electricsheepasia/asia-ilo-luu-xlu4-sex-edu-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: - 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, education and rural / urban areas | Asia (ILOSTAT)" --- # Composite rate of labour underutilization (LU4) by sex, education and rural / urban areas | Asia (ILOSTAT) 🌏 **7,422 observations** · **25 Asia countries** · **1999–2025** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **7,422 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_EDU_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_EDU_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` | 1,102 | 1999 | 2024 | | `VNM` | 736 | 2010 | 2024 | | `THA` | 701 | 2010 | 2024 | | `LKA` | 613 | 2010 | 2024 | | `PSE` | 475 | 2015 | 2022 | | `TUR` | 450 | 2004 | 2013 | | `BRN` | 410 | 2014 | 2024 | | `JOR` | 360 | 2017 | 2024 | | `IDN` | 315 | 2016 | 2023 | | `MNG` | 267 | 2019 | 2024 | | `GEO` | 243 | 2019 | 2024 | | `AFG` | 215 | 2014 | 2021 | | `BGD` | 202 | 2013 | 2024 | | `PHL` | 195 | 2017 | 2023 | | `KHM` | 192 | 2007 | 2019 | | ... | _10 more countries_ | | | ## Indicators (sample) - `LUU_XLU4_SEX_EDU_GEO_RT` — Composite rate of labour underutilization (LU4) by sex, education 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_EDU_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.) | `EDU_AGGREGATE_TOTAL` | | `classif1.label` | `string` | — | `Education (Aggregate levels): Total` | | `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` | — | `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-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_EDU_GEO_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="LUU_XLU4_SEX_EDU_GEO_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "LUU_XLU4_SEX_EDU_GEO_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_luu_xlu4_sex_edu_geo_rt_composite_rate_of_labour_underutilization_lu4_by_s_2025, title = {Composite rate of labour underutilization (LU4) by sex, education and rural / urban areas | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=LUU_XLU4_SEX_EDU_GEO_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-luu-xlu4-sex-edu-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_EDU_GEO_RT_This dataset contains 7,422 observations across 25 Asian countries, spanning from 1999 to 2025, focusing on Other measures of labour underutilization, specifically the Composite rate of labour underutilization (LU4) by sex, education and rural / urban areas (indicator code: LUU_XLU4_SEX_EDU_GEO_RT). The data is sourced from the International Labour Organizations (ILO) ILOSTAT database, retrieved via its REST API, and repackaged by Electric Sheep Asia to provide a unified, machine-learning-ready format. The dataset is organized in tabular form, with columns including country code (ref_area), country name (ref_area.label), data source (source.label), indicator code (indicator), indicator label (indicator.label), sex disaggregation (sex), education classification (classif1), area classification (classif2), year (time), observed value (obs_value), and observation status (obs_status). It provides annual frequency data, covering three sex dimensions (total, male, female) and includes classifications for education levels and rural/urban areas. Usage notes include: data is annual; multiple sources may exist for the same country-year, but only the ILO-selected best source is used; disaggregation columns are non-null only when the indicator publishes breakdowns. The dataset is suitable for tabular classification, tabular regression, and time-series forecasting tasks, licensed under cc-by-4.0.

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）旗下的ILOSTAT中央统计数据库，是衡量亚洲地区劳动力利用不足的复合指标（LU4）的专业数据集。构建过程中，数据通过ILOSTAT的REST API接口直接提取，原始指标代码为LUU_XLU4_SEX_EDU_GEO_RT，随后依据ISO 3166-1 alpha-3标准筛选出亚洲25个国家的观测记录。ILOSTAT采用国际劳工统计学家会议（ICLS）定义对各国劳动力调查的微观数据进行标准化处理，并在source.label列中标注数据来源以保障可追溯性。最终数据集由Electric Sheep Asia团队以Parquet格式重新打包并发布在HuggingFace平台上，便于机器学习场景下的直接调用。

使用方法

用户可通过HuggingFace Datasets库的load_dataset函数便捷加载该数据集，首次调用即可获得包含所有字段的DataFrame格式数据。针对特定国家的分析，可利用ref_area列进行过滤，例如筛选印度尼西亚（IDN）的数据。对于时间序列分析，建议先按指标代码进行筛选，再依据time列排序后使用plot方法可视化obs_value的变化趋势。若需构建国家与年份的交叉矩阵，可运用pivot_table方法将数据重塑为以time为行、ref_area为列的表格形式，便于进行面板数据分析或计量建模。数据集遵循CC-BY-4.0许可协议，使用时需同时引用ILO原始数据及Electric Sheep Asia的再包装版本。

