electricsheepeurope/europe-ilo-ees-tees-sex-edu-nb-employees-by-sex-and-education-thousands

--- 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 and education (thousands) | Europe (ILOSTAT)" --- # Employees by sex and education (thousands) | Europe (ILOSTAT) 🇪🇺 **34,361 observations** · **39 Europe countries** · **1987–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **34,361 observations** of `Employees` data across **39 Europe countries**, spanning **1987–2025**, 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_EDU_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_EDU_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 | |---------|-----:|-----------:|----------:| | `GRC` | 1,726 | 1987 | 2025 | | `CHE` | 1,459 | 1991 | 2025 | | `GBR` | 1,188 | 1992 | 2025 | | `CZE` | 1,170 | 1998 | 2024 | | `FRA` | 1,101 | 1993 | 2024 | | `IRL` | 1,070 | 1992 | 2024 | | `NLD` | 1,047 | 1996 | 2024 | | `PRT` | 1,038 | 1992 | 2025 | | `LUX` | 1,027 | 1992 | 2024 | | `ITA` | 1,004 | 1992 | 2024 | | `ESP` | 1,000 | 1992 | 2025 | | `BEL` | 1,000 | 1992 | 2024 | | `DNK` | 973 | 1992 | 2024 | | `DEU` | 970 | 1992 | 2024 | | `NOR` | 963 | 1996 | 2024 | | ... | _24 more countries_ | | | ## Indicators (sample) - `EES_TEES_SEX_EDU_NB` — Employees by sex and education (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_EDU_NB` | | `indicator.label` | `string` | Indicator name in English | `Employees by sex and education (thous…` | | `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.) | `EDU_AGGREGATE_TOTAL` | | `classif1.label` | `string` | — | `Education (Aggregate levels): 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` | — | `C3:5578` | | `note_classif.label` | `string` | — | `Nonstandard education level: Includin…` | | `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-edu-nb-employees-by-sex-and-education-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_EDU_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EES_TEES_SEX_EDU_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EES_TEES_SEX_EDU_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_ees_tees_sex_edu_nb_employees_by_sex_and_education_thousands_2025, title = {Employees by sex and education (thousands) | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EES_TEES_SEX_EDU_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-ees-tees-sex-edu-nb-employees-by-sex-and-education-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_EDU_NB_This dataset contains 34,361 observations of Employees by sex and education (thousands) data across 39 Europe countries, spanning the years 1987 to 2025, covering 1 distinct indicator (EES_TEES_SEX_EDU_NB). The data is sourced from ILOSTAT, the ILOs central statistics database and a leading global source for labour statistics, which harmonizes data from national labour force surveys, household income surveys, establishment surveys, and administrative records. Repackaged by Electric Sheep Europe, it is part of a mission to provide a unified, ML-ready data layer for Europe on HuggingFace. The schema includes columns for country code, country name, data source, indicator code, indicator name, sex disaggregation, education classification, observation year, observed value, observation status flags, and related notes, providing breakdowns by dimensions such as sex (total, male, female).

搜集汇总

数据集介绍

构建方式

该数据集以国际劳工组织（ILO）权威统计数据库ILOSTAT为数据源，通过其REST API接口（https://rplumber.ilo.org/data/indicator?id=EES_TEES_SEX_EDU_NB）直接抽取原始指标数据，并依据ISO3国家代码筛选出39个欧洲国家，最终形成覆盖1987至2025年、共计34,361条观测值的结构化表格。ILOSTAT本身依据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统一协调，数据集中保留了来源标签列以支持溯源。由Electric Sheep Europe对抽取结果进行标准化重打包，输出为HuggingFace数据集格式，便于机器学习与统计分析使用。

特点

数据集聚焦于欧洲地区按性别与教育程度分类的雇员人数（以千人为单位），涵盖39个国家、时间跨度长达39年，具有显著的面板数据特征。其核心变量包括国家代码、来源信息、指标代码、性别分类（总计、男性、女性）、教育分类、年份及观测值，并附有观测状态标志和多类注释标签，用以标识数据可靠性、序列断裂或非标准教育层级等质量信息。数据集以单一指标为主，但通过性别与教育维度的交叉提供细粒度分解，规模适中，适合进行跨国比较、趋势分析与回归建模。

