electricsheepeurope/europe-ilo-emp-pifl-sex-ocu-dsb-nb-employment-outside-the-formal-sector-by-sex-occupa

--- 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: "Employment outside the formal sector by sex, occupation and disability status (thousands) | Europe (ILOSTAT)" --- # Employment outside the formal sector by sex, occupation and disability status (thousands) | Europe (ILOSTAT) 🇪🇺 **13,744 observations** · **31 Europe countries** · **2007–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **13,744 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_OCU_DSB_NB) - **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_DSB_NB` 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 | |---------|-----:|-----------:|----------:| | `NLD` | 595 | 2007 | 2024 | | `PRT` | 588 | 2007 | 2024 | | `SVN` | 588 | 2007 | 2024 | | `FRA` | 550 | 2007 | 2024 | | `POL` | 537 | 2007 | 2024 | | `SWE` | 535 | 2007 | 2024 | | `FIN` | 517 | 2007 | 2024 | | `ITA` | 516 | 2007 | 2024 | | `ESP` | 516 | 2007 | 2024 | | `CZE` | 515 | 2007 | 2024 | | `SVK` | 511 | 2007 | 2024 | | `LVA` | 497 | 2007 | 2024 | | `EST` | 493 | 2007 | 2024 | | `NOR` | 486 | 2007 | 2024 | | `GBR` | 478 | 2007 | 2018 | | ... | _16 more countries_ | | | ## Indicators (sample) - `EMP_PIFL_SEX_OCU_DSB_NB` — Employment outside the formal sector by sex, occupation and disability status (thousands) ## 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_DSB_NB` | | `indicator.label` | `string` | Indicator name in English | `Employment outside the formal sector …` | | `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) | `157.365` | | `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-ocu-dsb-nb-employment-outside-the-formal-sector-by-sex-occupa") 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_DSB_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_PIFL_SEX_OCU_DSB_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_PIFL_SEX_OCU_DSB_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_emp_pifl_sex_ocu_dsb_nb_employment_outside_the_formal_sector_by_sex_occupa_2025, title = {Employment outside the formal sector by sex, occupation and disability status (thousands) | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_PIFL_SEX_OCU_DSB_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-emp-pifl-sex-ocu-dsb-nb-employment-outside-the-formal-sector-by-sex-occupa}} } ``` ## 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_DSB_NB_This dataset contains data on employment outside the formal sector by sex, occupation and disability status (in thousands) in Europe. Specifically, it includes 13,744 observations across 31 European countries from 2007 to 2025, focusing on one core indicator: EMP_PIFL_SEX_OCU_DSB_NB, which stands for Employment outside the formal sector by sex, occupation and disability status (thousands). The data is sourced from the ILOSTAT database of the International Labour Organization (ILO), processed and repackaged for machine learning tasks such as tabular classification, regression, and time-series forecasting. It includes fields like country codes, sex disaggregation (total, male, female), occupation skill level, disability status, year, and observed values, along with data quality notes and usage examples.

搜集汇总

数据集介绍

构建方式

该数据集以国际劳工组织（ILO）的ILOSTAT中央统计数据库为原始来源，通过ILOSTAT REST API接口直接提取指标EMP_PIFL_SEX_OCU_DSB_NB的原始记录，并依据ISO 3166-1 alpha-3国家代码筛选出31个欧洲国家的数据子集。ILOSTAT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协调，采用国际劳工统计学家会议（ICLS）标准定义加以规范，数据中保留source.label字段以追溯原始调查来源，最终由Electric Sheep Europe以Parquet格式重新封装发布，形成结构规整、可直接加载的机器学习就绪数据集。

特点

数据集涵盖2007年至2025年间的13744条观测记录，涉及31个欧洲国家，聚焦于非正规部门就业这一非正规经济核心议题。其显著特征在于多维度交叉分类：按性别（总计、男性、女性）、职业技能水平与残疾状况进行细分，并通过obs_status、note_classif等字段标注数据可靠性及非标准定义等质量信息。数据以年度频率发布，机构覆盖范围广，时间跨度长，且字段命名规范、类型明确，支持表格分类、回归与时间序列预测等多种分析任务，为欧洲非正规就业的跨国比较与趋势研究提供了一致化、可复用的数据基础。

