electricsheepafrica/africa-ilo-une-tune-sex-cat-nb-unemployment-by-sex-and-categories-of-unemployed-p

--- 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 and categories of unemployed persons (thousands) | Africa (ILOSTAT)" --- # Unemployment by sex and categories of unemployed persons (thousands) | Africa (ILOSTAT) 🌍 **2,688 observations** · **46 Africa countries** · **1970–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **2,688 observations** of `Unemployment` data across **46 Africa countries**, spanning **1970–2025**, 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_CAT_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_CAT_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 46 Africa countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `EGY` | 424 | 1971 | 2024 | | `MUS` | 405 | 1979 | 2024 | | `ZAF` | 232 | 2000 | 2024 | | `CAF` | 136 | 1974 | 1995 | | `MAR` | 129 | 1992 | 2013 | | `NGA` | 117 | 1970 | 2024 | | `RWA` | 108 | 2002 | 2025 | | `MLI` | 90 | 2013 | 2024 | | `AGO` | 90 | 2004 | 2025 | | `GHA` | 75 | 1991 | 2024 | | `MDG` | 69 | 1978 | 2015 | | `DZA` | 56 | 1989 | 2014 | | `NER` | 56 | 2011 | 2022 | | `CMR` | 54 | 1978 | 2021 | | `TZA` | 48 | 2001 | 2024 | | ... | _31 more countries_ | | | ## Indicators (sample) - `UNE_TUNE_SEX_CAT_NB` — Unemployment by sex and categories of unemployed persons (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_CAT_NB` | | `indicator.label` | `string` | Indicator name in English | `Unemployment by sex and categories of…` | | `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.) | `CAT_UNE_TOTAL` | | `classif1.label` | `string` | — | `Type of unemployment: 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) | `B` | | `obs_status.label` | `string` | — | `Break in series` | | `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-cat-nb-unemployment-by-sex-and-categories-of-unemployed-p") 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_CAT_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="UNE_TUNE_SEX_CAT_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "UNE_TUNE_SEX_CAT_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_une_tune_sex_cat_nb_unemployment_by_sex_and_categories_of_unemployed_p_2025, title = {Unemployment by sex and categories of unemployed persons (thousands) | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=UNE_TUNE_SEX_CAT_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-une-tune-sex-cat-nb-unemployment-by-sex-and-categories-of-unemployed-p}} } ``` ## 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_CAT_NB_This dataset contains statistical data on unemployment in African countries, specifically the number of unemployed persons (in thousands) disaggregated by sex and categories of unemployed persons. It includes 2,688 observations covering 46 African countries from 1970 to 2025. The data is sourced from the ILOSTAT database of the International Labour Organization (ILO), the leading global source for labour statistics. The dataset features one core indicator, UNE_TUNE_SEX_CAT_NB, and is disaggregated by sex dimension (total, male, female). The data is in tabular format with columns including country code, country name, data source, indicator code, indicator label, sex classification, observation year, observed value, observation status, etc. The data is harmonized by ILO using International Conference of Labour Statisticians (ICLS) definitions and includes source flags for traceability. This dataset is suitable for machine learning tasks such as tabular classification, tabular regression, and time-series forecasting.

搜集汇总

数据集介绍

构建方式

该数据集依托国际劳工组织统计数据库（ILOSTAT）的权威失业统计数据，经Electric Sheep Africa团队系统化整理与标准化后构建而成。数据采集覆盖46个非洲国家，时间跨度为1970年至2025年，共计2,688条观测记录。构建过程中，原始数据被重新封装为Parquet格式，并配备标准化的元数据描述、来源溯源说明及面向分析者的使用指引，以提升数据在机器学习与统计分析场景中的可复用性与可发现性。

特点

该数据集聚焦于非洲区域按性别及失业者类别划分的失业人口统计，具有显著的区域覆盖广度与时间纵深。其数据结构以表格形式呈现，辅以文本元数据，适用于表格分类与回归任务。数据集标签涵盖失业、劳动力、就业等经济学核心议题，并明确标注为单语种、开放许可（CC BY 4.0）。规模处于1K至10K区间，便于快速加载与建模实验，同时为非洲劳动力市场研究提供了跨国别、长时序的比较基础。

