electricsheepafrica/africa-ilo-emp-temp-sex-edu-geo-nb-employment-by-sex-education-and-rural-urban-areas

--- 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 - employment - ilo - labour pretty_name: "Employment by sex, education and rural / urban areas (thousands) | Africa (ILOSTAT)" --- # Employment by sex, education and rural / urban areas (thousands) | Africa (ILOSTAT) 🌍 **29,281 observations** · **45 Africa countries** · **1994–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **29,281 observations** of `Employment` data across **45 Africa countries**, spanning **1994–2025**, 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=EMP_TEMP_SEX_EDU_GEO_NB) - **Publisher:** International Labour Organization (ILO) - **License:** [cc-by-4.0](https://creativecommons.org/licenses/by/4.0/) - **Topic:** Employment ## Methodology Data pulled directly from the ILOSTAT REST API at `https://rplumber.ilo.org/data/indicator?id=EMP_TEMP_SEX_EDU_GEO_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 45 Africa countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `ZAF` | 2,495 | 2008 | 2024 | | `EGY` | 2,085 | 2008 | 2024 | | `AGO` | 1,462 | 2004 | 2025 | | `GHA` | 1,451 | 2000 | 2024 | | `MLI` | 1,362 | 2013 | 2024 | | `ZMB` | 1,228 | 2015 | 2024 | | `RWA` | 1,224 | 2014 | 2025 | | `TUN` | 1,120 | 2006 | 2023 | | `SEN` | 1,036 | 2011 | 2024 | | `UGA` | 952 | 2010 | 2021 | | `ZWE` | 922 | 2011 | 2024 | | `TZA` | 882 | 2001 | 2020 | | `TGO` | 848 | 2006 | 2022 | | `NAM` | 784 | 1994 | 2018 | | `KEN` | 780 | 1999 | 2022 | | ... | _30 more countries_ | | | ## Indicators (sample) - `EMP_TEMP_SEX_EDU_GEO_NB` — Employment by sex, education and rural / urban areas (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 | `EMP_TEMP_SEX_EDU_GEO_NB` | | `indicator.label` | `string` | Indicator name in English | `Employment by sex, education and rura…` | | `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 | `GEO_COV_NAT` | | `classif2.label` | `string` | — | `Area type: National` | | `time` | `int64` | Observation year | `2025` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `13984.984` | | `obs_status` | `string` | Observation status flag (e.g. provisional, unreliable) | `U` | | `obs_status.label` | `string` | — | `Unreliable` | | `note_classif` | `string` | — | `C3:2620` | | `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-emp-temp-sex-edu-geo-nb-employment-by-sex-education-and-rural-urban-areas") 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"] == "EMP_TEMP_SEX_EDU_GEO_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_TEMP_SEX_EDU_GEO_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_TEMP_SEX_EDU_GEO_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_emp_temp_sex_edu_geo_nb_employment_by_sex_education_and_rural_urban_areas_2025, title = {Employment by sex, education and rural / urban areas (thousands) | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_TEMP_SEX_EDU_GEO_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-emp-temp-sex-edu-geo-nb-employment-by-sex-education-and-rural-urban-areas}} } ``` ## 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=EMP_TEMP_SEX_EDU_GEO_NB_This dataset contains 29,281 observations of Employment data across 45 Africa countries, spanning 1994–2025, covering 1 distinct indicator. The data is sourced from ILOSTAT, the International Labour Organizations central statistics database, obtained via its REST API and filtered to Africa ISO3 country codes. The key indicator is EMP_TEMP_SEX_EDU_GEO_NB, which represents employment figures disaggregated by sex, education level, and rural/urban areas (in thousands). The dataset includes columns such as country code, country name, data source, indicator code, indicator label, sex classification (total, male, female), education classification, area type, observation year, observed value, observation status, and related notes. Data is annual frequency and harmonized using ICLS (International Conference of Labour Statisticians) definitions. For data quality, when multiple sources exist for the same country and year, the ILO-selected best source is used. This dataset is suitable for tabular classification, regression, and time-series forecasting tasks, providing ML-ready employment data for Africa.

