electricsheepafrica/africa-ilo-luu-xlu3-sex-age-rt-combined-rate-of-unemployment-and-potential-labour

--- 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 - other-measures-of-labour-underutilization - ilo - labour - employment pretty_name: "Combined rate of unemployment and potential labour force (LU3) by sex and age (%) | Africa (ILOSTAT)" --- # Combined rate of unemployment and potential labour force (LU3) by sex and age (%) | Africa (ILOSTAT) 🌍 **8,609 observations** · **43 Africa countries** · **1994–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **8,609 observations** of `Other measures of labour underutilization` data across **43 Africa countries**, spanning **1994–2025**, 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=LUU_XLU3_SEX_AGE_RT) - **Publisher:** International Labour Organization (ILO) - **License:** [cc-by-4.0](https://creativecommons.org/licenses/by/4.0/) - **Topic:** Other measures of labour underutilization ## Methodology Data pulled directly from the ILOSTAT REST API at `https://rplumber.ilo.org/data/indicator?id=LUU_XLU3_SEX_AGE_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 43 Africa countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `ZAF` | 1,125 | 2000 | 2024 | | `MUS` | 988 | 2001 | 2024 | | `RWA` | 440 | 2014 | 2025 | | `AGO` | 405 | 2011 | 2025 | | `MLI` | 399 | 2013 | 2024 | | `SYC` | 371 | 2014 | 2024 | | `ZMB` | 360 | 2017 | 2024 | | `SEN` | 315 | 2015 | 2024 | | `ZWE` | 270 | 2014 | 2024 | | `CIV` | 254 | 2012 | 2022 | | `KEN` | 225 | 2005 | 2022 | | `BFA` | 223 | 2014 | 2024 | | `MWI` | 219 | 2005 | 2024 | | `NGA` | 214 | 2011 | 2024 | | `GMB` | 180 | 2012 | 2025 | | ... | _28 more countries_ | | | ## Indicators (sample) - `LUU_XLU3_SEX_AGE_RT` — Combined rate of unemployment and potential labour force (LU3) by sex and age (%) ## 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 | `LUU_XLU3_SEX_AGE_RT` | | `indicator.label` | `string` | Indicator name in English | `Combined rate of unemployment and pot…` | | `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+` | | `time` | `int64` | Observation year | `2025` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `26.837` | | `obs_status` | `string` | Observation status flag (e.g. provisional, unreliable) | `B` | | `obs_status.label` | `string` | — | `Break in series` | | `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-luu-xlu3-sex-age-rt-combined-rate-of-unemployment-and-potential-labour") 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"] == "LUU_XLU3_SEX_AGE_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="LUU_XLU3_SEX_AGE_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "LUU_XLU3_SEX_AGE_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_luu_xlu3_sex_age_rt_combined_rate_of_unemployment_and_potential_labour_2025, title = {Combined rate of unemployment and potential labour force (LU3) by sex and age (%) | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=LUU_XLU3_SEX_AGE_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-luu-xlu3-sex-age-rt-combined-rate-of-unemployment-and-potential-labour}} } ``` ## 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=LUU_XLU3_SEX_AGE_RT_This dataset contains 8,609 observations of Other measures of labour underutilization data across 43 Africa countries, spanning 1994–2025, focusing on the Combined rate of unemployment and potential labour force (LU3) by sex and age (%) indicator (LUU_XLU3_SEX_AGE_RT). Sourced from the International Labour Organization (ILO) ILOSTAT database via API, filtered to African countries, and harmonized using International Conference of Labour Statisticians (ICLS) definitions. The data is provided in tabular format with columns including country code, country name, source, indicator code, sex disaggregation (total, male, female), age classification, year, observed value, and observation status, suitable for tabular classification, regression, and time-series forecasting tasks. Repackaged by Electric Sheep Africa for ML-ready use under CC BY 4.0 license.

