electricsheepafrica/africa-ilo-ees-tees-sex-geo-mts-nb-employees-by-sex-rural-urban-areas-and-marital-sta

该数据集包含非洲44个国家从1994年至2025年的雇员数据，共有17,548个观测值，涵盖1个核心指标：按性别、城乡地区和婚姻状况划分的雇员数量（千人）。数据来源于国际劳工组织(ILO)的ILOSTAT数据库，通过ILOSTAT REST API获取，并过滤为非洲国家。数据集包含详细的列结构，如国家代码(ref_area)、国家名称(ref_area.label)、数据来源(source和source.label)、指标代码(indicator)和指标标签(indicator.label)、性别分类(sex和sex.label)、分类变量(classif1和classif1.label、classif2和classif2.label)、观测年份(time)、观测值(obs_value)、观测状态(obs_status)以及相关注释(note_indicator和note_source)。数据按性别(总计、男性、女性)进行细分，并可能包含其他分类维度。数据为年度频率，ILO会为同一国家×年份选择最佳来源。该数据集由Electric Sheep Africa重新打包，旨在为非洲提供统一的、机器学习就绪的数据层。This dataset contains employee data of 44 African countries spanning from 1994 to 2025, with a total of 17,548 observations, covering 1 core indicator: the number of employees (in thousands) categorized by gender, urban-rural region and marital status. The data is sourced from the ILOSTAT database of the International Labour Organization (ILO), obtained via the ILOSTAT REST API, and filtered to retain only African countries. The dataset features a detailed column structure, including country code (ref_area), country name (ref_area.label), data source (source and source.label), indicator code (indicator) and indicator label (indicator.label), gender classification (sex and sex.label), categorical variables (classif1 and classif1.label, classif2 and classif2.label), observation year (time), observed value (obs_value), observation status (obs_status), as well as relevant notes (note_indicator and note_source). The data is segmented by gender (total, male, female), and may include other categorical dimensions. It is of annual frequency, and the ILO will select the optimal data source for each country × year combination. This dataset was repackaged by Electric Sheep Africa, with the aim of providing a unified, machine learning-ready data layer for Africa.