electricsheepeurope/europe-ilo-emp-xtru-sex-dsb-nb-time-related-underemployment-by-sex-and-disability

--- license: cc-by-4.0 language: - en task_categories: - tabular-classification - tabular-regression - time-series-forecasting multilinguality: monolingual size_categories: - 1K<n<10K tags: - tabular - europe - ilostat - time-related-underemployment - ilo - labour - employment pretty_name: "Time-related underemployment by sex and disability status (thousands) | Europe (ILOSTAT)" --- # Time-related underemployment by sex and disability status (thousands) | Europe (ILOSTAT) 🇪🇺 **3,348 observations** · **31 Europe countries** · **2004–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **3,348 observations** of `Time-related underemployment` data across **31 Europe countries**, spanning **2004–2025**, 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=EMP_XTRU_SEX_DSB_NB) - **Publisher:** International Labour Organization (ILO) - **License:** [cc-by-4.0](https://creativecommons.org/licenses/by/4.0/) - **Topic:** Time-related underemployment ## Methodology Data pulled directly from the ILOSTAT REST API at `https://rplumber.ilo.org/data/indicator?id=EMP_XTRU_SEX_DSB_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 31 Europe countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `GBR` | 186 | 2005 | 2025 | | `ITA` | 144 | 2004 | 2023 | | `FRA` | 139 | 2004 | 2023 | | `ESP` | 135 | 2004 | 2023 | | `GRC` | 134 | 2004 | 2023 | | `PRT` | 125 | 2004 | 2023 | | `POL` | 121 | 2005 | 2023 | | `LUX` | 120 | 2004 | 2023 | | `DEU` | 119 | 2005 | 2020 | | `BEL` | 117 | 2004 | 2023 | | `FIN` | 116 | 2004 | 2023 | | `SWE` | 112 | 2004 | 2023 | | `AUT` | 109 | 2004 | 2023 | | `NOR` | 108 | 2004 | 2023 | | `CHE` | 107 | 2007 | 2023 | | ... | _16 more countries_ | | | ## Indicators (sample) - `EMP_XTRU_SEX_DSB_NB` — Time-related underemployment by sex and disability status (thousands) ## Schema | Column | Type | Description | Example | |--------|------|-------------|---------| | `ref_area` | `string` | ISO 3166-1 alpha-3 country code | `AUT` | | `ref_area.label` | `string` | Country name in English | `Austria` | | `source` | `string` | ILOSTAT source code (e.g. labour force survey) | `BB:275` | | `source.label` | `string` | Source name in English | `HIES - EU Statistics on Income and Li…` | | `indicator` | `string` | ILOSTAT indicator code | `EMP_XTRU_SEX_DSB_NB` | | `indicator.label` | `string` | Indicator name in English | `Time-related underemployment by sex a…` | | `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.) | `DSB_STATUS_TOTAL` | | `classif1.label` | `string` | — | `Disability status: Total` | | `time` | `int64` | Observation year | `2023` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `29.221` | | `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_T2:85` | | `note_source.label` | `string` | — | `Repository: ILO-STATISTICS - Micro da…` | ## Disaggregation dimensions The following columns provide disaggregation dimensions: - **`sex`** (3 unique values): `SEX_T`, `SEX_F`, `SEX_M` ## 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-emp-xtru-sex-dsb-nb-time-related-underemployment-by-sex-and-disability") 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"] == "EMP_XTRU_SEX_DSB_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_XTRU_SEX_DSB_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_XTRU_SEX_DSB_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_emp_xtru_sex_dsb_nb_time_related_underemployment_by_sex_and_disability_2025, title = {Time-related underemployment by sex and disability status (thousands) | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_XTRU_SEX_DSB_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-emp-xtru-sex-dsb-nb-time-related-underemployment-by-sex-and-disability}} } ``` ## 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=EMP_XTRU_SEX_DSB_NB_This is a statistical dataset on time-related underemployment across 31 European countries, sourced from the International Labour Organization (ILO) ILOSTAT database. The dataset covers the period from 2004 to 2025 and contains 3,348 observations. The primary indicator is time-related underemployment by sex and disability status (in thousands). The data includes country codes, years, observed values, data sources, sex disaggregation (total, male, female), disability status classifications, and data quality notes. The dataset is suitable for tasks such as tabular classification, regression, and time-series forecasting.

