electricsheepafrica/africa-ilo-une-tune-sex-geo-mts-nb-unemployment-by-sex-rural-urban-area-and-marital-s

--- 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 - unemployment - ilo - labour - employment pretty_name: "Unemployment by sex, rural / urban area and marital status (thousands) | Africa (ILOSTAT)" --- # Unemployment by sex, rural / urban area and marital status (thousands) | Africa (ILOSTAT) 🌍 **16,738 observations** · **45 Africa countries** · **1994–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **16,738 observations** of `Unemployment` data across **45 Africa countries**, spanning **1994–2025**, 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=UNE_TUNE_SEX_GEO_MTS_NB) - **Publisher:** International Labour Organization (ILO) - **License:** [cc-by-4.0](https://creativecommons.org/licenses/by/4.0/) - **Topic:** Unemployment ## Methodology Data pulled directly from the ILOSTAT REST API at `https://rplumber.ilo.org/data/indicator?id=UNE_TUNE_SEX_GEO_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 45 Africa countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `EGY` | 1,455 | 2008 | 2024 | | `ZAF` | 1,404 | 2008 | 2024 | | `TUN` | 1,102 | 2006 | 2023 | | `GHA` | 808 | 2000 | 2024 | | `RWA` | 775 | 2014 | 2025 | | `AGO` | 766 | 2004 | 2025 | | `MLI` | 740 | 2013 | 2024 | | `ZMB` | 620 | 2015 | 2024 | | `ZWE` | 535 | 2011 | 2024 | | `NAM` | 514 | 1994 | 2018 | | `SEN` | 500 | 2011 | 2024 | | `NGA` | 449 | 2011 | 2024 | | `KEN` | 444 | 1999 | 2022 | | `TZA` | 430 | 2001 | 2020 | | `BFA` | 400 | 2014 | 2024 | | ... | _30 more countries_ | | | ## Indicators (sample) - `UNE_TUNE_SEX_GEO_MTS_NB` — Unemployment by sex, rural / urban area 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 | `UNE_TUNE_SEX_GEO_MTS_NB` | | `indicator.label` | `string` | Indicator name in English | `Unemployment by sex, rural / urban ar…` | | `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.) | `GEO_COV_NAT` | | `classif1.label` | `string` | — | `Area type: National` | | `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) | `1621.696` | | `obs_status` | `string` | Observation status flag (e.g. provisional, unreliable) | `U` | | `obs_status.label` | `string` | — | `Unreliable` | | `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-une-tune-sex-geo-mts-nb-unemployment-by-sex-rural-urban-area-and-marital-s") 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"] == "UNE_TUNE_SEX_GEO_MTS_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="UNE_TUNE_SEX_GEO_MTS_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "UNE_TUNE_SEX_GEO_MTS_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_une_tune_sex_geo_mts_nb_unemployment_by_sex_rural_urban_area_and_marital_s_2025, title = {Unemployment by sex, rural / urban area and marital status (thousands) | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=UNE_TUNE_SEX_GEO_MTS_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-une-tune-sex-geo-mts-nb-unemployment-by-sex-rural-urban-area-and-marital-s}} } ``` ## 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=UNE_TUNE_SEX_GEO_MTS_NB_This dataset contains unemployment data for 45 African countries from 1994 to 2025, with 16,738 observations covering one core indicator: UNE_TUNE_SEX_GEO_MTS_NB, which represents unemployment by sex, rural/urban area, and marital status (in thousands). The data is sourced from the International Labour Organization (ILO)s ILOSTAT database, retrieved via API and harmonized using International Conference of Labour Statisticians (ICLS) definitions. It includes columns such as country code, country name, data source, indicator code, sex classification (total, male, female), area type classification, marital status classification, observation year, observed value (unemployment count), and data quality flags (e.g., reliability status). The data is annual in frequency and provides disaggregation dimensions like sex and geographic coverage, suitable for tasks such as tabular classification, regression, and time-series forecasting. The dataset is repackaged by Electric Sheep Africa to support machine learning and research applications.

