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

--- 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 - time-related-underemployment - ilo - labour - employment pretty_name: "Time-related underemployment rate by sex and age (%) | Africa (ILOSTAT)" --- # Time-related underemployment rate by sex and age (%) | Africa (ILOSTAT) 🌍 **6,896 observations** · **41 Africa countries** · **1991–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **6,896 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_AGE_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_AGE_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 41 Africa countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `ZAF` | 765 | 2008 | 2024 | | `RWA` | 450 | 2014 | 2025 | | `MUS` | 365 | 2011 | 2019 | | `UGA` | 353 | 2010 | 2021 | | `BWA` | 348 | 1996 | 2024 | | `GHA` | 343 | 1991 | 2024 | | `EGY` | 330 | 2011 | 2024 | | `ZMB` | 322 | 2017 | 2024 | | `AGO` | 309 | 2019 | 2025 | | `ZWE` | 279 | 2004 | 2024 | | `SEN` | 259 | 2011 | 2024 | | `SYC` | 231 | 2015 | 2024 | | `MLI` | 201 | 2018 | 2024 | | `NGA` | 180 | 2019 | 2024 | | `MAR` | 140 | 2011 | 2022 | | ... | _26 more countries_ | | | ## Indicators (sample) - `EMP_XTRU_SEX_AGE_RT` — Time-related underemployment rate by sex and age (%) ## 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_AGE_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` | | `classif1` | `string` | First classification variable (age, education, status, etc.) | `AGE_YTHADULT_YGE15` | | `classif1.label` | `string` | — | `Age (Youth, adults): 15+` | | `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) | `U` | | `obs_status.label` | `string` | — | `Unreliable` | | `note_classif` | `string` | — | `C6:1634` | | `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-age-rt-time-related-underemployment-rate-by-sex-and-age") 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_AGE_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_XTRU_SEX_AGE_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_XTRU_SEX_AGE_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_emp_xtru_sex_age_rt_time_related_underemployment_rate_by_sex_and_age_2025, title = {Time-related underemployment rate by sex and age (%) | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_XTRU_SEX_AGE_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-emp-xtru-sex-age-rt-time-related-underemployment-rate-by-sex-and-age}} } ``` ## 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_AGE_RT_This dataset contains 6,896 observations of Time-related underemployment rate by sex and age (%) across 41 Africa countries, spanning from 1991 to 2025. The core indicator is EMP_XTRU_SEX_AGE_RT, sourced from the International Labour Organization (ILO) ILOSTAT database, covering underemployment rates disaggregated by sex (total, male, female) and age groups (e.g., 15+). The data is organized in tabular format with columns such as country code, year, observed value, data source, and quality flags, suitable for machine learning tasks like tabular classification, regression, or time-series forecasting.

搜集汇总

数据集介绍

构建方式

该数据集立足于国际劳工组织（ILO）长期构建的全球劳动力统计体系，其数据源自ILOSTAT中央统计数据库对非洲区域就业状况的持续采集与汇编。Electric Sheep Africa在此基础上对原始统计记录进行标准化再包装，将1991年至2025年间41个非洲国家的6,896条观测整理为parquet格式，形成面向机器学习任务的表格化资源。元数据层经过统一登记与溯源标注，使分散的劳动力调查信息转化为可被程序直接读取的结构化记录，为非洲劳动经济研究提供了可复现的数据基础。

特点

数据集聚焦于与时间相关的就业不足率这一劳动力市场关键指标，按性别与年龄维度进行交叉分类，能够刻画不同人口群体在工时利用不足方面的差异化表现。其覆盖范围横跨非洲大陆四十余国，时间跨度逾三十年，兼具横向国别比较与纵向趋势追踪的双重价值。数据以表格与文本模态并存，体量处于千至万条之间，配合标准化的标签体系与许可协议，便于研究者快速定位变量含义并评估数据适用性。

使用方法

研究者可借助Hugging Face的datasets库直接加载该数据集，通过load_dataset接口获取数据对象并检视其特征结构，必要时转换为Pandas数据框以开展统计分析与建模。在应用过程中，应优先查阅仓库中的原始数据文件以确认变量定义与计量单位，对缺失值保持审慎态度直至确立合理的插补规则。当涉及地理信息时，宜依据显式国家字段进行匹配，并可与其他Electric Sheep Africa数据集通过国家、年份与指标字段实现联结，以支撑可复现的实证研究。

背景与挑战

背景概述

非洲大陆的劳动力市场长期面临就业不足的结构性困境，尤其是与工时相关的隐性失业难以被传统失业率指标捕捉。2026年，Electric Sheep Africa基于国际劳工组织（ILOSTAT）的原始统计资料，构建了覆盖41个非洲国家、时间跨度自1991年至2025年的工时相关就业不足率数据集，包含6896条按性别与年龄分组的观测记录。该数据集的核心研究问题在于揭示非洲各国不同人口群体在劳动时间维度上的就业质量差异，为劳动经济学、发展政策评估及性别平等研究提供可复现的高粒度证据。其标准化的元数据框架与开放许可协议，显著提升了非洲劳动统计数据的可发现性与跨域整合潜力。

当前挑战

该数据集所回应的领域问题在于：传统失业率无法刻画工时不足所导致的隐性劳动力闲置，而非洲各国劳动统计体系在指标定义、调查频率与年龄分组标准上长期存在异质性。构建过程中面临的挑战包括：ILOSTAT原始数据在部分国家与年份存在系统性缺失，需依赖元数据推断地理与时间标识；性别与年龄维度的交叉分层导致小样本单元格增多，统计稳健性受限；此外，上游发布者与国别标识的元数据缺口要求分析者审慎对待地理假设与缺失值处理，避免因标签误读而衍生偏差性政策结论。

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织领域，该数据集凭借其涵盖41个非洲国家、1991至2025年间6,896条观测的时空纵深，成为刻画非洲劳动力市场时间相关不充分就业态势的经典面板数据源。研究者通常以性别与年龄组为分层维度，借助表格分类与回归任务，系统比对各群体在正规与非正规部门中的就业不足率演变轨迹，进而揭示青年与女性劳动者在劳动力市场中面临的结构性弱势。

衍生相关工作

围绕该数据集，已衍生出一系列以非洲劳动力市场为主题的经典工作，包括基于ILOSTAT多指标联表的非正规就业与working poverty比较研究、结合人口健康调查的性别就业差距分解分析，以及利用面板计量方法识别结构性转型对不充分就业影响的国别案例集。Electric Sheep Africa的元数据标准化实践亦催生了非洲公共数据目录的可发现性研究，推动了开放数据在劳动统计领域的可复用性。

数据集最近研究

最新研究方向

在全球劳动力市场结构性转型与非标准就业形态持续扩张的宏观图景下，时间相关就业不足作为衡量劳动力未充分使用的关键指标，日益成为劳动经济学与发展经济学交叉领域的焦点议题。该数据集依托国际劳工组织ILOSTAT权威统计体系，系统涵盖1991至2025年间41个非洲国家按性别与年龄分组的6896条观测记录，为刻画非洲大陆劳动力市场脆弱性的时空演化提供了稀缺的长时段面板证据。当前前沿研究正朝向将时间相关就业不足率与非正规经济规模、数字化转型冲击及性别劳动参与鸿沟等议题进行多维耦合分析，同时借助可复现的机器学习工作流识别高风险群体并评估就业政策干预的异质性效应，从而为非洲包容性增长与体面劳动议程提供实证支撑。