electricsheepasia/asia-ilo-emp-xtru-sex-edu-mts-rt-time-related-underemployment-rate-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 - time-related-underemployment - ilo - labour - employment pretty_name: "Time-related underemployment rate by sex, education and marital status (%) | Asia (ILOSTAT)" --- # Time-related underemployment rate by sex, education and marital status (%) | Asia (ILOSTAT) 🌏 **10,054 observations** · **29 Asia countries** · **1996–2025** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **10,054 observations** of `Time-related underemployment` data across **29 Asia countries**, spanning **1996–2025**, 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=EMP_XTRU_SEX_EDU_MTS_RT) - **Publisher:** International Labour Organization (ILO) - **License:** [cc-by-4.0](https://creativecommons.org/licenses/by/4.0/) - **Topic:** Time-related underemployment ## Methodology Data pulled directly from the ILOSTAT REST API at `https://rplumber.ilo.org/data/indicator?id=EMP_XTRU_SEX_EDU_MTS_RT` 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 29 Asia countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `IRN` | 1,021 | 2005 | 2024 | | `TUR` | 961 | 2004 | 2024 | | `CYP` | 848 | 1999 | 2020 | | `VNM` | 729 | 2010 | 2024 | | `THA` | 700 | 2010 | 2024 | | `KOR` | 624 | 2012 | 2025 | | `LKA` | 579 | 2010 | 2024 | | `PAK` | 563 | 2006 | 2025 | | `KHM` | 553 | 1996 | 2023 | | `MNG` | 431 | 2013 | 2024 | | `PSE` | 391 | 2015 | 2025 | | `BRN` | 372 | 2014 | 2024 | | `IDN` | 360 | 2016 | 2023 | | `BGD` | 240 | 2013 | 2024 | | `JOR` | 223 | 2017 | 2024 | | ... | _14 more countries_ | | | ## Indicators (sample) - `EMP_XTRU_SEX_EDU_MTS_RT` — Time-related underemployment rate by sex, education and marital status (%) ## 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 | `EMP_XTRU_SEX_EDU_MTS_RT` | | `indicator.label` | `string` | Indicator name in English | `Time-related underemployment rate by …` | | `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) | `7.665` | | `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-emp-xtru-sex-edu-mts-rt-time-related-underemployment-rate-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"] == "EMP_XTRU_SEX_EDU_MTS_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_XTRU_SEX_EDU_MTS_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_XTRU_SEX_EDU_MTS_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_emp_xtru_sex_edu_mts_rt_time_related_underemployment_rate_by_sex_education_2025, title = {Time-related underemployment rate by sex, education and marital status (%) | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_XTRU_SEX_EDU_MTS_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-emp-xtru-sex-edu-mts-rt-time-related-underemployment-rate-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 2026-05-26 via the Electric Sheep pipeline. Source URL: https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_XTRU_SEX_EDU_MTS_RT_This dataset contains 10,054 observations of Time-related underemployment data across 29 Asia countries, spanning the years 1996 to 2025, covering 1 distinct indicator: Time-related underemployment rate by sex, education and marital status (%) (indicator code: EMP_XTRU_SEX_EDU_MTS_RT). The data is sourced from the ILOSTAT database of the International Labour Organization (ILO). It provides multi-dimensional disaggregated data, including country, data source, observation year, observed value, and classifications by sex (total, male, female), education (aggregate total), and marital status (aggregate total). The data is harmonized by the ILO and includes observation status flags and source notes, 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

鉴于国际劳工组织（ILO）在全球劳动力统计领域的权威地位，该数据集依托ILOSTAT中央统计数据库，通过REST API接口直接获取指标EMP_XTRU_SEX_EDU_MTS_RT的原始记录，并依据亚洲ISO3国家代码进行筛选。ILOSTAT采用国际劳工统计学家会议（ICLS）定义对各国劳动力调查微观数据进行统一 harmonization，数据来源涵盖劳动力调查、家庭收入调查等，来源信息在source.label列中标注以便追溯。最终由Electric Sheep Asia重新打包为Parquet格式，形成覆盖29个亚洲国家、1996至2025年、共计10,054条观测的标准化数据集。

