electricsheepeurope/europe-ilo-eip-dwap-sex-geo-dsb-rt-inactivity-rate-by-sex-rural-urban-areas-and-disab

--- 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, rural / urban areas and disability status (%) | Europe (ILOSTAT)" --- # Inactivity rate by sex, rural / urban areas and disability status (%) | Europe (ILOSTAT) 🇪🇺 **14,891 observations** · **30 Europe countries** · **2002–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **14,891 observations** of `Other measures of labour underutilization` data across **30 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_GEO_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_GEO_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 30 Europe countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `LUX` | 575 | 2004 | 2024 | | `ITA` | 573 | 2004 | 2024 | | `FRA` | 569 | 2004 | 2024 | | `FIN` | 567 | 2004 | 2024 | | `ESP` | 567 | 2004 | 2024 | | `AUT` | 567 | 2004 | 2024 | | `GRC` | 567 | 2004 | 2024 | | `IRL` | 567 | 2004 | 2024 | | `SWE` | 567 | 2004 | 2024 | | `PRT` | 567 | 2004 | 2024 | | `NOR` | 567 | 2004 | 2024 | | `BEL` | 567 | 2004 | 2024 | | `EST` | 567 | 2004 | 2024 | | `CZE` | 547 | 2005 | 2024 | | `SVK` | 540 | 2005 | 2024 | | ... | _15 more countries_ | | | ## Indicators (sample) - `EIP_DWAP_SEX_GEO_DSB_RT` — Inactivity rate by sex, rural / urban areas 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_GEO_DSB_RT` | | `indicator.label` | `string` | Indicator name in English | `Inactivity rate by sex, rural / urban…` | | `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.) | `GEO_COV_NAT` | | `classif1.label` | `string` | — | `Area type: National` | | `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-geo-dsb-rt-inactivity-rate-by-sex-rural-urban-areas-and-disab") 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_GEO_DSB_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EIP_DWAP_SEX_GEO_DSB_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EIP_DWAP_SEX_GEO_DSB_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_eip_dwap_sex_geo_dsb_rt_inactivity_rate_by_sex_rural_urban_areas_and_disab_2025, title = {Inactivity rate by sex, rural / urban areas and disability status (%) | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EIP_DWAP_SEX_GEO_DSB_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-eip-dwap-sex-geo-dsb-rt-inactivity-rate-by-sex-rural-urban-areas-and-disab}} } ``` ## 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_GEO_DSB_RT_This dataset contains 14,891 observations of inactivity rate data by sex, rural/urban areas, and disability status (%), sourced from the International Labour Organizations ILOSTAT database under the topic Other measures of labour underutilization. It covers 30 European countries from 2002 to 2025, with one specific indicator (EIP_DWAP_SEX_GEO_DSB_RT). Data is retrieved via the ILOSTAT REST API, filtered to European country codes, and harmonized using International Conference of Labour Statisticians (ICLS) definitions for consistency and traceability. The dataset includes columns such as country code, source, indicator, sex, classification variables, year, observed value, and is 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）的核心统计数据库ILOSTAT，聚焦于欧洲地区基于性别、城乡地域及残疾状态划分的经济不活动率指标。数据通过ILOSTAT的REST API接口直接拉取，并依据ILO劳动统计学家国际会议（ICLS）制定的标准对原始调查微观数据进行统一化处理。为保障地理覆盖的完整性，数据被限定至欧洲的ISO3国家代码范围，并保留了‘source.label’列以追踪数据源的可溯性。最终构建了一个包含14,891条观测、覆盖30个欧洲国家、时间跨度从2002年至2025年的结构化数据集。

使用方法

研究人员可通过HuggingFace的`datasets`库便捷加载该数据，执行`load_dataset()`后即可将训练集转换为Pandas DataFrame进行后续操作。典型应用包括针对单一国家（如德国，`ref_area == DEU`）进行时间序列分析，或对特定指标（如`EIP_DWAP_SEX_GEO_DSB_RT`）基于‘time’与‘obs_value’绘制趋势图。更复杂的场景下，用户可利用透视表功能构建以年份为行、国家为列的矩阵，为面板数据分析或机器学习建模提供规整的数据结构。数据以Parquet格式存储，兼顾了高效读取与后续兼容性。

