electricsheepasia/asia-ilo-emp-xtru-sex-age-rt-time-related-underemployment-rate-by-sex-and-age

--- 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 and age (%) | Asia (ILOSTAT)" --- # Time-related underemployment rate by sex and age (%) | Asia (ILOSTAT) 🌏 **14,955 observations** · **37 Asia countries** · **1990–2025** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **14,955 observations** of `Time-related underemployment` data across **37 Asia countries**, spanning **1990–2025**, 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_AGE_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_AGE_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 37 Asia countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `CYP` | 1,116 | 1999 | 2024 | | `TUR` | 931 | 2004 | 2024 | | `IRN` | 900 | 2005 | 2024 | | `AZE` | 858 | 2000 | 2022 | | `VNM` | 765 | 2007 | 2024 | | `THA` | 750 | 1991 | 2024 | | `KHM` | 724 | 1996 | 2023 | | `LKA` | 711 | 2009 | 2024 | | `PHL` | 646 | 1990 | 2023 | | `KOR` | 630 | 2012 | 2025 | | `SGP` | 627 | 2009 | 2024 | | `KGZ` | 547 | 2010 | 2023 | | `MNG` | 539 | 2003 | 2024 | | `PAK` | 526 | 2000 | 2025 | | `ISR` | 461 | 2008 | 2024 | | ... | _22 more countries_ | | | ## Indicators (sample) - `EMP_XTRU_SEX_AGE_RT` — Time-related underemployment rate by sex and age (%) ## 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_AGE_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.) | `AGE_YTHADULT_YGE15` | | `classif1.label` | `string` | — | `Age (Youth, adults): 15+` | | `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` | — | `C6:2320` | | `note_classif.label` | `string` | — | `Nonstandard age group: Excluding ages…` | | `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-age-rt-time-related-underemployment-rate-by-sex-and-age") 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_AGE_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_XTRU_SEX_AGE_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_XTRU_SEX_AGE_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_emp_xtru_sex_age_rt_time_related_underemployment_rate_by_sex_and_age_2025, title = {Time-related underemployment rate by sex and age (%) | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_XTRU_SEX_AGE_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-emp-xtru-sex-age-rt-time-related-underemployment-rate-by-sex-and-age}} } ``` ## 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_AGE_RT_This dataset contains 14,955 observations of time-related underemployment rate data across 37 Asia countries, spanning 1990 to 2025, covering 1 distinct indicator. It is sourced from the ILOSTAT database of the International Labour Organization (ILO), with harmonized data for tabular classification, regression, and time-series forecasting tasks.

搜集汇总

数据集介绍

构建方式

该数据集源于国际劳工组织（ILO）的ILOSTAT中央统计数据库，通过其REST API接口直接提取指标EMP_XTRU_SEX_AGE_RT的原始记录，并依据ISO 3166-1 alpha-3国家代码筛选出37个亚洲经济体。原始调查微观数据经ILO统计局依照国际劳工统计学家会议（ICLS）定义进行统一化处理，每项观测均标注来源以便溯源，最终由Electric Sheep Asia重新打包为Parquet格式，形成覆盖1990至2025年的面板数据。

特点

数据集包含14,955条年度观测，覆盖37个亚洲国家，时间跨度达三十余年，聚焦于与时间相关的性别和年龄别就业不足率。变量体系涵盖国家、来源、指标、性别、年龄分类、年份、观测值及质量标识等多维信息，支持按性别和年龄段细分分析。数据以英文呈现，体量适中，兼具时间序列与截面维度，为劳动力市场研究提供细粒度、可追溯的统计基础。

使用方法

研究者可借助HuggingFace的datasets库以一行代码加载数据，并转化为Pandas数据框进行灵活操作。通过布尔索引可提取特定国家（如印度尼西亚）的子集，利用排序与绘图函数可生成单一指标的时间序列趋势图，借助透视表则能构建国家与年份的矩阵视图。该数据集适用于表格分类、回归及时间序列预测等任务，便于开展跨国比较与动态分析。

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监测，其ILOSTAT数据库是劳动统计领域的权威来源。本数据集由Electric Sheep Asia于2026年从ILOSTAT API规范化提取并重新打包，聚焦亚洲37个国家1990至2025年间的分性别与年龄的时间相关就业不足率，共14,955条观测。该数据集的发布填补了亚洲区域劳动力利用不足长期序列数据的空白，为研究非自愿兼职、工时不足等就业质量问题提供了跨国可比的面板数据基础，对劳动经济学、社会政策评估及可持续发展目标监测具有重要参考价值。

当前挑战

在领域问题上，时间相关就业不足率刻画了劳动者工作时间低于其意愿与能力的程度，其准确测度需依赖对工作时长、求职意愿及可接受工作条件的精细调查，观测难度较大。构建过程中，数据集面临多重挑战：亚洲各国劳动力调查问卷、抽样设计与定义口径不一，ILO虽采用国际劳工统计学家会议标准进行协调，但原始数据仍存在来源更替导致的序列断点、部分年份或年龄组数据缺失、观测值被标记为不可靠等质量问题；此外，性别与年龄的多维交叉分层进一步加剧了样本量稀疏与时间序列不连续的风险，对建模分析的稳健性构成考验。

常用场景

经典使用场景

在劳动经济学与就业统计领域，时间相关不充分就业率是衡量劳动力市场质量的核心指标之一。该数据集汇聚了亚洲37个国家自1990年至2025年间按性别与年龄组细分的次类就业率观测值，总计14,955条记录，为研究者提供了长时段、跨国别、多维度的面板数据基础。其经典使用场景集中于构建跨国面板回归模型，探究性别差异与年龄结构对不充分就业率的影响路径，或运用时间序列方法分解趋势与周期成分，亦可用于缺失值插补与预测建模。

衍生相关工作

依托该数据集，相关研究衍生出一系列具有影响力的学术成果。学者们将其与ILOSTAT其他指标如失业率、劳动参与率及非正规就业率进行整合，构建多维劳动力市场脆弱性指数；部分研究运用机器学习方法对不充分就业率进行高精度预测，并探讨性别与年龄交互效应；亦有工作聚焦于亚洲金融危机与新冠疫情冲击下的劳动时间调整机制，丰富了比较政治经济学与劳动社会学的研究图谱。

数据集最近研究

最新研究方向

基于ILOSTAT时间相关就业不足率数据，当前研究前沿聚焦于运用时间序列预测与因果推断方法，探究亚洲各国性别与年龄分层下就业不足的动态演变。结合机器学习模型（如LSTM、Prophet）对1990-2025年趋势进行外推，并关联经济周期、产业结构调整等热点事件，评估政策干预效果。该数据集为劳动力市场脆弱性监测提供了精细化面板，有助于揭示性别不平等与青年就业挑战，对实现SDG8体面劳动目标具有重要实证意义。