特点

该数据集聚焦于亚洲地区与时间相关的就业不足率，以性别、教育程度和婚姻状况为 disaggregation 维度，提供年度频率的量化观测。其核心特点在于多维度交叉分类：性别涵盖总计、男性和女性三类，教育程度和婚姻状况通过classif1与classif2列区分聚合层级，并附有详细的注释标签以说明非标准教育水平或序列中断等数据质量信息。数据覆盖范围广泛，最早可追溯至1996年，部分国家更新至2025年，且观测状态标志（obs_status）有助于识别临时性或不可靠估计值。

使用方法

研究人员可借助HuggingFace datasets库以单行代码加载该数据集，并转换为Pandas DataFrame进行灵活分析。典型用法包括按国家代码筛选特定区域数据，例如提取印度尼西亚的全部记录；或针对单一指标构建时间序列，观察就业不足率的演变趋势；亦可利用透视表功能将数据重塑为国家与年份的二维矩阵，便于跨国比较。此外，数据集的列结构清晰，支持回归、分类及时间序列预测等机器学习任务，使用时建议结合obs_status和note系列字段评估数据可靠性，并遵循CC-BY-4.0许可要求规范引用。

背景与挑战

背景概述

国际劳工组织（ILO）长期致力于全球劳动力市场统计的标准化与传播，其核心数据库ILOSTAT为衡量体面劳动与可持续发展目标提供了权威基准。在此背景下，Electric Sheep Asia于2025年对ILOSTAT中亚洲地区时间相关就业不足率数据进行系统性重包装，涵盖29个亚洲国家、1996至2025年间逾万条观测，涉及性别、教育程度与婚姻状况的多维分解。该数据集回应了亚洲劳动力市场中就业质量难以量化比较的研究缺口，为劳动经济学、社会政策评估及时间序列预测提供了细粒度、可追溯的标准化数据基础，对推动区域就业政策循证决策具有参考价值。

当前挑战

时间相关就业不足率作为衡量劳动力未充分使用的关键指标，其领域核心难题在于跨国家、跨年份的测量口径差异与数据稀疏性问题，尤其在教育程度与婚姻状况交叉分解后，部分国家观测严重缺失。构建过程中，挑战集中于ILOSTAT原始微数据的国际标准协调与来源优选，不同国家劳动力调查的抽样设计、季节调整方法及定义边界差异导致序列断裂频发，同时非标准教育分类与临时性数据标记加剧了数据清洗与可比性维护的复杂度。

常用场景

经典使用场景

在劳动经济学与就业结构分析的领域中，时间相关不充分就业率长期被视为衡量劳动力市场质量的关键标尺。该数据集汇聚了亚洲29个国家1996至2025年间按性别、教育程度与婚姻状况交叉分类的就业不充分指标，其最经典的使用场景在于构建跨国面板数据模型，用以刻画不同社会经济群体在劳动时间配置上的异质性。研究者常借助该数据开展时间序列预测与分类任务，以揭示亚洲地区不充分就业的动态演变轨迹。

衍生相关工作

围绕该数据集已衍生出一系列关联性研究工作，涵盖劳动参与率预测模型、教育分层与就业稳定性的因果推断，以及基于ILOSTAT框架的跨国比较分析。部分研究将其与工资、非正规就业等指标进行链接，构建多维劳动市场脆弱性指数。此外，该数据集的规范化封装方式亦促进了机器学习方法在劳动统计领域的应用探索，催生了若干面向亚洲区域就业质量评估的开源工具与可视化应用。

数据集最近研究

最新研究方向

在全球劳动力市场经历结构性变革的背景下，时间相关就业不足作为衡量劳动力资源未充分配置的关键指标，日益受到劳动经济学与不平等研究的关注。该数据集凭借其跨越近三十年的亚洲多国面板数据，整合性别、教育程度与婚姻状况的多维分层信息，为解析教育扩张与性别差异如何交互作用于就业不足提供了独特视角。当前前沿研究聚焦于运用机器学习方法进行就业不足率的时序预测与国别异质性识别，并借助因果推断框架探讨教育错配、婚姻角色约束对劳动力利用效率的长期影响，为亚洲地区实现体面劳动目标与包容性增长提供实证基础。