搜集汇总

数据集介绍

构建方式

该数据集源自国际劳工组织（ILO）的ILOSTAT中央统计数据库，系全球劳动统计领域的权威来源。Electric Sheep Africa对原始数据进行了重新打包与标准化处理，将其转化为适用于机器学习任务的Parquet格式。构建过程中，整合了1994年至2025年间45个非洲国家的失业统计数据，共计16,738条观测记录，覆盖按性别、城乡区域及婚姻状况分类的失业指标，并附带了标准化的元数据、数据溯源说明及分析导向的上下文信息。

特点

数据集聚焦于非洲地区的失业问题，具有显著的区域代表性与多维分类特征。其核心特点在于同时纳入性别、城乡分布与婚姻状况三个维度，为剖析劳动力市场中的结构性差异提供了细粒度视角。数据规模介于10K至100K之间，以表格数据与文本模态呈现，并以Parquet格式存储，便于高效读取与处理。作为Electric Sheep Africa目录的一部分，该数据集附带标准化的发现元数据与来源注释，支持可复现的分析工作流。

使用方法

用户可通过Hugging Face的datasets库直接加载该数据集，利用load_dataset函数获取数据对象，并通过特征查看与切片操作初步探查数据结构。对于表格数据，可将其转换为Pandas DataFrame以进行更灵活的统计分析。在使用过程中，建议先检查模式与缺失值情况，再依据地理、时间和子群列进行变量画像，必要时可与其他Electric Sheep Africa数据集基于国家、年份和指标字段进行连接，以构建可复现的分析流程。

背景与挑战

背景概述

非洲劳动力市场长期面临失业统计碎片化与性别、城乡及婚姻状况维度缺失的困境，国际劳工组织（ILO）通过ILOSTAT数据库持续采集相关指标，为政策分析与学术研究提供基础。Electric Sheep Africa于2026年将该数据集整理发布至Hugging Face平台，覆盖45个非洲国家、1994年至2025年间共16738条观测记录，系统呈现按性别、城乡区域和婚姻状况分类的失业人口规模（以千计）。该数据集为探究非洲失业结构性差异、评估劳动力市场政策效果以及推动性别与区域均衡研究提供了可复现的跨国面板数据支撑，对发展经济学与劳动经济学领域具有重要参考价值。

当前挑战

该数据集所应对的核心领域问题在于非洲失业数据的多维异质性刻画与跨国可比性难题，即如何在性别、城乡和婚姻状况交叉分类下保持统计口径一致。构建过程中，数据整合面临源数据缺失、指标定义随国家与年份变动、城乡划分标准不统一以及婚姻状况分类差异等挑战，同时需在保留原始缺失值与标准化元数据之间取得平衡。此外，部分国家元数据中上游出版者与国别标识缺失，进一步增加溯源与验证难度，要求使用者在建模前审慎核查变量单位、定义及方法学说明。

常用场景

经典使用场景

在劳动经济学与非洲发展研究的交汇处，该数据集凭借其精细化的人口分组维度，成为剖析失业结构性差异的经典工具。研究者常以性别、城乡地域及婚姻状况为交叉切片，运用列联表分析与方差分解方法，揭示不同社会群体在劳动力市场中的边缘化程度。例如，通过对比已婚女性与未婚女性在城乡之间的失业率梯度，可量化婚姻状态对女性劳动参与的制度性约束。此类分析不仅呈现了非洲45国在1994至2025年间失业模式的时空演变，还为跨国比较研究提供了统一口径的观测单元。

实际应用

在政策制定与项目评估层面，该数据集为非洲各国劳动部门及国际发展机构提供了精准的靶向依据。例如，针对农村已婚女性失业率显著偏高的区域，社会保障项目可据此设计兼顾育儿支持与技能培训的干预方案。同时，数据集支持构建失业预警指标，辅助非盟及各国统计局监测城乡劳动力市场动态。私营部门亦可用其评估特定区域的人力资本闲置状况，优化投资选址与招聘策略。此类应用凸显了细粒度失业统计在减贫与包容性增长议程中的操作价值。

衍生相关工作

围绕该数据集，衍生出一系列聚焦非洲劳动力市场异质性的经典研究。部分学者将其与ILOSTAT其他指标（如就业部门、工时）链接，构建多维度劳动市场脆弱性指数；另一些工作则利用其时空覆盖，开展婚姻状况与失业持续期的面板因果推断。此外，该数据集常被用于训练和验证机器学习模型，以预测特定子群体的失业风险，并催生了面向非洲政策模拟的微观仿真工具。这些衍生工作不仅拓展了原始数据的分析边界，也强化了Electric Sheep Africa目录下数据集间的互操作性。