electricsheepeurope/europe-ilo-ees-tees-sex-ifl-eco-nb-employees-by-sex-informal-formal-job-and-economic

--- 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: "Employees by sex, informal/formal job and economic activity (thousands) | Europe (ILOSTAT)" --- # Employees by sex, informal/formal job and economic activity (thousands) | Europe (ILOSTAT) 🇪🇺 **24,840 observations** · **5 Europe countries** · **2003–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **24,840 observations** of `Informal economy` data across **5 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=EES_TEES_SEX_IFL_ECO_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=EES_TEES_SEX_IFL_ECO_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 5 Europe countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `MDA` | 5,761 | 2003 | 2025 | | `SRB` | 5,069 | 2007 | 2025 | | `BIH` | 4,961 | 2006 | 2024 | | `MKD` | 4,579 | 2009 | 2025 | | `RUS` | 4,470 | 2010 | 2025 | ## Indicators (sample) - `EES_TEES_SEX_IFL_ECO_NB` — Employees by sex, informal/formal job and economic activity (thousands) ## Schema | Column | Type | Description | Example | |--------|------|-------------|---------| | `ref_area` | `string` | ISO 3166-1 alpha-3 country code | `BIH` | | `ref_area.label` | `string` | Country name in English | `Bosnia and Herzegovina` | | `source` | `string` | ILOSTAT source code (e.g. labour force survey) | `BA:493` | | `source.label` | `string` | Source name in English | `LFS - Labour Force Survey` | | `indicator` | `string` | ILOSTAT indicator code | `EES_TEES_SEX_IFL_ECO_NB` | | `indicator.label` | `string` | Indicator name in English | `Employees by sex, informal/formal job…` | | `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.) | `IFL_NATURE_TOTAL` | | `classif1.label` | `string` | — | `Nature of job: Total` | | `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) | `1069.429` | | `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` | | `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-ees-tees-sex-ifl-eco-nb-employees-by-sex-informal-formal-job-and-economic") 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"] == "EES_TEES_SEX_IFL_ECO_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EES_TEES_SEX_IFL_ECO_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EES_TEES_SEX_IFL_ECO_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_ees_tees_sex_ifl_eco_nb_employees_by_sex_informal_formal_job_and_economic_2025, title = {Employees by sex, informal/formal job and economic activity (thousands) | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EES_TEES_SEX_IFL_ECO_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-ees-tees-sex-ifl-eco-nb-employees-by-sex-informal-formal-job-and-economic}} } ``` ## 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=EES_TEES_SEX_IFL_ECO_NB_This dataset, titled Employees by sex, informal/formal job and economic activity (thousands) | Europe (ILOSTAT), contains 24,840 observations across 5 European countries (Moldova, Serbia, Bosnia and Herzegovina, North Macedonia, Russia), spanning the years 2003 to 2025. It focuses on a core indicator: Employees by sex, informal/formal job and economic activity (thousands), which analyzes informal economy aspects in the European labor market. The data is sourced from the International Labour Organization (ILO) ILOSTAT database, retrieved via API and filtered to European ISO3 country codes. It includes annual observations disaggregated by dimensions such as sex (total, male, female), nature of job (informal/formal), and economic activity sector. The dataset schema comprises columns for country codes, indicator codes, sex classification, observation year, observed values, and status flags, making it suitable for tasks like tabular classification, regression, and time-series forecasting. The data is harmonized by ILO and includes source and quality caveats, supporting machine learning and research applications.

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接口直接拉取指标代码为EES_TEES_SEX_IFL_ECO_NB的原始数据，并筛选出欧洲地区ISO3国家代码的记录。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等原始微观数据进行统一协调，数据来源在source.label列中予以标注以确保可追溯性。最终由Electric Sheep Europe重新打包为HuggingFace数据集，收录摩尔多瓦、塞尔维亚、波黑、北马其顿和俄罗斯五个欧洲国家2003至2025年间的非正规经济就业观测记录，共24840条。

