electricsheepeurope/europe-ilo-eap-teap-sex-age-edu-nb-labour-force-by-sex-age-and-education-thousands

--- 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 - labour-force - ilo - labour - employment pretty_name: "Labour force by sex, age and education (thousands) | Europe (ILOSTAT)" --- # Labour force by sex, age and education (thousands) | Europe (ILOSTAT) 🇪🇺 **542,139 observations** · **41 Europe countries** · **1987–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **542,139 observations** of `Labour force` data across **41 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=EAP_TEAP_SEX_AGE_EDU_NB) - **Publisher:** International Labour Organization (ILO) - **License:** [cc-by-4.0](https://creativecommons.org/licenses/by/4.0/) - **Topic:** Labour force ## Methodology Data pulled directly from the ILOSTAT REST API at `https://rplumber.ilo.org/data/indicator?id=EAP_TEAP_SEX_AGE_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 41 Europe countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `GRC` | 26,547 | 1987 | 2025 | | `CHE` | 21,607 | 1991 | 2025 | | `GBR` | 19,009 | 1992 | 2025 | | `CZE` | 18,024 | 1991 | 2025 | | `FRA` | 17,286 | 1993 | 2025 | | `IRL` | 16,773 | 1992 | 2025 | | `NLD` | 16,153 | 1996 | 2025 | | `ITA` | 15,671 | 1992 | 2025 | | `BEL` | 15,456 | 1992 | 2025 | | `PRT` | 15,416 | 1992 | 2025 | | `DEU` | 15,287 | 1992 | 2025 | | `ESP` | 14,952 | 1992 | 2025 | | `ROU` | 14,922 | 1995 | 2025 | | `SWE` | 14,908 | 1995 | 2025 | | `DNK` | 14,856 | 1992 | 2025 | | ... | _26 more countries_ | | | ## Indicators (sample) - `EAP_TEAP_SEX_AGE_EDU_NB` — Labour force by sex, age 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 | `EAP_TEAP_SEX_AGE_EDU_NB` | | `indicator.label` | `string` | Indicator name in English | `Labour force by sex, age and educatio…` | | `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.) | `AGE_YTHADULT_YGE15` | | `classif1.label` | `string` | — | `Age (Youth, adults): 15+` | | `classif2` | `string` | Second classification variable where applicable | `EDU_AGGREGATE_TOTAL` | | `classif2.label` | `string` | — | `Education (Aggregate levels): Total` | | `time` | `int64` | Observation year | `2024` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `1283.995` | | `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-eap-teap-sex-age-edu-nb-labour-force-by-sex-age-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"] == "EAP_TEAP_SEX_AGE_EDU_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EAP_TEAP_SEX_AGE_EDU_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EAP_TEAP_SEX_AGE_EDU_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_eap_teap_sex_age_edu_nb_labour_force_by_sex_age_and_education_thousands_2025, title = {Labour force by sex, age and education (thousands) | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EAP_TEAP_SEX_AGE_EDU_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-eap-teap-sex-age-edu-nb-labour-force-by-sex-age-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 2026-05-25 via the Electric Sheep pipeline. Source URL: https://www.ilo.org/shinyapps/bulkexplorer/?id=EAP_TEAP_SEX_AGE_EDU_NB_This dataset contains 542,139 observations of labour force data across 41 European countries, spanning from 1987 to 2025, with the specific indicator Labour force by sex, age and education (thousands). The data is sourced from ILOSTAT, the International Labour Organizations central statistics database, which is a leading global source for labour statistics, covering indicators such as employment, unemployment, wages, and more. Data was pulled directly from the ILOSTAT REST API and filtered to Europe ISO3 country codes. The schema includes columns like country code, country name, data source, indicator code, sex disaggregation, age and education classifications, observation year, observed value, and status flags. The data is published at annual frequency, and for cases with multiple sources per country×year, the ILO-selected best source is used. The dataset is suitable for tabular classification, regression, and time-series forecasting tasks, and is released under the CC-BY-4.0 license.

