electricsheepafrica/africa-ilo-emp-xtru-sex-rt-time-related-underemployment-rate-by-sex

--- 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 - time-related-underemployment - ilo - labour - employment pretty_name: "Time-related underemployment rate by sex (%) | Africa (ILOSTAT)" --- # Time-related underemployment rate by sex (%) | Africa (ILOSTAT) 🌍 **463 observations** · **36 Africa countries** · **1991–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **463 observations** of `Time-related underemployment` data across **36 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=EMP_XTRU_SEX_RT) - **Publisher:** International Labour Organization (ILO) - **License:** [cc-by-4.0](https://creativecommons.org/licenses/by/4.0/) - **Topic:** Time-related underemployment ## Methodology Data pulled directly from the ILOSTAT REST API at `https://rplumber.ilo.org/data/indicator?id=EMP_XTRU_SEX_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 36 Africa countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `ZAF` | 51 | 2008 | 2024 | | `RWA` | 30 | 2014 | 2025 | | `MUS` | 27 | 2011 | 2019 | | `BWA` | 24 | 1996 | 2024 | | `ZMB` | 24 | 2017 | 2024 | | `GHA` | 24 | 1991 | 2024 | | `ZWE` | 22 | 2004 | 2024 | | `AGO` | 21 | 2019 | 2025 | | `SEN` | 21 | 2011 | 2024 | | `UGA` | 21 | 2010 | 2021 | | `SYC` | 18 | 2015 | 2024 | | `TZA` | 15 | 1991 | 2020 | | `MLI` | 15 | 2018 | 2024 | | `EGY` | 15 | 2016 | 2024 | | `NGA` | 12 | 2019 | 2024 | | ... | _21 more countries_ | | | ## Indicators (sample) - `EMP_XTRU_SEX_RT` — Time-related underemployment rate by sex (%) ## 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 | `EMP_XTRU_SEX_RT` | | `indicator.label` | `string` | Indicator name in English | `Time-related underemployment rate by …` | | `sex` | `string` | Disaggregation by sex (SEX_T = total, SEX_M = male, SEX_F = female) | `SEX_T` | | `sex.label` | `string` | — | `Total` | | `time` | `int64` | Observation year | `2025` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `0.837` | | `obs_status` | `string` | Observation status flag (e.g. provisional, unreliable) | `B` | | `obs_status.label` | `string` | — | `Break in series` | | `note_classif` | `string` | — | `C6:2289` | | `note_classif.label` | `string` | — | `Nonstandard age group: Including ages…` | | `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-emp-xtru-sex-rt-time-related-underemployment-rate-by-sex") 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"] == "EMP_XTRU_SEX_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_XTRU_SEX_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_XTRU_SEX_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_emp_xtru_sex_rt_time_related_underemployment_rate_by_sex_2025, title = {Time-related underemployment rate by sex (%) | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_XTRU_SEX_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-emp-xtru-sex-rt-time-related-underemployment-rate-by-sex}} } ``` ## 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=EMP_XTRU_SEX_RT_This dataset contains 463 observations of time-related underemployment rate by sex (%) across 36 African countries from 1991 to 2025. The data is sourced from the ILOSTAT database of the International Labour Organization (ILO), retrieved via REST API and filtered for African countries. It covers one core indicator (EMP_XTRU_SEX_RT), representing the time-related underemployment rate by sex (%). The schema includes columns for country code, country name, data source, indicator code, sex disaggregation, observation year, observed value, observation status flags, and related notes. Data is disaggregated by sex (total, male, female) and includes data quality caveats such as annual frequency, best source selection, and non-null conditions for disaggregation columns. The dataset is stored in tabular format and suitable for tabular classification, regression, and time-series forecasting tasks.

