electricsheepasia/asia-ilo-luu-xlu4-sex-age-mts-rt-composite-rate-of-labour-underutilization-lu4-by-s

--- 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: "Composite rate of labour underutilization (LU4) by sex, age and marital status (%) | Asia (ILOSTAT)" --- # Composite rate of labour underutilization (LU4) by sex, age and marital status (%) | Asia (ILOSTAT) 🌏 **11,862 observations** · **28 Asia countries** · **1999–2025** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **11,862 observations** of `Other measures of labour underutilization` data across **28 Asia countries**, spanning **1999–2025**, 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_XLU4_SEX_AGE_MTS_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_XLU4_SEX_AGE_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 28 Asia countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `CYP` | 1,501 | 1999 | 2020 | | `VNM` | 1,154 | 2010 | 2024 | | `THA` | 1,041 | 2010 | 2024 | | `LKA` | 999 | 2010 | 2024 | | `TUR` | 764 | 2004 | 2013 | | `PSE` | 689 | 2015 | 2025 | | `BRN` | 608 | 2014 | 2024 | | `PHL` | 601 | 2017 | 2023 | | `JOR` | 539 | 2017 | 2024 | | `IDN` | 504 | 2016 | 2023 | | `MNG` | 411 | 2019 | 2024 | | `AFG` | 392 | 2012 | 2021 | | `BGD` | 365 | 2013 | 2024 | | `KHM` | 345 | 2003 | 2019 | | `ARM` | 288 | 2014 | 2017 | | ... | _13 more countries_ | | | ## Indicators (sample) - `LUU_XLU4_SEX_AGE_MTS_RT` — Composite rate of labour underutilization (LU4) by sex, age 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 | `LUU_XLU4_SEX_AGE_MTS_RT` | | `indicator.label` | `string` | Indicator name in English | `Composite rate of labour underutiliza…` | | `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 | `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) | `19.229` | | `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-xlu4-sex-age-mts-rt-composite-rate-of-labour-underutilization-lu4-by-s") 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_XLU4_SEX_AGE_MTS_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="LUU_XLU4_SEX_AGE_MTS_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "LUU_XLU4_SEX_AGE_MTS_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_luu_xlu4_sex_age_mts_rt_composite_rate_of_labour_underutilization_lu4_by_s_2025, title = {Composite rate of labour underutilization (LU4) by sex, age and marital status (%) | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=LUU_XLU4_SEX_AGE_MTS_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-luu-xlu4-sex-age-mts-rt-composite-rate-of-labour-underutilization-lu4-by-s}} } ``` ## 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=LUU_XLU4_SEX_AGE_MTS_RT_This dataset contains 11,862 observations of the composite rate of labour underutilization (LU4) by sex, age and marital status (%) across 28 Asia countries, spanning from 1999 to 2025, covering 1 distinct indicator. It is sourced from the ILOSTAT database of the International Labour Organization (ILO), repackaged for machine learning use, and suitable for tabular classification, regression, and time-series forecasting tasks.

搜集汇总

数据集介绍

构建方式

本数据集源自国际劳工组织（ILO）的ILOSTAT中央统计数据库，通过其REST API接口直接获取原始指标数据，并依据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标准化处理。数据采集后，针对亚洲28个国家的ISO3国家代码进行地理过滤，整合为涵盖1999年至2025年间的统一面板数据。数据集由Electric Sheep Asia团队重新封装，以Parquet格式存储，确保机器学习就绪的易用性，并在每行中保留来源字段以支持数据溯源。

特点

该数据集聚焦亚洲地区劳动力利用不足的综合度量指标（LU4），包含11,862条观测记录，覆盖28个国家，时间跨度长达27年。数据按性别、年龄及婚姻状况进行细粒度分组，提供了如男性、女性、15岁以上青年与成人等分类维度，并带有观测状态标志（如不可靠）与断点注释，方便用户评估数据质量。其单一指标设计降低了分析复杂度，同时保留了丰富的分层信息，是研究亚洲劳动力市场结构性变化的宝贵资源。

