electricsheepeurope/europe-ilo-ees-tees-sex-mjh-nb-employees-by-sex-and-multiple-job-holding-thousand

--- 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 and multiple-job holding (thousands) | Europe (ILOSTAT)" --- # Employees by sex and multiple-job holding (thousands) | Europe (ILOSTAT) 🇪🇺 **10,004 observations** · **38 Europe countries** · **1983–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **10,004 observations** of `Employees` data across **38 Europe countries**, spanning **1983–2025**, 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_MJH_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_MJH_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 38 Europe countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `GBR` | 360 | 1983 | 2025 | | `FRA` | 357 | 1983 | 2024 | | `DNK` | 351 | 1983 | 2024 | | `DEU` | 351 | 1983 | 2024 | | `BEL` | 351 | 1983 | 2024 | | `IRL` | 351 | 1983 | 2024 | | `GRC` | 351 | 1987 | 2025 | | `ITA` | 351 | 1983 | 2024 | | `LUX` | 351 | 1983 | 2024 | | `PRT` | 342 | 1986 | 2025 | | `ESP` | 342 | 1986 | 2025 | | `NLD` | 339 | 1983 | 2024 | | `CHE` | 315 | 1991 | 2025 | | `CZE` | 288 | 1993 | 2024 | | `AUT` | 279 | 1995 | 2025 | | ... | _23 more countries_ | | | ## Indicators (sample) - `EES_TEES_SEX_MJH_NB` — Employees by sex and multiple-job holding (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_MJH_NB` | | `indicator.label` | `string` | Indicator name in English | `Employees by sex and multiple-job hol…` | | `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.) | `MJH_AGGREGATE_TOTAL` | | `classif1.label` | `string` | — | `Multiple job holders: 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) | `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-mjh-nb-employees-by-sex-and-multiple-job-holding-thousand") 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_MJH_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EES_TEES_SEX_MJH_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EES_TEES_SEX_MJH_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_ees_tees_sex_mjh_nb_employees_by_sex_and_multiple_job_holding_thousand_2025, title = {Employees by sex and multiple-job holding (thousands) | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EES_TEES_SEX_MJH_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-ees-tees-sex-mjh-nb-employees-by-sex-and-multiple-job-holding-thousand}} } ``` ## 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_MJH_NB_This dataset contains employee statistics for 38 European countries from 1983 to 2025, focusing on the number of employees (in thousands) disaggregated by sex and multiple-job holding status. It includes 10,004 observations with the core indicator EES_TEES_SEX_MJH_NB (Employees by sex and multiple-job holding). The data is sourced from the International Labour Organizations (ILO) ILOSTAT database, retrieved via API and harmonized using International Conference of Labour Statisticians (ICLS) definitions. The dataset provides detailed disaggregation dimensions including sex (total, male, female) and multiple-job holding classifications, along with country codes, data sources, observation years, values, and data status flags. The data is presented at annual frequency and is suitable for tabular classification, regression, and time-series forecasting tasks.

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接口直接提取指标EES_TEES_SEX_MJH_NB的原始数据，并依据ISO3国家代码筛选出38个欧洲国家。ILOSTAT对各国劳动力调查、家庭收入调查等微观数据进行统一协调，遵循国际劳工统计学家会议（ICLS）的定义标准，确保跨国可比性。Electric Sheep Europe对提取的数据进行重新打包，以Parquet格式发布，并保留源标签列以支持溯源，最终形成涵盖1983至2025年的10004条观测记录。

特点

数据集聚焦于欧洲地区雇员按性别与多重职业持有情况的分布，时间跨度逾四十年，覆盖38个国家，年度频率，具备高时空分辨率。其核心特点在于提供按性别（总计、男性、女性）的细分维度，并包含观测状态标志和注释列，以标识序列断裂、方法修订等数据质量信息。所有指标值以千人为单位，便于横向比较。数据集以表格形式组织，支持分类、回归及时间序列预测等多类任务，且为单语言英语，适合劳动经济学与性别研究领域的定量分析。

