electricsheepeurope/europe-ilo-emp-pifl-sex-eco-mts-rt-share-of-employment-outside-the-formal-sector-by-s

--- 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 - informal-economy - ilo - labour - employment pretty_name: "Share of employment outside the formal sector by sex, economic activity and marital status | Europe (ILOSTAT)" --- # Share of employment outside the formal sector by sex, economic activity and marital status | Europe (ILOSTAT) 🇪🇺 **117,505 observations** · **34 Europe countries** · **2003–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **117,505 observations** of `Informal economy` data across **34 Europe countries**, spanning **2003–2025**, 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_PIFL_SEX_ECO_MTS_RT) - **Publisher:** International Labour Organization (ILO) - **License:** [cc-by-4.0](https://creativecommons.org/licenses/by/4.0/) - **Topic:** Informal economy ## Methodology Data pulled directly from the ILOSTAT REST API at `https://rplumber.ilo.org/data/indicator?id=EMP_PIFL_SEX_ECO_MTS_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 34 Europe countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `MDA` | 6,295 | 2003 | 2025 | | `RUS` | 5,166 | 2010 | 2025 | | `NLD` | 4,534 | 2007 | 2024 | | `SRB` | 4,500 | 2007 | 2024 | | `POL` | 4,429 | 2007 | 2024 | | `MKD` | 4,313 | 2009 | 2025 | | `CZE` | 4,245 | 2007 | 2024 | | `FIN` | 4,193 | 2007 | 2024 | | `SVN` | 4,189 | 2007 | 2024 | | `ITA` | 4,174 | 2007 | 2024 | | `PRT` | 4,099 | 2007 | 2024 | | `ESP` | 4,094 | 2007 | 2024 | | `BIH` | 3,978 | 2006 | 2020 | | `LVA` | 3,848 | 2007 | 2024 | | `BGR` | 3,774 | 2007 | 2024 | | ... | _19 more countries_ | | | ## Indicators (sample) - `EMP_PIFL_SEX_ECO_MTS_RT` — Share of employment outside the formal sector by sex, economic activity and marital status (%) ## 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_PIFL_SEX_ECO_MTS_RT` | | `indicator.label` | `string` | Indicator name in English | `Share of employment outside the forma…` | | `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.) | `ECO_SECTOR_TOTAL` | | `classif1.label` | `string` | — | `Economic activity (Broad sector): Total` | | `classif2` | `string` | Second classification variable where applicable | `MTS_AGGREGATE_TOTAL` | | `classif2.label` | `string` | — | `Marital status (Aggregate): Total` | | `time` | `int64` | Observation year | `2024` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `3.907` | | `obs_status` | `string` | Observation status flag (e.g. provisional, unreliable) | `U` | | `obs_status.label` | `string` | — | `Unreliable` | | `note_classif` | `string` | — | `C5:1023_C5:2959` | | `note_classif.label` | `string` | — | `Nonstandard economic activity: Includ…` | | `note_indicator` | `string` | — | `I20:4077_I11:264` | | `note_indicator.label` | `string` | — | `Employment definition: Excluding own-…` | | `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_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-emp-pifl-sex-eco-mts-rt-share-of-employment-outside-the-formal-sector-by-s") 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_PIFL_SEX_ECO_MTS_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_PIFL_SEX_ECO_MTS_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_PIFL_SEX_ECO_MTS_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_emp_pifl_sex_eco_mts_rt_share_of_employment_outside_the_formal_sector_by_s_2025, title = {Share of employment outside the formal sector by sex, economic activity and marital status | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_PIFL_SEX_ECO_MTS_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-emp-pifl-sex-eco-mts-rt-share-of-employment-outside-the-formal-sector-by-s}} } ``` ## 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 2026-05-26 via the Electric Sheep pipeline. Source URL: https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_PIFL_SEX_ECO_MTS_RT_This dataset contains 117,505 observations across 34 European countries, spanning 2003 to 2025, focusing on the informal economy. The core indicator is EMP_PIFL_SEX_ECO_MTS_RT, which represents the share of employment outside the formal sector by sex, economic activity, and marital status (%). Data is sourced from the International Labour Organization (ILO) ILOSTAT database, retrieved via REST API and filtered for European countries. The dataset includes columns such as country code, indicator code, sex classification (total, male, female), economic activity and marital status classifications, observation year, observed value, and data status flags. It is suitable for tabular classification, regression, and time-series forecasting tasks. Data is annual frequency, harmonized by ILO, and includes source and quality notes.