搜集汇总

数据集介绍

构建方式

该数据集依托国际劳工组织（ILO）的ILOSTAT中央统计数据库，以非洲劳动力市场监测为背景，由Electric Sheep Africa团队对原始统计资料进行标准化封装而成。构建过程聚焦于时间相关不充分就业率这一指标，按性别维度拆分，覆盖36个非洲国家自1991年至2025年的观测记录，合计463条。数据以Parquet列式格式存储，并配以统一的元数据清单、溯源说明与加载指引，确保从源数据库到Hugging Face平台的可复现迁移。

特点

数据集体量精炼，观测数低于一千，属经济学与金融领域的小型表格数据集。其核心特征在于以性别为分层变量刻画非洲各国时间相关不充分就业的长期演变，时间跨度逾三十年，兼顾跨国比较与趋势追踪。数据模态涵盖表格与文本，采用CC BY 4.0开放许可，并附有标准化的元数据标签，便于在非洲数据发现框架下被检索与复用。

使用方法

使用者可通过Hugging Face的datasets库以load_dataset函数直接加载该数据集，随后遍历其数据划分以检视特征结构与样本内容。对于表格型数据，可借助to_pandas方法转换为数据框，进而开展缺失值审查、变量画像及按地理、时间与性别维度的分组分析。在建模之前应核验变量定义与计量单位，并保留缺失值以待合理的插补规则，亦可依据显式的国家、年份与指标字段与其他非洲数据集进行关联。

背景与挑战

背景概述

在全球劳动力市场监测体系中，就业不足问题长期构成衡量劳动资源利用效率的关键维度，而时间相关就业不足率则直接刻画了劳动者因工时不足而无法充分参与生产活动的程度。国际劳工组织（ILO）自二十世纪中叶起持续构建全球劳动统计数据库（ILOSTAT），为各国劳动政策制定提供基准性证据。Electric Sheep Africa于2026年将ILOSTAT中非洲区域的时间相关就业不足率按性别分类数据整理发布至Hugging Face平台，覆盖36个非洲国家、1991至2025年间共463条观测记录，旨在为非洲劳动经济研究提供可复现的标准化数据基础设施。

当前挑战

该数据集所回应的是劳动经济学中长期存在的数据可及性与可比性难题：非洲各国劳动力调查在抽样设计、指标定义与报告周期上存在显著异质性，导致跨国比较与时间序列分析面临系统性偏差风险。构建过程中，元数据清单暴露出国家字段与上游发布者信息缺失的问题，ISO3国家编码亦未明确声明，研究者须依赖标题或源元数据进行地理推断。此外，样本量低于千条且覆盖跨度长达三十余年，缺失值的处理策略将直接影响回归建模与分类任务的稳健性。如何在保持数据原始性的同时构建可辩护的插补规则，构成该数据集下游应用的核心挑战。

常用场景

经典使用场景

在劳动经济学与非洲区域发展研究的交汇处，时间相关不充分就业率数据构成了衡量劳动力市场质量的核心标尺。该数据集汇聚了36个非洲国家自1991年至2025年间共计463条观测记录，按性别分列的时间相关不充分就业率指标，为研究者提供了纵贯三十余年的稀缺面板证据。经典使用场景体现于跨国比较分析与性别差异测度之中：研究者借助该数据集可系统刻画非洲各国不充分就业的时序演变轨迹，并在性别维度上解构劳动力利用不足的结构性差异，进而与ILOSTAT全球劳工统计框架形成有效对接。

解决学术问题

针对非洲劳动力市场研究中长期存在的数据碎片化与可比性不足问题，该数据集以标准化元数据与统一指标口径回应了若干关键学术关切。其核心贡献在于：填补了非洲区域时间相关不充分就业性别分化证据的系统性空白，使跨国面板计量分析得以在一致的定义框架下展开；同时通过覆盖1991至2025年的长时序观测，为检验结构性转型理论、劳动力市场摩擦假说以及性别不平等持续性命题提供了可复现的经验基础。该数据集的存在显著降低了非洲劳动统计研究的进入壁垒，并增强了实证结论的外部效度。

衍生相关工作

依托Electric Sheep Africa编目体系，该数据集催生了一系列聚焦非洲劳动统计的衍生研究与工程实践。典型工作包括：基于该数据集与其他ILOSTAT非洲指标的跨国面板回归分析，考察不充分就业与经济增长、教育投入之间的动态关联；结合性别维度开展的非洲女性劳动参与质量研究；以及在Hugging Face平台上构建的可复现笔记本与自动化数据质量评估流程。这些工作共同拓展了非洲劳动数据的开放科学生态，并为后续非洲劳工统计机器学习应用奠定了方法基础。