electricsheepeurope/europe-ilo-ees-tees-sex-eco-mts-nb-employees-by-sex-economic-activity-and-marital-sta

--- license: cc-by-4.0 language: - en task_categories: - tabular-classification - tabular-regression - time-series-forecasting multilinguality: monolingual size_categories: - 100K<n<1M tags: - tabular - europe - ilostat - employees - ilo - labour - employment pretty_name: "Employees by sex, economic activity and marital status (thousands) | Europe (ILOSTAT)" --- # Employees by sex, economic activity and marital status (thousands) | Europe (ILOSTAT) 🇪🇺 **162,169 observations** · **39 Europe countries** · **1991–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **162,169 observations** of `Employees` data across **39 Europe countries**, spanning **1991–2025**, 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_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_ECO_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 39 Europe countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `CHE` | 12,652 | 1991 | 2025 | | `GBR` | 9,124 | 1992 | 2025 | | `POL` | 8,314 | 2000 | 2025 | | `MDA` | 8,069 | 2000 | 2025 | | `CZE` | 7,882 | 1993 | 2024 | | `AUT` | 7,170 | 1995 | 2025 | | `FRA` | 7,103 | 2005 | 2024 | | `MKD` | 6,023 | 2006 | 2025 | | `RUS` | 5,184 | 2010 | 2025 | | `ALB` | 5,019 | 2007 | 2024 | | `BIH` | 4,921 | 2001 | 2020 | | `ESP` | 4,572 | 1992 | 2025 | | `SRB` | 4,066 | 2007 | 2020 | | `NLD` | 3,665 | 1992 | 2020 | | `DNK` | 3,598 | 1992 | 2020 | | ... | _24 more countries_ | | | ## Indicators (sample) - `EES_TEES_SEX_ECO_MTS_NB` — Employees by sex, economic activity 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_ECO_MTS_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 | `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_classif` | `string` | — | `—` | | `note_classif.label` | `string` | — | `—` | | `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-mts-nb-employees-by-sex-economic-activity-and-marital-sta") 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_MTS_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EES_TEES_SEX_ECO_MTS_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EES_TEES_SEX_ECO_MTS_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_ees_tees_sex_eco_mts_nb_employees_by_sex_economic_activity_and_marital_sta_2025, title = {Employees by sex, economic activity and marital status (thousands) | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EES_TEES_SEX_ECO_MTS_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-ees-tees-sex-eco-mts-nb-employees-by-sex-economic-activity-and-marital-sta}} } ``` ## 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_MTS_NB_This dataset contains 162,169 observations of employees data across 39 Europe countries, categorized by sex, economic activity, and marital status (in thousands), spanning the years 1991 to 2025, and covering 1 distinct indicator. The data is sourced from the International Labour Organization (ILO) ILOSTAT database, retrieved via REST API and filtered for European countries. It includes dimensions such as country codes, sex disaggregation, economic activity, and marital status, and is suitable for tasks like tabular classification, regression, and time-series forecasting.

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ILOSTAT统计数据库，通过ILOSTAT REST API直接获取指标EES_TEES_SEX_ECO_MTS_NB的原始记录，并依据欧洲ISO3国家代码进行筛选，最终形成覆盖39个欧洲国家的面板数据。原始调查微数据经国际劳工统计学家会议定义标准化，来源信息以source.label字段标识以便追溯。数据经Electric Sheep Europe重新打包为Parquet格式，并借助HuggingFace平台发布，从而提升机器学习工作流的易用性。

特点

数据集包含162169条观测，时间跨度为1991至2025年，覆盖39个欧洲国家，仅含一项指标即按性别、经济活动与婚姻状况划分的雇员人数（千人）。变量体系涵盖国家代码、来源、指标、性别、经济活动分类、婚姻状况分类、年份、观测值及各类注释标签，支持多维度剖析。性别维度提供总计、男性与女性三类取值，且所有分类变量在指标未发布细分时保持空值，结构清晰且便于筛选。

使用方法

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。可按ref_area字段筛选特定国家，或按indicator字段选取目标指标，结合时间排序绘制时间序列图以观察演变趋势。利用pivot_table方法可将数据重塑为国家×年份矩阵，便于横向比较。该数据集适用于表格分类、表格回归及时间序列预测等任务，为劳动经济学研究提供细粒度数据支撑。

背景与挑战

背景概述

国际劳工组织（ILO）长期致力于全球劳动力市场统计数据的汇编与标准化，其ILOSTAT数据库为劳动经济学研究提供了跨国可比的基础数据。在性别平等与就业结构变迁日益受到关注的背景下，按性别、经济活动部门和婚姻状况细分雇员数据的需求显著上升。该数据集由Electric Sheep Europe于2026年从ILOSTAT REST API摄取并重新打包，覆盖39个欧洲国家、1991至2025年间共162,169条观测，聚焦雇员人数（千人）这一核心指标。其发布为探究欧洲劳动力市场中性别差异、行业分布与婚姻状况的交互影响提供了长时序、多国别的微观统计基础，对劳动政策评估和经济社会学研究具有重要参考价值。

当前挑战

该数据集所回应的领域问题在于刻画欧洲各国雇员规模在性别、经济活动与婚姻状况多维交叉下的动态演变，其挑战源于劳动力统计的固有复杂性。构建过程中的主要困难包括：各国劳动力调查的抽样设计与变量定义存在系统性差异，需依赖国际劳工统计学家会议（ICLS）标准进行协调；部分国家或年份存在序列中断、数据可靠性不足及源记录缺失等问题，统一时空覆盖难度较大；婚姻状况与经济活动的分类维度并非所有国家均持续发布，导致非平衡面板结构；此外，非正式部门就业的测量偏差与自雇/雇员界定的跨国不一致，亦对数据可比性构成持续挑战。

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研究中，该数据集凭借其涵盖39个欧洲国家、跨越1991至2025年的162169条观测记录，成为剖析雇员规模变迁的经典面板数据源。研究者常以性别、经济活动部门与婚姻状况为三重分类维度，构建跨国比较框架，用以揭示不同社会制度与经济周期下劳动力供给结构的异质性。时间序列分析与面板回归模型在此数据上被广泛运用，以捕捉就业人口在性别与婚姻状态间的动态分布规律。

实际应用

在政策评估与劳动力市场监测领域，该数据集为国际组织与各国统计部门提供了追踪就业结构变化的实用工具。决策者可借助其时间序列特征，评估特定经济政策或社会变迁对已婚与未婚雇员、不同性别及经济部门就业规模的影响，进而为就业促进政策、性别平等措施及家庭支持方案的设计提供数据支撑。此外，咨询机构与智库亦利用此数据构建预警指标，以识别就业市场中潜在的脆弱群体与结构性风险。

衍生相关工作

该数据集衍生了一系列围绕欧洲劳动力市场比较的经典工作，包括基于ILOSTAT框架的性别就业差距再评估、婚姻状态与劳动参与率的跨国面板分析，以及经济部门分类下的就业弹性研究。部分研究进一步将其与欧盟劳动力调查微观数据对接，用以校验宏观汇总的可靠性；另有学者利用其长时间跨度，检验经济危机对已婚女性雇员比例的冲击效应。这些工作共同丰富了劳动统计数据的二次分析生态。