搜集汇总

数据集介绍

构建方式

该数据集由Electric Sheep Europe基于国际劳工组织（ILO）的ILOSTAT数据库构建，聚焦欧洲31国在2004至2025年间的时间相关非充分就业状况，并按性别与残疾状态进行细分。数据通过ILOSTAT REST API直接获取原始指标EMP_XTRU_SEX_DSB_NB，经ISO3国家代码筛选至欧洲范围，保留了来源信息以保证可追溯性。数据集涵盖3348条观测记录，字段包括国家、性别、分类变量、年份及观测值等，并依据劳动力调查等权威来源进行统一处理。

特点

数据集具备多维度的细分特性，特别关注性别与残疾状态的非充分就业差异，提供了3个性别类别及残疾状态分类。时间跨度长达22年，覆盖31个欧洲国家，形成平衡的面板结构，支持跨国家、跨时间序列的深度分析。数据来源于ILOSTAT这一权威统计数据库，采用国际劳工统计学家会议定义进行统一，并附带数据质量标记（如不可靠值）及注释字段，为研究人员提供了高质量、可比性的劳动力市场数据基础。

使用方法

使用者可通过Hugging Face的datasets库便捷加载数据，并转换为Pandas DataFrame进行探究。典型应用包括筛选特定国家（如德国）的子集，绘制单一指标的时间序列趋势图，或利用透视表功能构建国家×年份矩阵以进行面板数据分析。数据集结构清晰，字段命名规范，便于开展性别与残疾状况下的非充分就业比较研究、劳动力政策评估及经济趋势预测等任务。

背景与挑战

背景概述

该数据集由Electric Sheep Europe于2025年基于国际劳工组织（ILO）的ILOSTAT数据库重新打包而成，聚焦于欧洲31国2004至2025年间按性别与残疾状态划分的时间相关不充分就业指标（千人次）。ILOSTAT作为全球劳动统计的权威来源，依据国际劳工统计学家会议（ICLS）标准对各国劳动力调查数据进行协调统一，确保了跨国可比性。此数据集的核心研究问题在于揭示不同性别及残疾群体在劳动力市场中面临的不充分就业状况，为劳动经济学、社会政策及可持续发展目标（SDG）的监测提供了精细化的数据支撑。其引入的残疾状态维度，填补了现有劳动力统计中关于弱势群体就业质量研究的空白，对推动包容性就业政策的制定具有重要参考价值。

当前挑战

该数据集所应对的领域挑战在于准确衡量劳动力市场中的时间相关不充分就业，尤其是对残疾人群体的细分分析，这要求数据不仅在性别维度上具备细致划分，还需克服残疾定义跨国差异、统计口径不一致等难题。在构建过程中，数据集面临多重挑战：首先，不同国家及历史时期对‘残疾’的界定存在非标准定义，需依赖ILOSTAT的注记字段进行标识与修正；其次，年度数据频率限制了高频波动分析，且部分国家数据缺失或来源混杂，需通过‘最佳来源’选择机制确保数据质量；再次，观测状态标记（如‘不可靠’）提示部分数据存在统计不确定性，使用时需谨慎处理；最后，时间序列中的方法论修订与数据断裂（如注记字段所示）增加了长周期比较分析的复杂性。

常用场景

经典使用场景

该数据集聚焦于欧洲31国2004至2025年间按性别与残疾状态细分的时间相关非充分就业指标，源于国际劳工组织ILOSTAT数据库，经规范整理为表格形式，适用于分类、回归及时间序列预测等监督学习任务。研究者常将其用于构建劳动经济学中的就业质量预测模型，通过性别、残疾状态等特征推断非充分就业水平，亦可作为面板数据分析的基准，探索欧洲劳动力市场中弱势群体的就业结构演变。

实际应用

在实践层面，该数据集服务于欧盟及各国劳动部门、社会保障机构及非政府组织，用于监测可持续发展目标中体面工作指标的进展。其时间序列特性便于政策制定者识别非充分就业的周期性波动与结构性趋势，从而精准调配职业培训、就业援助等资源。此外，数据集亦可用于企业人力资源规划，辅助多元包容策略的制定，以及为非政府组织撰写政策简报提供量化依据，推动基于证据的社会决策。

衍生相关工作

该数据集衍生出系列相关工作，包括构建欧洲劳动力市场非充分就业的预测模型，如结合宏观经济指标的时间序列外推；开发可视化工具有效对比各国残疾群体就业境况；以及设计公平性算法，检验模型在不同性别与残疾亚组间的偏差。此外，其作为ILOSTAT的欧洲子集，常与教育、工资等指标整合，开展多维贫困与工作质量的研究，促进了跨学科合作与数据基础设施的完善。