electricsheepafrica/africa-ilo-luu-xlu2-sex-geo-rt-combined-rate-of-time-related-underemployment-and

--- 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 time-related underemployment and unemployment (LU2) by sex and rural / ur | Africa (ILOSTAT)" --- # Combined rate of time-related underemployment and unemployment (LU2) by sex and rural / ur | Africa (ILOSTAT) 🌍 **1,131 observations** · **32 Africa countries** · **1996–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **1,131 observations** of `Other measures of labour underutilization` data across **32 Africa countries**, spanning **1996–2025**, 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_XLU2_SEX_GEO_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_XLU2_SEX_GEO_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 32 Africa countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `ZAF` | 156 | 2008 | 2024 | | `RWA` | 90 | 2014 | 2025 | | `ZMB` | 72 | 2017 | 2024 | | `AGO` | 66 | 2019 | 2025 | | `SEN` | 63 | 2011 | 2024 | | `GHA` | 63 | 2006 | 2024 | | `ZWE` | 63 | 2011 | 2024 | | `UGA` | 63 | 2010 | 2021 | | `MLI` | 45 | 2018 | 2024 | | `EGY` | 45 | 2016 | 2024 | | `NGA` | 36 | 2019 | 2024 | | `SLE` | 27 | 2003 | 2018 | | `GMB` | 27 | 2012 | 2025 | | `KEN` | 27 | 2019 | 2022 | | `CIV` | 27 | 2016 | 2019 | | ... | _17 more countries_ | | | ## Indicators (sample) - `LUU_XLU2_SEX_GEO_RT` — Combined rate of time-related underemployment and unemployment (LU2) by sex and rural / urban areas (%) ## 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_XLU2_SEX_GEO_RT` | | `indicator.label` | `string` | Indicator name in English | `Combined rate of time-related underem…` | | `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.) | `GEO_COV_NAT` | | `classif1.label` | `string` | — | `Area type: National` | | `time` | `int64` | Observation year | `2025` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `11.141` | | `obs_status` | `string` | Observation status flag (e.g. provisional, unreliable) | `U` | | `obs_status.label` | `string` | — | `Unreliable` | | `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-xlu2-sex-geo-rt-combined-rate-of-time-related-underemployment-and") 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_XLU2_SEX_GEO_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="LUU_XLU2_SEX_GEO_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "LUU_XLU2_SEX_GEO_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_luu_xlu2_sex_geo_rt_combined_rate_of_time_related_underemployment_and_2025, title = {Combined rate of time-related underemployment and unemployment (LU2) by sex and rural / ur | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=LUU_XLU2_SEX_GEO_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-luu-xlu2-sex-geo-rt-combined-rate-of-time-related-underemployment-and}} } ``` ## 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_XLU2_SEX_GEO_RT_This dataset contains 1,131 observations of the "Combined rate of time-related underemployment and unemployment (LU2) by sex and rural/urban areas" across 32 African countries, spanning from 1996 to 2025. The data is sourced from the International Labour Organization (ILO) ILOSTAT database, retrieved via API, filtered, and repackaged. It covers one core indicator: LUU_XLU2_SEX_GEO_RT, representing the combined rate of time-related underemployment and unemployment by sex and rural/urban areas (%). The dataset includes fields such as country code, country name, data source, indicator code, sex classification (total, male, female), year, observed value, observation status, etc. It is suitable for machine learning tasks like tabular classification, regression, and time-series forecasting, providing standardized data for labor market research in Africa.

