electricsheepeurope/europe-ilo-ees-tees-sex-eco-dsb-nb-employees-by-sex-economic-activity-and-disability

--- 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, economic activity and disability status (thousands) | Europe (ILOSTAT)" --- # Employees by sex, economic activity and disability status (thousands) | Europe (ILOSTAT) 🇪🇺 **59,337 observations** · **32 Europe countries** · **2004–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **59,337 observations** of `Employees` data across **32 Europe countries**, spanning **2004–2025**, 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_ECO_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_ECO_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` | 2,596 | 2005 | 2025 | | `FRA` | 2,310 | 2004 | 2024 | | `EST` | 2,227 | 2004 | 2024 | | `AUT` | 2,165 | 2004 | 2024 | | `POL` | 2,164 | 2005 | 2024 | | `SWE` | 2,132 | 2004 | 2024 | | `NOR` | 2,106 | 2004 | 2024 | | `ITA` | 2,100 | 2004 | 2024 | | `SVN` | 2,093 | 2005 | 2024 | | `ESP` | 2,082 | 2004 | 2024 | | `SVK` | 2,070 | 2005 | 2024 | | `PRT` | 2,051 | 2004 | 2024 | | `NLD` | 2,040 | 2005 | 2024 | | `FIN` | 2,035 | 2004 | 2024 | | `DNK` | 2,019 | 2004 | 2024 | | ... | _17 more countries_ | | | ## Indicators (sample) - `EES_TEES_SEX_ECO_DSB_NB` — Employees by sex, economic activity 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_ECO_DSB_NB` | | `indicator.label` | `string` | Indicator name in English | `Employees by sex, economic activity a…` | | `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.) | `ECO_SECTOR_TOTAL` | | `classif1.label` | `string` | — | `Economic activity (Broad sector): 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-eco-dsb-nb-employees-by-sex-economic-activity-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_ECO_DSB_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EES_TEES_SEX_ECO_DSB_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EES_TEES_SEX_ECO_DSB_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_ees_tees_sex_eco_dsb_nb_employees_by_sex_economic_activity_and_disability_2025, title = {Employees by sex, economic activity and disability status (thousands) | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EES_TEES_SEX_ECO_DSB_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-ees-tees-sex-eco-dsb-nb-employees-by-sex-economic-activity-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_ECO_DSB_NB_This dataset contains annual statistics on the number of employees (in thousands) disaggregated by sex, economic activity, and disability status for 32 European countries from 2004 to 2025. It comprises 59,337 observations covering one core indicator (EES_TEES_SEX_ECO_DSB_NB), sourced from the International Labour Organization (ILO) ILOSTAT database. The dataset provides detailed dimensional breakdowns including sex (total, male, female), economic activity, and disability status, along with information such as country codes, data sources, observed values, observation statuses, and relevant notes. The data is harmonized by the ILO following International Conference of Labour Statisticians (ICLS) definitions and is designed as a machine-learning-ready structured dataset for labor market research.

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接口直接抽取指标代码为EES_TEES_SEX_ECO_DSB_NB的原始记录，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统一harmonisation，数据管道保留source.label字段以追溯原始调查来源。Electric Sheep Europe对拉取结果进行模式规范化，以Parquet格式重新封装，确保字段类型一致、缺失值显式标记，最终形成涵盖2004至2025年的面板数据。

特点

数据集包含59,337条观测，覆盖32个欧洲国家，时间跨度为2004至2025年，以年度频率记录按性别、经济活动部门和残疾状态分类的雇员人数（单位：千人）。性别维度区分总计、男性和女性三类，经济活动与残疾状态分类变量按指标发布情况非空。观测值附有obs_status质量标志，如临时性或不稳定估计，并设置note_classif、note_indicator、note_source等多级注释字段，记录非标准定义、序列断点及数据来源信息，为数据质量评估提供充分依据。

使用方法

研究者可通过HuggingFace datasets库以load_dataset函数加载数据集，并转换为Pandas DataFrame进行后续分析。典型用法包括按ref_area字段筛选单一国家，或按indicator字段提取特定指标的时间序列并排序绘图。利用pivot_table可将数据重整为国家与年份的二维矩阵，便于横向比较或面板回归。若需结合其他ILOSTAT指标，可复用相同加载流程，通过indicator字段区分序列。使用时应留意obs_status与note字段提示的估计可靠性及定义变化。

背景与挑战

背景概述

伴随全球对体面劳动与包容性就业议题的持续关切，国际劳工组织（ILO）依托其核心统计数据库ILOSTAT，系统汇编了覆盖二百余个经济体的劳动力市场指标。该数据集由Electric Sheep Europe于2025年自ILOSTAT REST API抽取并重新封装，聚焦欧洲32国2004至2025年间按性别、经济活动及残疾状况分列的雇员规模，共含59,337条观测。其核心研究问题在于揭示残疾状态与性别双重维度下就业结构的异质性，为评估残疾群体劳动力市场融入程度提供跨国可比的时间序列证据，对劳动经济学、社会政策评估及SDG体面工作目标的监测具有重要参考价值。

当前挑战

该数据集所回应的领域问题，在于残疾状况与就业参与的关联长期受制于定义分歧而难以跨国比较，ILOSTAT虽以国际劳工统计学家会议（ICLS）标准加以协调，却仍受各国调查工具差异的掣肘。构建过程中的挑战同样显著：部分国家残疾定义偏离国际标准，观测状态被标注为不可靠或临时性，序列中屡现方法修订所致的中断，且各细分维度仅在特定指标下发布，导致数据缺失与非平衡面板并存；此外，多来源冲突时仅保留ILO甄选的单一最优来源，虽提升一致性，却可能牺牲信息丰富度，对建模者的缺失值处理与异质性识别能力构成考验。

常用场景

经典使用场景

在劳动经济学与残障就业研究的交叉领域，该数据集以性别、经济活动部门与残障状态三重维度刻画欧洲三十二国雇员规模的时序演变，其最经典的使用场景在于构建跨国面板数据，用以检验残障身份与就业参与之间的结构性关联。研究者依托2004至2025年的年度观测，既可横向比较同一时点不同国家在残障雇员占比上的梯度差异，亦可纵向追踪各国残障就业政策干预前后的趋势变化，从而在统一分类框架下识别性别与行业部门对残障就业机会的调节效应。

解决学术问题

该数据集有效回应了残障就业统计中长期存在的可比性难题。由于各国劳动力调查在残障定义、抽样口径与行业分类上参差不齐，跨国比较往往陷入口径失谐的困境，而ILOSTAT依据国际劳工统计学家会议标准对原始微观调查数据进行协调化处理，并在来源标签中保留原始出处与定义偏差备注，使研究者得以在控制数据质量的前提下开展跨时空的量化分析，为残障就业差距的测度、性别不平等在残障群体中的叠加效应等议题提供了可复现的实证基础。

衍生相关工作

围绕该数据集衍生的经典工作主要集中于残障就业差距的跨国分解与性别交叉性分析。部分研究以其为基准，结合欧洲社会调查等微观数据，检验宏观残障就业率与个体就业概率之间的一致性；亦有学者将其纳入更广泛的ILOSTAT指标体系中，用于构建残障包容性指数或开展时间序列预测建模。这些工作不仅拓展了残障统计数据的分析边界，也推动了残障就业研究从描述性比较向因果推断与政策模拟的方向演进。