electricsheepeurope/europe-ilo-emp-pifl-sex-eco-geo-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: - 10K<n<100K tags: - tabular - europe - ilostat - informal-economy - ilo - labour - employment pretty_name: "Share of employment outside the formal sector by sex, economic activity and rural / urban | Europe (ILOSTAT)" --- # Share of employment outside the formal sector by sex, economic activity and rural / urban | Europe (ILOSTAT) 🇪🇺 **44,872 observations** · **31 Europe countries** · **2003–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **44,872 observations** of `Informal economy` data across **31 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_GEO_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_GEO_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 31 Europe countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `MDA` | 2,338 | 2003 | 2025 | | `BIH` | 1,888 | 2006 | 2024 | | `POL` | 1,847 | 2007 | 2024 | | `FIN` | 1,782 | 2007 | 2024 | | `SRB` | 1,777 | 2007 | 2024 | | `FRA` | 1,746 | 2007 | 2024 | | `CZE` | 1,738 | 2007 | 2024 | | `PRT` | 1,736 | 2007 | 2024 | | `RUS` | 1,728 | 2010 | 2025 | | `ITA` | 1,710 | 2007 | 2024 | | `ESP` | 1,710 | 2007 | 2024 | | `NOR` | 1,681 | 2007 | 2024 | | `BGR` | 1,666 | 2007 | 2024 | | `SVK` | 1,625 | 2007 | 2024 | | `LVA` | 1,598 | 2007 | 2024 | | ... | _16 more countries_ | | | ## Indicators (sample) - `EMP_PIFL_SEX_ECO_GEO_RT` — Share of employment outside the formal sector by sex, economic activity and rural / urban areas (%) ## 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_GEO_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 | `GEO_COV_NAT` | | `classif2.label` | `string` | — | `Area type: National` | | `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` | — | `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_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-geo-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_GEO_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_PIFL_SEX_ECO_GEO_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_PIFL_SEX_ECO_GEO_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_emp_pifl_sex_eco_geo_rt_share_of_employment_outside_the_formal_sector_by_s_2025, title = {Share of employment outside the formal sector by sex, economic activity and rural / urban | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_PIFL_SEX_ECO_GEO_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-emp-pifl-sex-eco-geo-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_GEO_RT_This dataset contains data on the share of employment outside the formal sector in Europe, disaggregated by sex, economic activity, and rural/urban areas. It comprises 44,872 observations across 31 European countries, spanning the years 2003 to 2025, with a focus on one indicator: share of employment outside the formal sector (%). The data is sourced from the International Labour Organization (ILO) ILOSTAT database, retrieved via API and filtered to European ISO3 country codes. The dataset schema includes columns such as country code, sex classification (total, male, female), economic activity classification, rural/urban classification, observation year, and value, suitable for tabular classification, regression, and time-series forecasting tasks. Data quality notes indicate annual frequency, use of ILO-selected best source for multiple sources, and non-null disaggregation columns only when breakdowns are published. The dataset is designed to support research and machine learning applications for analyzing informal economy in European labor markets.

搜集汇总

数据集介绍

构建方式

该数据集基于国际劳工组织（ILO）的ILOSTAT中央统计数据库构建，通过REST API直接提取指标EMP_PIFL_SEX_ECO_GEO_RT的原始数据，并依据ISO3国家代码筛选出31个欧洲国家。ILOSTAT采用国际劳工统计学家会议（ICLS）的定义对各国劳动力调查、家庭收入调查等微观数据进行标准化调和，数据来源在source.label列中标注以保证可追溯性。最终由Electric Sheep Europe重新打包为Parquet格式，形成44,872条观测记录，覆盖2003至2025年。

