electricsheepasia/asia-ilo-luu-xlu4-sex-geo-mts-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, rural / urban area and marital sta | Asia (ILOSTAT)" --- # Composite rate of labour underutilization (LU4) by sex, rural / urban area and marital sta | Asia (ILOSTAT) 🌏 **4,415 observations** · **25 Asia countries** · **1999–2025** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **4,415 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_GEO_MTS_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_GEO_MTS_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` | 594 | 1999 | 2020 | | `VNM` | 451 | 2010 | 2024 | | `THA` | 405 | 2010 | 2024 | | `LKA` | 378 | 2010 | 2024 | | `TUR` | 288 | 2004 | 2013 | | `PSE` | 288 | 2015 | 2022 | | `PHL` | 249 | 2017 | 2023 | | `BRN` | 243 | 2014 | 2024 | | `JOR` | 216 | 2017 | 2024 | | `IDN` | 189 | 2016 | 2023 | | `MNG` | 162 | 2019 | 2024 | | `AFG` | 138 | 2014 | 2021 | | `KHM` | 112 | 2007 | 2019 | | `ARM` | 108 | 2014 | 2017 | | `PAK` | 108 | 2009 | 2025 | | ... | _10 more countries_ | | | ## Indicators (sample) - `LUU_XLU4_SEX_GEO_MTS_RT` — Composite rate of labour underutilization (LU4) by sex, rural / urban area and marital 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_GEO_MTS_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.) | `GEO_COV_NAT` | | `classif1.label` | `string` | — | `Area type: National` | | `classif2` | `string` | Second classification variable where applicable | `MTS_AGGREGATE_TOTAL` | | `classif2.label` | `string` | — | `Marital status (Aggregate): 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-geo-mts-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_GEO_MTS_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="LUU_XLU4_SEX_GEO_MTS_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "LUU_XLU4_SEX_GEO_MTS_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_luu_xlu4_sex_geo_mts_rt_composite_rate_of_labour_underutilization_lu4_by_s_2025, title = {Composite rate of labour underutilization (LU4) by sex, rural / urban area and marital sta | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=LUU_XLU4_SEX_GEO_MTS_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-luu-xlu4-sex-geo-mts-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_GEO_MTS_RT_This dataset contains 4,415 observations of the Composite rate of labour underutilization (LU4) across 25 Asia countries, spanning 1999–2025, covering 1 distinct indicator. The data is sourced from the ILOSTAT database of the International Labour Organization (ILO), retrieved via REST API and filtered to Asia ISO3 country codes. It provides the composite rate of labour underutilization disaggregated by sex, rural/urban area, and marital status, with detailed fields including country codes, data sources, observation years, values, and status flags. The data is harmonized by ILO using International Conference of Labour Statisticians (ICLS) definitions and is suitable for tabular classification, regression, and time-series forecasting tasks.

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中央统计数据库，作为全球劳动力统计的权威来源，其数据经由各国劳动力调查、家庭收入调查及行政记录等渠道汇集而成。数据集通过调用ILOSTAT REST API直接提取原始指标，并依据国际劳工统计学家会议（ICLS）的定义对微观数据进行标准化处理。在此基础之上，由Electric Sheep Asia团队进一步过滤筛选出亚洲地区25个国家的观测记录，最终构建为包含4,415条观测值、时间跨度自1999年至2025年的结构化数据集合。

特点

该数据集聚焦于劳动力利用不足的综合衡量指标（LU4），按照性别、城乡地域及婚姻状况进行了细致的维度分解，提供了从总人口到特定人群的分层观测视角。数据涵盖25个亚洲国家，并附有详尽的来源标记与观测状态标注，便于用户评估数据质量与序列一致性。此外，数据集以Parquet格式封装，经过清洗与规范化的模式处理，具备良好的机器可读性与跨平台兼容性，为区域劳动力市场的比较研究提供了高质量、标准化的数据基础。

