electricsheepasia/asia-ilo-luu-xlu4-sex-age-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: - 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 rural / urban areas (%) | Asia (ILOSTAT)" --- # Composite rate of labour underutilization (LU4) by sex, age and rural / urban areas (%) | Asia (ILOSTAT) 🌏 **11,762 observations** · **25 Asia countries** · **1999–2025** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **11,762 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_AGE_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_AGE_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` | 1,807 | 1999 | 2024 | | `VNM` | 1,248 | 2007 | 2024 | | `LKA` | 986 | 2010 | 2024 | | `THA` | 960 | 2010 | 2024 | | `TUR` | 720 | 2004 | 2013 | | `PSE` | 705 | 2015 | 2022 | | `BRN` | 623 | 2014 | 2024 | | `JOR` | 538 | 2017 | 2024 | | `PHL` | 528 | 2017 | 2023 | | `IDN` | 504 | 2016 | 2023 | | `GEO` | 432 | 2019 | 2024 | | `MNG` | 408 | 2019 | 2024 | | `AFG` | 300 | 2014 | 2021 | | `ARM` | 288 | 2014 | 2017 | | `BGD` | 287 | 2013 | 2024 | | ... | _10 more countries_ | | | ## Indicators (sample) - `LUU_XLU4_SEX_AGE_GEO_RT` — Composite rate of labour underutilization (LU4) by sex, age 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_AGE_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.) | `AGE_YTHADULT_YGE15` | | `classif1.label` | `string` | — | `Age (Youth, adults): 15+` | | `classif2` | `string` | Second classification variable where applicable | `GEO_COV_NAT` | | `classif2.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) | `U` | | `obs_status.label` | `string` | — | `Unreliable` | | `note_classif` | `string` | — | `—` | | `note_classif.label` | `string` | — | `—` | | `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-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_AGE_GEO_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="LUU_XLU4_SEX_AGE_GEO_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "LUU_XLU4_SEX_AGE_GEO_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_luu_xlu4_sex_age_geo_rt_composite_rate_of_labour_underutilization_lu4_by_s_2025, title = {Composite rate of labour underutilization (LU4) by sex, age and rural / urban areas (%) | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=LUU_XLU4_SEX_AGE_GEO_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-luu-xlu4-sex-age-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_AGE_GEO_RT_This dataset contains composite rates of labour underutilization (LU4) for 25 Asian countries from 1999 to 2025, disaggregated by sex, age, and rural/urban areas. Sourced from the International Labour Organization (ILO) ILOSTAT database via API and filtered to Asian countries, it includes 11,762 observations and one core indicator (LUU_XLU4_SEX_AGE_GEO_RT). The dataset features columns such as country code, indicator code, sex classification, age classification, rural/urban classification, observation year, observed value, and data quality flags, suitable for machine learning tasks like tabular classification, regression, and time-series forecasting. Data is published at annual frequency and harmonized by ILO using ICLS definitions for consistency.

搜集汇总

数据集介绍

构建方式

在劳动力统计领域，准确衡量劳动利用不足状况对于评估区域经济发展至关重要。该数据集源自国际劳工组织（ILO）的ILOSTAT数据库，通过REST API接口直接提取指标编码为LUU_XLU4_SEX_AGE_GEO_RT的原始数据，并依据亚洲ISO3国家代码进行地理范围筛选。ILOSTAT通过国际劳工统计学家会议（ICLS）定义对各国劳动力调查微观数据进行统一协调与标准化处理，最终凝聚为涵盖11,762条观测值的结构化表格，每条记录均附有来源标识以保证可追溯性。

使用方法

研究者可通过HuggingFace Datasets库的load_dataset函数一键加载该数据集，并直接转换为Pandas DataFrame进行后续分析。例如，通过按‘ref_area’字段筛选特定国家数据，或针对单一指标‘LUU_XLU4_SEX_AGE_GEO_RT’进行时间序列可视化，以捕捉劳动利用不足率的历史演变趋势。此外，利用pivot_table方法可将数据重塑为国家与年份交叉的矩阵形式，便于开展面板数据分析或跨区域横向比较，极大提升了从数据到洞察的转化效率。

