electricsheepafrica/africa-ilo-ees-tees-sex-ocu-mts-nb-employees-by-sex-occupation-and-marital-status-tho
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--- license: cc-by-4.0 language: - en task_categories: - tabular-classification - tabular-regression - time-series-forecasting multilinguality: monolingual size_categories: - 10K<n<100K tags: - tabular - africa - ilostat - employees - ilo - labour - employment pretty_name: "Employees by sex, occupation and marital status (thousands) | Africa (ILOSTAT)" --- # Employees by sex, occupation and marital status (thousands) | Africa (ILOSTAT) 🌍 **91,798 observations** · **48 Africa countries** · **1982–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **91,798 observations** of `Employees` data across **48 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=EES_TEES_SEX_OCU_MTS_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_OCU_MTS_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 48 Africa countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `ZAF` | 10,832 | 2000 | 2024 | | `MUS` | 7,528 | 2001 | 2024 | | `EGY` | 6,103 | 2009 | 2024 | | `GHA` | 4,075 | 1991 | 2024 | | `AGO` | 3,982 | 2004 | 2025 | | `MLI` | 3,636 | 2009 | 2024 | | `SYC` | 3,508 | 2014 | 2024 | | `RWA` | 3,479 | 2014 | 2025 | | `BWA` | 3,153 | 1996 | 2024 | | `ZMB` | 3,117 | 2015 | 2024 | | `ZWE` | 2,759 | 2011 | 2024 | | `NAM` | 2,715 | 1994 | 2018 | | `TZA` | 2,514 | 2001 | 2024 | | `SEN` | 2,125 | 2015 | 2024 | | `UGA` | 1,936 | 2010 | 2021 | | ... | _33 more countries_ | | | ## Indicators (sample) - `EES_TEES_SEX_OCU_MTS_NB` — Employees by sex, occupation and marital 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) | `BA:13951` | | `source.label` | `string` | Source name in English | `LFS - Employment Survey` | | `indicator` | `string` | ILOSTAT indicator code | `EES_TEES_SEX_OCU_MTS_NB` | | `indicator.label` | `string` | Indicator name in English | `Employees by sex, occupation and mari…` | | `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.) | `OCU_SKILL_TOTAL` | | `classif1.label` | `string` | — | `Occupation (Skill level): Total` | | `classif2` | `string` | Second classification variable where applicable | `MTS_AGGREGATE_TOTAL` | | `classif2.label` | `string` | — | `Marital status (Aggregate): 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` | `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 ```python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-ilo-ees-tees-sex-ocu-mts-nb-employees-by-sex-occupation-and-marital-status-tho") 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_OCU_MTS_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EES_TEES_SEX_OCU_MTS_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EES_TEES_SEX_OCU_MTS_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_ees_tees_sex_ocu_mts_nb_employees_by_sex_occupation_and_marital_status_tho_2025, title = {Employees by sex, occupation and marital status (thousands) | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EES_TEES_SEX_OCU_MTS_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-ees-tees-sex-ocu-mts-nb-employees-by-sex-occupation-and-marital-status-tho}} } ``` ## 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_OCU_MTS_NB_
This dataset contains 91,798 observations of employee data across 48 African countries, spanning from 1982 to 2025, covering 1 distinct indicator. The data is sourced from the International Labour Organizations (ILO) ILOSTAT database, retrieved via the REST API and filtered to African country codes. It includes statistics on employees by sex, occupation, and marital status (in thousands), with columns for country, data source, indicator, classification variables, time, observed value, observation status, and notes. The data is published at an annual frequency and includes quality caveats such as the use of ILO-selected best source and non-null conditions for disaggregation columns. The dataset is suitable for tasks like tabular classification, regression, and time-series forecasting, and is designed for analyzing labor markets in Africa.
