electricsheepafrica/africa-ilo-une-deap-sex-age-geo-rt-unemployment-rate-by-sex-age-and-rural-urban-areas

--- 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 rate by sex, age and rural / urban areas (%) | Africa (ILOSTAT)" --- # Unemployment rate by sex, age and rural / urban areas (%) | Africa (ILOSTAT) 🌍 **30,879 observations** · **47 Africa countries** · **1991–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **30,879 observations** of `Unemployment` data across **47 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=UNE_DEAP_SEX_AGE_GEO_RT) - **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_DEAP_SEX_AGE_GEO_RT` 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 47 Africa countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `ZAF` | 2,478 | 2008 | 2024 | | `TUN` | 2,193 | 2006 | 2023 | | `EGY` | 2,084 | 2008 | 2024 | | `AGO` | 1,491 | 2004 | 2025 | | `RWA` | 1,382 | 2012 | 2025 | | `GHA` | 1,359 | 2000 | 2024 | | `MLI` | 1,202 | 2013 | 2024 | | `ZMB` | 1,093 | 1998 | 2024 | | `SEN` | 966 | 2011 | 2024 | | `ZWE` | 965 | 2004 | 2024 | | `NAM` | 922 | 1994 | 2018 | | `NGA` | 869 | 2011 | 2024 | | `KEN` | 797 | 1999 | 2022 | | `TZA` | 777 | 1991 | 2020 | | `BFA` | 756 | 2005 | 2024 | | ... | _32 more countries_ | | | ## Indicators (sample) - `UNE_DEAP_SEX_AGE_GEO_RT` — Unemployment rate by sex, age and rural / urban areas (%) ## 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_DEAP_SEX_AGE_GEO_RT` | | `indicator.label` | `string` | Indicator name in English | `Unemployment rate by sex, age and rur…` | | `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.) | `AGE_YTHADULT_YGE15` | | `classif1.label` | `string` | — | `Age (Youth, adults): 15+` | | `classif2` | `string` | Second classification variable where applicable | `GEO_COV_NAT` | | `classif2.label` | `string` | — | `Area type: National` | | `time` | `int64` | Observation year | `2025` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `10.391` | | `obs_status` | `string` | Observation status flag (e.g. provisional, unreliable) | `U` | | `obs_status.label` | `string` | — | `Unreliable` | | `note_classif` | `string` | — | `C6:2330` | | `note_classif.label` | `string` | — | `Nonstandard age group: Excluding age 65` | | `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-deap-sex-age-geo-rt-unemployment-rate-by-sex-age-and-rural-urban-areas") 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_DEAP_SEX_AGE_GEO_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="UNE_DEAP_SEX_AGE_GEO_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "UNE_DEAP_SEX_AGE_GEO_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_une_deap_sex_age_geo_rt_unemployment_rate_by_sex_age_and_rural_urban_areas_2025, title = {Unemployment rate by sex, age and rural / urban areas (%) | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=UNE_DEAP_SEX_AGE_GEO_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-une-deap-sex-age-geo-rt-unemployment-rate-by-sex-age-and-rural-urban-areas}} } ``` ## 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_DEAP_SEX_AGE_GEO_RT_This is a tabular dataset on unemployment rates in Africa, containing 30,879 observations across 47 African countries from 1991 to 2025. The core indicator is the unemployment rate by sex, age, and rural/urban areas (%), sourced from the International Labour Organizations ILOSTAT database. The dataset provides a detailed schema including country codes, country names, data sources, indicator codes, sex disaggregation (total, male, female), age classification, area classification, year, observed values, and data quality flags, suitable for tasks such as tabular classification, regression, and time-series forecasting.

