electricsheepeurope/europe-ilo-emp-pifl-sex-ocu-edu-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 education (thousands) | Europe (ILOSTAT)" --- # Employment outside the formal sector by sex, occupation and education (thousands) | Europe (ILOSTAT) 🇪🇺 **85,271 observations** · **35 Europe countries** · **2003–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **85,271 observations** of `Informal economy` data across **35 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_EDU_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_EDU_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 35 Europe countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `RUS` | 6,972 | 2010 | 2025 | | `MDA` | 6,946 | 2003 | 2025 | | `BIH` | 5,955 | 2006 | 2024 | | `SRB` | 5,284 | 2007 | 2024 | | `MKD` | 4,949 | 2009 | 2025 | | `NLD` | 3,232 | 2007 | 2024 | | `ESP` | 2,683 | 2007 | 2024 | | `ITA` | 2,619 | 2007 | 2024 | | `FIN` | 2,566 | 2007 | 2024 | | `PRT` | 2,491 | 2007 | 2024 | | `GBR` | 2,450 | 2007 | 2018 | | `POL` | 2,414 | 2007 | 2024 | | `SVN` | 2,380 | 2007 | 2024 | | `SWE` | 2,231 | 2007 | 2024 | | `CZE` | 2,171 | 2007 | 2024 | | ... | _20 more countries_ | | | ## Indicators (sample) - `EMP_PIFL_SEX_OCU_EDU_NB` — Employment outside the formal sector by sex, occupation and education (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_EDU_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 | `EDU_AGGREGATE_TOTAL` | | `classif2.label` | `string` | — | `Education (Aggregate levels): 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` | — | `—` | | `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-edu-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_EDU_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_PIFL_SEX_OCU_EDU_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_PIFL_SEX_OCU_EDU_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_emp_pifl_sex_ocu_edu_nb_employment_outside_the_formal_sector_by_sex_occupa_2025, title = {Employment outside the formal sector by sex, occupation and education (thousands) | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_PIFL_SEX_OCU_EDU_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-emp-pifl-sex-ocu-edu-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_EDU_NB_This dataset contains 85,271 observations across 35 European countries, spanning from 2003 to 2025, focusing on the Informal economy topic with the indicator Employment outside the formal sector by sex, occupation and education (thousands). It is sourced from ILOSTAT (the ILOs central statistics database), retrieved via API and filtered to European country codes. The dataset includes columns such as country code, country name, source, indicator, sex disaggregation, time, observed value, etc., and is suitable for tabular classification, regression, and time-series forecasting tasks. Repackaged by Electric Sheep Europe in Parquet format for machine learning readiness.

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接口直接获取非正规部门就业指标EMP_PIFL_SEX_OCU_EDU_NB的原始数据，并以欧洲ISO3国家代码为条件进行筛选。ILOSTAT依据国际劳工统计学家会议（ICLS）的定义对各国劳动力调查、家庭收入调查及行政记录等微观数据进行统一协调与标准化处理，来源信息记录于source.label字段以供追溯。数据经Electric Sheep Europe重新打包为Parquet格式并发布至HuggingFace平台，最终形成涵盖35个欧洲国家、时间跨度为2003至2025年的85,271条观测记录。

特点

该数据集聚焦于欧洲地区非正规部门就业的性别、职业与教育维度差异，时间序列自2003年延伸至2025年，包含35个欧洲国家的年度观测数据。数据以表格形式组织，涵盖ref_area、sex、classif1、classif2等关键分类字段，支持按性别、职业技能水平和教育程度进行交叉分析。观测值以千人为单位，并附有obs_status质量标志及note_indicator等注释字段，用以标识临时性数据、不可靠估计或序列中断等情况，为分析欧洲非正规就业的结构性特征提供了细粒度的跨国面板数据基础。

使用方法

该数据集可通过HuggingFace的datasets库以load_dataset函数直接加载，并转换为Pandas数据框进行后续分析。研究者可利用ref_area字段筛选特定国家，或依据indicator字段提取目标指标并沿时间维度排序以构建时间序列趋势图。借助pivot_table方法可将数据重塑为国家与年份的交叉矩阵，便于开展跨国比较或面板回归分析。此外，数据可应用于表格分类、表格回归及时间序列预测等机器学习任务，使用时需注意obs_status和note_indicator字段所提示的数据质量与序列连续性问题。

背景与挑战

背景概述

非正规部门就业的测度长期构成劳动统计领域的核心议题，国际劳工组织（ILO）自二十世纪九十年代起持续推动相关统计标准与数据基础设施建设。本数据集由ILO统计部门基于ILOSTAT中央数据库构建，经Electric Sheep Europe于2025年重新封装发布，覆盖35个欧洲国家、2003至2025年间85,271条观测记录，以性别、职业与教育三维度刻画正规部门以外就业规模。该数据回应了非正规经济跨国可比统计的迫切需求，为监测可持续发展目标中体面劳动指标提供了关键数据支撑。

当前挑战

数据集所服务的领域问题在于非正规就业的界定与量化本身面临概念模糊与跨国口径不一，国际劳工统计学家会议（ICLS）定义的落地常受各国调查工具差异制约。构建过程中，ILOSTAT须整合劳动力调查、家庭收入调查与行政记录等多源数据，并以元数据标注方法学修订所致的序列断裂，数据中存在的不可靠标记与缺失分类维度进一步增加了时序分析与跨国比较的复杂性，如何在异质性来源间实现统计协调仍是持续挑战。

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的规模与结构演变。研究者依托其覆盖三十五国、逾八万条观测的跨国面板结构，按性别、职业与教育维度对非正规就业进行系统分解，进而识别不同区域与年份间非正规就业的异质性模式。时间序列特征亦使其适用于趋势外推与周期性波动的建模分析，为理解欧洲劳动力市场二元结构提供基础数据支撑。

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难题。通过国际劳工组织依据国际劳工统计学家会议定义所进行的标准化调和，其消解了各国劳动力调查在概念界定与分类口径上的异质性，使跨国比较与跨期分析得以在统一框架下展开。这一特性为检验制度环境、教育禀赋与性别差异对非正规就业的影响提供了可靠的经验基础，推动了非正规经济理论从描述性研究向因果推断的范式演进。

衍生相关工作

围绕该数据集，学界与数据社区衍生出若干具有代表性的工作。其一为基于其面板结构展开的非正规就业收敛性研究，考察欧洲一体化进程中各国非正规就业比重的趋同或分化。其二为将其与性别工资差距、教育回报率等指标进行联合建模的交叉研究，揭示非正规就业在劳动力市场不平等中的中介作用。此外，Electric Sheep Europe对其进行的Parquet格式重封装与统一数据卡发布，亦促进了机器学习方法在劳动统计领域的可复现应用。