特点

该数据集聚焦于欧洲非正规经济领域的劳动力市场统计，以性别、非正规/正规就业身份及经济活动部门为核心分类维度，覆盖五国逾二十年的年度数据，时间跨度自2003年延伸至2025年。数据以表格形式组织，包含国家代码、来源标识、指标代码、性别分类及经济活动分类等字段，并附有观测状态标记与来源注释列，便于识别暂定值或不可靠数据。其单语种、CC-BY-4.0许可的特性降低了使用门槛，适用于表格分类、回归及时序预测等多种机器学习任务。

使用方法

研究者可通过HuggingFace datasets库以一行代码加载该数据集，并转换为Pandas DataFrame进行灵活分析。典型操作包括按ref_area字段筛选特定国家，按indicator字段提取目标指标的时间序列并可视化，或利用pivot_table方法将数据重塑为国家与年份的交叉矩阵。该数据集支持直接用于非正规经济就业规模的趋势分析、性别差异比较以及跨国面板研究，也可作为机器学习模型在表格预测任务中的训练或评估资源。

背景与挑战

背景概述

国际劳工组织（ILO）自1919年成立以来，始终致力于全球劳动统计的标准化与推广，其ILOSTAT数据库堪为劳动领域最为权威的跨国数据源。在此背景下，europe-ilo-ees-tees-sex-ifl-eco-nb-employees-by-sex-informal-formal-job-and-economic数据集由Electric Sheep Europe于2025年整合发布，涵盖五个欧洲国家2003至2025年间非正规与正规就业的性别及经济活动分布，共计24,840条观测。该数据集直接回应了非正规经济测度这一长期困扰劳动经济学界的难题，为探究性别就业差异、非正规部门演变及经济周期影响提供了精细化的量化基础，对促进体面劳动和性别平等的政策评估具有重要参考价值。

当前挑战

该数据集所面对的领域问题在于非正规就业的界定与度量始终存在跨国可比性难题，不同国家劳动统计口径的差异易导致系统性偏差。构建过程中，原始调查数据的缺失值、异常值以及分类标准不统一（如非正规就业定义随ICLS决议更新而变化）构成主要障碍；此外，部分国家数据存在序列中断或观测状态标记为不可靠，需要谨慎处理。时间跨度内经济结构转型与统计方法修订也使得趋势分析需额外校正，这些因素共同对数据建模与因果推断提出了方法论挑战。

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，该数据集承载着刻画欧洲五国非正规就业性别差异与经济部门分布的核心使命。其经典使用场景聚焦于以国别×年度为分析单元，围绕性别（男/女/总计）与非正规/正规就业身份构建多维面板数据结构，进而支撑对非正规就业规模演变趋势的时序建模与跨国比较分析。研究者常借助该数据集开展就业结构的性别分层研究、非正规经济周期波动识别以及部门异质性检验等任务。

衍生相关工作

围绕该数据集已衍生出一系列具有影响力的研究工作。在方法论层面，研究者开发了基于ILO分类标准的面板数据插补与调和算法；在实证层面，产生了关于东南欧非正规就业收敛性的比较研究、性别就业正规化与经济增长关系的跨国分析，以及结合劳动力调查微观数据的非正规就业决定因素探究。这些工作进一步拓展了ILOSTAT数据在劳动经济学与性别研究中的应用边界。

数据集最近研究

最新研究方向

在全球劳动力市场非正规性持续引发关注的背景下，该数据集覆盖欧洲五国2003至2025年的非正规与正规就业性别差异及经济活动分布，为探究转型经济体非正规就业的性别结构变迁提供了微观基础。当前前沿研究聚焦于利用此类面板数据，结合机器学习方法识别非正规就业的驱动因素及其对经济周期的非线性响应，尤其关注女性在非正规部门中的脆弱性及其与行业分割的关联。世界银行与国际劳工组织近期联合发布的非正规经济监测报告，凸显了性别视角下就业质量追踪的紧迫性。该数据集的时间跨度和国别对比能力，有助于评估结构性改革与危机冲击对非正规就业性别差距的异质性影响，为包容性劳动力市场政策提供实证依据，并推动可持续发目标中体面劳动指标的量化监测。