electricsheepafrica/africa-ilo-pop-3wap-sex-geo-trs-nb-youth-working-age-population-by-sex-rural-urban-ar

Youth working-age population by sex, rural / urban areas and stages of transition (thousan | Africa (ILOSTAT)

🌍 3,267 observations · 26 Africa countries · 2012–2025 · Repackaged by Electric Sheep Africa

TL;DR

This dataset contains 3,267 observations of Population data across 26 Africa countries, spanning 2012–2025, 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_TRS_NB and filtered to Africa 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

26 Africa countries · top rows shown below, sorted by row count:

Country	Rows	First year	Last year
ZMB	374	2012	2024
ZAF	372	2015	2024
RWA	324	2017	2025
SEN	252	2015	2024
EGY	225	2012	2019
UGA	198	2012	2021
ZWE	144	2019	2024
NAM	144	2012	2018
LBR	134	2012	2017
NGA	121	2022	2024
MWI	116	2012	2014
GMB	108	2012	2025
TGO	90	2012	2014
MDG	81	2015	2022
COG	72	2015	2022
...	11 more countries

Indicators (sample)

• POP_3WAP_SEX_GEO_TRS_NB — Youth working-age population by sex, rural / urban areas and stages of transition (thousands)

Schema

Column	Type	Description	Example
ref_area	string	ISO 3166-1 alpha-3 country code	BEN
ref_area.label	string	Country name in English	Benin
source	string	ILOSTAT source code (e.g. labour force survey)	BX:6368
source.label	string	Source name in English	HS - School to Work Transition Survey
indicator	string	ILOSTAT indicator code	POP_3WAP_SEX_GEO_TRS_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	TRS_STAGE_TOTAL
classif2.label	string	—	Transition stages: Total
time	int64	Observation year	2012
obs_value	float64	Observed indicator value (unit varies — see indicator definition)	2853.882
obs_status	string	Observation status flag (e.g. provisional, unreliable)	B
obs_status.label	string	—	Break in series
note_indicator	string	—	I11:264
note_indicator.label	string	—	Break in series: Methodology revised
note_source	string	—	R1:3513_T3:2481
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("electricsheepafrica/africa-ilo-pop-3wap-sex-geo-trs-nb-youth-working-age-population-by-sex-rural-urban-ar")
df = ds["train"].to_pandas()
print(df.head())

Filter to one country

kenya = df[df["ref_area"] == "KEN"]

Time-series for a single indicator

sample = (df[df["indicator"] == "POP_3WAP_SEX_GEO_TRS_NB"]
          .sort_values("time"))
sample.plot(x="time", y="obs_value", title="POP_3WAP_SEX_GEO_TRS_NB")

Pivot to country × year matrix

matrix = (df[df["indicator"] == "POP_3WAP_SEX_GEO_TRS_NB"]
          .pivot_table(index="time", columns="ref_area", values="obs_value"))
print(matrix.tail())

Citation

@misc{africa_ilo_pop_3wap_sex_geo_trs_nb_youth_working_age_population_by_sex_rural_urban_ar_2025,
  title        = {Youth working-age population by sex, rural / urban areas and stages of transition (thousan | Africa (ILOSTAT)},
  author       = {International Labour Organization (ILO)},
  year         = {2025},
  url          = {https://www.ilo.org/shinyapps/bulkexplorer/?id=POP_3WAP_SEX_GEO_TRS_NB},
  publisher    = {HuggingFace Datasets, repackaged by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-pop-3wap-sex-geo-trs-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 Africa repackaging.

About Electric Sheep

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

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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_GEO_TRS_NB