electricsheepafrica/africa-ilo-emp-nifl-sex-geo-mts-nb-informal-employment-by-sex-rural-urban-areas-and-m

该数据集包含来自国际劳工组织（ILO）ILOSTAT数据库的非正规就业数据，专门针对非洲地区。数据集名称为“按性别、城乡地区和婚姻状况分列的非正规就业（千）| 非洲（ILOSTAT）”，涵盖了40个非洲国家从1999年至2025年的12,354个观测值。核心指标为EMP_NIFL_SEX_GEO_MTS_NB，即按性别、城乡地区和婚姻状况分列的非正规就业人数（以千计）。数据通过ILOSTAT REST API获取，并经过ILO根据国际劳工统计学家会议（ICLS）定义进行标准化处理。数据集结构包括以下列：国家代码（ref_area）、国家名称（ref_area.label）、数据来源代码和标签（source、source.label）、指标代码和标签（indicator、indicator.label）、性别分类（sex、sex.label）、第一分类变量（如地区类型，classif1、classif1.label）、第二分类变量（如婚姻状况，classif2、classif2.label）、年份（time）、观测值（obs_value）、观测状态（obs_status、obs_status.label）以及相关注释（note_indicator、note_source等）。数据按性别维度进行细分，包括总计（SEX_T）、男性（SEX_M）和女性（SEX_F）。数据集主要用于表格分类、表格回归和时间序列预测任务，适用于劳动经济学、非正规经济研究和非洲区域分析。数据以年度频率发布，并包含数据质量说明，如使用ILO选择的“最佳来源”。数据集由Electric Sheep Africa重新打包，以支持机器学习和研究使用。This dataset contains informal employment data from the International Labour Organization (ILO) ILOSTAT database, specifically focused on Africa. The dataset is titled Informal employment by sex, rural / urban areas and marital status (thousands) | Africa (ILOSTAT) and includes 12,354 observations across 40 African countries spanning from 1999 to 2025. The core indicator is EMP_NIFL_SEX_GEO_MTS_NB, which represents informal employment by sex, rural/urban areas, and marital status in thousands. Data is sourced directly from the ILOSTAT REST API and harmonized by the ILO using International Conference of Labour Statisticians (ICLS) definitions. The dataset schema includes columns such as country code (ref_area), country name (ref_area.label), source code and label (source, source.label), indicator code and label (indicator, indicator.label), sex disaggregation (sex, sex.label), first classification variable (e.g., area type, classif1, classif1.label), second classification variable (e.g., marital status, classif2, classif2.label), year (time), observed value (obs_value), observation status (obs_status, obs_status.label), and related notes (note_indicator, note_source, etc.). Data is disaggregated by sex dimensions, including total (SEX_T), male (SEX_M), and female (SEX_F). The dataset is intended for tabular classification, tabular regression, and time-series forecasting tasks, suitable for labor economics, informal economy research, and African regional analysis. Data is published at annual frequency and includes quality caveats, such as the use of ILO-selected best source when multiple sources exist. The dataset is repackaged by Electric Sheep Africa to support machine learning and research applications.