electricsheepeurope/europe-ilo-ees-tees-age-ec2-nb-employees-by-age-and-economic-activity-isic-level

--- 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 - employees - ilo - labour - employment pretty_name: "Employees by age and economic activity - ISIC level 2 (thousands) | Europe (ILOSTAT)" --- # Employees by age and economic activity - ISIC level 2 (thousands) | Europe (ILOSTAT) 🇪🇺 **62,133 observations** · **16 Europe countries** · **1993–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **62,133 observations** of `Employees` data across **16 Europe countries**, spanning **1993–2025**, 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_AGE_EC2_NB) - **Publisher:** International Labour Organization (ILO) - **License:** [cc-by-4.0](https://creativecommons.org/licenses/by/4.0/) - **Topic:** Employees ## Methodology Data pulled directly from the ILOSTAT REST API at `https://rplumber.ilo.org/data/indicator?id=EES_TEES_AGE_EC2_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 16 Europe countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `GRC` | 6,530 | 1993 | 2025 | | `PRT` | 5,999 | 1998 | 2025 | | `CHE` | 5,835 | 1996 | 2025 | | `CZE` | 5,341 | 1993 | 2024 | | `SVK` | 5,132 | 1998 | 2023 | | `GBR` | 5,115 | 2005 | 2025 | | `AUT` | 4,862 | 2004 | 2025 | | `FRA` | 4,860 | 2005 | 2024 | | `MKD` | 3,656 | 2006 | 2025 | | `BIH` | 3,325 | 2001 | 2024 | | `ITA` | 3,013 | 2008 | 2020 | | `ALB` | 2,712 | 2007 | 2024 | | `SRB` | 2,426 | 2007 | 2019 | | `BLR` | 1,924 | 2016 | 2024 | | `UKR` | 964 | 2018 | 2021 | | ... | _1 more countries_ | | | ## Indicators (sample) - `EES_TEES_AGE_EC2_NB` — Employees by age and economic activity - ISIC level 2 (thousands) ## Schema | Column | Type | Description | Example | |--------|------|-------------|---------| | `ref_area` | `string` | ISO 3166-1 alpha-3 country code | `ALB` | | `ref_area.label` | `string` | Country name in English | `Albania` | | `source` | `string` | ILOSTAT source code (e.g. labour force survey) | `BA:480` | | `source.label` | `string` | Source name in English | `LFS - Labour Force Survey` | | `indicator` | `string` | ILOSTAT indicator code | `EES_TEES_AGE_EC2_NB` | | `indicator.label` | `string` | Indicator name in English | `Employees by age and economic activit…` | | `classif1` | `string` | First classification variable (age, education, status, etc.) | `AGE_YTHADULT_YGE15` | | `classif1.label` | `string` | — | `Age (Youth, adults): 15+` | | `classif2` | `string` | Second classification variable where applicable | `EC2_ISIC4_TOTAL` | | `classif2.label` | `string` | — | `Economic activity (ISIC-Rev.4), 2 dig…` | | `time` | `int64` | Observation year | `2024` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `537.005` | | `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…` | ## 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-age-ec2-nb-employees-by-age-and-economic-activity-isic-level") 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_AGE_EC2_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EES_TEES_AGE_EC2_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EES_TEES_AGE_EC2_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_ees_tees_age_ec2_nb_employees_by_age_and_economic_activity_isic_level_2025, title = {Employees by age and economic activity - ISIC level 2 (thousands) | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EES_TEES_AGE_EC2_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-ees-tees-age-ec2-nb-employees-by-age-and-economic-activity-isic-level}} } ``` ## 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_AGE_EC2_NB_This dataset contains the Employees by age and economic activity - ISIC level 2 (thousands) indicator data from the International Labour Organization (ILO) ILOSTAT database, covering 16 European countries from 1993 to 2025. It includes 62,133 observations focused on one core indicator: EES_TEES_AGE_EC2_NB, which measures the number of employees (in thousands) disaggregated by age groups and economic activity (based on ISIC Rev.4 2-digit classification). The data is sourced from national labour force surveys, household income surveys, establishment surveys, and administrative records, and is harmonized by the ILO for international comparability. The dataset is presented in tabular format with columns such as country code, country name, data source, indicator code, classification variables (e.g., age group and economic activity), observation year, observed value (number of employees), and data status flags. The data is annual frequency and suitable for tasks like tabular classification, regression, and time-series forecasting. The dataset is repackaged by Electric Sheep Europe for ease of use in machine learning projects.

