electricsheepafrica/africa-ilo-emp-xtru-sex-age-mts-nb-time-related-underemployment-by-sex-age-and-marita

--- license: cc-by-4.0 language: - en task_categories: - tabular-classification - tabular-regression - time-series-forecasting multilinguality: monolingual size_categories: - 10K<n<100K tags: - tabular - africa - ilostat - time-related-underemployment - ilo - labour - employment pretty_name: "Time-related underemployment by sex, age and marital status (thousands) | Africa (ILOSTAT)" --- # Time-related underemployment by sex, age and marital status (thousands) | Africa (ILOSTAT) 🌍 **11,040 observations** · **35 Africa countries** · **1991–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **11,040 observations** of `Time-related underemployment` data across **35 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_MTS_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_AGE_MTS_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 35 Africa countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `ZAF` | 1,357 | 2008 | 2024 | | `RWA` | 801 | 2014 | 2025 | | `GHA` | 652 | 1991 | 2024 | | `MUS` | 625 | 2011 | 2019 | | `BWA` | 598 | 1996 | 2024 | | `ZMB` | 563 | 2017 | 2024 | | `UGA` | 546 | 2010 | 2021 | | `ZWE` | 539 | 2011 | 2024 | | `AGO` | 535 | 2019 | 2025 | | `SEN` | 429 | 2011 | 2024 | | `MLI` | 394 | 2018 | 2024 | | `SYC` | 383 | 2015 | 2024 | | `EGY` | 324 | 2016 | 2024 | | `NGA` | 323 | 2019 | 2024 | | `ETH` | 243 | 2005 | 2021 | | ... | _20 more countries_ | | | ## Indicators (sample) - `EMP_XTRU_SEX_AGE_MTS_NB` — Time-related underemployment by sex, age and marital status (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_AGE_MTS_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` | | `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 | `MTS_AGGREGATE_TOTAL` | | `classif2.label` | `string` | — | `Marital status (Aggregate): 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) | `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-mts-nb-time-related-underemployment-by-sex-age-and-marita") 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_MTS_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_XTRU_SEX_AGE_MTS_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_XTRU_SEX_AGE_MTS_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_emp_xtru_sex_age_mts_nb_time_related_underemployment_by_sex_age_and_marita_2025, title = {Time-related underemployment by sex, age and marital status (thousands) | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_XTRU_SEX_AGE_MTS_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-emp-xtru-sex-age-mts-nb-time-related-underemployment-by-sex-age-and-marita}} } ``` ## 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_MTS_NB_This dataset contains Time-related underemployment by sex, age and marital status (thousands) data from the International Labour Organization (ILO) ILOSTAT database, covering 35 Africa countries from 1991 to 2025, with 11,040 observations. Data is pulled directly from the ILOSTAT REST API and filtered to Africa ISO3 country codes, focusing on the labour statistics indicator EMP_XTRU_SEX_AGE_MTS_NB, which measures time-related underemployment disaggregated by sex, age, and marital status (in thousands). The dataset supports tabular classification, regression, and time-series forecasting tasks, with columns including country codes, indicators, sex, classification variables, time, and observed values. Data is annual frequency, using the ILO-selected best source method, and disaggregation columns are provided when the indicator publishes breakdowns. It is monolingual (English), size category 10K<n<100K, licensed under cc-by-4.0, and repackaged by Electric Sheep Africa for a unified, ML-ready data layer.

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统计数据库（ILOSTAT）的原始统计记录，由Electric Sheep Africa团队进行系统性整理与标准化再发布。原始数据源自各国劳动力调查与官方上报，覆盖非洲35个国家自1991年至2025年的观测记录，共计11,040条。构建过程中，团队对变量名称、性别与年龄分组、婚姻状况分类等维度进行了统一编码，并补充了标准化的元数据说明、来源注释与分析指引，最终以Parquet格式封装，以便于在Hugging Face平台实现高效加载与版本管理。

