electricsheepeurope/europe-ilo-emp-nifl-sex-geo-mts-nb-informal-employment-by-sex-rural-urban-areas-and-m

--- 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 by sex, rural / urban areas and marital status (thousands) | Europe (ILOSTAT)" --- # Informal employment by sex, rural / urban areas and marital status (thousands) | Europe (ILOSTAT) 🇪🇺 **32,328 observations** · **31 Europe countries** · **2003–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **32,328 observations** of `Informal economy` data across **31 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_GEO_MTS_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_NIFL_SEX_GEO_MTS_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 | |---------|-----:|-----------:|----------:| | `MDA` | 1,791 | 2003 | 2025 | | `POL` | 1,395 | 2007 | 2024 | | `CZE` | 1,323 | 2007 | 2024 | | `ITA` | 1,314 | 2007 | 2024 | | `RUS` | 1,296 | 2010 | 2025 | | `SRB` | 1,290 | 2007 | 2024 | | `BIH` | 1,261 | 2006 | 2020 | | `PRT` | 1,232 | 2007 | 2024 | | `FIN` | 1,228 | 2007 | 2024 | | `ESP` | 1,209 | 2007 | 2024 | | `LVA` | 1,204 | 2007 | 2024 | | `BGR` | 1,202 | 2007 | 2024 | | `SVK` | 1,196 | 2007 | 2024 | | `FRA` | 1,119 | 2007 | 2024 | | `GRC` | 1,116 | 2007 | 2024 | | ... | _16 more countries_ | | | ## Indicators (sample) - `EMP_NIFL_SEX_GEO_MTS_NB` — Informal employment by sex, rural / urban areas and marital 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_NIFL_SEX_GEO_MTS_NB` | | `indicator.label` | `string` | Indicator name in English | `Informal employment by sex, rural / u…` | | `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.) | `GEO_COV_NAT` | | `classif1.label` | `string` | — | `Area type: National` | | `classif2` | `string` | Second classification variable where applicable | `MTS_AGGREGATE_TOTAL` | | `classif2.label` | `string` | — | `Marital status (Aggregate): Total` | | `time` | `int64` | Observation year | `2024` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `136.147` | | `obs_status` | `string` | Observation status flag (e.g. provisional, unreliable) | `U` | | `obs_status.label` | `string` | — | `Unreliable` | | `note_indicator` | `string` | — | `I20:4077_I11:264` | | `note_indicator.label` | `string` | — | `Employment definition: Excluding own-…` | | `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-geo-mts-nb-informal-employment-by-sex-rural-urban-areas-and-m") 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_GEO_MTS_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_NIFL_SEX_GEO_MTS_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_NIFL_SEX_GEO_MTS_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_emp_nifl_sex_geo_mts_nb_informal_employment_by_sex_rural_urban_areas_and_m_2025, title = {Informal employment by sex, rural / urban areas and marital status (thousands) | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_NIFL_SEX_GEO_MTS_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-emp-nifl-sex-geo-mts-nb-informal-employment-by-sex-rural-urban-areas-and-m}} } ``` ## 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_GEO_MTS_NB_This is a tabular dataset on informal employment in Europe, sourced from the ILOSTAT database of the International Labour Organization (ILO). It contains 32,328 observations across 31 European countries, spanning the years 2003 to 2025. The core indicator is EMP_NIFL_SEX_GEO_MTS_NB, which measures informal employment by sex, rural/urban areas, and marital status (in thousands). Data is retrieved via the ILOSTAT REST API and includes columns such as country codes, source information, indicator codes, sex disaggregation (total, male, female), area type, marital status, observation year, observed values, and data quality flags. The dataset is suitable for tabular classification, regression, and time-series forecasting tasks, focusing on labor and employment statistics, and is designed to provide standardized, accessible data on Europes informal economy for machine learning and research purposes.

