electricsheepeurope/europe-ilo-ees-tees-sex-est-geo-nb-employees-by-sex-establishment-size-and-rural-urba

--- license: cc-by-4.0 language: - en task_categories: - tabular-classification - tabular-regression - time-series-forecasting multilinguality: monolingual size_categories: - 10K<n<100K tags: - tabular - europe - ilostat - employees - ilo - labour - employment pretty_name: "Employees by sex, establishment size and rural / urban areas (thousands) | Europe (ILOSTAT)" --- # Employees by sex, establishment size and rural / urban areas (thousands) | Europe (ILOSTAT) 🇪🇺 **11,559 observations** · **9 Europe countries** · **1992–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **11,559 observations** of `Employees` data across **9 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_EST_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_EST_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 9 Europe countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `MDA` | 2,687 | 2003 | 2025 | | `BIH` | 2,267 | 2001 | 2024 | | `AUT` | 2,178 | 2004 | 2025 | | `SRB` | 1,890 | 2007 | 2025 | | `PRT` | 1,170 | 2007 | 2016 | | `POL` | 585 | 2021 | 2025 | | `GRC` | 504 | 1992 | 2005 | | `CZE` | 234 | 2019 | 2020 | | `ALB` | 44 | 2008 | 2008 | ## Indicators (sample) - `EES_TEES_SEX_EST_GEO_NB` — Employees by sex, establishment size 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_EST_GEO_NB` | | `indicator.label` | `string` | Indicator name in English | `Employees by sex, establishment size …` | | `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.) | `EST_AGGREGATE_TOTAL` | | `classif1.label` | `string` | — | `Establishment size (Aggregate): Total` | | `classif2` | `string` | Second classification variable where applicable | `GEO_COV_NAT` | | `classif2.label` | `string` | — | `Area type: National` | | `time` | `int64` | Observation year | `2008` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `494.157` | | `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-est-geo-nb-employees-by-sex-establishment-size-and-rural-urba") 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_EST_GEO_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EES_TEES_SEX_EST_GEO_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EES_TEES_SEX_EST_GEO_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_ees_tees_sex_est_geo_nb_employees_by_sex_establishment_size_and_rural_urba_2025, title = {Employees by sex, establishment size 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_EST_GEO_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-ees-tees-sex-est-geo-nb-employees-by-sex-establishment-size-and-rural-urba}} } ``` ## 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_EST_GEO_NB_This dataset contains employee data for 9 European countries, spanning from 1992 to 2025, with 11,559 observations. The indicator is EES_TEES_SEX_EST_GEO_NB, which represents employees by sex, establishment size, and rural/urban areas (in thousands). The data is sourced from the International Labour Organization (ILO)s ILOSTAT statistical database, retrieved via REST API and filtered to European country codes. The dataset includes multiple dimensions: country (using ISO 3166-1 alpha-3 codes), data source (e.g., labor force surveys), sex (total, male, female), establishment size classification, rural/urban classification, year, observed value (number of employees), observation status (e.g., unreliable), etc. The data is annual frequency and includes data quality caveats, such as using the ILO-selected best source when multiple sources exist for the same country×year. The dataset is stored in tabular format and is suitable for tabular classification, regression, and time-series forecasting tasks.

搜集汇总

数据集介绍

构建方式

该数据集依托国际劳工组织（ILO）的ILOSTAT中央统计数据库，通过REST API接口直接提取指标EES_TEES_SEX_EST_GEO_NB的原始数据，并依据ISO3国家代码筛选出9个欧洲国家。ILOSTAT对源自国家劳动力调查、家庭收入调查及行政记录的微观数据进行协调，采用国际劳工统计学家会议（ICLS）标准定义，确保跨国可比性。Electric Sheep Europe对提取的数据进行重新封装，规范模式并输出为Parquet格式，最终形成包含11559条观测、覆盖1992至2025年的面板数据集。

特点

数据集聚焦欧洲九国雇员规模，以千人计量，按性别、机构规模及城乡区域三重维度细分，时间跨度逾三十年。数据以年度频率呈现，包含来源标签、指标注释及观测状态等元数据，可追溯原始调查信息。分类变量如性别（总量、男性、女性）和机构规模等仅在指标发布对应细分时非空，且每个国家-年份组合采用ILO优选的最佳来源，兼顾了统计协调性与数据可用性。

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数据框进行灵活操作。典型用法包括按国家代码过滤子集、对单一指标按时间排序绘制趋势图，或利用透视表生成国家×年份矩阵以开展横截面或时序分析。该数据集适用于表格分类、回归及时间序列预测任务，使用时需遵循CC-BY-4.0许可，并同时引用ILO原始来源与Electric Sheep Europe的重新封装工作。

背景与挑战

背景概述

国际劳工组织（ILO）长期致力于全球劳动力市场统计的标准化与可比性建设，其核心数据库ILOSTAT汇集了来自各国劳动力调查、家庭收入调查及行政记录的多源数据。在此背景下，欧洲地区就业结构变迁与性别平等议题日益受到政策制定者与学术界的关注。Electric Sheep Europe于2025年前后对该指标进行规范化重打包，旨在为机器学习社区提供可直接调用的表格化劳动力数据。该数据集覆盖9个欧洲国家、1992至2025年的11559条观测，聚焦不同性别、机构规模及城乡区域下的雇员数量，为劳动经济学、区域发展及性别就业差异研究提供了细粒度的时间序列基础。

当前挑战

该数据集所回应的领域问题在于：如何从多源、非标准化的国家调查数据中提炼出可跨国比较的就业结构指标，并揭示性别与城乡维度下的雇员分布规律。构建过程中的挑战尤为显著：各国统计口径与调查年份差异导致时间序列断裂，如希腊数据止于2005年而摩尔多瓦延续至2025年；部分观测值被标记为“不可靠”或含方法论修订断点，需谨慎处理缺失与异常；机构规模与城乡分类在不同国家的界定亦难完全统一。此外，性别维度虽有三类取值，但细分交叉后的样本稀疏性可能制约高精度建模与稳健推断。

常用场景

经典使用场景

在劳动经济学与就业统计领域，该数据集凭借其覆盖九国、横跨三十余年的性别—企业规模—城乡维度的雇员观测值，构成了分析欧洲劳动力市场结构性差异的经典基础。研究者惯常运用其构建面板数据模型，考察性别就业差距如何随企业规模递增或递减，以及城乡分割对女性就业参与率的调节效应。时间序列预测任务亦频繁采用该数据集，以评估不同国家就业结构的收敛或分化趋势。

解决学术问题

该数据集有效回应了劳动经济学中长期存在的若干学术困惑：性别就业构成是否随企业规模扩大而趋于均衡，城乡二元结构如何塑造不同性别雇员的分布模式，以及经济转型期东南欧国家与中欧国家之间的就业结构差异何以持续。通过提供国际劳工组织统一方法学协调后的可比数据，该数据集使跨国比较研究得以控制调查工具差异，从而提升因果推断的可靠性，对理解制度变迁与劳动力市场结果之间的关联具有实质意义。

衍生相关工作

依托该数据集衍生的经典工作涵盖多个方向：有研究利用其构建贝叶斯层次模型以估计小域性别就业率，弥补部分国家样本量不足的缺陷；亦有学者将其与欧洲劳动力调查微观数据链接，探究企业规模溢价中的性别异质性；在机器学习领域，该数据集被用作表格回归与时间序列预测的基准，催生了针对缺失值与序列中断的稳健建模方法。这些工作共同拓展了就业统计数据的分析边界。