electricsheepafrica/africa-ilo-emp-xtru-sex-nb-time-related-underemployment-by-sex-thousands

--- 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 by sex (thousands) | Africa (ILOSTAT)" --- # Time-related underemployment by sex (thousands) | Africa (ILOSTAT) 🌍 **520 observations** · **41 Africa countries** · **1991–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **520 observations** of `Time-related underemployment` data across **41 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_NB) - **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_NB` 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 41 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 | | `UGA` | 27 | 2009 | 2021 | | `ZMB` | 24 | 2017 | 2024 | | `EGY` | 24 | 2011 | 2024 | | `GHA` | 24 | 1991 | 2024 | | `BWA` | 24 | 1996 | 2024 | | `ZWE` | 22 | 2004 | 2024 | | `SEN` | 21 | 2011 | 2024 | | `AGO` | 21 | 2019 | 2025 | | `SYC` | 18 | 2015 | 2024 | | `TZA` | 15 | 1991 | 2020 | | `MLI` | 15 | 2018 | 2024 | | `MAR` | 12 | 2011 | 2022 | | ... | _26 more countries_ | | | ## Indicators (sample) - `EMP_XTRU_SEX_NB` — Time-related underemployment by sex (thousands) ## 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_NB` | | `indicator.label` | `string` | Indicator name in English | `Time-related underemployment by sex (…` | | `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) | `117.022` | | `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-nb-time-related-underemployment-by-sex-thousands") 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_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_XTRU_SEX_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_XTRU_SEX_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_emp_xtru_sex_nb_time_related_underemployment_by_sex_thousands_2025, title = {Time-related underemployment by sex (thousands) | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_XTRU_SEX_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-emp-xtru-sex-nb-time-related-underemployment-by-sex-thousands}} } ``` ## 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_NB_This dataset contains 520 observations of Time-related underemployment by sex (thousands) data across 41 Africa countries, spanning 1991–2025, covering 1 distinct indicator (EMP_XTRU_SEX_NB). The data is sourced from the ILOSTAT database of the International Labour Organization (ILO), with normalized schema including country codes, years, sex disaggregation, observed values, and source information, 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统计，经Electric Sheep Africa以标准化元数据流程重新封装而成。构建过程以ILOSTAT原始指标为数据内核，保留按性别分列的时间相关就业不足人数（以千为单位）记录，覆盖41个非洲国家自1991年至2025年的观测，最终形成520条结构化观测，以Parquet格式发布，并附以统一的来源注释、许可标识与清单快照，使非洲劳动统计证据得以在可复现的分析框架中被检索与调用。

特点

数据集聚焦非洲区域时间相关就业不足的性别差异，涵盖41个国家、逾三十年时间跨度，指标口径由ILOSTAT统一界定，具备跨国与跨期比较的基础。其体量控制在千条以内，结构紧凑而轻量，便于快速加载与剖析。数据以表格与文本模态并存，附带标准化元数据、来源溯源说明以及分析导向的上下文提示，既服务于探索性统计，也适配于劳动经济学领域中的分组比较与趋势观察。

使用方法

使用者可借助Hugging Face datasets库以load_dataset接口直接载入数据，获取数据划分与特征结构，并在需要时通过to_pandas转换为数据框以进行后续统计建模。分析前宜先行检视变量定义、单位与缺失模式，对缺失值暂作保留，待确立可辩护的插补规则后再行处理。若涉地理维度，应明确使用数据中显式的国家字段；若地理信息仅由标题或来源元数据隐含，则需在下游分析中记录该假设，并可依据国家、年份与指标字段同其他非洲数据集进行联结。

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构建与完善，其ILOSTAT数据库已成为劳动经济学实证研究不可或缺的基础设施。在非洲大陆，就业不足问题因非正规经济占比高、季节波动显著而尤为突出，时间相关就业不足指标直接刻画了劳动者工时低于其意愿水平的结构性困境。Electric Sheep Africa于2026年将该指标以标准化元数据形式发布至Hugging Face平台，覆盖41个非洲国家、1991至2025年间共520条观测记录，按性别分列，旨在为非洲劳动市场研究提供可复现、可检索的开放数据资源，对推动区域就业政策评估与性别差异分析具有基础性支撑意义。

当前挑战

该数据集所涉及的领域问题在于如何准确度量并跨国比较非洲各地区时间相关就业不足的真实水平，其难点根植于各国统计口径差异、非正规部门抽样偏差及性别维度数据报告的系统性缺失。在构建过程中，研究面临的主要挑战包括：原始ILOSTAT数据中部分国家与年份的缺失值需在不引入偏误的前提下审慎处理；元数据清单中country与upstream_publisher字段存在信息缺口，须依赖标题与来源语境推断地理覆盖范围；此外，小样本规模（n<1K）限制了高维建模与细粒度性别差异推断的统计效力，研究者须在建模前充分评估缺失模式与单位定义，以避免对政策含义的过度解读。

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叉领域中，对就业质量的精细化测度始终是核心议题。该数据集以性别为分类维度，系统记录了1991至2025年间41个非洲国家与时间相关的就业不足人口数量（以千人为单位），为研究者提供了跨越三十余年的长时序面板数据。其最经典的使用场景在于构建非洲各国就业不足程度的性别差异比较分析，通过将男性与女性的时间相关就业不足数据进行配对观察，揭示劳动力市场中性别结构性失衡的演变轨迹，进而服务于区域就业政策的效果评估与跨国比较研究。

衍生相关工作

围绕该数据集，Electric Sheep Africa已构建起以非洲公开数据发现为目标的元数据目录体系，将时间相关就业不足指标与劳动力参与率、失业率、部门就业结构等数据集进行关联索引。研究者在此基础上衍生出多维劳动市场脆弱性评估框架，以及将ILOSTAT指标与非洲各国人口普查微观数据融合的实证研究。此类工作拓展了非洲就业统计数据的应用边界，也为跨国劳动比较数据库的标准化建设提供了可借鉴的工程范式。

数据集最近研究

最新研究方向

在非洲劳动力市场结构性转型与体面劳动赤字持续引发关注的背景下，该数据集以国际劳工组织ILOSTAT的标准化统计框架为依托，聚焦1991至2025年间41个非洲国家按性别分列的时间相关不充分就业规模，为研判性别维度下的劳动利用不足提供了长时序、跨国别的量化依据。当前相关研究的前沿方向在于将此类宏观劳动统计与家庭调查、劳动力流动及非正规经济数据加以融合，借助面板计量与机器学习方法识别性别差异在就业质量恶化过程中的传导机制，并评估工业化、数字化与气候变化冲击对女性劳动参与稳定性的异质性影响。该数据集的价值在于以可复现的开放格式支撑非洲就业政策模拟与可持续发展目标中体面劳动指标的监测，对推动性别敏感的劳动力市场治理具有实证参考意义。