electricsheepeurope/europe-ilo-ees-tees-sex-ocu-ins-nb-employees-by-sex-occupation-and-public-private-sec

--- 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, occupation and public/private sector (thousands) | Europe (ILOSTAT)" --- # Employees by sex, occupation and public/private sector (thousands) | Europe (ILOSTAT) 🇪🇺 **42,257 observations** · **14 Europe countries** · **1993–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **42,257 observations** of `Employees` data across **14 Europe countries**, spanning **1993–2025**, 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_OCU_INS_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_OCU_INS_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 14 Europe countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `CHE` | 4,454 | 1996 | 2025 | | `GRC` | 3,894 | 1993 | 2020 | | `POL` | 3,689 | 2000 | 2025 | | `ESP` | 3,588 | 2000 | 2025 | | `MDA` | 3,566 | 2000 | 2025 | | `GBR` | 3,139 | 2005 | 2025 | | `FRA` | 3,026 | 2005 | 2024 | | `AUT` | 3,014 | 2004 | 2025 | | `BIH` | 2,761 | 2001 | 2024 | | `MKD` | 2,692 | 2006 | 2025 | | `SVK` | 2,581 | 2006 | 2023 | | `ALB` | 2,482 | 2002 | 2024 | | `SRB` | 1,834 | 2007 | 2020 | | `MNE` | 1,537 | 2011 | 2024 | ## Indicators (sample) - `EES_TEES_SEX_OCU_INS_NB` — Employees by sex, occupation and public/private sector (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_OCU_INS_NB` | | `indicator.label` | `string` | Indicator name in English | `Employees by sex, occupation and publ…` | | `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.) | `OCU_SKILL_TOTAL` | | `classif1.label` | `string` | — | `Occupation (Skill level): Total` | | `classif2` | `string` | Second classification variable where applicable | `INS_SECTOR_TOTAL` | | `classif2.label` | `string` | — | `Institutional sector: 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-ocu-ins-nb-employees-by-sex-occupation-and-public-private-sec") 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_OCU_INS_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EES_TEES_SEX_OCU_INS_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EES_TEES_SEX_OCU_INS_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_ees_tees_sex_ocu_ins_nb_employees_by_sex_occupation_and_public_private_sec_2025, title = {Employees by sex, occupation and public/private sector (thousands) | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EES_TEES_SEX_OCU_INS_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-ees-tees-sex-ocu-ins-nb-employees-by-sex-occupation-and-public-private-sec}} } ``` ## 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_OCU_INS_NB_This dataset contains 42,257 observations of Employees data across 14 Europe countries, spanning 1993–2025, covering 1 distinct indicator: Employees by sex, occupation and public/private sector (thousands). It is sourced from ILOSTAT, the ILOs central statistics database, and includes disaggregation by sex (total, male, female), occupation, and institutional sector. The data is suitable for tabular classification, regression, and time-series forecasting tasks, with columns such as country code, year, observed value, source, and classification variables, supporting labor market and economic analysis.

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REST API接口直接提取指标EES_TEES_SEX_OCU_INS_NB的原始观测记录，并依据ISO3国家代码筛选出14个欧洲国家。ILO对各国的劳动力调查、家庭收入调查及行政记录等微观数据进行协调与标准化处理，遵循国际劳工统计学家会议（ICLS）的定义框架，确保跨国可比性。Electric Sheep Europe在此基础上对数据架构进行规范化，将其打包为HuggingFace数据集，保留源标签列以支持溯源，最终形成包含42,257条年度观测记录的结构化表格数据。

特点

该数据集涵盖1993年至2025年间的长时间跨度，涉及14个欧洲国家，总计逾四万条观测记录。数据以性别、职业和公共/私营部门三个维度进行交叉分类，提供按 sex、classif1 和 classif2 等多层级分解的细粒度信息。每条记录包含指标代码、来源标识、观测值、观测状态标志及注释信息等丰富元数据，可支撑性别就业差异、职业结构变迁与部门分布等劳动经济学议题的实证分析。数据频率为年度，部分指标存在临时性或不可靠标记，需在使用时加以甄别。

使用方法

研究者可通过HuggingFace datasets库以一行代码加载该数据集，并转换为Pandas数据框进行后续操作。典型用法包括按 ref_area 字段筛选特定国家的子集，或依据 indicator 与 time 字段提取单一指标的时间序列并绘制趋势图。利用 pivot_table 方法可将数据重塑为国家×年份的矩阵形式，便于开展跨国比较。在分析过程中，建议关注 obs_status 列以评估数据质量，并参考 note_indicator 与 note_source 等注释字段识别系列断点或方法修订，从而确保结论的稳健性。

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可比性，其ILOSTAT数据库整合了200余个经济体的劳动力调查与行政记录，成为劳动经济学研究的基石。本数据集由Electric Sheep Europe于2025年重新打包发布，源自ILOSTAT的REST API，聚焦1993至2025年间14个欧洲国家按性别、职业及公共/私营部门划分的雇员数据（单位：千人），共计42,257条观测。该数据集填补了欧洲劳动市场细分维度长时间序列的空白，为探究性别职业隔离、公共部门就业结构变迁等议题提供了关键实证基础，对劳动政策评估与学术研究具有显著影响力。

当前挑战

在领域问题上，该数据集直面劳动统计中性别、职业与部门交叉分类的稀疏性与异质性难题，如何从多源调查中稳健估计细分人群的就业规模是核心挑战。构建过程中，ILO需协调各国差异化的劳动力调查定义、抽样框架及数据发布时滞，并通过ICLS标准进行事后协调，同时应对数据缺失、序列中断及不可靠观测的标注。Electric Sheep Europe的再包装则需解析嵌套的API响应、统一多语言分类标签，并确保与原始元数据的可追溯性，以支撑时间序列预测与公平性分析等下游任务。

常用场景

经典使用场景

在劳动经济学与性别研究的交汇处，该数据集凭借其涵盖14个欧洲国家、跨越三十余年的42,257条观测记录，成为剖析就业结构变迁的经典素材。研究者常运用其按性别、职业及公共/私营部门划分的雇员人数序列，构建面板数据模型，以揭示女性在公共部门与高技能职业中的代表性演变。时间序列预测任务亦频繁借助此数据集，评估经济周期与政策干预对就业分布的滞后效应，尤其是在中东欧转型经济体中的表现。

实际应用

在政策制定与劳动力市场分析中，该数据集为欧盟及候选国的就业战略评估提供了量化基线。国际组织可据此监测体面劳动目标进展，国家统计机构用以校验本国劳动力调查的部门分类精度。咨询机构则借助其预测公共部门人员规模，辅助财政规划。人力资源研究亦利用职业与性别交叉维度，诊断行业人才管道中的性别失衡，为多元化招聘政策提供实证依据。

衍生相关工作

基于此数据集，衍生了一系列聚焦欧洲就业性别差异的经典研究。学者们将其与EU-LFS微观数据链接，生成职业隔离指数；或与国民账户数据融合，分析公共部门就业的宏观经济效应。在机器学习领域，该数据集成为表格回归与时间序列基准的测试平台，推动了缺失值插补与层次预测方法的发展。这些工作共同丰富了ILOSTAT数据在比较劳动研究中的应用生态。