electricsheepeurope/europe-ilo-emp-nifl-sex-eco-rt-informal-employment-rate-by-sex-and-economic-activ

--- 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 and economic activity (%) | Europe (ILOSTAT)" --- # Informal employment rate by sex and economic activity (%) | Europe (ILOSTAT) 🇪🇺 **34,834 observations** · **34 Europe countries** · **2003–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **34,834 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_NIFL_SEX_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_NIFL_SEX_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 | |---------|-----:|-----------:|----------:| | `MDA` | 1,644 | 2003 | 2025 | | `SRB` | 1,545 | 2007 | 2025 | | `BIH` | 1,514 | 2006 | 2024 | | `RUS` | 1,455 | 2010 | 2025 | | `POL` | 1,391 | 2007 | 2024 | | `MKD` | 1,340 | 2009 | 2025 | | `NLD` | 1,260 | 2007 | 2024 | | `FRA` | 1,246 | 2007 | 2024 | | `SVN` | 1,227 | 2007 | 2024 | | `ITA` | 1,219 | 2007 | 2024 | | `ESP` | 1,212 | 2007 | 2024 | | `CZE` | 1,200 | 2007 | 2024 | | `SVK` | 1,162 | 2007 | 2024 | | `FIN` | 1,150 | 2007 | 2024 | | `PRT` | 1,139 | 2007 | 2024 | | ... | _19 more countries_ | | | ## Indicators (sample) - `EMP_NIFL_SEX_ECO_RT` — Informal employment rate by sex 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_NIFL_SEX_ECO_RT` | | `indicator.label` | `string` | Indicator name in English | `Informal employment rate by sex and e…` | | `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.) | `ECO_SECTOR_TOTAL` | | `classif1.label` | `string` | — | `Economic activity (Broad sector): 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` | — | `C5:1023_C5:2959` | | `note_classif.label` | `string` | — | `Nonstandard economic activity: Includ…` | | `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-nifl-sex-eco-rt-informal-employment-rate-by-sex-and-economic-activ") 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_ECO_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_NIFL_SEX_ECO_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_NIFL_SEX_ECO_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_emp_nifl_sex_eco_rt_informal_employment_rate_by_sex_and_economic_activ_2025, title = {Informal employment rate by sex and economic activity (%) | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_NIFL_SEX_ECO_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-emp-nifl-sex-eco-rt-informal-employment-rate-by-sex-and-economic-activ}} } ``` ## 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_NIFL_SEX_ECO_RT_This dataset, titled Informal employment rate by sex and economic activity (%) | Europe (ILOSTAT), contains 34,834 observations across 34 European countries, spanning the years 2003 to 2025. It is sourced from the International Labour Organization (ILO)s ILOSTAT database, a leading global source for labour statistics, focusing on the informal economy topic. The core indicator is EMP_NIFL_SEX_ECO_RT, which represents the informal employment rate by sex and economic activity (%). Data is pulled from the ILOSTAT REST API and filtered to European countries, harmonized using International Conference of Labour Statisticians (ICLS) definitions. The dataset includes columns such as country code, indicator, sex disaggregation (total, male, female), year, observed value, and is suitable for tabular classification, regression, and time-series forecasting tasks. It is released under the CC-BY-4.0 license, repackaged by Electric Sheep Europe as part of a unified, ML-ready data layer for Europe.

