electricsheepasia/asia-ilo-eip-xplf-sex-geo-mts-nb-potential-labour-force-by-sex-rural-urban-area-and

--- 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, rural / urban area and marital status (thousands) | Asia (ILOSTAT)" --- # Potential labour force by sex, rural / urban area and marital status (thousands) | Asia (ILOSTAT) 🌏 **5,331 observations** · **26 Asia countries** · **1999–2025** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **5,331 observations** of `Other measures of labour underutilization` data across **26 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_GEO_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_GEO_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 26 Asia countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `CYP` | 574 | 1999 | 2020 | | `PHL` | 535 | 2007 | 2023 | | `VNM` | 428 | 2010 | 2024 | | `THA` | 403 | 2010 | 2024 | | `PSE` | 396 | 2012 | 2022 | | `TUR` | 396 | 2000 | 2013 | | `LKA` | 350 | 2010 | 2024 | | `ARM` | 317 | 2007 | 2018 | | `BRN` | 243 | 2014 | 2024 | | `IDN` | 216 | 2015 | 2023 | | `JOR` | 216 | 2017 | 2024 | | `MNG` | 162 | 2019 | 2024 | | `AFG` | 135 | 2014 | 2021 | | `MMR` | 134 | 2015 | 2020 | | `TLS` | 132 | 2010 | 2022 | | ... | _11 more countries_ | | | ## Indicators (sample) - `EIP_XPLF_SEX_GEO_MTS_NB` — Potential labour force by sex, rural / urban area 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_GEO_MTS_NB` | | `indicator.label` | `string` | Indicator name in English | `Potential labour force by sex, rural …` | | `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.) | `GEO_COV_NAT` | | `classif1.label` | `string` | — | `Area type: National` | | `classif2` | `string` | Second classification variable where applicable | `MTS_AGGREGATE_TOTAL` | | `classif2.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) | `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_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-geo-mts-nb-potential-labour-force-by-sex-rural-urban-area-and") 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_GEO_MTS_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EIP_XPLF_SEX_GEO_MTS_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EIP_XPLF_SEX_GEO_MTS_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_eip_xplf_sex_geo_mts_nb_potential_labour_force_by_sex_rural_urban_area_and_2025, title = {Potential labour force by sex, rural / urban area and marital status (thousands) | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EIP_XPLF_SEX_GEO_MTS_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-eip-xplf-sex-geo-mts-nb-potential-labour-force-by-sex-rural-urban-area-and}} } ``` ## 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_GEO_MTS_NB_This dataset contains potential labour force data by sex, rural/urban area, and marital status (in thousands) for Asia, sourced from the International Labour Organization (ILO) ILOSTAT database. It covers 26 Asian countries from 1999 to 2025, with 5,331 observations and one distinct indicator: EIP_XPLF_SEX_GEO_MTS_NB (Potential labour force by sex, rural/urban area and marital status). The data is provided at an annual frequency and includes columns such as country code, country name, data source, indicator code, sex disaggregation (total, male, female), classification variables (e.g., area type, marital status), observation year, observed value, and status flags. The data is harmonized using International Conference of Labour Statisticians (ICLS) definitions, with quality caveats like the use of the best source for each country-year and non-null disaggregation columns only when published. It is suitable for tabular classification, regression, and time-series forecasting tasks, designed as an ML-ready data layer for labour market research 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核心统计数据库，通过其REST API接口直接获取原始指标数据，并依据亚洲ISO3国家代码进行地域筛选和过滤。数据经过ILO基于国际劳工统计学家会议（ICLS）定义的标准方法进行统一化处理，确保不同来源的微观调查数据具备可比性。最终由Electric Sheep Asia团队进行重新封装，整合为5,331条观测记录，覆盖26个亚洲国家，时间跨度从1999年至2025年。

使用方法

使用者可通过HuggingFace的`datasets`库轻松加载该数据集，一条命令即可将数据转化为Pandas DataFrame进行分析。典型应用包括：筛选特定国家的子集进行纵向趋势分析，利用`time`和`obs_value`列绘制时间序列曲线；或通过数据透视表构建国家×年份的观测值矩阵，以进行跨国比较研究。数据集的分列设计（如`sex`、`classif1`、`classif2`）支持灵活的维度切片，适合用于表格分类、回归以及时间序列预测等机器学习任务。