使用方法

研究者可通过HuggingFace的datasets库以一行代码加载数据集，并转换为Pandas数据框进行灵活操作。典型用法包括：按国家代码筛选特定国家的时间序列，例如提取德国的全部记录；针对单一指标绘制时间趋势图，观察多重职业持有的演变；或通过透视表将数据重塑为“年份×国家”矩阵，便于面板数据分析。此外，可结合观测状态列进行数据清洗，剔除不可靠或断裂的观测值。该数据集亦适用于构建预测模型，如利用历史数据预测未来雇员多重职业持有趋势。

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，其核心数据库ILOSTAT汇聚了200余个经济体的劳动力调查数据，为就业、失业及体面劳动等议题提供权威依据。在此背景下，欧洲地区多职业持有现象因经济结构转型与非标准就业扩张而日益凸显，该数据集由国际劳工组织统计部门主导，经Electric Sheep Europe于2025年重新封装发布，系统收录了1983至2025年间38个欧洲国家按性别分列的多职业雇员数据，共计10,004条观测值。作为ILOSTAT官方指标的精细化呈现，它为探究欧洲劳动力市场中性别差异与多重就业的动态关联提供了关键数据支撑，对劳动经济学与就业政策研究具有重要参考价值。

当前挑战

多职业持有统计的领域难题根植于其定义的多重性与数据采集的异质性。各国劳动力调查对“多重职业”的界定标准不一，部分国家仅涵盖主要职业之外的有酬工作，另一些则包含自雇或家庭帮工，致使跨国比较面临概念非等效的困境。构建过程中，原始微观数据的来源差异显著，不同调查的抽样设计、参考周期与覆盖范围不尽相同，尽管ILO已依据国际劳工统计学家会议决议进行协调，数据断点与方法修订仍频繁出现在时间序列中，观测状态标志所提示的序列断裂即为明证。此外，性别维度的细分在部分年份或国家存在缺失，可能削弱对女性多重就业趋势的精确刻画，而年度频率的局限亦难以捕捉季节性波动或短期政策效应。

常用场景

经典使用场景

在劳动经济学与就业统计研究领域，该数据集最经典的使用场景在于对欧洲多国多重职业持有现象的长期趋势进行量化刻画。研究者常以国别与性别为分组维度，构建1983年至2025年的跨国面板数据，通过时间序列分解或面板回归模型，考察多重职业持有率随经济周期、劳动力市场制度变迁的演变轨迹。其典型的分析范式包括将观测值转换为就业占比，进而比较男女两性在多重职业参与上的结构性差异，并借助可视化手段呈现各国间的趋同与分化格局。

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后续研究。部分学者将其与欧洲劳动力调查微观数据链接，深入探究多重职业持有者的职业组合特征与收入效应；亦有研究将其与宏观经济指标匹配，构建预警模型以识别就业质量下降的早期信号。在方法论层面，该数据常被用作时间序列预测与面板因果推断的基准数据集，推动缺失值插补、序列断点校正等统计技术的发展，并为劳动统计领域的可重复研究树立了数据共享典范。

数据集最近研究

最新研究方向

在全球劳动力市场弹性化与非标准就业形态持续扩张的宏观图景下，欧洲多职业持有现象日益成为劳动经济学与就业政策研究的焦点。该数据集依托ILOSTAT权威统计框架，以1983至2025年38个欧洲国家的年度观测为经度，以性别与多重职业持有状态的交叉分层为纬度，构筑起逾万条高粒度时序记录。当前前沿研究正沿三条脉络展开：其一，运用面板协整与因果推断技术，解析经济周期波动、数字化平台用工与多重职业参与率之间的动态关联；其二，借助性别异质性分解方法，揭示照护责任、职业隔离与女性多重就业行为之间的结构性张力；其三，将多重职业持有作为非标准就业的核心表征，嵌入欧洲劳动力市场二元化与社保覆盖缺口评估模型。该数据集亦为欧盟就业指导框架与ILO体面劳动议程的监测评估提供关键实证基底，对理解后疫情时代劳动供给碎片化及其福利效应具有重要学术价值与政策意涵。