electricsheepafrica/africa-ilo-emp-xtru-sex-edu-mts-nb-time-related-underemployment-by-sex-education-and
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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 - time-related-underemployment - ilo - labour - employment pretty_name: "Time-related underemployment by sex, education and marital status (thousands) | Africa (ILOSTAT)" --- # Time-related underemployment by sex, education and marital status (thousands) | Africa (ILOSTAT) 🌍 **6,216 observations** · **34 Africa countries** · **1991–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **6,216 observations** of `Time-related underemployment` data across **34 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=EMP_XTRU_SEX_EDU_MTS_NB) - **Publisher:** International Labour Organization (ILO) - **License:** [cc-by-4.0](https://creativecommons.org/licenses/by/4.0/) - **Topic:** Time-related underemployment ## Methodology Data pulled directly from the ILOSTAT REST API at `https://rplumber.ilo.org/data/indicator?id=EMP_XTRU_SEX_EDU_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 34 Africa countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `ZAF` | 859 | 2008 | 2024 | | `RWA` | 448 | 2014 | 2025 | | `ZMB` | 388 | 2017 | 2024 | | `GHA` | 366 | 1991 | 2024 | | `UGA` | 328 | 2010 | 2021 | | `MUS` | 308 | 2012 | 2019 | | `AGO` | 304 | 2019 | 2025 | | `ZWE` | 289 | 2011 | 2024 | | `BWA` | 284 | 2006 | 2024 | | `SEN` | 227 | 2011 | 2024 | | `MLI` | 208 | 2018 | 2024 | | `EGY` | 197 | 2016 | 2024 | | `NGA` | 190 | 2019 | 2024 | | `SYC` | 177 | 2015 | 2024 | | `ETH` | 162 | 2005 | 2021 | | ... | _19 more countries_ | | | ## Indicators (sample) - `EMP_XTRU_SEX_EDU_MTS_NB` — Time-related underemployment by sex, education 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 | `EMP_XTRU_SEX_EDU_MTS_NB` | | `indicator.label` | `string` | Indicator name in English | `Time-related underemployment 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.) | `EDU_AGGREGATE_TOTAL` | | `classif1.label` | `string` | — | `Education (Aggregate levels): 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) | `117.022` | | `obs_status` | `string` | Observation status flag (e.g. provisional, unreliable) | `U` | | `obs_status.label` | `string` | — | `Unreliable` | | `note_classif` | `string` | — | `C3:2620` | | `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-emp-xtru-sex-edu-mts-nb-time-related-underemployment-by-sex-education-and") 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"] == "EMP_XTRU_SEX_EDU_MTS_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_XTRU_SEX_EDU_MTS_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_XTRU_SEX_EDU_MTS_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_emp_xtru_sex_edu_mts_nb_time_related_underemployment_by_sex_education_and_2025, title = {Time-related underemployment by sex, education and marital status (thousands) | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_XTRU_SEX_EDU_MTS_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-emp-xtru-sex-edu-mts-nb-time-related-underemployment-by-sex-education-and}} } ``` ## 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=EMP_XTRU_SEX_EDU_MTS_NB_
This dataset is titled Time-related underemployment by sex, education and marital status (thousands) | Africa (ILOSTAT) and contains 6,216 observations across 34 African countries, spanning from 1991 to 2025. The core indicator is Time-related underemployment, sourced from the International Labour Organization (ILO) ILOSTAT database, retrieved via API and filtered to African ISO3 country codes. It is structured in tabular format with columns such as country code (ref_area), sex (sex), education level (classif1), marital status (classif2), year (time), and observed value (obs_value), supporting disaggregation by sex, education, marital status, and other dimensions. The data is annual frequency and provides statistics on underemployment, suitable for tabular classification, regression, and time-series forecasting tasks. For data quality, ILOSTAT harmonizes raw survey microdata using ICLS definitions, with source and observation status flags included. Usage examples include loading the dataset, filtering by country, time-series analysis, and pivoting to matrices. The dataset is released under the CC-BY-4.0 license and repackaged by Electric Sheep Africa as part of a unified, ML-ready data layer for Africa on HuggingFace.
