electricsheepasia/asia-ilo-eip-teip-sex-edu-geo-nb-persons-outside-the-labour-force-by-sex-education

--- 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 - asia - ilostat - other-measures-of-labour-underutilization - ilo - labour - employment pretty_name: "Persons outside the labour force by sex, education and rural / urban areas (thousands) | Asia (ILOSTAT)" --- # Persons outside the labour force by sex, education and rural / urban areas (thousands) | Asia (ILOSTAT) 🌏 **41,104 observations** · **30 Asia countries** · **1970–2025** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **41,104 observations** of `Other measures of labour underutilization` data across **30 Asia countries**, spanning **1970–2025**, 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_TEIP_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_TEIP_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 30 Asia countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `PSE` | 3,933 | 2000 | 2022 | | `IDN` | 3,718 | 1990 | 2023 | | `CYP` | 3,154 | 1999 | 2024 | | `KHM` | 2,445 | 1996 | 2023 | | `MNG` | 2,286 | 2003 | 2024 | | `VNM` | 2,192 | 2010 | 2024 | | `PAK` | 2,163 | 2005 | 2025 | | `THA` | 2,030 | 2007 | 2024 | | `GEO` | 2,013 | 2009 | 2024 | | `ARM` | 2,006 | 2001 | 2023 | | `LKA` | 1,837 | 2010 | 2024 | | `IND` | 1,827 | 1994 | 2025 | | `KOR` | 1,772 | 2000 | 2025 | | `BRN` | 1,260 | 2014 | 2024 | | `JOR` | 1,003 | 2017 | 2024 | | ... | _15 more countries_ | | | ## Indicators (sample) - `EIP_TEIP_SEX_EDU_GEO_NB` — Persons outside the 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_TEIP_SEX_EDU_GEO_NB` | | `indicator.label` | `string` | Indicator name in English | `Persons outside the labour force by s…` | | `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) | `8230.246` | | `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`** (4 unique values): `SEX_T`, `SEX_M`, `SEX_F`, `SEX_O` ## 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-teip-sex-edu-geo-nb-persons-outside-the-labour-force-by-sex-education") 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_TEIP_SEX_EDU_GEO_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EIP_TEIP_SEX_EDU_GEO_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EIP_TEIP_SEX_EDU_GEO_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_eip_teip_sex_edu_geo_nb_persons_outside_the_labour_force_by_sex_education_2025, title = {Persons outside the 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_TEIP_SEX_EDU_GEO_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-eip-teip-sex-edu-geo-nb-persons-outside-the-labour-force-by-sex-education}} } ``` ## 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_TEIP_SEX_EDU_GEO_NB_This dataset contains 41,104 observations of Persons outside the labour force by sex, education and rural/urban areas (thousands) across 30 Asia countries from 1970 to 2025, with the core indicator EIP_TEIP_SEX_EDU_GEO_NB. Data is sourced from the International Labour Organization (ILO) ILOSTAT database, collected through labour force surveys, household income surveys, establishment surveys, and administrative records, and harmonized using International Conference of Labour Statisticians (ICLS) definitions. The dataset is provided in tabular format, including columns for country codes, year, sex disaggregation, education level, rural/urban area, observed values, and data quality flags, suitable for tasks such as tabular classification, regression, and time-series forecasting. It is released under the CC-BY-4.0 license and repackaged by Electric Sheep Asia for machine learning readiness.

搜集汇总

数据集介绍

构建方式

在亚太地区劳动市场研究的广阔图景中，对非劳动力人口的细致刻画始终是理解区域就业结构的关键拼图。该数据集由Electric Sheep Asia团队精心重制，其构建路径清晰且权威：数据通过ILOSTAT官方REST API直接获取，筛选出亚太地区30个国家的ISO3代码后，以年频次进行整合。原始数据基于ILO统一的国际劳工统计学家会议定义，对各国劳动力调查、家庭收支调查等微观数据进行协调与标准化，每一笔观测均保留来源标签以确保全程可追溯。最终形成包含41,104条记录、横跨1970至2025年的结构化表格，字段涵盖性别、教育程度、城乡地域等核心维度的精细拆解。

