electricsheepafrica/africa-ilo-une-tune-sex-dur-mts-nb-unemployment-by-sex-duration-and-marital-status-th

--- 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, duration and marital status (thousands) | Africa (ILOSTAT)" --- # Unemployment by sex, duration and marital status (thousands) | Africa (ILOSTAT) 🌍 **8,985 observations** · **43 Africa countries** · **1991–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **8,985 observations** of `Unemployment` data across **43 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_DUR_MTS_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_DUR_MTS_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 43 Africa countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `ZAF` | 1,118 | 2000 | 2024 | | `MUS` | 945 | 2001 | 2024 | | `EGY` | 786 | 2008 | 2024 | | `RWA` | 447 | 2014 | 2025 | | `AGO` | 388 | 2009 | 2025 | | `TUN` | 373 | 2010 | 2021 | | `SYC` | 324 | 2014 | 2024 | | `GHA` | 323 | 1991 | 2024 | | `BWA` | 317 | 1996 | 2024 | | `MLI` | 305 | 2013 | 2024 | | `SEN` | 304 | 2015 | 2024 | | `ZMB` | 297 | 2017 | 2024 | | `ZWE` | 291 | 2014 | 2024 | | `NAM` | 197 | 2012 | 2018 | | `TZA` | 179 | 2001 | 2024 | | ... | _28 more countries_ | | | ## Indicators (sample) - `UNE_TUNE_SEX_DUR_MTS_NB` — Unemployment by sex, duration and marital status (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_DUR_MTS_NB` | | `indicator.label` | `string` | Indicator name in English | `Unemployment by sex, duration and mar…` | | `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.) | `DUR_AGGREGATE_TOTAL` | | `classif1.label` | `string` | — | `Duration (Aggregate): Total` | | `classif2` | `string` | Second classification variable where applicable | `MTS_AGGREGATE_TOTAL` | | `classif2.label` | `string` | — | `Marital status (Aggregate): 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) | `U` | | `obs_status.label` | `string` | — | `Unreliable` | | `note_classif` | `string` | — | `C7:2844` | | `note_classif.label` | `string` | — | `Nonstandard duration of unemployment:…` | | `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-dur-mts-nb-unemployment-by-sex-duration-and-marital-status-th") 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_DUR_MTS_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="UNE_TUNE_SEX_DUR_MTS_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "UNE_TUNE_SEX_DUR_MTS_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_une_tune_sex_dur_mts_nb_unemployment_by_sex_duration_and_marital_status_th_2025, title = {Unemployment by sex, duration and marital status (thousands) | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=UNE_TUNE_SEX_DUR_MTS_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-une-tune-sex-dur-mts-nb-unemployment-by-sex-duration-and-marital-status-th}} } ``` ## 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_DUR_MTS_NB_This dataset contains 8,985 observations of unemployment data across 43 African countries, spanning from 1991 to 2025. The primary indicator is UNE_TUNE_SEX_DUR_MTS_NB, which represents unemployment by sex, duration, and marital status (in thousands). The data is sourced from the ILOSTAT database of the International Labour Organization (ILO), retrieved via API and harmonized. It includes disaggregation dimensions such as sex (total, male, female), along with fields for country codes, data sources, observation years, values, and quality flags. The data is published at annual frequency and is suitable for tabular classification, regression, and time-series forecasting tasks.

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（ILO）中央统计数据库ILOSTAT，经Electric Sheep Africa团队系统化整理与标准化后发布。构建过程涵盖对非洲地区43个国家1991至2025年间按性别、失业持续时间和婚姻状况分类的失业统计数据进行采集、清洗与统一编码，形成包含8985条观测值的结构化数据档案。原始指标以千人为单位记录，最终以Parquet格式封装，并附有标准元数据、来源注释与使用指南，旨在促进非洲劳动力市场数据的可发现性与可复用性。

使用方法

研究者可通过Hugging Face datasets库以load_dataset函数直接加载该数据集，获取默认划分并检视特征结构与样本内容。对于表格型数据，可将其转换为Pandas DataFrame以便进行统计分析与可视化。使用时应从仓库文件与数据查看器入手，核对变量定义、单位及缺失值处理方式，并利用明确的国别、年份与指标字段进行跨数据集联结。建议在建模前完成模式探查与缺失机制评估，保留原始缺失值直至确立合理的插补规则，并在成果中规范引用来源与仓库信息。

