electricsheepeurope/europe-ilo-eip-dwap-sex-age-dsb-rt-inactivity-rate-by-sex-age-and-disability-status

--- 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 - other-measures-of-labour-underutilization - ilo - labour - employment pretty_name: "Inactivity rate by sex, age and disability status (%) | Europe (ILOSTAT)" --- # Inactivity rate by sex, age and disability status (%) | Europe (ILOSTAT) 🇪🇺 **48,253 observations** · **32 Europe countries** · **2002–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **48,253 observations** of `Other measures of labour underutilization` data across **32 Europe countries**, spanning **2002–2025**, 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=EIP_DWAP_SEX_AGE_DSB_RT) - **Publisher:** International Labour Organization (ILO) - **License:** [cc-by-4.0](https://creativecommons.org/licenses/by/4.0/) - **Topic:** Other measures of labour underutilization ## Methodology Data pulled directly from the ILOSTAT REST API at `https://rplumber.ilo.org/data/indicator?id=EIP_DWAP_SEX_AGE_DSB_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 32 Europe countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `BEL` | 1,701 | 2004 | 2024 | | `GBR` | 1,701 | 2005 | 2025 | | `FRA` | 1,701 | 2004 | 2024 | | `EST` | 1,701 | 2004 | 2024 | | `ITA` | 1,701 | 2004 | 2024 | | `ESP` | 1,699 | 2004 | 2024 | | `IRL` | 1,697 | 2004 | 2024 | | `PRT` | 1,693 | 2004 | 2024 | | `FIN` | 1,689 | 2004 | 2024 | | `AUT` | 1,681 | 2004 | 2024 | | `LUX` | 1,673 | 2004 | 2024 | | `GRC` | 1,673 | 2004 | 2024 | | `NOR` | 1,653 | 2004 | 2024 | | `SWE` | 1,648 | 2004 | 2024 | | `HUN` | 1,620 | 2005 | 2024 | | ... | _17 more countries_ | | | ## Indicators (sample) - `EIP_DWAP_SEX_AGE_DSB_RT` — Inactivity rate by sex, age and disability status (%) ## 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) | `BB:7401` | | `source.label` | `string` | Source name in English | `HIES - Living Standards Survey` | | `indicator` | `string` | ILOSTAT indicator code | `EIP_DWAP_SEX_AGE_DSB_RT` | | `indicator.label` | `string` | Indicator name in English | `Inactivity rate by sex, age and disab…` | | `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 | `DSB_STATUS_TOTAL` | | `classif2.label` | `string` | — | `Disability status: Total` | | `time` | `int64` | Observation year | `2012` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `59.202` | | `obs_status` | `string` | Observation status flag (e.g. provisional, unreliable) | `U` | | `obs_status.label` | `string` | — | `Unreliable` | | `note_classif` | `string` | — | `C14:6260` | | `note_classif.label` | `string` | — | `Nonstandard definition of disability:…` | | `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-eip-dwap-sex-age-dsb-rt-inactivity-rate-by-sex-age-and-disability-status") 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"] == "EIP_DWAP_SEX_AGE_DSB_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EIP_DWAP_SEX_AGE_DSB_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EIP_DWAP_SEX_AGE_DSB_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_eip_dwap_sex_age_dsb_rt_inactivity_rate_by_sex_age_and_disability_status_2025, title = {Inactivity rate by sex, age and disability status (%) | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EIP_DWAP_SEX_AGE_DSB_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-eip-dwap-sex-age-dsb-rt-inactivity-rate-by-sex-age-and-disability-status}} } ``` ## 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 2026-05-27 via the Electric Sheep pipeline. Source URL: https://www.ilo.org/shinyapps/bulkexplorer/?id=EIP_DWAP_SEX_AGE_DSB_RT_This dataset contains 48,253 observations of Other measures of labour underutilization data across 32 Europe countries, spanning 2002–2025, covering the Inactivity rate by sex, age and disability status (%) indicator. The data is sourced from the International Labour Organizations ILOSTAT database and provides statistics on labour force non-participation rates disaggregated by sex, age groups, and disability status.

