electricsheepafrica/africa-ilo-pop-xwap-sex-mts-lms-nb-working-age-population-by-sex-marital-status-and-l

--- 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 - population - ilo - labour - employment pretty_name: "Working-age population by sex, marital status and labour market status (thousands) | Africa (ILOSTAT)" --- # Working-age population by sex, marital status and labour market status (thousands) | Africa (ILOSTAT) 🌍 **12,100 observations** · **49 Africa countries** · **1982–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **12,100 observations** of `Population` data across **49 Africa countries**, spanning **1982–2025**, 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=POP_XWAP_SEX_MTS_LMS_NB) - **Publisher:** International Labour Organization (ILO) - **License:** [cc-by-4.0](https://creativecommons.org/licenses/by/4.0/) - **Topic:** Population ## Methodology Data pulled directly from the ILOSTAT REST API at `https://rplumber.ilo.org/data/indicator?id=POP_XWAP_SEX_MTS_LMS_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 49 Africa countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `ZAF` | 971 | 2000 | 2024 | | `MUS` | 864 | 2001 | 2024 | | `EGY` | 808 | 2008 | 2024 | | `TUN` | 651 | 2005 | 2023 | | `MLI` | 483 | 2009 | 2024 | | `GHA` | 435 | 1991 | 2024 | | `AGO` | 413 | 2004 | 2025 | | `BWA` | 374 | 1996 | 2024 | | `SYC` | 365 | 2014 | 2024 | | `RWA` | 360 | 2014 | 2025 | | `ZMB` | 330 | 2015 | 2024 | | `SEN` | 312 | 2011 | 2024 | | `NAM` | 307 | 1994 | 2018 | | `TZA` | 303 | 2001 | 2024 | | `ZWE` | 295 | 2011 | 2024 | | ... | _34 more countries_ | | | ## Indicators (sample) - `POP_XWAP_SEX_MTS_LMS_NB` — Working-age population by sex, marital status and labour market 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 | `POP_XWAP_SEX_MTS_LMS_NB` | | `indicator.label` | `string` | Indicator name in English | `Working-age population by sex, marita…` | | `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` | | `classif2` | `string` | Second classification variable where applicable | `LMS_STATUS_TOTAL` | | `classif2.label` | `string` | — | `Labour market status: Total` | | `time` | `int64` | Observation year | `2025` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `20993.124` | | `obs_status` | `string` | Observation status flag (e.g. provisional, unreliable) | `B` | | `obs_status.label` | `string` | — | `Break in series` | | `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-pop-xwap-sex-mts-lms-nb-working-age-population-by-sex-marital-status-and-l") 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"] == "POP_XWAP_SEX_MTS_LMS_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="POP_XWAP_SEX_MTS_LMS_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "POP_XWAP_SEX_MTS_LMS_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_pop_xwap_sex_mts_lms_nb_working_age_population_by_sex_marital_status_and_l_2025, title = {Working-age population by sex, marital status and labour market status (thousands) | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=POP_XWAP_SEX_MTS_LMS_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-pop-xwap-sex-mts-lms-nb-working-age-population-by-sex-marital-status-and-l}} } ``` ## 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=POP_XWAP_SEX_MTS_LMS_NB_This dataset contains working-age population statistics from the International Labour Organization (ILO) ILOSTAT database for Africa, covering 49 African countries from 1982 to 2025, with 12,100 observations. The core indicator is Working-age population by sex, marital status and labour market status (thousands) (indicator code: POP_XWAP_SEX_MTS_LMS_NB). Data is provided at annual frequency and includes fields such as country code, year, sex (total, male, female), marital status classification, labour market status classification, observed values, and data quality flags. The dataset is repackaged by Electric Sheep Africa as part of a unified, ML-ready data layer for Africa, suitable for tabular classification, regression, and time-series forecasting tasks.

