electricsheepasia/asia-ilo-eip-xplf-sex-edu-geo-nb-potential-labour-force-by-sex-education-and-rural

--- 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, education and rural / urban areas (thousands) | Asia (ILOSTAT)" --- # Potential labour force by sex, education and rural / urban areas (thousands) | Asia (ILOSTAT) 🌏 **7,801 observations** · **26 Asia countries** · **1999–2025** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **7,801 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_EDU_GEO_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_EDU_GEO_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` | 930 | 1999 | 2024 | | `VNM` | 702 | 2010 | 2024 | | `PSE` | 633 | 2012 | 2022 | | `TUR` | 621 | 2000 | 2013 | | `THA` | 606 | 2010 | 2024 | | `LKA` | 460 | 2010 | 2024 | | `ARM` | 395 | 2007 | 2018 | | `BRN` | 394 | 2014 | 2024 | | `IDN` | 360 | 2015 | 2023 | | `JOR` | 358 | 2017 | 2024 | | `MNG` | 248 | 2019 | 2024 | | `GEO` | 232 | 2019 | 2024 | | `AFG` | 207 | 2014 | 2021 | | `MMR` | 202 | 2015 | 2020 | | `PHL` | 195 | 2017 | 2023 | | ... | _11 more countries_ | | | ## Indicators (sample) - `EIP_XPLF_SEX_EDU_GEO_NB` — Potential labour force by sex, education and rural / urban areas (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_EDU_GEO_NB` | | `indicator.label` | `string` | Indicator name in English | `Potential labour force by sex, educat…` | | `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.) | `EDU_AGGREGATE_TOTAL` | | `classif1.label` | `string` | — | `Education (Aggregate levels): Total` | | `classif2` | `string` | Second classification variable where applicable | `GEO_COV_NAT` | | `classif2.label` | `string` | — | `Area type: National` | | `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_classif` | `string` | — | `C3:2620` | | `note_classif.label` | `string` | — | `Nonstandard education level: Includin…` | | `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-edu-geo-nb-potential-labour-force-by-sex-education-and-rural") 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_EDU_GEO_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EIP_XPLF_SEX_EDU_GEO_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EIP_XPLF_SEX_EDU_GEO_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_eip_xplf_sex_edu_geo_nb_potential_labour_force_by_sex_education_and_rural_2025, title = {Potential labour force by sex, education and rural / urban areas (thousands) | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EIP_XPLF_SEX_EDU_GEO_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-eip-xplf-sex-edu-geo-nb-potential-labour-force-by-sex-education-and-rural}} } ``` ## 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_EDU_GEO_NB_This dataset contains potential labour force data for 26 Asian countries from 1999 to 2025, disaggregated by sex, education level, and rural/urban areas (in thousands). It includes 7,801 observations and covers one main indicator: EIP_XPLF_SEX_EDU_GEO_NB (Potential labour force by sex, education and rural/urban areas). The data is sourced from the International Labour Organization (ILO) ILOSTAT database, retrieved via API and filtered for Asian countries. The dataset features columns such as country code, country name, data source, indicator code, sex classification, education classification, rural/urban classification, observation year, observed value, observation status flags, and related notes. Data is published annually, with some entries marked as provisional or unreliable. It is suitable for tabular classification, regression, and time-series forecasting tasks, and is designed to provide a standardized, ML-ready data layer for Asian labour statistics.

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，通过调用其REST API接口，提取了名为EIP_XPLF_SEX_EDU_GEO_NB的指标数据。数据筛选聚焦于亚洲地区26个国家的观测值，经由Electric Sheep Asia团队精心重封装而成。构建过程中，ILOSTAT依据国际劳工统计学家会议（ICLS）的定义，对原始调查的微观数据进行了一致性整合与标准化处理，并在source.label字段中保留了数据来源的完整追溯信息，确保了数据的透明性与可复现性。

特点

数据集收录了7,801条观测记录，时间跨度从1999年至2025年，覆盖了亚洲26个国家。其核心指标关注的是按性别、教育程度以及城乡区域划分的潜在劳动力人口（以千人为单位），为研究劳动力未充分利用状况提供了精细化的分类维度。数据还包含了观测状态标记、系列中断注释等元信息，方便用户评估数据质量。整体而言，该数据集具有跨国家、长时序、多维度细分的特点，是研究亚洲劳动力市场结构变迁的宝贵资源。

