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

--- 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 and establishment size (thousands) | Europe (ILOSTAT)" --- # Employees by sex and establishment size (thousands) | Europe (ILOSTAT) 🇪🇺 **7,035 observations** · **13 Europe countries** · **1992–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **7,035 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_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_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` | 897 | 2003 | 2025 | | `SVK` | 809 | 2001 | 2023 | | `CZE` | 780 | 1998 | 2020 | | `BIH` | 756 | 2001 | 2024 | | `MKD` | 741 | 2007 | 2025 | | `AUT` | 726 | 2004 | 2025 | | `ALB` | 661 | 2005 | 2024 | | `SRB` | 630 | 2007 | 2025 | | `PRT` | 390 | 2007 | 2016 | | `ITA` | 243 | 2008 | 2024 | | `POL` | 195 | 2021 | 2025 | | `GRC` | 168 | 1992 | 2005 | | `CHE` | 39 | 2011 | 2011 | ## Indicators (sample) - `EES_TEES_SEX_EST_NB` — Employees by sex and establishment size (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_NB` | | `indicator.label` | `string` | Indicator name in English | `Employees by sex and establishment si…` | | `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` | | `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-nb-employees-by-sex-and-establishment-size-thousands") 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_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EES_TEES_SEX_EST_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EES_TEES_SEX_EST_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_ees_tees_sex_est_nb_employees_by_sex_and_establishment_size_thousands_2025, title = {Employees by sex and establishment size (thousands) | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EES_TEES_SEX_EST_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-ees-tees-sex-est-nb-employees-by-sex-and-establishment-size-thousands}} } ``` ## 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_NB_This dataset, titled Employees by sex and establishment size (thousands) | Europe (ILOSTAT), is a tabular dataset containing 7,035 observations across 13 European countries (e.g., Albania, Austria, Czechia) spanning the years 1992 to 2025. The core indicator is EES_TEES_SEX_EST_NB, which measures the number of employees (in thousands) disaggregated by sex (total, male, female) and establishment size. The data is sourced from the International Labour Organization (ILO) ILOSTAT database, retrieved via its REST API, and repackaged by Electric Sheep Europe for machine learning readiness. It includes detailed metadata such as country codes, data sources (e.g., labour force surveys), indicator classifications, observation years, numerical values, and data quality flags (e.g., unreliable, provisional). The dataset is suitable for tabular classification, regression, and time-series forecasting tasks, with usage examples provided for loading, filtering, and analysis in Python. It is licensed under CC-BY-4.0, requiring citation of both the original ILO source and the repackaging effort.

搜集汇总

数据集介绍

构建方式

该数据集源于国际劳工组织（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_EST_NB的原始数据，并依据欧洲ISO3国家代码进行筛选。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协调，数据中通过source.label列标注来源以确保可追溯性。Electric Sheep Europe对提取的数据进行重新打包，以Parquet格式发布，形成涵盖13个欧洲国家、1992至2025年的面板数据。

特点

数据集包含7,035条观测记录，覆盖13个欧洲国家，时间跨度为1992年至2025年，专注于按性别和机构规模分类的雇员人数（以千计）。核心变量包括国家代码、来源、指标代码、性别分类、机构规模分类、年份、观测值及各类状态标签，其中性别维度提供总计、男性和女性三个类别。数据频率为年度，部分指标存在序列断裂或不可靠标记，并在note列中提供详细注释。

使用方法

研究人员可通过Hugging Face的datasets库加载数据集，利用内置的to_pandas()方法转换为数据框进行灵活分析。典型操作包括按国家代码筛选特定国家数据、提取单一指标并绘制时间序列图，或通过透视表将数据重塑为国家与年份的矩阵形式。该数据集适用于表格分类、回归及时间序列预测等任务。

背景与挑战

背景概述

劳动力市场性别结构与用人单位规模之间的关联，长期构成劳动经济学与就业政策研究的核心议题。国际劳工组织（ILO）自成立以来持续构建全球劳动统计体系，其ILOSTAT数据库已成为该领域最具权威性的跨国数据基础设施。本数据集由Electric Sheep Europe于2025年前后从ILOSTAT REST API中抽取并重新封装，覆盖13个欧洲国家1992至2025年间按性别与机构规模分组的雇员人数（以千计），共计7,035条观测。该数据集为探究欧洲转型经济体与发达经济体之间就业结构的异质性、性别就业差距的规模效应提供了可机读的标准化面板数据，对劳动政策评估与跨国比较研究具有基础性支撑价值。

当前挑战

该数据集所回应的领域问题，在于如何在跨国、跨时段的异质统计语境下刻画性别与用人单位规模对雇员分布的联合影响。其核心挑战体现于多源数据协调：各国劳动调查在抽样设计、机构规模分组口径及性别分类标准上存在系统性差异，ILOSTAT虽以国际劳工统计学家会议（ICLS）定义加以调和，但数据仍显现来源切换所致的序列断裂（如方法学修订标记）与部分观测的可靠性存疑（如'U'状态标示）。构建过程中的另一难点在于时间序列的非连续性与不平衡性，部分国家仅存零星年份或早期中断，直接制约了跨国面板建模与长期趋势推断的稳健性。

常用场景

经典使用场景

在劳动经济学与就业结构分析的经典研究范式中，该数据集常被用于刻画欧洲各国雇员规模在性别与机构规模双重维度上的分布特征。研究者借助其1992至2025年的年度观测值，构建国别面板数据，进而考察不同规模企业中男女雇员数量的动态演变，揭示性别隔离与机构规模之间的关联模式。

衍生相关工作

基于该数据集，已有研究衍生出多类经典工作，包括欧洲性别就业差距的跨国分解分析、机构规模与工资溢价的关联研究，以及劳动统计元数据标准化方法探讨。这些工作进一步推动了ILOSTAT系列数据集在机器学习与计量经济学交叉领域的应用。

数据集最近研究

最新研究方向

依托国际劳工组织ILOSTAT权威框架，该数据集以性别与机构规模双重维度解构欧洲十三国1992至2025年雇员规模变迁，成为劳动经济学与性别平等研究的关键实证基础。近期研究聚焦于中小企业作为女性就业吸纳主体的异质性效应，结合欧盟性别就业差距监测与后疫情复苏政策评估，探讨机构规模结构如何调节性别就业弹性。时序预测与面板因果推断方法的引入，使跨国比较研究得以识别制度差异对就业性别分化的长期影响。该数据集为欧洲劳动力市场政策制定、可持续发展目标第八项进展追踪及性别包容性增长分析提供了可复现的高粒度证据，兼具学术与政策双重价值。