特点

数据集聚焦于非正规部门就业份额，按性别、经济活动部门和城乡区域三维度细分，涵盖31个欧洲国家，时间跨度达23年。变量结构丰富，包含ref_area、source、indicator、sex、classif1、classif2、time、obs_value等字段，并附有观测状态标志和注释标签，便于识别数据质量与方法变更。年度频率，部分指标存在多源时由ILO择优选取，所有分类维度仅在指标发布相应分解时非空，整体规模适中，适合多任务建模。

使用方法

通过HuggingFace datasets库的load_dataset函数加载，转换为Pandas DataFrame后即可进行筛选、排序和聚合分析。例如按ref_area过滤特定国家，按indicator和时间排序绘制时间序列图，或利用pivot_table生成国家×年份矩阵。该数据集支持表格分类、回归及时间序列预测任务，使用时应遵循CC-BY-4.0许可，并同时引用ILO原始来源与Electric Sheep Europe的再包装工作。

背景与挑战

背景概述

非正规经济就业的测度始终是发展经济学与劳动经济学的核心议题，其数据基础直接关系到体面劳动议程的监测与政策评估。国际劳工组织（ILO）自二十世纪中叶起持续推动非正规部门统计标准的国际协调，并依托国际劳工统计学家会议（ICLS）决议构建全球可比指标。2025年，Electric Sheep Europe基于ILOSTAT数据库，将EMP_PIFL_SEX_ECO_GEO_RT指标整理为覆盖31个欧洲国家、2003至2025年共44,872条观测的标准化数据集。该数据集按性别、经济活动部门及城乡维度细分，为欧洲非正规就业的结构性分析提供了统一、可复现的数据基础，对劳动市场政策研究与可持续发展目标（SDG 8）的进展监测具有重要参考价值。

当前挑战

该数据集所应对的领域问题在于非正规就业的跨国可比测度。非正规经济活动的定义边界随各国法律框架与统计实践而异，ILO虽以ICLS标准进行协调，但不同来源（劳动力调查、住户收入调查、行政记录）在抽样设计、参考期与覆盖范围上的差异仍构成显著挑战。构建过程中的具体困难包括：部分国家观测年份不连续，如摩尔多瓦覆盖2003至2025年而多数国家仅自2007年起；同一国家×年份存在多源数据时需依赖ILO的“最优来源”择选规则；观测状态标志（如“不可靠”）与序列断点提示（如方法修订）要求使用者在建模时加以甄别；性别、经济活动与城乡维度的细分并非所有国家均予发布，导致面板数据结构不平衡，对时间序列预测与跨国比较分析提出了更高的数据治理要求。

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，该数据集凭借其覆盖31个欧洲国家、时间跨度逾二十年的非正规部门就业份额统计，成为刻画欧洲非正规就业性别差异与城乡分化的基准性数据资源。研究者常借助其按性别、经济活动门类及城乡属性交叉分类的观测值，构建面板数据模型，以揭示非正规就业在区域间的时空演变规律。该数据集亦广泛应用于时间序列预测任务，通过历史观测值拟合趋势，为劳动力市场动态监测提供定量支撑。

实际应用

在政策制定与社会保障领域，该数据集为国际组织及各国劳工部门评估非正规就业规模、设计社保扩面政策提供了可操作的数据依据。其分性别与城乡维度的指标可辅助识别脆弱就业群体，支持靶向性就业促进项目与减贫干预的成效监测。同时，该数据集亦被用于构建劳动力市场预警指标，帮助相关机构及时捕捉非正规就业异常波动，为宏观就业政策调整提供参考。

衍生相关工作

围绕该数据集，研究者已衍生出一系列经典工作，包括欧洲非正规就业收敛性分析、性别不平等与非正规经济的关联性研究，以及基于机器学习方法对非正规就业份额的预测建模。部分学者将其与其他ILOSTAT指标及世界银行发展指标整合，构建了多维度劳动力市场脆弱性指数。这些工作不仅拓展了非正规经济研究的分析边界，也为后续欧洲劳动力市场一体化研究奠定了数据与方法论基础。