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中央统计数据库ILOSTAT所发布的非洲区域失业率统计，经由Electric Sheep Africa对原始公开数据进行系统化整理与标准化封装而构建。其采集范围覆盖47个非洲国家，时间跨度自1991年至2025年，共计30,879条观测记录，围绕性别、年龄组及城乡地域三个维度对失业率指标进行细分。数据以parquet列式格式存储，附有标准化元数据、来源溯源说明及面向分析者的使用指引，确保数据结构一致且便于跨域复用。

特点

数据集以非洲劳动力市场为核心议题，兼具时间序列与横截面双重属性，涵盖经济学与金融领域的表格型及文本型模态。其规模处于一万至十万行区间，变量设计精细，可支持按性别、年龄段与城乡属性展开交叉分析。数据保留原始缺失值，未施加主观插补，并明确标注元数据缺口与字段定义，为研究者提供透明且可追溯的证据基础，适用于跨国比较、趋势刻画及劳动经济学的实证探索。

使用方法

研究者可借助Hugging Face datasets库以一行代码加载该数据集，通过查看features与样本切片快速把握schema结构，并可转换为Pandas数据框以便后续建模。在分析前需核对变量定义、单位与来源方法，审慎处理缺失值，避免仅凭标签推断政策含义。建议依据国家、年份及指标字段与其他非洲数据集进行联结，构建可复现的分析流程，并在成果中同时引用ILOSTAT原始来源与Electric Sheep Africa的工程化仓库。

背景与挑战

背景概述

失业率作为劳动力市场健康程度的核心标尺，其按性别、年龄及城乡地域的精细化分解，长期以来是发展经济学与劳动政策研究的基石。国际劳工组织（ILO）依托ILOSTAT数据库，持续汇集全球劳动力统计资料，构成了该领域最权威的跨国数据来源。在此背景下，Electric Sheep Africa于2026年前后对非洲区域的ILOSTAT失业率指标进行了系统性的元数据整理与格式再封装，催生出覆盖47个非洲国家、时间跨度自1991年至2025年、共计30879条观测的专题数据集。该数据集的核心研究问题在于如何以标准化、可复现的方式呈现非洲各国失业状况在人口结构维度上的异质性，为区域劳动力市场比较分析与政策评估提供结构化的证据基础，对非洲发展经济学实证研究具有显著的支撑价值。

当前挑战

该数据集所回应的领域问题，在于非洲劳动力市场统计长期面临口径不一、覆盖参差与结构性维度缺失的困境，跨国失业率的性别与城乡差异比较因此难以在统一框架下展开。在构建过程中，数据整合面临若干具体挑战：元数据清单中country与upstream_publisher字段存在缺失，需借助标题与来源信息进行审慎推断；各成员国上报数据的统计定义、调查方法与参考期不尽一致，直接比较可能引入偏差；城乡与年龄组的分类标准在国别间存在异质性，统一编码需要额外假设；此外，缺失值处理尚缺乏可辩护的插补规则，若轻率填补可能扭曲劳动市场真实图景。这些挑战要求使用者在建模前对变量定义与单位进行充分核验。

常用场景

经典使用场景

在劳动经济学与区域发展研究的交叉语境下，该数据集最经典的使用场景是围绕非洲劳动力市场的结构性差异展开分层计量分析。研究者可借助性别、年龄组与城乡居所三重维度，刻画1991至2025年间47个非洲国家失业率的异质性分布，进而运用双向固定效应模型或面板分位数回归，探讨不同人口群体在经济周期与政策冲击下的就业脆弱性。此类分析常与非洲大陆自由贸易区、青年就业倡议等宏观议题相呼应，构成发展经济学实证研究的基础性数据支撑。

实际应用

在政策实践层面，该数据集可服务于国际发展机构与非洲各国劳动部门的就业监测与干预评估。借助分性别、分年龄与城乡维度的失业率序列，政策制定者能够识别青年群体或农村女性等高风险人群，优化职业培训与社会保障资源的空间配置。同时，该数据亦可嵌入世界银行、非洲开发银行等机构的国别诊断报告中，为就业促进型项目的立项论证与绩效评估提供量化依据。

衍生相关工作

基于该数据集及其所属的Electric Sheep Africa元数据目录，已衍生出一系列围绕非洲劳动市场的可复现研究实践。典型工作包括构建非洲失业率面板数据库的标准化流程、开发面向政策分析的交互式仪表盘，以及将本数据与世界发展指标、非洲开发银行社会经济数据库进行跨国别匹配的实证探索。这些工作共同拓展了非洲开放数据生态在劳动统计领域的应用边界。