electricsheepeurope/europe-ilo-emp-pifl-sex-ocu-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, occupation and marital status (%) | Europe (ILOSTAT)" --- # Share of employment outside the formal sector by sex, occupation and marital status (%) | Europe (ILOSTAT) 🇪🇺 **126,713 observations** · **34 Europe countries** · **2003–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **126,713 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_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_OCU_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` | 6,050 | 2003 | 2025 | | `RUS` | 6,031 | 2010 | 2025 | | `NLD` | 5,439 | 2007 | 2024 | | `ITA` | 5,023 | 2007 | 2024 | | `POL` | 4,882 | 2007 | 2024 | | `ESP` | 4,837 | 2007 | 2024 | | `CZE` | 4,754 | 2007 | 2024 | | `PRT` | 4,680 | 2007 | 2024 | | `SRB` | 4,547 | 2007 | 2024 | | `SVN` | 4,441 | 2007 | 2024 | | `FIN` | 4,340 | 2007 | 2024 | | `BIH` | 4,205 | 2006 | 2020 | | `MKD` | 4,157 | 2009 | 2025 | | `SVK` | 4,113 | 2007 | 2024 | | `LVA` | 4,043 | 2007 | 2024 | | ... | _19 more countries_ | | | ## Indicators (sample) - `EMP_PIFL_SEX_OCU_MTS_RT` — Share of employment outside the formal sector by sex, occupation 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_OCU_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.) | `OCU_SKILL_TOTAL` | | `classif1.label` | `string` | — | `Occupation (Skill level): 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` | — | `—` | | `note_classif.label` | `string` | — | `—` | | `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-ocu-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_OCU_MTS_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_PIFL_SEX_OCU_MTS_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_PIFL_SEX_OCU_MTS_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_emp_pifl_sex_ocu_mts_rt_share_of_employment_outside_the_formal_sector_by_s_2025, title = {Share of employment outside the formal sector by sex, occupation and marital status (%) | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_PIFL_SEX_OCU_MTS_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-emp-pifl-sex-ocu-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_OCU_MTS_RT_This dataset contains 126,713 observations across 34 European countries from 2003 to 2025, focusing on the informal economy indicator Share of employment outside the formal sector by sex, occupation and marital status (%). The data is sourced from the International Labour Organization (ILO) ILOSTAT database, retrieved via REST API and filtered to European ISO3 country codes. It includes fields such as country code, country name, data source, indicator code, sex disaggregation (total, male, female), occupation classification, marital status classification, observation year, observed value, observation status flags, etc., suitable for tabular classification, regression, and time-series forecasting tasks. The data is published at annual frequency, with data quality caveats and usage examples provided, supporting filtering, time-series analysis, and pivot operations. The dataset is repackaged by Electric Sheep Europe and released under the CC-BY-4.0 license.

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接口直接抽取指标EMP_PIFL_SEX_OCU_MTS_RT的原始记录，并依据ISO3国家代码筛选出欧洲区域数据。ILOSTAT采用国际劳工统计学家会议（ICLS）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，数据中通过source.label字段标注原始来源以保障可追溯性。Electric Sheep Europe对抽取结果进行规范化封装，以Parquet格式发布，形成覆盖2003至2025年、包含126,713条观测的即用型数据集。

使用方法

研究者可通过HuggingFace datasets库以一行代码加载数据并转换为Pandas DataFrame，快速开展探索性分析。典型用法包括按ref_area字段筛选特定国家子集、按indicator字段提取目标指标并沿time排序绘制时序曲线，以及利用pivot_table构建国家×年份矩阵以支持面板回归或预测建模。数据亦适用于表格分类、回归与时间序列预测等任务场景，使用时应关注obs_status标志所提示的数据可靠性，并遵循CC-BY-4.0许可要求同时引用ILO原始来源与Electric Sheep Europe的再封装工作。

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，其数据基础直接影响体面劳动议程的监测与政策评估。国际劳工组织（ILO）依托国际劳工统计学家会议（ICLS）确立的定义框架，通过ILOSTAT数据库系统整合各国劳动力调查与家庭收入调查等微观数据，构建了跨200余经济体的可比劳动统计体系。该数据集由Electric Sheep Europe于2025年基于ILOSTAT API重新封装发布，覆盖34个欧洲国家2003至2025年间126,713条观测记录，按性别、职业与婚姻状况细分非正规部门就业占比。其核心价值在于为欧洲非正规就业的性别差异、职业结构分化及婚姻状况关联研究提供标准化面板数据基础，对监测可持续发展目标第八项体面劳动指标具有重要支撑意义。

当前挑战

该数据集所回应的领域问题在于非正规就业测度的跨国可比性与多维分解难题。非正规部门就业占比的估算高度依赖各国调查工具、抽样设计及非正规经济界定标准的差异，即便经ICLS框架调和，残余的方法论异质性仍可能削弱跨国比较的稳健性。构建过程中，数据面临来源碎片化与质量参差的挑战：同一国家年份存在多源调查时仅保留ILO优选来源，但部分观测带有不可靠或暂定状态标记；性别、职业与婚姻状况的三维交叉分类导致部分单元格样本量稀疏，加剧估计的不稳定性；2003至2025年的时间跨度内调查方法演进亦引入序列断裂风险。此外，年度频率的数据结构限制了季节性就业波动的捕捉能力，非正规就业本身固有的隐蔽性更使调查覆盖率成为系统性隐忧。

常用场景

经典使用场景

在国际劳动经济学与非正规经济研究领域，该数据集构成刻画欧洲非正规就业性别差异与职业分层格局的核心实证资源。依托国际劳工组织统一协调的劳动力调查微观数据，研究者得以按性别、职业类别与婚姻状况三重维度，系统测算2003至2025年间34个欧洲国家正规部门之外就业份额的时空演变，进而辨识女性、低技能职业群体及特定婚姻状态人群在非正规劳动市场中的结构性脆弱处境。

解决学术问题

该数据集有效回应了非正规就业测度中长期存在的可比性困境，即各国调查口径、就业定义与社会保障边界差异所导致的跨国比较偏误。凭借国际劳工组织基于国际劳工统计学家会议定义的标准化调和处理，并辅以来源标签的溯源机制，该数据集为检验非正规就业的性别鸿沟假说、职业隔离理论以及婚姻状况与劳动市场边缘化关联等议题提供了跨国可比的实证基础，推动了非正规经济研究从碎片化国别描述走向系统化比较分析。

实际应用

在政策实践层面，该数据集为欧盟及其成员国劳动监察机构、社会保障部门与国际发展组织监测非正规就业风险提供了可操作的量化依据。政策制定者可借助其识别非正规就业高发国家、群体与时段，进而优化劳动市场规制、扩展社会保障覆盖面并设计针对女性与弱势职业群体的就业促进干预。同时，该数据集亦可服务于企业合规评估、非政府组织倡导以及国际比较报告中的体面劳动指标监测。

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切之际，该数据集为解析欧洲非正规就业的结构性异质性提供了关键支撑。当前研究前沿聚焦于性别、职业与婚姻状况的多维交互如何塑造非正规就业的分布格局，并结合时序预测方法刻画2003至2025年间的演化轨迹。这一方向与ILO推动的非正规经济统计标准化进程以及欧盟体面劳动议程紧密关联，有助于识别脆弱群体在正式部门之外的集聚特征。其影响在于为跨国比较研究提供可复用的基准，并支撑面向包容性劳动力市场的政策模拟与因果推断。