使用方法

用户可通过HuggingFace Datasets库的一键加载函数load_dataset()快速获取数据，并转换为Pandas DataFrame进行后续分析。典型应用包括按国家筛选特定序列（如印度尼西亚）、对单一指标（如LUU_XLU4_SEX_AGE_MTS_RT）绘制时间序列图，或利用pivot_table将数据重组为国家×年份的矩阵格式，便于面板数据回归或跨区域比较。数据还可直接用于表格分类、回归或时间序列预测等机器学习任务，极大降低了数据预处理的成本。

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（ILOSTAT）于2025年发布，经Electric Sheep Asia重新整理后发布于HuggingFace平台。其核心研究问题聚焦于亚洲地区劳动力利用不足的综合测度，具体通过“综合劳动力利用不足率（LU4）”这一指标，按性别、年龄和婚姻状况进行细分，以揭示亚洲28个国家在1999至2025年间劳动力市场的结构性特征。数据集涵盖11,862条观测值，为区域劳动经济学、社会分层及可持续发展目标（SDGs）中体面劳动指标的监测提供了高质量的基础数据。作为ILOSTAT全球劳动统计体系的重要组成部分，该数据集在推动亚洲劳动力市场比较研究、政策评估以及跨国时间序列分析方面具有显著影响力。

当前挑战

该数据集应对的领域挑战在于劳动力利用不足的多维度测度难题：传统失业率往往低估实际劳动市场闲置程度，而LU4指标综合了失业、时间相关就业不足及潜在劳动力，能够更全面地反映劳动力资源未被充分利用的现状。构建过程中面临的关键挑战包括：跨国数据源异质性导致的概念协调困难，需依赖ILO国际劳工统计学家会议（ICLS）统一定义进行标准化处理；不同国家调查方法（如劳动力调查、行政记录）的差异使得数据可比性受限，需通过‘最佳来源’筛选逻辑进行处理；以及部分观测值存在质量标识（如“不可靠”、“方法修订导致序列中断”），需要用户在建模时谨慎处理数据断裂与质量标记，以确保分析结果的稳健性。

常用场景

经典使用场景

在劳动经济学与跨国比较研究领域，本数据集堪称探索亚洲劳动力市场结构性失衡的经典工具。它聚焦于综合劳动力利用不足率（LU4），这一指标超越了传统失业率，将时间相关就业不足与潜在劳动力大军纳入考量，为描绘亚洲28国1999至2025年间劳动力闲置的全景图提供了独特视角。研究者常借助此数据集，按性别、年龄及婚姻状况进行精细分层，系统剖析不同社会群体在劳动力市场中的脆弱性差异，尤其适用于检验经济发展阶段、社会政策变迁与劳动力吸纳能力之间的动态关联。其年度时间序列特性亦使得面板数据回归、时间序列分解等高级计量方法大有用武之地。

实际应用

在实际应用层面，该数据集为国际发展机构、国家劳动部门及宏观经济政策制定者提供了无可替代的决策辅助。国际劳工组织（ILO）可借此监测成员国在推动体面劳动方面的进展，特别是围绕可持续发展目标（SDG）中关于充分和生产性就业的具体指标。亚洲各国政府能够依据LU4的变动趋势，及时调整职业培训计划、就业服务体系建设与社会保障覆盖范围。非政府组织与智库则运用此类数据开展区域劳动市场简报，识别高脆弱性群体，并倡导更具包容性的劳动法规改革。此外，金融机构在进行国家风险评估时，亦将劳动力利用不足率视为社会稳定性与消费潜力的关键前瞻信号。

衍生相关工作

围绕这一数据集，劳动经济学与数据科学领域已衍生出一系列富有开创性的研究脉络。在计量方法上，面板协整分析与误差修正模型被广泛应用于检验LU4与经济增长、工资刚性的长期均衡关系。在机器学习应用中，研究人员利用该数据集的分类与回归特征，构建预测模型以早期预警劳动力市场危机的爆发，或模拟不同政策情境（如最低工资调整、移民政策放宽）对劳动力利用效率的冲击。脱胎于ILOSTAT数据库的标准化清洗流程，亦催生了多个专注于亚洲区域的时间序列数据集合，推动了跨国劳动统计的融合与复用，促进了开放科学运动在实证经济学领域的深入渗透。