingFace Datasets, repackaged by Electric Sheep Europe},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-pop-3wap-sex-edu-tra-nb-youth-working-age-population-by-sex-education-and}}
}

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 2026-05-27 via the Electric Sheep pipeline. Source URL: https://www.ilo.org/shinyapps/bulkexplorer/?id=POP_3WAP_SEX_EDU_TRA_NB