electricsheepafrica/africa-ilo-une-tune-sex-edu-cct-nb-unemployment-by-sex-education-and-citizenship-thou

--- 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 - international-migrant-stock - ilo - labour - employment pretty_name: "Unemployment by sex, education and citizenship (thousands) | Africa (ILOSTAT)" --- # Unemployment by sex, education and citizenship (thousands) | Africa (ILOSTAT) 🌍 **4,865 observations** · **42 Africa countries** · **1991–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **4,865 observations** of `International migrant stock` data across **42 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_TUNE_SEX_EDU_CCT_NB) - **Publisher:** International Labour Organization (ILO) - **License:** [cc-by-4.0](https://creativecommons.org/licenses/by/4.0/) - **Topic:** International migrant stock ## Methodology Data pulled directly from the ILOSTAT REST API at `https://rplumber.ilo.org/data/indicator?id=UNE_TUNE_SEX_EDU_CCT_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 42 Africa countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `GHA` | 429 | 1991 | 2024 | | `RWA` | 382 | 2014 | 2025 | | `MLI` | 349 | 2013 | 2024 | | `BWA` | 315 | 2006 | 2024 | | `NAM` | 309 | 1994 | 2018 | | `CIV` | 240 | 2012 | 2022 | | `SEN` | 222 | 2011 | 2024 | | `SYC` | 208 | 2014 | 2024 | | `ZWE` | 200 | 2014 | 2024 | | `ZMB` | 166 | 2017 | 2024 | | `GMB` | 153 | 2012 | 2025 | | `BDI` | 101 | 2006 | 2020 | | `BFA` | 99 | 2018 | 2023 | | `SWZ` | 99 | 2016 | 2023 | | `TGO` | 93 | 2017 | 2022 | | ... | _27 more countries_ | | | ## Indicators (sample) - `UNE_TUNE_SEX_EDU_CCT_NB` — Unemployment by sex, education and citizenship (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) | `BB:16199` | | `source.label` | `string` | Source name in English | `HIES - Survey on Expenditure, Revenue…` | | `indicator` | `string` | ILOSTAT indicator code | `UNE_TUNE_SEX_EDU_CCT_NB` | | `indicator.label` | `string` | Indicator name in English | `Unemployment by sex, education and ci…` | | `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.) | `EDU_AGGREGATE_TOTAL` | | `classif1.label` | `string` | — | `Education (Aggregate levels): Total` | | `classif2` | `string` | Second classification variable where applicable | `CCT_CIT_TOTAL` | | `classif2.label` | `string` | — | `Citizenship: Total` | | `time` | `int64` | Observation year | `2019` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `1639.157` | | `obs_status` | `string` | Observation status flag (e.g. provisional, unreliable) | `U` | | `obs_status.label` | `string` | — | `Unreliable` | | `note_classif` | `string` | — | `C3:2620` | | `note_classif.label` | `string` | — | `Nonstandard education level: Includin…` | | `note_indicator` | `string` | — | `T5:1429` | | `note_indicator.label` | `string` | — | `Unemployment definition: Two criteria…` | | `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-edu-cct-nb-unemployment-by-sex-education-and-citizenship-thou") 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_EDU_CCT_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="UNE_TUNE_SEX_EDU_CCT_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "UNE_TUNE_SEX_EDU_CCT_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_une_tune_sex_edu_cct_nb_unemployment_by_sex_education_and_citizenship_thou_2025, title = {Unemployment by sex, education and citizenship (thousands) | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=UNE_TUNE_SEX_EDU_CCT_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-une-tune-sex-edu-cct-nb-unemployment-by-sex-education-and-citizenship-thou}} } ``` ## 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_EDU_CCT_NB_This dataset contains international migrant stock data for 42 African countries from 1991 to 2025, specifically focusing on unemployment figures disaggregated by sex, education, and citizenship (in thousands). It includes 4,865 observations and covers one key indicator: UNE_TUNE_SEX_EDU_CCT_NB. The data is sourced from the ILOSTAT database of the International Labour Organization (ILO), extracted via API and filtered to African country codes. Organized in tabular format, the dataset includes columns such as country code, country name, data source, indicator code, indicator name, sex classification, education classification, citizenship classification, observation year, observed value, and data status flags. Data is disaggregated by dimensions like sex (total, male, female), making it suitable for tasks like tabular classification, regression, and time-series forecasting. Repackaged by Electric Sheep Africa, it is licensed under CC-BY-4.0.