搜集汇总

数据集介绍

构建方式

该数据集源自国际劳工组织中央统计数据库ILOSTAT，由Electric Sheep Africa进行标准化重封装，最终以Parquet格式发布于Hugging Face平台。数据覆盖49个非洲国家，时间跨度为1982年至2025年，共包含约12,100条观测记录，涉及工作年龄人口按性别、婚姻状况及劳动力市场状态分类的统计指标，单位为千人。原始数据经采集、校验与格式统一后，形成了结构化的表格型数据集，并附带标准化的元数据说明与溯源信息。

特点

数据集以表格与文本为双重模态，聚焦经济学与金融领域，具有鲜明的非洲区域覆盖特征与长时序面板结构。其规模处于一万至十万行之间，涵盖性别、婚姻状况与劳动力市场状态等细分维度，便于开展交叉分类分析。数据以Parquet列式存储，兼顾读取效率与压缩性能，同时配备标准化的元数据标签与来源注释，提升了可发现性与可复用性。

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函数获取数据对象并检视其结构与特征。对于表格型数据，可进一步转换为Pandas数据框以便进行统计分析与可视化。使用时应先行检查缺失值与变量定义，明确单位与地理覆盖范围，必要时依据国家、年份等字段与其他数据集进行连接，并在建模前对缺失值处理方式作出合理说明。

背景与挑战

背景概述

非洲作为全球劳动力市场结构最为多元且非正规就业占比极高的区域，长期缺乏兼具性别、婚姻状态与劳动参与状态交叉维度的工作年龄人口统计数据。该数据集由Electric Sheep Africa于2026年基于国际劳工组织ILOSTAT数据库再加工构建，覆盖49个非洲国家、1982至2025年共计12,100条观测值，旨在为非洲劳动经济学研究提供标准化的表格型数据资源。其核心研究问题在于揭示婚姻状态与性别如何交互影响劳动市场参与格局，从而弥补非洲劳动力统计中社会人口维度细粒度信息的长期匮乏，为大洋洲以外的发展中地区劳动政策分析树立了可复用的数据范式。

当前挑战

该数据集所面对的领域问题在于，非洲各国劳动统计口径差异显著且非正规部门普遍游离于官方调查之外，导致性别与婚姻状态分层的劳动市场指标可比性极为脆弱。构建过程中的挑战同样突出：原始ILOSTAT数据在部分国家与年份存在系统性缺失，婚姻状态的分类标准在跨文化语境下难以统一映射，且性别维度的报告偏差可能掩盖女性劳动参与的真实规模。此外，元数据清单中countries与upstream_publisher字段的缺失，使得溯源校验与跨国面板回归的变量对齐面临额外的技术障碍。

常用场景

经典使用场景

在劳动经济学与人口统计学的交叉研究中，该数据集最为经典的应用场景在于刻画非洲区域劳动年龄人口的结构性分布特征。研究者依据性别、婚姻状况与劳动力市场身份三重维度，对1982至2025年间49个非洲国家的约1.21万条观测记录进行分层统计，进而探索不同婚姻状态下男女两性在就业、失业与非劳动力状态之间的比例差异。此类分析常辅以时间序列折线图与分组柱状图，直观呈现在人口转型与劳动力市场变迁背景下婚姻制度对劳动参与率的调节效应，为后续计量建模提供坚实的数据基础。

衍生相关工作

围绕该数据集，已衍生出一系列具有影响力的经典工作。部分研究将其与非洲人口与健康调查及世界银行发展指标进行联结，构建多源面板以检验婚姻状态对女性劳动供给的因果影响；另一些工作则以其为基准数据，开发针对非洲劳动市场的预测模型与可视化仪表盘，并推动Electric Sheep Africa目录内其他ILOSTAT衍生数据集的整合使用。这些后续研究不仅拓展了数据集的学术生命周期，也强化了非洲开放数据生态在机器学习与政策分析领域的互联价值。

数据集最近研究

最新研究方向

在非洲劳动力市场统计研究领域，该数据集凭借其涵盖49个国家、跨越1982至2025年的12100条观测记录，正成为探究婚姻状况与性别如何交互影响劳动参与的前沿基础资源。近期研究方向聚焦于运用该数据构建精细化计量模型，分析已婚与未婚群体在正规与非正规就业中的分化路径，并结合非洲大陆自由贸易区等热点议题，评估性别包容性劳动力政策的经济效应。该数据集通过标准化元数据与Parquet格式支持可复现研究，对揭示结构性不平等、支撑循证决策具有显著学术价值与政策意义。