使用方法

研究者可借助Hugging Face datasets库以load_dataset函数一键加载该数据集，并转换为Pandas数据框进行后续分析。使用时可通过ref_area字段筛选特定国家，或利用indicator字段提取单一指标的时间序列，进而调用绘图函数观察就业变动趋势；亦可借助pivot_table将数据重塑为国家与年份的交叉矩阵，便于开展面板数据分析或跨国比较。数据集遵循CC-BY-4.0许可协议，使用时需同时引用国际劳工组织原始来源与Electric Sheep Europe的再封装工作，以确保数据溯源的规范性与合法性。

背景与挑战

背景概述

非正规经济中的就业测量长期构成劳动统计领域的核心议题，其界定与跨国可比性直接关系到体面劳动监测与可持续发展目标的评估。国际劳工组织（ILO）依托国际劳工统计学家会议（ICLS）确立的界定框架，通过ILOSTAT数据库系统整合各国劳动力调查、家庭收入调查与行政记录，构建了全球领先的劳动统计汇编。本数据集由Electric Sheep Europe于2025年从ILOSTAT REST API提取并重新封装，聚焦2007至2025年间31个欧洲国家按性别、职业与残疾状况分类的非正规部门外就业观测，共计13,744条记录，为劳动经济学、社会政策评估与跨国比较研究提供了结构化的高质量数据基础。

当前挑战

非正规就业的界定在各国统计实践中存在显著异质性，不同调查工具对非正规部门、非正规就业与非正规经济的概念操作化迥异，导致跨国与跨时点比较面临根本性的测量误差风险。数据中obs_status列标记的不可靠观测与note_classif列所记录的非标准残疾定义，折射出原始调查在抽样设计、变量定义与覆盖范围上的参差。部分国家如英国的数据止于2018年，时间序列的不平衡对趋势分析与预测建模构成障碍。性别、职业与残疾状况三重交叉分类在多数国家样本量有限，稀疏单元下的估计稳定性与统计功效面临挑战，残疾状况分类的非标准化尤为突出，限制了该维度上的可靠推断。

常用场景

经典使用场景

在劳动经济学与非正规经济研究的交汇处，该数据集构成了剖析欧洲非正规部门就业结构的核心素材。研究者通常将其用于按性别、职业与残疾状态三维交叉的非正规就业水平比较分析，借助2007至2025年的长时序面板，考察不同技能层级职业中非正规就业的性别差异与残障群体边缘化程度。典型操作包括将obs_value透视为国家×年份矩阵，拟合非正规就业占比的收敛趋势，或运用固定效应模型分离国别制度禀赋与时间冲击对非正规就业规模的净效应。

解决学术问题

该数据集直面的学术难题在于非正规就业测度的跨国可比性缺失。ILO借助国际劳工统计学家会议定义对各国劳动力调查微数据进行协调，使此前碎片化的国别统计得以纳入统一分类框架。这为检验非正规部门女性化假说、残障惩罚效应以及职业技能层级与非正规性的关联提供了可复现的经验基础，亦支撑了关于欧洲劳动力市场二元结构的计量识别，其意义在于将非正规就业从描述性统计提升为可假设检验的实证对象。

衍生相关工作

围绕该数据集衍生的经典工作多见于非正规就业的跨国比较与残障劳动市场结果研究。学者利用其性别与职业交叉维度，扩展出非正规就业性别差距的分解分析；结合残疾状态分类，推动了残障就业惩罚的跨国证据积累。Electric Sheep Europe将其纳入统一ML就绪数据层，催生了基于表格回归与时序预测的基准建模工作，并与ILOSTAT其他指标数据集形成互补，支撑欧洲劳动力市场面板数据的集成研究。