electricsheepafrica/africa-ilo-pop-3wap-sex-age-dsb-nb-youth-working-age-population-by-sex-age-and-disabi

Youth working-age population by sex, age and disability status (thousands) | Africa (ILOSTAT)

🌍 4,858 observations · 42 Africa countries · 1998–2025 · Repackaged by Electric Sheep Africa

TL;DR

This dataset contains 4,858 observations of Population data across 42 Africa countries, spanning 1998–2025, 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_AGE_DSB_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

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

Country	Rows	First year	Last year
RWA	360	2014	2025
ZMB	324	2012	2024
GHA	287	2010	2024
SEN	252	2015	2024
TGO	251	2006	2022
BWA	243	2009	2024
ZWE	216	2014	2024
TZA	215	2008	2024
CIV	192	1998	2022
UGA	178	2010	2021
GMB	144	2012	2025
BFA	144	2006	2024
BEN	144	2012	2022
LBR	137	2010	2017
SYC	127	2019	2024
...	27 more countries

Indicators (sample)

• POP_3WAP_SEX_AGE_DSB_NB — Youth working-age population by sex, age and disability status (thousands)

Schema

Column	Type	Description	Example
ref_area	string	ISO 3166-1 alpha-3 country code	AGO
ref_area.label	string	Country name in English	Angola
source	string	ILOSTAT source code (e.g. labour force survey)	AA:835
source.label	string	Source name in English	PC - Population Census
indicator	string	ILOSTAT indicator code	POP_3WAP_SEX_AGE_DSB_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.)	AGE_YTHBANDS_Y15-29
classif1.label	string	—	Age (Youth bands): 15-29
classif2	string	Second classification variable where applicable	DSB_STATUS_TOTAL
classif2.label	string	—	Disability status: Total
time	int64	Observation year	2014
obs_value	float64	Observed indicator value (unit varies — see indicator definition)	6497.559
obs_status	string	Observation status flag (e.g. provisional, unreliable)	U
obs_status.label	string	—	Unreliable
note_classif	float64	—	—
note_classif.label	float64	—	—
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("electricsheepafrica/africa-ilo-pop-3wap-sex-age-dsb-nb-youth-working-age-population-by-sex-age-and-disabi")
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_AGE_DSB_NB"]
          .sort_values("time"))
sample.plot(x="time", y="obs_value", title="POP_3WAP_SEX_AGE_DSB_NB")

Pivot to country × year matrix

matrix = (df[df["indicator"] == "POP_3WAP_SEX_AGE_DSB_NB"]
          .pivot_table(index="time", columns="ref_area", values="obs_value"))
print(matrix.tail())

Citation

@misc{africa_ilo_pop_3wap_sex_age_dsb_nb_youth_working_age_population_by_sex_age_and_disabi_2025,
  title        = {Youth working-age population by sex, age and disability status (thousands) | Africa (ILOSTAT)},
  author       = {International Labour Organization (ILO)},
  year         = {2025},
  url          = {https://www.ilo.org/shinyapps/bulkexplorer/?id=POP_3WAP_SEX_AGE_DSB_NB},
  publisher    = {HuggingFace Datasets, repackaged by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-pop-3wap-sex-age-dsb-nb-youth-working-age-population-by-sex-age-and-disabi}}
}

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_AGE_DSB_NB