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数据库，通过其REST API接口直接抽取特定指标（EIP_DWAP_SEX_AGE_DSB_RT）的原始数据。在获取原始数据后，数据集构建者依据国际劳工统计学家会议（ICLS）的定义对来自劳动力调查、家庭收入调查等来源的微观数据进行标准化处理，并筛选出覆盖32个欧洲国家的观测记录。所有数据均经过去重与格式统一，保留了来源标识列以保障可追溯性，最终形成包含48,253条观测值的结构化表格数据集。

特点

该数据集的核心特点在于其精细的分层结构，不仅按性别（总计、男性、女性）、年龄组和残疾状况进行多维细分，还提供了详尽的数据质量标记列（如观测状态、系列断裂注释等），使研究者能够精准评估每个数据点的可靠性。时间跨度覆盖2002年至2025年，且每个国家-年份组合下的数据来源均经过ILO的'最佳来源'优选策略。此外，数据集引入了标准化分类编码体系，支持灵活的子集筛选与跨国家时间序列分析。

使用方法

研究者可通过HuggingFace Datasets库的load_dataset()函数一键加载该数据集，并转换为Pandas DataFrame进行后续处理。推荐的使用路径包括：按国家代码过滤特定国家的观测序列，利用时间列与观测值列绘制单指标的时间演变趋势图，或通过数据透视表构建国家×年份的矩阵以进行比较分析。数据集中的标准化编码列（如sex、classif1）便于用户按性别、年龄或残疾状态等维度快速切片，从而深入挖掘欧洲劳动力市场非活跃率的差异化模式。

背景与挑战

背景概述

该数据集由国际劳工组织（ILO）于2025年发布，经由Electric Sheep Europe团队重新整理并托管于HuggingFace平台，聚焦欧洲32个国家2002至2025年间按性别、年龄和残疾状况划分的劳动力非参与率。作为ILOSTAT数据库的子集，其核心研究问题在于揭示残疾人群体的劳动力市场边缘化程度，填补了传统失业率指标无法捕捉的隐蔽性劳动力闲置现象。数据集涵盖48,253条观测记录，为劳动经济学、社会保障政策及可持续发展目标（SDG）中的体面劳动指标提供了跨国家、跨时段的标准化比较基础，对推动包容性劳动力市场研究具有重要影响力。

当前挑战

该数据集所解决的领域问题在于量化因残疾、年龄或性别歧视导致的隐性劳动力闲置，超越传统失业率统计的局限性，促进对劳动市场结构缺陷的深入理解。构建过程中遭遇的挑战包括：不同国家调查方法（如劳动力调查与收入调查）的差异导致数据可比性下降，需依赖ILO的数据协调标准（ICLS）；残障定义的非标准化（如分类变量中的'非标准残疾定义'注记）引发测量偏差；时间序列中的方法论修订和数据中断标记（如'断点'字段）要求研究者谨慎处理历史趋势推断；此外，多源数据融合时'最佳来源'的筛选规则可能掩盖原始数据的不确定性，增加了复现分析的复杂性。

常用场景

经典使用场景

该数据集的核心使用场景在于分析欧洲不同性别、年龄及残疾状态人群的劳动力不参与率（inactivity rate），为劳动经济学、社会政策与人口健康交叉领域提供了高质量的微观与宏观结合数据。研究者通常利用其丰富的分类维度（如性别、年龄分段、残疾状态）进行分组比较，或将其作为时间序列数据，观察2002至2025年间各国劳动力市场结构性变化的长期趋势。数据集还常被用于构建面板数据模型，以探究残疾状态对劳动参与的影响在不同福利制度国家间的异质性。

衍生相关工作

围绕该数据集已衍生出一系列经典工作。在方法论层面，研究者开发了处理ILOSTAT多来源标志（如'最佳来源'选择、方法变更断点标注）的插值与稳健性检验框架，以及基于'残疾状态'与'年龄'双重分类的不活动率分解算法。在实证研究方向上，基于该数据的工作群组包括：利用差分法评估2008年金融危机后欧洲各国残疾就业保护法改革的外生冲击效应，使用双向固定效应模型检验性别-残疾交互项在不同代际群体中的显著性，以及运用聚类分析识别欧洲'残疾劳动排斥'的四种典型福利体制。这些研究推动了社会政策评估从截面描述向因果推断的转型。

数据集最近研究

最新研究方向

在全球劳动力市场结构性转型与包容性发展议题备受瞩目的当下，该数据集聚焦于欧洲32国2002至2025年间按性别、年龄及残疾状况细分的非经济活动率，为揭示劳动力利用不足的多维异质性提供了关键量化基础。前沿研究方向已从宏观失业率转向微观群体的劳动参与壁垒，尤其关注残疾人群在人工智能与绿色转型浪潮中被边缘化的风险。该数据与ILOSTAT国际劳工统计标准接轨，可支撑时序预测建模、横向比较政策效应及交叉性不平等分析，在欧盟积极推行《残疾权利战略》与“2030年就业率目标”的背景下，为评估社会包容政策的实施效果与识别弱势群体结构困境提供了不可或缺的数据根基。