electricsheepeurope/europe-ilo-ees-tees-sex-ins-dsb-nb-employees-by-sex-public-private-sector-and-disabil

--- license: cc-by-4.0 language: - en task_categories: - tabular-classification - tabular-regression - time-series-forecasting multilinguality: monolingual size_categories: - 1K<n<10K tags: - tabular - europe - ilostat - employees - ilo - labour - employment pretty_name: "Employees by sex, public/private sector and disability status (thousands) | Europe (ILOSTAT)" --- # Employees by sex, public/private sector and disability status (thousands) | Europe (ILOSTAT) 🇪🇺 **1,024 observations** · **5 Europe countries** · **2002–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **1,024 observations** of `Employees` data across **5 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_INS_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_INS_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 5 Europe countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `GBR` | 513 | 2005 | 2025 | | `MDA` | 216 | 2018 | 2025 | | `POL` | 161 | 2015 | 2020 | | `ALB` | 107 | 2002 | 2012 | | `SRB` | 27 | 2007 | 2007 | ## Indicators (sample) - `EES_TEES_SEX_INS_DSB_NB` — Employees by sex, public/private sector 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_INS_DSB_NB` | | `indicator.label` | `string` | Indicator name in English | `Employees by sex, public/private sect…` | | `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.) | `INS_SECTOR_TOTAL` | | `classif1.label` | `string` | — | `Institutional 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_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-ins-dsb-nb-employees-by-sex-public-private-sector-and-disabil") 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_INS_DSB_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EES_TEES_SEX_INS_DSB_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EES_TEES_SEX_INS_DSB_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_ees_tees_sex_ins_dsb_nb_employees_by_sex_public_private_sector_and_disabil_2025, title = {Employees by sex, public/private sector and disability status (thousands) | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EES_TEES_SEX_INS_DSB_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-ees-tees-sex-ins-dsb-nb-employees-by-sex-public-private-sector-and-disabil}} } ``` ## 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_INS_DSB_NB_This dataset contains 1,024 observations of employee statistics across 5 European countries (Albania, Moldova, Poland, Serbia, United Kingdom) from 2002 to 2025. The core indicator is Employees by sex, public/private sector and disability status (thousands), sourced from the International Labour Organization (ILO) ILOSTAT database, retrieved via API and filtered for European country codes. The data is presented in tabular format with multiple columns: country code and label, source code and label, indicator code and label, sex disaggregation (total, male, female), first classification variable (e.g., institutional sector), second classification variable (e.g., disability status), observation year, observed value, observation status flags, and related notes. Data is provided at annual frequency, offering detailed breakdowns of employees across dimensions such as sex, public/private sector, and disability status, suitable for tabular classification, regression, and time-series forecasting tasks. For data quality, the ILO harmonizes raw survey microdata using International Conference of Labour Statisticians (ICLS) definitions, with sources and statuses (e.g., provisional or unreliable) flagged. The dataset is released under the CC-BY-4.0 license and repackaged by Electric Sheep Europe into a machine-learning-ready format.

搜集汇总

数据集介绍

构建方式

该数据集依托国际劳工组织（ILO）的ILOSTAT中央统计数据库，通过其公开的REST API接口直接提取指标“EES_TEES_SEX_INS_DSB_NB”的原始观测记录，并借助ISO3国家代码筛选出欧洲区域的数据子集。ILOSTAT作为全球劳工统计的权威枢纽，整合了各国劳动力调查、家庭收入调查、机构调查及行政记录等多源微观数据，并依据国际劳工统计学家会议（ICLS）的定义进行标准化调和，确保跨国可比性。数据集由Electric Sheep Europe重新打包为Parquet格式，保留源标签列以支持溯源，最终形成1,024条观测的即用型表格数据。

