electricsheepafrica/africa-ilo-une-tune-sex-eco-edu-nb-unemployment-of-previously-employed-persons-by-sex

--- 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 of previously employed persons by sex, former economic activity and education | Africa (ILOSTAT)" --- # Unemployment of previously employed persons by sex, former economic activity and education | Africa (ILOSTAT) 🌍 **13,254 observations** · **27 Africa countries** · **1989–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **13,254 observations** of `Unemployment` data across **27 Africa countries**, spanning **1989–2025**, 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_ECO_EDU_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_ECO_EDU_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 27 Africa countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `ZAF` | 3,805 | 1998 | 2024 | | `EGY` | 2,774 | 2008 | 2024 | | `MUS` | 2,311 | 2003 | 2024 | | `AGO` | 970 | 2004 | 2025 | | `RWA` | 691 | 1996 | 2021 | | `TUN` | 595 | 1989 | 2019 | | `MAR` | 306 | 1990 | 2022 | | `NAM` | 193 | 1997 | 2018 | | `MDG` | 178 | 2003 | 2015 | | `BWA` | 169 | 2001 | 2010 | | `UGA` | 144 | 2012 | 2012 | | `GMB` | 127 | 2012 | 2012 | | `BFA` | 107 | 2018 | 2018 | | `SOM` | 106 | 2019 | 2019 | | `BDI` | 96 | 2014 | 2014 | | ... | _12 more countries_ | | | ## Indicators (sample) - `UNE_TUNE_SEX_ECO_EDU_NB` — Unemployment of previously employed persons by sex, former economic activity and education (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_ECO_EDU_NB` | | `indicator.label` | `string` | Indicator name in English | `Unemployment of previously employed p…` | | `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.) | `ECO_SECTOR_TOTAL` | | `classif1.label` | `string` | — | `Economic activity (Broad sector): Total` | | `classif2` | `string` | Second classification variable where applicable | `EDU_AGGREGATE_TOTAL` | | `classif2.label` | `string` | — | `Education (Aggregate levels): Total` | | `time` | `int64` | Observation year | `2025` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `655.15` | | `obs_status` | `string` | Observation status flag (e.g. provisional, unreliable) | `U` | | `obs_status.label` | `string` | — | `Unreliable` | | `note_classif` | `string` | — | `C6:1061` | | `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-eco-edu-nb-unemployment-of-previously-employed-persons-by-sex") 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_ECO_EDU_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="UNE_TUNE_SEX_ECO_EDU_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "UNE_TUNE_SEX_ECO_EDU_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_une_tune_sex_eco_edu_nb_unemployment_of_previously_employed_persons_by_sex_2025, title = {Unemployment of previously employed persons by sex, former economic activity and education | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=UNE_TUNE_SEX_ECO_EDU_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-une-tune-sex-eco-edu-nb-unemployment-of-previously-employed-persons-by-sex}} } ``` ## 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_ECO_EDU_NB_This dataset contains 13,254 observations of unemployment data across 27 African countries, spanning from 1989 to 2025, with the primary indicator UNE_TUNE_SEX_ECO_EDU_NB, which represents unemployment of previously employed persons by sex, former economic activity and education (in thousands). The data is sourced from the International Labour Organization (ILO) ILOSTAT database, retrieved via API and filtered to African ISO3 country codes. It includes statistics on unemployment, covering fields such as country, data source, indicator, sex disaggregation (total, male, female), economic activity and education classifications, observation year, observed value, and status flags. The data is annual frequency, harmonized by ILO using International Conference of Labour Statisticians (ICLS) definitions for consistency and traceability. This dataset is suitable for tabular classification, regression, and time-series forecasting tasks, and is part of a unified, ML-ready data layer for Africa on HuggingFace.

