electricsheepeurope/europe-ilo-ees-tees-sex-age-ins-nb-employees-by-sex-age-and-public-private-sector-tho

--- 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 sex, age and public/private sector (thousands) | Europe (ILOSTAT)" --- # Employees by sex, age and public/private sector (thousands) | Europe (ILOSTAT) 🇪🇺 **44,142 observations** · **14 Europe countries** · **1987–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **44,142 observations** of `Employees` data across **14 Europe countries**, spanning **1987–2025**, 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_AGE_INS_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_SEX_AGE_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 14 Europe countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `GRC` | 4,896 | 1987 | 2020 | | `CHE` | 4,320 | 1996 | 2025 | | `MDA` | 3,744 | 2000 | 2025 | | `POL` | 3,744 | 2000 | 2025 | | `ESP` | 3,744 | 2000 | 2025 | | `AUT` | 3,154 | 2004 | 2025 | | `GBR` | 3,024 | 2005 | 2025 | | `FRA` | 2,880 | 2005 | 2024 | | `BIH` | 2,806 | 2001 | 2024 | | `MKD` | 2,798 | 2006 | 2025 | | `ALB` | 2,786 | 2002 | 2024 | | `SVK` | 2,592 | 2006 | 2023 | | `SRB` | 1,978 | 2007 | 2020 | | `MNE` | 1,676 | 2011 | 2024 | ## Indicators (sample) - `EES_TEES_SEX_AGE_INS_NB` — Employees by sex, age and public/private sector (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_SEX_AGE_INS_NB` | | `indicator.label` | `string` | Indicator name in English | `Employees by sex, age and public/priv…` | | `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.) | `AGE_YTHADULT_YGE15` | | `classif1.label` | `string` | — | `Age (Youth, adults): 15+` | | `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) | `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…` | ## 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-age-ins-nb-employees-by-sex-age-and-public-private-sector-tho") 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_AGE_INS_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EES_TEES_SEX_AGE_INS_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EES_TEES_SEX_AGE_INS_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_ees_tees_sex_age_ins_nb_employees_by_sex_age_and_public_private_sector_tho_2025, title = {Employees by sex, age 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_AGE_INS_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-ees-tees-sex-age-ins-nb-employees-by-sex-age-and-public-private-sector-tho}} } ``` ## 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_AGE_INS_NB_This dataset contains European employee data from the International Labour Organization (ILO) ILOSTAT database, specifically the indicator Employees by sex, age and public/private sector (thousands). It covers 14 European countries (including Albania, Austria, Bosnia and Herzegovina, France, Greece, Macedonia, Moldova, Montenegro, Poland, Serbia, Slovakia, Spain, Switzerland, United Kingdom), spanning the years 1987 to 2025, with 44,142 observations. The data is disaggregated by sex (total, male, female), age groups, and institutional sector (public/private), and includes columns such as country code, source, observed value, and observation status. Data is provided at annual frequency and is suitable for tasks like tabular classification, regression, and time-series forecasting.

搜集汇总

数据集介绍

构建方式

该数据集以国际劳工组织（ILO）的ILOSTAT中央统计数据库为源，通过其REST API接口（https://rplumber.ilo.org/data/indicator?id=EES_TEES_SEX_AGE_INS_NB）直接提取指标数据，并依据ISO3国家代码筛选欧洲地区，最终由Electric Sheep Europe重新打包发布。原始调查微观数据经ILOSTAT依照国际劳工统计学家会议（ICLS）定义进行标准化调和，来源在source.label列中标注以确保可追溯性，覆盖14个欧洲国家、1987至2025年共计44,142条观测记录。

使用方法

研究人员可通过HuggingFace的datasets库以一行代码加载数据集，并使用to_pandas()方法转换为数据框进行后续分析。典型操作包括按ref_area列筛选特定国家（如df[df["ref_area"] == "DEU"]），按indicator列提取单一指标并依time列排序绘制时间序列图，或利用pivot_table将数据重塑为国家×年份矩阵以开展横向比较。该数据集遵循CC-BY-4.0许可协议，使用时需同时引用国际劳工组织原始来源及Electric Sheep Europe的再包装工作。

