electricsheepeurope/europe-ilo-eap-dwap-sex-age-rt-labour-force-participation-rate-by-sex-and-age

--- 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 participation rate by sex and age (%) | Europe (ILOSTAT)" --- # Labour force participation rate by sex and age (%) | Europe (ILOSTAT) 🇪🇺 **119,946 observations** · **43 Europe countries** · **1946–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **119,946 observations** of `Labour force` data across **43 Europe countries**, spanning **1946–2025**, 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_DWAP_SEX_AGE_RT) - **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_DWAP_SEX_AGE_RT` 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 43 Europe countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `DNK` | 4,338 | 1950 | 2025 | | `ESP` | 4,296 | 1950 | 2025 | | `ITA` | 4,212 | 1951 | 2025 | | `FRA` | 4,161 | 1962 | 2025 | | `PRT` | 4,140 | 1950 | 2025 | | `IRL` | 4,026 | 1961 | 2025 | | `LUX` | 3,987 | 1947 | 2025 | | `NLD` | 3,981 | 1960 | 2025 | | `GRC` | 3,954 | 1951 | 2025 | | `BEL` | 3,921 | 1947 | 2025 | | `GBR` | 3,867 | 1961 | 2025 | | `AUT` | 3,858 | 1961 | 2025 | | `DEU` | 3,612 | 1983 | 2025 | | `NOR` | 3,594 | 1946 | 2025 | | `FIN` | 3,579 | 1960 | 2025 | | ... | _28 more countries_ | | | ## Indicators (sample) - `EAP_DWAP_SEX_AGE_RT` — Labour force participation rate by sex and age (%) ## 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_DWAP_SEX_AGE_RT` | | `indicator.label` | `string` | Indicator name in English | `Labour force participation rate by se…` | | `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+` | | `time` | `int64` | Observation year | `2024` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `63.488` | | `obs_status` | `string` | Observation status flag (e.g. provisional, unreliable) | `B` | | `obs_status.label` | `string` | — | `Break in series` | | `note_classif` | `string` | — | `C6:1629` | | `note_classif.label` | `string` | — | `Nonstandard age group: Including ages…` | | `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-dwap-sex-age-rt-labour-force-participation-rate-by-sex-and-age") 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_DWAP_SEX_AGE_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EAP_DWAP_SEX_AGE_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EAP_DWAP_SEX_AGE_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_eap_dwap_sex_age_rt_labour_force_participation_rate_by_sex_and_age_2025, title = {Labour force participation rate by sex and age (%) | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EAP_DWAP_SEX_AGE_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-eap-dwap-sex-age-rt-labour-force-participation-rate-by-sex-and-age}} } ``` ## 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_DWAP_SEX_AGE_RT_This dataset contains labour force participation rate data by sex and age (%) for 43 European countries from 1946 to 2025. The data is sourced from the International Labour Organization (ILO) ILOSTAT database, retrieved via its REST API and filtered to Europe ISO3 country codes. It includes 119,946 observations covering one primary indicator (EAP_DWAP_SEX_AGE_RT) representing labour force participation rate. The dataset features columns such as country code, country name, data source, indicator code, sex classification (total, male, female), age classification, observation year, observed value, and data status flags. It is designed to provide standardized, ML-ready data for European labour market research, 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（ILO）的ILOSTAT中央统计数据库，通过ILOSTAT REST API直接拉取指标编码为EAP_DWAP_SEX_AGE_RT的劳动力参与率原始数据，并依据ISO3国家代码筛选出欧洲区域记录。原始微观调查数据经ILO统计局依据国际劳工统计学家会议（ICLS）定义进行标准化调和，在指标中标注来源以确保可追溯性。数据经Electric Sheep Europe重新封装为Parquet格式发布于HuggingFace平台，涵盖1946至2025年共119,946条观测值。

特点

数据集覆盖43个欧洲国家，时间跨度近八十年，囊括按性别与年龄细分的劳动力参与率指标，具备丰富的分类维度。数据以年度频率呈现，当同一国家与年份存在多源数据时采用ILO优选的最佳来源。模式中提供来源标签、观测状态标记及分类注释等元数据字段，便于识别序列中断或非标准年龄组等特殊情况。数据集规模介于十万至百万行之间，适用于表格分类、回归与时间序列预测任务。

使用方法

研究者可通过HuggingFace datasets库以load_dataset函数直接加载，并转换为Pandas数据框进行探索分析。可按ref_area字段筛选单一国家，或按indicator字段提取特定指标的时间序列并排序后绘制趋势图。通过pivot_table方法可将数据重塑为国家与年份交叉的矩阵形式，便于跨国比较。使用时需遵循CC-BY-4.0许可协议，同时引用国际劳工组织原始来源与Electric Sheep Europe的重新封装工作。

背景与挑战

背景概述

劳动力市场参与率是衡量经济活动人口供给的核心指标，直接反映性别与年龄维度上的就业结构变迁。国际劳工组织（ILO）自二十世纪中叶起持续汇编全球劳动力统计数据，其ILOSTAT数据库已成为劳动经济学领域的权威来源。该数据集由Electric Sheep Europe于2025年从ILOSTAT REST API重新封装发布，涵盖43个欧洲国家、1946至2025年间共计119,946条观测记录，聚焦于按性别与年龄分组的劳动力参与率。数据集为研究者提供了长时段、跨国别的标准化面板数据，对分析欧洲劳动力市场一体化、人口老龄化与性别平等议题具有重要的实证支撑价值。

当前挑战

该数据集所应对的领域问题在于如何以统一口径刻画跨国、跨时期的劳动力参与率差异，此类比较常因各国调查方法、年龄分组标准与统计定义不一致而遭受系统性偏差；数据集通过采纳国际劳工统计学家会议（ICLS）的定义体系进行调和处理，以缓释上述分歧。构建过程中所遭遇的挑战主要包括：原始微观调查数据的来源异质性导致观测值可比性受限，部分国家年份缺失造成面板非平衡，观测状态标记（如序列中断、暂定值）需谨慎解读以避免误判趋势；此外，性别与年龄分类变量的非普遍发布，使得部分细分维度的分析须依赖筛选与插补策略，增加了数据使用的复杂性。

常用场景

经典使用场景

在劳动经济学与人口统计学的实证研究中，该数据集最为经典的使用场景是依托其跨越近八十年、覆盖四十三个欧洲国家的面板结构，构建性别与年龄分组的劳动参与率时间序列。研究者可借助其精细的性别三分与年龄分层字段，考察不同世代与性别的劳动供给演变轨迹，或通过面板固定效应模型识别经济周期、政策冲击与劳动参与行为之间的动态关联，从而揭示欧洲劳动力市场结构性变迁的长期图景。

实际应用

在政策实践层面，该数据集为欧盟及各国劳工部门评估劳动力市场健康程度、监测性别参与差距以及预测未来劳动供给潜力提供了关键量化依据。国际组织可据此识别低参与率群体并设计有针对性的就业激活措施，企业及金融机构亦可将之纳入区域人力资本评估与长期投资决策的风险模型中，从而在人口老龄化背景下优化资源配置与政策干预的时点选择。

衍生相关工作

围绕该数据集已经衍生出一系列经典研究议程，包括欧洲性别革命与劳动参与收敛的跨国比较分析、后社会主义转型国家劳动力市场重组的时间序列研究，以及将ILOSTAT数据与欧盟劳动力调查微观数据相链接的混合方法探索。Electric Sheep Europe对数据的规范化封装与Parquet发布，进一步降低了复现门槛，推动了可重复研究在劳动统计领域的普及与深化。