背景与挑战

背景概述

该数据集由国际劳工组织（ILO）统计司于2025年整理发布，由Electric Sheep Asia重新封装于HuggingFace平台，聚焦亚洲地区劳动力利用不足的综合衡量指标（LU4）。劳动力利用不足是衡量经济体劳动力市场健康程度的关键维度，超越了传统的失业率统计，涵盖了时间相关的不充分就业、潜在劳动力及闲置劳动力等复杂情形。数据集涵盖1999年至2025年间25个亚洲国家的7,422条观测数据，按性别、教育水平及城乡地域进行精细分层，为研究亚洲劳动力市场的结构性差异、区域发展不平衡及社会政策效果提供了权威且可复用的数据基础，在劳动经济学、发展经济学及公共政策分析领域具有重要影响力。

当前挑战

该数据集所应对的核心领域挑战在于，传统的失业率指标无法全面反映劳动力市场的闲置程度，尤其在新兴经济体和非正规经济主导的区域，劳动力利用不足的测量需要整合多个维度（时间、技能匹配、工作意愿）的复杂信息。在构建过程中，面临的挑战包括：跨国家与跨年度的数据源异质性，各国劳动力调查方法、定义及质量参差不齐，需依赖ILO基于国际劳工统计学家会议（ICLS）标准进行繁琐的协调统一；数据中部分观测值被标记为‘不可靠’或存在方法论断裂（break in series），需谨慎处理连续性与可比性问题；此外，按教育水平和城乡地域的分层导致了大量稀疏观测，增加了统计分析中的缺失值处理与样本代表性评估难度。

常用场景

经典使用场景

该数据集整合了国际劳工组织ILOSTAT数据库中亚洲25个国家1999至2025年间劳动力利用不足的综合率（LU4）指标，并按性别、教育程度及城乡区域进行了细致的分类。经典的使用场景包括利用其结构化表格数据进行分类与回归任务，以预测不同人口群体在特定年份的劳动力利用不足状况；同时，由于其时间跨度长、国家覆盖面广，该数据集极其适合用于时间序列预测建模，分析亚洲各国劳动力市场效率的动态演变趋势，评估结构性失业与不充分就业的长期规律。

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长期以来因缺乏标准化、跨区域可比微观数据而导致的研究瓶颈。研究者得以突破单一国家分析的局限，对亚洲多元经济体进行系统的横截面与纵向对比，探索性别不平等、教育回报递减以及城乡二元结构如何系统性影响劳动力资源错配。其持续更新的监测能力使学术研究能够精准捕捉政策干预（如技能培训计划、区域发展倡议）对劳动力利用不足率的冲击效应，为验证人力资本理论、劳动力市场分割假说提供了坚实的数据底座，显著推动了该领域实证研究的可信度与普适性。

实际应用

在实际应用层面，该数据集是国际发展机构、各国劳动部门及智库进行劳动力市场监测与政策评估的核心工具。通过实时追踪LU4指标的变化，决策者能够精准识别出就业质量最脆弱的群体——例如，受教育程度低的农村女性——从而设计有针对性的就业促进与社会保障项目。此外，数据集还可服务于跨国企业的区域投资决策，用以评估特定市场的劳动力冗余程度与人力成本压力，或为人道主义组织在移民与劳务输出研究中提供基准参考，助力实现联合国可持续发展目标中体面劳动与经济增长的量化追踪。

数据集最近研究

最新研究方向

该数据集聚焦于亚洲地区劳动力利用不足的综合衡量指标（LU4），结合性别、教育程度与城乡地理维度的交叉分析，为后疫情时代劳动力市场结构性失衡研究提供了前沿数据支撑。当前研究热点包括：利用该时间序列数据追踪东南亚及南亚国家在数字化转型与产业升级过程中，青年女性与低教育群体面临的就业质量下降问题；通过城乡对比揭示城市化进程中农村劳动力向非正规部门转移的隐性失业现象；以及结合国际劳工组织（ILO）最新方法论，探索LU4作为传统失业率补充指标在衡量SDG目标8（体面工作）进展中的政策敏感性。该数据集填补了亚洲地区劳动力利用不足细分数据的空白，尤其为性别平等与教育回报率等交叉性议题的实证研究提供了可靠基准。