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

--- license: cc-by-4.0 language: - en task_categories: - tabular-classification - tabular-regression - time-series-forecasting multilinguality: monolingual size_categories: - 100K<n<1M tags: - tabular - africa - ilostat - population - ilo - labour - employment pretty_name: "Working-age population by sex, age and education (thousands) | Africa (ILOSTAT)" --- # Working-age population by sex, age and education (thousands) | Africa (ILOSTAT) 🌍 **198,833 observations** · **50 Africa countries** · **1982–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **198,833 observations** of `Population` data across **50 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_AGE_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_AGE_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 50 Africa countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `ZAF` | 18,744 | 2000 | 2024 | | `MUS` | 15,124 | 2001 | 2024 | | `EGY` | 11,161 | 2008 | 2024 | | `MLI` | 8,361 | 2009 | 2024 | | `GHA` | 8,257 | 1991 | 2024 | | `AGO` | 7,294 | 2004 | 2025 | | `TZA` | 6,663 | 2001 | 2024 | | `RWA` | 6,393 | 2014 | 2025 | | `ZMB` | 6,262 | 2015 | 2024 | | `TUN` | 6,215 | 2005 | 2023 | | `BWA` | 5,951 | 2006 | 2024 | | `SEN` | 5,408 | 2011 | 2024 | | `UGA` | 5,135 | 2010 | 2021 | | `TGO` | 4,981 | 2006 | 2022 | | `ZWE` | 4,834 | 2011 | 2024 | | ... | _35 more countries_ | | | ## Indicators (sample) - `POP_XWAP_SEX_AGE_EDU_NB` — Working-age population by sex, age 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_AGE_EDU_NB` | | `indicator.label` | `string` | Indicator name in English | `Working-age population by sex, age an…` | | `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 | `EDU_AGGREGATE_TOTAL` | | `classif2.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-age-edu-nb-working-age-population-by-sex-age-and-education-th") 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_AGE_EDU_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="POP_XWAP_SEX_AGE_EDU_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "POP_XWAP_SEX_AGE_EDU_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_pop_xwap_sex_age_edu_nb_working_age_population_by_sex_age_and_education_th_2025, title = {Working-age population by sex, age and education (thousands) | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=POP_XWAP_SEX_AGE_EDU_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-pop-xwap-sex-age-edu-nb-working-age-population-by-sex-age-and-education-th}} } ``` ## 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_AGE_EDU_NB_This dataset, titled Working-age population by sex, age and education (thousands) | Africa (ILOSTAT), contains 198,833 observations across 50 African countries, spanning from 1982 to 2025, and covers 1 distinct indicator. It is sourced from the International Labour Organization (ILO) ILOSTAT database and repackaged by Electric Sheep Africa to provide a unified, ML-ready data layer for Africa. The dataset focuses on working-age population statistics, disaggregated by sex, age, and education, measured in thousands. Data is pulled directly from the ILOSTAT REST API, filtered to Africa ISO3 country codes, and harmonized using International Conference of Labour Statisticians (ICLS) definitions. The schema includes columns such as country code, source, indicator, sex, classification variables, time, observed value, and data quality flags. It is suitable for tabular classification, regression, and time-series forecasting tasks, supporting analyses of population trends and labor market dynamics in 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（ILO）的中央统计数据库ILOSTAT，由Electric Sheep Africa进行标准化重打包与元数据增强。原始数据经结构化提取后，以Parquet格式存储，覆盖1982年至2025年期间50个非洲国家的劳动年龄人口统计，按性别、年龄组和教育程度分类，以千人为单位记录。数据构建过程保留了源数据的统计口径与缺失值，并附有明确的数据来源标注与引用信息，确保可追溯性与分析可重复性。

特点

数据集包含198,833条观测记录，横跨非洲50个国家逾四十年的劳动年龄人口数据，兼具时间序列与横截面维度。其核心特征在于按性别、年龄和教育程度三重维度细分的劳动年龄人口规模，为劳动力市场结构分析提供细粒度基础。数据以表格与文本模态呈现，适用于分类与回归任务，并已标准化元数据标签，支持非洲数据发现与跨数据集链接。

