electricsheepeurope/europe-ilo-pop-xwap-sex-age-mts-nb-working-age-population-by-sex-age-and-marital-stat

--- license: cc-by-4.0 language: - en task_categories: - tabular-classification - tabular-regression - time-series-forecasting multilinguality: monolingual size_categories: - 100K<n<1M tags: - tabular - europe - ilostat - population - ilo - labour - employment pretty_name: "Working-age population by sex, age and marital status (thousands) | Europe (ILOSTAT)" --- # Working-age population by sex, age and marital status (thousands) | Europe (ILOSTAT) 🇪🇺 **227,322 observations** · **39 Europe countries** · **1983–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **227,322 observations** of `Population` data across **39 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=POP_XWAP_SEX_AGE_MTS_NB) - **Publisher:** International Labour Organization (ILO) - **License:** [cc-by-4.0](https://creativecommons.org/licenses/by/4.0/) - **Topic:** Population ## Methodology Data pulled directly from the ILOSTAT REST API at `https://rplumber.ilo.org/data/indicator?id=POP_XWAP_SEX_AGE_MTS_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 39 Europe countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `CHE` | 15,179 | 1991 | 2025 | | `CZE` | 12,024 | 1993 | 2024 | | `GBR` | 12,019 | 1983 | 2025 | | `MDA` | 10,553 | 2000 | 2025 | | `POL` | 10,211 | 1997 | 2025 | | `AUT` | 9,452 | 1995 | 2025 | | `MKD` | 8,821 | 2005 | 2025 | | `FRA` | 8,373 | 2005 | 2024 | | `ALB` | 7,567 | 2002 | 2024 | | `BIH` | 7,261 | 2001 | 2020 | | `IRL` | 6,966 | 1983 | 2023 | | `RUS` | 6,912 | 2010 | 2025 | | `ESP` | 6,892 | 1986 | 2025 | | `ITA` | 6,048 | 1983 | 2024 | | `LUX` | 5,898 | 1983 | 2020 | | ... | _24 more countries_ | | | ## Indicators (sample) - `POP_XWAP_SEX_AGE_MTS_NB` — Working-age population by sex, age and marital status (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 | `POP_XWAP_SEX_AGE_MTS_NB` | | `indicator.label` | `string` | Indicator name in English | `Working-age population by sex, age an…` | | `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 | `MTS_AGGREGATE_TOTAL` | | `classif2.label` | `string` | — | `Marital status (Aggregate): Total` | | `time` | `int64` | Observation year | `2024` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `2022.424` | | `obs_status` | `string` | Observation status flag (e.g. provisional, unreliable) | `U` | | `obs_status.label` | `string` | — | `Unreliable` | | `note_classif` | `string` | — | `C6:1688` | | `note_classif.label` | `string` | — | `Nonstandard age group: Excluding ages…` | | `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-pop-xwap-sex-age-mts-nb-working-age-population-by-sex-age-and-marital-stat") 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"] == "POP_XWAP_SEX_AGE_MTS_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="POP_XWAP_SEX_AGE_MTS_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "POP_XWAP_SEX_AGE_MTS_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_pop_xwap_sex_age_mts_nb_working_age_population_by_sex_age_and_marital_stat_2025, title = {Working-age population by sex, age and marital status (thousands) | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=POP_XWAP_SEX_AGE_MTS_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-pop-xwap-sex-age-mts-nb-working-age-population-by-sex-age-and-marital-stat}} } ``` ## 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 2026-05-25 via the Electric Sheep pipeline. Source URL: https://www.ilo.org/shinyapps/bulkexplorer/?id=POP_XWAP_SEX_AGE_MTS_NB_This dataset contains 227,322 observations of working-age population data across 39 Europe countries, spanning 1983–2025, covering 1 distinct indicator (POP_XWAP_SEX_AGE_MTS_NB). The data is disaggregated by sex, age, and marital status (in thousands). It is sourced from the International Labour Organization (ILO) ILOSTAT database, pulled via the REST API and filtered to European ISO3 country codes. The dataset includes detailed columns such as country codes, indicator labels, sex classification, time, observed values, and source notes, with data quality caveats and usage examples provided. It is suitable for tabular classification, regression, and time-series forecasting tasks.

