electricsheepeurope/europe-ilo-emp-pifl-sex-edu-dsb-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, education and disability status (%) | Europe (ILOSTAT)" --- # Share of employment outside the formal sector by sex, education and disability status (%) | Europe (ILOSTAT) 🇪🇺 **14,265 observations** · **31 Europe countries** · **2007–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **14,265 observations** of `Informal economy` data across **31 Europe countries**, spanning **2007–2025**, 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_EDU_DSB_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_EDU_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 31 Europe countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `PRT` | 664 | 2007 | 2024 | | `FIN` | 640 | 2007 | 2024 | | `NLD` | 610 | 2007 | 2024 | | `ESP` | 599 | 2007 | 2024 | | `ITA` | 574 | 2007 | 2024 | | `SWE` | 560 | 2007 | 2024 | | `FRA` | 554 | 2007 | 2024 | | `POL` | 538 | 2007 | 2024 | | `NOR` | 536 | 2007 | 2024 | | `BGR` | 526 | 2007 | 2024 | | `LUX` | 511 | 2007 | 2024 | | `CZE` | 511 | 2007 | 2024 | | `SVK` | 509 | 2007 | 2024 | | `GBR` | 508 | 2007 | 2018 | | `LVA` | 499 | 2007 | 2024 | | ... | _16 more countries_ | | | ## Indicators (sample) - `EMP_PIFL_SEX_EDU_DSB_RT` — Share of employment outside the formal sector by sex, education and disability 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_EDU_DSB_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.) | `EDU_AGGREGATE_TOTAL` | | `classif1.label` | `string` | — | `Education (Aggregate levels): Total` | | `classif2` | `string` | Second classification variable where applicable | `DSB_STATUS_TOTAL` | | `classif2.label` | `string` | — | `Disability status: 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` | — | `C14:6260` | | `note_classif.label` | `string` | — | `Nonstandard definition of disability:…` | | `note_indicator` | `string` | — | `—` | | `note_indicator.label` | `string` | — | `—` | | `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-edu-dsb-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_EDU_DSB_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_PIFL_SEX_EDU_DSB_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_PIFL_SEX_EDU_DSB_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_emp_pifl_sex_edu_dsb_rt_share_of_employment_outside_the_formal_sector_by_s_2025, title = {Share of employment outside the formal sector by sex, education and disability status (%) | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_PIFL_SEX_EDU_DSB_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-emp-pifl-sex-edu-dsb-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_EDU_DSB_RT_This dataset contains 14,265 observations of informal economy data across 31 European countries, spanning from 2007 to 2025, covering 1 distinct indicator: Share of employment outside the formal sector by sex, education and disability status (%) (EMP_PIFL_SEX_EDU_DSB_RT). The data is sourced from the International Labour Organization (ILO) ILOSTAT database, pulled directly via the REST API and filtered to European ISO3 country codes. It is harmonized by ILO using International Conference of Labour Statisticians (ICLS) definitions, with source traceability through columns like source.label. The dataset includes disaggregation dimensions such as sex, education, and disability status, and provides data quality notes, such as annual frequency, best-source selection, and non-null conditions for classification variables. Stored in a tabular format, it is suitable for tasks like tabular classification, regression, and time-series forecasting, designed for machine learning and research purposes.

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统计数据库，通过ILOSTAT REST API接口直接提取指标代码为EMP_PIFL_SEX_EDU_DSB_RT的原始数据，并依据ISO 3166-1 alpha-3国家代码筛选出31个欧洲国家。ILOSTAT依据国际劳工统计学家会议（ICLS）的定义对各国劳动力调查、家庭收入调查及行政记录等微观数据进行统一调和，数据经Electric Sheep Europe重新打包为Parquet格式，完整保留来源标签及注释字段以保证可追溯性。