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，获取数据集对象后遍历可用分片并检查特征结构。对于表格分析，可将分片转换为Pandas数据框以进行缺失值探查、变量分布剖析及按地理、时间与子群体维度的剖面分析。建议在建模前审慎处理缺失值，并利用显式国家、年份与指标字段与其他Electric Sheep Africa数据集进行连接，以构建可复现的分析工作流。

背景与挑战

背景概述

在非洲劳动力市场深度转型与人力资本核算需求日益迫切的背景下，非洲工作年龄人口按性别、年龄与教育程度分列的数据集应运而生。该数据集由Electric Sheep Africa基于国际劳工组织（ILO）的ILOSTAT中央统计数据库整理发布，涵盖1982年至2025年间50个非洲国家的198,833条观测记录，以千人为单位系统刻画了工作年龄人口的教育结构特征。其核心研究问题在于揭示非洲各国劳动力人口在教育维度上的性别与年龄分布差异，为劳动经济学、教育政策评估及人力资本测算提供可复现的微观证据基础。作为标准化元数据目录的组成部分，该数据集显著提升了非洲劳动统计数据的可发现性与互操作性，对区域就业政策制定与实证研究具有重要参考价值。

当前挑战

该数据集所回应的领域问题在于非洲工作年龄人口教育结构数据的长期碎片化与跨国可比性不足，传统统计来源往往缺乏按性别、年龄与教育三重维度交叉分类的完整时序记录。构建过程中的核心挑战包括：ILOSTAT原始数据在不同国家与年份间的报告口径、教育分类标准（如ISCED版本）及人口估算方法存在异质性，直接整合易引入系统性偏差；部分国家与年份存在缺失值，需在保留原始缺失状态与审慎插补之间取得平衡；元数据清单中country与upstream_publisher字段的缺失增加了溯源与地理归属的不确定性；此外，将非标准化统计表格转化为ML-ready的Parquet格式并保持单位与定义的一致性，亦对数据工程的严谨性提出较高要求。

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，该数据集常被用于刻画非洲各国劳动年龄人口的结构性特征。凭借其覆盖50个非洲国家、时间跨度自1982年至2025年的198833条观测记录，研究者可依据性别、年龄组与教育程度三个维度对劳动年龄人口进行精细化分层。此类分层数据在计算劳动参与率、教育回报率以及人口红利窗口期等指标时具有基础性价值，并可与ILOSTAT的其他劳动力市场指标进行横向联结，构建多维度分析框架。

衍生相关工作

围绕该数据集及其所属的Electric Sheep Africa目录，已衍生出一系列面向非洲劳动统计的标准化数据工程工作。相关实践包括以parquet格式统一封装ILOSTAT指标、建立可复现的加载与转换流程，以及通过显式国家、年份与指标字段实现多数据集联结。这些工作推动了非洲开放数据生态中元数据规范化与机器学习就绪数据集的建设，并为后续劳动经济学与人口统计学的可复现研究提供了基础设施层面的支撑。

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口红利议题持续升温的背景下，该数据集以1982至2025年、覆盖50个非洲国家的198,833条观测，系统刻画了工作年龄人口在性别、年龄与教育维度上的分布格局，为解析人力资本存量与就业潜力提供了精细化面板基础。当前前沿研究正依托此类数据，将教育分层变量嵌入人口—经济耦合模型，评估不同教育梯队对部门劳动生产率与产业升级的异质性贡献，并借助可复现的表格建模流程检验性别与代际差异在劳动力供给弹性中的调节效应。数据集所承载的长期时序与跨国可比性，亦使其成为追踪非洲人口结构变迁、审视教育与就业错配以及支撑区域政策模拟的关键经验素材，对深化发展经济学与劳动经济学的交叉研究具有显著的方法论与实证价值。