electricsheepeurope/europe-ilo-ees-tees-sex-eco-edu-nb-employees-by-sex-economic-activity-and-education-t

--- license: cc-by-4.0 language: - en task_categories: - tabular-classification - tabular-regression - time-series-forecasting multilinguality: monolingual size_categories: - 100K<n<1M tags: - tabular - europe - ilostat - employees - ilo - labour - employment pretty_name: "Employees by sex, economic activity and education (thousands) | Europe (ILOSTAT)" --- # Employees by sex, economic activity and education (thousands) | Europe (ILOSTAT) 🇪🇺 **909,567 observations** · **39 Europe countries** · **1991–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **909,567 observations** of `Employees` data across **39 Europe countries**, spanning **1991–2025**, covering **1 distinct indicators**. ## About the source **ILOSTAT** is the ILO's central statistics database, the leading global source for labour statistics. It compiles indicators across employment, unemployment, wages, working time, child labour, informal economy, social protection, occupational injuries, and SDG decent work targets — drawing on national labour force surveys, household income surveys, establishment surveys, and administrative records. Coverage spans 200+ economies, with the ILO's Department of Statistics responsible for harmonisation. - **Source:** [ILOSTAT](https://www.ilo.org/shinyapps/bulkexplorer/?id=EES_TEES_SEX_ECO_EDU_NB) - **Publisher:** International Labour Organization (ILO) - **License:** [cc-by-4.0](https://creativecommons.org/licenses/by/4.0/) - **Topic:** Employees ## Methodology Data pulled directly from the ILOSTAT REST API at `https://rplumber.ilo.org/data/indicator?id=EES_TEES_SEX_ECO_EDU_NB` and filtered to Europe ISO3 country codes. ILOSTAT harmonises raw survey microdata using ICLS (International Conference of Labour Statisticians) definitions; sources are flagged in the `source.label` column for traceability. ## Geographic coverage 39 Europe countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `GRC` | 40,088 | 1993 | 2025 | | `CHE` | 39,323 | 1991 | 2025 | | `GBR` | 38,983 | 1993 | 2025 | | `FRA` | 35,165 | 1993 | 2024 | | `IRL` | 31,446 | 1993 | 2024 | | `NLD` | 30,641 | 1996 | 2024 | | `ITA` | 29,398 | 1992 | 2024 | | `SWE` | 29,326 | 1995 | 2024 | | `PRT` | 28,980 | 1993 | 2025 | | `DEU` | 28,305 | 1993 | 2024 | | `DNK` | 28,279 | 1993 | 2024 | | `BEL` | 26,630 | 1993 | 2024 | | `AUT` | 26,511 | 1995 | 2025 | | `ESP` | 26,243 | 1993 | 2025 | | `HUN` | 24,873 | 1997 | 2024 | | ... | _24 more countries_ | | | ## Indicators (sample) - `EES_TEES_SEX_ECO_EDU_NB` — Employees by sex, economic activity and education (thousands) ## Schema | Column | Type | Description | Example | |--------|------|-------------|---------| | `ref_area` | `string` | ISO 3166-1 alpha-3 country code | `ALB` | | `ref_area.label` | `string` | Country name in English | `Albania` | | `source` | `string` | ILOSTAT source code (e.g. labour force survey) | `BA:480` | | `source.label` | `string` | Source name in English | `LFS - Labour Force Survey` | | `indicator` | `string` | ILOSTAT indicator code | `EES_TEES_SEX_ECO_EDU_NB` | | `indicator.label` | `string` | Indicator name in English | `Employees by sex, economic activity a…` | | `sex` | `string` | Disaggregation by sex (SEX_T = total, SEX_M = male, SEX_F = female) | `SEX_T` | | `sex.label` | `string` | — | `Total` | | `classif1` | `string` | First classification variable (age, education, status, etc.) | `ECO_SECTOR_TOTAL` | | `classif1.label` | `string` | — | `Economic activity (Broad sector): Total` | | `classif2` | `string` | Second classification variable where applicable | `EDU_AGGREGATE_TOTAL` | | `classif2.label` | `string` | — | `Education (Aggregate