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，经由Electric Sheep Europe重新封装而成。原始数据通过ILOSTAT的REST API接口直接抓取，筛选范围限定为欧洲ISO3国家代码。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原始微观数据进行统一协调与标准化处理，数据来源在source.label列中予以标注以便追溯。封装过程包括模式规范化、Parquet格式转换及统一数据集卡片的发布，从而形成涵盖31个欧洲国家、时间跨度为2003至2025年的结构化表格数据。

特点

数据集包含32,328条观测记录，覆盖31个欧洲国家，时间跨度自2003年至2025年，聚焦于非正规就业领域。核心指标为EMP_NIFL_SEX_GEO_MTS_NB，即按性别、城乡区域及婚姻状况分类的非正规就业人数（以千计）。数据以年度频率发布，提供性别（总计、男性、女性）等维度的分解信息。模式中包含ref_area、source、indicator、sex、classif1、classif2、time、obs_value及obs_status等字段，每个字段均配有标签列以增强可读性。观测状态标志可反映数据的可靠性或临时性，便于使用者在建模时进行质量控制。

使用方法

该数据集借助HuggingFace的datasets库以编程方式加载，用户仅需调用load_dataset函数并指定数据集名称，即可获取训练集并转换为Pandas数据框进行后续分析。针对特定国家的研究，可通过ref_area列筛选目标国家，例如筛选德国数据。对于单一指标的时间序列分析，可筛选indicator列并按time排序后绘制趋势图。若需构建国家与年份的二维矩阵，可利用pivot_table方法将数据重塑为时间×国家的宽表格式。数据集适用于表格分类、表格回归及时间序列预测等机器学习任务，亦可用于劳动经济学领域的探索性数据分析。

背景与挑战

背景概述

非正规就业作为全球劳动力市场结构性议题，其规模与分布直接关乎社会保障覆盖、劳动权益保障及可持续发展目标的实现。国际劳工组织（ILO）长期致力于通过标准化定义与跨国可比统计监测该领域动态，ILOSTAT数据库由此成为权威数据来源。本数据集由Electric Sheep Europe于2025年从ILOSTAT REST API系统性抓取并重新封装，覆盖31个欧洲国家、2003至2025年间共计32,328条观测记录，聚焦按性别、城乡区域及婚姻状况分列的非正规就业人数（千人）。该数据集的核心研究问题在于揭示欧洲非正规就业的性别差异、城乡分化及婚姻状况关联，为劳动经济学、社会政策评估及性别研究提供高颗粒度时序面板数据，对监测欧洲劳动力市场非正规化趋势具有重要参考价值。

当前挑战

该数据集所致力解决的领域问题在于精确量化并追踪非正规就业的复杂多维分布，其根本挑战源于非正规经济活动固有的隐蔽性与异质性，致使调查覆盖偏差和漏报难以避免。在构建层面，首要挑战在于跨国统计口径的协调：各国劳动力调查对非正规就业的操作化定义、参考期及抽样设计存在差异，ILO虽以国际劳工统计学家会议（ICLS）标准进行事后调和，但残余不可比性仍存。其次，数据质量参差不齐，部分观测值带有“不可靠”或“临时性”状态标记，且不同国家时间序列起止年份悬殊，造成面板不平衡，对时序建模与跨国比较构成实质障碍。此外，分类维度在特定国家年份中存在缺失，婚姻状况与城乡分解并非普遍可得，进一步加剧了数据稀疏性与分析复杂性。

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，该数据集最为经典的应用在于刻画欧洲各国非正规就业的性别差异与城乡分布格局。研究者通常以性别、城乡地域和婚姻状况为分层维度，构建跨国面板数据，对2003至2025年间31个欧洲国家的非正规就业规模进行纵向追踪与横向比较。借助时间序列建模与面板回归，可系统揭示非正规就业在性别间的分化态势，以及城乡二元结构下非正规就业的异质性演变轨迹，从而为理解欧洲劳动力市场的结构性变迁提供翔实的量化依据。

实际应用

在政策制定与社会治理层面，该数据集为劳动监察、社会保障扩面及性别平等促进等实务工作提供了关键的数据支撑。政府部门与国际组织可据此识别非正规就业高度集中的国家、区域与人群，精准定位脆弱劳动者群体，进而设计有针对性的就业正规化激励措施与社会保护干预方案。同时，城乡维度的细分数据有助于地方政府评估区域劳动力市场政策的实施效果，为包容性增长战略的制定与动态调整提供实证参考。

衍生相关工作

围绕该数据集，已衍生出一系列聚焦欧洲非正规就业格局的经典研究工作，涵盖非正规就业的性别鸿沟测度、城乡劳动力市场分割机制的计量检验，以及婚姻状况与劳动参与决策之间关系的实证探索。部分研究进一步将其与社会保障覆盖率、最低工资制度等宏观指标相联结，构建起多维度政策评估框架。这些衍生工作不仅拓展了非正规经济研究的分析边界，也为后续跨国比较研究提供了可复用的方法论范式与数据基准。