electricsheepafrica/africa-ilo-pop-xwap-sex-age-nb-working-age-population-by-sex-and-age-thousands

--- 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 age (thousands) | Africa (ILOSTAT)" --- # Working-age population by sex and age (thousands) | Africa (ILOSTAT) 🌍 **41,753 observations** · **53 Africa countries** · **1950–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **41,753 observations** of `Population` data across **53 Africa countries**, spanning **1950–2025**, 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_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_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 53 Africa countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `MUS` | 2,997 | 1952 | 2024 | | `EGY` | 2,709 | 1960 | 2024 | | `ZAF` | 2,377 | 1960 | 2024 | | `TUN` | 2,043 | 1956 | 2023 | | `BWA` | 1,438 | 1964 | 2024 | | `MLI` | 1,300 | 1976 | 2024 | | `RWA` | 1,266 | 1978 | 2025 | | `SYC` | 1,134 | 1960 | 2024 | | `ZMB` | 1,055 | 1969 | 2024 | | `ZWE` | 1,041 | 1982 | 2024 | | `GHA` | 1,041 | 1960 | 2024 | | `MAR` | 1,035 | 1960 | 2022 | | `NAM` | 1,020 | 1960 | 2018 | | `AGO` | 948 | 1960 | 2025 | | `TZA` | 924 | 1978 | 2024 | | ... | _38 more countries_ | | | ## Indicators (sample) - `POP_XWAP_SEX_AGE_NB` — Working-age population by sex and age (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_NB` | | `indicator.label` | `string` | Indicator name in English | `Working-age population by sex and age…` | | `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+` | | `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` | `string` | — | `C6:2309` | | `note_classif.label` | `string` | — | `Nonstandard age group: Including ages…` | | `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-nb-working-age-population-by-sex-and-age-thousands") 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_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="POP_XWAP_SEX_AGE_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "POP_XWAP_SEX_AGE_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_pop_xwap_sex_age_nb_working_age_population_by_sex_and_age_thousands_2025, title = {Working-age population by sex and age (thousands) | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=POP_XWAP_SEX_AGE_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-pop-xwap-sex-age-nb-working-age-population-by-sex-and-age-thousands}} } ``` ## 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_NB_This dataset contains working-age population data by sex and age (in thousands) for 53 African countries from 1950 to 2025. It includes 41,753 observations and covers one key indicator: POP_XWAP_SEX_AGE_NB (Working-age population by sex and age). The data is sourced from the International Labour Organization (ILO) ILOSTAT database, retrieved via API and filtered to African ISO3 country codes. The dataset features columns such as country code, country name, source, indicator code, sex disaggregation, age classification, year, observed value, and status flags. Data is annual frequency and harmonized by ILO using International Conference of Labour Statisticians (ICLS) definitions. It is suitable for tabular classification, regression, and time-series forecasting tasks, supporting research on African demographic and labor market trends.

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核心统计数据库ILOSTAT，由Electric Sheep Africa依据其元数据清单进行系统性再包装与标准化编目。原始数据经采集汇聚后，转化为Parquet列式存储格式，覆盖非洲53个国家自1950年至2025年的劳动年龄人口统计，按性别与年龄组分别记录以千人为单位的观测值，总计41753条记录。构建过程强调元数据追溯与格式统一，在保留原始发布方权利的前提下，形成可供非洲数据发现与复用的规范化数据集。

特点

此数据集聚焦非洲区域劳动年龄人口的性别与年龄结构，时间跨度逾七十年，兼具长时序与跨国别比较的维度优势。数据以表格与文本模态呈现，体量介于一万至十万条之间，结构规整、指标明确，便于开展人口结构与劳动力供给的量化分析。数据集附带标准化元数据、来源说明与分析导向的上下文提示，标签体系涵盖人口、劳工、就业等主题，并遵循CC BY 4.0开放许可，利于学术研究与可复现工作流的构建。

使用方法

研究者可借助Hugging Face datasets库以一行代码加载该数据集，获取各拆分信息与特征结构，并可按需将表格拆分转换为Pandas数据框以开展进一步分析。使用前应核查仓库文件中的变量定义、单位及缺失值分布，避免基于标签直接推断政策含义；当涉及地理维度时，宜依据显式国家字段进行合并与分组，若地理信息仅隐含于标题或来源元数据，则需在分析中明确记录相关假设，并保留缺失值直至确立合理的插补规则。

背景与挑战

背景概述

在劳动力市场分析与人口结构变迁的研究中，劳动年龄人口的分性别与分年龄组统计数据构成测算劳动参与率、就业弹性及抚养比等核心指标的基础。鉴于非洲大陆各国人口动态异质性强、数据可得性参差，国际劳工组织（ILO）通过ILOSTAT数据库长期汇编跨国劳动统计，为区域比较与政策评估提供权威依据。该数据集由Electric Sheep Africa于2026年标准化发布，整合了ILOSTAT中1950至2025年间53个非洲国家约4.2万条观测记录，以表格与文本格式呈现劳动年龄人口（千人）的分性别、分年龄组估计值，旨在为非洲经济金融研究提供可复现的机器学习就绪型数据资源，对人口经济学与劳动计量分析具有基础性支撑意义。

当前挑战

该数据集所应对的核心领域挑战在于非洲劳动年龄人口数据的时空异质性与可比性困境：各国统计口径、年龄分组边界及性别分类定义存在差异，且1950年代早期观测多依赖模型回溯估计而非直接普查，造成跨国面板分析中的测量误差与结构性断点。构建过程中，元数据清单显示国家标识与上游出版者字段存在缺失，ISO3覆盖未予声明，需依赖标题或来源元数据推断地理归属，增加了数据链接与去重难度。此外，缺失值处理策略需兼顾估计值与真实统计的混淆风险，分析者须在建模前审慎核验变量定义、单位及方法学注释，以免从标签表面推断政策含义而引入偏误。

常用场景

经典使用场景

在劳动经济学与人口统计学的实证研究中，该数据集最经典的使用场景是构建非洲区域的工作年龄人口结构面板，用以刻画1950年至2025年间53个非洲国家按性别与年龄分组的劳动供给基础。研究者常将其与就业、失业及劳动参与率等指标进行纵向匹配，从而在人口红利、年龄结构转型与劳动力市场动态等议题上展开跨国比较分析，为理解非洲大陆人口变迁与经济发展的耦合关系提供基础性的数据支撑。

衍生相关工作

围绕该数据集，Electric Sheep Africa 及其他研究团队衍生出一系列相关经典工作，包括非洲劳动市场指标面板的构建、跨国人口红利测算框架的开发，以及将工作年龄人口数据与教育、健康、迁移等主题数据集进行多源融合的探索性研究。这些工作进一步推动了非洲开放数据生态的建设，催生了面向机器学习建模的标准化劳动统计数据集，并为后续关于非正规就业、性别劳动差距及青年失业问题的实证文献奠定了数据基础。

数据集最近研究

最新研究方向

在非洲劳动经济学与人口结构转型研究领域，该数据集凭借对1950—2025年53个非洲国家按性别与年龄分组的劳动年龄人口的精细化覆盖，正推动学界深入探究人口红利窗口期与就业市场弹性之间的动态耦合关系。围绕国际劳工组织统计口径的标准化优势，前沿工作聚焦于将此类面板数据嵌入可计算一般均衡模型，以评估青年劳动供给冲击、性别参与率差异及跨境人口迁移对区域经济收敛的潜在影响。该数据集亦为联合国可持续发展目标中体面劳动议程的监测提供可复现的实证基础，其公开元数据治理模式对提升非洲统计能力建设具有示范意义。