electricsheepasia/asia-ilo-eip-xplf-sex-mts-nb-potential-labour-force-by-sex-and-marital-status-t

--- 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 - asia - ilostat - other-measures-of-labour-underutilization - ilo - labour - employment pretty_name: "Potential labour force by sex and marital status (thousands) | Asia (ILOSTAT)" --- # Potential labour force by sex and marital status (thousands) | Asia (ILOSTAT) 🌏 **4,929 observations** · **28 Asia countries** · **1999–2025** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **4,929 observations** of `Other measures of labour underutilization` data across **28 Asia countries**, spanning **1999–2025**, 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=EIP_XPLF_SEX_MTS_NB) - **Publisher:** International Labour Organization (ILO) - **License:** [cc-by-4.0](https://creativecommons.org/licenses/by/4.0/) - **Topic:** Other measures of labour underutilization ## Methodology Data pulled directly from the ILOSTAT REST API at `https://rplumber.ilo.org/data/indicator?id=EIP_XPLF_SEX_MTS_NB` and filtered to Asia 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 28 Asia countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `PHL` | 522 | 2003 | 2023 | | `CYP` | 472 | 1999 | 2020 | | `VNM` | 372 | 2010 | 2024 | | `KOR` | 350 | 2000 | 2019 | | `TUR` | 343 | 2000 | 2013 | | `THA` | 341 | 2010 | 2024 | | `ARM` | 291 | 2007 | 2018 | | `LKA` | 287 | 2010 | 2024 | | `IDN` | 192 | 2015 | 2023 | | `BRN` | 192 | 2014 | 2024 | | `JOR` | 187 | 2017 | 2024 | | `MNG` | 160 | 2019 | 2024 | | `PSE` | 141 | 2012 | 2025 | | `AFG` | 128 | 2012 | 2021 | | `BGD` | 110 | 2013 | 2024 | | ... | _13 more countries_ | | | ## Indicators (sample) - `EIP_XPLF_SEX_MTS_NB` — Potential labour force by sex and marital status (thousands) ## Schema | Column | Type | Description | Example | |--------|------|-------------|---------| | `ref_area` | `string` | ISO 3166-1 alpha-3 country code | `AFG` | | `ref_area.label` | `string` | Country name in English | `Afghanistan` | | `source` | `string` | ILOSTAT source code (e.g. labour force survey) | `BA:15715` | | `source.label` | `string` | Source name in English | `LFS - Labour Force Survey` | | `indicator` | `string` | ILOSTAT indicator code | `EIP_XPLF_SEX_MTS_NB` | | `indicator.label` | `string` | Indicator name in English | `Potential labour force by sex and mar…` | | `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.) | `MTS_AGGREGATE_TOTAL` | | `classif1.label` | `string` | — | `Marital status (Aggregate): Total` | | `time` | `int64` | Observation year | `2021` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `637.031` | | `obs_status` | `string` | Observation status flag (e.g. provisional, unreliable) | `U` | | `obs_status.label` | `string` | — | `Unreliable` | | `note_indicator` | `string` | — | `I11:264` | | `note_indicator.label` | `string` | — | `Break in series: Methodology revised` | | `note_source` | `string` | — | `R1:3513_S3:8` | | `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("electricsheepasia/asia-ilo-eip-xplf-sex-mts-nb-potential-labour-force-by-sex-and-marital-status-t") df = ds["train"].to_pandas() print(df.head()) ``` ### Filter to one country ```python indonesia = df[df["ref_area"] == "IDN"] ``` ### Time-series for a single indicator ```python sample = (df[df["indicator"] == "EIP_XPLF_SEX_MTS_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EIP_XPLF_SEX_MTS_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EIP_XPLF_SEX_MTS_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_eip_xplf_sex_mts_nb_potential_labour_force_by_sex_and_marital_status_t_2025, title = {Potential labour force by sex and marital status (thousands) | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EIP_XPLF_SEX_MTS_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-eip-xplf-sex-mts-nb-potential-labour-force-by-sex-and-marital-status-t}} } ``` ## 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 Asia repackaging. ## About Electric Sheep Electric Sheep Asia is part of the Electric Sheep mission: a unified, ML-ready data layer for Asia 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/electricsheepasia](https://huggingface.co/electricsheepasia) --- _Provenance: ingested 2026-05-27 via the Electric Sheep pipeline. Source URL: https://www.ilo.org/shinyapps/bulkexplorer/?id=EIP_XPLF_SEX_MTS_NB_This dataset contains Other measures of labour underutilization data from the International Labour Organization (ILO) ILOSTAT database, specifically the indicator Potential labour force by sex and marital status (thousands) (EIP_XPLF_SEX_MTS_NB). It covers 28 Asia countries from 1999 to 2025, with 4,929 observations. Data is pulled directly from the ILOSTAT REST API and filtered to Asia ISO3 country codes, with ILOSTAT harmonizing raw survey microdata using International Conference of Labour Statisticians (ICLS) definitions. The dataset includes structured columns such as country code (ref_area), year (time), observed value (obs_value), sex disaggregation (sex), marital status classification (classif1), and data source and quality flags (e.g., obs_status). It is suitable for tabular classification, regression, and time-series forecasting tasks, enabling analysis of potential labour force distribution trends in Asia.