搜集汇总

数据集介绍

构建方式

该数据集由Electric Sheep Europe团队从国际劳工组织（ILO）的中央统计数据库ILOSTAT中提取并重新封装而成。原始数据通过ILOSTAT的REST API接口获取，以劳动年龄人口按性别、年龄和婚姻状况分类的指标（POP_XWAP_SEX_AGE_MTS_NB）为核心，并依据ISO3国家代码筛选出39个欧洲国家。ILOSTAT依据国际劳工统计学家会议（ICLS）的定义对各国劳动力调查等微观数据进行协调与标准化，数据来源在source.label列中标注以保证可追溯性。最终以Parquet格式存储，包含227,322条观测记录，时间跨度为1983年至2025年，遵循CC-BY-4.0许可协议。

特点

数据集涵盖39个欧洲国家，时间跨度逾四十年，包含227,322条年度观测值，具有显著的时空覆盖广度。数据结构化程度高，包含国家代码、数据来源、指标代码、性别、年龄分类、婚姻状况、年份及观测值等列，并附有详细的标签与注释列以标识数据质量、非标准年龄组及序列断裂等问题。其多维度分类特征（性别、年龄、婚姻状况）使其适用于表格分类、回归及时间序列预测等多类任务，且所有数据均来自权威的ILOSTAT，经过国际标准协调，兼具可靠性与可复用性。

使用方法

用户可通过Hugging Face的datasets库以一行代码加载该数据集，并转换为Pandas数据框进行后续分析。典型操作包括按国家代码筛选特定国家的数据、选取单一指标并按时间排序以绘制时间序列图、以及利用透视表生成国家与年份的矩阵以便比较。此外，数据集自带的分类列（如性别、年龄组、婚姻状况）支持多维度的分组聚合与统计建模，适用于人口结构变迁、劳动力供给趋势等研究，亦可作为机器学习任务中的特征工程基础。

背景与挑战

背景概述

国际劳工组织（ILO）长期致力于全球劳动力市场统计数据的标准化与传播，其ILOSTAT数据库汇集了来自各国劳动力调查和行政记录的就业、失业及人口指标。在此背景下，欧洲地区适龄劳动人口按性别、年龄和婚姻状况分类的统计数据对于理解区域劳动力供给结构、制定社会保障政策以及分析人口趋势具有关键意义。该数据集由Electric Sheep Europe于2025年从ILOSTAT REST API提取并重新打包发布，覆盖39个欧洲国家、1983至2025年间共计227,322条观测记录，为劳动经济学与人口学研究提供了统一、可复用的数据基础，其影响力在于降低了跨国比较研究的门槛，推动了基于机器学习的时间序列预测与分类建模。

当前挑战

该数据集所应对的领域问题在于如何将来源各异、口径不一的国家劳动力调查数据协调为可比的跨国面板序列，同时保留婚姻状况等细分维度，以支持适龄劳动人口结构的精细化分析。构建过程中面临多重挑战：原始数据来自不同国家的抽样调查，年龄段分类与婚姻状况定义存在国别差异，需依据国际劳工统计学家会议（ICLS）标准进行事后协调；部分国家时间序列存在断点或方法修订，表现为观测状态标记为‘不可靠’或‘系列中断’；数据中还包含非标准年龄分组与临时性估计值，需通过源标签和注释字段进行追溯与甄别。此外，不同国家数据起始年份差异显著，导致面板不平衡，对时间序列建模的稳健性提出更高要求。

常用场景

经典使用场景

在劳动经济学与人口统计学的交叉研究中，该数据集凭借其长时段、多国别、细粒度的结构化字段，成为剖析欧洲劳动年龄人口婚姻状况与人口结构变迁的经典素材。研究者常借助时间序列分析与面板数据模型，探究性别、年龄与婚姻状态在劳动供给弹性中的调节效应；同时，该数据集亦被广泛用于构建欧洲各国劳动年龄人口的分层预测模型，以评估老龄化背景下婚姻结构变动对劳动力储备的潜在冲击。

实际应用

在政策制定与商业决策领域，该数据集为欧洲各国社会保障机构预测养老金支出与医疗资源配置提供了关键的人口结构基线。跨国企业可依据不同年龄段与婚姻状况的劳动人口分布，优化区域招聘策略与弹性用工方案；劳动力市场分析机构则利用其时间序列特征，监测婚姻状态变迁对行业用工短缺的传导效应。此外，该数据集亦有助于非政府组织精准定位脆弱劳动群体，设计面向特定婚姻状况与年龄段的就业扶持干预。

衍生相关工作

以该数据集为基石，后续研究衍生出若干经典工作：一是欧洲跨国劳动参与率分解研究，将婚姻状况纳入布林德-奥克斯分解框架，量化婚姻结构变动对性别劳动参与差距的贡献度；二是基于该数据训练的时间序列预测模型，用于中期劳动年龄人口规模的情景推演；三是将本数据集与欧洲社会调查微观数据融合的匹配研究，检验宏观婚姻结构与个体劳动决策的一致性。这些工作共同拓展了ILOSTAT数据在计算社会科学中的应用边界。