特点

数据集聚焦于非洲地区与时间相关的就业不足现象，按性别、年龄及婚姻状况进行多维交叉统计，单位为千人。其核心特征在于时间跨度长、国别覆盖广，且保留了原始统计中的缺失值，为研究者提供了审慎处理数据缺失的空间。数据以表格与文本多模态形式呈现，标签体系涵盖ILOSTAT、劳动就业、经济金融等主题，并附有标准化元数据与来源溯源信息，有助于在跨国比较与子群体分析中保持变量定义的一致性与可追溯性。

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，获取默认划分后利用features与切片操作检查字段结构。对于表格型分析，可将Dataset对象转换为Pandas DataFrame，进而开展缺失值诊断、按地理与时间维度的变量画像以及子群体比较。建议在建模前核实变量单位与定义，并利用显式的国家、年份与指标字段与其他Electric Sheep Africa数据集进行连接，同时以可复现的笔记本形式引用原始来源与再发布仓库。

背景与挑战

背景概述

伴随全球劳动力市场结构转型与非标准就业形态的扩张，时间相关不足就业日益成为衡量劳动力利用不足的关键维度。国际劳工组织（ILO）长期通过ILOSTAT数据库系统采集各国劳动力调查数据，为跨国比较提供权威基准。Electric Sheep Africa于2026年将ILOSTAT中非洲地区按性别、年龄与婚姻状况分类的时间相关不足就业数据（以千人为单位）重新封装发布，涵盖35个非洲国家、1991至2025年间共计11,040条观测记录。该数据集为非洲劳动经济学研究、性别与年龄分层就业政策评估以及可持续发展目标监测提供了结构化的实证基础，其开放许可与标准化元数据显著提升了非洲数据的可发现性与可复用性。

当前挑战

该数据集所应对的领域问题在于：时间相关不足就业的测度长期受制于各国劳动力调查口径差异、非正规经济部门覆盖不足以及婚姻状况等社会人口变量跨国可比性薄弱，致使非洲区域层面的劳动利用不足态势难以被精确刻画。构建过程中，数据整理者面临多重挑战：源数据缺失国家与上游发布者等关键元数据字段，需依赖标题与来源上下文进行地理推断；不同年份与国家的指标定义可能存在隐性偏差，要求分析者在建模前审慎核验变量单位与缺失机制；此外，性别、年龄与婚姻状况交叉分类导致部分单元格样本稀疏，对统计推断的稳健性构成潜在威胁。

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，该数据集构筑起一座以性别、年龄与婚姻状况为经纬的时间相关就业不足观测框架。其经典使用场景体现于跨国别、跨时期的劳动市场脆弱性测度研究，研究者借助1991年至2025年间35个非洲国家的万余人次观测，系统描绘就业时间不足的分布形态与演变轨迹。依托表格分类与回归任务的双重适配性，该数据集常被用于识别就业不足的高风险人群特征，并量化人口结构变量对劳动时间配置的边际影响，从而为非洲劳动市场的一体化比较分析提供可复现的微观证据基础。

实际应用

在政策实践与劳动力市场治理层面，该数据集为非洲各国劳工部门、国际组织及社会政策研究机构提供了可操作的证据支撑。就业服务机构可依据性别、年龄与婚姻状况维度的就业不足分布，精准识别需优先干预的人群，如青年已婚女性或中高龄非正规就业者。国际组织在制定体面劳动国别规划时，可援引该数据的时间序列特征评估既有政策成效，并据此调整技能培训与就业匹配项目的资源配置。此外，金融机构与包容性增长项目亦可将其纳入社会经济风险评估模型，以提升对劳动收入不稳定性的预测能力。

衍生相关工作

该数据集作为Electric Sheep Africa目录的组成部分，衍生出一系列围绕非洲劳动市场结构化元数据与机器学习就绪格式的经典工作。研究者将其与目录内其他ILOSTAT指标数据集进行国别—年份键值连接，构建多指标劳动脆弱性面板，用于聚类分析与预测建模。部分工作聚焦于缺失值处理与地理编码标准化，推动了非洲公开数据集在可复现研究中的方法学讨论。另有研究以该数据为基准，开发性别敏感的就业不足预警指标，并在Hugging Face平台上形成可交互的分析笔记本，促进了数据发现、溯源标注与跨领域复用实践的规范化。