electricsheepafrica/africa-ilo-pop-xwap-sex-geo-lms-nb-working-age-population-by-sex-rural-urban-areas-an

--- 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 - population - ilo - labour - employment pretty_name: "Working-age population by sex, rural / urban areas and labour market status (thousands) | Africa (ILOSTAT)" --- # Working-age population by sex, rural / urban areas and labour market status (thousands) | Africa (ILOSTAT) 🌍 **8,788 observations** · **45 Africa countries** · **1994–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **8,788 observations** of `Population` data across **45 Africa countries**, spanning **1994–2025**, 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_GEO_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_GEO_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 45 Africa countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `ZAF` | 624 | 2008 | 2024 | | `EGY` | 612 | 2008 | 2024 | | `TUN` | 576 | 2006 | 2023 | | `AGO` | 407 | 2004 | 2025 | | `MLI` | 396 | 2013 | 2024 | | `GHA` | 360 | 2000 | 2024 | | `RWA` | 360 | 2014 | 2025 | | `ZMB` | 336 | 2015 | 2024 | | `SEN` | 288 | 2011 | 2024 | | `NAM` | 252 | 1994 | 2018 | | `UGA` | 252 | 2010 | 2021 | | `NGA` | 252 | 2011 | 2024 | | `ZWE` | 252 | 2011 | 2024 | | `TZA` | 251 | 2001 | 2020 | | `KEN` | 216 | 1999 | 2022 | | ... | _30 more countries_ | | | ## Indicators (sample) - `POP_XWAP_SEX_GEO_LMS_NB` — Working-age population by sex, rural / urban areas 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_GEO_LMS_NB` | | `indicator.label` | `string` | Indicator name in English | `Working-age population by sex, rural …` | | `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.) | `GEO_COV_NAT` | | `classif1.label` | `string` | — | `Area type: National` | | `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) | `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-pop-xwap-sex-geo-lms-nb-working-age-population-by-sex-rural-urban-areas-an") 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_GEO_LMS_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="POP_XWAP_SEX_GEO_LMS_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "POP_XWAP_SEX_GEO_LMS_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_pop_xwap_sex_geo_lms_nb_working_age_population_by_sex_rural_urban_areas_an_2025, title = {Working-age population by sex, rural / urban areas 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_GEO_LMS_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-pop-xwap-sex-geo-lms-nb-working-age-population-by-sex-rural-urban-areas-an}} } ``` ## 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_GEO_LMS_NB_This dataset contains working-age population statistics for African countries from the International Labour Organization (ILO) ILOSTAT database. It comprises 8,788 observations across 45 African countries spanning the years 1994 to 2025. The core indicator is POP_XWAP_SEX_GEO_LMS_NB, which represents the working-age population (in thousands) disaggregated by sex (total, male, female), rural/urban area type (e.g., national, urban, rural), and labour market status (e.g., total, employed, unemployed). Data is provided at annual frequency and includes columns for country code, country name, data source, indicator code, sex classification, geographic classification, labour market status classification, observation year, observed value, observation status flags, and relevant notes. The dataset has been repackaged by Electric Sheep Africa as part of a unified, ML-ready data layer for Africa.

搜集汇总

数据集介绍

构建方式

该数据集源于国际劳工组织中央统计数据库ILOSTAT，经Electric Sheep Africa进行元数据标准化与仓储工程化处理后发布。其构建整合了1994年至2025年间45个非洲国家的劳动力市场观测记录，总计8788条，围绕工作年龄人口按性别、城乡地域及劳动力市场状态进行分层聚合。原始统计以千人计量单位呈现，经格式转换、字段统一与缺失值保留等步骤，最终以parquet列式存储格式封装，形成可复现的非洲劳动经济结构化证据层。

特点

数据集聚焦非洲区域工作年龄人口的经济活动特征，涵盖性别、城乡分布与劳动市场状态等多维交叉属性，时间跨度逾三十年，具备较强的面板数据潜质。其体量处于千至万级区间，兼顾分析灵活性与计算效率，格式采用parquet以优化列式读取性能。元数据标注了经济金融领域标签及人口、就业、劳动力等主题词，但国家字段与上游发布者信息存在缺失，需在建模前依据源文件确认变量定义与计量单位。