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）的中央统计数据库ILOSTAT，后者依托各国劳动力调查、住户收入调查、机构调查及行政记录等多源数据，依据国际劳工统计学家会议（ICLS）定义进行标准化调和。Electric Sheep Europe通过ILOSTAT的REST API接口直接提取指标EES_TEES_AGE_EC2_NB，以欧洲ISO3国家代码为筛选条件，将原始数据封装为Parquet格式并发布至HuggingFace平台。数据集涵盖1993至2025年间的年度观测值，涉及16个欧洲国家，共计62,133条记录。数据中保留了来源标签与注释字段，以保障统计过程的可追溯性。

特点

该数据集聚焦于按年龄与经济 activity（ISIC第2级）分组的雇员人数（千人），提供跨度逾三十年的面板数据。其核心特征在于多维分类体系：除国家和年份外，还包含年龄分组（如青年与成年人15岁以上）及经济 activity 分类（ISIC-Rev.4二位码），使得研究者能够同时考察人口结构与产业结构的交互效应。数据以表格形式组织，包含国家代码、来源、指标代码、分类变量、观测值及状态标志等字段，其中部分观测值附有可靠性标注或序列断点说明。数据集规模适中，介于一万至十万条之间，语言为英语，遵循CC-BY-4.0许可协议。

使用方法

研究者可通过HuggingFace的datasets库以单行代码加载数据集，并转换为Pandas DataFrame进行后续分析。典型用法包括：按国家代码筛选子集以开展国别研究；按指标代码提取时间序列并利用绘图函数观察趋势演变；或通过透视表将数据重塑为国家×年份矩阵，便于面板回归或跨国比较。数据集亦适用于表格分类、回归及时间序列预测等机器学习任务。使用时应留意观测状态标志及序列断点注释，以确保分析结果的稳健性。引用时需同时标注ILO原始来源与Electric Sheep Europe的再封装工作。

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传播，其ILOSTAT数据库汇集了200余个经济体的劳动力调查数据，为就业、失业、工资等核心指标提供权威来源。该数据集由Electric Sheep Europe于2026年重新打包发布，整合了1993至2025年间16个欧洲国家按年龄分组及ISIC Rev.4二级经济活动分类的雇员人数（千人）数据，共计62,133条观测。其核心研究问题在于揭示欧洲劳动力市场在年龄结构与行业分布上的长期演变规律，为劳动经济学、人口学及区域政策研究提供细粒度面板数据，推动了跨国比较与时间序列分析的实证进展。

当前挑战

该数据集所应对的领域问题在于，传统劳动力统计往往缺乏跨国家、跨年龄与跨行业的统一协调数据，难以支撑对欧洲就业结构转型的精细刻画，如青年与成年劳动力在不同经济部门间的再分配。构建过程中，主要挑战包括：原始数据源自各国劳动力调查，抽样设计、覆盖范围和变量定义存在异质性，ILO虽依ICLS标准进行协调，但序列断裂（如方法修订）和不可靠观测仍频繁出现；部分国家时间跨度短、缺失年份多，导致面板不平衡；ISIC二级分类的细分维度使得某些单元格样本量不足，影响统计稳健性。

常用场景

经典使用场景

在劳动经济学与区域科学交叉领域，该数据集最为经典的用途在于构建跨国别的年龄-行业二维就业矩阵，以刻画欧洲劳动力市场的结构性特征。研究者可依据ISIC第二级分类与年龄组变量，将十六个欧洲国家自1993年至2025年的雇员数据重塑为面板结构，进而考察不同年龄群体在制造业、服务业等细分行业中的分布演变。此类分析常见于劳动供给弹性估计、行业年龄极化测度以及经济周期中青年与成年雇员替代关系的实证检验，为理解欧洲一体化进程中劳动力资源配置的异质性提供了细粒度观测基础。

衍生相关工作

围绕该数据集衍生的经典工作主要体现在比较劳动经济学与时间序列预测两个方向。一方面，研究者将其与ILOSTAT其他指标（如失业率、工时、工资）链接，构建多指标劳动市场韧性指数，用于分析欧洲国家在金融危机与公共卫生事件中的行业就业调整路径。另一方面，该数据集被广泛用于基准测试时间序列预测模型与面板回归方法，例如检验年龄-行业就业序列的平稳性、协整关系与结构突变点。部分机器学习研究亦以其为训练语料，探索小样本跨国面板下的迁移学习与缺失值插补策略，推动了劳动统计数据的二次开发利用。

数据集最近研究

最新研究方向

在劳动经济学与人口结构变迁的交汇处，该数据集正推动着面向欧洲劳动力市场韧性评估的前沿探索。研究者日益关注年龄分层与ISIC二分位行业分类的交互效应，以刻画青年、壮年与老龄雇员在制造业、服务业等板块的分布动态。伴随欧盟绿色转型与数字化战略的推进，相关热点集中于识别产业结构调整对特定年龄群体就业的异质性冲击，并借助时间序列预测模型评估政策干预的滞后效应。该数据资源为跨国比较提供了长达三十余年的细粒度观测，其价值在于支撑劳动力供给弹性、行业年龄极化及区域就业脆弱性的实证检验，进而为欧洲就业政策协调与人力资本配置提供量化依据。