特点

数据集涵盖2007年至2025年共14,265条观测记录，覆盖31个欧洲国家，时间跨度长达十九年。核心指标为按性别、教育程度和残疾状况分类的非正规部门就业份额，所有记录均包含来源代码、指标标签、分类变量及观测状态标记，其中残疾状况的非标准定义等特殊情形通过注释字段予以说明。数据以年度频率发布，分类维度包括性别（总计、男性、女性）以及教育和残疾状况的细分层级。

使用方法

研究者可通过HuggingFace datasets库以load_dataset函数直接加载数据集并转换为Pandas DataFrame进行探索性分析。典型用法包括按国家代码筛选特定国家的子集、针对单一指标构建时间序列并绘图，以及将数据透视为国家与年份的交叉矩阵以观察跨国趋势。数据集支持表格分类、表格回归和时间序列预测等机器学习任务，使用时需注意部分观测值带有不可靠或临时性状态标记，引用时应同时标注ILO原始来源与Electric Sheep Europe的再包装工作。

背景与挑战

背景概述

非正规经济部门的就业测算始终是劳动经济学与发展经济学的核心议题，其数据质量直接关系到社会保障政策与体面劳动议程的推进。国际劳工组织（ILO）依托ILOSTAT数据库，依据国际劳工统计学家会议（ICLS）定义，系统汇集各国劳动力调查与家庭收入调查数据，构建了覆盖200余个经济体的劳动统计体系。该数据集由Electric Sheep Europe于2025年从ILOSTAT REST API摄取并重新封装，聚焦欧洲31国2007至2025年间按性别、教育程度与残疾状况分列的非正规部门就业占比，共含14,265条观测，旨在为跨境比较研究与机器学习建模提供标准化、可复用的表格数据基础。

当前挑战

该数据集所回应的领域难题在于非正规就业的跨时空可比性：各国对非正规部门的界定、残疾状况的测量口径及教育分类标准长期存在异质性，ILOSTAT虽经统一协调，仍难以完全消除定义差异对跨国比较的干扰。构建过程中的主要挑战包括：部分国家观测值被标记为不可靠或临时性，需依赖状态标志加以甄别；同一国家—年份存在多源数据时仅保留ILO遴选的最佳来源，可能掩盖源间分歧；性别、教育与残疾三维交叉分层导致部分单元格样本稀疏，削弱了统计推断的稳健性；英国等国的时序在2018年中断，造成面板结构不平衡。上述因素共同制约着基于该数据的因果识别与预测建模精度。

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，该数据集最经典的使用场景在于刻画欧洲各国非正规部门就业份额的时序演变轨迹。依托ILOSTAT统一口径的年度观测值，研究者可按国别、性别、教育程度与残疾状态等多维截面进行分层比较，构建国别×年份的面板矩阵，进而透视2007至2025年间欧洲劳动力市场正规化进程的异质性。借助时间序列建模与跨国对比分析，该数据集为识别非正规就业的结构性模式提供了坚实的经验基础。

衍生相关工作

围绕该数据集已衍生出一系列与ILO统计体系相关的研究与工程实践。Electric Sheep Europe将其纳入欧洲统一数据层，衍生出标准化的HuggingFace数据集卡片与加载脚本，促进了非正规经济指标的机器学习可用性。在学术侧，该数据常被用于跨国面板回归、非正规就业决定因素分析及SDG体面劳动指标监测等经典工作，成为劳动统计再分析与政策比较研究的重要基础资源。

数据集最近研究

最新研究方向

在全球非正规就业持续占据总就业半数以上的背景下，该数据集为解析欧洲非正规就业的结构性成因提供了关键实证基础。当前研究前沿聚焦于性别、教育水平与残疾状态三重弱势身份的交叉性分析，借助纵向数据与机器学习方法，揭示非正规就业在不同社会群体间的异质性分布与动态演化。相关热点事件如国际劳工组织推动的“向正规化转型”议程及欧盟性别平等战略，均高度依赖此类细分数据来评估政策干预效果。该数据集在促进体面劳动监测、社会保障覆盖评估以及弱势群体就业歧视识别方面具有重要影响与深远意义。