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

--- 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, age and rural / urban areas (%) | Africa (ILOSTAT)" --- # Time-related underemployment rate by sex, age and rural / urban areas (%) | Africa (ILOSTAT) 🌍 **8,168 observations** · **32 Africa countries** · **1996–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **8,168 observations** of `Time-related underemployment` 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=EMP_XTRU_SEX_AGE_GEO_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_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` | 1,233 | 2008 | 2024 | | `RWA` | 706 | 2014 | 2025 | | `ZMB` | 494 | 2017 | 2024 | | `UGA` | 485 | 2010 | 2021 | | `AGO` | 474 | 2019 | 2025 | | `GHA` | 458 | 2006 | 2024 | | `ZWE` | 433 | 2011 | 2024 | | `SEN` | 385 | 2011 | 2024 | | `MLI` | 307 | 2018 | 2024 | | `EGY` | 288 | 2016 | 2024 | | `NGA` | 283 | 2019 | 2024 | | `KEN` | 216 | 2019 | 2022 | | `CIV` | 206 | 2016 | 2019 | | `GMB` | 203 | 2012 | 2025 | | `SLE` | 184 | 2003 | 2018 | | ... | _17 more countries_ | | | ## Indicators (sample) - `EMP_XTRU_SEX_AGE_GEO_RT` — Time-related underemployment rate by sex, age 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 | `EMP_XTRU_SEX_AGE_GEO_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+` | | `classif2` | `string` | Second classification variable where applicable | `GEO_COV_NAT` | | `classif2.label` | `string` | — | `Area type: National` | | `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` | `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-emp-xtru-sex-age-geo-rt-time-related-underemployment-rate-by-sex-age-and-r") 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_GEO_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_XTRU_SEX_AGE_GEO_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_XTRU_SEX_AGE_GEO_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_emp_xtru_sex_age_geo_rt_time_related_underemployment_rate_by_sex_age_and_r_2025, title = {Time-related underemployment rate by sex, age and rural / urban areas (%) | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_XTRU_SEX_AGE_GEO_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-emp-xtru-sex-age-geo-rt-time-related-underemployment-rate-by-sex-age-and-r}} } ``` ## 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_GEO_RT_This dataset contains time-related underemployment rate data for 32 African countries from 1996 to 2025, with 8,168 observations. The data is sourced from the International Labour Organization (ILO) ILOSTAT database, with the core indicator being Time-related underemployment rate by sex, age and rural/urban areas (%). The dataset is organized in tabular format, including fields such as country code, country name, data source, indicator code, sex classification (total, male, female), age classification, area type, observation year, observed value, and data status. The data is harmonized by ILO based on International Conference of Labour Statisticians (ICLS) definitions and is suitable for machine learning tasks such as tabular classification, regression, and time-series forecasting.

搜集汇总

数据集介绍

构建方式

该数据集系基于国际劳工组织（ILO）核心统计数据库ILOSTAT的原始记录，由Electric Sheep Africa团队实施系统化工程重构而成。构建过程遵循元数据驱动的标准化流程，对1996年至2025年间32个非洲国家的与时间相关的就业不足率数据进行采集、清洗与结构化封装，最终以Parquet格式存储，形成涵盖8,168条观测值的可复现数据资产。各国数据经统一编码后，保留了性别、年龄组及城乡地域等关键维度，缺失值予以保留以便下游分析者依据明确规则处理。

特点

数据集聚焦非洲区域劳动力市场中与时间相关的就业不足现象，时间跨度近三十年，覆盖32个国家，具备显著的时空广度与面板结构特征。其核心变量为按性别、年龄及城乡区域交叉分类的就业不足率百分比，可支撑多维度的异质性分析。数据以表格与文本双模态呈现，体量介于一千至一万条之间，归属于经济学与金融领域，附有标准化元数据标签及来源溯源信息，便于在非洲数据发现框架内进行索引与关联。

使用方法

研究者可借助Hugging Face datasets库以单行代码加载该数据集，通过查看数据集的features与切片预览以确认schema结构。若需进行统计分析，可将相应split转换为Pandas DataFrame，进而实施缺失值诊断、按地理与时间维度的变量轮廓刻画，或与其他Electric Sheep Africa数据集基于国家、年份及指标字段进行连接。建模之前应依据README指引核对变量定义与计量单位，并明确记录地理覆盖的假设条件，以确保分析过程的可复现性与结论的稳健性。

背景与挑战

背景概述

国际劳工组织（ILO）自二十世纪中叶以来持续构建全球劳动力统计体系，ILOSTAT作为其核心数据库，为监测就业质量与体面劳动提供了权威基准。在此框架下，非洲地区长期面临就业不足统计碎片化、性别与城乡维度信息匮乏的困境。Electric Sheep Africa于2026年将ILOSTAT中非洲时间相关就业不足率数据整理为标准化数据集，涵盖1996至2025年间32个非洲国家的8168条观测记录，按性别、年龄组和城乡地域分层。该数据集填补了非洲劳动力市场细粒度比较数据的空白，为追踪可持续发展目标中充分就业与体面劳动进展提供了可复用的分析基础。

当前挑战

就业不足率的跨国比较面临统计口径异质性的根本挑战，各国对工时阈值的界定、非正规部门抽样覆盖以及季节性劳动力波动的处理方式存在显著差异，致使跨国估计的可比性受限。数据构建过程中，原始ILOSTAT记录存在元数据缺失，特别是国家级上游出版者信息与ISO3地理编码的未声明状态，削弱了溯源完整性与地理关联能力。性别、年龄与城乡三重维度的交叉分层在部分国家年份中产生稀疏单元格，导致估计值缺失或置信区间过宽。此外，非正规经济中自雇与家庭帮工的工作时间测量本身存在系统性偏差，使得时间相关就业不足率可能低估实际劳动力利用不足的程度。

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中，该数据集凭借其覆盖32个非洲国家、跨越1996至2025年的时间相关不充分就业率面板数据，成为刻画非洲劳动力市场结构性低效的经典素材。研究者常以性别、年龄组及城乡二元地理为分层维度，构建多水平回归模型或时间序列分解框架，用以识别不充分就业率在人口亚群体间的异质性轨迹。其标准化地理与时间字段亦使其成为跨国比较与政策评估的基准数据源。

实际应用

在政策实践层面，该数据集为非洲各国劳工部门、国际发展机构及区域经济共同体提供监测时间相关不充分就业动态的实证工具。用户可据此绘制国别与次区域层面的就业质量热力图，识别性别与年龄敏感群体的脆弱性，并评估就业促进计划与农村生计多样化项目的干预成效。其帕奎特格式亦便于与宏观经济、教育及人口健康数据链接，支撑循证决策与资源配置。

衍生相关工作

依托该数据集，学界与开源社区已衍生出若干经典工作，包括非洲劳动力市场脆弱性指数构建、性别就业差距的时空分解研究，以及城乡不充分就业与贫困关联的微观计量分析。这些工作进一步整合国际劳工组织统计数据库与其他非洲开放数据资源，形成元数据驱动的分析流水线，并催生了面向政策模拟的可交互仪表板与可复现笔记本，持续拓展该数据集在比较劳动研究中的应用边界。