electricsheepasia/asia-ilo-luu-xlu4-sex-geo-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: - 1K<n<10K tags: - tabular - asia - ilostat - other-measures-of-labour-underutilization - ilo - labour - employment pretty_name: "Composite rate of labour underutilization (LU4) by sex and rural / urban areas (%) | Asia (ILOSTAT)" --- # Composite rate of labour underutilization (LU4) by sex and rural / urban areas (%) | Asia (ILOSTAT) 🌏 **1,512 observations** · **25 Asia countries** · **1999–2025** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **1,512 observations** of `Other measures of labour underutilization` data across **25 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_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_XLU4_SEX_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` | 234 | 1999 | 2024 | | `VNM` | 156 | 2007 | 2024 | | `LKA` | 126 | 2010 | 2024 | | `THA` | 120 | 2010 | 2024 | | `PSE` | 96 | 2015 | 2022 | | `TUR` | 90 | 2004 | 2013 | | `BRN` | 81 | 2014 | 2024 | | `JOR` | 72 | 2017 | 2024 | | `PHL` | 66 | 2017 | 2023 | | `IDN` | 63 | 2016 | 2023 | | `GEO` | 54 | 2019 | 2024 | | `MNG` | 54 | 2019 | 2024 | | `AFG` | 39 | 2014 | 2021 | | `KHM` | 36 | 2007 | 2019 | | `PAK` | 36 | 2009 | 2025 | | ... | _10 more countries_ | | | ## Indicators (sample) - `LUU_XLU4_SEX_GEO_RT` — Composite rate of labour underutilization (LU4) by sex 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_XLU4_SEX_GEO_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.) | `GEO_COV_NAT` | | `classif1.label` | `string` | — | `Area type: National` | | `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) | `B` | | `obs_status.label` | `string` | — | `Break in series` | | `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-geo-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_GEO_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="LUU_XLU4_SEX_GEO_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "LUU_XLU4_SEX_GEO_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_luu_xlu4_sex_geo_rt_composite_rate_of_labour_underutilization_lu4_by_s_2025, title = {Composite rate of labour underutilization (LU4) by sex and rural / urban areas (%) | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=LUU_XLU4_SEX_GEO_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-luu-xlu4-sex-geo-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_GEO_RT_This dataset contains composite rates of labour underutilization (LU4) by sex and rural/urban areas for 25 Asian countries from 1999 to 2025. Sourced from the International Labour Organization (ILO) ILOSTAT database via REST API and filtered for Asia, it includes 1,512 observations covering one indicator (LUU_XLU4_SEX_GEO_RT), which represents the composite rate of labour underutilization (%). The data features columns such as country codes, source information, indicator codes, sex disaggregation (total, male, female), year, observed values, and status flags. It is designed to provide standardized, ML-ready data for studying labour markets in Asia, suitable for tasks like tabular classification, regression, and time-series forecasting.

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，经由Electric Sheep Asia团队从ILOSTAT REST API直接抽取并重新打包而成。数据筛选聚焦于亚洲地区的ISO3国家代码，涵盖了25个亚洲国家从1999年至2025年期间的年度观测值，共计1512条记录。在构建过程中，ILOSTAT依据国际劳工统计学家会议（ICLS）的定义对原始调查微观数据进行统一协调处理，并在source.label字段中标注数据来源，以确保每一条观测值的可追溯性与透明性。数据集仅收录年度频次的数据，且当同一国家年份存在多个来源时，优先采用ILO选定的“最佳来源”。

特点

本数据集的核心特点在于其聚焦于劳动力利用不足的综合度量指标，即LUU_XLU4_SEX_GEO_RT，用以按性别及城乡区域划分的复合劳动力利用不足率（%）。数据结构清晰，包含ref_area（国家代码）、sex（性别分类，含总、男、女三类）、classif1（区域类型，如全国或城乡）、time（年份）及obs_value（观测值）等关键变量。此外，数据集附带了细致的元数据标签，如obs_status用于标记观测状态（如序列中断），note_indicator则提供了方法修订等注释信息，极大地增强了数据的可解释性与可信度。该数据集为亚洲地区劳动力市场研究提供了标准化、跨时空的比较基础。

使用方法

该数据集整合于Hugging Face Datasets生态中，用户可通过一行Python代码轻松加载：使用`load_dataset`函数从`electricsheepasia`仓库中直接导入，并转换为Pandas DataFrame进行后续分析。典型应用场景包括聚焦特定国家的子集筛选，如通过`ref_area`过滤印度尼西亚的数据；或针对单一指标进行时间序列分析，利用`obs_value`和`time`绘制趋势图。研究者还可通过数据透视功能，将数据重塑为国家×年份的矩阵形式，便于进行面板数据回归或跨国家比较分析。数据集采用cc-by-4.0许可协议，鼓励学术引用与二次使用，同时要求注明原始来源及Electric Sheep Asia的再包装贡献。

