electricsheepafrica/africa-ilo-emp-pifl-sex-age-edu-nb-employment-outside-the-formal-sector-by-sex-age-an

该数据集包含非洲45个国家从1999年至2025年的非正规部门就业统计数据，共有110,975条观测记录，聚焦于单一指标EMP_PIFL_SEX_AGE_EDU_NB（按性别、年龄和教育程度划分的非正规部门就业人数，以千计）。数据来源于国际劳工组织（ILO）的ILOSTAT数据库，通过REST API获取并经过标准化处理，使用ICLS（国际劳工统计学家会议）定义对原始调查数据进行协调。数据集包含多维度分类变量，如性别（总计、男性、女性）、年龄组和教育水平，并提供国家代码、年份、观测值及数据质量标志（如可靠性状态）。数据以年度频率发布，覆盖南非、毛里求斯、埃及等国家，适用于表格分类、回归和时间序列预测等任务。数据集由Electric Sheep Africa重新打包，采用CC-BY-4.0许可，旨在为非洲提供机器学习就绪的数据层。This dataset contains 110,975 observations of informal sector employment statistics for 45 African countries spanning from 1999 to 2025, focusing on a single indicator EMP_PIFL_SEX_AGE_EDU_NB (Employment outside the formal sector by sex, age and education, in thousands). The data is sourced from the International Labour Organizations (ILO) ILOSTAT database, retrieved via REST API and harmonized using ICLS (International Conference of Labour Statisticians) definitions for raw survey microdata. It includes multi-dimensional disaggregation variables such as sex (total, male, female), age groups, and education levels, with columns for country codes, year, observed values, and data quality flags (e.g., reliability status). The data is published at annual frequency and covers countries like South Africa, Mauritius, and Egypt, suitable for tabular classification, regression, and time-series forecasting tasks. Repackaged by Electric Sheep Africa under the CC-BY-4.0 license, it aims to provide a machine-learning-ready data layer for Africa.