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，通过其REST API接口直接获取指标EMP_NIFL_SEX_ECO_RT的原始记录，并依据ISO3国家代码筛选出34个欧洲国家。ILOSTAT采用国际劳工统计学家会议（ICLS）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协调，数据经Electric Sheep Europe重新打包为Parquet格式，保留来源标签与备注字段，确保可追溯性。

特点

数据集涵盖2003至2025年间34个欧洲国家的34834条观测记录，以性别和经济活动部门为分解维度，包含性别（总计、男性、女性）及经济活动分类等字段。每条记录附有观测状态、来源注释和分类注释等元数据，便于评估数据质量。时间序列为年度频率，部分指标存在多来源时由ILO择选最佳来源，结构清晰且支持表格分类、回归与时间序列预测任务。

使用方法

通过HuggingFace的datasets库调用load_dataset()函数即可加载数据集，返回的DataFrame可直接用于分析。使用者可依据ref_area字段筛选特定国家，按indicator字段提取目标指标并进行时间排序，或利用pivot_table生成国家×年份矩阵。示例代码展示了筛选德国数据、绘制单一指标时间序列以及构建面板矩阵的操作，便于开展跨国比较与趋势分析。

背景与挑战

背景概述

非正规就业构成全球劳动力市场的重要议题，其规模与结构直接关联社会保障覆盖、劳动权益保护及经济脆弱性。国际劳工组织（ILO）自成立以来持续推动劳动统计标准化，ILOSTAT数据库汇集全球200余个经济体的劳动力调查数据。该数据集由Electric Sheep Europe于2025年重新包装发布，基于ILO的EMP_NIFL_SEX_ECO_RT指标，涵盖欧洲34国2003至2025年非正规就业率，按性别和经济活动部门分类。数据源自各国劳动力调查与家庭收入调查，经国际劳工统计学家会议（ICLS）定义协调，为欧洲非正规就业的跨国比较与趋势分析提供了标准化基础。

当前挑战

该数据集所应对的核心领域问题在于非正规就业的准确测度与跨国可比性。非正规就业定义在不同国家统计体系中存在差异，且涉及自雇、家庭帮工等复杂就业形态，导致数据收集与协调面临显著困难。构建过程中，ILO依赖各国调查的原始微观数据，存在调查年份不连续、指标口径不一致、部分观测值被标记为不可靠或临时等问题。此外，数据的时间跨度虽长，但各国起始年份参差不齐，部分国家数据缺失较多，对时间序列分析与跨国面板建模构成挑战。

常用场景

经典使用场景

在全球劳动力市场结构性转型的宏大叙事中，非正规就业始终是透视发展中国家与转型经济体就业质量的核心棱镜。该数据集最为经典的使用场景，在于依托国际劳工组织（ILO）所构建的跨国可比统计框架，对欧洲34国自2003年至2025年间非正规就业率的时序演变进行精细化刻画。研究者可藉由性别与经济部门双重维度的交叉分类，系统识别非正规就业在农业、工业与服务部门间的分布异质性，进而揭示经济周期波动、制度变迁与劳动力市场弹性化之间的动态关联。其年度频率的观测结构亦使之成为面板数据建模与合成控制法评估的天然素材。

实际应用

在政策实践层面，该数据集构成劳动监察机构、国际发展组织与智库开展就业质量评估的重要信息基础设施。决策者可据以识别非正规就业高发的人群与行业，从而精准设计社会保障扩面策略、技能培训项目与劳动法规执行方案。对于从事欧洲劳动力市场研究的咨询机构而言，该数据可用于构建国别风险画像、评估区域一体化政策对就业形态的溢出效应。数据科学从业者亦能利用其丰富的分类变量与时间跨度，开发就业趋势预测模型或异常检测算法，服务于公共就业服务的智能化转型。

衍生相关工作

以ILOSTAT非正规就业指标为基石的衍生研究，已广泛嵌入发展经济学与劳动社会学的知识生产链条。若干经典工作围绕非正规就业的性别差距展开，探讨照护经济与就业脆弱性之间的互构关系，亦有研究将其作为关键协变量纳入多维贫困测度框架，检验就业形态对家庭福利的传导效应。在区域研究领域，该数据集被用于比较中东欧与南欧国家在欧盟一体化进程中的劳动力市场趋同与分化。伴随机器学习方法的渗透，近期亦有工作尝试以该数据为训练语料，构建非正规就业率高风险国家的分类预测模型。