electricsheepafrica/africa-ilo-ees-tees-sex-how-nb-employees-by-sex-and-weekly-hours-actually-worked
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--- license: cc-by-4.0 language: - en task_categories: - tabular-classification - tabular-regression - time-series-forecasting multilinguality: monolingual size_categories: - 1K<n<10K tags: - tabular - africa - ilostat - employees - ilo - labour - employment pretty_name: "Employees by sex and weekly hours actually worked (thousands) | Africa (ILOSTAT)" --- # Employees by sex and weekly hours actually worked (thousands) | Africa (ILOSTAT) 🌍 **5,674 observations** · **44 Africa countries** · **1991–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **5,674 observations** of `Employees` data across **44 Africa 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](https://www.ilo.org/shinyapps/bulkexplorer/?id=EES_TEES_SEX_HOW_NB) - **Publisher:** International Labour Organization (ILO) - **License:** [cc-by-4.0](https://creativecommons.org/licenses/by/4.0/) - **Topic:** Employees ## Methodology Data pulled directly from the ILOSTAT REST API at `https://rplumber.ilo.org/data/indicator?id=EES_TEES_SEX_HOW_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 44 Africa countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `ZAF` | 624 | 2000 | 2024 | | `MUS` | 586 | 2001 | 2024 | | `EGY` | 366 | 2008 | 2024 | | `RWA` | 240 | 2014 | 2025 | | `MLI` | 233 | 2010 | 2024 | | `SYC` | 229 | 2014 | 2024 | | `BWA` | 216 | 1996 | 2024 | | `UGA` | 202 | 2010 | 2021 | | `GHA` | 201 | 1991 | 2024 | | `ZMB` | 193 | 2017 | 2024 | | `AGO` | 186 | 2019 | 2025 | | `TZA` | 174 | 2001 | 2024 | | `NAM` | 173 | 1994 | 2018 | | `ZWE` | 165 | 2011 | 2024 | | `SEN` | 158 | 2015 | 2024 | | ... | _29 more countries_ | | | ## Indicators (sample) - `EES_TEES_SEX_HOW_NB` — Employees by sex and weekly hours actually worked (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) | `BA:13951` | | `source.label` | `string` | Source name in English | `LFS - Employment Survey` | | `indicator` | `string` | ILOSTAT indicator code | `EES_TEES_SEX_HOW_NB` | | `indicator.label` | `string` | Indicator name in English | `Employees by sex and weekly hours act…` | | `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.) | `HOW_BANDS_TOTAL` | | `classif1.label` | `string` | — | `Hour bands: Total` | | `time` | `int64` | Observation year | `2025` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `3788.156` | | `obs_status` | `string` | Observation status flag (e.g. provisional, unreliable) | `U` | | `obs_status.label` | `string` | — | `Unreliable` | | `note_classif` | `string` | — | `C8:1600` | | `note_classif.label` | `string` | — | `Nonstandard hours band: Including 49+…` | | `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-ees-tees-sex-how-nb-employees-by-sex-and-weekly-hours-actually-worked") 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"] == "EES_TEES_SEX_HOW_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EES_TEES_SEX_HOW_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EES_TEES_SEX_HOW_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_ees_tees_sex_how_nb_employees_by_sex_and_weekly_hours_actually_worked_2025, title = {Employees by sex and weekly hours actually worked (thousands) | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EES_TEES_SEX_HOW_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-ees-tees-sex-how-nb-employees-by-sex-and-weekly-hours-actually-worked}} } ``` ## 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=EES_TEES_SEX_HOW_NB_
This is a tabular dataset on employee data across 44 African countries, focusing on the indicator Employees by sex and weekly hours actually worked (thousands). It contains 5,674 observations spanning from 1991 to 2025, sourced from the International Labour Organization (ILO)s ILOSTAT statistics database. The dataset provides detailed disaggregation of employee counts by sex (total, male, female) and weekly hours worked groups, suitable for machine learning tasks such as tabular classification, regression, or time-series forecasting. Data is pulled directly from the ILOSTAT REST API, normalized for consistency and traceability, and covers countries like South Africa, Mauritius, Egypt, with each observation including country codes, year, indicator values, data sources, and quality flags.
