electricsheepeurope/europe-ilo-emp-pifl-sex-oc2-nb-employment-outside-the-formal-sector-by-sex-and-oc

--- 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 and occupation - ISCO level 2 (thousands) | Europe (ILOSTAT)" --- # Employment outside the formal sector by sex and occupation - ISCO level 2 (thousands) | Europe (ILOSTAT) 🇪🇺 **24,512 observations** · **33 Europe countries** · **2006–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **24,512 observations** of `Informal economy` data across **33 Europe countries**, spanning **2006–2025**, 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_OC2_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_OC2_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 33 Europe countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `RUS` | 1,698 | 2010 | 2025 | | `NLD` | 1,367 | 2007 | 2024 | | `CZE` | 1,344 | 2007 | 2024 | | `ITA` | 1,219 | 2007 | 2024 | | `POL` | 1,179 | 2007 | 2024 | | `ESP` | 1,137 | 2007 | 2024 | | `GBR` | 1,057 | 2007 | 2018 | | `SRB` | 1,051 | 2007 | 2024 | | `FIN` | 1,040 | 2007 | 2024 | | `PRT` | 1,034 | 2007 | 2024 | | `BIH` | 1,009 | 2006 | 2024 | | `SVK` | 979 | 2007 | 2024 | | `MKD` | 828 | 2009 | 2025 | | `NOR` | 767 | 2007 | 2023 | | `LVA` | 746 | 2007 | 2024 | | ... | _18 more countries_ | | | ## Indicators (sample) - `EMP_PIFL_SEX_OC2_NB` — Employment outside the formal sector by sex and occupation - ISCO level 2 (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_OC2_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.) | `OC2_ISCO08_TOTAL` | | `classif1.label` | `string` | — | `Occupation (ISCO-08), 2 digit level: …` | | `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_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-oc2-nb-employment-outside-the-formal-sector-by-sex-and-oc") 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_OC2_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_PIFL_SEX_OC2_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_PIFL_SEX_OC2_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_emp_pifl_sex_oc2_nb_employment_outside_the_formal_sector_by_sex_and_oc_2025, title = {Employment outside the formal sector by sex and occupation - ISCO level 2 (thousands) | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_PIFL_SEX_OC2_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-emp-pifl-sex-oc2-nb-employment-outside-the-formal-sector-by-sex-and-oc}} } ``` ## 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_OC2_NB_This dataset contains 24,512 observations of informal economy employment data across 33 European countries from 2006 to 2025, disaggregated by sex and occupation (ISCO level 2) in thousands. It covers one key indicator (EMP_PIFL_SEX_OC2_NB), sourced from the International Labour Organizations ILOSTAT database, repackaged and normalized by Electric Sheep Europe for machine learning tasks such as tabular classification, regression, and time-series forecasting.

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REST API接口直接拉取指标EMP_PIFL_SEX_OC2_NB的原始记录，并依据ISO3国家代码筛选出欧洲地区数据。ILOSTAT采用国际劳工统计学家会议（ICLS）标准定义对各国劳动力调查、家庭收入调查等微观数据进行统一调和，数据中保留来源标签以确保可追溯性。Electric Sheep Europe对原始数据进行重新打包，规范列名与格式，最终形成涵盖33个欧洲国家、2006至2025年间24512条观测值的结构化表格数据。

特点

数据集聚焦于非正规部门就业议题，以性别和ISCO-08二位职业分类为双重 disaggregation 维度，提供总就业、男性与女性三类性别分组。数据以千人为计量单位，覆盖俄罗斯、荷兰、捷克、意大利等33个欧洲国家，时间跨度近二十年。每个观测值附有来源标识、观测状态标记以及指标注释，能够反映数据质量与序列断裂情况。该数据集属于中等规模表格数据，适用于分类、回归及时间序列预测等多类机器学习任务。

使用方法

用户可通过HuggingFace的datasets库调用load_dataset函数加载该数据集，并转换为Pandas DataFrame进行后续分析。典型用法包括按ref_area列筛选特定国家、按indicator列提取单一指标并绘制时间序列趋势图，或利用pivot_table将数据重塑为国家与年份的交叉矩阵。数据集亦支持直接用于表格分类、回归及时间序列预测模型的训练与评估，使用时应遵循CC-BY-4.0许可协议并同时引用ILO原始来源与Electric Sheep Europe的再包装工作。

背景与挑战

背景概述

在全球经济结构转型与非正规经济持续扩张的宏观背景下，国际劳工组织（ILO）长期致力于监测各国劳动力市场中的非正规就业状况。该数据集由ILO统计局基于各国劳动力调查、家庭收入调查及行政记录等多源数据，经国际劳工统计学家会议（ICLS）定义统一协调后，由Electric Sheep Europe于2025年重新封装发布。其核心研究问题在于精准刻画欧洲33国2006至2025年间正规部门以外就业人口按性别与职业（ISCO-08二位码）分类的分布特征与演变趋势，为劳动经济学、社会政策评估及可持续发展目标（SDG）中体面劳动指标的追踪提供关键数据支撑，对理解欧洲非正规就业的结构性差异具有重要实证价值。

当前挑战

该数据集所应对的领域问题，在于非正规就业本身定义模糊、跨国统计口径差异显著，且非正规部门活动常游离于官方登记体系之外，导致数据收集与跨国比较面临天然障碍。在构建过程中，ILO需协调不同国家调查工具、参考期与抽样设计的异质性，部分国家数据存在年份断档或来源更迭，如英国仅覆盖至2018年；部分观测值带有“不可靠”或“序列断裂”状态标记，提示方法学修订或样本不足。此外，性别与职业维度的交叉 disaggregation 并非所有国家均完整发布，进一步加剧了数据稀疏性与缺失机制的非随机性，对时序建模与跨国面板分析构成实质性挑战。

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，该数据集最典型的使用场景在于刻画欧洲各国非正规部门就业的性别与职业结构差异。研究者依托24,512条观测记录，按ISCO-08二级职业分类与性别维度，构建2006至2025年的面板数据，用于比较不同国家非正规就业的规模、演变轨迹及其在男性与女性之间的分布特征。借助时间序列分析与跨国横截面比较，可揭示非正规就业在转型经济体与发达市场经济体之间的异质性模式。

解决学术问题

该数据集为解答非正规就业的性别分割与职业隔离等学术议题提供了关键证据。以往研究常受限于跨国可比数据匮乏，难以系统检验非正规就业是否在特定职业中呈现女性化趋势。本数据集通过ILO统一协调的ICLS定义与标准化分类，使学者能够控制国家与年份效应，识别性别与职业交互作用对非正规就业概率的影响，进而深化对劳动力市场二元结构、社会保障覆盖缺口及体面劳动赤字形成机制的理解。

衍生相关工作

围绕该数据集及其源指标，已衍生出一系列经典研究。ILO定期发布的《世界非正规就业报告》以此为基准，系统评估全球及区域非正规就业趋势；大量跨国计量研究将其与GDP、教育、贸易开放度等宏观变量匹配，检验非正规就业的经济周期敏感性与制度决定因素；此外，该数据还被用于校准可计算一般均衡模型与微观模拟模型，评估税收、最低工资及社会保障改革对非正规部门的溢出效应，推动了劳动统计方法学的持续改进。