electricsheepeurope/europe-ilo-ees-tees-sex-ocu-dsb-nb-employees-by-sex-occupation-and-disability-status

--- 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, occupation and disability status (thousands) | Europe (ILOSTAT)" --- # Employees by sex, occupation and disability status (thousands) | Europe (ILOSTAT) 🇪🇺 **24,079 observations** · **32 Europe countries** · **2002–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **24,079 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_OCU_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_OCU_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` | 928 | 2005 | 2025 | | `FRA` | 900 | 2004 | 2024 | | `ESP` | 880 | 2004 | 2024 | | `ITA` | 876 | 2004 | 2024 | | `AUT` | 874 | 2004 | 2024 | | `EST` | 868 | 2004 | 2024 | | `BEL` | 861 | 2004 | 2024 | | `PRT` | 857 | 2004 | 2024 | | `LUX` | 855 | 2004 | 2024 | | `SVN` | 854 | 2005 | 2024 | | `GRC` | 846 | 2004 | 2024 | | `SWE` | 841 | 2004 | 2024 | | `HUN` | 837 | 2005 | 2024 | | `POL` | 835 | 2005 | 2024 | | `NOR` | 829 | 2004 | 2024 | | ... | _17 more countries_ | | | ## Indicators (sample) - `EES_TEES_SEX_OCU_DSB_NB` — Employees by sex, occupation 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_OCU_DSB_NB` | | `indicator.label` | `string` | Indicator name in English | `Employees by sex, occupation and disa…` | | `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 | `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-ocu-dsb-nb-employees-by-sex-occupation-and-disability-status") 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_DSB_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EES_TEES_SEX_OCU_DSB_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EES_TEES_SEX_OCU_DSB_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_ees_tees_sex_ocu_dsb_nb_employees_by_sex_occupation_and_disability_status_2025, title = {Employees by sex, occupation and disability status (thousands) | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EES_TEES_SEX_OCU_DSB_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-ees-tees-sex-ocu-dsb-nb-employees-by-sex-occupation-and-disability-status}} } ``` ## 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_DSB_NB_This dataset is a tabular dataset containing employee data for 32 European countries from 2002 to 2025, with a total of 24,079 observations. The core indicator is EES_TEES_SEX_OCU_DSB_NB, which represents employees by sex, occupation, and disability status (in thousands). The data is sourced from the International Labour Organization (ILO) ILOSTAT database, retrieved via REST API and harmonized using International Conference of Labour Statisticians (ICLS) definitions. It includes multiple dimensions such as country, data source, sex (total, male, female), occupation classification, and disability status classification, along with year, observed value, and data status flags. The data is annual frequency and suitable for tabular classification, regression, and time-series forecasting tasks, primarily used for labor market analysis and research. The dataset is repackaged by Electric Sheep Europe in Parquet format for machine learning readiness and released under the CC-BY-4.0 license.

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接口直接提取指标EES_TEES_SEX_OCU_DSB_NB的原始数据，并依据ISO3国家代码筛选出32个欧洲国家。ILOSTAT采用国际劳工统计学家会议（ICLS）定义对各国劳动力调查、家庭收入调查及行政记录等微观数据进行标准化处理，Electric Sheep Europe在此基础上重新封装为HuggingFace数据集，保留来源标签以确保可追溯性。

特点

数据集涵盖2002至2025年间24,079条观测记录，以千人为单位度量按性别、职业和残疾状况分类的雇员数量。数据以年度频率发布，包含ref_area、sex、classif1、classif2等多维分类字段，以及观测值、状态标志和详尽注释列。数据经ILO统一协调，优先选用各国最佳来源，并附带质量警示标志（如临时性、不可靠），为跨欧洲劳动力市场研究提供细粒度、可比较的面板数据。

使用方法

研究者可通过HuggingFace的datasets库以load_dataset()函数直接加载，并转换为Pandas数据框进行灵活操作。典型用法包括按ref_area筛选特定国家（如DEU），按indicator和时间排序绘制单指标时间序列，或利用pivot_table构建国家×年份矩阵以支持面板分析与预测建模。数据集适用于表格分类、回归及时间序列预测任务，调用时需遵循CC-BY-4.0许可并引用ILO与Electric Sheep Europe。

背景与挑战

背景概述

在全球化劳动力市场性别平等与残障融合议题日益凸显的背景下，国际劳工组织（ILO）长期致力于构建涵盖体面劳动多维指标的权威统计体系。该数据集由Electric Sheep Europe于2025年基于ILOSTAT数据库重新封装发布，收录了2002至2025年间32个欧洲国家约2.4万条观测记录，聚焦按性别、职业与残障状况分类的雇员人数。其核心研究问题在于揭示欧洲劳动力市场中性别与残障双重维度的就业结构差异，为评估包容性就业政策成效提供了跨国可比的高质量面板数据，对劳动经济学、社会政策评估及残障研究领域具有重要的实证支撑价值。

当前挑战

该数据集所回应的领域问题在于，性别与残障身份交织下的职业隔离与就业参与差距长期缺乏统一口径的跨国量化依据，传统劳动力统计往往仅单独呈现性别或残障单一维度的分布，难以刻画交叉群体在职业层级中的真实处境。构建过程中的主要挑战则源于多源异构数据的协调：32国分别依托劳动力调查、家庭收入调查与行政记录等不同采集机制，残障定义存在国别差异且部分年份出现方法论修订导致的序列断裂，部分观测值标注为“不可靠”或“临时性”，加之职业分类与残障状态分类的双重嵌套导致样本量稀疏，均对数据一致性与时序可比性构成显著制约。

常用场景

经典使用场景

在劳动经济学与残疾研究领域，该数据集最经典的使用场景是构建欧洲32国跨性别的职业分布与残疾状况的劳动力市场参与面板。研究者借助其2002至2025年的年度观测，按性别和职业技能层级对残疾雇员进行细粒度刻画，用以分析残疾就业的结构性差异与动态演变。

衍生相关工作

围绕该数据集，已衍生出多项经典研究，如欧洲残疾就业差距的分解分析、职业隔离的性别与残疾交互效应探讨，以及基于时间序列的残疾就业周期波动预测。这些工作常与Eurostat的SILC数据交叉验证，并催生了针对残疾就业的机器学习分类与回归模型。

数据集最近研究

最新研究方向

伴随全球对残疾人劳动权益保障的持续关切，基于国际劳工组织ILOSTAT的规范统计，该数据集为解析欧洲32国2002至2025年间残疾人就业结构提供了精细化的性别与职业分层依据。当前研究前沿聚焦于运用长时序跨面板数据，识别残疾雇员在技能层级职业中的性别隔离模式，并结合欧盟残疾人就业战略等政策热点，评估各国在消除就业歧视与促进融合就业方面的进展差异。该数据对揭示结构性不平等、支撑循证社会政策制定具有关键意义，亦为劳动经济学与残障研究的交叉创新提供实证基础。