electricsheepafrica/africa-ilo-une-tune-sex-geo-dsb-nb-unemployment-by-sex-rural-urban-areas-and-disabili

--- 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 - unemployment - ilo - labour - employment pretty_name: "Unemployment by sex, rural / urban areas and disability status (thousands) | Africa (ILOSTAT)" --- # Unemployment by sex, rural / urban areas and disability status (thousands) | Africa (ILOSTAT) 🌍 **2,277 observations** · **37 Africa countries** · **2006–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **2,277 observations** of `Unemployment` data across **37 Africa countries**, spanning **2006–2025**, 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_DSB_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_DSB_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 37 Africa countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `RWA` | 254 | 2014 | 2025 | | `GHA` | 190 | 2010 | 2024 | | `ZMB` | 159 | 2015 | 2024 | | `ZWE` | 159 | 2014 | 2024 | | `SEN` | 131 | 2015 | 2024 | | `CIV` | 90 | 2016 | 2022 | | `GMB` | 89 | 2012 | 2025 | | `TZA` | 86 | 2008 | 2020 | | `TGO` | 83 | 2006 | 2022 | | `UGA` | 78 | 2010 | 2021 | | `SWZ` | 75 | 2016 | 2023 | | `BFA` | 59 | 2019 | 2024 | | `NGA` | 57 | 2011 | 2019 | | `LSO` | 51 | 2019 | 2024 | | `ETH` | 50 | 2013 | 2021 | | ... | _22 more countries_ | | | ## Indicators (sample) - `UNE_TUNE_SEX_GEO_DSB_NB` — Unemployment by sex, rural / urban areas and disability 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) | `AA:835` | | `source.label` | `string` | Source name in English | `PC - Population Census` | | `indicator` | `string` | ILOSTAT indicator code | `UNE_TUNE_SEX_GEO_DSB_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 | `DSB_STATUS_TOTAL` | | `classif2.label` | `string` | — | `Disability status: Total` | | `time` | `int64` | Observation year | `2014` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `560.654` | | `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-une-tune-sex-geo-dsb-nb-unemployment-by-sex-rural-urban-areas-and-disabili") 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_DSB_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="UNE_TUNE_SEX_GEO_DSB_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "UNE_TUNE_SEX_GEO_DSB_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_une_tune_sex_geo_dsb_nb_unemployment_by_sex_rural_urban_areas_and_disabili_2025, title = {Unemployment by sex, rural / urban areas and disability status (thousands) | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=UNE_TUNE_SEX_GEO_DSB_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-une-tune-sex-geo-dsb-nb-unemployment-by-sex-rural-urban-areas-and-disabili}} } ``` ## 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_DSB_NB_This dataset contains unemployment data for Africa, disaggregated by sex, rural/urban areas, and disability status. It includes 2,277 observations across 37 African countries, spanning 2006 to 2025, with 1 distinct indicator (unemployment in thousands). The data is sourced from the International Labour Organization (ILO) ILOSTAT database, repackaged by Electric Sheep Africa for machine learning readiness. It provides a detailed schema including country codes, data sources, indicator codes, sex classifications, area types, disability status, observation years, and values, along with data quality caveats and usage examples.

搜集汇总

数据集介绍

构建方式

该数据集以国际劳工组织中央统计数据库（ILOSTAT）为原始数据源，由Electric Sheep Africa团队进行系统化整编与元数据标准化。整编过程涵盖对原始失业统计记录的提取、字段清洗与格式统一，并补充了面向非洲数据发现场景的溯源说明、许可声明与分析导向的上下文注释，最终以Parquet格式封装为Hugging Face数据集仓库。数据集覆盖37个非洲国家，时间跨度为2006年至2025年，共包含2,277条观测记录，涉及按性别、城乡区域及残疾状况分类的失业人口规模（以千人为单位）指标。

