electricsheepeurope/europe-ilo-emp-pifl-sex-ocu-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 and occupation (%) | Europe (ILOSTAT)" --- # Share of employment outside the formal sector by sex and occupation (%) | Europe (ILOSTAT) 🇪🇺 **21,003 observations** · **34 Europe countries** · **2003–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **21,003 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_OCU_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_OCU_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 | |---------|-----:|-----------:|----------:| | `NLD` | 853 | 2007 | 2024 | | `MDA` | 817 | 2003 | 2025 | | `SVN` | 785 | 2007 | 2024 | | `POL` | 757 | 2007 | 2024 | | `SWE` | 751 | 2007 | 2024 | | `BIH` | 747 | 2006 | 2024 | | `ITA` | 744 | 2007 | 2024 | | `ESP` | 743 | 2007 | 2024 | | `PRT` | 733 | 2007 | 2024 | | `CZE` | 730 | 2007 | 2024 | | `FRA` | 717 | 2007 | 2024 | | `FIN` | 706 | 2007 | 2024 | | `NOR` | 699 | 2007 | 2024 | | `BEL` | 683 | 2007 | 2024 | | `SVK` | 683 | 2007 | 2024 | | ... | _19 more countries_ | | | ## Indicators (sample) - `EMP_PIFL_SEX_OCU_RT` — Share of employment outside the formal sector by sex and occupation (%) ## 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_OCU_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.) | `OCU_SKILL_TOTAL` | | `classif1.label` | `string` | — | `Occupation (Skill level): 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` | — | `—` | | `note_classif.label` | `string` | — | `—` | | `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-ocu-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_OCU_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_PIFL_SEX_OCU_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_PIFL_SEX_OCU_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_emp_pifl_sex_ocu_rt_share_of_employment_outside_the_formal_sector_by_s_2025, title = {Share of employment outside the formal sector by sex and occupation (%) | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_PIFL_SEX_OCU_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-emp-pifl-sex-ocu-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_OCU_RT_This dataset contains 21,003 observations across 34 European countries from 2003 to 2025, focusing on the informal economy indicator EMP_PIFL_SEX_OCU_RT, which represents the share of employment outside the formal sector by sex and occupation (%). The data is sourced from the International Labour Organizations (ILO) ILOSTAT database, retrieved via REST API and filtered to European country codes, with harmonization based on International Conference of Labour Statisticians (ICLS) definitions. It includes columns such as country code, year, sex disaggregation, observed values, and data quality flags, suitable for tabular classification, regression, and time-series forecasting tasks. The data is published at annual frequency and includes caveats like provisional or unreliable data markers.

搜集汇总

数据集介绍

构建方式

该数据集依托国际劳工组织（ILO）的ILOSTAT中央统计数据库，通过其REST API接口直接抽取指标EMP_PIFL_SEX_OCU_RT的原始数据，并依据欧洲ISO3国家代码进行筛选，最终由Electric Sheep Europe完成重新打包与标准化发布。所有数据均源自各国劳动力调查、家庭收入调查等微观调查，经ILO按照国际劳工统计学家会议（ICLS）定义统一协调，确保了跨国可比性与统计严谨性。

使用方法

使用者可通过HuggingFace的datasets库以一行代码加载数据集，并转换为Pandas数据框进行灵活操作。典型用法包括按国家代码筛选特定国家的数据、针对单一指标绘制时间序列图，以及利用透视表将数据重塑为国家×年份矩阵。数据支持表格分类、回归与时间序列预测等任务，便于研究者开展非正规就业的跨国比较与趋势分析。

背景与挑战

背景概述

非正规经济就业的测度长期构成劳动统计领域的核心议题，其数据质量直接关乎体面劳动议程的监测与政策评估。国际劳工组织（ILO）依托国际劳动统计学家会议（ICLS）确立的界定标准，通过ILOSTAT平台系统整合各国劳动力调查与住户收入调查等微观数据，构建了跨200余经济体的劳动统计数据库。本研究聚焦的欧洲子集由Electric Sheep Europe于2025年完成规范化封装，覆盖34个欧洲国家、2003至2025年间共21,003条观测记录，以性别与职业技能等级为分层维度，刻画正规部门以外就业占比的时序演变。该数据集为非正规就业的跨国比较、性别差异分析与职业结构研究提供了标准化、可直接调用的数据基础，对劳动经济学与不平等研究具有显著的支撑价值。

当前挑战

非正规就业统计的根本难题在于概念边界随ICLS决议修订而迁移，各国执行口径与调查工具存在系统性差异，致使跨国可比性始终面临结构性约束。就本数据集而言，构建过程中的突出挑战体现于三方面：其一，ILOSTAT对同一国家—年份维度可能并存多源观测，仅保留ILO甄选的“最优来源”，虽保障一致性却牺牲了来源间差异的可追溯性；其二，部分观测标注为“不可靠”或伴随“方法修订导致序列断裂”的说明，提示时序建模需审慎处理结构性断点与异常值；其三，性别与职业分类维度仅在该指标发布细分时才非空，稀疏分层结构对交叉分析构成限制，要求研究者在聚合与细分之间作出权衡。

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与性别差异。研究者可借助其按性别与职业技能层级分解的年度观测值，构建跨国面板数据，考察非正规就业在总就业中的占比如何随经济周期、制度变迁与劳动力市场改革而波动。凭借34国、2003至2025年的长时段覆盖，该数据集成为比较欧洲非正规就业结构异质性的基础性数据资源，常用于绘制趋势图、编制国别矩阵以及开展性别维度的横截面比较分析。

解决学术问题

该数据集有效回应了非正规就业测量中长期存在的跨国可比性难题。通过ILOSTAT依据国际劳工统计学家会议定义对各国劳动力调查与家庭收入调查的原始微观数据进行统一调和，它缓解了因各国统计口径、调查来源与职业分类差异所致的比较偏误。研究者据此得以检验非正规就业与性别不平等、技能结构、社会保障覆盖之间的理论假设，并在面板计量框架下识别制度因素对非正规性的因果效应。该数据集为衡量体面劳动目标进展、评估非正规经济规模提供了标准化依据，对劳动政策研究具有重要参考价值。

实际应用

在政策实践层面，该数据集为国际组织与各国劳动部门监测非正规就业态势提供了可操作的工具。社会保障机构可依据女性与低技能职业群体中非正规就业份额的高低，识别覆盖缺口并设计针对性的扩展策略；就业服务机构可结合时序变化评估劳动力市场正规化改革的成效。数据分析人员亦可将其与其他ILOSTAT指标或宏观经济变量连接，构建预警模型或区域比较仪表板，辅助制定就业促进与社会保护政策，服务于可持续发展目标中体面劳动议程的落实。

数据集最近研究

最新研究方向

在全球非正规经济研究日益受到国际组织与学术界关注的背景下，该数据集基于ILOSTAT的EMP_PIFL_SEX_OCU_RT指标，系统记录了2003至2025年间34个欧洲国家按性别与职业分类的非正规部门就业占比，为探究后疫情时代欧洲劳动力市场非正规化的时空分异提供了高分辨率面板数据。近年来，该领域的前沿研究聚焦于非正规就业的性别差异与职业隔离机制，以及非正规经济对社会保障覆盖与可持续生计的冲击；与此同时，欧盟“平台工作指令”的出台与ILO关于向正规化转型的倡议构成关键政策热点。该数据集支持时间序列预测与跨国比较分析，对评估非正规就业的长期趋势、监测体面劳动目标进展具有重要的实证价值与政策意涵。