electricsheepafrica/africa-ilo-eap-dwap-sex-age-edu-rt-labour-force-participation-rate-by-sex-age-and-edu
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--- license: cc-by-4.0 language: - en task_categories: - tabular-classification - tabular-regression - time-series-forecasting multilinguality: monolingual size_categories: - 100K<n<1M tags: - tabular - africa - ilostat - labour-force - ilo - labour - employment pretty_name: "Labour force participation rate by sex, age and education (%) | Africa (ILOSTAT)" --- # Labour force participation rate by sex, age and education (%) | Africa (ILOSTAT) 🌍 **186,586 observations** · **49 Africa countries** · **1982–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **186,586 observations** of `Labour force` data across **49 Africa countries**, spanning **1982–2025**, 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](https://www.ilo.org/shinyapps/bulkexplorer/?id=EAP_DWAP_SEX_AGE_EDU_RT) - **Publisher:** International Labour Organization (ILO) - **License:** [cc-by-4.0](https://creativecommons.org/licenses/by/4.0/) - **Topic:** Labour force ## Methodology Data pulled directly from the ILOSTAT REST API at `https://rplumber.ilo.org/data/indicator?id=EAP_DWAP_SEX_AGE_EDU_RT` 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 49 Africa countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `ZAF` | 18,564 | 2000 | 2024 | | `MUS` | 13,464 | 2001 | 2024 | | `EGY` | 10,883 | 2008 | 2024 | | `GHA` | 7,935 | 1991 | 2024 | | `MLI` | 7,454 | 2009 | 2024 | | `AGO` | 7,081 | 2004 | 2025 | | `TZA` | 6,469 | 2001 | 2024 | | `RWA` | 6,098 | 2014 | 2025 | | `ZMB` | 6,092 | 2015 | 2024 | | `TUN` | 5,987 | 2005 | 2023 | | `BWA` | 5,380 | 2006 | 2024 | | `SEN` | 5,070 | 2011 | 2024 | | `UGA` | 4,932 | 2010 | 2021 | | `TGO` | 4,755 | 2006 | 2022 | | `ZWE` | 4,594 | 2011 | 2024 | | ... | _34 more countries_ | | | ## Indicators (sample) - `EAP_DWAP_SEX_AGE_EDU_RT` — Labour force participation rate by sex, age and education (%) ## 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) | `BA:13951` | | `source.label` | `string` | Source name in English | `LFS - Employment Survey` | | `indicator` | `string` | ILOSTAT indicator code | `EAP_DWAP_SEX_AGE_EDU_RT` | | `indicator.label` | `string` | Indicator name in English | `Labour force participation rate by se…` | | `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 | `EDU_AGGREGATE_TOTAL` | | `classif2.label` | `string` | — | `Education (Aggregate levels): Total` | | `time` | `int64` | Observation year | `2025` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `74.342` | | `obs_status` | `string` | Observation status flag (e.g. provisional, unreliable) | `U` | | `obs_status.label` | `string` | — | `Unreliable` | | `note_classif` | `string` | — | `C3:3710` | | `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 ```python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-ilo-eap-dwap-sex-age-edu-rt-labour-force-participation-rate-by-sex-age-and-edu") df = ds["train"].to_pandas() print(df.head()) ``` ### Filter to one country ```python kenya = df[df["ref_area"] == "KEN"] ``` ### Time-series for a single indicator ```python sample = (df[df["indicator"] == "EAP_DWAP_SEX_AGE_EDU_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EAP_DWAP_SEX_AGE_EDU_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EAP_DWAP_SEX_AGE_EDU_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_eap_dwap_sex_age_edu_rt_labour_force_participation_rate_by_sex_age_and_edu_2025, title = {Labour force participation rate by sex, age and education (%) | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EAP_DWAP_SEX_AGE_EDU_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-eap-dwap-sex-age-edu-rt-labour-force-participation-rate-by-sex-age-and-edu}} } ``` ## License Released under [cc-by-4.0](https://creativecommons.org/licenses/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](https://huggingface.co/electricsheepafrica) --- _Provenance: ingested 2026-05-26 via the Electric Sheep pipeline. Source URL: https://www.ilo.org/shinyapps/bulkexplorer/?id=EAP_DWAP_SEX_AGE_EDU_RT_
This dataset contains statistics on the labour force participation rate by sex, age and education (%) for Africa, sourced from the International Labour Organization (ILOSTAT) database. It includes 186,586 observations across 49 African countries, spanning the years 1982 to 2025. The core indicator is EAP_DWAP_SEX_AGE_EDU_RT, representing the labour force participation rate, disaggregated by sex (total, male, female), age groups, and education levels. The schema comprises columns such as country code (ref_area), indicator code (indicator), year (time), observed value (obs_value), along with source, classification, and note columns. It is suitable for machine learning tasks like tabular classification, regression, and time-series forecasting, and can be used for analyzing labour market trends, policy research, and economic modeling in Africa.
