electricsheepasia/asia-ilo-eip-teip-sex-edu-mts-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: - 100K<n<1M tags: - tabular - asia - ilostat - other-measures-of-labour-underutilization - ilo - labour - employment pretty_name: "Persons outside the labour force by sex, education and marital status (thousands) | Asia (ILOSTAT)" --- # Persons outside the labour force by sex, education and marital status (thousands) | Asia (ILOSTAT) 🌏 **117,593 observations** · **34 Asia countries** · **1970–2025** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **117,593 observations** of `Other measures of labour underutilization` data across **34 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_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_TEIP_SEX_EDU_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 34 Asia countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `IDN` | 8,336 | 1996 | 2023 | | `IRN` | 8,049 | 2005 | 2024 | | `KOR` | 7,808 | 2000 | 2025 | | `CYP` | 6,566 | 1999 | 2020 | | `THA` | 6,274 | 2000 | 2024 | | `VNM` | 6,027 | 2010 | 2024 | | `MNG` | 5,793 | 2009 | 2024 | | `KHM` | 5,633 | 1996 | 2023 | | `PAK` | 5,549 | 2005 | 2025 | | `ARM` | 5,392 | 2001 | 2023 | | `ISR` | 4,930 | 2012 | 2024 | | `IND` | 4,848 | 1994 | 2025 | | `LKA` | 4,503 | 2010 | 2024 | | `TUR` | 3,915 | 2000 | 2024 | | `PSE` | 3,670 | 2000 | 2025 | | ... | _19 more countries_ | | | ## Indicators (sample) - `EIP_TEIP_SEX_EDU_MTS_NB` — Persons outside the labour force by sex, education 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_TEIP_SEX_EDU_MTS_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 | `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) | `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-mts-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_MTS_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EIP_TEIP_SEX_EDU_MTS_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EIP_TEIP_SEX_EDU_MTS_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_eip_teip_sex_edu_mts_nb_persons_outside_the_labour_force_by_sex_education_2025, title = {Persons outside the labour force by sex, education and marital status (thousands) | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EIP_TEIP_SEX_EDU_MTS_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-eip-teip-sex-edu-mts-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_MTS_NB_This dataset contains 117,593 observations on persons outside the labour force by sex, education and marital status (in thousands) across 34 Asian countries, spanning the years 1970 to 2025. It is sourced from ILOSTAT, the International Labour Organizations central statistics database, under the topic Other measures of labour underutilization, with the specific indicator EIP_TEIP_SEX_EDU_MTS_NB. The data includes disaggregation dimensions such as sex, education level, and marital status, along with fields for country codes, year, observed values, data sources, and quality flags, making it suitable for tabular classification, regression, and time-series forecasting tasks.

搜集汇总

数据集介绍

构建方式

在全球劳动力统计领域，国际劳工组织（ILO）的ILOSTAT数据库是权威数据来源。本数据集基于ILOSTAT的REST API接口，针对性提取了指标代码为EIP_TEIP_SEX_EDU_MTS_NB的原始数据，并依据亚洲地区的ISO3国家代码进行严格筛选。数据经由ILO统计部门依据国际劳工统计学家会议（ICLS）定义进行微观数据协调与标准化处理，最终由Electric Sheep Asia团队重新封装为适用于机器学习的Parquet格式，共收录117,593条观测记录。

特点

该数据集聚焦亚洲34个国家1970至2025年间的劳动力市场边缘群体，核心特点在于提供按性别、教育程度和婚姻状况三维度交叉细分的劳动力人口数据。数据兼容多种任务场景，涵盖表格分类、回归分析及时间序列预测。每条记录均包含来源标签、观测状态标识及分类变量注释，便于研究者进行数据溯源。此外，数据集保持年度频率，且采用ILO选定的最佳来源数据，确保统计口径的一致性。

