electricsheepasia/asia-ilo-luu-xlu4-sex-age-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: - 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 education (%) | Asia (ILOSTAT)" --- # Composite rate of labour underutilization (LU4) by sex, age and education (%) | Asia (ILOSTAT) 🌏 **19,702 observations** · **30 Asia countries** · **1999–2025** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **19,702 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_AGE_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_AGE_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` | 2,692 | 1999 | 2024 | | `VNM` | 1,864 | 2010 | 2024 | | `THA` | 1,668 | 2010 | 2024 | | `LKA` | 1,522 | 2010 | 2024 | | `TUR` | 1,181 | 2004 | 2013 | | `PSE` | 1,107 | 2015 | 2025 | | `BRN` | 939 | 2014 | 2024 | | `JOR` | 860 | 2017 | 2024 | | `IDN` | 838 | 2016 | 2023 | | `BGD` | 640 | 2013 | 2024 | | `GEO` | 623 | 2019 | 2024 | | `MNG` | 613 | 2019 | 2024 | | `KHM` | 576 | 2003 | 2019 | | `AFG` | 505 | 2014 | 2021 | | `PHL` | 480 | 2017 | 2023 | | ... | _15 more countries_ | | | ## Indicators (sample) - `LUU_XLU4_SEX_AGE_EDU_RT` — Composite rate of labour underutilization (LU4) by sex, age 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_AGE_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.) | `AGE_YTHADULT_YGE15` | | `classif1.label` | `string` | — | `Age (Youth, adults): 15+` | | `classif2` | `string` | Second classification variable where applicable | `EDU_AGGREGATE_TOTAL` | | `classif2.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-age-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_AGE_EDU_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="LUU_XLU4_SEX_AGE_EDU_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "LUU_XLU4_SEX_AGE_EDU_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_luu_xlu4_sex_age_edu_rt_composite_rate_of_labour_underutilization_lu4_by_s_2025, title = {Composite rate of labour underutilization (LU4) by sex, age and education (%) | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=LUU_XLU4_SEX_AGE_EDU_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-luu-xlu4-sex-age-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_AGE_EDU_RT_This dataset contains Other measures of labour underutilization data from the International Labour Organization (ILO) ILOSTAT database, specifically the Composite rate of labour underutilization (LU4) by sex, age and education (%). It covers 30 Asian countries from 1999 to 2025, with 19,702 observations. The data is sourced from the ILOSTAT REST API and filtered to Asian country codes, focusing on the composite labour underutilization rate disaggregated by sex, age, and education. The dataset includes columns such as country code, source information, indicator code, sex classification, age and education classifications, observation year, observed value, and status flags, making it suitable for tabular classification, regression, and time-series forecasting tasks. The data is published at an annual frequency and is harmonized using ILOs International Conference of Labour Statisticians (ICLS) definitions.

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统计数据库，聚焦于亚洲地区劳动力利用不足的综合率（LU4）指标，按性别、年龄及教育程度进行细致分层。数据通过ILOSTAT REST API直接抽取，并依据ISO 3166-1 alpha-3代码筛选出30个亚洲国家，时间跨度覆盖1999至2025年。原始调查微观数据经由ICLS定义统一整合，确保跨国可比性，且每种观测均标注来源代码以供溯源。数据集以表格形式封装为Parquet格式，共包含19,702条观测记录，每条记录详尽记录了国家、来源、指标、性别分类、教育分层、观测年份、数值及状态标记，构建过程兼顾了标准化与可追溯性。

使用方法

数据集通过HuggingFace datasets库实现即插即用，用户仅需调用load_dataset()函数即可将数据载入内存并转换为Pandas DataFrame进行剖析。针对特定国家，可利用ref_area字段进行条件过滤；对单一指标进行时间序列分析时，可按indicator字段筛选并依据time列排序后绘图，直观呈现趋势变化。研究者亦可将数据重塑为以年份为行、国家为列的透视矩阵，便于开展面板数据回归或跨经济体的横向比较。内置的多种分类维度支持用户灵活构建子样本，以满足定制化的研究需求。该数据集设计兼顾易用性与分析深度，适于劳动经济学、发展研究及政策评估等领域应用。

