electricsheepeurope/europe-ilo-ees-tees-sex-ocu-geo-nb-employees-by-sex-occupation-and-rural-urban-areas

--- 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, occupation and rural / urban areas (thousands) | Europe (ILOSTAT)" --- # Employees by sex, occupation and rural / urban areas (thousands) | Europe (ILOSTAT) 🇪🇺 **129,099 observations** · **39 Europe countries** · **1992–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **129,099 observations** of `Employees` data across **39 Europe countries**, spanning **1992–2025**, 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_OCU_GEO_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_OCU_GEO_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 | |---------|-----:|-----------:|----------:| | `FRA` | 5,015 | 1992 | 2024 | | `DEU` | 5,014 | 1992 | 2024 | | `NLD` | 4,988 | 1992 | 2024 | | `DNK` | 4,913 | 1992 | 2024 | | `GRC` | 4,866 | 1992 | 2025 | | `ITA` | 4,759 | 1992 | 2024 | | `IRL` | 4,751 | 1993 | 2024 | | `PRT` | 4,749 | 1992 | 2024 | | `ESP` | 4,698 | 1992 | 2024 | | `BEL` | 4,603 | 1993 | 2024 | | `GBR` | 4,536 | 1992 | 2019 | | `AUT` | 4,364 | 1995 | 2025 | | `FIN` | 4,338 | 1997 | 2024 | | `SWE` | 4,232 | 1997 | 2024 | | `LUX` | 3,997 | 1997 | 2024 | | ... | _24 more countries_ | | | ## Indicators (sample) - `EES_TEES_SEX_OCU_GEO_NB` — Employees by sex, occupation and rural / urban areas (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) | `BB:7401` | | `source.label` | `string` | Source name in English | `HIES - Living Standards Survey` | | `indicator` | `string` | ILOSTAT indicator code | `EES_TEES_SEX_OCU_GEO_NB` | | `indicator.label` | `string` | Indicator name in English | `Employees by sex, occupation and rura…` | | `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.) | `OCU_SKILL_TOTAL` | | `classif1.label` | `string` | — | `Occupation (Skill level): Total` | | `classif2` | `string` | Second classification variable where applicable | `GEO_COV_NAT` | | `classif2.label` | `string` | — | `Area type: National` | | `time` | `int64` | Observation year | `2012` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `421.776` | | `obs_status` | `string` | Observation status flag (e.g. provisional, unreliable) | `U` | | `obs_status.label` | `string` | — | `Unreliable` | | `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-ocu-geo-nb-employees-by-sex-occupation-and-rural-urban-areas") 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_OCU_GEO_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EES_TEES_SEX_OCU_GEO_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EES_TEES_SEX_OCU_GEO_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_ees_tees_sex_ocu_geo_nb_employees_by_sex_occupation_and_rural_urban_areas_2025, title = {Employees by sex, occupation and rural / urban areas (thousands) | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EES_TEES_SEX_OCU_GEO_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-ees-tees-sex-ocu-geo-nb-employees-by-sex-occupation-and-rural-urban-areas}} } ``` ## 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_OCU_GEO_NB_This dataset is sourced from the ILOSTAT statistical database of the International Labour Organization (ILO), repackaged by Electric Sheep Europe. It contains 129,099 observations across 39 European countries from 1992 to 2025, with the core indicator being Employees by sex, occupation and rural / urban areas (thousands) (EES_TEES_SEX_OCU_GEO_NB). The data is organized in tabular format, including columns such as country code, country name, data source, indicator code, indicator label, sex disaggregation (total, male, female), occupation classification (by skill level), rural/urban area classification, observation year, observed value (in thousands), observation status flags, and related notes. The dataset is suitable for tasks like tabular classification, regression, and time-series forecasting, enabling analysis of trends in Europes labour market across gender, occupation, and geographic dimensions. Data is retrieved via the ILOSTAT REST API and harmonized by the ILO using International Conference of Labour Statisticians (ICLS) definitions for cross-country comparability.

