electricsheepeurope/europe-ilo-emp-nifl-sex-ocu-dsb-rt-informal-employment-rate-by-sex-occupation-and-dis

--- 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: "Informal employment rate by sex, occupation and disability status (%) | Europe (ILOSTAT)" --- # Informal employment rate by sex, occupation and disability status (%) | Europe (ILOSTAT) 🇪🇺 **13,925 observations** · **31 Europe countries** · **2007–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **13,925 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_NIFL_SEX_OCU_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_NIFL_SEX_OCU_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 | |---------|-----:|-----------:|----------:| | `FRA` | 646 | 2007 | 2024 | | `POL` | 636 | 2007 | 2024 | | `NLD` | 602 | 2007 | 2024 | | `SVN` | 592 | 2007 | 2024 | | `PRT` | 579 | 2007 | 2024 | | `ITA` | 554 | 2007 | 2024 | | `SWE` | 532 | 2007 | 2024 | | `FIN` | 516 | 2007 | 2024 | | `CZE` | 514 | 2007 | 2024 | | `ESP` | 508 | 2007 | 2024 | | `SVK` | 508 | 2007 | 2024 | | `GRC` | 503 | 2007 | 2024 | | `LVA` | 496 | 2007 | 2024 | | `NOR` | 492 | 2007 | 2024 | | `EST` | 487 | 2007 | 2024 | | ... | _16 more countries_ | | | ## Indicators (sample) - `EMP_NIFL_SEX_OCU_DSB_RT` — Informal employment rate by sex, occupation 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_NIFL_SEX_OCU_DSB_RT` | | `indicator.label` | `string` | Indicator name in English | `Informal employment rate by sex, occu…` | | `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 | `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) | `3.38` | | `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-nifl-sex-ocu-dsb-rt-informal-employment-rate-by-sex-occupation-and-dis") 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_NIFL_SEX_OCU_DSB_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_NIFL_SEX_OCU_DSB_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_NIFL_SEX_OCU_DSB_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_emp_nifl_sex_ocu_dsb_rt_informal_employment_rate_by_sex_occupation_and_dis_2025, title = {Informal employment rate by sex, occupation and disability status (%) | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_NIFL_SEX_OCU_DSB_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-emp-nifl-sex-ocu-dsb-rt-informal-employment-rate-by-sex-occupation-and-dis}} } ``` ## 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 2026-05-27 via the Electric Sheep pipeline. Source URL: https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_NIFL_SEX_OCU_DSB_RT_This dataset contains 13,925 observations across 31 European countries (e.g., France, Poland, Netherlands), spanning the years 2007 to 2025. The topic is the informal economy, with a specific indicator for the informal employment rate by sex, occupation, and disability status (%). The data is sourced from the International Labour Organization (ILO) ILOSTAT database, retrieved via API and processed, suitable for tasks such as tabular classification, regression, and time-series forecasting. The dataset includes columns such as country code, country name, indicator code, indicator label, sex classification, classification variables, observation year, observed value, and provides data quality notes and usage examples.

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，聚焦于欧洲地区非正规就业率这一关键劳动力市场指标。构建过程严格遵循ILO的标准化流程，通过REST API接口从ILOSTAT官方数据源提取原始指标数据，并依据欧洲国家ISO 3166-1 alpha-3代码进行地域筛选，最终汇聚31个欧洲国家自2007年至2025年的年度观测值。数据集在保留原始分类维度（如性别、职业熟练程度及残疾状况）的基础上，进行了规范化的结构重排，整合为统一的Parquet格式，确保数据完整性与跨年可比性，从而为区域劳动力市场研究提供坚实的数据基础。

特点

此数据集的核心优势在于其多维度分类粒度与广阔的时间跨度。它涵盖了性别（总体、男、女）、职业技能等级及残疾状况的交叉分类，能够细腻揭示非正规就业在不同人群中的分布特征。13,925条观测记录覆盖长达19年的年度数据，为时间序列分析与趋势预测提供了充足样本。特别值得强调的是，数据集完整保留了ILOSTAT的元数据字段，包括数据来源标识、观测状态标记（如可靠与否）及各类注释，这极大提升了数据的可追溯性与透明性，便于研究者准确评估数据质量并进行严谨的计量分析。

使用方法

该数据集被设计为直接可用的机器学习就绪格式，通过HuggingFace的datasets库即可一键加载，转换为Pandas DataFrame后能便捷地开展各类分析。使用者可按国家代码进行快速筛选，聚焦特定经济体的非正规就业动态；也可针对单一指标进行时间序列的可视化与建模，观测其演变轨迹。此外，数据集支持透视表操作，可轻松构建国家—年份的面板数据矩阵，为跨国的比较研究和面板回归等高级计量分析提供了极大便利。结合其丰富的分类与注释字段，该数据集是研究欧洲劳动力市场结构、评估政策影响及构建预测模型的优质数据资产。

背景与挑战

背景概述

该数据集由Electric Sheep Europe于2025年重新打包发布，源自国际劳工组织（ILO）的ILOSTAT数据库，聚焦于欧洲31个国家2007至2025年间按性别、职业和残疾状况划分的非正规就业率。作为全球劳动统计的权威来源，ILOSTAT通过整合各国劳动力调查、住户收支调查等微观数据，为政策制定者与研究者提供衡量劳动市场非正规性的关键指标。该数据集的核心研究问题在于揭示不同人口特征群体在非正规就业中的结构性差异，支撑对劳动力市场包容性及社会保护覆盖率的实证分析。凭借其跨年度、跨国界的粒度化信息，该数据集已成为追踪欧洲非正规经济演变、评估可持续发展目标（SDG）中体面劳动进展的重要基础资源。

当前挑战

构建此数据集面临多重挑战。在领域层面，非正规就业的定义与测量方式因国家而异，虽经ILO依据国际劳工统计学家会议（ICLS）准则进行协调，但不同来源的统计口径差异仍可能导致跨国比较时的偏差。在数据构建过程中，挑战在于整合来自31国、多种调查系统的异构数据，需处理缺失值、异常标记（如obs_status中的'U'表示不可靠）以及残疾状态等维度中非标准定义的兼容问题。此外，确保时间序列的连续性与一致性、筛选同一国家年份中的'最佳来源'，亦需精细的溯源管理，以保证数据的可信度与可复现性。

常用场景

经典使用场景

该数据集汇聚了欧洲31国2007至2025年间非正规就业率的年度观测值，按性别、职业和残疾状态精细拆解，为劳动经济学与公共政策研究提供了宝贵的时间序列面板数据。研究者常借助此类数据开展跨国比较分析，探究非正规就业在人口亚群中的分布异质性，或运用面板回归、时间序列预测等计量方法，揭示非正规就业率的动态演变规律及其宏观经济关联。

解决学术问题

该数据集直面非正规就业测量中的经典学术难题，即如何在不同国籍统计框架下实现口径统一。ILOSTAT依据国际劳工统计学家会议标准对原始调查微观数据进行协调，极大提升了数据的跨国可比性，为探究非正规就业的决定因素、社会经济影响及其与正式就业的转换机制等长期争论的议题提供了坚实实证基础，有力推动了劳动经济学与福利国家研究的量化进展。

衍生相关工作

基于此数据集，衍生出一系列研究劳动市场分层与不平等的前沿工作。经典应用包括构建非正规就业的多维脆弱性指数，分析性别、残疾状态与职业技能水平对非正规就业概率的交互效应，以及在宏观层面探讨非正规就业率与制度质量、经济周期之间的关联。这些工作不仅深化了对欧洲劳动力市场细分结构的认知，也为政策仿真与预测提供了模型基础。