使用方法

用户可通过Hugging Face的datasets库便捷地加载该数据集，仅需一行代码load_dataset即可获取。加载后可将数据转化为Pandas DataFrame格式，方便进行后续分析。使用者能轻松按国家代码（如ref_area为IDN）进行过滤，针对特定国家进行深入分析。同时，利用obs_value列和时间列，可以直观地绘制某个指标的时间序列图。此外，通过数据透视功能，能够快速构建以年份为行、国家为列的分析矩阵，便于开展跨国比较研究。

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ILOSTAT数据库发布，并由Electric Sheep Asia团队重新整理至HuggingFace平台，专注于亚洲地区潜在劳动力市场的精细刻画。核心研究问题在于通过性别、教育程度及城乡地域的交叉维度，揭示亚洲26个国家在1999至2025年间“其他劳动力利用不足指标”的时空演变规律。该数据集涵盖了7801条观测记录，为学术界和政策制定者提供了空前粒度的劳动力统计素材，极大地推动了关于非充分就业、潜在劳动力储备等边缘劳动形态的量化研究，特别是在区域发展经济学与国际劳动标准比较领域具有奠基性的影响力。

当前挑战

该数据集所解决的领域问题是传统失业率指标无法捕捉的劳动力利用不足现象，如因信心不足或条件限制而退出市场的潜在劳动力，其挑战在于如何通过ILOSTAT的协调定义将来自不同国家劳动力调查、家计调查等多源异构数据进行标准化整合。构建过程中的核心挑战则体现在三个层面：其一，跨国家数据源的质量参差不齐，部分观测值被标记为不可靠或临时的状态标志；其二，年度频率限制了高精度时序建模，而月度或季度子系列未被收录；其三，细粒度分类维度（如性别、教育等级）仅在特定指标下可用，导致多维联合分析时样本量骤降，增加了统计推断的局限性。

常用场景

经典使用场景

该数据集收录了1999至2025年间亚洲26个国家的潜在劳动力人口数据，按性别、教育程度和城乡区域进行细致分层。研究人员常将其用于时间序列分析与面板数据建模，以探究亚洲各国劳动力市场中的隐性失业与就业不足现象。经典的学术应用包括构建多元回归模型，量化教育水平对不同性别群体劳动参与率的差异化影响，以及分析城镇化进程中潜在劳动力规模的结构性变化。数据集中提供的分类维度与年度观测，为开展跨国比较和长期趋势研究提供了坚实的数据基础。

衍生相关工作

该数据集催生了一系列衍生性的研究工作，包括基于面板数据的分位数回归分析、运用机器学习方法预测潜在劳动力变化趋势的论文，以及聚焦特定国家如越南、土耳其的劳动力市场转型深度案例研究。部分学者将其与ILO的其他劳动力指标数据集（如失业率、工资水平）进行融合，构建综合性的劳动力市场健康状况指数。在学术界，该数据集也成为讨论劳动统计方法协调化与数据可复现性议题的典型范例。

数据集最近研究

最新研究方向

在亚太地区劳动力市场结构性转型与包容性发展议题持续升温的背景下，该数据集聚焦于“潜在劳动力”这一前沿概念，通过性别、教育与城乡地理的交叉维度，刻画了26个亚洲国家在1999年至2025年间劳动力未充分利用的复杂图景。其研究的创新性体现在对传统失业统计边界的突破——将那些虽有工作意愿但因各种障碍（如家庭责任、教育错配或基础设施限制）而处于劳动力市场边缘的人群纳入分析框架，从而为评估后疫情时代亚洲经济复苏的脆弱性、衡量女性劳动参与率提升的瓶颈，以及理解数字化与自动化浪潮下教育与就业之间的错配效应提供了关键的量化基础。该数据集已成为国际劳工组织（ILO）与区域发展机构合作探究非正规就业转型、城乡劳动力再配置以及可持续生计政策制定的重要依据，特别是在追踪“潜在劳动力”如何受极端气候事件或地缘经济波动影响而动态变化方面，展现出不可替代的学术价值与现实意义。