electricsheepafrica/africa-ilo-une-deap-sex-edu-cbr-rt-unemployment-rate-by-sex-education-and-place-of-bi

--- 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 rate by sex, education and place of birth (%) | Africa (ILOSTAT)" --- # Unemployment rate by sex, education and place of birth (%) | Africa (ILOSTAT) 🌍 **4,103 observations** · **33 Africa countries** · **1991–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **4,103 observations** of `International migrant stock` data across **33 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_DEAP_SEX_EDU_CBR_RT) - **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_DEAP_SEX_EDU_CBR_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 33 Africa countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `GHA` | 468 | 1991 | 2024 | | `AGO` | 360 | 2009 | 2025 | | `RWA` | 346 | 2014 | 2025 | | `ZMB` | 241 | 2017 | 2024 | | `ZWE` | 212 | 2014 | 2024 | | `MWI` | 180 | 2005 | 2024 | | `EGY` | 162 | 2008 | 2011 | | `GMB` | 157 | 2012 | 2025 | | `LBR` | 155 | 2010 | 2017 | | `MLI` | 141 | 2020 | 2024 | | `ZAF` | 124 | 2011 | 2017 | | `UGA` | 120 | 2010 | 2021 | | `BEN` | 119 | 2011 | 2022 | | `BFA` | 117 | 2018 | 2024 | | `KEN` | 109 | 2019 | 2022 | | ... | _18 more countries_ | | | ## Indicators (sample) - `UNE_DEAP_SEX_EDU_CBR_RT` — Unemployment rate by sex, education and place of birth (%) ## 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_EDU_CBR_RT` | | `indicator.label` | `string` | Indicator name in English | `Unemployment rate by sex, education a…` | | `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 | `CBR_BIR_TOTAL` | | `classif2.label` | `string` | — | `Place of birth: 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` | `string` | — | `C3:2620` | | `note_classif.label` | `string` | — | `Nonstandard education level: Includin…` | | `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-edu-cbr-rt-unemployment-rate-by-sex-education-and-place-of-bi") 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_EDU_CBR_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="UNE_DEAP_SEX_EDU_CBR_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "UNE_DEAP_SEX_EDU_CBR_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_une_deap_sex_edu_cbr_rt_unemployment_rate_by_sex_education_and_place_of_bi_2025, title = {Unemployment rate by sex, education and place of birth (%) | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=UNE_DEAP_SEX_EDU_CBR_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-une-deap-sex-edu-cbr-rt-unemployment-rate-by-sex-education-and-place-of-bi}} } ``` ## 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_EDU_CBR_RT_This dataset, named Unemployment rate by sex, education and place of birth (%) | Africa (ILOSTAT), contains statistical information from the International Labour Organization (ILO) ILOSTAT database. It focuses on the African region, covering annual unemployment rate data for 33 African countries from 1991 to 2025, with a total of 4,103 observations. The core indicator is UNE_DEAP_SEX_EDU_CBR_RT, which represents the unemployment rate percentage disaggregated by sex (total, male, female), education (aggregate levels), and place of birth (total). The data structure includes fields such as country code, indicator code, sex classification, education classification, place of birth classification, observation year, observed value, as well as data source and quality notes. The dataset aims to provide machine learning-ready, standardized data for studying labor markets, migration, and employment disparities in Africa.

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）核心统计数据库ILOSTAT，经Electric Sheep Africa标准化工程重新封装而成。其构建依托元数据驱动的编目流程，对非洲区域1991至2025年间按性别、教育程度及出生地分类的失业率指标进行系统性采集与整合，覆盖33个非洲国家，最终凝聚为4103条观测记录，并以Parquet列式格式存储，形成兼具可追溯性与分析就绪性的表格化数据资源。

特点

数据集聚焦非洲劳动力市场中的失业率差异，以性别、教育水平和出生地三重维度刻画群体异质性，兼具时间跨度长与国别覆盖广的双重优势。元数据标注涵盖经济学金融领域标签、表格与文本模态，并内置来源溯源、加载指引与分析情境说明，便于研究者快速把握数据结构，同时保留原始缺失值以待审慎处理。

使用方法

研究者可借助Hugging Face datasets库直接加载数据集，通过迭代获取拆分名称并检视特征结构，亦可转换为Pandas数据框以支持进一步统计建模。使用时应优先核查数据文件中的变量定义与单位，利用显式国家列进行地理定位，并在下游分析中记录地理假设。建议在建模前评估缺失模式，并与其他Electric Sheep Africa数据集按国家、年份及指标字段进行联接，以构建可复现的分析工作流。

背景与挑战

背景概述

在非洲劳动力市场信息长期碎片化的背景下，国际劳工组织（ILO）通过ILOSTAT数据库持续采集各国劳动力调查数据，为跨国比较提供统计基础。Electric Sheep Africa于2026年将其中关于失业率的指标进行标准化整理，形成涵盖33个非洲国家、1991至2025年共4,103条观测的公开数据集，旨在以性别、教育程度与出生地三重维度刻画非洲失业结构。该数据集依托Hugging Face平台发布，遵循CC BY 4.0许可，为非洲经济金融领域的可复现研究与国际移民劳动力市场分析提供了关键数据支撑，亦推动了区域性开放数据基础设施的建设。

当前挑战

该数据集所回应的核心领域问题在于如何以一致口径衡量非洲各国失业率在性别、教育及出生地维度上的异质性，从而弥补跨国劳动力统计长期存在的可比性缺口。构建过程中面临多重挑战：其一，原始ILOSTAT数据源自各国调查，指标定义、抽样方法与报告周期参差不齐，难以直接聚合；其二，出生地与移民身份变量在多数非洲国家统计中覆盖不全，导致观测缺失与样本偏倚；其三，元数据中country与upstream_publisher字段存在空缺，地理归属依赖标题推断，削弱了数据可追溯性；其四，1K至10K的样本规模在细分交叉维度后更显稀疏，限制了精细建模的稳健性。

常用场景

经典使用场景

在劳动经济学与人口统计学的交叉领域中，剖析失业率的结构性差异始终是核心议题。该数据集汇聚了33个非洲国家自1991年至2025年间逾四千条观测记录，以性别、教育程度与出生地三重维度精细刻画失业率分布，为研究者提供了纵贯三十余年的跨国比较面板。其经典用法在于借助双向固定效应模型或分层线性模型，解构非洲劳动力市场中性别鸿沟、教育回报异质性以及移民身份对就业机会的差异化影响，进而描绘出区域失业格局的时空演化轨迹。

解决学术问题

该数据集直面发展经济学中长期悬而未决的难题：教育扩张为何未能同步消解非洲青年的失业困境？性别与出生地差异在多大程度上独立于教育禀赋而发挥作用？凭借标准化元数据与可溯源的国家—年份—子群标识，研究者得以分离教育升级效应与结构性排斥效应，检验移民身份是否构成额外的就业壁垒。其学术意义在于为人力资本理论在低收入国家的适用边界提供实证约束，并为跨国劳动力市场制度比较奠定可复现的数据基石。

衍生相关工作

基于该数据集及其所属的Electric Sheep Africa元数据目录，衍生出一系列围绕非洲劳动力市场不平等的可复现研究。学者将其与ILOSTAT其他指标数据集进行跨表联结，构建出涵盖就业、失业与非正规就业的多维劳动脆弱性指数。部分工作运用机器学习方法对缺失值进行稳健插补，并训练梯度提升模型预测各国失业率走势。该数据集亦被纳入非洲数据发现平台，催生了标准化数据卡与自动化元数据生成流程等基础设施性成果，推动了非洲开放数据的规范化流通。