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

--- 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 education (thousands) | Europe (ILOSTAT)" --- # Employees by sex, informal/formal job and education (thousands) | Europe (ILOSTAT) 🇪🇺 **10,162 observations** · **5 Europe countries** · **2003–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **10,162 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_EDU_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_EDU_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 | |---------|-----:|-----------:|----------:| | `SRB` | 2,434 | 2007 | 2025 | | `BIH` | 2,119 | 2006 | 2024 | | `MDA` | 2,044 | 2003 | 2025 | | `MKD` | 1,981 | 2009 | 2025 | | `RUS` | 1,584 | 2010 | 2025 | ## Indicators (sample) - `EES_TEES_SEX_IFL_EDU_NB` — Employees by sex, informal/formal job and education (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_EDU_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 | `EDU_AGGREGATE_TOTAL` | | `classif2.label` | `string` | — | `Education (Aggregate levels): 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-edu-nb-employees-by-sex-informal-formal-job-and-education") 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_EDU_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EES_TEES_SEX_IFL_EDU_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EES_TEES_SEX_IFL_EDU_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_ees_tees_sex_ifl_edu_nb_employees_by_sex_informal_formal_job_and_education_2025, title = {Employees by sex, informal/formal job and education (thousands) | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EES_TEES_SEX_IFL_EDU_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-ees-tees-sex-ifl-edu-nb-employees-by-sex-informal-formal-job-and-education}} } ``` ## 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_EDU_NB_This dataset contains 10,162 observations of informal economy data across 5 Europe countries, spanning 2003–2025, covering 1 distinct indicator. Specifically, it provides statistics on employees by sex, informal/formal job and education (in thousands), sourced from the International Labour Organization (ILO) ILOSTAT database and repackaged by Electric Sheep Europe for machine learning readiness. The dataset supports tabular classification, tabular regression, and time-series forecasting tasks, suitable for labor market analysis and economic research.

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，原始数据通过ILOSTAT REST API接口直接抓取，并依据欧洲ISO3国家代码进行筛选，最终由Electric Sheep Europe重新打包发布。数据采集过程严格遵循国际劳工统计学家会议（ICLS）的定义标准，对来自各国劳动力调查、家庭收入调查及行政记录等原始微观数据进行统一协调，并在source.label列中标注数据来源以便溯源，确保了统计口径的一致性与可追溯性。

特点

数据集覆盖5个欧洲国家，时间跨度为2003年至2025年，包含10,162条观测记录，以表格形式呈现非正规与正规就业中按性别及教育程度分类的雇员人数（千人）。数据按性别（总计、男性、女性）进行 disaggregation，并附带丰富的元数据字段，如来源名称、指标标签、观测状态标记及方法修订说明等，支持表格分类、回归及时间序列预测等任务，体量适中且结构清晰。

使用方法

用户可通过HuggingFace datasets库以一行代码加载数据集，并转换为Pandas DataFrame展开分析。典型操作包括按国家代码筛选子集、针对单一指标绘制时间序列图、以及利用透视表生成国家与年份的交叉矩阵。官方示例展示了过滤德国数据、提取特定指标序列及构建面板矩阵的具体步骤，为研究者提供了即取即用的便捷接口。

背景与挑战

背景概述

非正规经济就业的测度长期以来构成劳动统计领域的核心难题，其规模与结构直接关系到体面劳动议程的推进与社会保护政策的制定。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，通过ILOSTAT数据库系统性地汇集各国劳动力调查与住户调查数据，对非正规就业进行跨国可比的口径统一。该数据集由Electric Sheep Europe于2025年重新封装发布，源自ILOSTAT官方API，涵盖塞尔维亚、波黑、摩尔多瓦、北马其顿与俄罗斯五个欧洲国家2003至2025年间按性别、非正规/正规就业及教育程度分列的雇员人数观测值，共计10,162条记录。其核心研究问题在于揭示非正规就业在不同性别与教育水平群体间的分布差异及其时间演变趋势，为比较劳动经济学与非正规部门研究提供细粒度的量化基础。

当前挑战

该数据集所回应的领域问题在于非正规就业统计的跨国可比性与性别及教育维度下的结构性差异识别，这一议题因各国非正规部门界定标准不一、调查工具异质而长期面临测量困境。构建过程中的具体挑战包括：五国劳动力调查的抽样设计与问卷设计存在差异，须依赖ICLS定义进行事后协调；部分国家序列存在方法学修订所致的断点，观测值亦被标注为不可靠或临时性，影响时序分析的一致性；教育分类与非正规/正规就业分类在各国间的映射关系不完全对称，造成交叉维度分析时的样本稀疏；此外，2025年数据尚属初步统计，序列末端存在修订风险，对预测类任务的稳定性构成制约。

常用场景

经典使用场景

在劳动经济学与非正规经济研究的交叉领域中，该数据集凭借按性别、非正规/正规就业形态与教育程度三重维度分组的雇员数量年度序列，构成了刻画欧洲转型经济体劳动力市场分层结构的经典数据基础。研究者通常将其用于构建面板数据模型，以考察2003至2025年间塞尔维亚、波黑、摩尔多瓦、北马其顿与俄罗斯五国非正规就业规模的演变轨迹，并借助性别与教育维度的交互分类，揭示不同人力资本群体在正规与非正规部门之间的分布差异。

实际应用

在政策实践层面，该数据集为国际组织与各国劳动部门监测体面劳动目标进展提供了量化依据，可用于识别非正规就业集中度较高的性别与教育群体，进而指导社会保障扩面与技能培训资源的精准配置。其年度频率与国别覆盖特征亦适用于劳动力市场风险评估、非正规经济规模预测模型的训练，以及面向欧洲邻国政策对话的跨国比较报告编制。

衍生相关工作

围绕该数据集衍生的典型工作包括基于ILOSTAT非正规就业序列的转型经济体劳动力市场二元结构比较研究，以及将教育维度纳入非正规就业决定因素分析的多层模型探索。部分研究进一步将其与ILO其他体面劳动指标数据集链接，构建涵盖工资、工时与社会保护的综合性劳动统计面板，亦有工作利用其时间序列特征开展非正规就业占比的预测建模与情景推演。