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统计数据库（ILOSTAT）中关于劳动未充分利用的标准化指标，由Electric Sheep Africa团队依据公开元数据清单进行系统化重包装与索引。构建过程聚焦于非洲区域，从原始来源提取涵盖43个非洲国家的失业与潜在劳动力综合比率（LU3）数据，按性别与年龄维度进行结构化整理，形成包含8609条观测值的表格化数据集。所有变量均保留原始定义与计量单位，并以Parquet格式存储，同时配套标准化的元数据说明、来源注释与加载指引，确保数据溯源清晰且可复现。

特点

该数据集在时空覆盖上具有显著广度，囊括1994年至2025年间43个非洲国家的劳动市场核心指标，以性别和年龄为关键分组变量，提供失业与潜在劳动力综合比率的百分比数值。数据以表格与文本双模态呈现，规模介于一千至一万行之间，适用于分类与回归任务。其标签体系围绕经济学与金融领域构建，聚焦劳动未充分就业主题，并携带明确的非洲区域标签与开放数据属性。数据集保留缺失值，不进行推断性填充，强调在建模前需核实变量定义与单位，以保证分析的中立性与准确性。

使用方法

研究者可通过Hugging Face的datasets库直接加载该数据集，利用load_dataset函数获取数据对象并检视其结构与特征。对于表格型数据，可将其转换为Pandas数据框以便进行统计分析与可视化探索。使用过程中建议先检查数据模式与缺失情况，再依据国家、年份及性别年龄等显式字段进行分组剖析。该数据集可与其他Electric Sheep Africa系列数据集通过国家、年份和指标字段进行关联，构建可复现的分析流程。使用时应遵循CC BY 4.0许可协议，并在成果中引用原始来源与Hugging Face仓库链接。

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标准，其ILOSTAT数据库是衡量各国就业与劳动力利用状况的权威来源。在此框架下，Electric Sheep Africa于2026年将非洲地区ILOSTAT数据系统化整理并发布至Hugging Face平台，形成涵盖43个非洲国家、1994至2025年间8609条观测的LU3指标数据集。该数据集聚焦于综合失业率与潜在劳动力比率这一关键劳动利用不足指标，按性别与年龄组细分，为非洲劳动力市场结构性问题的量化研究提供了标准化、可复现的数据基础，对区域经济政策评估与可持续发展目标监测具有重要支撑意义。

当前挑战

LU3指标本身面临概念界定与跨时空可比性的固有难题。不同国家对潜在劳动力的统计口径存在差异，非正式经济部门就业状态的模糊性进一步加剧了测量偏差，使得跨国、跨年份的严格比较需要复杂校准。数据构建过程中，元数据清单存在国家标识与上游发布者字段缺失，地理覆盖依赖标题隐含信息而非显式编码，要求分析者在建模前明确假设并审慎处理缺失值。此外，1994至2025年的长时段序列需应对调查方法变更、数据修订与报告延迟等问题，这些挑战共同构成了从原始数据到可靠政策洞见之间的核心障碍。

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，该数据集最经典的使用场景在于刻画非洲大陆劳动力未充分利用的结构性特征。研究者依托国际劳工组织（ILOSTAT）的标准化框架，以LU3指标——即失业率与潜在劳动力之和占总扩展劳动力的比重——为核心测量工具，按性别与年龄组进行分层比较。凭借覆盖43个非洲国家、逾8600条观测记录以及1994至2025年的长时序跨度，该数据集支撑起跨国面板分析与队列世代比较，用以揭示青年与女性群体在劳动力市场中更为脆弱的边缘化处境。

实际应用

在政策实践层面，该数据集为非洲各国劳工部门、区域经济共同体以及国际发展机构提供了量化决策依据。政策制定者可借助其分性别、分年龄的细粒度指标，精准识别青年失业高发国与女性劳动参与洼地，从而有的放矢地设计技能培训、就业补贴与生育支持等干预措施。国际组织亦可将其纳入可持续发展目标中体面劳动议程的监测体系，追踪各国在减少劳动未充分利用方面的进展。此外，金融机构与咨询公司可将其用于国别风险评估中的社会稳定性分析，为投资布局提供劳动市场维度的情报支撑。

衍生相关工作

该数据集作为Electric Sheep Africa元数据清单的组成部分，已衍生出一系列围绕非洲劳动市场数据基础设施的关联工作。在Hugging Face平台上，其与同一目录下的ILOSTAT失业率、劳动参与率、非正规就业等数据集形成了可横向连接的数据生态，支撑研究者构建多维劳动市场指标矩阵。基于该数据集的元数据标准化实践，Electric Sheep Africa进一步开发了面向非洲公共数据的发现与加载工具链，如load_dataset接口的统一封装与数据质量注释规范。学术层面，已有研究将其用于训练非洲劳动市场预测模型与构建跨国比较的可复现notebook，并成为非洲经济金融领域机器学习基准测试的候选数据源之一。