electricsheepeurope/europe-ilo-ees-tees-sex-age-mts-nb-employees-by-sex-age-and-marital-status-thousands

--- 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, age and marital status (thousands) | Europe (ILOSTAT)" --- # Employees by sex, age and marital status (thousands) | Europe (ILOSTAT) 🇪🇺 **218,979 observations** · **39 Europe countries** · **1983–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **218,979 observations** of `Employees` data across **39 Europe countries**, spanning **1983–2025**, 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_AGE_MTS_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_AGE_MTS_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 | |---------|-----:|-----------:|----------:| | `CHE` | 14,783 | 1991 | 2025 | | `CZE` | 11,875 | 1993 | 2024 | | `GBR` | 11,821 | 1983 | 2025 | | `MDA` | 10,142 | 2000 | 2025 | | `POL` | 10,035 | 1997 | 2025 | | `AUT` | 9,405 | 1995 | 2025 | | `FRA` | 7,961 | 2005 | 2024 | | `MKD` | 7,409 | 2006 | 2025 | | `RUS` | 6,894 | 2010 | 2025 | | `ESP` | 6,869 | 1986 | 2025 | | `ALB` | 6,729 | 2002 | 2024 | | `IRL` | 6,690 | 1983 | 2023 | | `ITA` | 6,048 | 1983 | 2024 | | `BIH` | 6,047 | 2001 | 2020 | | `DEU` | 5,472 | 1983 | 2020 | | ... | _24 more countries_ | | | ## Indicators (sample) - `EES_TEES_SEX_AGE_MTS_NB` — Employees by sex, age and marital status (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_AGE_MTS_NB` | | `indicator.label` | `string` | Indicator name in English | `Employees by sex, age and marital sta…` | | `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.) | `AGE_YTHADULT_YGE15` | | `classif1.label` | `string` | — | `Age (Youth, adults): 15+` | | `classif2` | `string` | Second classification variable where applicable | `MTS_AGGREGATE_TOTAL` | | `classif2.label` | `string` | — | `Marital status (Aggregate): 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` | — | `C6:1688` | | `note_classif.label` | `string` | — | `Nonstandard age group: Excluding ages…` | | `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-age-mts-nb-employees-by-sex-age-and-marital-status-thousands") 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_AGE_MTS_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EES_TEES_SEX_AGE_MTS_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EES_TEES_SEX_AGE_MTS_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_ees_tees_sex_age_mts_nb_employees_by_sex_age_and_marital_status_thousands_2025, title = {Employees by sex, age and marital status (thousands) | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EES_TEES_SEX_AGE_MTS_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-ees-tees-sex-age-mts-nb-employees-by-sex-age-and-marital-status-thousands}} } ``` ## 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_AGE_MTS_NB_This dataset contains 218,979 observations of employee data across 39 European countries from 1983 to 2025, focusing on the indicator Employees by sex, age and marital status (thousands). The data is sourced from the ILOSTAT database of the International Labour Organization (ILO), retrieved via REST API and filtered for European countries. It is presented in tabular format with columns such as country code (ref_area), country name (ref_area.label), data source (source), indicator code (indicator), sex disaggregation (sex), age and marital status classifications (classif1, classif2), year (time), observed value (obs_value), and more, suitable for tabular classification, regression, and time-series forecasting tasks. The data is harmonized using ICLS definitions and includes quality flags (e.g., observation status). Repackaged by Electric Sheep Europe, it aims to provide a unified, ML-ready data layer for Europe, enabling researchers and developers to quickly load and use the data.

