electricsheepeurope/europe-ilo-ees-tees-sex-est-mts-nb-employees-by-sex-establishment-size-and-marital-st

--- 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, establishment size and marital status (thousands) | Europe (ILOSTAT)" --- # Employees by sex, establishment size and marital status (thousands) | Europe (ILOSTAT) 🇪🇺 **47,935 observations** · **13 Europe countries** · **1992–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **47,935 observations** of `Employees` data across **13 Europe countries**, spanning **1992–2025**, 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_MTS_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_MTS_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 13 Europe countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `MDA` | 7,610 | 2003 | 2025 | | `MKD` | 6,228 | 2007 | 2025 | | `CZE` | 6,207 | 1998 | 2020 | | `AUT` | 5,779 | 2004 | 2025 | | `BIH` | 5,072 | 2001 | 2020 | | `ALB` | 4,675 | 2005 | 2024 | | `SRB` | 4,309 | 2007 | 2020 | | `PRT` | 3,405 | 2007 | 2016 | | `SVK` | 2,096 | 2001 | 2020 | | `POL` | 1,556 | 2021 | 2025 | | `GRC` | 504 | 1992 | 2005 | | `CHE` | 350 | 2011 | 2011 | | `ITA` | 144 | 2021 | 2024 | ## Indicators (sample) - `EES_TEES_SEX_EST_MTS_NB` — Employees by sex, establishment size and marital 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) | `BA:480` | | `source.label` | `string` | Source name in English | `LFS - Labour Force Survey` | | `indicator` | `string` | ILOSTAT indicator code | `EES_TEES_SEX_EST_MTS_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 | `MTS_AGGREGATE_TOTAL` | | `classif2.label` | `string` | — | `Marital status (Aggregate): Total` | | `time` | `int64` | Observation year | `2024` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `537.005` | | `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-est-mts-nb-employees-by-sex-establishment-size-and-marital-st") 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_MTS_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EES_TEES_SEX_EST_MTS_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EES_TEES_SEX_EST_MTS_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_ees_tees_sex_est_mts_nb_employees_by_sex_establishment_size_and_marital_st_2025, title = {Employees by sex, establishment size and marital status (thousands) | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EES_TEES_SEX_EST_MTS_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-ees-tees-sex-est-mts-nb-employees-by-sex-establishment-size-and-marital-st}} } ``` ## 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_MTS_NB_This dataset contains employee statistics from the International Labour Organization (ILO) ILOSTAT database, covering 13 European countries (including Albania, Austria, Bosnia and Herzegovina, Czechia, Greece, Italy, Moldova, North Macedonia, Poland, Portugal, Serbia, Slovakia, Switzerland) from 1992 to 2025, with 47,935 observations. The core indicator is EES_TEES_SEX_EST_MTS_NB, which represents employees (in thousands) disaggregated by sex (total, male, female), establishment size (total), and marital status (total). The dataset is provided in tabular format, including fields such as country code, year, observed value, data source, indicator classification, and observation status, making it suitable for tasks like tabular classification, regression, or time-series forecasting. The data is harmonized by ILO using International Conference of Labour Statisticians (ICLS) definitions and includes source and quality flags.

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统计数据库，通过ILOSTAT REST API直接提取指标EES_TEES_SEX_EST_MTS_NB的原始数据，并依据ISO3国家代码筛选出13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等微观数据进行标准化调和，数据中标记了来源以便追溯。Electric Sheep Europe对获取的数据进行规范化封装，以Parquet格式重新发布，构建了涵盖1992年至2025年的47935条观测记录。

使用方法

使用该数据集时，可通过HuggingFace的datasets库调用load_dataset函数加载，并转换为Pandas数据框进行后续分析。例如筛选特定国家（如德国）的数据，或针对单一指标按年份排序绘制时间序列趋势图，亦可通过透视表生成国家与年份的交叉矩阵。数据集适用于劳动经济学研究、跨国比较分析及预测建模，使用时需遵循CC-BY-4.0许可并同时引用ILO原始来源与Electric Sheep Europe的再包装工作。