搜集汇总

数据集介绍

构建方式

该数据集隶属于Electric Sheep Africa在Hugging Face平台发布的非洲公开数据目录，其构建遵循标准化元数据编目流程，将国际劳工组织统计数据库（ILOSTAT）中关于非洲区域失业状况的原始统计记录进行重新封装。数据源自ILOSTAT这一全球劳工统计的核心权威数据库，涵盖42个非洲国家在1991年至2025年间的4,865条观测记录，以性别、教育程度和公民身份为分类维度，按千人单位计量失业人口。Electric Sheep Africa对原始数据实施格式统一化处理，生成parquet文件并附加来源标注、加载指引与分析背景，从而在保留原始统计语义的前提下提升数据在机器学习工作流中的可发现性与可用性。

使用方法

使用者可通过Hugging Face datasets库以load_dataset函数直接加载该数据集，获取分片结构后遍历特征字段并预览样本记录。针对表格型数据，可将首个分片转换为Pandas数据框，便于开展统计探查与建模准备。建议在使用时先行检查数据模式与缺失值分布，按地理、时间及子群体维度勾勒变量轮廓，并在存在显式国家、年份与指标字段时与其他Electric Sheep Africa数据集进行联接。分析过程应保留缺失值直至确立可辩护的插补规则，并在可复现笔记本中同时引用原始来源背景与Hugging Face仓库地址，以确保研究路径的透明性与可追溯性。

背景与挑战

背景概述

国际劳工组织（ILO）长期致力于全球劳动力市场统计，其ILOSTAT数据库是劳动统计领域的权威来源。非洲大陆的劳动力市场受到性别、教育程度与公民身份等多重结构性因素影响，但相关数据长期分散且标准不一。在此背景下，Electric Sheep Africa于2026年基于ILOSTAT数据构建了该数据集，涵盖1991至2025年间42个非洲国家的4,865条观测记录，旨在为非洲失业问题的跨群体比较研究提供标准化、机器可读的数据基础。该数据集整合了性别、教育水平与公民身份三个维度，填补了非洲劳动力市场中多维交叉分析的数据空白，对推动区域就业政策研究与实证分析具有重要参考价值。

当前挑战

该数据集所应对的领域问题在于，非洲失业统计长期面临数据碎片化、口径不统一以及多维交叉指标缺失的困境，难以支撑性别、教育程度与公民身份交互作用下的精细分析。在构建过程中，主要挑战包括：上游ILOSTAT数据在国别覆盖与时间序列上存在非均衡性，部分国家与年份缺失显著；公民身份维度在非洲多国统计实践中定义与采集标准不一，跨国可比性受限；元数据中地理标识与上游出版者信息不完整，增加了数据溯源与变量确认的难度；此外，以千人为单位的失业计数数据在跨经济体规模差异下直接比较易产生误导，需结合劳动力总量进行标准化处理。

常用场景

经典使用场景

在劳动经济学与非洲区域研究的交叉领域，该数据集最为经典的使用场景在于刻画不同性别、教育程度与公民身份群体在非洲各国劳动力市场中的失业结构。研究者可依托其覆盖1991至2025年、42个非洲国家的近五千条观测记录，构建多维度的失业率面板，比较本土公民与移民群体在初级、中等及高等教育水平上的失业风险差异，进而揭示性别与教育在劳动力市场排斥中的交互作用。

解决学术问题

该数据集有效回应了非洲劳动力市场研究中长期存在的微观异质性证据匮乏问题。传统研究多依赖国别调查或宏观失业率，难以在统一框架下同时控制性别、教育层级与公民身份三个关键变量。该数据集以标准化表格形式整合ILOSTAT指标，使研究者能够检验移民身份是否加剧教育回报的分化，并为解释非洲青年高失业率与技能错配并存的悖论提供可复现的数据基础，其意义在于推动跨国比较从描述性统计走向结构化的因果推断。

实际应用

在实际应用层面，该数据集为国际组织、非洲各国劳工部门及发展机构提供了可操作的监测工具。政策分析人员可据此识别失业风险高度集中的子群体，例如低教育水平的女性移民或特定公民身份下的中等教育持有者，从而设计更具靶向性的就业促进与技能培训项目。同时，该数据集可嵌入世界银行、国际劳工组织等机构的国别诊断报告，辅助制定移民融入与教育政策，并为非政府组织评估劳动力市场干预成效提供基准参照。

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非正规就业持续扩张的背景下，该数据集聚焦于非洲42国1991至2025年间按性别、教育程度与公民身份三重维度交叉划分的失业人口规模，为解析移民身份与人力资本禀赋如何共同形塑就业脆弱性提供了稀缺的跨国面板证据。当前前沿研究正借助此类具备元数据溯源保障的表格数据，探索公民身份差异对教育回报率的调节效应，以及性别—教育—身份交互项在失业持续期建模中的非线性贡献。该数据集经Electric Sheep Africa标准化元数据封装后，与ILOSTAT原始统计框架形成可复现的对照链条，其价值在于支撑非洲移民劳工政策评估、技能错配诊断及包容性增长测度等热点议题，并为机器学习驱动的劳动力市场分层预测提供可检验的基准特征集。