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

--- 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, education and labour market status (thousands) | Africa (ILOSTAT)" --- # Working-age population by sex, education and labour market status (thousands) | Africa (ILOSTAT) 🌍 **48,695 observations** · **49 Africa countries** · **1982–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **48,695 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_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_EDU_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` | 4,693 | 2000 | 2024 | | `MUS` | 3,817 | 2001 | 2024 | | `EGY` | 2,787 | 2008 | 2024 | | `GHA` | 2,055 | 1991 | 2024 | | `MLI` | 2,017 | 2009 | 2024 | | `AGO` | 1,936 | 2004 | 2025 | | `RWA` | 1,654 | 2014 | 2025 | | `ZMB` | 1,576 | 2015 | 2024 | | `TUN` | 1,555 | 2005 | 2023 | | `SEN` | 1,390 | 2011 | 2024 | | `BWA` | 1,308 | 2006 | 2024 | | `TGO` | 1,279 | 2006 | 2022 | | `TZA` | 1,279 | 2001 | 2024 | | `ZWE` | 1,215 | 2011 | 2024 | | `UGA` | 1,207 | 2010 | 2021 | | ... | _34 more countries_ | | | ## Indicators (sample) - `POP_XWAP_SEX_EDU_LMS_NB` — Working-age population by sex, education 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_EDU_LMS_NB` | | `indicator.label` | `string` | Indicator name in English | `Working-age population by sex, educat…` | | `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` | | `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` | `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-lms-nb-working-age-population-by-sex-education-and-labour") 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_LMS_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="POP_XWAP_SEX_EDU_LMS_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "POP_XWAP_SEX_EDU_LMS_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_pop_xwap_sex_edu_lms_nb_working_age_population_by_sex_education_and_labour_2025, title = {Working-age population by sex, education 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_EDU_LMS_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-pop-xwap-sex-edu-lms-nb-working-age-population-by-sex-education-and-labour}} } ``` ## 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_LMS_NB_This is a statistical dataset on the working-age population in Africa, containing 48,695 observations across 49 African countries from 1982 to 2025. The core indicator is Working-age population by sex, education and labour market status (thousands) (corresponding to ILOSTAT code POP_XWAP_SEX_EDU_LMS_NB). The data is sourced from the International Labour Organization (ILO)s ILOSTAT database, a leading global source for labour statistics, which harmonizes data from national labour force surveys, household income surveys, establishment surveys, and administrative records. The dataset includes detailed column structures such as country code (ISO 3166-1 alpha-3), country name, source code and label, indicator code and label, sex disaggregation (total, male, female), education and labour market status classifications, observation year, observed value (in thousands), observation status flags, and related notes. Data is disaggregated by dimensions like sex, education, and labour market status, but disaggregation columns are non-null only when the indicator publishes that breakdown. The dataset is at annual frequency, and when multiple sources exist for the same country×year, the ILO-selected best source is used. This dataset is 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

该数据集依托国际劳工组织统计数据库的权威劳动统计资料，由Electric Sheep Africa团队对原始数据进行系统性重封装与标准化处理，最终形成涵盖48,695条观测记录的结构化数据集。构建过程以非洲49个国家为地理单元，将1982年至2025年间按性别、教育程度与劳动力市场状态划分的working-age人口数据整合为统一格式，并以parquet格式存储，同时配以标准化元数据、加载指引与来源说明，从而在保留原始统计口径的前提下提升数据的可发现性与复用效率。

特点

该数据集以非洲区域劳动经济研究为定位，兼具时间跨度长、地理覆盖广与维度细分明确的特点。其观测规模介于一万至十万条之间，聚焦working-age人口在性别、教育程度及劳动力市场状态等维度的交叉分布，单位为千人，属于经济学与金融学领域的表格型数据。数据以单一语言英语呈现，并附有主题标签体系与出处标注，便于研究者在非洲数据发现框架下进行跨领域比较与可复现分析。

使用方法

使用者可通过Hugging Face datasets库以load_dataset函数直接载入该数据集，并借助数据查看器检视仓库文件结构与特征定义。对于表格型数据，可将其转换为Pandas数据框以便进一步处理；在建模前应先行检查schema、缺失值分布与变量单位，避免依赖标签直接推断政策含义。分析中宜结合显式国家、年份与指标字段开展地理、时间及子群画像，并在跨数据集联接时明确字段对应关系，同时保留缺失值的原始状态直至确立可辩护的插补规则。

背景与挑战

背景概述

国际劳工组织（ILO）自二十世纪中叶起持续构建全球劳动力统计体系，ILOSTAT数据库即其核心成果之一，为各国就业政策与学术研究提供基准数据。该数据集由Electric Sheep Africa于2026年标准化发布，源自ILOSTAT，覆盖49个非洲国家、1982至2025年间约48,695条观测，以性别、教育程度与劳动力市场状态为维度刻画工作年龄人口。其核心研究问题在于揭示非洲劳动力市场中教育结构与性别差异的交互作用，为区域人力资本评估与就业政策制定提供可复现的量化依据，对发展经济学与劳动经济学领域具有基础性支撑意义。

当前挑战

该数据集所回应的领域难题在于：非洲劳动力市场统计长期存在性别与教育维度细粒度数据稀疏、跨国可比性不足等困境，难以支撑精准的结构性分析。构建过程中的挑战亦不容忽视，原始ILOSTAT数据在各国上报口径、教育分类标准及劳动力状态定义上存在异质性，需经标准化元数据映射方能形成统一框架；同时，数据集存在国家与上游发布者等元数据缺口，要求使用者在建模前审慎核验变量定义与单位，并保留缺失值以待合理插补，避免因标签推断而产生政策误读。

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，该数据集构筑了一座以非洲大陆为空间尺度、跨越1982至2025年长时段、覆盖49个国家、并以性别、教育程度与劳动力市场状态三重维度加以分割的观察窗口。研究者据此得以系统描绘非洲工作年龄人口的结构性图景，尤为经典的应用在于构建多层次面板模型以解析教育资本在性别维度上的非对称分布如何塑造劳动参与率与失业率的国别差异。数据以千人为计量单位，便于跨年跨区域的标准化比较，为描述性统计、趋势外推以及跨国收敛性检验提供了坚实的数据基底。

解决学术问题

长期以来，非洲劳动力市场研究饱受数据碎片化与口径不统一之困扰，性别与教育维度的交互效应往往在宏观统计中被遮蔽。该数据集凭借ILOSTAT的权威标准化框架，有效缓解了跨国比较中的概念不可通约问题，使得学者得以在统一分类体系下追问教育扩张是否切实转化为女性劳动参与之提升，抑或受制于结构性壁垒而停滞。其时间跨度之长亦为识别长期趋势与结构性断点提供了可能，推动了从静态描述向动态因果推断的范式迁移，对非洲发展经济学与性别经济学的实证积累具有基础性意义。

衍生相关工作

作为Electric Sheep Africa在Hugging Face平台上发布之非洲公共数据目录的组成部分，该数据集启发了若干衍生性研究工作。其标准化元数据与Parquet格式便于与同目录下其他ILOSTAT指标数据集进行跨国、跨指标的联结分析，催生了关于非洲人力资本与就业结构的综合研究。在机器学习社区中，该数据集被用于表格数据的分类与回归基准测试，亦被纳入非洲经济预测模型的训练语料。相关方法论文献与可复现笔记本逐步积累，推动了非洲开放数据生态的 methodological 成熟。