背景与挑战

背景概述

该数据集由国际劳工组织（ILO）于2025年通过其核心统计数据库ILOSTAT发布，并由Electric Sheep Asia团队重新封装至HuggingFace平台。其核心研究问题聚焦于亚洲地区潜在劳动力人口的结构特征，涵盖性别、城乡区域及婚姻状况等多维度的交叉分析。数据覆盖1999至2025年间26个亚洲国家，共计5331条观测记录，为劳动经济学、人口社会学及可持续发展目标（SDG）相关研究提供了珍贵的纵向比较素材。作为ILO在劳动力利用不足度量领域的关键数据产品，该数据集对理解亚洲新兴经济体的隐性失业、非正规就业及劳动力市场弹性具有重要参考价值，尤其在分析女性劳动参与率、城乡就业差异等议题上填补了精细化的数据空白。

当前挑战

该数据集面临的挑战首先源于其所解决的领域问题：劳动力利用不足的度量本身即存在概念歧义，潜在劳动力（如求职倦怠者、临时失业边缘人群）的界定高度依赖调查问卷设计与国际劳动统计学家会议（ICLS）标准的本土化适配，导致跨国可比性常受质疑。在构建过程中，数据整合面临多重瓶颈：不同国家劳动力调查的采样框架、时间粒度与分类体系（如婚姻状况的本地定义）存在显著差异，ILO虽通过算法选择“最佳来源”进行协调，但部分年份因方法论修订（如指标注释中的序列断裂标记）引入时序不连续性。此外，乡村区域数据稀疏性显著——26国中仅部分国家提供了城乡细分统计，且亚组样本量在低生育率国家（如塞浦路斯）与数据中断国（如阿富汗）之间波动剧烈，对机器学习的时序预测任务构成噪声干扰。

常用场景

经典使用场景

该数据集涵盖了1999年至2025年间亚洲26个国家的潜在劳动力数据，按性别、城乡区域和婚姻状况进行细致分层，包含5331条观测记录。经典使用场景聚焦于时间序列分析与面板数据建模，研究人员可借助该数据集追踪亚洲各国潜在劳动力规模的动态演变，通过性别与城乡维度的交叉对比，揭示劳动力市场中的结构性差异。数据集的年度频率与多国覆盖特性使其特别适用于构建预测模型，例如利用自回归移动平均（ARIMA）或长短期记忆网络（LSTM）对潜在劳动力趋势进行前瞻性估计，为劳动经济学中“劳动力闲置”问题的量化分析提供基础支撑。

解决学术问题

数据集主要服务于劳动经济学与人口统计学领域的核心学术议题，特别是国际劳工组织（ILO）提出的“劳动力利用不足”测度框架。它解决了传统失业率指标难以捕捉的隐性劳动力闲置问题，如因家庭责任或社会观念放弃求职的潜在劳动力。通过剖析不同性别与婚姻状况群体的潜在劳动供给差异，学术研究得以验证性别歧视、城乡二元结构等社会经济因素如何影响劳动力参与决策。该数据集的引入推动了关于“就业不足”与“潜在劳动力”的实证研究，深化了对可持续发展目标（SDG）中体面劳动指标的跨文化比较与政策评估。

实际应用

在实际应用层面，该数据集为政府机构、国际组织及政策智库提供了制定精准就业战略的数据基石。例如，亚洲各国劳动部门可依据分性别、分区域的潜在劳动力规模，设计差异化的职业培训方案，引导女性或农村闲置劳动力回归劳动力市场。企业人力资源规划亦可借助该数据集评估区域劳动力储备，为投资选址与用工配置提供参考。在灾害应急与疫后复苏场景中，数据集帮助快速评估劳动力冲击程度，如新冠疫情后亚洲农民工返乡潮对潜在劳动力池的影响，从而优化社会保障与就业激励政策的资源投放。

数据集最近研究

最新研究方向

该数据集聚焦于亚洲地区潜在劳动力在性别、城乡与婚姻状况维度的精细分层，为劳动经济学中劳动力未充分利用的测量提供了关键数据支撑。当前前沿研究方向集中于利用时间序列预测与分类模型，分析新冠疫情后亚洲劳动力市场的结构性变化，特别关注女性与未婚群体在城乡迁移与就业决策中的动态特征。该数据集与ILO推动的体面劳动议程紧密相连，其跨26国、覆盖近30年的观测数据，为政策制定者与学术界验证劳动力市场制度、性别平等政策及城乡差距演化提供了实证基础。