electricsheepasia/asia-ilo-luu-xlu4-sex-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: - n<1K tags: - tabular - asia - ilostat - other-measures-of-labour-underutilization - ilo - labour - employment pretty_name: "Composite rate of labour underutilization (LU4) by sex (%) | Asia (ILOSTAT)" --- # Composite rate of labour underutilization (LU4) by sex (%) | Asia (ILOSTAT) 🌏 **582 observations** · **31 Asia countries** · **1999–2025** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **582 observations** of `Other measures of labour underutilization` data across **31 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_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_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 31 Asia countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `CYP` | 78 | 1999 | 2024 | | `VNM` | 51 | 2007 | 2024 | | `LKA` | 42 | 2010 | 2024 | | `KGZ` | 39 | 2011 | 2023 | | `THA` | 39 | 2010 | 2024 | | `TUR` | 30 | 2004 | 2013 | | `PSE` | 30 | 2015 | 2025 | | `BRN` | 27 | 2014 | 2024 | | `JOR` | 24 | 2017 | 2024 | | `PHL` | 21 | 2017 | 2023 | | `IDN` | 21 | 2016 | 2023 | | `GEO` | 18 | 2019 | 2024 | | `MNG` | 18 | 2019 | 2024 | | `KHM` | 15 | 2003 | 2019 | | `AFG` | 15 | 2012 | 2021 | | ... | _16 more countries_ | | | ## Indicators (sample) - `LUU_XLU4_SEX_RT` — Composite rate of labour underutilization (LU4) by sex (%) ## 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_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` | | `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-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_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="LUU_XLU4_SEX_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "LUU_XLU4_SEX_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_luu_xlu4_sex_rt_composite_rate_of_labour_underutilization_lu4_by_s_2025, title = {Composite rate of labour underutilization (LU4) by sex (%) | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=LUU_XLU4_SEX_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-luu-xlu4-sex-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_RT_This dataset contains 582 observations of the composite rate of labour underutilization (LU4) by sex (%) across 31 Asia countries from 1999 to 2025, covering 1 distinct indicator. The data is sourced from the International Labour Organization (ILO) ILOSTAT database, repackaged by Electric Sheep Asia, and is intended for tabular classification, regression, and time-series forecasting tasks. It includes fields such as country codes, years, sex disaggregation, observed values, and is licensed under CC-BY-4.0.

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统计局（ILOSTAT）的核心劳动力统计数据库，汇集了31个亚洲国家在1999至2025年间关于劳动力利用不足综合率（LU4）的年度观察数据，共包含582条记录。数据通过ILOSTAT的REST API接口获取，并依据亚洲国家ISO3代码进行地域筛选。原始调查微观数据经ILO采用国际劳工统计学家会议（ICLS）定义进行统一协调，每项观测的原始来源在source.label列中予以标注，以保障数据的可追溯性与规范性。

特点

该数据集以劳动力利用不足综合率（LU4）为核心指标，按性别（总、男性、女性）提供细粒度分解，覆盖31个亚洲国家长达二十余年的时序数据，为区域劳动力市场研究提供了丰富素材。数据集遵循CC-BY-4.0许可协议，具备完整的元数据架构，包括观测状态、来源注释与断点说明等，有助于用户精确辨识数据质量。同时，各字段标准化设计，便于直接开展跨国家、跨时间的比较分析与可视化呈现。

使用方法

用户可通过HuggingFace的datasets库以一行代码加载数据，便捷地将观测值转换为Pandas DataFrame进行操作。数据集支持按国家筛选、按时间排序绘制趋势图，以及透视形成国家×年份矩阵，适用于面板数据分析。对于时间序列建模和分类回归任务，数据提供了干净的数值型目标变量与分类维度变量，可直接用于机器学习模型的训练与评估。此外，原数据引用与数据集打包方均已明确，便于学术研究中规范引用与合规使用。

背景与挑战

背景概述

该数据集由国际劳工组织（ILO）的ILOSTAT数据库编制，并由Electric Sheep Asia于2025年重新封装发布，聚焦亚洲31个国家在1999至2025年间劳动力利用不足的综合比率（LU4），按性别分列，共涵盖582条观测记录。ILOSTAT作为全球劳动统计的权威来源，依据国际劳工统计学家会议（ICLS）标准整合各国劳动力调查数据，为研究亚洲区域劳动力市场结构、性别差异及发展趋势提供了标准化、可比的量化基础。该数据集在时间序列分析、区域比较及政策评估等领域具有重要应用价值，推动了劳动经济学与数据科学交叉研究的发展。

当前挑战

该领域面临的核心挑战在于劳动力利用不足的多维性，LU4作为综合指标需整合时间相关不充分就业、求职者及潜在劳动力等多重维度，传统统计框架难以全面捕捉非正规经济下的隐性失业。数据构建过程中，不同国家的调查方法、指标定义及数据质量参差不齐，需通过ILO的协调机制进行标准化处理，但部分国家存在数据缺失、系列中断或方法论修订，如观测状态标记为'Break in series'，影响了时间序列的连续性和可比性。此外，按性别分列的细化虽有助于性别差异分析，但性别分类的单一性可能忽略非二元性别群体，且数据覆盖的国家和时间跨度不均，对跨国与跨期比较构成挑战。

常用场景

经典使用场景

该数据集聚焦于亚洲31个国家1999至2025年间劳动力利用不足综合率（LU4）的性别分列年度观测值，共计582条记录，其核心应用场景为劳动力市场的时间序列分析与跨国比较研究。凭借其涵盖二十余载的纵向数据结构和标准化的ILOSTAT指标口径，研究者可借此剖析亚洲区域劳动力闲置的演变轨迹，并进行性别维度的差异化探讨。在实证研究中，它常用于面板数据回归分析，以评估宏观经济政策或社会变迁对劳动力有效配置的影响，同时为劳动经济学中关于非充分就业的量化研究提供坚实的数据基础。

实际应用

在实际应用层面，该数据集对政策制定者与国际组织具有显著价值。各国劳动部门可依据分性别、分国的LU4数据，精准识别劳动力闲置高风险群体，从而制定更具靶向性的就业促进计划。国际劳工组织（ILO）及区域发展机构可利用这些历史序列监测千年发展目标中体面工作的进展，评估干预措施效果，并优化资源分配。此外，研究机构与咨询公司能够基于此数据构建预测模型，为劳动力市场趋势提供前瞻性洞察，辅助企业投资决策与政府宏观规划。其标准化、机器可读的格式亦便于整合进更广泛的经济监测平台，实现跨领域的数据融合应用。

衍生相关工作

此数据集的发布推动了多项延伸性学术工作的发展。其一，基于其时间序列特性，研究者已将其应用于劳动力市场预测模型的构建，采用ARIMA等经典模型或LSTM等深度学习方法，对亚洲各国未来劳动力闲置率进行推估。其二，该数据常与ILOSTAT其他指标（如失业率、就业率）结合，衍生出关于劳动力市场多维健康指数的综合性研究。其三，数据中性别维度的细分催生了聚焦于女性劳动力利用不足的性别经济学分析，探讨影响女性就业的系统性障碍。其四，地理覆盖的多样性支持了跨国比较研究平台的建设，此类工作常运用聚类分析等方法识别具有相似劳动力结构特征的国家群体，为区域劳动力政策协调提供了科学依据。