背景与挑战

背景概述

该数据集由国际劳工组织（ILO）下属的ILOSTAT数据库整理发布，并由Electric Sheep Europe于2025年重新封装至HuggingFace平台，专注于欧洲地区劳动参与率的细分维度。核心指标为“按性别、城乡区域及残疾状况划分的不活动率”，覆盖30个欧洲国家，时间跨度从2002年至2025年，共计14,891条观测记录。该数据集旨在弥补传统劳动统计中针对边缘群体（如女性、农村人口及残疾人）劳动参与度量化分析的空白，为劳动经济学、社会保障政策及可持续发展目标（SDG）评估提供高质量的精细粒度数据支持。其引入ILO统一统计标准，有效提升了跨国可比性与研究可复现性，对欧洲劳动力市场分层研究具有重要基准价值。

当前挑战

该数据集所解决的领域核心挑战在于：传统劳动参与率指标往往忽略性别、地域及残疾状况的交叉影响，导致政策制定缺乏对弱势群体劳动排斥现象的精准刻画。构建过程中面临的挑战包括：1) 多源数据整合难度高，需调和各国劳动调查在定义、抽样方法与统计口径上的差异，例如部分国家采用非标准残疾定义（如note_classif标注需通过C14:6260代码追溯）；2) 数据质量标注复杂，观测值中存在不稳定（U）及方法变更（break in series）等状态标识，研究者需谨慎处理时间序列的连续性；3) 细粒度分层导致稀疏性，例如按性别、城乡与残疾状况交叉后，部分国家或年份的样本量不足，影响统计推断的稳健性。

常用场景

经典使用场景

在劳动力市场研究中，该数据集被广泛用于分析欧洲各国因性别、城乡地域和残疾状况导致的经济不活跃率差异。研究者可借助其丰富的分类维度，构建面板数据模型，探索结构性就业障碍。例如，通过按性别和地区细分的不活跃率，揭示残疾群体在城乡劳动力市场中的参与壁垒，为比较不同福利制度下的劳动排斥模式提供实证基础。

解决学术问题

该数据集解决了劳动经济学中关于‘隐性失业’与‘劳动力闲置’测量的关键难题，尤其是针对边缘群体（如残疾人和农村居民）。传统失业率常低估劳动力市场的真实闲置程度，而此数据通过国际劳工组织标准化的不活跃率指标，为评估全面就业目标及联合国可持续发展目标（SDG 8）的进展提供了可靠的时间序列依据。其跨年度覆盖使研究者能够追踪2002至2025年间欧洲劳动力结构的动态演变。

实际应用

在实际应用中，该数据集可辅助政策制定者识别特定人群（如农村残疾女性）的就业缺口，从而设计精准的就业促进计划。社会研究机构可利用其城乡与性别交叉分类，评估区域发展政策的包容性效果。此外，国际组织可借助时间序列数据监测劳动力市场改革对弱势群体的长期影响，优化资源分配。

数据集最近研究

最新研究方向

该数据集聚焦于欧洲劳动力市场中因性别、城乡区域及残疾状况导致的劳动参与率差异，为劳动经济学与社会政策研究提供了跨时空的精细颗粒度数据支撑。当前前沿研究方向集中于利用该长时序面板数据，结合机器学习模型（如时间序列预测与分类算法），剖析欧洲各国在新冠疫情后劳动力市场恢复过程中的结构性不平等，特别是残疾人群体的就业排斥与城乡间经济韧性差异。此外，该数据集与欧盟2030年包容性增长目标紧密关联，研究者正借助其多维分解维度，量化不同政策干预（如远程办公推广与社会保障改革）对弱势群体劳动参与率的异质性影响，为制定精准的就业促进策略提供实证依据。