特点

该数据集的核心特色在于其多维度的精细颗粒度与区域聚焦深度。它不仅提供了‘非劳动力人口’这一总量指标，更通过性别、教育水平及城乡地域的三重分类维度，揭示出亚洲劳动参与率背后复杂的社会结构性差异。数据收录了1970至2025年间30个亚太国家的时序信息，时间跨度之长与地理覆盖之广，为跨国比较与长周期趋势分析提供了坚实底座。尤为重要的是，每一条记录均附有数据来源标签与观测状态标记（如‘不可靠’），使得研究者能够在宏观统计的严谨性与微观数据的真实性之间获取关键的判断依据。

使用方法

该数据集的接入极为便捷，完全贴合现代数据科学工作流。用户仅需通过HuggingFace的datasets库调用load_dataset函数，即可一键将数据载入内存，并以Pandas DataFrame格式进行后续探索。在具体操作中，研究者可按国别ISO代码进行筛选以聚焦单一经济体，亦可通过指示器代码对特定指标进行时序分析。对于需要展示面板结构的场景，可利用透视表功能将数据重塑为国家×年份的矩阵形式，从而灵活适配从简单描述统计到复杂面板回归的多样化分析需求。

背景与挑战

背景概述

在国际劳工组织（ILO）长期致力于推动体面劳动与劳动力统计标准化的背景下，该数据集由ILO统计司通过ILOSTAT平台构建，并由Electric Sheep Asia于2025年重新打包发布，聚焦亚洲30个国家1970至2025年间非劳动力人口的数据。其核心研究问题在于揭示性别、教育水平与城乡地域对劳动力市场边缘化群体的结构性影响，为分析劳动力未充分利用现象提供精细化的分类视角。作为全球劳动力统计权威来源的二次整合产物，该数据集凭借逾四万条观测记录与标准化架构，显著增强了亚洲区域劳动经济学实证研究的数据可及性与可比性，对人力资源开发与社会政策评估具有重要参考价值。

当前挑战

该数据集所应对的领域挑战在于，传统劳动力统计多集中于就业与失业人群，而对因家庭责任、求学或丧失信心等原因退出的非劳动力群体关注不足，难以全面刻画劳动力未充分利用的复杂现实。构建过程中，ILO需协调30个国家迥异的调查设计、教育分类体系与城乡定义标准，并处理数据源切换、方法论修订及观测值可靠性标注等技术难题。同时，国家间数据可得性不均（如巴勒斯坦与印度尼西亚的观测数量远超其他国家）以及某些年份数据缺失，进一步增加了整合与比较分析的难度，要求研究者审慎处理数据质量提示并理解分类变量的语义差异。

常用场景

经典使用场景

在劳动经济学与社会分层研究中，该数据集经典地用于分析亚洲地区劳动力市场边缘群体的结构性特征。通过融合性别、教育程度与城乡地理三重维度，研究者能够精准刻画非经济活动人口中，因教育瓶颈、地理隔离或性别歧视而长期游离于劳动力市场之外的群体规模与演变趋势。其1970至2025年间覆盖30个国家的海量观测，为跨时、跨国的比较研究提供了坚实的数据基础，特别适用于评估经济发展、教育扩张与城镇化进程对劳动力参与率的异质性影响。

解决学术问题

该数据集直面传统劳动力统计中被忽视的学术盲区——即因丧失信心、家庭责任或教育限制而退出市场、未被计入失业率的人群。它解决了如何将‘潜在劳动力’与‘非经济活动’从概念上剥离并量化测量的难题，推动学术研究从单纯关注就业率转向更全面的劳动力利用不足指标。这一数据资源使学者得以检验人力资本理论、性别角色分工理论在亚洲语境下的适用性，并揭示了教育与地域不平等如何代际传递、固化社会排斥，从而为政策干预提供了精准的靶点。

衍生相关工作

基于该数据集，衍生出多个方向的重要研究工作。其一，机器学习和时间序列预测模型被用于构建亚洲国家非劳动力人口短期与长期变动的前瞻性模拟，探索性别与教育维度的动态收敛性。其二，学者将其与国际移民、GDP增长等外部面板数据融合，利用因果推断方法检验劳动参与率下降的经济驱动因素。此外，部分研究还将其与家庭微观调查数据进行层次匹配，构建了多尺度社会分层模型，用以解析从宏观结构性约束到个体决策的传导链条，显著推动了计算社会学与劳动经济学的交叉融合。