electricsheepasia/asia-ilo-luu-xlu4-sex-edu-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 education (%) | Asia (ILOSTAT)" --- # Composite rate of labour underutilization (LU4) by sex and education (%) | Asia (ILOSTAT) 🌏 **6,825 observations** · **30 Asia countries** · **1999–2025** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **6,825 observations** of `Other measures of labour underutilization` data across **30 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_EDU_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_EDU_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 30 Asia countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `CYP` | 1,024 | 1999 | 2024 | | `VNM` | 650 | 2010 | 2024 | | `THA` | 588 | 2010 | 2024 | | `LKA` | 538 | 2010 | 2024 | | `PSE` | 423 | 2015 | 2025 | | `BRN` | 395 | 2014 | 2024 | | `JOR` | 336 | 2017 | 2024 | | `IDN` | 244 | 2016 | 2023 | | `BGD` | 237 | 2013 | 2024 | | `MNG` | 234 | 2019 | 2024 | | `GEO` | 234 | 2019 | 2024 | | `KHM` | 205 | 2003 | 2019 | | `AFG` | 185 | 2014 | 2021 | | `PHL` | 184 | 2017 | 2023 | | `PAK` | 156 | 2009 | 2025 | | ... | _15 more countries_ | | | ## Indicators (sample) - `LUU_XLU4_SEX_EDU_RT` — Composite rate of labour underutilization (LU4) by sex and education (%) ## 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_EDU_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.) | `EDU_AGGREGATE_TOTAL` | | `classif1.label` | `string` | — | `Education (Aggregate levels): 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` | — | `C3:2620` | | `note_classif.label` | `string` | — | `Nonstandard education level: Includin…` | | `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-edu-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_EDU_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="LUU_XLU4_SEX_EDU_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "LUU_XLU4_SEX_EDU_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_luu_xlu4_sex_edu_rt_composite_rate_of_labour_underutilization_lu4_by_s_2025, title = {Composite rate of labour underutilization (LU4) by sex and education (%) | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=LUU_XLU4_SEX_EDU_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-luu-xlu4-sex-edu-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_EDU_RT_This dataset contains 6,825 observations of Other measures of labour underutilization data across 30 Asia countries, spanning 1999–2025, covering 1 distinct indicator: Composite rate of labour underutilization (LU4) by sex and education (%). It is sourced from ILOSTAT (ILOs central statistics database), repackaged for machine learning readiness, and includes columns such as country codes, sex disaggregation, education classification, year, and observed values. The dataset is designed for tabular classification, regression, and time-series forecasting tasks, with data quality notes and usage examples provided.

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ILOSTAT数据库，经由Electric Sheep Asia团队对原始API接口进行系统性抽取与清洗而得。数据获取路径限定于亚洲地区ISO3国家代码范围内，通过ILO统计部门依据国际劳工统计学家会议定义对各国劳动力调查微观数据进行协调与标准化处理。每一条观测记录均包含来源标签，确保数据可追溯性；最终汇集涵盖1999年至2025年间30个亚洲国家的6825条观测，以整齐的表格格式呈现。

使用方法

使用者可通过HuggingFace Datasets库中的load_dataset函数一键加载该数据集并转换为Pandas DataFrame格式进行后续处理。针对具体国家的研究需求，可利用ref_area字段进行条件筛选以提取单国子集。对于时间序列分析，建议按指标编码与年份排序后直接进行可视化与建模。若需构建国家间的对比矩阵，则可通过透视表功能以时间为行、国家为列、观测值为值进行重塑操作。