levels): Total` | | `time` | `int64` | Observation year | `2024` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `537.005` | | `obs_status` | `string` | Observation status flag (e.g. provisional, unreliable) | `U` | | `obs_status.label` | `string` | — | `Unreliable` | | `note_classif` | `string` | — | `C3:5578` | | `note_classif.label` | `string` | — | `Nonstandard education level: Includin…` | | `note_indicator` | `string` | — | `I11:264` | | `note_indicator.label` | `string` | — | `Break in series: Methodology revised` | | `note_source` | `string` | — | `R1:3513` | | `note_source.label` | `string` | — | `Repository: ILO-STATISTICS - Micro da…` | ## Disaggregation dimensions The following columns provide disaggregation dimensions: - **`sex`** (3 unique values): `SEX_T`, `SEX_M`, `SEX_F` ## Data quality & caveats - Data is annual frequency. Some indicators also publish monthly or quarterly series — those are not included here. - When an indicator has multiple sources for the same country×year, the ILO-selected 'best source' is used. - Disaggregation columns (`sex`, `classif1`, `classif2`) are non-null only when the indicator publishes that breakdown. ## Usage ```python from datasets import load_dataset ds = load_dataset("electricsheepeurope/europe-ilo-ees-tees-sex-eco-edu-nb-employees-by-sex-economic-activity-and-education-t") df = ds["train"].to_pandas() print(df.head()) ``` ### Filter to one country ```python germany = df[df["ref_area"] == "DEU"] ``` ### Time-series for a single indicator ```python sample = (df[df["indicator"] == "EES_TEES_SEX_ECO_EDU_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EES_TEES_SEX_ECO_EDU_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EES_TEES_SEX_ECO_EDU_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_ees_tees_sex_eco_edu_nb_employees_by_sex_economic_activity_and_education_t_2025, title = {Employees by sex, economic activity and education (thousands) | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EES_TEES_SEX_ECO_EDU_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-ees-tees-sex-eco-edu-nb-employees-by-sex-economic-activity-and-education-t}} } ``` ## License Released under [cc-by-4.0](https://creativecommons.org/licenses/by/4.0/). Original data © International Labour Organization (ILO). When using this dataset, please cite both the original source above and the Electric Sheep Europe repackaging. ## About Electric Sheep Electric Sheep Europe is part of the Electric Sheep mission: a unified, ML-ready data layer for Europe on HuggingFace. We pull data from authoritative open sources, normalize the schemas, package as Parquet, and publish with consistent dataset cards so researchers and developers can use `load_dataset()` to start working in seconds. Browse the full collection: [huggingface.co/electricsheepeurope](https://huggingface.co/electricsheepeurope) --- _Provenance: ingested 2026-05-26 via the Electric Sheep pipeline. Source URL: https://www.ilo.org/shinyapps/bulkexplorer/?id=EES_TEES_SEX_ECO_EDU_NB_This dataset contains 909,567 observations of Employees data across 39 Europe countries, spanning 1991–2025, covering 1 distinct indicator: EES_TEES_SEX_ECO_EDU_NB — Employees by sex, economic activity and education (thousands). The data is sourced from ILOSTAT, the ILOs central statistics database, which harmonizes raw survey microdata using International Conference of Labour Statisticians (ICLS) definitions. The dataset includes columns for country code, country name, source, indicator code, sex disaggregation (total, male, female), economic activity classification, education classification, year, observed value, and observation status flags. It is designed for tabular classification, regression, and time-series forecasting tasks.