使用方法

借助Hugging Face datasets库可便捷加载该数据集，通过load_dataset函数获取数据对象后，利用features属性检视字段结构，并抽取首条记录以验证数据形态。对于表格型分析，可将指定拆分转换为Pandas数据框，进而开展缺失值诊断、地理与时间维度的变量刻画。建议在建模前审阅仓库文件确认国家列与指标定义，必要时与其他Electric Sheep Africa数据集按国家、年份及指标字段进行连接，并在成果中同时引用ILOSTAT源出处与本仓储地址。

背景与挑战

背景概述

国际劳工组织（ILO）长期致力于全球劳动力市场统计数据的收集与标准化，其ILOSTAT数据库是劳动经济学研究的重要基础设施。然而，非洲地区在劳动年龄人口统计方面长期存在数据碎片化、性别与城乡维度缺失等问题，制约了该区域劳动力市场政策的精准制定。2026年，Electric Sheep Africa基于ILOSTAT原始数据，整合并发布了覆盖45个非洲国家、1994至2025年共8,788条观测记录的劳动年龄人口数据集，按性别、城乡区域及劳动力市场状态进行细分。该数据集为非洲劳动经济学研究提供了标准化的可复现数据支撑，推动了区域劳动力市场分析的精细化发展。

当前挑战

该数据集所应对的核心领域挑战在于：非洲劳动力市场统计长期缺乏按性别与城乡维度交叉分类的系统性数据，导致性别就业差距和城乡劳动参与率差异难以被准确量化，制约了针对性就业政策的制定。在构建过程中，数据集面临多重挑战：原始ILOSTAT数据来源分散，不同国家的统计口径与报告标准存在异质性；部分年份和国家的数据缺失严重，需在保留缺失值与合理插补之间审慎权衡；城乡分类定义在各国间缺乏统一标准，影响跨国比较的可靠性；元数据中country与upstream_publisher字段的信息缺口，增加了数据溯源与归因的难度。

常用场景

经典使用场景

在劳动经济学与区域发展研究的交汇处，该数据集构筑了一方以性别、城乡地域及劳动力市场状态为经纬的非洲劳动年龄人口观测平台。研究者借此得以在1994至2025年的长时段内，对45个非洲国家进行跨国比较与面板数据分析，揭示不同性别与城乡群体在就业、失业及非劳动力状态间的分布变迁。其经典用法在于通过分层交叉表格与趋势建模，刻画非洲劳动力供给结构的异质性，为后续计量模型提供坚实的人口基数输入。

衍生相关工作

围绕该数据集，Electric Sheep Africa的元数据目录催生了一系列以非洲开放数据为核心的衍生工作，包括跨国劳动市场面板的构建、城乡性别就业差距的分解分析，以及与其他ILOSTAT指标联立的复合指数开发。部分研究将其与非洲经济研究联盟的微观调查数据对接，形成宏观—微观嵌套的劳动供给模型。这些工作共同拓展了非洲劳动统计在机器学习与可复现研究中的边界，并为后续数据集标准化提供了范例。

数据集最近研究

最新研究方向

在非洲劳动力市场结构转型与城乡发展不平衡的宏大叙事下，该数据集所承载的按性别、城乡地域及劳动力市场状态细分的劳动年龄人口统计，正成为洞察非洲人口红利兑现路径的关键实证基础。当前研究前沿聚焦于利用此类高粒度面板数据，结合空间分析与因果推断方法，解构非洲各国在1994至2025年间劳动参与率的性别鸿沟与城乡分化，尤其关注非正规就业扩张、青年失业固化以及女性劳动供给对家庭福利的传导效应。关联热点涵盖国际劳工组织体面劳动议程、非洲大陆自由贸易区对劳动力配置的潜在重塑，以及后疫情时代生计脆弱性评估。该数据集的学术价值在于为跨国比较提供标准化观测单元，其影响延伸至社会保障政策设计、城乡融合发展战略及包容性增长监测，构成非洲经济结构转型研究中不可或缺的量化基石。