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ILOSTAT中央统计数据库，通过REST API接口直接提取指标EES_TEES_SEX_AGE_MTS_NB的原始观测记录，并依据ISO3国家代码筛选出39个欧洲国家。原始调查微数据经国际劳工统计学家会议（ICLS）定义体系进行跨国协调与标准化处理，确保不同来源的劳动力调查数据具备可比性。数据经Electric Sheep Europe重新打包为Parquet格式，保留来源标签与注释字段，以增强溯源性。

使用方法

研究者可通过HuggingFace的datasets库以load_dataset函数直接加载数据，并转换为Pandas数据框进行后续分析。典型操作包括按国家代码筛选特定区域、针对单一指标构建时间序列、或透视生成国家与年份的矩阵表。数据亦可结合分类标签进行分组聚合，以探究性别、年龄及婚姻状况对就业结构的交互影响。

背景与挑战

背景概述

劳动力市场统计历来是研判社会经济结构与制定公共政策的基石。国际劳工组织（ILO）长期致力于全球劳动统计的标准化与传播，其ILOSTAT数据库汇聚了两百余个经济体的就业、失业与劳动条件指标。该数据集由Electric Sheep Europe于2025年从ILOSTAT REST API抓取并重新封装，覆盖39个欧洲国家、1983至2025年间218,979条观测记录，聚焦按性别、年龄与婚姻状况分列的雇员人数（千人）。其核心研究问题在于揭示欧洲各国劳动力构成的性别与年龄异质性，并为劳动经济学、人口结构变迁及社会保障政策的跨国比较提供高粒度时序数据基础。

当前挑战

该数据集所应对的领域问题在于劳动力统计中多维分类交叉下的时序可比性：性别、年龄与婚姻状况三重维度的细分使得许多国家在特定年份的单元格出现稀疏或缺失，为建模带来结构性不均衡。构建过程中，数据依赖各国劳动调查的原始微数据，ILO虽以国际劳工统计学家会议定义为基准进行协调，但各国调查口径、抽样设计与报告制度差异显著，导致时间序列存在方法学修订引起的中断，且部分观测被标注为不可靠或临时性数值。此外，婚姻状况分类的跨国映射与聚合层级的非标准化，进一步增加了数据清洗与语义对齐的复杂度。

常用场景

经典使用场景

在劳动经济学与人口统计学交叉研究中，该数据集最经典的用途在于刻画欧洲39国1983至2025年间雇员规模在性别、年龄及婚姻状态多维交叉下的动态分布。研究者可借助其长时段、高频次的观测值，构建面板数据模型与时间序列预测模型，用以检验婚姻状态对劳动参与率的调节效应，或分析不同年龄队列中性别就业差距的收敛趋势。

解决学术问题

该数据集有效回应了跨国比较研究中长期存在的口径不一与数据碎片化难题。依托国际劳工组织统一协调的ICLS定义与来源标注体系，研究者得以在一致框架下探讨性别、年龄与婚姻状态对雇员数量的交互影响，为验证劳动供给的生命周期假说、评估家庭结构变迁的就业效应提供了可靠的经验基础，并推动了相关领域实证研究的可复现性。

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弱势群体就业状况提供了量化依据。借助性别、年龄与婚姻状态的精细分类，决策者可识别单亲家庭、青年群体或中老年女性等特定人群的就业短板，从而设计更具靶向性的职业培训、育儿支持与弹性工作制度。企业人力资源部门亦可参考其趋势研判，优化招聘策略与多元化用工规划。

数据集最近研究

最新研究方向

在劳动经济学与人口统计学交叉领域，该数据集正推动对婚姻状况如何调节性别与年龄维度就业差异的精细化探究。前沿研究聚焦于利用其长时序、多国面板结构，识别婚姻状态变迁对女性劳动参与率的异质性影响，以及经济周期中已婚与未婚群体就业弹性的分化。结合ILOSTAT的标准化编码与数据质量标记，学者得以校正调查方法断裂带来的偏差，进而检验家庭分工理论、生命历程理论在欧洲福利国家体制下的适用性。相关热点延伸至后疫情时代劳动力市场重构、人口老龄化对就业结构的压力，以及性别平等政策的效果评估。该数据集为跨国比较与因果推断提供了关键微观基础，对完善社会保护与促进包容性增长具有重要参考价值。