electricsheepafrica/africa-ilo-une-tune-sex-age-nb-unemployment-by-sex-and-age-thousands

--- 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 and age (thousands) | Africa (ILOSTAT)" --- # Unemployment by sex and age (thousands) | Africa (ILOSTAT) 🌍 **30,515 observations** · **54 Africa countries** · **1969–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **30,515 observations** of `Unemployment` data across **54 Africa countries**, spanning **1969–2025**, 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_AGE_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_AGE_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 54 Africa countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `MUS` | 2,205 | 1969 | 2024 | | `ZAF` | 2,110 | 1995 | 2024 | | `TUN` | 1,824 | 1989 | 2023 | | `EGY` | 1,788 | 1970 | 2024 | | `MAR` | 1,236 | 1969 | 2022 | | `BWA` | 1,165 | 1984 | 2024 | | `RWA` | 900 | 1989 | 2025 | | `GHA` | 894 | 1989 | 2024 | | `AGO` | 886 | 1984 | 2025 | | `MLI` | 874 | 2004 | 2024 | | `ZMB` | 843 | 1986 | 2024 | | `ZWE` | 833 | 1982 | 2024 | | `DZA` | 797 | 1975 | 2017 | | `SYC` | 765 | 1994 | 2024 | | `TZA` | 710 | 1991 | 2024 | | ... | _39 more countries_ | | | ## Indicators (sample) - `UNE_TUNE_SEX_AGE_NB` — Unemployment by sex and age (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_AGE_NB` | | `indicator.label` | `string` | Indicator name in English | `Unemployment by sex and age (thousands)` | | `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+` | | `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_classif` | `string` | — | `C6:2274` | | `note_classif.label` | `string` | — | `Nonstandard age group: Including ages…` | | `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-age-nb-unemployment-by-sex-and-age-thousands") 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_AGE_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="UNE_TUNE_SEX_AGE_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "UNE_TUNE_SEX_AGE_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_une_tune_sex_age_nb_unemployment_by_sex_and_age_thousands_2025, title = {Unemployment by sex and age (thousands) | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=UNE_TUNE_SEX_AGE_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-une-tune-sex-age-nb-unemployment-by-sex-and-age-thousands}} } ``` ## 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_AGE_NB_This dataset contains unemployment data by sex and age (in thousands) for 54 African countries from 1969 to 2025, sourced from ILOSTAT (the International Labour Organizations central statistics database). It includes 30,515 observations covering one key indicator: UNE_TUNE_SEX_AGE_NB, which represents unemployment by sex and age (thousands). The data is retrieved via the ILOSTAT REST API, normalized with fields such as country codes, years, sex disaggregation, observed values, and is suitable for tasks like 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的失业指标，经Electric Sheep Africa进行元数据标准化与重新封装而构建。原始数据覆盖非洲54个国家，时间跨度为1969年至2025年，共包含30515条观测记录，以千人为单位统计按性别和年龄分组的失业人数。构建过程中保留了源数据的指标定义与单位信息，同时补充了标准化元数据、加载指南和来源说明，以便于非洲数据发现与分析。

特点

数据集以表格与文本模态呈现，采用parquet格式存储，规模介于1万至10万条之间，属于经济学与金融领域。其核心特点在于覆盖范围广泛，囊括非洲大陆54个国家逾半个世纪的失业统计，并按性别和年龄维度细分，为劳动力市场研究提供结构化证据。数据保留缺失值，附带明确的来源溯源、许可信息和使用建议，便于可重复分析。

使用方法

研究者可通过Hugging Face datasets库加载该数据集，先检视仓库文件与数据查看器，利用table.features和table[:3]了解模式与样本。表格数据可转换为Pandas DataFrame进行地理、时间及子群分析，并与其他Electric Sheep Africa数据集按国家、年份和指标字段联接。使用中应确认变量定义与单位，保留缺失值直至有合理插补规则，避免仅凭标签推断政策含义。

背景与挑战

背景概述

非洲大陆的劳动力市场长期面临结构性失衡与数据碎片化的双重困境，国际劳工组织（ILO）作为全球劳工统计的权威机构，其ILOSTAT数据库构成了监测各国就业与失业动态的核心基础设施。该数据集由Electric Sheep Africa于2026年前后完成工程化整理与标准化发布，收录了1969年至2025年间54个非洲国家按性别与年龄分组的失业人口数据，共计30,515条观测记录，以Parquet格式提供并附有标准化元数据与溯源说明。其核心研究问题在于揭示非洲各国失业状况在性别与年龄维度上的异质性分布及其时序演变规律，为劳动经济学实证研究与区域政策评估提供可复现的跨国面板数据基础，对推动非洲数据驱动型决策具有基础性意义。

当前挑战

该数据集所应对的领域问题在于非洲失业统计长期存在的覆盖不均衡与跨国可比性不足，各国劳动力调查方法、年龄分组标准及失业定义存在显著差异，导致横向比较面临方法论障碍。构建过程中，Electric Sheep Africa需从ILOSTAT多源异构数据中整合时序记录，处理缺失值、口径调整与元数据标注不一致等问题，且部分国家的性别与年龄细分数据存在系统性缺报。此外，数据来源的元数据完整性受限，国家字段与上游发布者信息在清单中存在缺口，促使研究者在建模前必须审慎核验变量定义、单位与统计方法，以避免对失业标签作出缺乏依据的政策性推断。

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汇处，该数据集扮演着非洲失业态势量化刻画的基础性角色。依托国际劳工组织统计司的权威框架，其经典用法在于构建以性别与年龄组为双重视角的面板数据模型，追踪1969年至2025年间54个非洲国家失业人口（以千人为单位）的时序演变轨迹。研究者借由这一结构化表格，得以在统一口径下开展跨国家、跨时期的比较分析，进而揭示青年群体与女性劳动者在非洲劳动力市场中所承受的差异化失业压力，为后续的计量建模提供扎实的数据底座。

实际应用

在政策设计与国际发展实践的场域中，该数据集为非洲各国劳工部门、区域经济共同体及国际援助机构提供了关键的情境感知工具。决策者可依据分性别、分年龄的失业规模估算，精准定位就业干预的优先人群与地理区域，优化职业培训与社会保障资源的配置。同时，世界银行、非洲开发银行等多边机构在编制国别诊断报告与项目评估时，亦可将其作为劳动力市场脆弱性的基准指标，支撑从应急性就业计划到中长期人力资本投资的策略制定。

衍生相关工作

以该数据集为起点，一系列衍生研究在非洲劳动经济学领域渐次展开。部分学者将其与人口普查、家庭调查等微观数据链接，探究失业持续期与教育回报之间的异质性关联；另有研究将其嵌入一般均衡模型，模拟贸易冲击或技术变迁对非洲不同人口群体就业的差异化影响。此外，该数据集亦激发了关于失业统计方法本身的批判性反思，推动了非正规经济部门就业测量的方法论改进，并成为多份国际组织旗舰报告与区域就业展望的核心引证来源。