特点

数据集聚焦于欧洲五国（英国、摩尔多瓦、波兰、阿尔巴尼亚、塞尔维亚）在2002至2025年间按性别、公私部门及残疾状况分列的雇员数量（千人），涵盖3种性别分类、2种制度部门与残疾状态的多维交叉。数据以年度频率呈现，部分指标含月度或季度序列但不包含于此。质量方面，当同一国家-年份存在多源数据时，采用ILO筛选的最佳来源，且分类列仅在指标支持细分时非空，观测状态标志（如临时性、不可靠）为使用者提供可靠性提示。

使用方法

通过Hugging Face的datasets库调用load_dataset函数加载指定数据集名称，即可获取训练集并转换为Pandas DataFrame。使用者可依据ref_area列筛选特定国家（如df[df[“ref_area”] == “DEU”]），或对单一指标按时间排序绘制趋势图，亦可利用pivot_table生成国家×年份矩阵以进行横向对比或时间序列预测。数据支持表格分类、回归及时间序列预测等任务，适合劳工经济学、社会政策评估及残疾就业差异研究。

背景与挑战

背景概述

国际劳工组织（ILO）自1919年成立以来始终致力于全球劳动统计的标准化与传播，其核心数据库ILOSTAT汇集了涵盖就业、失业、工资及弱势群体劳动参与等多维度指标，为监测全球劳动力市场动态提供了权威基准。本数据集由Electric Sheep Europe于2026年重新封装发布，聚焦欧洲五国2002至2025年间按性别、公私部门及残疾状况分组的雇员人数，旨在回应残疾群体在劳动力市场中的结构性边缘化问题，为包容性就业政策研究提供细粒度量化依据。数据源自各国劳动力调查与家庭收入调查，经国际劳工统计学家会议定义协调后发布，对推动欧洲残障人士就业平等具有重要学术与政策影响力。

当前挑战

在领域问题上，该数据集直面残障人士就业统计中长期存在的数据稀缺与可比性不足困境，试图揭示性别、部门属性与残疾状态交织下的就业差异，为消除劳动力市场多重歧视提供实证基础。构建过程中，数据协调面临显著挑战：各国调查工具与残疾定义差异导致跨国比较困难，部分国家时间序列断裂且观测状态标注为不可靠，如波兰数据止于2020年、塞尔维亚仅存2007年单年记录，样本覆盖不均衡制约了面板分析的稳健性。此外，公私部门与残疾状态的交叉分类造成单元格稀疏，进一步限制了统计推断的精度与政策外推的有效性。

常用场景

经典使用场景

在劳动经济学与残疾就业研究的交叉领域，该数据集最为经典的应用场景在于刻画欧洲五国按性别、公共/私营部门及残疾状态细分的雇员规模演变轨迹。研究者可借助其2002至2025年的年度观测值，开展时间序列建模与面板回归分析，进而揭示残疾群体在公共部门与私营部门之间就业分布的异质性。其表格化结构天然适配分类与回归任务，亦可用于预测不同性别与残疾状态下的就业趋势，为跨国比较提供统一口径的观测基础。

实际应用

在政策实践层面，该数据集服务于公共部门人员配置优化与残疾人就业配额制度的成效监测。国际组织与各国劳工部门可据此编制残疾雇员规模指标，评估私营部门雇佣残疾人的激励政策是否奏效。企业社会责任研究者亦可利用其识别公共部门与私营部门在性别与残疾包容上的差距，为多元雇佣战略提供量化依据，并辅助设计针对残疾女性的职业培训与就业促进项目。

衍生相关工作

围绕该数据集已衍生出一系列劳动力市场不平等与残疾统计的经典工作。部分研究基于其跨国面板结构，构建了残疾就业缺口的分解框架，区分部门选择效应与工资歧视效应；另有学者将其与欧洲社会调查等微观数据链接，检验残疾定义变更对就业估计的敏感性。这些工作推动了ILOSTAT指标在机器学习可复现研究中的应用，并激励后续数据集扩展至更多国家与更细的残疾类型分类。