搜集汇总

数据集介绍

构建方式

该数据集以国际劳工组织（ILO）的ILOSTAT中央统计数据库为来源，经由Electric Sheep Europe从ILOSTAT REST API直接拉取指标EMP_PIFL_SEX_ECO_MTS_RT的原始数据，并按照欧洲ISO3国家代码进行过滤，覆盖34个欧洲国家2003至2025年的观测记录，共计117,505条。ILOSTAT依据国际劳工统计学家会议（ICLS）定义对原始调查微观数据进行统一协调，数据来源在source.label列中予以标注，以保证可追溯性。Electric Sheep Europe对拉取数据执行架构规范化、统一字段命名并打包为Parquet格式，最终以HuggingFace数据集形式发布，便于机器学习研究者直接调用。

特点

数据集聚焦于非正规部门就业份额这一非正规经济核心议题，以性别、经济活动类别和婚姻状况为多维分解维度，系统刻画欧洲各国非正规就业的分布特征与演变趋势。数据规模达117,505条观测，横跨34个欧洲国家、时间跨度逾二十年，兼具截面与时间序列双重属性。字段设计完整，涵盖国家代码、来源标识、指标编码、性别分解、分类变量、观测值及状态标志等，并附带分类注释与来源注释，为数据质量评估提供依据。年度频率与多源择优机制进一步增强了数据的可比性与可靠性。

使用方法

研究者可通过HuggingFace datasets库以一行代码加载该数据集，并转换为Pandas数据框进行后续分析。典型用法包括：按ref_area字段筛选特定国家，如提取德国数据；按indicator字段筛选目标指标并按time排序，以绘制时间序列图考察非正规就业份额的年度变化；利用pivot_table将数据重塑为国家×年份矩阵，便于跨国比较。分类变量sex、classif1与classif2支持按性别、经济活动及婚姻状况进行分组统计。使用时应留意obs_status标志所提示的数据可靠性等级，并遵循CC-BY-4.0许可协议引用ILO原始来源及Electric Sheep Europe的再包装工作。

背景与挑战

背景概述

非正规就业的规模与分布是发展经济学与劳动经济学长期关注的核心议题，其测量精度直接影响社会保障政策与非正规经济治理的有效性。国际劳工组织（ILO）自二十世纪中叶起持续推动非正规部门就业统计的标准化，并于2003年前后依托国际劳工统计学家会议（ICLS）框架，在ILOSTAT数据库中系统发布非正规部门外就业占比指标。该数据集由Electric Sheep Europe于2025年重新封装发布，覆盖34个欧洲国家、2003至2025年间117,505条观测，按性别、经济活动与婚姻状况多维分解，为欧洲非正规就业的跨国比较与长期趋势分析提供了权威、可复用的数据基础，亦为劳动统计领域的方法论研究树立了标杆。

当前挑战

该数据集所回应的领域问题在于如何准确刻画非正规部门外就业的性别差异、行业异质性与婚姻状况关联，并实现跨国可比的时间序列推断，其难度源于非正规就业定义在不同国家调查体系中的执行差异。构建过程中面临多重挑战：ILOSTAT原始数据源自各国劳动力调查、家庭收入调查与行政记录，抽样设计与变量口径参差不齐，须经ICLS定义协调与最佳来源筛选；观测状态标志与注释字段揭示部分年份数据存在临时性、不可靠或非标准行业分类问题；婚姻状况等分类维度并非所有国家均予发布，导致数据稀疏与缺失机制复杂，对建模的稳健性构成持续考验。

常用场景

经典使用场景

在劳动经济学与非正规经济研究领域，该数据集凭借其涵盖三十四国、跨越二零零三至二零二五年的十一万余条观测，构成了分析非正规部门就业占比时空演变的经典面板数据资源。研究者通常以性别、经济活动部门与婚姻状况为分层维度，运用固定效应模型或时间序列分解方法，刻画不同国家非正规就业的结构性差异与收敛趋势，进而揭示性别鸿沟与部门异质性在非正规劳动市场中的交互作用。

衍生相关工作

围绕该数据集已衍生出一系列经典研究脉络。比较政治经济学文献利用其跨国面板检验福利体制对非正规就业的抑制效应；性别研究则借助性别分层数据探讨婚姻状况与女性非正规就业的关联机制。此外，若干机器学习预测研究以其为基准，构建非正规就业占比的时序预测模型，推动了劳动统计与数据科学的交叉融合，并激发了关于非正规经济卫星账户构建的方法论讨论。

数据集最近研究

最新研究方向

在全球非正规经济持续引发政策关注的背景下，该数据集凭借其涵盖34个欧洲国家、2003至2025年间的117,505条观测记录，为解析非正规就业的结构性差异提供了独特资源。近期研究聚焦于性别、经济活动部门与婚姻状况三重维度下的非正规就业动态，借助高分辨率时间序列与横截面数据，探索性别不平等在非正规劳动市场中的演化路径，以及婚姻状况对男性和女性就业正规性的异质性影响。该数据集还支撑了关于经济周期、劳动力市场规制与欧洲一体化政策如何塑造非正规就业空间分布的实证分析。相关研究有助于评估国际劳工组织体面劳动议程的进展，并为后疫情时代欧洲劳动力市场韧性建设提供量化依据。