electricsheepeurope/europe-ilo-ees-tees-sex-mts-dsb-nb-employees-by-sex-marital-status-and-disability-sta

--- 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, marital status and disability status (thousands) | Europe (ILOSTAT)" --- # Employees by sex, marital status and disability status (thousands) | Europe (ILOSTAT) 🇪🇺 **43,690 observations** · **32 Europe countries** · **2002–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **43,690 observations** of `Employees` data across **32 Europe countries**, spanning **2002–2025**, 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_MTS_DSB_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_MTS_DSB_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 32 Europe countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `GBR` | 1,687 | 2005 | 2025 | | `PRT` | 1,663 | 2004 | 2024 | | `BEL` | 1,642 | 2004 | 2024 | | `ITA` | 1,620 | 2004 | 2024 | | `SVN` | 1,620 | 2005 | 2024 | | `ESP` | 1,615 | 2004 | 2024 | | `FIN` | 1,551 | 2004 | 2024 | | `NLD` | 1,534 | 2005 | 2024 | | `IRL` | 1,522 | 2004 | 2024 | | `SWE` | 1,518 | 2004 | 2024 | | `LUX` | 1,504 | 2004 | 2024 | | `EST` | 1,495 | 2004 | 2024 | | `FRA` | 1,485 | 2004 | 2024 | | `CZE` | 1,477 | 2005 | 2024 | | `GRC` | 1,471 | 2004 | 2024 | | ... | _17 more countries_ | | | ## Indicators (sample) - `EES_TEES_SEX_MTS_DSB_NB` — Employees by sex, marital status and disability 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) | `BB:7401` | | `source.label` | `string` | Source name in English | `HIES - Living Standards Survey` | | `indicator` | `string` | ILOSTAT indicator code | `EES_TEES_SEX_MTS_DSB_NB` | | `indicator.label` | `string` | Indicator name in English | `Employees by sex, marital status and …` | | `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.) | `MTS_AGGREGATE_TOTAL` | | `classif1.label` | `string` | — | `Marital status (Aggregate): Total` | | `classif2` | `string` | Second classification variable where applicable | `DSB_STATUS_TOTAL` | | `classif2.label` | `string` | — | `Disability status: Total` | | `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_classif` | `string` | — | `C14:6260` | | `note_classif.label` | `string` | — | `Nonstandard definition of disability:…` | | `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-mts-dsb-nb-employees-by-sex-marital-status-and-disability-sta") 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_MTS_DSB_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EES_TEES_SEX_MTS_DSB_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EES_TEES_SEX_MTS_DSB_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_ees_tees_sex_mts_dsb_nb_employees_by_sex_marital_status_and_disability_sta_2025, title = {Employees by sex, marital status and disability status (thousands) | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EES_TEES_SEX_MTS_DSB_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-ees-tees-sex-mts-dsb-nb-employees-by-sex-marital-status-and-disability-sta}} } ``` ## 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_MTS_DSB_NB_This dataset contains employee statistics for European countries from the International Labour Organization (ILO) ILOSTAT database, specifically the indicator Employees by sex, marital status and disability status (thousands). It covers 32 European countries, spans the years 2002 to 2025, and includes 43,690 observations. The data is provided at an annual frequency, with columns such as country code, indicator code, sex disaggregation (total, male, female), marital status classification (total), disability status classification (total), year, observed value (in thousands), and data source and quality flags. The dataset is designed to provide machine learning-ready European labour market data for tasks like tabular classification, regression, or time-series forecasting. It has been repackaged by Electric Sheep Europe and published on the HuggingFace platform with a consistent schema.