搜集汇总

数据集介绍

构建方式

该数据集源于国际劳工组织（ILO）的权威统计数据库ILOSTAT，其构建依托于一个标准化的数据管道。研究团队通过ILOSTAT的REST API接口，以程序化方式提取了指标代码为EES_TEES_SEX_OCU_GEO_NB的原始数据，该指标专门记录按性别、职业及城乡地域分类的雇员人数。数据提取后，依据ISO 3166-1 alpha-3国家代码，筛选出39个欧洲国家的观测记录，并保留了原始调查来源标签以确保数据溯源。最终由Electric Sheep Europe进行重新打包与模式规范化，生成便于机器学习使用的Parquet格式数据集，涵盖1992年至2025年的129,099条观测。

特点

该数据集的核心特征体现在其多维度的劳动力市场解构能力上。数据集以性别（总计、男性、女性）、职业分类（基于技能水平）、城乡地域类型为核心分层变量，细致刻画了欧洲各国雇员分布的异质性。其地理覆盖范围广泛，横跨39个欧洲国家，时间跨度逾三十年，为探究长期就业结构变迁提供了丰富的截面与时序信息。数据以英文单语呈现，体量适中，属于十万至百万行级别，适用于表格分类、回归及时间序列预测等多类任务。各列均附带清晰的标签与状态标志，如观测状态注释，增强了数据解读的严谨性。

使用方法

使用者可通过Hugging Face的datasets库以单行代码便捷加载本数据集，并直接转换为Pandas数据框进行后续分析。在具体应用中，研究者可依据ref_area列筛选特定国家（如德国），或对indicator列进行过滤以聚焦单一指标，进而利用time列构建时间序列图，观察雇员人数的演变轨迹。此外，通过透视表操作，可将数据重塑为国家与年份的矩阵形式，便于开展跨国比较或面板数据分析。数据集亦支持直接用于训练机器学习模型，其丰富的分类变量与数值目标变量为表格预测任务提供了标准化的输入结构。

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体系的构建与完善。ILOSTAT作为其核心统计数据库，汇集了来自各国劳动力调查、家庭收入调查及行政记录的微观数据，经国际劳工统计学家会议（ICLS）定义标准化后向全球发布。本数据集由Electric Sheep Europe于2025年重新封装，聚焦欧洲39国1992至2025年间按性别、职业及城乡区域划分的雇员数据，共计129,099条观测。该数据集为探究欧洲劳动力市场结构性变迁、性别职业隔离及城乡就业差异提供了宝贵的跨国面板数据，对劳动经济学与区域发展研究具有重要支撑价值。

当前挑战

该数据集所应对的核心领域问题在于刻画欧洲劳动力市场中性别、职业与城乡维度的雇员分布格局，服务于就业政策评估与不平等研究。构建过程中的挑战尤为显著：跨国数据源自各异，各国的劳动力调查口径、职业分类标准与城乡定义存在系统性差异，ILOSTAT虽以ICLS框架调和，仍难免断点与不可靠标记频现；部分国家（如英国数据止于2019年）或年份（如希腊延伸至2025年）覆盖不均衡，导致面板结构不平衡；城乡维度仅在国家层面（GEO_COV_NAT）呈报，缺乏次国家层级细分，限制了空间异质性的深入挖掘；观察状态标注（如暂定、不可靠）需在建模时审慎处理，否则将引入估计偏误。

常用场景

经典使用场景

在劳动经济学与区域发展研究的经典范式中，该数据集常被用于刻画欧洲39国雇员在性别、职业与城乡维度上的分布格局。研究者借助其1992至2025年的长时序截面观测，构建面板数据模型，考察性别职业隔离的时空演变，以及城乡劳动力市场结构的差异化特征。其细粒度分类变量与标准化国别编码，为跨国比较与趋势检验提供了坚实的经验基础。

解决学术问题

该数据集有效回应了劳动统计领域长期存在的若干学术难题：如何以统一口径衡量跨国性别职业分布差异，如何识别城乡区域在就业结构中的系统性分野，以及如何追踪经济周期与政策干预对就业构成的动态影响。通过ILO标准化协调，它缓解了各国调查定义不一致导致的比较偏误，为实证劳动经济学、社会分层与区域科学提供了可复现的高质量证据，推动了跨国劳动市场研究的规范化。

衍生相关工作

围绕该数据集，已衍生出一系列经典研究工作，包括基于面板回归的性别职业隔离指数测算、城乡就业弹性估计，以及运用机器学习方法对就业结构进行预测的探索。部分研究将其与ILO其他指标（如工资、工时）链接，构建多维劳动市场脆弱性指数。这些工作不仅拓展了ILOSTAT数据的分析边界，也催生了面向欧洲劳动力市场的一体化数据产品与可视化仪表板，形成了持续演进的学术与政策应用生态。