搜集汇总

数据集介绍

构建方式

该数据集源于国际劳工组织统计数据库（ILOSTAT）所发布的与时间相关就业不足与失业综合比率（LU2）指标，由Electric Sheep Africa团队依据公开元数据清单进行再包装与标准化处理。构建过程以非洲区域为地理边界，筛选出覆盖32个非洲国家自1996年至2025年的观测记录，共计1131条。数据以表格形式存储为Parquet格式，保留性别与城乡维度信息，同时附带标准化的来源注释、许可协议及使用指引，形成可直接用于分析的结构化数据集。

使用方法

使用者可通过Hugging Face的datasets库以编程方式加载该数据集，获取数据结构与特征信息，并可按需转换为Pandas数据框以进行表格化分析。加载后应首先检查各字段的缺失情况与量纲定义，结合国家、年份、性别及城乡等维度开展分组统计与趋势分析。该数据集可与其他Electric Sheep Africa发布的非洲数据集通过国家与年份字段进行关联，构建多指标交叉分析框架。使用时应遵循CC BY 4.0许可协议，并在研究中标注原始来源与再包装方信息。

背景与挑战

背景概述

国际劳工组织（ILO）长期致力于全球劳动力市场统计体系的完善，其ILOSTAT数据库为衡量就业不足与失业提供了跨国可比的标准指标。在此背景下，Electric Sheep Africa于2026年将非洲地区ILOSTAT中关于时间相关就业不足与失业综合比率（LU2）的数据集进行标准化重发布，覆盖32个非洲国家、1131条观测记录，时间跨度为1996至2025年。该数据集按性别与城乡维度细分，旨在揭示非洲劳动力市场中被传统失业率所遮蔽的就业不足问题，为区域经济政策制定与劳动力市场研究提供了关键的实证基础。

当前挑战

该数据集所回应的核心领域挑战在于，传统失业率指标无法捕捉工时不足但仍在就业的边缘劳动力群体，而LU2综合比率正是为弥补这一盲区而设计。在构建层面，数据集面临多重困难：ILOSTAT原始数据在非洲各国的采集口径、统计频率与报告标准存在显著异质性，性别与城乡维度的细分数据缺失值普遍存在，且元数据清单中部分字段（如国家标识、上游发布机构）尚未完整声明，这些因素共同对数据的一致性与跨国比较分析的有效性构成制约。

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构建跨国别、跨性别的劳动力未充分利用率面板分析。研究者依托其覆盖32个非洲国家、1996至2025年的1131条观测记录，针对LU2指标——即与时间相关的就业不足与失业的复合比率——展开时序演变与截面差异的实证检验。该场景通常结合城乡二分维度，运用固定效应模型或面板门槛回归，刻画非洲劳动力市场结构性扭曲的时空特征，进而为区域就业政策评估提供量化基准。

解决学术问题

该数据集切实回应了非洲劳动力市场研究中长期存在的度量窄化与性别盲视两大难题。传统失业率难以捕捉非充分就业群体，而LU2复合指标将工时不足与完全失业纳入统一框架，弥补了单一指标的遮蔽效应。同时，按性别与城乡分层的观测结构，使研究者得以检验女性与农村劳动者在劳动力未充分利用中的异质性暴露，为理解非洲非正规经济下的就业脆弱性提供了可复现的数据支撑，推动了劳动统计方法在发展中地区的适配性讨论。

实际应用

在政策实务层面，该数据集为国际组织与非洲各国劳工部门提供了监测体面劳动目标进展的实证工具。通过追踪LU2指标在性别与城乡维度上的动态变化，决策者可识别就业不足高发区域，优化职业培训与就业创造项目的资源配置。此外，该数据集亦服务于世界银行、国际劳工组织等机构的国别诊断报告，支撑社会保障扩面与农村生计多样化政策的循证设计，并为企业投资非洲劳动密集型产业时的区位劳动力供给评估提供参考。

数据集最近研究

最新研究方向

伴随国际劳工组织体面劳动议程与非正规就业理论在非洲的深化，该数据集所承载的时间相关不充分就业与失业复合率（LU2）正成为劳动经济学前沿的关键观测维度。研究动态集中于将按性别与城乡二元结构分层的面板数据嵌入非洲劳动力市场韧性评估框架，探究性别鸿沟与城乡分化如何在时间维度上交织。该数据集以其跨三十二国、近三十年的连续观测，为校准非正规经济中隐性失业的估计偏差提供了实证基座，亦支撑着与贫困脆弱性、气候冲击等议题的交叉分析，对弥合非洲就业统计盲区具有显著的学术与政策意义。