electricsheepasia/asia-ilo-luu-xlu2-sex-age-geo-rt-combined-rate-of-time-related-underemployment-and

--- 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: "Combined rate of time-related underemployment and unemployment (LU2) by sex, age and rural | Asia (ILOSTAT)" --- # Combined rate of time-related underemployment and unemployment (LU2) by sex, age and rural | Asia (ILOSTAT) 🌏 **14,693 observations** · **25 Asia countries** · **1996–2025** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **14,693 observations** of `Other measures of labour underutilization` data across **25 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=LUU_XLU2_SEX_AGE_GEO_RT) - **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=LUU_XLU2_SEX_AGE_GEO_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 25 Asia countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `CYP` | 1,770 | 1999 | 2024 | | `VNM` | 1,244 | 2007 | 2024 | | `KHM` | 1,064 | 1996 | 2023 | | `LKA` | 983 | 2010 | 2024 | | `THA` | 960 | 2010 | 2024 | | `KOR` | 936 | 2013 | 2025 | | `MNG` | 929 | 2010 | 2024 | | `PAK` | 858 | 2006 | 2025 | | `TUR` | 708 | 2004 | 2013 | | `PSE` | 695 | 2015 | 2022 | | `BRN` | 591 | 2014 | 2024 | | `IDN` | 576 | 2016 | 2023 | | `PHL` | 528 | 2017 | 2023 | | `JOR` | 504 | 2017 | 2024 | | `GEO` | 432 | 2019 | 2024 | | ... | _10 more countries_ | | | ## Indicators (sample) - `LUU_XLU2_SEX_AGE_GEO_RT` — Combined rate of time-related underemployment and unemployment (LU2) by sex, age and rural / urban areas (%) ## 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 | `LUU_XLU2_SEX_AGE_GEO_RT` | | `indicator.label` | `string` | Indicator name in English | `Combined rate of time-related underem…` | | `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+` | | `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) | `12.91` | | `obs_status` | `string` | Observation status flag (e.g. provisional, unreliable) | `U` | | `obs_status.label` | `string` | — | `Unreliable` | | `note_classif` | `string` | — | `—` | | `note_classif.label` | `string` | — | `—` | | `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-luu-xlu2-sex-age-geo-rt-combined-rate-of-time-related-underemployment-and") 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"] == "LUU_XLU2_SEX_AGE_GEO_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="LUU_XLU2_SEX_AGE_GEO_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "LUU_XLU2_SEX_AGE_GEO_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_luu_xlu2_sex_age_geo_rt_combined_rate_of_time_related_underemployment_and_2025, title = {Combined rate of time-related underemployment and unemployment (LU2) by sex, age and rural | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=LUU_XLU2_SEX_AGE_GEO_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-luu-xlu2-sex-age-geo-rt-combined-rate-of-time-related-underemployment-and}} } ``` ## 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=LUU_XLU2_SEX_AGE_GEO_RT_This dataset contains 14,693 observations of Other measures of labour underutilization data across 25 Asia countries, spanning 1996–2025, covering 1 distinct indicator: LUU_XLU2_SEX_AGE_GEO_RT — Combined rate of time-related underemployment and unemployment (LU2) by sex, age and rural / urban areas (%). The data is sourced from ILOSTAT, the ILOs central statistics database, and is repackaged by Electric Sheep Asia for ML-ready use, with a schema including columns for country, year, indicator value, sex disaggregation, and other metadata.

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中央统计数据库，通过REST API接口直接获取指标LUU_XLU2_SEX_AGE_GEO_RT的原始数据，并依据亚洲ISO3国家代码进行筛选。原始调查微观数据经国际劳工统计学家会议（ICLS）定义进行标准化调和，确保跨国可比性；数据来源在source.label列中标注以备追溯，最终由Electric Sheep Asia重新打包为机器学习就绪的Parquet格式。

特点

数据集涵盖1996至2025年间25个亚洲国家的14,693条观测记录，聚焦于结合时间相关不充分就业与失业的综合比率（LU2）。数据按性别、年龄及城乡地域进行多维分解，包含国家代码、指标代码、观测值及质量标志等字段，并以年度频率呈现，为劳动市场分析提供细粒度面板数据。

使用方法

研究者可通过HuggingFace的datasets库调用load_dataset函数加载数据集，并转换为Pandas DataFrame进行后续分析。典型操作包括按国家代码筛选子集、提取单一指标进行时间序列可视化，以及利用透视表生成国家×年份矩阵，以支持分类、回归或时间序列预测等任务。

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可比性建设，其ILOSTAT数据库已成为劳动经济学研究的重要基础设施。亚洲地区劳动力市场结构异质性显著，非正规就业与时间相关就业不足现象普遍，然而跨国可比数据长期匮乏。在此背景下，Electric Sheep Asia于2025年基于ILOSTAT REST API重新封装了亚洲25国1996至2025年LU2指标数据集，涵盖14,693条观测记录，整合性别、年龄及城乡维度。该数据集为监测亚洲劳动力利用不足的时空演变、评估体面劳动目标进展提供了关键实证基础，对劳动政策制定与SDG相关研究具有重要参考价值。

当前挑战

劳动力利用不足的测度本身面临概念界定与跨国外比较的双重困境。LU2指标融合了时间相关就业不足与失业两种状态，其操作化定义在不同国家劳动力调查中的执行存在差异，可能导致观测值可比性受损。数据集构建过程中，ILO虽采用国际劳工统计学家会议标准进行调和，但原始调查的覆盖面、抽样设计与数据质量参差不齐，部分国家年份数据缺失或标记为不可靠。此外，分类维度在部分国家未完整发布，年龄与城乡分组的细化程度不一，序列中断现象亦有记录，对时间序列建模与跨国面板分析构成实质性制约。

常用场景

经典使用场景

在劳动经济学与就业统计研究中，该数据集凭借其覆盖亚洲25国、跨越近三十年的面板结构，成为刻画劳动力利用不足长期演变态势的经典素材。研究者通常以时间序列建模与跨国比较分析为核心路径，将性别、年龄组及城乡属性作为关键分层维度，考察时间相关就业不足与失业合并比率（LU2）的周期波动与结构性差异。借助面板回归、聚类分析与趋势分解等方法，可有效识别不同区域劳动力市场脆弱性的异质性特征。

解决学术问题

该数据集直面劳动统计学中长期存在的测度口径不统一与跨国可比性不足难题。依托国际劳工组织基于国际劳工统计学家会议定义的调和化处理，它将时间相关就业不足与失业合并为统一指标，使研究者得以在一致框架下探讨劳动力未充分利用问题。其价值在于弥合了传统失业率无法反映工时不足的局限，为检验性别差距、青年就业困境及城乡二元结构等学术命题提供了可靠的经验基础，对推动包容性就业政策研究具有实质意义。

衍生相关工作

围绕该数据集，后续研究衍生出若干经典工作方向。一方面，学者将其与ILOSTAT其他劳动力未充分利用指标（如LU1、LU3、LU4）进行整合，构建多维就业脆弱性指数；另一方面，机器学习社区利用其表格与时间序列特性，开展缺失值插补、跨国预测及聚类分型等任务。此外，部分研究将其与性别工资差距、非正规就业等数据集链接，探讨劳动力市场多重剥夺的交互机制，拓展了亚洲就业比较研究的分析边界。