electricsheepeurope/europe-ilo-emp-pifl-sex-age-eco-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, age and economic activity (%) | Europe (ILOSTAT)" --- # Share of employment outside the formal sector by sex, age and economic activity (%) | Europe (ILOSTAT) 🇪🇺 **288,567 observations** · **34 Europe countries** · **2003–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **288,567 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_AGE_ECO_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_AGE_ECO_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 | |---------|-----:|-----------:|----------:| | `RUS` | 15,172 | 2010 | 2025 | | `MDA` | 14,467 | 2003 | 2025 | | `BIH` | 11,880 | 2006 | 2024 | | `SRB` | 11,673 | 2007 | 2024 | | `POL` | 11,435 | 2007 | 2024 | | `NLD` | 10,935 | 2007 | 2024 | | `CZE` | 10,818 | 2007 | 2024 | | `MKD` | 10,690 | 2009 | 2025 | | `FIN` | 10,540 | 2007 | 2024 | | `ITA` | 10,463 | 2007 | 2024 | | `PRT` | 10,359 | 2007 | 2024 | | `SVN` | 10,347 | 2007 | 2024 | | `ESP` | 10,056 | 2007 | 2024 | | `SVK` | 9,272 | 2007 | 2024 | | `FRA` | 9,209 | 2007 | 2024 | | ... | _19 more countries_ | | | ## Indicators (sample) - `EMP_PIFL_SEX_AGE_ECO_RT` — Share of employment outside the formal sector by sex, age and economic activity (%) ## 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_AGE_ECO_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.) | `AGE_YTHADULT_YGE15` | | `classif1.label` | `string` | — | `Age (Youth, adults): 15+` | | `classif2` | `string` | Second classification variable where applicable | `ECO_SECTOR_TOTAL` | | `classif2.label` | `string` | — | `Economic activity (Broad sector): 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` | — | `C6:1058` | | `note_classif.label` | `string` | — | `Nonstandard age group: Excluding age 15` | | `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-age-eco-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_AGE_ECO_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_PIFL_SEX_AGE_ECO_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_PIFL_SEX_AGE_ECO_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_emp_pifl_sex_age_eco_rt_share_of_employment_outside_the_formal_sector_by_s_2025, title = {Share of employment outside the formal sector by sex, age and economic activity (%) | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_PIFL_SEX_AGE_ECO_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-emp-pifl-sex-age-eco-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_AGE_ECO_RT_This dataset contains 288,567 observations of informal economy employment data across 34 Europe countries, spanning 2003–2025, with the core indicator Share of employment outside the formal sector by sex, age and economic activity (%). It is sourced from the ILOSTAT database of the International Labour Organization (ILO), retrieved via API and filtered for European countries, covering fields such as country, sex, age, economic activity, year, and observed values for analyzing the distribution and trends of informal employment in Europe.

搜集汇总

数据集介绍

构建方式

该数据集源于国际劳工组织（ILO）的ILOSTAT中央统计数据库，通过REST API接口直接提取非正规经济部门就业份额指标（EMP_PIFL_SEX_AGE_ECO_RT），并以欧洲ISO3国家代码为筛选条件进行子集化处理。原始数据由各国劳动力调查、家庭收入调查及行政记录汇编而成，ILOSTAT依据国际劳工统计学家会议（ICLS）定义的标准化框架对微观数据进行调和，保留源标签以确保可追溯性，最终由Electric Sheep Europe重新打包为HuggingFace数据集。

使用方法

研究者可通过HuggingFace的datasets库以load_dataset函数加载数据，并转换为Pandas数据框进行后续处理。支持按国家代码筛选特定国家子集，或针对单一指标提取时间序列以分析趋势变化，亦可利用透视表功能将数据重塑为国家与年份的矩阵形式。该数据集适用于表格分类、回归及时间序列预测等任务，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

非正规经济部门就业的测度长期以来构成劳动统计领域的核心议题，其数据质量直接关乎体面劳动议程的监测与政策制定。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，汇聚各国劳动力调查、家庭收入调查及行政记录，构建了ILOSTAT这一全球权威劳动统计数据库。本数据集由Electric Sheep Europe于2025年从ILOSTAT REST API抽取并重新封装，聚焦欧洲34国2003至2025年间非正规部门就业占比，涵盖性别、年龄与经济 activity 等多维分解，含288,567条观测。该数据集为比较欧洲非正规就业的性别差异、年龄梯度及行业分布提供了标准化、可复用的数据基础。

当前挑战

该数据集所应对的领域问题在于非正规就业界定的跨国异质性与统计口径非一致性，不同国家对非正规部门的操作化定义存在显著分歧，致使横向比较面临概念效度威胁。构建过程中的挑战体现为多源异构数据的协调：原始调查微数据经ICLS标准调和后，仍存在序列中断、观测可靠性标记及非标准年龄分组等数据质量瑕疵，且部分国家年份存在来源切换导致的测量断裂。此外，性别、年龄与经济 activity 三维分解维度并非在所有国家×年份组合中完整可得，对建模时的缺失机制处理与时间序列连续性构成实质性约束。

常用场景

经典使用场景

在非正规经济就业研究领域，该数据集凭借其涵盖34个欧洲国家、跨越2003至2025年的288,567条观测记录，成为刻画非正规部门就业占比动态演变的核心资源。研究者常将其用于按性别、年龄和经济活动部门划分的非正规就业比例的时间序列分析与跨国比较，典型做法包括构建国别面板以考察非正规就业的收敛或分化趋势，以及利用年龄与性别维度揭示青年、女性等群体在非正规部门中的脆弱性分布。

解决学术问题

该数据集有效回应了非正规就业测量中跨国可比性不足与时间序列断裂的学术难题。通过ILOSTAT基于国际劳工统计学家会议定义进行的标准化调和，它为探究非正规就业与经济发展水平、劳动力市场制度及性别平等之间的关联提供了可复现的实证基础，推动了关于非正规经济规模估算、结构转型与非正规性持续机制等议题的定量研究，对劳动经济学与发展经济学的交叉对话具有显著意义。

实际应用

在实际应用层面，该数据集为国际组织、政府部门及社会合作伙伴监测非正规就业进展提供了操作化工具。政策分析者可借助其识别非正规就业高发的人群与部门，评估劳动法规、社会保障扩面及正规化干预措施的成效；同时，机器学习从业者可将其用于表格分类、回归及时序预测任务，例如预测特定国家或人群的非正规就业占比走势，从而支持循证决策与资源精准投放。

数据集最近研究

最新研究方向

在全球非正规经济研究日益深化的背景下，该数据集凭借其覆盖34个欧洲国家、跨越2003至2025年的28万余条观测记录，为劳动经济学与计量社会科学提供了独特的面板数据资源。当前前沿研究聚焦于非正规就业的性别与年龄分层机制，借助该数据集的高维分类变量（如sex、classif1、classif2），学者们得以精细刻画青年、女性及特定经济活动部门中非正规就业比例的动态演化。与此同时，欧洲劳动力市场转型、移民融入与后疫情时代就业脆弱性等热点议题，亦推动该数据集在时间序列预测与政策评估中的广泛应用。其标准化ILOSTAT编码与可追溯的来源标注，确保了跨国比较研究的信度与效度，对理解欧洲非正规经济结构变迁及制定包容性劳动政策具有重要的实证支撑意义。