africa-ilo-eap-dwap-sex-edu-cct-rt-labour-force-participation-rate-by-sex-education-a
收藏资源简介：
该数据集是一个关于非洲劳动力市场的结构化表格数据集，由Electric Sheep Africa从国际劳工组织（ILO）的官方统计数据库ILOSTAT中提取并重新打包。核心指标是“按性别、教育程度和公民身份划分的劳动力参与率（%）”，旨在为研究非洲各国劳动力结构、移民对劳动力市场的影响以及相关社会经济问题提供标准化的数据支持。数据集包含6,876个观测值，覆盖42个非洲国家，时间跨度为1991年至2025年，以年度频率呈现，每条记录代表特定国家、特定年份在特定人口细分维度（如性别、教育、公民身份）下的劳动力参与率数值。它包含22个字段，详细记录了国家信息（ISO代码和名称）、数据来源（如家庭收入支出调查、劳动力调查等）、指标代码与标签、多个分类维度（性别、教育程度、公民身份）的代码与标签、观测年份、观测数值以及数据质量标志（如可靠性状态）和各类方法说明注释。数据集适用于多种数据分析与机器学习任务，包括劳动力市场的跨国与跨时间比较研究、时间序列预测、基于分类特征的回归分析（如预测劳动力参与率）、以及探索性别、教育背景和移民身份等因素对劳动力参与的影响。数据已预处理为Parquet格式，可通过Hugging Face datasets库直接加载使用。
This dataset is a structured tabular dataset on the African labor market, extracted and repackaged by Electric Sheep Africa from the International Labour Organization (ILO)s official statistical database, ILOSTAT. The core indicator is Labor force participation rate by sex, education, and citizenship (%), designed to provide standardized data support for studying labor force structures in African countries, the impact of migration on the labor market, and related socio-economic issues. The dataset contains 6,876 observations, covering 42 African countries with a time span from 1991 to 2025. Data is presented at an annual frequency, with each record representing the labor force participation rate value for a specific country, year, and population segmentation dimension (such as gender, education, citizenship). It includes 22 fields, detailing country information (ISO codes and names), data sources (e.g., household income and expenditure surveys, labor force surveys), indicator codes and labels, codes and labels for multiple classification dimensions (gender, education level, citizenship), observation year, observation value, as well as data quality flags (e.g., reliability status) and various methodological notes. The dataset is suitable for various data analysis and machine learning tasks, including cross-country and cross-time comparative studies of labor markets, time series forecasting, regression analysis based on categorical features (e.g., predicting labor force participation rates), and exploring the effects of factors such as gender, educational background, and migration status on labor force participation. The data has been preprocessed into Parquet format and can be directly loaded using the Hugging Face datasets library.
数据集概述
- 数据集名称: Labour force participation rate by sex, education and citizenship (%) | Africa (ILOSTAT)
- 数据集规模: 6,876 条观测记录
- 地理覆盖: 42 个非洲国家
- 时间范围: 1991 年至 2025 年
- 指标数量: 1 个独特指标
- 许可协议: cc-by-4.0
- 语言: 英文
- 任务类型: 表格分类、表格回归、时间序列预测
- 数据标签: 表格数据、非洲、ILOSTAT、国际移民存量、ILO、劳动力、就业
数据来源
- 原始来源: ILOSTAT bulk explorer (EAP_DWAP_SEX_EDU_CCT_RT)
- 发布机构: 国际劳工组织 (ILO)
- 数据获取方式: 通过 ILOSTAT REST API 直接拉取，并过滤至非洲 ISO3 国家代码
- 数据整理方: Electric Sheep Africa
核心指标
EAP_DWAP_SEX_EDU_CCT_RT— 按性别、教育程度和公民身份划分的劳动力参与率 (%)
数据模式 (Schema)
| 列名 | 类型 | 描述 | 示例 |
|---|---|---|---|
ref_area |
string | ISO 3166-1 alpha-3 国家代码 | AGO |
ref_area.label |
string | 国家英文名称 | Angola |
source |
string | ILOSTAT 来源代码 | BB:16199 |
source.label |
string | 来源英文名称 | HIES - Survey on Expenditure, Revenue… |
indicator |
string | ILOSTAT 指标代码 | EAP_DWAP_SEX_EDU_CCT_RT |
indicator.label |
string | 指标英文名称 | Labour force participation rate by se… |
sex |
string | 性别分类 (SEX_T=总计, SEX_M=男性, SEX_F=女性) | SEX_T |
sex.label |
string | 性别标签 | Total |
classif1 |
string | 第一个分类变量 (年龄、教育、身份等) | EDU_AGGREGATE_TOTAL |
classif1.label |
string | 分类标签 | Education (Aggregate levels): Total |
classif2 |
string | 第二个分类变量 | CCT_CIT_TOTAL |
classif2.label |
string | 分类标签 | Citizenship: Total |
time |
int64 | 观测年份 | 2019 |
obs_value |
float64 | 观测指标值 | 75.318 |
obs_status |
string | 观测状态标志 | U |
obs_status.label |
string | 状态标签 | Unreliable |
note_classif |
string | 分类注释代码 | C3:2620 |
note_classif.label |
string | 分类注释描述 | Nonstandard education level: Includin… |
note_indicator |
string | 指标注释代码 | I11:264 |
note_indicator.label |
string | 指标注释描述 | Break in series: Methodology revised |
note_source |
string | 来源注释代码 | R1:3513 |
note_source.label |
string | 来源注释描述 | Repository: ILO-STATISTICS - Micro da… |
分类维度
- 性别 (
sex): 3 个唯一值 (SEX_T,SEX_M,SEX_F)
地理覆盖情况 (部分示例)
| 国家 (代码) | 观测行数 | 最早年份 | 最晚年份 |
|---|---|---|---|
GHA |
558 | 1991 | 2024 |
SYC |
507 | 2014 | 2024 |
RWA |
442 | 2014 | 2025 |
MLI |
441 | 2013 | 2024 |
NAM |
374 | 1994 | 2018 |
BWA |
349 | 2006 | 2024 |
SEN |
328 | 2011 | 2024 |
ZWE |
306 | 2014 | 2024 |
CIV |
305 | 2012 | 2022 |
TZA |
265 | 2001 | 2024 |
| 其他 27 个国家 | ... | ... | ... |
数据质量与注意事项
- 数据为年度频率，不包含月度或季度系列。
- 当同一国家×年份存在多个来源时，使用 ILO 选定的“最佳来源”。
- 分类列 (
sex,classif1,classif2) 仅在指标发布该细分时非空。
使用示例
python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-ilo-eap-dwap-sex-edu-cct-rt-labour-force-participation-rate-by-sex-education-a") df = ds["train"].to_pandas()
引用方式
bibtex @misc{africa_ilo_eap_dwap_sex_edu_cct_rt_labour_force_participation_rate_by_sex_education_a_2025, title = {Labour force participation rate by sex, education and citizenship (%) | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EAP_DWAP_SEX_EDU_CCT_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-eap-dwap-sex-edu-cct-rt-labour-force-participation-rate-by-sex-education-a}} }