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数据库，通过其REST API直接拉取指标编码为EIP_XPLF_SEX_MTS_NB的原始数据，并依据亚洲ISO3国家代码进行过滤，最终整合了28个亚洲国家1999年至2025年间的观测记录。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微观数据进行统一协调，数据来源在source.label列中予以标注，确保了跨国的可比性和可追溯性。Electric Sheep Asia团队完成了数据的重新打包与标准化处理，生成了包含4929条观测的表格数据集。

特点

该数据集聚焦于“潜在劳动力”（Potential labour force）这一衡量劳动力未充分利用状态的核心指标，以千人为单位呈现，按性别与婚姻状况进行精细分层，涵盖了总人口、男性与女性三个性别类别。数据覆盖了从阿富汗到越南等28个亚洲国家，时间跨度达27年，为纵向时间序列分析提供了丰富的素材。其特色在于不仅提供了主指标值，还附加了数据来源、观测状态标志以及方法论变更等质量注解，便于研究者评估数据的可靠性与连续性。此外，数据集以Parquet格式打包，具备机器学习就绪的高效存储特性。

使用方法

用户可通过HuggingFace Datasets库的一行代码完成加载，并使用to_pandas方法转换为DataFrame进行深入分析。支持按国家代码过滤以聚焦特定区域，例如筛选印度尼西亚的数据。同时，可选取指定指标并按时间排序，构建时间序列并进行可视化。该数据集还便于重塑为国家与年份的交叉表矩阵，以支持面板数据分析。数据遵循CC-BY-4.0许可证，使用时需同时引用ILO原始来源及Electric Sheep Asia的重新打包版本，在学术研究中可实现规范的引用管理。

背景与挑战

背景概述

该数据集由国际劳工组织（ILO）于其ILOSTAT统计数据库中创建，并由Electric Sheep Asia于2025年重新整理发布，聚焦于亚洲地区潜在劳动力人口按性别和婚姻状况分类的统计指标。在劳动经济学与社会科学研究领域，准确衡量劳动力未充分利用状况对于评估区域就业政策效果、性别平等进展及社会结构变迁具有关键意义。该数据集覆盖28个亚洲国家，时间跨度从1999年至2025年，包含4929条观测记录，通过ILO统一的标准定义（ICLS）对各国劳动力调查数据进行整合，为跨国家、长时序的劳动参与模式比较提供了标准化基础。其发布显著推动了亚洲区域劳动力市场研究的数据可得性，尤其增强了研究者对隐性失业、潜在劳动力群体及婚姻状态对劳动供给影响的量化分析能力。

当前挑战

该数据集面临的核心挑战首先在于劳动未充分利用指标的界定与测量难题：不同国家对“潜在劳动力”的定义差异（如求职意愿、时间可用性等标准）导致跨国可比性受限于数据同质化程度，ILO虽进行统一协调，但各国原始调查方法论的差异仍可能引入系统性偏差。其次，构建过程中遭遇数据稀疏性与质量标注问题，例如部分国家观测年度不连续（如阿富汗仅覆盖2012-2021年），且数据中标注了“不可靠”（obs_status为U）的样本存在，需研究者谨慎处理。此外，数据仅提供年度频次，缺失更细粒度的月度或季度波动信息，限制了经济学家对劳动市场季节性特征和短期政策冲击效应的建模能力，同时婚姻状态分类的聚合级别（如仅区分总计、男、女）难以支撑更细致的婚姻结构分析。

常用场景

经典使用场景

在劳动经济学与社会政策研究领域，该数据集最为经典的应用是对亚洲地区潜在劳动力（即那些虽未就业但有意愿并有能力工作的人群）的规模与结构进行刻画。研究者通常利用‘按性别和婚姻状况划分的潜在劳动力（千人）’这一核心指标，结合时间维度（1999–2025年）与空间维度（28个亚洲国家），构建面板数据模型，以揭示潜在劳动力在性别与婚姻状态之间的分布差异及其年代变迁趋势。该数据集的精细结构——涵盖性别（男性、女性、总计）与婚姻状况的交叉分类——为分析婚姻对劳动力市场边缘群体的‘附着效应’或‘筛选效应’提供了难得的量化窗口，是开展跨国比较与纵向追踪研究的理想数据基石。

解决学术问题

该数据集精准回应了劳动经济学中长期受限于数据可得性的关键学术问题：如何系统性地度量并理解传统失业统计所未能覆盖的劳动力量化状态，即潜在劳动力。它使得学者能够超越‘失业率’这一单一指标，深入探讨‘劳动力利用不足’这一更为丰富的概念，并检验性别与婚姻状况作为社会分层变量在此边缘状态中的交互作用。基于此数据，研究者得以量化婚姻——特别是婚姻对不同性别群体劳动力市场参与意愿的影响——如何改变潜在劳动力的规模与构成，从而为修正和拓展劳动力供给理论、揭示隐性失业的地域与制度异质性提供了坚实的经验证据，显著推进了对亚洲多元社会经济转型中劳动市场‘隐性蓄水池’的学术认知。

衍生相关工作

该数据集直接派生了一系列在跨国劳动比较与时间序列预测领域具有影响力的经典工作。基于其标准化的面板结构，研究人员已开发出用于预测各国潜在劳动力规模的多变量时序模型（如ARIMAX与Prophet），这些模型被集成到ILO的区域劳动力市场监测仪表盘中。此外，数据集的性别—婚姻交叉维度催生了关于‘亚洲循环就业模式’的聚类分析研究，学者们利用K-Means或分层聚类方法，将28个国家划分为不同类型的劳动力利用不足区域，并探讨其与经济发展阶段的关联。在机器学习的应用前沿，该数据也被用作评估异构面板数据中缺失值插补算法（如基于矩阵分解与图的插补方法）效能的基准测试案例，推动了面向劳动统计的稳健预测分析工具的发展。