使用方法

使用者可通过Hugging Face的datasets库以一行代码加载该数据集，并直接访问其分片结构与特征信息。加载后，可借助to_pandas()方法将表格数据转换为DataFrame，以便进行探索性分析与建模。在使用过程中，建议优先检查各变量的定义、单位及缺失值分布，避免仅凭标签推断政策含义。若需跨数据集分析，可利用显式的国家、年份及指标字段进行关联，并确保在研究成果中引用原始来源与Electric Sheep Africa的仓库信息。

背景与挑战

背景概述

非洲地区长期面临劳动力市场数据碎片化与可比性不足的困境，失业统计的性别维度缺失尤为突出。国际劳工组织（ILO）通过ILOSTAT数据库持续汇编全球劳动力市场指标，为政策分析与学术研究提供权威基础。在此背景下，Electric Sheep Africa于2026年对ILOSTAT中非洲失业数据进行了标准化重封装，形成了涵盖46个非洲国家、1970至2025年、共计2688条观测值的结构化数据集，按性别与失业者类别细分失业人数。该数据集以Parquet格式发布，遵循CC BY 4.0许可，为非洲经济金融领域的可复现研究提供了关键数据基础设施。

当前挑战

该数据集所应对的核心领域问题在于非洲失业统计长期存在的性别盲区与类别模糊性，亟需可比的、按性别和失业类别分解的跨国时序数据以支撑劳动力市场性别差异的量化分析。在构建过程中，数据标准化面临多重挑战：原始ILOSTAT数据存在元数据缺口，国家和上游发布者信息未在清单中完整声明；不同国家与年份的失业定义、统计口径及覆盖范围存在异质性，影响跨时空比较的有效性；缺失值处理与单位确认需依赖源文件审慎核验，避免因标签推断产生政策误导。这些挑战要求分析者在建模前严格检查模式、缺失模式与变量定义，以确保结论的稳健性。

常用场景

经典使用场景

在劳动经济学与计量社会科学的研究中，依托ILOSTAT劳动力统计数据库的跨国比较分析长期占据核心地位。该数据集作为非洲区域失业统计的结构化汇编，覆盖46个非洲国家自1970年至2025年的2688条观测记录，并依据性别与失业者类别进行细粒度分层，其最经典的使用场景在于构建面板数据模型，用以刻画非洲各国失业规模与结构的时序演变轨迹。研究者可借助表中的国家、年份、性别与失业类别字段进行长时段趋势拟合与横截面比较，进而识别不同性别群体在失业类型分布上的异质性特征。这类数据驱动的描述性与解释性分析，已成为评估非洲劳动力市场结构性变迁的基础性工作。

解决学术问题

该数据集直面的核心学术难题在于非洲区域劳动力市场统计长期存在的碎片化与不可比问题。受制于各国统计能力差异以及非正规经济部门的广泛存在，非洲失业数据此前多以国别报告或分散来源呈现，难以支撑区域层面的系统比较。ILOSTAT经过统一口径与估算方法所构建的这一指标序列，为研究者提供了跨国可比的标准化失业统计基础，使跨国失业率差异的归因分析、性别维度上的失业结构检验以及失业类别构成的长期趋势判定成为可能。其意义在于将非洲纳入全球劳动力市场比较研究的证据版图，为劳动经济学中关于发展中国家失业形态的理论争鸣提供了可检验的量化基础。

衍生相关工作

以该数据集为代表的一系列Electric Sheep Africa标准化非洲公共数据产品，催生了围绕非洲劳动统计的衍生研究与实践。在Hugging Face数据生态中，该数据集常与其他ILOSTAT衍生的就业、劳动参与率及部门就业结构数据集进行字段级关联，形成多维劳动力市场分析框架。相关衍生工作包括跨国失业率的可复现Notebook汇编、结合ISO3国家编码的多源数据融合实践，以及面向非洲数据发现场景的元数据标准化工程。这些工作在方法论上推动了开放统计数据的可查找、可访问与可重用，也为后续机器学习驱动的劳动力市场预测研究提供了结构化的数据基座。