使用方法

分析者可通过Hugging Face数据集加载接口直接获取数据，调用load_dataset函数加载指定仓库后，利用features属性查看字段结构并预览前若干行样本。对于表格类分析任务，可将首个数据划分转换为Pandas数据框，以便执行缺失值探查、变量分布剖析及按地理、时间与子群体维度的剖面统计。数据支持与其他Electric Sheep Africa数据集基于国家、年份与指标字段进行联结，从而构建更具解释力的跨域分析。在建模之前，建议保留原始缺失值直至确立可辩护的插补规则，且不应仅凭标签推断政策含义，而应在原始来源材料中核实定义、单位与方法细节。

背景与挑战

背景概述

在国际劳工统计体系中，按性别、城乡与残疾状况细分失业数据，是评估劳动力市场结构性不平等与包容性增长的关键证据。该数据集由Electric Sheep Africa基于国际劳工组织ILOSTAT数据库整理发布，覆盖37个非洲国家、2006至2025年间共2277条观测记录，以表格与文本形式在Hugging Face平台开放共享，遵循CC BY 4.0许可协议。其核心研究问题在于揭示非洲地区不同人口群体失业率的异质性分布，为劳动经济学、社会政策与可持续发展研究提供可复现的跨国面板数据基础，对推动非洲数据驱动决策具有基础性意义。

当前挑战

该数据集所面对的领域挑战在于，失业现象的测量高度依赖各国劳动统计口径与调查方法，跨国可比性受到非正式就业普遍、残疾界定多元及城乡划分标准差异的显著制约。构建过程中的挑战则体现为元数据缺口，如国家字段与上游发布者信息尚未完整声明，部分地理标识依赖标题推断；此外，缺失值的处理尺度、指标单位的一致性校验以及跨年度数据拼接中的断裂风险，均对建模者的数据清洗与假设管理能力提出较高要求。

常用场景

经典使用场景

在劳动经济学与非洲区域发展研究的交叉领域，该数据集凭借对性别、城乡地域及残障状态三重维度的失业人口分层刻画，构成了开展异质性劳动力市场分析的经典素材。研究者通常将其加载为表格型数据框，围绕2006至2025年间37个非洲国家的2277条观测记录，借助分组统计与面板回归手段，考察不同社会群体失业率的时序演变与截面差异，进而揭示非洲大陆劳动力市场结构性分化的基本图景。

解决学术问题

该数据集有效回应了非洲劳动统计中弱势群体失业状况数据长期匮乏的学术困境。以往研究多受限于性别或城乡单一维度的粗粒度指标，难以同时纳入残障身份这一常被忽略的交叉变量。本数据集通过提供统一口径下的细分失业人口估计，使学者得以检验残障与性别、地域之间的交互效应，为理解多重劣势叠加如何塑造就业机会不平等提供了可复现的实证基础，对推动包容性劳动政策研究具有实质意义。

实际应用

在国际组织与非洲各国政策实践中，该数据集可服务于就业促进项目的精准定位与效果评估。政府部门与开发机构能够依据分性别、分城乡及残障状态的失业规模，识别高失业风险群体并优化资源配置，例如为农村残障女性设计专项技能培训或社会保障干预。同时，其标准化元数据与清晰来源标注便于与其他非洲社会经济数据集整合，支撑国别比较与区域监测报告的编制。

数据集最近研究

最新研究方向

在全球劳动力市场结构性转型与残障包容性发展议程深度交织的背景下，该数据集以其涵盖37个非洲国家、跨越2006至2025年的2,277条观测记录，为解析性别、城乡地域与残障状态三重维度下的失业异质性提供了稀缺的跨国面板证据。当前前沿研究正依托此类微观分层数据，聚焦残障群体在城乡劳动力市场中的双重边缘化机制，以及性别差异如何与地域属性交互影响就业可及性，进而回应国际劳工组织关于包容性就业监测的方法论革新。该数据集亦推动了非洲区域失业统计从总量估算向弱势群体精细化追踪的范式迁移，为评估社会保障政策的靶向效能与弥合数据鸿沟提供了关键实证基础，对实现可持续发展目标中的充分就业与体面工作议程具有深远的监测与倡导意义。