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（https://rplumber.ilo.org/data/indicator?id=EES_TEES_SEX_MTS_DSB_NB）直接抽取原始指标数据，并依据ISO3国家代码筛选出32个欧洲国家。ILOSTAT依据国际劳工统计学家会议（ICLS）的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，数据来源在source.label列中标注以保证可追溯性。Electric Sheep Europe对抽取的数据进行重新打包，统一模式并发布为Parquet格式，最终形成涵盖2002至2025年、共计43,690条观测的记录。

特点

数据集聚焦于欧洲地区雇员按性别、婚姻状况和残疾状况的分布，以千人为单位呈现。其核心特征在于多维度的交叉分类：性别分为总计、男性和女性三类，婚姻状况和残疾状况分别通过classif1和classif2列进行聚合或细分，为分析劳动力市场中的社会人口差异提供了 granular 视角。时间跨度长达二十余年，覆盖32个国家，年度频率数据兼具时空双重维度。每条记录附带观测状态标志及分类注释，便于用户评估数据可靠性和定义差异。

使用方法

研究者可通过HuggingFace datasets库以一行代码加载数据集，并转换为Pandas DataFrame进行灵活操作。典型用法包括：筛选特定国家（如df[df['ref_area']=='DEU']）以开展国别研究；针对单一指标按时间排序后绘制时间序列图，观察雇员数量的演变趋势；或利用透视表将数据重塑为国家×年份矩阵，便于横向比较。数据亦可直接用于表格分类、回归及时间序列预测等机器学习任务，为劳动经济学和社会政策分析提供结构化输入。

背景与挑战

背景概述

国际劳工组织（ILO）长期致力于构建全球劳动力市场统计标准，其ILOSTAT数据库为就业、失业及体面劳动等核心议题提供权威数据支撑。在此背景下，Electric Sheep Europe对ILOSTAT原始数据进行了系统性重封装，于2025年发布涵盖32个欧洲国家、2002至2025年间43690条观测值的员工数据集，按性别、婚姻状况与残疾状态进行多维交叉分类。该数据集填补了欧洲区域残疾与婚姻状态交互维度下就业统计的空白，为探究劳动力市场中的结构性不平等提供了精细化的数据基础，对劳动经济学、社会政策评估及残障就业研究具有重要参考价值。

当前挑战

该数据集所对应的领域问题聚焦于残疾与婚姻状况交互影响下的就业态势建模与预测，其核心挑战源于分类维度的稀疏性与数据质量的异质性。在构建过程中，ILO虽采用国际劳工统计学家会议（ICLS）定义对各国调查微观数据进行调和，但不同国家数据来源多样，涵盖劳动力调查、家庭收入调查及行政记录，导致指标定义、抽样方法与覆盖范围存在系统性差异。部分观测值带有“不可靠”或“序列断裂”等状态标识，加之残疾定义非标准化问题突出，致使跨国家与跨时间的可比性受损。此外，婚姻状况与残疾状态的部分交叉单元格样本量不足，进一步加剧了统计推断与时间序列建模的不确定性。

常用场景

经典使用场景

在劳动经济学与社会政策研究中，该数据集常被用于构建跨国面板数据，以剖析性别、婚姻状况与残疾身份三重维度交织下的就业参与差异。研究者借助2002至2025年32个欧洲国家的逾四万条观测，通过双向固定效应模型或时间序列分解，考察已婚残障女性等交叉弱势群体的就业演变轨迹，进而揭示劳动力市场分层机制。

实际应用

在政策实践层面，该数据集为欧盟及各国劳动主管部门评估残障融合就业政策、家庭友好型劳动制度以及性别平等立法的实效提供了量化依据。社会保障机构可据此识别特定婚姻与残障状态组合下的就业低谷群体，优化职业培训与就业补贴的靶向配置，企业亦能借助该数据开展多元包容性雇佣的基准比对与合规审查。

衍生相关工作

围绕该数据集已衍生出一系列经典研究，包括基于ILOSTAT多指标联动的欧洲残障就业缺口分解分析、婚姻状况调节下性别工资差异的队列研究，以及以该数据为训练语料构建的表格数据预测模型。Electric Sheep Europe的标准化封装亦催生了跨国劳动统计数据的自动化管道研究，推动了可复现劳动计量学的发展。