背景与挑战

背景概述

国际劳工组织（ILO）长期致力于构建全球劳动力市场统计基准，ILOSTAT数据库即为这一努力的核心成果，为劳动经济学与政策研究提供跨国可比数据。在此基础上，Electric Sheep Africa于2026年对ILOSTAT中非洲区域失业数据进行了系统化整理与元数据标准化，发布该数据集，涵盖1991至2025年间43个非洲国家的8985条观测，按性别、失业持续时间和婚姻状况分类。该数据集回应了非洲劳动力市场细分研究数据稀缺的问题，其结构化与元数据标注为分析失业的性别差异与婚姻状态关联提供了可复现基础，对非洲就业政策评估具有参考价值。

当前挑战

该数据集所面对的核心挑战在于多维度失业统计的稀疏性与异质性。按性别、持续时间和婚姻状况三重交叉分类，导致部分国家与年份组合存在大量缺失值；非洲各国统计能力差异显著，数据收集口径与年份覆盖不一致，婚姻状况等变量定义在不同文化语境下缺乏统一标准，失业持续时间的数据记录精度参差不齐。此外，元数据中上游发布者与国别字段的缺失，增加了数据溯源与合并分析的难度。如何在保留原始缺失结构的前提下，实现跨国跨时可比，并避免因标签简化而曲解政策含义，是构建与应用中的关键难题。

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，失业率的性别差异、失业持续期结构以及婚姻状况对劳动力市场参与的影响始终是核心议题。该数据集以千人计量单位系统汇编了1991至2025年间43个非洲国家的失业观测记录，天然适配于按性别、失业持续期和婚姻状况进行分组比较的经典分析范式。研究者可借助这一结构化面板，刻画不同人口子群体在非洲各国失业风险中的分布形态，进而识别性别与婚姻状态在失业持续期上的交互效应，构成理解非洲劳动力市场分层机制的基准性使用路径。

解决学术问题

该数据集直面的学术问题是非洲劳动力市场中失业持续时间与人口特征之间的关联机制长期缺乏跨国可比证据。既有研究多受限于单一国家调查或零星年份，难以支撑跨时段、跨区域的稳健推断。本数据集通过统一指标口径覆盖近三十五年、四十余国的观测，使学者得以检验婚姻状况是否在性别维度上调节失业持续期，并评估结构性调整政策对弱势群体失业风险的异质性冲击，为非洲劳动经济学实证研究提供了可复现的数据基础，其意义在于将碎片化国别证据整合为可比较的跨国知识积累。

实际应用

在政策实践层面，该数据集可为非洲区域组织与各国劳动部门提供失业风险人群的精细化画像。国际机构在制定就业保障与社会保护方案时，可依据按性别和婚姻状况分解的失业持续期数据，识别长期失业集中的人群类别，从而优化职业培训、收入支持与再就业服务的资源配置。非政府组织亦可利用这些分国别、分年度的指标监测特定群体的劳动力市场边缘化趋势，为项目评估与倡导行动提供量化依据，提升干预措施的针对性与时效性。

数据集最近研究

最新研究方向

在全球劳动力市场性别差距持续引发政策关注的背景下，该数据集依托ILOSTAT权威统计框架，以非洲43国1991至2025年近九千条观测为基石，将失业问题的分析切口从单一失业率拓展至性别、失业持续期与婚姻状态的交叉维度。当前前沿研究愈发聚焦于婚姻状态与失业持续期之间的交互效应，尤其是女性因家庭角色固化而面临的长期失业风险，以及非正式就业对失业统计口径的稀释作用。该数据集为非洲劳动力市场分层研究、性别经济学实证以及社会保障政策仿真提供了细粒度面板支撑，其标准化元数据与可复现的加载路径亦呼应了开放科学浪潮下对非洲数据基础设施建设的迫切需求，对弥合区域数据鸿沟具有切实的方法论意义。