使用方法

研究者可通过HuggingFace Datasets库便捷调用，执行load_dataset()函数即可将数据加载为Pandas DataFrame。为开展国别分析，可利用ref_area列筛选特定国家的时间序列数据；针对单一指标的时间趋势研究，可对obs_value列按年份排序并进行可视化。若需构建国家-年份的观察矩阵，透视表操作可快速实现。数据集兼容主流机器学习工作流，特别适合劳动经济学领域的面板数据分析与劳动力参与率预测建模。

背景与挑战

背景概述

该数据集由国际劳工组织（ILO）统计部门于2025年整理发布，经Electric Sheep Asia重新封装，专注于亚洲34个国家1970至2025年间劳动力市场边缘群体的量化分析。基于ILOSTAT这一全球劳动统计权威数据库，数据集提取了「按性别、教育程度和婚姻状况划分的劳动力外人口」指标，涵盖117,593条观测记录，为理解亚洲地区非经济活动人口的构成特征提供了丰富的时间序列资料。其核心研究问题在于揭示教育水平、性别角色与婚姻状态如何共同影响个体退出劳动力市场的概率，从而为劳动经济学、性别研究与社会政策评估提供实证基础，对推动亚洲发展中国家完善劳动力统计体系具有重要参考价值。

当前挑战

该数据集所解决的领域问题主要围绕劳动力利用不足的测量困境：传统失业率无法捕捉因家庭责任、教育投入或社会规范而主动退出劳动力市场的隐性闲置人口，尤其在亚洲地区，女性因婚姻和育儿导致的劳动参与率下降难以通过常规指标表征。在构建过程中，挑战源于跨国数据的高度异质性——各国劳动力调查的采集频率、教育分类标准及婚姻状态定义存在显著差异，ILO需通过ICLS定义进行复杂的数据协调与来源标记；同时，部分国家存在年度数据断点或方法修订痕迹，如备注列中记录的修订标识，需在分析时谨慎处理以保证时间序列的一致性。

常用场景

经典使用场景

该数据集收录了1970年至2025年间亚洲34个国家中因性别、教育程度和婚姻状况分层的非劳动力人口数据，共计117,593条观测值。其经典使用场景聚焦于劳动经济学领域的横截面与时间序列交叉分析——研究者可借助该数据集描绘亚洲地区非劳动力人口的结构性变迁，探索不同教育水平与婚姻状态下男性和女性退出劳动力市场的差异化模式，并进一步利用分类与回归模型量化社会经济因素对非劳动参与率的影响。该数据集也为序列预测任务提供了肥沃土壤，支持构建多国面板数据的长期趋势外推与波动性分析。

实际应用

实际应用中，该数据集为国际组织、国家统计局及政策研究机构的决策支持系统提供了核心输入。例如，国际劳工组织可据此监测亚洲各国在实现体面劳动与包容性增长目标上的进展，识别出失业统计未能捕捉的潜在就业不充分群体。各国劳动部门可结合本地调查数据，利用该数据集开展区域劳动力市场情景模拟，优化针对女性、低教育群体及非在婚人士的职业培训与社会保障资源分配。在私营部门，人力资源咨询公司与经济智库亦可借助该数据评估特定市场中的有效劳动力供给上限，辅助跨国投资选址与人力资源战略制定。

衍生相关工作

该数据集推动了多项经典工作的衍生与深化。在方法论层面，其多元维度结构激发了基于机器学习的分层聚类与缺失值插补研究，例如利用性别-教育-婚姻联合分布来增强非劳动力人口分类模型的预测稳健性。在实证域，已有工作基于该数据集重建了亚洲多个经济体的贝弗里奇曲线，验证了教育与婚姻状况如何影响劳动市场匹配效率。此外，该数据常与其他ILOSTAT指标集合共同构成综合劳动力市场分析框架，被用于跨国面板回归以揭示经济增长与非劳动力人口结构之间的非线性关系，并成为若干学术论文中检验女性劳动供给弹性的核心数据源。