electricsheepeurope/europe-ilo-emp-nifl-sex-ins-mts-rt-informal-employment-rate-by-sex-public-private-sec

该数据集名为按性别、公共/私营部门和婚姻状况划分的非正规就业率（%）| 欧洲（ILOSTAT），是一个关于欧洲非正规就业的表格型数据集。数据集包含4,606个观测值，覆盖4个欧洲国家（摩尔多瓦（MDA）、北马其顿（MKD）、波黑（BIH）、塞尔维亚（SRB）），时间跨度为2003年至2025年。核心指标为EMP_NIFL_SEX_INS_MTS_RT，即按性别、公共/私营部门和婚姻状况划分的非正规就业率（百分比）。数据来源于国际劳工组织（ILO）的ILOSTAT数据库，通过其REST API获取，并经过处理筛选出欧洲国家数据。数据集结构包括多个字段：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）、分类变量1和2（classif1, classif1.label, classif2, classif2.label）、观测年份（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）。数据为年度频率，当同一国家×年份有多个数据来源时，使用ILO选择的最佳来源。分类列（如性别、分类变量）仅在指标发布细分数据时非空。数据集适用于表格分类、回归和时间序列预测任务，可用于分析欧洲非正规就业的趋势、差异和影响因素。This dataset is named Informal employment rate by sex, public/private sector and marital status (%) | Europe (ILOSTAT) and is a tabular dataset on informal employment in Europe. It contains 4,606 observations across 4 European countries (Moldova (MDA), North Macedonia (MKD), Bosnia and Herzegovina (BIH), Serbia (SRB)), spanning the years 2003 to 2025. The core indicator is EMP_NIFL_SEX_INS_MTS_RT, which represents the informal employment rate by sex, public/private sector, and marital status (%). The data is sourced from the International Labour Organization (ILO) ILOSTAT database, retrieved via its REST API and filtered to European country codes. The dataset schema includes multiple fields: 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), classification variables 1 and 2 (classif1, classif1.label, classif2, classif2.label), observation year (time), observed value (obs_value), observation status (obs_status, obs_status.label), and related notes (note_indicator, note_source, etc.). The data is annual frequency; when multiple sources exist for the same country×year, the ILO-selected best source is used. Disaggregation columns (e.g., sex, classification variables) are non-null only when the indicator publishes that breakdown. The dataset is suitable for tabular classification, regression, and time-series forecasting tasks, and can be used to analyze trends, disparities, and influencing factors of informal employment in Europe.