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EAP_TEAP_SEX_AGE_EDU_NB的原始记录，并依据ISO3国家代码筛选出欧洲区域数据。ILOSTAT对各成员国提交的劳动力调查、家庭收入调查及行政记录等微观数据进行统一协调，遵循国际劳工统计学家会议（ICLS）的定义标准，对性别、年龄和教育维度进行标准化分类。Electric Sheep Europe对原始数据实施模式规范化、格式统一与Parquet封装，确保数据可直接用于机器学习任务。

特点

数据集涵盖1987至2025年41个欧洲国家的542,139条观测记录，以千人为单位呈现按性别、年龄和教育程度分组的劳动力规模。数据包含丰富的元信息列，如来源标签、观测状态标志、分类注释和指标注释，便于追溯数据质量与统计口径变更。性别维度提供总计、男性和女性三类取值，年龄与教育维度分别通过classif1和classif2列进行多层次分类。数据结构为典型的表格型时间序列，支持分类、回归和时序预测等多种分析范式。

使用方法

研究人员可通过HuggingFace datasets库以一行代码加载数据，并转换为Pandas DataFrame进行灵活操作。典型用法包括按国家代码筛选特定国家数据、按指标代码提取单一指标的时间序列、以及通过透视表构建国家与年份的矩阵视图。数据可直接用于劳动力参与率的趋势分析、性别与教育维度的差异比较、以及基于历史观测的预测建模。使用时应关注obs_status标志和注释列所提示的数据可靠性及序列断裂问题，并遵循CC-BY-4.0许可协议引用原始来源与再封装方。

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的标准化与可比性建设，其ILOSTAT数据库整合了各国劳动力调查、家庭收入调查及行政记录等多元数据源，覆盖逾两百个经济体。在此背景下，欧洲地区劳动力参与率的性别与年龄差异、教育层级对劳动供给的结构性影响，始终是劳动经济学与人口学研究的重要议题。该数据集由Electric Sheep Europe于2025年重新封装发布，原始数据源自ILO统计司，涵盖1987至2025年间41个欧洲国家542139条观测记录，以千人计量单位系统呈现按性别、年龄组和教育水平交叉分类的劳动力规模，为长时段跨国比较研究提供了标准化、可直接调用的数据基础。

当前挑战

该数据集所应对的核心领域问题在于劳动力供给结构异质性的精细化度量和跨时空可比性建构。传统劳动力统计往往以总量或单一维度呈现，难以揭示性别、年龄与教育三重分层交互作用下劳动力参与的差异化模式。构建过程中的主要挑战体现在：各国劳动力调查的抽样设计、教育分类标准及年龄分组方式存在差异，ILO虽依据国际劳工统计学家会议定义进行调和，但教育层级归类仍面临非标准情形；部分国家早期年份数据缺失或存在序列断点，观测状态标注为不可靠或临时性的记录需审慎处理；来源切换导致的方法论修订亦对时间序列一致性构成威胁。

常用场景

经典使用场景

在劳动经济学与人口统计学领域，该数据集最经典的使用场景在于刻画欧洲劳动力市场的结构性特征。研究者依托其涵盖41个欧洲国家、1987至2025年逾54万条观测的丰富维度，按性别、年龄组与受教育程度对劳动力规模进行精细化分解。此类分析常用于揭示不同人口群体劳动参与率的长期演变轨迹，例如青年与成年劳动力在教育层级上的分布差异，或男性与女性在特定年龄段的劳动力供给弹性。该数据集亦为跨国比较研究提供了标准化框架，使得中东欧与西欧国家在转型前后的劳动力结构变迁得以系统呈现。

实际应用

在实际应用层面，该数据集为政策制定者与国际组织提供了劳动力供给侧监测的量化依据。欧盟委员会及各国劳动部门可借助其分性别、年龄与教育的劳动力存量数据，评估特定群体（如低学历青年或高龄女性）的就业脆弱性，从而设计靶向性的职业培训与就业促进方案。人力资源机构与跨国企业亦可利用该数据集进行区域劳动力禀赋的比较分析，辅助投资选址与人才招聘策略的制定。此外，该数据集的年度频率与标准化编码使其能够无缝接入宏观经济预测模型，为劳动力短缺预警与移民政策评估提供数据支撑。

衍生相关工作

围绕该数据集，学术界与政策研究机构衍生出一系列经典工作。国际劳工组织基于同源数据定期发布《世界就业与社会展望》报告，其中关于欧洲劳动力市场性别差距与教育回报的分析即以此为底层数据。欧洲改善生活与工作条件基金会亦利用该数据集开展代际劳动参与的比较研究。在学术领域，多篇发表于《劳动经济学杂志》与《欧洲经济评论》的论文借助其面板结构，探究教育政策改革对劳动力供给的长期效应。近年来，随着机器学习方法在社会科学中的普及，该数据集亦被用于训练劳动参与率预测模型，推动了时间序列预测与面板数据因果推断方法的发展。