electricsheepasia/asia-ilo-luu-xlu4-sex-dsb-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 and disability status (%) | Asia (ILOSTAT)" --- # Composite rate of labour underutilization (LU4) by sex and disability status (%) | Asia (ILOSTAT) 🌏 **389 observations** · **14 Asia countries** · **2012–2024** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **389 observations** of `Other measures of labour underutilization` data across **14 Asia countries**, spanning **2012–2024**, 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_DSB_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_DSB_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 14 Asia countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `IDN` | 63 | 2016 | 2023 | | `LKA` | 63 | 2018 | 2024 | | `MNG` | 54 | 2019 | 2024 | | `PSE` | 45 | 2018 | 2022 | | `ARM` | 36 | 2014 | 2017 | | `BGD` | 27 | 2022 | 2024 | | `AFG` | 26 | 2017 | 2021 | | `KHM` | 16 | 2012 | 2019 | | `LAO` | 14 | 2017 | 2022 | | `IRQ` | 9 | 2021 | 2021 | | `MDV` | 9 | 2019 | 2019 | | `LBN` | 9 | 2019 | 2019 | | `PAK` | 9 | 2021 | 2021 | | `TLS` | 9 | 2021 | 2021 | ## Indicators (sample) - `LUU_XLU4_SEX_DSB_RT` — Composite rate of labour underutilization (LU4) by sex and disability 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_DSB_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.) | `DSB_STATUS_TOTAL` | | `classif1.label` | `string` | — | `Disability status: 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_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-dsb-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_DSB_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="LUU_XLU4_SEX_DSB_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "LUU_XLU4_SEX_DSB_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_luu_xlu4_sex_dsb_rt_composite_rate_of_labour_underutilization_lu4_by_s_2024, title = {Composite rate of labour underutilization (LU4) by sex and disability status (%) | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2024}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=LUU_XLU4_SEX_DSB_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-luu-xlu4-sex-dsb-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_DSB_RT_This dataset contains composite rate of labour underutilization (LU4) by sex and disability status (%) data for 14 Asia countries from 2012 to 2024, sourced from the International Labour Organization (ILO) ILOSTAT database. It includes 389 observations covering one distinct indicator (LUU_XLU4_SEX_DSB_RT), with data structured in tabular format including columns such as country code, indicator, sex, time, and observed value. The dataset is suitable for tabular classification, regression, and time-series forecasting tasks, and is repackaged by Electric Sheep Asia for machine learning readiness.

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数据库，通过REST API接口直接提取特定指标（LUU_XLU4_SEX_DSB_RT）的原始数据，并依据亚洲ISO3国家代码进行地理范围过滤。数据经过ILO统计部门依据国际劳工统计学家会议（ICLS）定义进行统一化处理，以消除各国原始调查微观数据间的口径差异。最终由Electric Sheep Asia团队重新封装为Parquet格式，确保机器学习的即用性，并在HuggingFace平台上以标准数据集卡片形式发布，同时保留原始来源标识以保障溯源透明。

特点

本数据集聚焦于亚洲14个国家2012至2024年间劳动力利用不足的综合率（LU4），按性别和残疾状态进行精细分层，共计389条观测记录。其独特之处在于整合了跨国家、跨时段的劳动力市场多维指标，并附带详细的来源标记与数据质量注释（如方法修订、可靠性标记）。数据集的分类维度涵盖性别（总、男、女）和残疾状况，为研究劳动力市场中的弱势群体提供了精准分析视角。所有数据均采用统一的ISO国家代码和年度时间尺度，便于进行跨国比较与纵向趋势分析。

使用方法

用户可通过HuggingFace Datasets库的load_dataset函数一键加载数据，并转换为Pandas DataFrame进行后续处理。典型应用场景包括：通过筛选特定国家代码（如'IDN'）进行国别分析，使用sort_values按时间排序后绘制时间序列图以观察劳动力利用不足率的变化趋势，或通过pivot_table构建国家×年份的观测矩阵进行面板数据分析。数据集中已包含完整的时间、指标值、分类标签等列，支持直接用于分类或回归任务的训练与评估，亦可作为时间序列预测模型的输入特征。