使用方法

使用者可通过HuggingFace的datasets库以一行代码加载数据集，并将其转换为Pandas数据框进行后续操作。典型用法包括按国家代码筛选特定国家的子集、针对单一指标绘制时间序列图以观察演变趋势，以及利用透视表将数据重塑为国家与年份的交叉矩阵。数据集支持表格分类、回归与时间序列预测等任务，亦可结合性别与教育分类变量开展分组分析。加载后建议检查obs_status等质量标志列，以识别并处理不可靠或临时性观测值，确保分析结果的稳健性。

背景与挑战

背景概述

国际劳工组织长期致力于全球劳动力市场统计体系之构建，其核心数据库ILOSTAT承载着二百余个经济体的就业、失业、工资及劳动条件等关键指标，为比较劳动经济学与政策评估奠定了实证基础。该数据集由Electric Sheep Europe于2025年自ILOSTAT REST API萃取并重新封装，聚焦1987至2025年间三十九個欧洲国家按性别与教育程度划分的雇员人数（以千计），共计三万四千余条观测。其核心研究问题在于揭示欧洲劳动力市场中性别与教育维度上的就业结构变迁，为不平等测度、人力资本配置及劳动政策效应分析提供长时序、跨国别的微观统计支撑，对劳动经济学、教育经济学及社会政策研究具有显著参考价值。

当前挑战

就领域问题而言，该数据集应对的是劳动力市场统计中性别与教育交叉分类的精细化测度难题——长期以来，跨国可比的教育层级划分与性别分类标准差异，致使欧洲就业结构的系统性比较面临口径不一致的障碍，亟需统一框架下的长期序列数据予以支撑。就构建过程而言，挑战主要体现为：原始数据源自各国劳动力调查与行政记录，抽样设计、覆盖范围及变量定义参差不齐，ILOSTAT虽以国际劳工统计学家会议定义为基准进行调和，仍难免产生时序断裂与观测状态标注；部分国家或年份存在缺失与不可靠值，需借助源标签与注释字段进行追溯与甄别；教育分类的非标准层级与汇总口径更增加了数据清洗与建模的复杂性。

常用场景

经典使用场景

在劳动经济学与计量社会科学领域，该数据集构成了剖析欧洲劳动力市场结构性变迁的经典面板数据资源。研究者通常以国家与年份为双重维度，围绕雇员数量的性别与教育程度分布展开时间序列建模与横截面比较分析，既可刻画各国人力资本构成的动态演化轨迹，亦能借助教育分层变量检验性别就业差距的收敛或分化趋势。其年度频率与跨国可比性使其成为探究教育扩张如何重塑就业结构的理想素材。

衍生相关工作

围绕该数据集及ILOSTAT同类指标，学界衍生出诸多经典研究脉络。比较政治经济学文献中，大量跨国回归分析以教育分层就业数据为自变量，探讨去工业化与女性劳动参与率之间的因果关系。增长核算研究中，学者将其与教育存量数据结合，估计人力资本对生产率增长的贡献弹性。欧洲就业政策评估领域，亦有系列工作利用其面板结构检验里斯本战略与欧洲2020目标在成员国的就业效应。

数据集最近研究

最新研究方向

在劳动经济学与教育社会学交叉领域，基于ILOSTAT跨国面板数据的性别与教育维度就业结构研究正成为前沿方向。该数据集覆盖39个欧洲国家、1987至2025年的34,361条观测记录，为探究教育分层如何塑造性别就业差异提供了独特的长时序视角。近期研究聚焦于利用此数据检验教育扩张对女性就业参与率的异质性影响，并结合欧洲性别平等指数与劳动力市场政策变迁，分析不同教育层级下性别就业缺口的收敛趋势。同时，该数据被用于机器学习模型中的时间序列预测与因果推断，以评估技能溢价、产业结构转型及人口老龄化对分性别就业的冲击，为欧洲就业政策制定与终身学习体系优化提供实证依据。