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统计资料，经Electric Sheep Africa团队以标准化工程流程重新封装而成。原始数据涵盖1989年至2025年间27个非洲国家的13254条观测记录，围绕曾就业人员的失业状况，按性别、先前经济活动及教育程度进行分类。构建过程中，团队保留了源数据的缺失值并以Parquet格式统一存储，同时为数据发现编制了标准化的元数据说明与使用指引，确保数据在非洲经济金融研究场景中的可追溯性与可复用性。

特点

该数据集以表格与文本为主要模态，体量位于一万至十万行区间，覆盖非洲多国的长期失业统计，具有明显的时间跨度与地理广度。其核心特征在于同时呈现性别、前经济活动与教育程度三个维度的交叉分类，为考察曾就业群体失业的结构性差异提供了细粒度观测。数据以英文单语呈现，遵循CC BY 4.0许可协议，配有明确的数据质量提示与来源说明，便于研究者在建模前核查变量定义、单位及缺失情况。

使用方法

研究者可通过Hugging Face datasets库的load_dataset函数直接加载该数据集，获取数据集对象后遍历其划分并检视特征结构，亦可将其转换为Pandas数据框以便开展表格分析。使用时应先检查数据架构与缺失模式，结合国家、年份和指标字段进行地理、时间与子群变量的剖面分析。若需与其他Electric Sheep Africa数据集整合，可利用显式的国家、年份及指标字段进行连接，并在下游分析中保留缺失值直至确立合理的插补规则，同时引用原始来源与Hugging Face仓库。

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业顽疾，研判曾就业人员的失业动态与性别、前职业及教育水平之间的关联，对制定精准的劳动力市场政策至关重要。国际劳工组织（ILO）长期维护的ILOSTAT数据库为全球劳动统计提供了权威基准，而Electric Sheep Africa于2026年基于该源数据工程化发布此非洲专项数据集，覆盖27个非洲国家、时间跨度自1989年至2025年，共13254条观测记录，凝聚了数据工程团队对非洲公共数据的标准化整合努力。该数据集聚焦曾被雇佣者的失业分布，为劳动经济学、性别研究与教育回报分析提供了可复现的实证基础。

当前挑战

该数据集所应对的核心领域问题在于刻画非洲曾就业群体的失业状况，并揭示性别、前经济活动类别与教育程度三重维度下的差异化失业风险，此类多维度交叉的失业分析在非洲区域层面长期缺乏统一且可比较的微观证据。构建过程中的挑战亦不容忽视，数据集需跨越近四十年、二十七个国家的异质性统计口径进行对齐，且元数据中缺失国别与上游发布者等关键字段，原始来源的变量定义、计量单位与采样方法须经审慎核验，缺失值的合理保留与后续插补规则的建立构成分析链条上的关键难点。

常用场景

经典使用场景

在劳动经济学与非洲发展研究的交叉领域，该数据集构成了剖析失业者职业轨迹的典型素材。其经典使用场景聚焦于依据性别、前经济部门与教育程度三个维度，刻画非洲27国自1989至2025年间曾就业者失业规模的结构性变迁。研究者常借此构建面板数据模型，检验不同教育层级群体在退出劳动市场后的脆弱性差异，以及性别因素在失业持续期中的调节效应，从而揭示非洲劳动力市场转型进程中的异质性特征。

衍生相关工作

围绕该数据集已衍生出若干具有代表性的后续研究。部分学者将其与非洲各国劳动力调查微观数据链接，构建了多层级失业风险预测模型；亦有工作以此为基础，发展了针对前就业者技能可迁移性的分类指标体系，并应用于比较不同教育层级的再就业速度。在方法论层面，该数据集激发了关于ILOSTAT指标在非洲语境下效度检验的讨论，推动了区域劳动统计元数据标准化进程，并为Electric Sheep Africa系列数据集的互操作性设计提供了关键参照。

数据集最近研究

最新研究方向

在非洲劳动力市场结构性转型与性别平等议题持续升温的背景下，该数据集依托ILOSTAT权威统计框架，覆盖27个非洲国家1989至2025年间逾万条观测记录，近期研究聚焦于以性别、前职业经济活动与教育程度为三重分层维度，刻画曾就业者失业路径的异质性特征。学界日益关注教育资本与行业退出模式如何交互作用于再就业概率，并借助可复现的表格建模方法检验女性在农业与服务业收缩中的脆弱性。该数据集亦被纳入非洲开放数据治理与元数据标准化运动，为跨国比较劳动政策、评估技能错配及设计针对性社会保障干预提供实证基础。