electricsheepeurope/europe-ilo-ees-tees-sex-ifl-ins-nb-employees-by-sex-informal-formal-job-and-public-pr

--- license: cc-by-4.0 language: - en task_categories: - tabular-classification - tabular-regression - time-series-forecasting multilinguality: monolingual size_categories: - 1K<n<10K tags: - tabular - europe - ilostat - informal-economy - ilo - labour - employment pretty_name: "Employees by sex, informal/formal job and public/private sector (thousands) | Europe (ILOSTAT)" --- # Employees by sex, informal/formal job and public/private sector (thousands) | Europe (ILOSTAT) 🇪🇺 **1,941 observations** · **4 Europe countries** · **2003–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **1,941 observations** of `Informal economy` data across **4 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_INS_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_INS_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 4 Europe countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `MDA` | 591 | 2003 | 2025 | | `BIH` | 513 | 2006 | 2024 | | `MKD` | 459 | 2009 | 2025 | | `SRB` | 378 | 2007 | 2020 | ## Indicators (sample) - `EES_TEES_SEX_IFL_INS_NB` — Employees by sex, informal/formal job and public/private sector (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_INS_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 | `INS_SECTOR_TOTAL` | | `classif2.label` | `string` | — | `Institutional 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) | `B` | | `obs_status.label` | `string` | — | `Break in series` | | `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-ins-nb-employees-by-sex-informal-formal-job-and-public-pr") 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_INS_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EES_TEES_SEX_IFL_INS_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EES_TEES_SEX_IFL_INS_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_ees_tees_sex_ifl_ins_nb_employees_by_sex_informal_formal_job_and_public_pr_2025, title = {Employees by sex, informal/formal job and public/private sector (thousands) | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EES_TEES_SEX_IFL_INS_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-ees-tees-sex-ifl-ins-nb-employees-by-sex-informal-formal-job-and-public-pr}} } ``` ## 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_INS_NB_This dataset contains informal economy data from the International Labour Organization (ILO) ILOSTAT database for Europe, specifically the indicator EES_TEES_SEX_IFL_INS_NB, which represents employees by sex, informal/formal job, and public/private sector (in thousands). It covers 4 European countries (Moldova, Bosnia and Herzegovina, North Macedonia, Serbia) from 2003 to 2025, with 1,941 observations. The data is annual and includes columns such as country code, country name, data source, indicator code, indicator label, sex disaggregation (total, male, female), classification variables (e.g., nature of job, institutional sector), observation year, observed value, observation status, and notes. The data is harmonized by ILO using International Conference of Labour Statisticians (ICLS) definitions and includes source flags for traceability. It is suitable for tabular classification, regression, and time-series forecasting tasks, enabling analysis of employment patterns in Europes informal economy.

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统计数据库（ILOSTAT），由Electric Sheep Europe团队通过ILOSTAT REST API接口抓取指标EES_TEES_SEX_IFL_INS_NB的原始数据，并依据欧洲ISO3国家代码进行筛选与重整。原始数据经ILOSTAT依照国际劳工统计学家会议（ICLS）定义对各国劳动力调查等微观数据进行标准化调和，数据集保留了来源标识与注释信息以确保可追溯性，最终以Parquet格式打包发布，涵盖摩尔多瓦、波黑、北马其顿与塞尔维亚四国自2003年至2025年的观测记录。

特点

数据集包含1,941条年度观测，覆盖4个欧洲国家，时间跨度逾二十年，聚焦于按性别、非正规/正规就业及公共/私营部门分类的雇员人数（千人）。数据以表格形式组织，包含国家代码、来源、指标、性别、分类变量、年份及观测值等字段，并附带观测状态与注释说明，便于识别序列断裂等数据质量问题。该数据集为单语英语资源，具有明确的许可协议（CC-BY-4.0），支持表格分类、回归及时间序列预测等多种机器学习任务。

使用方法

通过HuggingFace数据集库可便捷加载该数据集，使用load_dataset函数获取训练集并转换为Pandas数据框以进行后续分析。研究者可按国家代码筛选特定国家的数据，或针对单一指标提取时间序列并绘制趋势图，亦可利用透视表将数据重塑为国家与年份的矩阵形式，从而支撑跨国比较与预测建模等应用场景。

背景与挑战

背景概述

非正规经济就业的测度长期构成劳动统计领域的核心议题，其关乎体面劳动议程的监测与包容性增长政策的制定。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，自本世纪初起系统编纂非正规就业指标，为跨国比较提供基准。该数据集由Electric Sheep Europe于2025年经ILOSTAT REST API抓取并重新封装，涵盖摩尔多瓦、波黑、北马其顿与塞尔维亚四国2003至2025年间1941条观测，以性别、非正规/正规就业及公共/私营部门为分类维度，为转型经济体非正规就业的性别差异与部门结构研究提供精细化数据支撑。

当前挑战

该数据集所回应的领域问题在于，非正规就业统计长期面临定义标准不一、跨国可比性薄弱与性别维度数据稀缺等困境，亟需标准化、可追溯的跨国指标以支撑实证研究。构建过程中的挑战亦不容忽视：各国劳动力调查的抽样设计与采集口径存在异质性，ILO虽以ICLS定义加以调和，仍难免序列断裂与观测状态异常；部分国家年份缺失或数据仅覆盖至2020年，制约时序完整性；性别与部门分类并非所有指标均予发布，导致非空值分布不均，对建模时的缺失值处理与跨类别比较构成挑战。

常用场景

经典使用场景

在劳动经济学与非正规经济研究的交汇领域，该数据集凭借按性别、非正规/正规就业形态以及公共/私营部门交叉细分的雇员数量序列，成为刻画欧洲转型经济体劳动力市场结构的经典面板数据。研究者通常以摩尔多瓦、波斯尼亚和黑塞哥维那、北马其顿与塞尔维亚四国为观测单元，运用其2003至2025年的年度观测值，构建非正规就业比重的时序轨迹，进而识别性别差异与部门再分配之间的联动特征。依托HuggingFace平台的标准化封装，该数据可经由load_dataset接口便捷载入，并直接适配表格分类、表格回归与时间序列预测等任务范式，支撑跨国比较与趋势拟合等基础分析流程。

实际应用

在政策评估与国际比较的实际场景中，该数据集可用于追踪目标国家非正规就业规模的变化，辅助判断劳动监察、社会保障扩面及正规化激励政策的实施成效。国际组织与各国统计部门可据此编制性别敏感的就业指标，识别公共部门与私营部门在吸纳劳动力方面的相对贡献，从而为制定针对性的就业促进策略提供依据。学术与咨询机构亦可将其作为基准数据，嵌入国别诊断报告或区域发展评估，支撑以证据为基础的政策建议。

衍生相关工作

围绕该数据集衍生的经典工作主要体现在ILOSTAT指标体系的扩展应用与区域劳动市场比较研究之中。研究者以该指标为核心，结合ILOSTAT发布的失业率、工资与工时等系列，构建多指标联动的分析框架，用以考察非正规就业与宏观经济周期之间的协动关系。Electric Sheep Europe的标准化封装进一步推动了此类数据的再利用，使跨国面板的机器学习建模与可复现研究成为可能，并为后续纳入更多欧洲国家与更细分类维度的数据集建设提供范式参照。