背景与挑战

背景概述

该数据集由国际劳工组织（ILO）统计部门发布，由Electric Sheep Asia于2025年重新整合并托管于HuggingFace平台，聚焦亚洲地区劳动利用不足的复合率（LU4）指标。数据集涵盖1999年至2025年间30个亚洲国家与地区的19,702条观测记录，按性别、年龄及教育程度进行细致分层，旨在揭示劳动力市场中潜在劳动力储备的规模与结构。作为ILOSTAT数据库的重要子集，该数据为劳动经济学、发展研究及政策制定提供了关键的量化基础，尤其有助于分析亚洲新兴经济体在就业质量、非正规就业及劳动力闲置等方面的动态特征，对联合国可持续发展目标（SDG）中体面工作的监测与评估具有重要支撑作用。

当前挑战

该数据集面临多维挑战。领域层面，劳动利用不足复合率（LU4）作为超越传统失业率的综合性指标，其定义与测算需协调不同国家劳动统计体系的差异，尤其在亚洲地区，非正规就业普遍，数据采集与口径统一难度显著。构建过程中，数据整合自ILOSTAT REST API，需处理多来源调查数据的异构性与时间序列的非连续性，如部分国家存在方法修订或断点（如note_indicator标示），且年份覆盖参差不齐（如土耳其仅2004-2013年）。此外，性别与教育分类的缺失值、观测可靠性标记（如obs_status为'Unreliable'）及非标准教育水平的注释（如note_classif）均增加了数据清洗与分析的复杂性，对研究者提出较高的数据预处理要求。

常用场景

经典使用场景

该数据集汇集了亚洲30个国家1999至2025年间劳动力利用不足综合率（LU4）的年度观测数据，按性别、年龄及教育程度进行细致分层，为劳动经济学领域的研究提供了宝贵的纵向面板数据。研究者常将其应用于时间序列分析，以追踪特定国家或区域劳动力市场疲软态势的演变轨迹；亦可构建跨国比较模型，剖析不同亚洲经济体中结构性劳动力错配的共性特征。此外，该数据集的表格化结构也便于利用机器学习方法，如分类与回归任务，来预测劳动力利用不足水平并识别其关键驱动因素。

解决学术问题

在学术层面，该数据集有效回应了劳动力市场研究中关于劳动力利用不足的多维测度问题。传统上，失业率往往低估了劳动力市场的真实闲置程度，而LU4指标综合了失业、时间相关就业不足及潜在劳动力等元素，为更全面刻画劳动力未充分利用状况提供了量化基准。借助性、年龄、教育等维度，学者能够深入探究不同人群在劳动力市场中的脆弱性差异，并检验人力资本理论、性别不平等及生命周期效应等经典命题在亚洲情境下的适用性，从而推动劳动经济学理论与实证研究的深化。

实际应用

在实际应用层面，该数据集对政策制定与国际组织具有重要价值。各国劳动部门可借此数据监测劳动力市场健康状况，评估就业政策的实效，并依据细分人群特征精准施策，如针对青年或低教育水平群体的就业促进计划。国际劳工组织及其他跨国机构亦可用于撰写区域劳动报告，支持可持续发展目标（SDG）中体面工作指标的跟踪与评估。此外，数据可服务人力资源规划与职业教育培训体系的调整，为优化劳动力资源配置提供数据支撑。

数据集最近研究

最新研究方向

该数据集基于ILOSTAT权威劳动统计，聚焦亚洲30国1999-2025年间综合劳动力利用不足率（LU4）的性别、年龄与教育维度分解，为劳动力市场结构性失衡研究提供了精细化的时序面板数据。当前前沿方向包括利用机器学习与时间序列模型揭示后疫情时代亚洲各国劳动力闲置的异质性演变轨迹，结合教育分层探讨人力资本配置效率与青年就业脆弱性，并借助因果推断方法评估政策干预对降低综合劳动力利用不足率的实效。该数据集的公开化与标准化处理显著降低了跨国比较分析的门槛，为区域劳动力市场监测、可持续发展目标（SDG）进展评估及包容性增长战略的制定提供了坚实的数据基石，推动了劳动经济学实证研究向更精细、更动态的方向深化。