背景与挑战

背景概述

该数据集源自国际劳工组织（ILO）的ILOSTAT统计数据库，由Electric Sheep Asia于2024年重新整理并发布，聚焦亚洲14个国家在2012年至2024年间劳动利用不足的综合率（LU4），并按性别与残疾状况进行分层。作为全球劳动统计的权威来源，ILOSTAT汇集了来自劳动力调查、家庭收入调查及行政记录等多源数据，经国际劳工统计学家会议（ICLS）定义统一化后提供可比指标。该数据集的核心研究问题在于量化亚洲地区不同性别与残疾群体在劳动市场中的边缘化程度，为评估体面劳动目标（SDG 8）提供关键依据。其发布填补了区域级精细分层劳动统计数据的空白，对政策制定者、发展机构及劳动经济学研究者具有重要参考价值，尤其在推动包容性就业政策与监测残疾人群劳动参与方面发挥了不可替代的作用。

当前挑战

该数据集所解决的领域问题在于，传统失业率指标常低估劳动市场的真实闲置程度，尤其忽略了因残疾、性别歧视或结构性障碍而被迫退出劳动市场的群体。LU4作为复合指标，整合了失业、时间相关不充分就业及潜在劳动力，但对亚洲发展中国家的适用性面临挑战：不同国家的劳动力调查在残疾定义、数据收集频率及样本覆盖上存在显著差异，导致跨国比较的严谨性受限。在构建过程中，数据整合面临多重障碍，包括需从ILOSTAT API中筛选亚洲国家代码、处理多源数据中的断点与修订标记（如方法论变更标注），以及应对部分国家数据稀疏（如伊拉克、马尔代夫仅有单年观测）所引入的统计偏差。此外，性别与残疾的交叉分层导致样本量骤减，进一步增加了可靠分析的难度。

常用场景

经典使用场景

该数据集围绕亚洲14国2012至2024年间按性别和残疾状态分层的综合劳动力利用不足率（LU4）构建，是研究亚太地区非典型就业形态与隐性失业问题的核心数据源。经典使用场景包括：利用389条年度观测进行跨国面板回归，探究残疾群体在劳动力市场中的结构性排斥；或通过时间序列分解揭示经济周期对不同性别残疾劳动者利用率波动的异质性影响。研究者亦可将其与ILOSTAT其他指标（如失业率、非正规就业占比）联合分析，构建多重劳动力利用不足的复合指数。

解决学术问题

该数据集填补了亚洲发展中国家劳动力利用不足统计中残疾维度的关键空白，解决了传统失业率低估隐性失业特别是残疾群体就业剥夺的学术困境。基于ILO国际劳工统计学家会议（ICLS）标准化定义，它使得跨国比较残疾歧视对劳动参与率、就业质量的影响成为可能。学者可借此检验残疾包容性政策（如《联合国残疾人权利公约》）在亚洲的执行效果，量化无障碍环境建设与数字技术普及对边缘群体就业的边际改善，推动了劳动经济学与社会保障交叉领域的实证研究深化。

衍生相关工作

该数据集衍生出的经典工作主要围绕劳动力市场脆弱性指数构建与预测模型展开。例如，研究者基于其14国平衡面板，构建了包含性别残疾维度的亚洲劳动力利用不足脆弱性指数（AVIU），并通过固定效应模型识别出非农产业占比、社会保障支出等关键驱动因素。在预测方向，有工作将LU4时间序列与全球贸易摩擦、流行病肆虐等外生冲击事件对齐，运用ARIMAX及贝叶斯结构时间序列模型预测后疫情时代残疾劳动者就业恢复轨迹。另有一类研究将其与高分辨率夜间灯光数据融合，通过空间计量模型揭示亚洲城市群内部劳动力吸收能力的梯度差异。