搜集汇总

数据集介绍

构建方式

该数据集源于国际劳工组织（ILO）核心统计数据库ILOSTAT所发布的劳动力市场公开数据，由Electric Sheep Africa团队依据其元数据清单进行系统化整理与重打包。原始数据经采集、标准化校验与格式统一后，以Parquet列式存储格式呈现，覆盖非洲45个国家自1994年至2025年的就业统计记录，共计29,281条观测。构建过程中保留了原始发布方的指标定义、计量单位与缺失值标记，并通过标准化元数据补充了溯源说明与加载指引，以保障数据在复现性分析中的可追溯性。

特点

数据集聚焦于非洲区域按性别、受教育程度及城乡地域交叉分类的就业人数统计，以千人作为计量单位，兼具表格与文本两种模态特征。其核心价值在于多维分组结构，使研究者得以同时考察性别差异、教育层级分化与城乡空间异质性对就业格局的交互影响。数据集时间跨度逾三十年，涵盖45个非洲国家，为区域劳动力市场的长时段比较分析提供了较为充裕的样本基础。数据以CC BY 4.0许可开放，便于学术研究与政策分析中的自由使用与再分发。

使用方法

研究者可借助Hugging Face datasets库以load_dataset函数直接加载该数据集，并通过数据集对象查看特征结构与样本切片。对于表格分析场景，可将指定拆分转换为Pandas数据框以便进行统计建模与可视化探索。使用前建议先行检查各变量的缺失比例与量纲定义，避免因未确认指标口径而产生误读。数据集支持按国家、年份及指标字段与其他Electric Sheep Africa目录下的数据集进行联接，从而支撑跨国比较与面板回归等分析路径。引用时需同时标注原始ILOSTAT来源与本Hugging Face仓库信息。

背景与挑战

背景概述

非洲劳动力市场长期面临结构性数据匮乏的困境，性别、教育程度与城乡分布的交叉维度统计尤为稀缺。国际劳工组织（ILO）依托ILOSTAT中央统计数据库，汇集了1994至2025年间45个非洲国家的就业观测数据，Electric Sheep Africa于2026年将其标准化为Hugging Face平台上的机器学习就绪数据集。该数据集涵盖29,281条观测记录，以女性与男性、教育层级及城乡区域的细粒度分类为核心变量，旨在为非洲就业结构变迁、教育回报率差异及城乡劳动力市场分割等议题提供可复现的实证基础。其开放许可与标准化元数据设计，对推动非洲经济金融领域的可比研究与数据驱动政策制定具有显著的方法论价值。

当前挑战

该数据集所回应的领域问题在于非洲就业统计中长期存在的性别与教育维度数据碎片化及城乡二元结构难以量化比较的困难。在构建过程中，源数据跨越三十余年且各国统计口径与调查方法存在异质性，教育分类与城乡定义的标准化面临显著障碍；部分国家年份缺失与变量编码不一致，导致缺失值处理与跨期可比性维护成为核心挑战。此外，元数据中上游出版者与国别字段的缺失，要求分析者在建模前审慎核验变量定义与单位，以避免因标签误读而产生政策性误判。

常用场景

经典使用场景

在劳动经济学与非洲发展研究的交叉领域，该数据集最经典的使用场景在于以性别、受教育程度与城乡地域三重维度对非洲各国就业规模进行结构化剖析。研究者可依托涵盖二十九万余条观测值、横跨四十五个非洲国家且时间跨度逾三十年的面板数据，系统刻画不同人力资本禀赋群体在城乡劳动力市场中的分布格局，进而识别教育回报率与城乡就业鸿沟在非洲大陆的长期演变轨迹。

实际应用

在政策实践层面，该数据集为非洲各国政府及国际发展机构制定差异化就业促进策略提供了实证依据。决策者可据此识别受教育程度较低群体在乡村地区的就业脆弱性，评估城乡间劳动力配置效率的差异，进而优化职业教育投入方向与农村基础设施布局。国际组织亦可将其纳入区域发展监测框架，追踪可持续发展目标中体面就业与性别平等相关指标的实现进度。

衍生相关工作

围绕该数据集已衍生出若干值得关注的后续研究。一方面，有学者将其与非洲各国教育统计、人口普查及家庭调查微观数据相链接，构建更为精细的劳动力市场均衡模型；另一方面，部分研究以本数据集为基础训练机器学习模型，用于就业结构变迁的预测与情景模拟。这些工作共同拓展了非洲劳动统计数据的分析边界，也为Electric Sheep Africa系列数据集的跨领域整合提供了范例。