electricsheepafrica/africa-ilo-luu-xlu2-sex-dsb-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: - n<1K 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 disability | Africa (ILOSTAT)" --- # Combined rate of time-related underemployment and unemployment (LU2) by sex and disability | Africa (ILOSTAT) 🌍 **633 observations** · **24 Africa countries** · **2005–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **633 observations** of `Other measures of labour underutilization` data across **24 Africa countries**, spanning **2005–2025**, 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_DSB_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_DSB_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 24 Africa countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `RWA` | 90 | 2014 | 2025 | | `ZWE` | 54 | 2014 | 2024 | | `BWA` | 53 | 2019 | 2024 | | `ZMB` | 53 | 2018 | 2024 | | `GHA` | 51 | 2013 | 2024 | | `SEN` | 39 | 2016 | 2024 | | `UGA` | 36 | 2010 | 2021 | | `ETH` | 27 | 2005 | 2021 | | `GMB` | 27 | 2012 | 2025 | | `SWZ` | 26 | 2016 | 2023 | | `SYC` | 24 | 2019 | 2024 | | `CIV` | 18 | 2016 | 2017 | | `LSO` | 18 | 2019 | 2024 | | `TZA` | 17 | 2014 | 2020 | | `LBR` | 17 | 2010 | 2017 | | ... | _9 more countries_ | | | ## Indicators (sample) - `LUU_XLU2_SEX_DSB_RT` — Combined rate of time-related underemployment and unemployment (LU2) by sex and disability status (%) ## Schema | Column | Type | Description | Example | |--------|------|-------------|---------| | `ref_area` | `string` | ISO 3166-1 alpha-3 country code | `BWA` | | `ref_area.label` | `string` | Country name in English | `Botswana` | | `source` | `string` | ILOSTAT source code (e.g. labour force survey) | `BX:15710` | | `source.label` | `string` | Source name in English | `HS - Multi-Topic Household Survey` | | `indicator` | `string` | ILOSTAT indicator code | `LUU_XLU2_SEX_DSB_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.) | `DSB_STATUS_TOTAL` | | `classif1.label` | `string` | — | `Disability status: Total` | | `time` | `int64` | Observation year | `2024` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `29.348` | | `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-xlu2-sex-dsb-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_DSB_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="LUU_XLU2_SEX_DSB_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "LUU_XLU2_SEX_DSB_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_luu_xlu2_sex_dsb_rt_combined_rate_of_time_related_underemployment_and_2025, title = {Combined rate of time-related underemployment and unemployment (LU2) by sex and disability | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=LUU_XLU2_SEX_DSB_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-luu-xlu2-sex-dsb-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_DSB_RT_This dataset contains 633 observations of the combined rate of time-related underemployment and unemployment (LU2) by sex and disability status across 24 Africa countries, spanning from 2005 to 2025, covering 1 distinct indicator. The data is sourced from the International Labour Organizations (ILO) ILOSTAT database, retrieved via API and filtered to African countries. It includes fields such as country code, country name, data source, indicator code, indicator name, sex disaggregation, disability status classification, observation year, observed value, and observation status. The data is published at an annual frequency and harmonized by ILO using International Conference of Labour Statisticians (ICLS) definitions. 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

该数据集源自国际劳工组织中央统计数据库（ILOSTAT）的权威发布，由Electric Sheep Africa团队进行系统化元数据标准加工与格式重构。原始数据涵盖非洲24个国家在2005至2025年间关于与时间相关的就业不足和失业的综合比率（LU2）的633条观测记录，构建过程沿用了ILOSTAT严格的统计口径与分类体系，将分散的性别与残疾维度指标整合为统一的Parquet格式，并附带标准化元数据说明与溯源注释，最终形成面向非洲数据发现场景的机器学习就绪型数据集。

特点

数据集聚焦于非洲区域劳动力市场中的就业不足与失业综合测度，以性别和残疾状况为关键分组变量，体现了对弱势群体就业处境的细致关注。其规模属于n<1K的小型数据集，涵盖24个非洲国家长达二十年的时序观测，兼具表格与文本双模态特征。Parquet格式保障了高效的列式存储与读取性能，标准化的元数据索引则显著提升了数据在非洲经济金融领域的可发现性与可复用性，为跨国比较与趋势分析提供了结构化的证据基础。

使用方法

研究者可通过Hugging Face datasets库直接加载该数据集，利用load_dataset函数获取完整的数据表结构，并借助to_pandas方法将表格数据转换为Pandas DataFrame以进行后续分析。在建模之前，建议先行检查数据模式与缺失值分布，明确国家、年份及指标字段的对应关系，审慎保留缺失值直至确立合理的插补规则。该数据集适宜与Electric Sheep Africa目录下其他数据集基于国家、年份等显式字段进行联结，以构建可复现的分析工作流并拓展多维度的劳动力市场研究。

背景与挑战

背景概述

在国际劳工统计领域，劳动力未充分利用的测度始终是洞察就业质量的核心议题。该数据集由Electric Sheep Africa于2026年整理发布，源自国际劳工组织ILOSTAT数据库，聚焦非洲24国2005至2025年间按性别与残疾状况分列的LU2综合比率，即时间相关就业不足与失业的合并指标。作为非洲公共数据目录的组成部分，其以标准化元数据和轻量级表格格式降低了区域劳动市场研究的门槛，为比较分析与可复现工作流提供了结构化证据基础。

当前挑战

该数据集所应对的领域问题在于，非洲劳动力市场普遍存在非正式就业与统计能力薄弱，传统失业率难以刻画时间相关就业不足的真实广度，需借助LU2复合指标予以补充。构建过程中，ILOSTAT原始数据在性别与残疾维度的覆盖参差不齐，部分国家与年份缺失显著，且元数据对国别和上游发布者的字段存在空白，迫使整理者依赖隐含地理线索。如何在保留缺失值的前提下确保跨国可比性与定义一致性，构成该数据集的主要挑战。

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汇处，该数据集最经典的使用场景在于对非洲地区劳动力未充分利用状况进行多维刻画。研究者依托其涵盖2005至2025年间24个非洲国家、633条观测记录的结构化表格，按性别与残疾状况分解时间相关就业不足与失业的复合比率（LU2），进而开展跨国家、跨时间的比较分析。借助这些数据，学者能够识别非洲劳动力市场中性别差异与残障群体边缘化的典型模式，为后续的计量建模与政策评估提供坚实的数据基础。

衍生相关工作

围绕该数据集，已有若干衍生性工作逐步展开。Electric Sheep Africa以此为基础构建了非洲公共数据目录，推动区域数据发现与元数据标准化；部分研究将其与非洲其他ILOSTAT指标数据集加以联结，拓展至劳动参与率、非正规就业等议题的联合分析；还有学者利用其性别与残疾分项数据开发预测模型，探索劳动未充分利用的驱动因素。这些工作共同丰富了非洲劳动统计数据的开放生态与应用纵深。

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体系的持续深化，将时间相关就业不足与失业率合并计算的LU2指标，正成为洞察非洲劳动力市场结构性困境的前沿工具。该数据集覆盖二十四国、逾六百余观测值，贯通2005至2025年，为性别与残障维度的交叉分析提供了稀缺的时序证据。当前研究聚焦于以LU2刻画非正规就业与隐性失业的交织机制，揭示残障群体在劳动力市场中遭受的双重边缘化，并借助可复现的表格建模路径评估经济波动与政策干预的异质性效应。其开放元数据架构亦推动了非洲经济金融领域数据基础设施的标准化进程，为跨国比较与循证决策奠定了实证根基。