electricsheepafrica/africa-ilo-emp-xtru-sex-mts-nb-time-related-underemployment-by-sex-and-marital-st

--- 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 by sex and marital status (thousands) | Africa (ILOSTAT)" --- # Time-related underemployment by sex and marital status (thousands) | Africa (ILOSTAT) 🌍 **3,611 observations** · **35 Africa countries** · **1991–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **3,611 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_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_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` | 459 | 2008 | 2024 | | `RWA` | 270 | 2014 | 2025 | | `GHA` | 216 | 1991 | 2024 | | `MUS` | 209 | 2011 | 2019 | | `BWA` | 190 | 1996 | 2024 | | `ZMB` | 187 | 2017 | 2024 | | `AGO` | 183 | 2019 | 2025 | | `ZWE` | 157 | 2011 | 2024 | | `UGA` | 153 | 2010 | 2021 | | `SEN` | 138 | 2011 | 2024 | | `SYC` | 131 | 2015 | 2024 | | `MLI` | 118 | 2018 | 2024 | | `EGY` | 110 | 2016 | 2024 | | `NGA` | 106 | 2019 | 2024 | | `KEN` | 81 | 2019 | 2022 | | ... | _20 more countries_ | | | ## Indicators (sample) - `EMP_XTRU_SEX_MTS_NB` — Time-related underemployment by sex 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_MTS_NB` | | `indicator.label` | `string` | Indicator name in English | `Time-related underemployment by sex a…` | | `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.) | `MTS_AGGREGATE_TOTAL` | | `classif1.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_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-mts-nb-time-related-underemployment-by-sex-and-marital-st") 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_MTS_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_XTRU_SEX_MTS_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_XTRU_SEX_MTS_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_emp_xtru_sex_mts_nb_time_related_underemployment_by_sex_and_marital_st_2025, title = {Time-related underemployment by sex and marital status (thousands) | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_XTRU_SEX_MTS_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-emp-xtru-sex-mts-nb-time-related-underemployment-by-sex-and-marital-st}} } ``` ## 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_MTS_NB_This dataset contains 3,611 observations of time-related underemployment data across 35 Africa countries, spanning from 1991 to 2025, with one core indicator Time-related underemployment by sex and marital status (thousands). The data is sourced from the International Labour Organization (ILO)s ILOSTAT database, retrieved via API and filtered to Africa ISO3 country codes. The dataset is organized in tabular format, including columns such as country code, country name, data source, indicator code, sex disaggregation (total, male, female), marital status classification, year, observed value (in thousands), observation status flags, etc. The data is annual frequency, covering labour statistics, and is suitable for tabular classification, regression, or time-series forecasting tasks. The dataset is repackaged by Electric Sheep Africa under the CC-BY-4.0 license, aiming to provide a unified, ML-ready data layer for Africa.

搜集汇总

数据集介绍

构建方式

该数据集源自国际劳工组织统计数据库（ILOSTAT），由Electric Sheep Africa团队进行元数据标准化与格式重构。原始数据聚焦非洲地区劳动力市场中与时间相关的就业不足状况，以千人为计量单位，按性别与婚姻状况分类统计。构建过程中，团队对来自ILOSTAT的公开统计数据进行系统整理，保留其原始统计口径与单位定义，并统一封装为Parquet格式，确保数据结构清晰、便于程序化读取。所有记录均以国家、年份、性别及婚姻状况等维度组织，形成覆盖多国、多时段的观测样本，为后续分析提供结构化基础。

特点

数据集具有显著的时空覆盖广度与分类维度特征，包含35个非洲国家自1991年至2025年的3611条观测记录，时间跨度逾三十年。其变量设计围绕就业不足这一核心劳动经济指标展开，区分性别与婚姻状况，使研究者能够考察不同社会人口群体在劳动力市场中的边缘化程度。数据以表格与文本模态呈现，采用标准化元数据标注，具备明确的来源归属与许可协议，属经济学与金融领域专题数据。分区大小介于一千至一万条之间，适合中等规模统计分析。

使用方法

研究者可通过Hugging Face数据集加载接口直接获取数据，调用load_dataset函数传入仓库标识符即可载入完整数据集，并利用features属性查看字段结构。对于表格型分析需求，可将数据集转换为Pandas数据框以执行描述统计、缺失值检验与分组比较。使用前需确认变量定义与计量单位，保留缺失值以待合理插补。数据支持按国家、年份及性别等字段进行筛选与合并，亦可与其他Electric Sheep Africa数据集通过显式国家与年份字段进行连接，以构建可复现的分析流程。

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严峻考验，其中与工时相关的就业不足问题尤为突出，直接关乎劳动者的收入水平与生计质量。国际劳工组织（ILO）作为全球劳工统计的权威机构，通过ILOSTAT数据库持续采集各国劳动力市场指标。该数据集由Electric Sheep Africa团队于2026年整理发布，基于ILOSTAT原始数据重新封装，涵盖1991至2025年间35个非洲国家的3,611条观测记录，按性别与婚姻状况分类呈现工时相关就业不足人口（以千计）。其核心研究问题在于揭示非洲劳动力市场中就业不足的性别差异与婚姻状况分化，为劳动经济学、社会政策评估与可持续发展目标监测提供可复现的实证基础，对非洲数据生态的建设与开放共享具有示范意义。

当前挑战

工时相关就业不足的测度本身即构成劳动统计领域的持久难题，其概念界定涉及工时阈值、意愿调查与收入参照等多重维度，各国统计口径的差异使跨国比较面临可比性风险。非洲区域数据的稀疏性与不均衡性进一步加剧了挑战：部分国家缺乏连续年份的观测，婚姻状况分类标准在不同文化语境下存在异质性，非正式部门就业的普遍存在更使数据采集的覆盖度与代表性受到制约。Electric Sheep Africa在元数据标准化过程中还需应对源数据字段缺失、地理标识不完整等结构性问题，如何在保留缺失值的同时提供清晰的使用指引，成为构建过程中需审慎权衡的技术要点。

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，该数据集凭借其对时间相关就业不足现象按性别与婚姻状况的细粒度刻画，成为探究非洲劳动力市场结构性困境的经典素材。研究者惯常将其用于估计不同性别与婚姻状态群体在特定时段内工作时长低于意愿水平的比例，并借助面板维度追踪1991至2025年间35个非洲国家的演变轨迹。此类分析往往以就业不足率为因变量，考察性别差异与婚姻角色如何交互影响劳动供给质量，从而为理解非洲非正规经济中的隐性失业提供量化依据。

实际应用

在政策实践层面，该数据集为国际组织与非洲各国劳工部门识别脆弱就业群体提供了操作化工具。通过按性别与婚姻状况筛选高就业不足率人群，政策制定者可更有针对性地设计工时保障、托幼支持与技能培训项目，以缓解已婚女性因照料责任而被迫缩短工时的困境。同时，该数据亦可用于监测可持续发展目标中关于充分生产性就业与体面工作的进展，辅助非政府组织在基层开展就业能力评估与干预效果追踪。

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衍生性研究。部分工作将其与ILOSTAT其他就业指标整合，构建多维度就业质量指数，用以比较不同婚姻状态群体的劳动福利差异。另有研究借助该数据训练表格分类与回归模型，预测各国就业不足率的未来走势，并探讨性别与婚姻变量在机器学习特征重要性中的位次。这些工作共同拓展了非洲劳动统计数据的再利用边界，亦为Electric Sheep Africa目录下其他数据集的交叉分析提供了方法参照。