electricsheepasia/asia-ilo-emp-pifl-sex-age-eco-nb-employment-outside-the-formal-sector-by-sex-age-an

该数据集名为“按性别、年龄和经济活动划分的非正规部门外就业人数（千）| 亚洲（ILOSTAT）”，包含191,481个观测值，覆盖28个亚洲国家，时间跨度为2000年至2025年。数据集专注于非正规经济领域，提供了一个核心指标：EMP_PIFL_SEX_AGE_ECO_NB，即按性别、年龄和经济活动划分的非正规部门外就业人数（以千计）。数据来源于国际劳工组织（ILO）的ILOSTAT统计数据库，通过API获取并过滤为亚洲国家代码。数据集包括多列信息，如国家代码（ref_area）、国家名称、数据来源（source）、指标代码（indicator）、性别（sex）、年龄分类（classif1）、经济活动分类（classif2）、年份（time）、观测值（obs_value）等，用于支持表格分类、回归和时间序列预测等任务。数据经过ILO的标准化处理，基于国际劳工统计学家会议（ICLS）定义，并包含数据质量标志（如观测状态）。数据集适用于研究亚洲非正规就业趋势、性别和年龄差异分析等场景，并提供了Python代码示例以便快速加载和使用。This dataset, titled Employment outside the formal sector by sex, age and economic activity (thousands) | Asia (ILOSTAT), contains 191,481 observations across 28 Asia countries, spanning the years 2000 to 2025. It focuses on the informal economy, featuring one key indicator: EMP_PIFL_SEX_AGE_ECO_NB, which measures employment outside the formal sector by sex, age, and economic activity (in thousands). The data is sourced from the International Labour Organization (ILO)s ILOSTAT database, retrieved via API and filtered to Asia ISO3 country codes. The dataset includes columns such as country code (ref_area), country name, data source (source), indicator code (indicator), sex (sex), age classification (classif1), economic activity classification (classif2), year (time), observed value (obs_value), and more, designed for tabular classification, regression, and time-series forecasting tasks. The data is harmonized by ILO using International Conference of Labour Statisticians (ICLS) definitions and includes quality flags (e.g., observation status). It is suitable for analyzing informal employment trends, gender and age disparities in Asia, and comes with Python usage examples for easy loading and filtering.