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，劳动力利用不足的精准测度成为劳动经济学与可持续发展研究的关键议题。国际劳工组织（ILO）于其成立百年之际持续深耕劳动统计领域，其旗舰数据库ILOSTAT整合了来自200多个经济体的劳动力调查与行政记录数据，为政策制定与学术研究提供了坚实的数据基石。在此框架下，亚洲综合劳动力利用不足率（LU4）数据集应运而生，由Electric Sheep Asia于2025年重新封装发布，收录了1999年至2025年间覆盖25个亚洲国家的11,762条观测值。该数据集聚焦于按性别、年龄及城乡地域划分的复合劳动力利用不足率，突破了传统失业率指标的局限，系统捕捉了隐形失业、就业不足与劳动力市场附着松散等边缘化现象，为深入剖析亚洲发展中国家非正规经济与结构性就业矛盾提供了不可替代的量化工具，有力地推动了ILO体面劳动议程的区域化实证研究。

当前挑战

该数据集所应对的核心领域挑战在于，传统失业率指标严重低估了亚洲发展中国家劳动力市场的真实困境，大量劳动者以非正规就业、不充分就业或退出市场的方式被排除在统计视野之外，综合劳动力利用不足率（LU4）的建构本身即为攻克这一概念鸿沟的关键尝试。从数据构建过程而言，挑战具体体现在三个方面：其一，跨国数据来源的异质性显著，各国劳动力调查的定义、抽样框架与调查时间存在差异，ILOSTAT虽通过国际劳动统计学家会议（ICLS）标准进行了协调统一，但数据来源标识（source.label）仍表明溯源层面需严谨对待；其二，时间序列的断裂与标记问题突出，数据集中存在因方法论修订导致的中断（break in series）及不可靠观测值标记（obs_status为U），这要求在时间序列分析中谨慎处理结构性突变；其三，分类维度的稀疏性构成挑战，针对特定性别或城乡的细分数据存在缺失，限制了多维交叉分析的可能性，数据使用者需在分析粒度与统计可靠性之间寻求平衡。

常用场景

经典使用场景

该数据集收录了1999年至2025年间亚洲25个国家共计11,762条劳动力利用不足综合率（LU4）的观测记录，按性别、年龄及城乡地域进行精细分层。作为国际劳工组织（ILO）ILOSTAT数据库的核心指标之一，该数据集为研究亚洲地区劳动力市场的结构性失衡提供了宝贵的量化素材。经典使用场景包括：构建面板数据模型以探究不同人口亚群中劳动力闲置程度的动态演变趋势；利用分层回归分析性别与年龄因素对就业不充分状态的交互影响；以及通过时间序列预测方法（如ARIMA或Prophet模型）对各国劳动力利用不足的未来走向进行前瞻性推断。

解决学术问题

在劳动经济学与发展经济学领域，该数据集有效解决了多项长期困扰学界的核心问题。它首次以统一、可比的标准量化了亚洲范围内劳动力利用不足（而非仅关注失业率）的复合形态，从而突破了传统失业统计在捕捉隐性就业不充分（如非自愿兼职、技能错配导致的低效就业）方面的局限性。研究者可借助该数据揭示经济发展水平与劳动力闲置之间的非线性关系，评估社会保障政策对边缘劳动群体的实际覆盖效果，并检验全球化冲击与产业结构变迁如何通过性别与年龄渠道诱发差异化的劳动市场脆弱性。这些发现为优化就业政策、推动体面劳动议程（SDG 8.5）提供了坚实的实证基础。

实际应用

在实际应用层面，该数据集可作为亚洲各国劳动统计部门与跨国发展机构进行政策评估与决策支持的关键工具。例如，通过监测不同性别与年龄段群体在LU4指标上的波动，政策制定者能够精准识别在经济增长放缓期间遭受最大就业冲击的社会群体，并针对性地设计职业培训计划或社会保障兜底方案。国际组织（如世界银行、亚洲开发银行）可将其融入区域劳动力市场监测体系，用于比较各国在实现体面劳动目标方面的进展。此外，大型企业的人力资源战略规划亦可借鉴该数据，预判特定地区与领域的劳动力供给质量与配置效率。

数据集最近研究

最新研究方向

当前，该数据集在劳动经济学与可持续发展研究领域的前沿方向集中于运用精细化时序建模与多维分解技术，深入剖析亚洲地区劳动力利用不足（LU4）复合率的性别、年龄及城乡结构差异。结合ILOSTAT在2023年后更新数据采集方法与疫情后劳动力市场重塑的背景，研究者正利用此面板数据追踪新冠疫情对非正规就业与隐蔽失业的长期后效影响，并评估其与联合国可持续发展目标（SDG 8）中体面劳动指标的偏离程度。尤其在东南亚与西亚经济体，通过耦合此数据集与宏观政策变量，学者得以量化劳动参与弹性与技能错配的动态演变，为后疫情时代亚洲包容性复苏战略的制定奠定了实证基础。