背景与挑战

背景概述

在全球劳动力市场日益复杂的背景下，准确衡量劳动利用不足（labour underutilization）已成为劳动经济学和发展研究的重要议题。国际劳工组织（ILO）于2025年通过其核心统计数据库ILOSTAT发布了“亚洲劳动力利用不足综合率（LU4）按性别与城乡分布”数据集，由Electric Sheep Asia重新整理并托管于HuggingFace平台。该数据集涵盖1999至2025年间25个亚洲国家的1,512条观测值，聚焦于综合劳动利用不足率这一关键指标，为跨国比较和时间序列分析提供了标准化、可复用的数据基础。其核心研究问题在于如何超越传统的失业率指标，通过纳入潜在劳动力与边缘劳动力等多维信息，更全面地刻画亚洲各国劳动力市场的真实运行状况。该数据集的发布填补了亚洲区域在劳动利用不足精细维度上的数据空白，对推动SDG体面工作目标的量化评估、制定区域就业政策以及开展劳动力市场弹性研究具有重要支撑作用。

当前挑战

该数据集所针对的领域挑战在于，传统失业率指标无法捕捉劳动力市场中广泛存在的隐性闲置问题，例如因无合适工作而放弃求职的沮丧型失业者及非充分就业者。LU4综合率通过整合失业、时间相关不充分就业、潜在劳动力及边缘劳动力四个维度，为评估劳动资源真实利用效率提供了更严谨的度量标准，助力克服现有指标在政策诊断中的偏差。在构建过程中，数据集成面临显著的技术挑战：首先，ILOSTAT需对不同国家基于ICLS定义的原始调查微观数据进行统一协调，以解决因调查方法、时间口径及抽样设计差异导致的跨国不可比问题；其次，数据覆盖的时间跨度长达27年，期间部分国家的统计方法经历了重大修订（如断点标记B及注释注明的方法变更），需通过标注制度进行溯源，以维持序列的连贯性与分析的有效性；此外，数据中仅包含年度频率的观测，而月度或季度的高频数据缺失，限制了在短期波动分析中的应用潜力；最后，针对部分国家年份的组合，数据集采用ILO筛选的“最佳来源”而非全部来源，这种选择机制虽提高了数据质量，却可能引入源变迁导致的系统性偏差，需要研究者在使用时谨慎评估其影响。

常用场景

经典使用场景

在劳动经济学与区域发展研究中，本数据集被广泛用于分析亚洲国家劳动力利用不足的复合率（LU4）随时间的演变趋势，尤其是在性别与城乡维度的异质性表现。研究者可通过时间序列建模，揭露不同亚洲经济体在就业市场吸纳能力上的结构性差异，也可利用面板数据回归方法，探索经济增长、产业结构变迁与劳动利用不足之间的深层关联。该数据集涵盖1999年至2025年间25个亚洲国家的观测值，为跨国比较与纵向追踪提供了稳固的数据支撑。

衍生相关工作

基于该数据集衍生的相关工作包括构建亚洲劳动市场超时预测模型、开发劳动利用不平等指数、以及设计城乡劳动力流动与LU4联动的结构方程模型。部分研究者还将其与ILOSTAT其他指标（如失业率、非正规就业比例）进行多源融合，生成复合劳动韧性指数，用于评估经济冲击下的就业恢复能力。该数据集的标准化格式也为机器学习驱动的区域就业趋势分类与异常检测任务提供了可复用的基准数据，推动了自动社会监测的发展。

数据集最近研究

最新研究方向

该数据集聚焦于亚洲地区劳动力利用不足的综合率（LU4）的性别与城乡维度分解，为劳动经济学领域的前沿研究提供了高粒度的时间序列基础。当前研究方向围绕后疫情时代亚洲非正规就业的恢复韧性、性别就业差距的演化轨迹，以及年轻群体长期失业率的结构性成因展开。数据集覆盖1999至2025年间25个亚洲国家的多源调查数据，其空间异质性和时间序列长度使其成为探究劳动力市场碎片化与政策干预效果的理想样本。特别是，利用ILOSTAT统一的国际劳工统计标准，该数据集支持跨国产出缺口与影子经济规模的比较分析，进而为亚洲劳动力市场与可持续发展目标（SDG 8）的实证关联提供了可靠数据支撑，推动了区域劳动力治理向数据驱动型决策的转型。