electricsheepeurope/europe-ilo-ees-tees-sex-ocu-mts-nb-employees-by-sex-occupation-and-marital-status-tho

--- 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 marital status (thousands) | Europe (ILOSTAT)" --- # Employees by sex, occupation and marital status (thousands) | Europe (ILOSTAT) 🇪🇺 **202,765 observations** · **39 Europe countries** · **1991–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **202,765 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_OCU_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_OCU_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` | 15,484 | 1991 | 2025 | | `GBR` | 10,944 | 1992 | 2025 | | `POL` | 10,274 | 1997 | 2025 | | `MDA` | 10,077 | 2000 | 2025 | | `AUT` | 9,335 | 1995 | 2025 | | `CZE` | 9,205 | 2000 | 2024 | | `FRA` | 8,528 | 2005 | 2024 | | `MKD` | 7,569 | 2006 | 2025 | | `ALB` | 6,268 | 2002 | 2024 | | `RUS` | 6,048 | 2010 | 2025 | | `BIH` | 6,038 | 2001 | 2020 | | `ESP` | 6,035 | 1992 | 2025 | | `IRL` | 5,504 | 1992 | 2023 | | `SRB` | 4,876 | 2007 | 2020 | | `ITA` | 4,725 | 1992 | 2024 | | ... | _24 more countries_ | | | ## Indicators (sample) - `EES_TEES_SEX_OCU_MTS_NB` — Employees by sex, occupation 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_OCU_MTS_NB` | | `indicator.label` | `string` | Indicator name in English | `Employees by sex, occupation and mari…` | | `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 | `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` | — | `C4:1005` | | `note_classif.label` | `string` | — | `Nonstandard occupation: Including 7` | | `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-mts-nb-employees-by-sex-occupation-and-marital-status-tho") 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_MTS_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EES_TEES_SEX_OCU_MTS_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EES_TEES_SEX_OCU_MTS_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_ees_tees_sex_ocu_mts_nb_employees_by_sex_occupation_and_marital_status_tho_2025, title = {Employees by sex, occupation and marital status (thousands) | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EES_TEES_SEX_OCU_MTS_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-ees-tees-sex-ocu-mts-nb-employees-by-sex-occupation-and-marital-status-tho}} } ``` ## 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_MTS_NB_This dataset contains employee data for 39 European countries from 1991 to 2025, with the indicator Employees by sex, occupation and marital status (thousands). It includes 202,765 observations covering 1 distinct indicator. The data is sourced from the International Labour Organization (ILO) ILOSTAT database, retrieved via API and filtered to Europe ISO3 country codes. The dataset features multiple columns such as country code, country name, data source, indicator code, sex disaggregation, occupation classification, marital status classification, observation year, observed value, and observation status. Data is published at annual frequency, with quality caveats including the use of ILO-selected best source when multiple sources exist for the same country×year. The dataset is suitable for tabular classification, regression, and time-series forecasting tasks.

搜集汇总

数据集介绍

构建方式

该数据集源自国际劳工组织（ILO）的ILOSTAT中央统计数据库，通过其REST API接口直接抽取指标EES_TEES_SEX_OCU_MTS_NB的原始记录，并依据ISO3国家代码筛选出欧洲39国的数据。ILOSTAT依据国际劳工统计学家会议（ICLS）定义对各国劳动力调查等微观数据进行统一协调，数据中通过source.label列标注来源以确保可追溯性。Electric Sheep Europe对原始数据进行规范化重打包，发布为HuggingFace数据集，便于机器学习应用。

特点

数据集涵盖1991年至2025年欧洲39国的雇员统计，共202,765条观测，以千人为单位。核心字段包括国家代码、数据来源、指标代码、性别、职业分类、婚姻状况分类、年份、观测值及各类状态与注释标签。性别维度提供总计、男性和女性三个类别。数据以年度频率发布，部分指标未包含月度或季度序列，且当同一国家-年份存在多来源时仅保留ILO选定的最佳来源。

使用方法

通过HuggingFace的datasets库加载数据集，转换为Pandas DataFrame后可进行灵活分析。例如，可筛选特定国家（如德国）的子集，或对单一指标按时间排序绘制趋势图，还可透视生成国家×年份矩阵。数据集适用于表格分类、回归及时间序列预测等任务，使用时需注意观测状态标志和注释标签以理解数据质量。

背景与挑战

背景概述

在国际劳工统计领域，跨国、跨时期且按多维人口特征细分的就业数据长期稀缺，制约着劳动力市场结构变迁的量化研究。国际劳工组织（ILO）依托其ILOSTAT中央统计数据库，整合各国劳动力调查与行政记录，于2025年发布该数据集，并由Electric Sheep Europe重新封装。数据集涵盖39个欧洲国家、1991至2025年间202,765条观测，按性别、职业与婚姻状况细分雇员人数。其核心研究问题在于揭示婚姻状况与职业性别隔离如何交互影响就业参与，为劳动经济学、社会分层与性别研究提供了高粒度面板数据基础。

当前挑战

该数据集所回应的领域问题，在于传统就业统计往往忽略婚姻状况与职业技能的交叉维度，难以刻画性别与家庭角色对职业分布的差异化约束。构建过程中的挑战尤为显著：ILOSTAT原始微数据来源于各国调查工具与抽样框架，ICLS定义虽提供协调基准，但国家间职业分类与婚姻状况编码仍存在非标准化处理，导致部分序列出现方法修订断点。数据质量标志如obs_status中“不可靠”标记与注释列中的系列断裂提示，要求研究者在时序建模与跨国比较中审慎处理异质性与缺失机制。

常用场景

经典使用场景

在劳动经济学与人口统计学的交叉领域中，该数据集最经典的使用场景在于依托国际劳工组织标准化框架，对欧洲39国1991至2025年间按性别、职业与婚姻状况交叉分类的雇员规模进行纵贯比较分析。研究者可借此考察不同婚姻状态群体在职业层级中的分布变迁，以及性别维度下就业结构的长期演化轨迹，从而为劳动力市场分层研究提供细粒度的时间序列证据。

解决学术问题

该数据集有效回应了劳动社会学中关于婚姻状况与职业隔离之间关联机制的实证争议，弥补了跨国比较研究中长期缺乏统一口径细分类别数据的缺陷。通过提供经国际劳工统计学家会议定义协调后的观测值，它使学者得以在控制国家异质性的前提下检验婚姻溢价假说、性别职业隔离的收敛趋势及经济转型对东欧国家就业结构的差异化冲击，显著提升了相关因果推断的外部效度。

衍生相关工作

基于该数据集及其源流，已衍生出一系列经典研究，包括运用面板固定效应模型检验婚姻状况对职业声望获得影响的跨国比较论文，以及采用聚类轨迹分析刻画欧洲女性就业模式分化的时序研究。此外，若干开源工具包将其整合为劳动市场不平等指标的计算基准，推动了可复现社会科学研究基础设施的建设。