electricsheepafrica/africa-ilo-pop-xwap-sex-edu-nb-working-age-population-by-sex-and-education-thousa

--- 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 and education (thousands) | Africa (ILOSTAT)" --- # Working-age population by sex and education (thousands) | Africa (ILOSTAT) 🌍 **12,875 observations** · **49 Africa countries** · **1982–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **12,875 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_EDU_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_EDU_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` | 1,176 | 2000 | 2024 | | `MUS` | 1,053 | 2001 | 2024 | | `EGY` | 704 | 2008 | 2024 | | `GHA` | 540 | 1991 | 2024 | | `MLI` | 526 | 2009 | 2024 | | `AGO` | 502 | 2004 | 2025 | | `RWA` | 436 | 2014 | 2025 | | `ZMB` | 417 | 2015 | 2024 | | `TUN` | 393 | 2005 | 2023 | | `SEN` | 363 | 2011 | 2024 | | `BWA` | 353 | 2006 | 2024 | | `TZA` | 352 | 2001 | 2024 | | `TGO` | 351 | 2006 | 2022 | | `ZWE` | 336 | 2011 | 2024 | | `UGA` | 324 | 2010 | 2021 | | ... | _34 more countries_ | | | ## Indicators (sample) - `POP_XWAP_SEX_EDU_NB` — Working-age population by sex and education (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_EDU_NB` | | `indicator.label` | `string` | Indicator name in English | `Working-age population by sex and edu…` | | `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.) | `EDU_AGGREGATE_TOTAL` | | `classif1.label` | `string` | — | `Education (Aggregate levels): 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` | `string` | — | `C3:3710` | | `note_classif.label` | `string` | — | `Nonstandard education level: Includin…` | | `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-edu-nb-working-age-population-by-sex-and-education-thousa") 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_EDU_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="POP_XWAP_SEX_EDU_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "POP_XWAP_SEX_EDU_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_pop_xwap_sex_edu_nb_working_age_population_by_sex_and_education_thousa_2025, title = {Working-age population by sex and education (thousands) | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=POP_XWAP_SEX_EDU_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-pop-xwap-sex-edu-nb-working-age-population-by-sex-and-education-thousa}} } ``` ## 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_EDU_NB_This dataset contains 12,875 observations of Population data across 49 Africa countries, spanning 1982–2025, covering 1 distinct indicators.

搜集汇总

数据集介绍

构建方式

该数据集由Electric Sheep Africa团队基于国际劳工组织（ILOSTAT）的公开统计数据进行工程化重构，通过标准化元数据与统一格式封装而成。其数据来源于ILOSTAT中央统计数据库，覆盖非洲49个国家自1982年至2025年的劳动年龄人口统计，总计12,875条观测记录。构建过程中，团队提取了按性别与教育程度分列的劳动年龄人口数量（以千人为单位），并以Parquet格式重新组织，同时附加以元数据驱动的说明文档、来源追溯与使用指引，从而形成适用于机器学习与可复现分析的数据资产。

特点

该数据集聚焦于非洲区域劳动年龄人口的结构性特征，以性别和教育程度为双重视角，呈现人口维度的长期演变趋势。数据规模处于一万至十万条之间，涵盖近四十年时间跨度与四十九个非洲国家，具备良好的时空覆盖广度。其格式为Parquet，支持高效的列式读取与大规模分析。数据集附带详尽的来源注释与质量说明，强调保留缺失值并核实变量定义，体现了严谨的数据治理原则。标签体系涵盖劳动、就业、人口与经济金融等领域，便于跨域检索与关联分析。

使用方法

研究者可通过Hugging Face的datasets库直接加载该数据集，使用load_dataset函数指定仓库名称即可获取数据对象，进而查看特征结构与样本片段。对于表格型数据，可调用to_pandas方法转换为DataFrame以进行统计分析与可视化。在建模前，建议先检查数据架构与缺失情况，明确国家、年份与指标字段的对应关系，并审慎处理缺失值。该数据集可与其他Electric Sheep Africa目录下的数据集通过国家、年份等显式字段进行连接，以构建更为综合的非洲社会经济分析框架。

背景与挑战

背景概述

伴随非洲劳动力市场结构转型与人力资本积累问题日益受到计量经济学与劳动经济学关注，国际劳工组织（ILO）长期通过ILOSTAT数据库发布跨国劳动力统计。Electric Sheep Africa于2026年将ILOSTAT中非洲区域按性别与教育程度划分的劳动年龄人口（千人）数据整理为标准化数据集，覆盖49个非洲国家、1982至2025年间共12875条观测。该数据集以表格与文本格式发布，旨在为非洲人口结构、教育性别差异及就业潜力研究提供可复现的公开证据，非洲数据发现与跨国比较分析具有基础性支撑意义。

当前挑战

该数据集所回应的领域问题，在于非洲劳动年龄人口按性别与教育程度分解的长时间序列统计长期存在覆盖不均、口径不一与国别缺失等困难，制约了人力资本与劳动力供给的跨国比较研究。构建过程中，数据整合面临源指标定义与单位需逐项核对、部分国家年份缺失且不宜轻率插补、地理标识仅由标题或源元数据隐含而ISO3覆盖未明示等挑战，同时需在保留原始缺失值与提供可复现分析之间保持审慎平衡。

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领域中，按性别与教育程度分列的劳动年龄人口数据构成了分析人力资本存量与劳动力供给结构的基石。该数据集源自国际劳工组织统计数据库，覆盖49个非洲国家自1982年至2025年的12,875条观测记录，其最经典的使用场景在于构建面板数据模型，以考察不同教育层级与性别维度下劳动年龄人口的时序演变与跨国差异。研究者常以此为基础，计算教育基尼系数、性别平等指数等衍生指标，进而刻画非洲各国劳动力市场的结构性特征。

衍生相关工作

围绕该数据集，已衍生出一系列经典研究工作，包括非洲教育回报率的跨国比较、性别劳动参与差距的分解分析以及人口红利窗口期的国别识别。部分研究将其与非洲开发银行、世界银行的家庭调查数据链接，构建多源融合的劳动市场监测框架。Electric Sheep Africa在此基础上持续扩展非洲开放数据目录，推动形成以ILOSTAT为核心的元数据互联生态，为后续机器学习驱动的劳动力预测模型提供了标准化训练语料与基准特征集。

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人力资本积累议题持续升温的背景下，该数据集以国际劳工组织ILOSTAT为权威溯源，覆盖49个非洲国家1982至2025年间的劳动年龄人口按性别与教育程度划分的观测记录，为探究教育红利释放、性别就业差距演变及人口年龄结构变迁提供了长时序、跨国别的量化基础。当前前沿研究聚焦于将此类分层人口数据与技能错配、非正规就业及青年失业等热点议题相勾连，借助面板计量与机器学习方法识别教育扩张对劳动参与率的异质性效应，进而为非洲各国制定包容性就业政策与人力资本投资战略提供实证支撑，其跨世纪的时间纵深与多维分组特征亦有助于刻画区域劳动力供给的收敛与分化格局。