electricsheepafrica/africa-ilo-une-deap-sex-age-mts-rt-unemployment-rate-by-sex-age-and-marital-status

--- 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 rate by sex, age and marital status (%) | Africa (ILOSTAT)" --- # Unemployment rate by sex, age and marital status (%) | Africa (ILOSTAT) 🌍 **93,375 observations** · **49 Africa countries** · **1982–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **93,375 observations** of `Unemployment` data across **49 Africa countries**, spanning **1982–2025**, 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_DEAP_SEX_AGE_MTS_RT) - **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_DEAP_SEX_AGE_MTS_RT` 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 49 Africa countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `ZAF` | 9,969 | 2000 | 2024 | | `MUS` | 6,905 | 2001 | 2024 | | `EGY` | 5,671 | 2008 | 2024 | | `TUN` | 5,405 | 2005 | 2023 | | `GHA` | 3,935 | 1991 | 2024 | | `RWA` | 3,611 | 2014 | 2025 | | `AGO` | 3,593 | 2004 | 2025 | | `BWA` | 3,451 | 1996 | 2024 | | `MLI` | 3,238 | 2009 | 2024 | | `ZMB` | 2,492 | 2015 | 2024 | | `ZWE` | 2,463 | 2011 | 2024 | | `NAM` | 2,217 | 1994 | 2018 | | `SYC` | 2,213 | 2014 | 2024 | | `BFA` | 2,112 | 2006 | 2024 | | `SEN` | 2,096 | 2011 | 2024 | | ... | _34 more countries_ | | | ## Indicators (sample) - `UNE_DEAP_SEX_AGE_MTS_RT` — Unemployment rate by sex, age and marital status (%) ## 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_DEAP_SEX_AGE_MTS_RT` | | `indicator.label` | `string` | Indicator name in English | `Unemployment rate by sex, age 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.) | `AGE_YTHADULT_YGE15` | | `classif1.label` | `string` | — | `Age (Youth, adults): 15+` | | `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) | `10.391` | | `obs_status` | `string` | Observation status flag (e.g. provisional, unreliable) | `U` | | `obs_status.label` | `string` | — | `Unreliable` | | `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-deap-sex-age-mts-rt-unemployment-rate-by-sex-age-and-marital-status") 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_DEAP_SEX_AGE_MTS_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="UNE_DEAP_SEX_AGE_MTS_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "UNE_DEAP_SEX_AGE_MTS_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_une_deap_sex_age_mts_rt_unemployment_rate_by_sex_age_and_marital_status_2025, title = {Unemployment rate by sex, age and marital status (%) | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=UNE_DEAP_SEX_AGE_MTS_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-une-deap-sex-age-mts-rt-unemployment-rate-by-sex-age-and-marital-status}} } ``` ## 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_DEAP_SEX_AGE_MTS_RT_This dataset contains unemployment rate (%) data disaggregated by sex, age, and marital status for 49 African countries, spanning the years 1982 to 2025, with 93,375 observations. The data is sourced from the International Labour Organization (ILO)s ILOSTAT database, retrieved via its REST API and filtered to African country codes. It covers one core indicator: UNE_DEAP_SEX_AGE_MTS_RT (Unemployment rate by sex, age and marital status). The schema includes columns such as country code, country name, data source, indicator code, sex classification (total, male, female), age classification, marital status classification, observation year, observed value, and data status flags. The data is annual frequency, with ILOSTAT harmonizing raw survey microdata using International Conference of Labour Statisticians (ICLS) definitions for consistency. Data quality caveats note that when multiple sources exist for the same country×year, the ILO-selected best source is used, and disaggregation columns are non-null only when the indicator publishes that breakdown. The dataset is repackaged by Electric Sheep Africa as part of a unified, ML-ready data layer for Africa.

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核心统计数据库ILOSTAT的原始失业率观测记录，由Electric Sheep Africa团队进行系统化工程再造。构建过程涵盖对1982年至2025年间49个非洲国家失业率统计数据的采集、清洗与标准化封装，以Parquet格式统一存储，并配以结构化元数据标签，形成可追溯、可复现的开放数据资源。

特点

数据集涵盖93375条观测记录，按性别、年龄与婚姻状况三个维度交叉划分失业率指标，时间跨度逾四十年，覆盖非洲大陆49个国家。其以表格与文本双模态呈现，属经济学金融领域，体量介于一万至十万条之间，采用CC BY 4.0许可协议，兼具长时段纵向比较与多群体横向剖析的统计价值。

使用方法

研究者可借助Hugging Face datasets库直接加载该数据集，通过load_dataset函数获取数据对象并检视特征架构与样本内容。对表格型数据，可转换为Pandas数据框以便开展缺失值诊断、地域与时间维度的变量画像分析，亦可依据国家、年份与指标字段与其他Electric Sheep Africa数据集进行联结，构建可复现的分析流程。

背景与挑战

背景概述

非洲大陆的劳动力市场长期受制于非正规就业普遍、性别不平等与婚姻状况引致的分化，失业率的结构性差异难以在宏观总量中显现。国际劳工组织（ILO）自1982年起通过ILOSTAT持续汇编各国劳动力调查数据，为跨国比较提供基准。由Electric Sheep Africa团队于2020年代后期整理并发布的本数据集，汇集49个非洲国家、93,375条观测记录，时间跨度覆盖1982至2025年，系统呈现按性别、年龄与婚姻状况交叉分类的失业率。该数据集填补了非洲细分劳动力统计的可及性缺口，为劳动经济学、性别研究与区域政策评估提供了可复现的微观证据基础，并借助统一元数据架构提升了非洲公共数据的可发现性与可复用性。

当前挑战

本数据集所应对的核心领域问题在于消除失业统计中因性别、年龄与婚姻状况交叉而生的结构性盲区，此类细分维度在多数非洲国家统计体系中长期缺失或口径不一，致使政策制定难以精准触达脆弱群体。构建过程中的挑战集中体现于数据异质性：各国劳动力调查在年龄分组、婚姻状况分类及失业定义上存在差异，跨国可比性受到制约；时间序列跨越四十余年，统计方法与调查频率的变迁导致缺失值与口径断裂。此外，部分国家元数据中上游发布机构与国别标记未能完整声明，需在分析阶段结合源文件谨慎核验，以避免因标签简化而误释政策含义。

常用场景

经典使用场景

劳动力市场分层分析是该数据集最为经典的应用范式。依托国际劳工组织统计数据库所提供的九万余条观测记录，研究者得以在性别、年龄组与婚姻状况三个维度上对非洲四十九国的失业率进行精细化拆解。此类场景常见于面板数据回归、交叉表分析以及时间序列趋势刻画，用以揭示不同人口亚群体在1982年至2025年间的失业风险差异及其演化轨迹。

解决学术问题

该数据集有效回应了非洲劳动力市场中长期存在的结构异质性问题。传统宏观失业率指标往往掩盖了性别、年龄与婚姻状态交织形成的分化格局，而本数据集使研究者能够检验婚姻状况是否为特定性别与年龄群体的失业保护因素，进而推动关于非正规就业、家庭劳动分工与生命周期就业脆弱性的理论探讨，为比较政治经济学与发展经济学提供可复现的实证基础。

衍生相关工作

围绕该数据集，衍生出一系列面向非洲劳动市场的分析与建模工作。研究者将其与Electric Sheep Africa目录下的其他ILOSTAT指标数据集进行连接，构建多维劳动力市场画像；亦有工作以此为基础，开展失业率的机器学习预测、区域聚类比较以及性别就业差距的跨国计量研究，丰富了非洲开放数据生态的应用谱系。