搜集汇总

数据集介绍

构建方式

该数据集源自国际劳工组织（ILO）的核心统计数据库ILOSTAT，通过其REST API接口直接抽取指标EES_TEES_SEX_ECO_EDU_NB的原始记录，并筛选欧洲ISO3国家代码，最终由Electric Sheep Europe重新打包为HuggingFace数据集。ILOSTAT依据国际劳工统计学家会议（ICLS）定义对各国劳动力调查、家庭收入调查等微观数据进行标准化调和，确保跨国可比性，每条记录均标注来源标签以供溯源，涵盖1991至2025年间39个欧洲国家的909,567条观测值。

使用方法

研究者可通过HuggingFace的datasets库以一行代码加载数据集并转换为Pandas DataFrame，进而执行国家筛选、时间序列分析或构建国家×年份矩阵等操作。例如，过滤ref_area为DEU可获取德国数据，按指标排序后绘制趋势图，或利用pivot_table生成跨国比较矩阵。数据集适用于表格分类、回归及时间序列预测任务，使用时应遵循CC-BY-4.0许可，同时引用ILO原始来源与Electric Sheep Europe的再打包工作。

背景与挑战

背景概述

国际劳工组织（ILO）自1919年成立以来，始终致力于全球劳动统计的标准化与可比性建设，其ILOSTAT数据库是劳动经济学领域最具权威性的跨国数据源之一。在此背景下，Electric Sheep Europe于2025年将ILOSTAT中关于欧洲地区雇员数据的指标（EES_TEES_SEX_ECO_EDU_NB）进行规范化重打包，形成了覆盖39个欧洲国家、时间跨度为1991至2025年、包含909,567条观测值的面板数据集。该数据集聚焦于按性别、经济活动与教育水平细分的雇员人数，为探究欧洲劳动力市场结构变迁、教育回报差异及性别就业平等议题提供了坚实的量化基础，对劳动经济学、教育社会学及区域政策评估具有显著的支撑价值。

当前挑战

该数据集所应对的核心领域问题在于，如何从多源异构的国家劳动力调查中提炼出跨国可比的雇员规模估计，同时保持性别、经济活动部门与教育层级三维度的细粒度分解能力，这一任务因各国教育分类体系与产业分类标准的差异而面临协调困难。构建过程中，ILO虽采用国际劳工统计学家会议（ICLS）定义进行标准化，但原始数据仍不可避免地携带来源偏差、序列断裂及观测状态标记（如临时性、不可靠）等复杂特征。此外，部分国家在特定年份存在缺失或口径调整，如何在保持时间序列连续性的同时准确反映劳动力结构变化，构成了数据使用者在建模与推断时需审慎应对的关键挑战。

常用场景

经典使用场景

在劳动经济学与教育社会学交叉领域，该数据集凭借其对性别、经济活动与教育水平三维度雇佣关系的精细刻画，成为剖析欧洲劳动力市场结构分化的经典素材。研究者常以面板数据模型或时间序列分析为工具，考察1991年至2025年间39个欧洲国家不同受教育程度劳动者在各类经济部门中的就业规模变动，进而揭示性别隔离与教育回报在行业间的异质性表现。

解决学术问题

该数据集有效回应了劳动力市场中教育匹配与性别平等议题的量化研究需求，解决了跨国比较中因统计口径差异而导致的数据不可比问题。其统一采用国际劳工组织标准分类，使学者得以在一致框架下检验教育扩张对就业结构的影响、经济周期中性别就业弹性的差异，以及部门间人力资本配置效率等理论假设，为欧洲就业政策评估提供了可靠的经验基础。

实际应用

在政策实践层面，该数据集为欧盟及各国劳工部门监测就业结构变化、制定针对性技能培训与性别平等干预措施提供了量化依据。企业人力资源部门亦可借助其行业与教育维度的细分数据，研判不同学历劳动力供给趋势，优化招聘与人力规划。国际组织则利用其时间跨度优势，评估经济危机或结构性改革对特定群体就业的冲击。

数据集最近研究

最新研究方向

在全球劳动力市场性别平等议题持续升温的背景下，该数据集凭借其涵盖39个欧洲国家、跨越1991至2025年的90余万条观测记录，为探究性别、经济活动与教育水平三者交互作用下就业结构的动态演变提供了独特的长时序微观基础。当前前沿研究聚焦于运用机器学习与计量经济学方法，识别教育分层如何调节性别就业差距的经济周期敏感性，并借助预测建模评估政策干预对女性在知识密集型行业参与率的潜在影响。该数据集亦支撑了关于欧洲一体化进程中劳动力市场收敛性与结构性失衡的实证检验，为国际组织制定包容性增长策略提供了关键证据。