electricsheepafrica/africa-ilo-emp-temp-sex-ste-cct-nb-employment-by-sex-status-in-employment-and-citizen

该数据集名为按性别、就业状况和公民身份划分的就业（千人）| 非洲（ILOSTAT），包含11,362条观察数据，覆盖44个非洲国家，时间跨度为1991年至2025年。数据来源于国际劳工组织（ILO）的ILOSTAT数据库，主题为国际移民存量，具体指标为EMP_TEMP_SEX_STE_CCT_NB，即按性别、就业状况和公民身份划分的就业人数（以千人为单位）。数据集采用表格格式，包含多列信息，如国家代码、国家名称、数据来源、指标代码、性别分类（总计、男性、女性）、就业状况分类、公民身份分类、年份、观测值等。数据集适用于表格分类、表格回归和时间序列预测等自然语言处理任务，可用于分析非洲各国就业趋势、性别差异和移民影响。数据经过ILO的标准化处理，并包含数据质量说明，如年度频率、最佳来源选择等。数据集由Electric Sheep Africa重新打包，以Parquet格式发布，便于机器学习使用。The dataset is titled Employment by sex, status in employment and citizenship (thousands) | Africa (ILOSTAT) and contains 11,362 observations across 44 Africa countries, spanning the years 1991 to 2025. It is sourced from the International Labour Organization (ILO) ILOSTAT database, focusing on the topic of international migrant stock, with the specific indicator EMP_TEMP_SEX_STE_CCT_NB, which measures employment by sex, status in employment, and citizenship in thousands. The dataset is structured in tabular format, including columns such as country code, country name, data source, indicator code, sex disaggregation (total, male, female), employment status classification, citizenship classification, year, observed value, and more. It is suitable for natural language processing tasks like tabular classification, tabular regression, and time-series forecasting, enabling analysis of employment trends, gender disparities, and migration impacts across African countries. The data is harmonized by ILO and includes caveats on data quality, such as annual frequency and best-source selection. Repackaged by Electric Sheep Africa in Parquet format for machine learning readiness.