electricsheepeurope/europe-ilo-emp-pifl-sex-edu-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, education and rural / urban areas (% | Europe (ILOSTAT)" --- # Share of employment outside the formal sector by sex, education and rural / urban areas (% | Europe (ILOSTAT) 🇪🇺 **23,069 observations** · **31 Europe countries** · **2003–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **23,069 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_EDU_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_EDU_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` | 2,292 | 2003 | 2025 | | `BIH` | 1,898 | 2006 | 2024 | | `SRB` | 1,798 | 2007 | 2024 | | `RUS` | 1,581 | 2010 | 2025 | | `PRT` | 762 | 2007 | 2024 | | `FIN` | 753 | 2007 | 2024 | | `SWE` | 746 | 2007 | 2024 | | `ESP` | 744 | 2007 | 2024 | | `NOR` | 714 | 2007 | 2024 | | `ITA` | 708 | 2007 | 2024 | | `BGR` | 698 | 2007 | 2024 | | `FRA` | 673 | 2007 | 2024 | | `POL` | 647 | 2007 | 2024 | | `GRC` | 647 | 2007 | 2024 | | `CZE` | 638 | 2007 | 2024 | | ... | _16 more countries_ | | | ## Indicators (sample) - `EMP_PIFL_SEX_EDU_GEO_RT` — Share of employment outside the formal sector by sex, education 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_EDU_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.) | `EDU_AGGREGATE_TOTAL` | | `classif1.label` | `string` | — | `Education (Aggregate levels): 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` | — | `—` | | `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-edu-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_EDU_GEO_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_PIFL_SEX_EDU_GEO_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_PIFL_SEX_EDU_GEO_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_emp_pifl_sex_edu_geo_rt_share_of_employment_outside_the_formal_sector_by_s_2025, title = {Share of employment outside the formal sector by sex, education and rural / urban areas (% | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_PIFL_SEX_EDU_GEO_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-emp-pifl-sex-edu-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_EDU_GEO_RT_This dataset contains observations on the share of employment outside the formal sector in Europe, specifically the indicator Share of employment outside the formal sector by sex, education and rural / urban areas (%). It includes 23,069 observations across 31 European countries, spanning the years 2003 to 2025. The data is sourced from the International Labour Organization (ILO) ILOSTAT database, retrieved via REST API and filtered to European country codes, harmonized using International Conference of Labour Statisticians (ICLS) definitions. The dataset features dimensions such as country, data source, sex (total, male, female), education classification, rural/urban area classification, observation year, observed value (percentage), and data quality flags (e.g., unreliable, provisional). It is suitable for tabular classification, regression, and time-series forecasting tasks, designed to provide machine learning-ready data for studying the European labor market and informal economy.

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中央统计数据库，通过其REST API接口（https://rplumber.ilo.org/data/indicator?id=EMP_PIFL_SEX_EDU_GEO_RT）直接抽取非正规经济部门就业指标数据。原始调查微观数据依据国际劳工统计学家会议（ICLS）定义进行标准化调和，并以ISO3国家代码筛选出欧洲地区记录，最终由Electric Sheep Europe重新封装为Parquet格式发布于HuggingFace平台，确保了数据溯源的可追踪性与机器学习的即用性。

特点

数据集涵盖2003至2025年间31个欧洲国家的23,069条观测记录，聚焦于按性别、教育程度及城乡地域划分的非正规部门就业份额指标。其核心特征在于多维度的分类变量（如性别、教育聚合层级、城乡覆盖类型）与丰富的元数据列（包括来源标签、观测状态标志、指标注释等），为分析非正规就业的结构性差异提供了细粒度且可审计的数据基础。

使用方法

研究者可通过HuggingFace的datasets库以load_dataset()函数加载数据并转换为Pandas数据框，进而执行国家筛选、时间序列分析或构建国家×年份透视矩阵等操作。该数据集支持表格分类、回归及时间序列预测等任务，使用时需注意观测值的年度频率、来源选择性偏差以及分类维度非空仅当指标发布对应细分时才成立等特性，并应遵循CC-BY-4.0许可协议引用原始ILO来源。

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经济学与发展经济学的核心议题，其数据基础直接关乎体面劳动议程的政策成效评估。国际劳工组织（ILO）自二十世纪中叶起持续推动非正规就业统计的概念标准化与方法论协调，ILOSTAT作为全球劳动统计的权威中枢，汇集各国劳动力调查、住户收入调查及行政记录，依据国际劳工统计学家会议（ICLS）定义进行跨国调和。该数据集由Electric Sheep Europe于2025年从ILOSTAT REST API摄取并重新封装，覆盖31个欧洲国家、2003至2025年共23,069条观测，按性别、教育程度与城乡区域系统分解非正规部门就业占比，为欧洲非正规经济研究提供了迄今颗粒度较细的跨国面板资源，对监测可持续发展目标第八项亦具有基础性支撑意义。

当前挑战

该数据集所回应的领域问题在于，非正规就业的跨国可比测度始终面临定义歧义、抽样异质与报告偏差的多重困境，ILO虽以ICLS框架加以调和，但各国调查工具与参照期的差异仍构成固有噪声。构建过程中的具体挑战包括：原始指标在性别、教育维度上的分解覆盖参差不齐，部分国家年份存在缺失或仅发布聚合层级数值；数据中标注的序列中断（如方法论修订）、不可靠观测状态及来源更替，要求使用者在时序分析中审慎处理结构性断点；城乡分类与教育分类的编码在不同国家间未完全对齐，跨国比较须依赖元数据追溯来源标签以甄别口径差异。

常用场景

经典使用场景

在非正规经济部门就业比例的跨国比较研究中，该数据集构成了一项关键的经验基础。依托国际劳工组织（ILO）所提供之标准化劳动力调查数据，研究者得以在统一的口径之下，系统考察欧洲三十一国自2003年至2025年间非正规就业的规模与演变轨迹。此类数据的经典运用，常见于以性别、教育程度与城乡属性为分析维度的分层比较研究，借由这些维度揭示非正规就业分布的结构性差异与时间趋势。

解决学术问题

非正规就业的测度长期受制于定义歧异与数据可比性不足的困境，该数据集通过国际劳工统计学家会议（ICLS）所确立的定义框架与ILO的统一调和程序，有效缓解了跨国比较中的口径不一致问题。其学术价值在于，为劳动经济学与发展经济学中关于非正规部门规模决定因素、教育回报差异以及性别就业不平等的研究提供了可复现的量化依据，从而推动该领域从个案描述向跨国实证的范式转变。

衍生相关工作

围绕ILOSTAT所衍生的研究文献已形成较为丰富的积累，包括非正规就业与经济增长关系的面板分析、性别差距在非正规部门中的分解研究，以及教育禀赋对非正规就业概率影响的微观计量工作。部分研究进一步将该数据集与其他跨国劳动力调查数据相结合，构建更为综合的非正规经济指标体系，亦有学者以其为基准对非正规就业的测度方法进行比较与反思。