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可比性。ILOSTAT作为其核心统计数据库，汇集了200余个经济体的劳动力调查与行政记录，为就业、失业及劳动条件研究提供权威基准。该数据集由Electric Sheep Europe于2025年重新封装发布，覆盖14个欧洲国家、1987至2025年间44,142条观察记录，聚焦按性别、年龄及公共/私营部门分类的雇员人数。其核心研究问题在于揭示欧洲劳动力市场结构变迁，尤其是性别与年龄维度的就业分化，以及公共部门就业的演变趋势。该数据集为劳动经济学、社会政策评估及性别研究提供了精细化、长时序的跨国面板数据，有力支撑了就业政策的效果分析与国际比较研究。

当前挑战

该数据集所应对的领域问题在于劳动力市场统计的跨国可比性与细粒度分解。传统就业统计常受限于定义差异、覆盖范围不一及分类标准缺失，难以精确刻画性别、年龄与部门交织下的就业格局。构建过程中，数据源自各国劳动力调查与行政记录，其抽样设计、问卷设计及统计口径存在显著异质性，ILO虽依据国际劳工统计学家会议（ICLS）定义进行调和，但仍面临源数据缺失、时序断裂及指标不可靠等挑战。此外，部分国家数据仅覆盖特定年份，且公共/私营部门分类在各国间定义不一致，导致面板数据存在非随机缺失，对模型的稳健性与泛化能力构成考验。数据集中的观测状态标志与断点注释亦提示使用者需审慎处理数据质量与连续性。

常用场景

经典使用场景

在劳动经济学与人口统计学领域，针对公共部门与私营部门就业结构的跨国比较研究长期倚赖于标准化、可复用的面板数据。该数据集汇聚了14个欧洲国家自1987年至2025年间共计44,142条观测记录，以性别、年龄组及机构部门（公共/私营）为分类维度，提供了雇员人数的年度统计。其最经典的使用场景在于构建国别—年份—性别的多维面板矩阵，用以刻画欧洲劳动力市场中部门就业分布的动态演变轨迹，尤其适于开展性别维度的公共部门就业规模时序分析与跨国收敛性检验。

解决学术问题

该数据集有效回应了劳动统计研究中长期存在的跨国可比性难题。由于各国劳动力调查在年龄分组、部门界定与性别分类上存在方法学异质性，原始微观数据难以直接横向比较。ILO通过国际劳工统计学家会议（ICLS）定义对原始调查数据进行协调化处理，并提供来源标签与观测状态标识，使研究者能够在统一框架下探讨公共与私营部门雇员规模的性别差异、年龄结构效应以及部门间劳动力流动等议题。其意义在于为跨国劳动政策评估与性别平等就业研究提供了可溯源、可验证的高质量统计基础。

实际应用

在政策实践层面，该数据集为欧洲各国政府及国际组织监测公共部门就业规模、评估性别就业差距以及研判劳动力市场结构变迁提供了量化依据。典型应用包括：测算公共部门女性雇员占比的长期趋势，分析青年与成年劳动者在公共/私营部门间的分布差异，以及结合时间序列模型对部门就业进行短期预测。此外，该数据集亦可嵌入机器学习流水线，用于表格分类、回归与时间序列预测任务的基准测试，服务于学术研究与商业分析等多种实际场景。

数据集最近研究

最新研究方向

在全球劳动力市场结构性变革与公共部门就业规模持续调整的背景下，基于ILOSTAT权威微观调查数据的研究正朝着高粒度异质性分析与跨国产出收敛检验方向纵深推进。该数据集涵盖14个欧洲国家1987至2025年间按性别、年龄及公私部门三维交叉分类的雇员观测值，为揭示性别就业差距的年龄梯度演变、公共部门女性就业的“庇护效应”以及转型经济体国有部门收缩对青年与中老年劳动者差异化冲击提供了独特面板支撑。当前前沿研究聚焦于运用断点回归与合成控制法识别方法论修订引致的序列断裂，并结合时间序列预测模型评估公私部门就业结构的长期趋势，其成果对欧洲性别平等政策与劳动力市场韧性建设具有重要实证参考价值。