背景与挑战

背景概述

在劳动经济学与可持续发展目标（SDGs）评估体系中，劳动力的利用不足程度是衡量经济体就业质量与结构性失衡的核心指标。国际劳工组织（ILO）自1999年起通过ILOSTAT数据库系统性地收集全球劳动统计资料，为多国比较提供标准化依据。该数据集由Electric Sheep Asia于2025年重新整理并发布，聚焦亚洲30个国家和地区，涵盖了1999至2025年间按性别与教育程度划分的劳动力利用不足综合率（LU4）的6,825条观测值。这一数据集突破了传统失业率指标的局限性，能够更全面地捕捉边缘劳动力、潜在劳动力及时间相关就业不足等隐性劳动市场问题，为亚洲区域的劳动市场研究、政策制定及经济预测提供了宝贵的多维度时间序列数据，尤其有助于揭示教育与性别差异在劳动力利用效率中的作用。

当前挑战

该数据集所解决的领域挑战主要在于：传统的失业率指标在劳动经济学中往往低估了实际劳动力资源浪费的程度，尤其在新兴经济体和社会保障体系不完善的亚洲国家，非正规就业、技能错配与就业意愿受阻等现象普遍存在。LU4指标通过整合失业、就业不足及劳动力边缘人群，提供了一个更为全面的劳动力利用不足画像，但仍面临数据可比性与时效性挑战。构建过程中的挑战包括：ILOSTAT数据源自不同国家劳动力调查，虽经国际劳工统计会议（ICLS）定义协调，但不同国家调查方法、问卷设计、教育分类标准（如classif1注释中出现的非标准教育层级）及数据质量标记（如不可靠值）使得跨国家、跨时间的纵向分析存在偏差，需要使用者谨慎处理断点与来源变动。

常用场景

经典使用场景

该数据集记录了1999年至2025年间亚洲30个国家按性别与教育程度分层的劳动力利用不足综合率（LU4），共计6825条观测值。其经典使用场景聚焦于跨国家、跨时期的劳动力市场结构性分析。研究者可借助这一结构化面板数据，揭示亚洲各国在劳动力资源闲置程度上的时空异质性，并通过性别与教育维度的解构，深入探讨不同人口群体在就业不足、潜在劳动力与失业状态之间的动态转化规律。时间序列与分类回归的分析范式在此天然适用，成为比较区域劳动经济学研究的基础工具。

解决学术问题

数据集直接回应了劳动经济学中长期存在的测量困境——如何超越传统失业率，更全面捕捉劳动力市场的闲置与低效配置。通过整合国际劳工组织（ILO）对标化调查数据的统一处理，LU4指标将失业、时间相关就业不足、潜在劳动力及边缘附着群体纳入统一框架。这解决了以往研究因统计口径差异导致的区域可比性缺失问题，为探究教育回报递减、性别就业鸿沟以及经济周期对不同教育层次劳动群体的非对称冲击等学术命题，提供了标准化、可复现的实证基础。

实际应用

在实际应用中，该数据集成为多边发展机构与各国政策制定者诊断劳动力市场健康状况的关键支撑。开发银行利用其中的性别与教育细分数据，评估技能培训项目的成效，并识别出因技能错配而陷入低利用状态的群体。亚洲国家劳动部门则借助时间序列变化，研判产业结构转型对劳动力吸纳能力的影响，进而优化职业教育和就业服务资源的区域配置。非政府组织亦可利用该数据追踪特定弱势人群的劳动参与障碍，为倡导包容性增长策略提供循证依据。

数据集最近研究

最新研究方向

当前，基于ILOSTAT构建的劳动利用不足综合率（LU4）数据集，正成为亚洲劳动力市场研究的前沿工具。研究者聚焦于通过性别与教育程度交叉分解，精准捕捉非典型就业与隐性失业的动态演化，尤其在后疫情时代经济复苏、数字转型与零工经济崛起的背景下，这一指标比传统失业率更具现实解释力。该数据集覆盖30个亚洲国家长达二十余年的观测，为跨国比较面板分析与时间序列预测提供了坚实的数据基础，推动了对劳动力资源错配、教育回报率差异及性别就业鸿沟的量化研究。其标准化架构与便捷的API接口，也加速了机器学习模型在劳动经济学与公共政策评估中的落地应用，对于理解亚洲复杂的劳动力市场结构与可持续发展目标（SDG）相关指标的监测具有重要实证价值。