使用方法

使用者可通过HuggingFace的`datasets`库中的`load_dataset`函数便捷加载该数据集，并将其转换为Pandas DataFrame以进行后续分析。在进行时间序列分析时，可通过筛选特定指标如`LUU_XLU4_SEX_GEO_MTS_RT`并按时间排序，轻松绘制趋势图。数据集中丰富的分类列，如`ref_area`（国家代码）与`sex`（性别），支持用户按国家、性别等维度进行分组筛选或构建透视表，从而灵活地探索不同群体与地区间劳动力利用情况的差异与演变规律。

背景与挑战

背景概述

劳动力资源的高效配置与充分就业是宏观经济健康运行的核心标志，而劳动利用不足作为超越传统失业率范畴的综合性指标，能够更全面地揭示劳动力市场的隐性失衡。在此背景下，国际劳工组织统计司（ILOSTAT）依托其庞大的数据采集与标准化体系，构建了包含复合劳动利用不足率在内的多维统计框架。由国际劳工组织（ILO）主导、Electric Sheep Asia在2025年进行封装整理的该数据集，汇集了亚洲25个国家1999至2025年间共4,415条观测记录，聚焦于按性别、城乡地域与婚姻状况划分的综合劳动利用不足率。该数据集的发布，为深入理解亚洲地区结构性就业难题、评估劳工政策效能及推动可持续生计目标的相关研究提供了坚实的数据基础，显著提升了该领域实证分析的可及性与精细度。

当前挑战

该数据集所应对的领域核心挑战在于，传统失业率指标往往低估了隐性失业、时间相关就业不足及劳动力市场边缘化群体的真实困境，尤其在亚洲多样化的发展阶段与劳动力结构下，单一的就业状态分类已难以刻画复杂的劳动利用全貌。构建过程中面临的主要挑战包括：首先，各国原始调查数据在问卷设计、抽样方法及统计口径上存在显著差异，ILOSTAT虽依据国际劳工统计学家会议的最新定义进行协调，但不同来源间的兼容性与可比性仍需审慎对待；其次，数据以年频发布，无法捕捉年内季节性波动与突发政策冲击下的时效性变化；此外，受限于特定国家在某些年份缺乏有效调查，时间序列的完整性与连续性受到制约，且在多元分类维度下，部分细分组别的样本量较小，可能影响统计推断的稳健性。

常用场景

经典使用场景

在劳动经济学与发展经济学的交汇领域，该数据集是分析亚洲地区劳动力利用不充分现象的核心工具。其经典用途在于通过复合劳动力利用不足率（LU4）这一综合指标，结合性别、城乡及婚姻状况的维度分解，刻画亚洲25个国家在1999年至2025年间的劳动力市场结构性特征。研究人员常利用该数据集进行跨国面板回归，揭示经济发展阶段、社会制度与劳动力闲置之间的复杂关联，或构建时间序列模型以追踪特定国家劳动力利用率的动态演变轨迹。

实际应用

在实际应用层面，该数据为国际劳工组织、各国劳动部门及发展机构提供了制定与评估就业政策的量化基础。通过对比不同性别和城乡群体的LU4值，政策制定者能够识别出最脆弱的劳动力群体，从而精准设计针对性的职业培训计划、灵活就业保障措施或区域产业扶持政策。此外，该数据集还支撑了跨国比较研究，帮助亚洲国家在可持续发展目标框架下监测体面工作的进展，并为区域经济合作组织调整劳动力流动与移民政策提供实证依据。

衍生相关工作

基于该数据衍生出了一系列具有影响力的学术工作，其中包括利用LU4指标构建亚洲劳动力市场脆弱性指数，为后续的区域经济韧性研究奠定指标基础；也有研究将其与教育水平、产业结构升级指标结合，通过面板协整分析探讨人力资本积累与劳动力利用效率的长期均衡关系。此外，部分学者运用该数据集验证了性别不平等指数与劳动力闲置率之间的U型曲线假说，丰富了关于女性劳动参与率转折点的理论讨论。这些成果进一步推动了劳动经济学、性别研究与发展社会学之间的跨学科对话。