electricsheepafrica/africa-ilo-pop-xwap-sex-age-lms-nb-working-age-population-by-sex-age-and-labour-marke

--- 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, age and labour market status (thousands) | Africa (ILOSTAT)" --- # Working-age population by sex, age and labour market status (thousands) | Africa (ILOSTAT) 🌍 **58,094 observations** · **49 Africa countries** · **1982–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **58,094 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_AGE_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_AGE_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` | 4,800 | 2000 | 2024 | | `MUS` | 4,454 | 2001 | 2024 | | `TUN` | 3,232 | 2005 | 2023 | | `EGY` | 3,152 | 2008 | 2024 | | `MLI` | 2,198 | 2009 | 2024 | | `AGO` | 2,098 | 2004 | 2025 | | `GHA` | 2,092 | 1991 | 2024 | | `BWA` | 1,912 | 1996 | 2024 | | `RWA` | 1,905 | 2014 | 2025 | | `ZMB` | 1,676 | 2015 | 2024 | | `SYC` | 1,655 | 2014 | 2024 | | `SEN` | 1,490 | 2011 | 2024 | | `TZA` | 1,472 | 2001 | 2024 | | `ZWE` | 1,338 | 2011 | 2024 | | `NAM` | 1,328 | 1994 | 2018 | | ... | _34 more countries_ | | | ## Indicators (sample) - `POP_XWAP_SEX_AGE_LMS_NB` — Working-age population by sex, age 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_AGE_LMS_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 | `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-age-lms-nb-working-age-population-by-sex-age-and-labour-marke") 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_LMS_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="POP_XWAP_SEX_AGE_LMS_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "POP_XWAP_SEX_AGE_LMS_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_pop_xwap_sex_age_lms_nb_working_age_population_by_sex_age_and_labour_marke_2025, title = {Working-age population by sex, age 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_AGE_LMS_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-pop-xwap-sex-age-lms-nb-working-age-population-by-sex-age-and-labour-marke}} } ``` ## 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_LMS_NB_This dataset contains 58,094 observations of working-age population data across 49 African countries from 1982 to 2025, covering 1 distinct indicator: working-age population by sex, age and labour market status (thousands). The data is sourced from the ILOSTAT database of the International Labour Organization (ILO), retrieved via API and repackaged. It is provided in tabular format with fields such as country code, indicator, sex classification, age classification, observation year, and observed value. The dataset is suitable for tasks like tabular classification, regression, and time-series forecasting, and is released under the CC-BY-4.0 license.

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统计数据库（ILOSTAT），系全球劳动统计领域的权威数据来源，经Electric Sheep Africa团队系统化整编而成。原始数据经重新打包为parquet格式，保留其标准化元数据框架，涵盖劳动年龄人口按性别、年龄及劳动力市场状态的分类统计。整编过程未改变原始统计口径，仅对数据格式与元数据描述进行规范化处理，以适配Hugging Face平台的数据发现与加载机制。数据集包含58094条观测记录，覆盖49个非洲国家，时间跨度为1982年至2025年，数据单位统一为千人。

特点

该数据集以表格型数据结构呈现，具备明确的领域标签与元数据标注，属于经济学与金融领域，聚焦非洲区域劳动市场特征。其核心特征在于多维度分类体系，即按性别、年龄组及劳动力市场状态对劳动年龄人口进行交叉统计，为劳动经济学研究提供细粒度数据支撑。数据规模处于万级至十万级之间，格式为parquet，便于高效读取与分析。许可协议为CC BY 4.0，支持开放获取与再利用。数据集以英文为主要语言，属于单语种资源，且元数据中标注了部分来源信息缺失的情况，需在使用时结合原始文献加以确认。

使用方法

研究者可通过Hugging Face的datasets库加载该数据集，使用load_dataset函数指定仓库名称即可获取数据对象，进而查看特征结构并预览样本记录。对于表格型分析，可将数据集转换为Pandas数据框以便进行统计建模与可视化。在使用过程中，建议首先检查数据模式与缺失值分布，明确地理、时间与子群变量的编码方式，避免直接以标签推断政策含义。若需与其他非洲数据集联合分析，应依据显式的国家、年份与指标字段进行匹配。加载示例如下：from datasets import load_dataset; ds = load_dataset("electricsheepafrica/africa-ilo-pop-xwap-sex-age-lms-nb-working-age-population-by-sex-age-and-labour-marke"); print(ds)。

背景与挑战

背景概述

非洲劳动力市场统计长期面临数据碎片化与覆盖不足的困境，国际劳工组织（ILO）虽通过ILOSTAT数据库提供全球劳动统计基准，但非洲区域的数据可得性与可比性始终受限。2026年，Electric Sheep Africa对ILOSTAT原始数据进行系统性重包装，构建了涵盖49个非洲国家、58,094条观测记录、时间跨度自1982年至2025年的劳动年龄人口数据集，按性别、年龄及劳动力市场状态进行三维分类。该数据集以标准化元数据与Parquet格式发布，旨在为非洲经济金融研究提供可复现的分析基础，其核心价值在于将分散的官方统计转化为机器学习就绪的结构化资源，服务于劳动经济学、人口统计学与发展政策评估等交叉领域。

当前挑战

该数据集所回应的领域问题在于非洲劳动力市场异质性极高，非正规就业普遍、统计口径不一，致使跨国比较与时序分析困难重重。构建过程中的挑战亦不容忽视：ILOSTAT原始数据存在国别覆盖不均衡、年龄分组标准不统一及劳动力市场状态定义差异等问题，重包装时需在保持源数据语义完整性的前提下进行标准化映射。此外，元数据中country与upstream_publisher字段的缺失进一步加大了溯源难度，分析者须在缺乏明确地理标识的情形下审慎处理缺失值，避免因插补不当而扭曲劳动参与率与就业结构的推断结果。

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，该数据集构成了剖析非洲大陆劳动力市场结构性特征的基石性资源。依托国际劳工组织（ILO）统计数据库的权威框架，其最经典的应用在于按性别、年龄组及劳动力市场状态（如就业、失业、非劳动力）对49个非洲国家1982至2025年的工作年龄人口进行精细化分层解析。研究者借此得以构建跨国别的面板数据模型，系统考察劳动参与率的性别差异、青年失业的年龄梯度以及非正规就业的规模演变，从而为理解非洲各国劳动力供给的动态机制提供微观层面的量化支撑。

实际应用

在政策制定与市场分析层面，该数据集为非洲各国政府、国际发展机构及金融机构提供了关键的决策依据。劳动力规划部门可依据分年龄性别的劳动市场状态分布，精准识别潜在劳动力储备与技能错配高发群体，进而设计针对性的职业培训与就业促进项目。发展银行与投资者则可将其作为评估区域人力资本禀赋与市场风险的基础图层，结合人口预测模型研判消费市场潜力与用工成本趋势，为基础设施投资与产业布局提供数据驱动的参照。

衍生相关工作

围绕该数据集已衍生出一系列聚焦非洲劳动市场异质性与韧性的经典研究。部分工作将其与世界银行发展指标及非洲各国住户调查微数据链接，构建多源融合的劳动市场监测框架；另有研究基于其长时序结构，运用年龄-时期-队列模型分解劳动参与率变动的结构性效应。此外，在机器学习领域，该数据集常被用作非洲经济金融表格分类与回归任务的基准，推动了面向低资源区域的自动化特征工程与缺失值稳健建模方法的发展。