electricsheepafrica/africa-ilo-luu-xlu3-sex-age-mts-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: - 10K<n<100K 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, age and marital sta | Africa (ILOSTAT)" --- # Combined rate of unemployment and potential labour force (LU3) by sex, age and marital sta | Africa (ILOSTAT) 🌍 **13,608 observations** · **42 Africa countries** · **1994–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **13,608 observations** of `Other measures of labour underutilization` data across **42 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_MTS_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_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 42 Africa countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `ZAF` | 1,852 | 2000 | 2024 | | `MUS` | 1,531 | 2001 | 2024 | | `RWA` | 701 | 2014 | 2025 | | `MLI` | 659 | 2013 | 2024 | | `AGO` | 648 | 2011 | 2025 | | `SYC` | 568 | 2014 | 2024 | | `ZMB` | 562 | 2017 | 2024 | | `SEN` | 519 | 2015 | 2024 | | `ZWE` | 480 | 2014 | 2024 | | `CIV` | 383 | 2012 | 2022 | | `KEN` | 364 | 2005 | 2022 | | `BFA` | 361 | 2014 | 2024 | | `NGA` | 331 | 2011 | 2024 | | `ETH` | 318 | 1999 | 2021 | | `GMB` | 307 | 2012 | 2025 | | ... | _27 more countries_ | | | ## Indicators (sample) - `LUU_XLU3_SEX_AGE_MTS_RT` — Combined rate of unemployment and potential labour force (LU3) 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 | `LUU_XLU3_SEX_AGE_MTS_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+` | | `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) | `26.837` | | `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-luu-xlu3-sex-age-mts-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_MTS_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="LUU_XLU3_SEX_AGE_MTS_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "LUU_XLU3_SEX_AGE_MTS_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_luu_xlu3_sex_age_mts_rt_combined_rate_of_unemployment_and_potential_labour_2025, title = {Combined rate of unemployment and potential labour force (LU3) by sex, age and marital sta | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=LUU_XLU3_SEX_AGE_MTS_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-luu-xlu3-sex-age-mts-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_MTS_RT_This dataset contains 13,608 observations of Other measures of labour underutilization data across 42 African countries, spanning from 1994 to 2025, with a focus on the indicator Combined rate of unemployment and potential labour force (LU3) by sex, age and marital status. It is sourced from the ILOSTAT database of the International Labour Organization (ILO), retrieved via REST API and filtered for African countries. The dataset features a tabular schema with columns for country codes, years, sex, age classifications, marital status, observed values, and data quality flags. It is designed for tasks such as tabular classification, regression, and time-series forecasting, facilitating analysis of labour markets in Africa.

搜集汇总

数据集介绍

构建方式

该数据集由Electric Sheep Africa团队从国际劳工组织（ILO）的ILOSTAT核心统计数据库中提取并重新打包而成，聚焦于非洲区域劳动力市场欠充分利用的复合指标。构建过程涵盖对原始统计数据的系统采集、标准化清洗与格式转换，覆盖1994年至2025年间42个非洲国家的13608条观测记录，最终以Parquet列式存储格式发布，并辅以标准化的元数据清单与溯源说明，确保数据在Hugging Face平台上的可发现性与可复现性。

使用方法

研究者可通过Hugging Face的datasets库直接加载该数据集，利用内置的split接口获取Pandas格式的表格数据，进而开展schema检查、缺失模式分析与分组统计。在建模或跨国比较时，建议以显式的国家、年份与指标字段作为连接键，与其他Electric Sheep Africa数据集或原始ILOSTAT数据进行整合。分析过程中需在代码或报告中标注数据来源、版本快照及许可条款，确保研究过程透明且可追溯。

背景与挑战

背景概述

劳动力市场统计长期作为发展经济学与政策评估的基石，国际劳工组织（ILO）所维护的ILOSTAT数据库为此提供了跨国可比的核心指标。非洲地区劳动力市场结构复杂，非正规就业普遍、失业统计口径不一，使得综合失业率与潜在劳动力（LU3）指标成为衡量劳动力未充分利用程度的关键工具。该数据集由Electric Sheep Africa于2026年整理发布，汇聚ILOSTAT中42个非洲国家自1994年至2025年的13,608条观测记录，按性别、年龄与婚姻状况细分，旨在为非洲劳动经济研究提供可复现、元数据完备的分析素材。其影响力在于降低了非洲跨国劳动力比较的数据门槛，支撑实证研究与政策监测。

当前挑战

该数据集所应对的领域问题在于劳动力未充分利用的量化，其挑战源于非洲各国统计能力参差、指标定义与调查方法存在异质性，失业与潜在劳动力的边界判定缺乏统一标准，且婚姻状况等人口维度在部分国家统计中缺失严重。构建过程中，元数据清单暴露出国家标识与上游发布机构字段的空缺，数据溯源信息不完整；时间跨度内调查口径变更亦可能引入结构性断点。分析者须在缺失值处理、变量单位确认与跨国可比性校准上审慎行事，避免因标签误读而推导出失当的政策含义。

常用场景

经典使用场景

在劳动力市场统计与贫困评估研究领域，该数据集凭借其覆盖42个非洲国家、横跨1994至2025年的13608条观测记录，成为刻画非洲劳动力未充分利用状况的经典面板数据来源。研究者通常以性别、年龄组与婚姻状况为分层维度，对LU3综合失业率与潜在劳动力比率进行时序比较与跨国横截面分析，进而揭示非洲大陆就业结构的异质性与演变轨迹。

解决学术问题

该数据集有效回应了非洲劳动力市场研究中长期存在的数据碎片化与指标口径不一问题。通过提供统一规范、来源可溯的ILOSTAT再包装数据，它使学者得以在一致框架下检验性别与婚姻状况对劳动力未充分利用的影响机制，并为非正规就业、青年失业等议题的跨国比较研究奠定实证基础，显著提升了相关结论的可复制性与外部效度。

实际应用

在政策实践层面，该数据集为国际组织、非洲各国劳工部门及发展机构提供了量化监测工具。决策者可依据分性别、分年龄组的LU3指标识别劳动力市场中的脆弱群体，评估就业促进政策的阶段性成效，并借助时间序列趋势为社会保障与技能培训资源的定向配置提供数据支撑，服务于减贫与体面劳动议程。

数据集最近研究

最新研究方向

伴随国际劳工组织对劳动力未充分利用测度框架的持续深化，LU3指标作为整合失业人口与潜在劳动力的复合型度量，正逐步成为非洲劳动力市场分析的关键工具。该数据集依托ILOSTAT权威统计，覆盖42个非洲国家1994至2025年的13,608条观测，按性别、年龄与婚姻状况细分，为探究结构性失业与隐性劳动力闲置的交互机制提供了长时序、多维度的实证基础。当前研究前沿聚焦于将此类表格数据与机器学习方法结合，用以识别劳动力市场脆弱群体、评估非正规就业冲击，以及检验性别与生命周期因素对劳动参与率的调节效应。在非洲人口红利窗口与就业压力并存的背景下，该数据集对制定包容性劳动力政策、追踪可持续发展目标进展具有重要的监测与预警意义。