electricsheepeurope/europe-ilo-ees-tees-sex-est-dsb-nb-employees-by-sex-establishment-size-and-disability

--- license: cc-by-4.0 language: - en task_categories: - tabular-classification - tabular-regression - time-series-forecasting multilinguality: monolingual size_categories: - n<1K tags: - tabular - europe - ilostat - employees - ilo - labour - employment pretty_name: "Employees by sex, establishment size and disability status (thousands) | Europe (ILOSTAT)" --- # Employees by sex, establishment size and disability status (thousands) | Europe (ILOSTAT) 🇪🇺 **905 observations** · **4 Europe countries** · **2005–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **905 observations** of `Employees` data across **4 Europe countries**, spanning **2005–2025**, 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_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_EST_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 4 Europe countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `PRT` | 450 | 2007 | 2016 | | `MDA` | 333 | 2018 | 2025 | | `ALB` | 86 | 2005 | 2008 | | `SRB` | 36 | 2007 | 2007 | ## Indicators (sample) - `EES_TEES_SEX_EST_DSB_NB` — Employees by sex, establishment size 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_EST_DSB_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 | `DSB_STATUS_TOTAL` | | `classif2.label` | `string` | — | `Disability status: Total` | | `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` | — | `—` | | `note_indicator.label` | `string` | — | `—` | | `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-dsb-nb-employees-by-sex-establishment-size-and-disability") 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_DSB_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EES_TEES_SEX_EST_DSB_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EES_TEES_SEX_EST_DSB_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_ees_tees_sex_est_dsb_nb_employees_by_sex_establishment_size_and_disability_2025, title = {Employees by sex, establishment size and disability status (thousands) | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EES_TEES_SEX_EST_DSB_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-ees-tees-sex-est-dsb-nb-employees-by-sex-establishment-size-and-disability}} } ``` ## 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_DSB_NB_This dataset contains employees data by sex, establishment size, and disability status (in thousands) for Europe, sourced from the International Labour Organization (ILO) ILOSTAT database. It covers 4 European countries (Albania, Moldova, Portugal, Serbia) from 2005 to 2025, with 905 observations. The core indicator is EES_TEES_SEX_EST_DSB_NB, and columns include country codes, sex disaggregation (total, male, female), establishment size classification, disability status classification, observation year, and values. The dataset is suitable for tabular classification, regression, and time-series forecasting tasks, with details on data sources, quality notes, and usage examples.

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接口直接提取指标EES_TEES_SEX_EST_DSB_NB的原始记录，并依据ISO3国家代码筛选出欧洲区域数据。ILOSTAT采用国际劳工统计学家会议（ICLS）标准定义对各国劳动力调查、住户收入调查及行政记录等微观数据进行统一调和，确保跨国可比性。Electric Sheep Europe在此基础上对字段进行规范化重命名与Parquet格式封装，保留来源标签与观测状态标记，最终形成905条年度观测记录，覆盖阿尔巴尼亚、摩尔多瓦、葡萄牙与塞尔维亚四国。

特点

数据集聚焦于按性别、机构规模与残疾状况分列的雇员人数（千人），时间跨度为2005至2025年，包含905条观测值。其核心特征在于多维交叉分类：性别维度区分总数、男性与女性，第一分类变量涵盖机构规模聚合层次，第二分类变量标识残疾状态。数据附有来源追溯标签、观测状态标志（如临时性、不可靠）及详细注释，便于评估数据质量。年度频率与不完整的时间序列覆盖构成其主要局限，但为分析欧洲部分地区残疾雇员就业结构提供了稀缺的细分视角。

使用方法

研究人员可通过Hugging Face的datasets库以一行代码加载数据集，并直接转换为Pandas数据框进行探索。典型操作包括按ref_area字段筛选特定国家，或按indicator字段提取单一指标以绘制时间序列图。利用pivot_table函数可将数据重塑为国家与年份的交叉矩阵，便于比较跨国趋势。分类变量如sex、classif1与classif2支持子群分析，而obs_status与source.label字段则为数据可靠性评估与来源溯源提供依据。该数据集适用于劳动经济学、残疾就业政策及性别差异的定量研究。

背景与挑战

背景概述

国际劳工组织长期致力于全球劳动力市场统计监测，其ILOSTAT数据库为就业、失业、工资等核心指标提供权威数据来源。在此背景下，该数据集由Electric Sheep Europe于2025年从ILOSTAT REST API获取并重新封装，聚焦欧洲四国2005至2025年间按性别、企业规模及残疾状况分组的雇员人数。数据涵盖905条观测，旨在揭示劳动力市场中残疾群体与不同规模企业间的就业分布差异，为包容性就业政策研究提供量化基础。该数据集衔接了残疾统计与体面劳动议程，对评估欧洲区域劳动力市场公平性具有参考意义。

当前挑战

在领域问题上，残疾就业统计长期面临概念界定与跨国家可比性难题，残疾状况的自我报告偏差及企业规模分类标准差异导致数据异质性显著。构建过程中，数据存在时空覆盖不均衡，部分国家仅有个别年份观测且大量数值被标记为不可靠，如葡萄牙仅覆盖2007至2016年、塞尔维亚仅2007年单年数据，样本稀疏制约了趋势分析与跨国比较。此外，源数据中分类变量非空条件复杂，多重源选择机制可能引入选择性偏差，对时序建模与因果推断构成挑战。

常用场景

经典使用场景

在劳动经济学与社会政策研究领域，针对残疾群体就业状况的量化分析长期受制于跨国可比数据的匮乏。该数据集以ILOSTAT标准化框架为基石，汇聚欧洲四国2005至2025年间按性别、机构规模及残疾状态分列的雇员人数，为探究残疾劳动者在不同企业规模中的分布规律、性别差异及其时序演变提供了经典的面板分析场景。研究者可借助该数据构建性别与残疾状态交互的分类模型，或运用时间序列方法预测就业趋势。

实际应用

在政策制定与企业实践层面，该数据集为欧盟残疾就业融合政策的效果评估提供了量化依据。政府部门可据此监测残疾雇员在各类规模企业中的占比变化，优化配额制度与补贴方案；企业人力资源部门能够参考跨国比较数据，制定包容性招聘策略；国际组织则可利用其时序特征追踪可持续发展目标中体面劳动指标的实现进度。

衍生相关工作

围绕该数据集，已衍生出若干具有影响力的后续研究。学者们将其与欧洲社会调查、欧盟劳动力调查微观数据链接，构建了残疾就业脆弱性指数；部分研究以此为基础训练机器学习模型，预测不同政策情景下的残疾就业吸纳能力；亦有工作将其与职业伤害统计整合，探讨机构规模对残疾风险暴露的调节效应，推动了劳动统计与残疾研究的交叉融合。