背景与挑战

背景概述

劳动统计作为洞察劳动力市场结构与社会经济变迁的基石，长期依赖于跨国可比数据的系统积累。国际劳工组织（ILO）自成立以来便致力于全球劳动统计的标准化与传播，其ILOSTAT数据库汇聚两百余个经济体的劳动力调查与行政记录，成为该领域最具权威性的数据基础设施。在此背景下，Electric Sheep Europe于2025年对ILOSTAT原始数据进行二次封装，构建了覆盖13个欧洲国家、逾4.7万条观测的雇员数据集。该数据集聚焦性别、企业规模与婚姻状况三重维度的雇员人数分布，时间跨度自1992年延伸至2025年，为劳动经济学、性别研究与组织社会学提供了精细化的量化素材。其价值在于将分散于各年度与国别的统计碎片整合为机器学习可即时调用的规范化资源，显著降低了跨国劳动比较研究的门槛。

当前挑战

该数据集所回应的领域问题，在于如何精准刻画雇佣关系的异质性结构——传统劳动力统计往往仅关注就业总量与失业率，而忽视了企业规模与婚姻状况等社会分层变量对雇佣模式的塑造作用。构建过程中的核心挑战则来自多重数据源的整合与质量管控。ILOSTAT原始数据由各国劳动力调查、家庭收入调查与行政记录拼接而成，统计口径与抽样方法各异，ILO虽以国际劳工统计学家会议（ICLS）定义进行调和，但部分国家仍存在方法学修订所导致的序列断裂，观测状态亦被标记为“不可靠”或“临时性”。此外，性别、企业规模与婚姻状况等分类维度仅当指标发布相应细分时才非空，稀疏矩阵特征对建模提出了额外的缺失值处理要求，时间跨度内部分国家起始年份滞后也限制了面板数据的平衡性。

常用场景

经典使用场景

在劳动经济学与人口统计学的交叉领域，该数据集为解析欧洲劳动力市场的结构性特征提供了不可多得的面板数据基础。其经典使用场景在于按性别、企业规模及婚姻状况三重维度对雇员人数进行细粒度分解，研究者可借此构建跨国比较的时间序列模型，刻画不同婚姻状态下的男性和女性在大型与小型企业中的就业分布变迁。借助1992年至2025年的连续观测，可有效识别经济周期、政策改革与人口结构变动对雇员构成的差异化冲击，尤其适用于探索婚姻状况如何调节企业规模与性别之间的就业关联。

解决学术问题

该数据集有效回应了劳动统计研究中长期存在的几类难题：一是跨国可比性不足，ILO通过统一应用国际劳工统计学家会议的定义对原始调查微数据进行协调，削弱了各国调查口径差异带来的估计偏误；二是细分维度交互效应难以捕捉，婚姻状况与企业规模的联合分布往往在汇总数据中被遮蔽，而本数据集保留了完整的交叉分类，使得性别、企业规模与婚姻状态的三阶交互得以实证检验；三是时间序列断裂问题，数据集标注了序列断点及数据可靠性状态，为结构突变检验和异常值诊断提供了透明依据，提升了学术研究的可复现性。

实际应用

在政策制定与商业决策层面，该数据集展现出广泛的应用价值。劳动监管部门可依据按企业规模分组的雇员性别与婚姻构成，评估家庭友好型政策在不同规模企业中的覆盖差异，识别非正规就业或脆弱就业的高风险群体。企业人力资源部门可借助婚姻状况与性别的交叉数据，优化招聘、保留与弹性工作安排策略，以应对不同雇员群体的需求分化。国际组织与智库则可利用13个欧洲国家的可比序列，监测体面劳动目标的进展，为就业促进项目提供基线参照。

数据集最近研究

最新研究方向

在全球劳动力市场性别平等议题持续升温的背景下，该数据集凭借其覆盖13个欧洲国家、逾三十年（1992–2025）的雇员统计，为探究性别、企业规模与婚姻状况三重维度下的就业结构变迁提供了独特素材。当前前沿研究聚焦于利用高分辨率面板数据识别婚姻状况对男女就业参与的异质性影响，并结合企业规模分层考察劳动市场分割与性别鸿沟的交互机制。该数据亦服务于欧洲性别平等战略及国际劳工组织体面劳动议程的监测评估，对揭示转型经济体劳动力市场动态、优化社会保障政策具有重要参考价值。