electricsheepasia/asia-ilo-luu-xlu4-sex-edu-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, education and marital status (%) | Asia (ILOSTAT)" --- # Composite rate of labour underutilization (LU4) by sex, education and marital status (%) | Asia (ILOSTAT) 🌏 **7,654 observations** · **28 Asia countries** · **1999–2025** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **7,654 observations** of `Other measures of labour underutilization` data across **28 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_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_EDU_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 28 Asia countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `CYP` | 939 | 1999 | 2020 | | `VNM` | 761 | 2010 | 2024 | | `THA` | 744 | 2010 | 2024 | | `LKA` | 632 | 2010 | 2024 | | `TUR` | 480 | 2004 | 2013 | | `PSE` | 461 | 2015 | 2025 | | `BRN` | 410 | 2014 | 2024 | | `JOR` | 360 | 2017 | 2024 | | `IDN` | 315 | 2016 | 2023 | | `MNG` | 270 | 2019 | 2024 | | `BGD` | 261 | 2013 | 2024 | | `KHM` | 249 | 2003 | 2019 | | `AFG` | 243 | 2014 | 2021 | | `PHL` | 223 | 2017 | 2023 | | `PAK` | 180 | 2009 | 2025 | | ... | _13 more countries_ | | | ## Indicators (sample) - `LUU_XLU4_SEX_EDU_MTS_RT` — Composite rate of labour underutilization (LU4) by sex, education 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_EDU_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.) | `EDU_AGGREGATE_TOTAL` | | `classif1.label` | `string` | — | `Education (Aggregate levels): Total` | | `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) | `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-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_EDU_MTS_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="LUU_XLU4_SEX_EDU_MTS_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "LUU_XLU4_SEX_EDU_MTS_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_luu_xlu4_sex_edu_mts_rt_composite_rate_of_labour_underutilization_lu4_by_s_2025, title = {Composite rate of labour underutilization (LU4) by sex, education and marital status (%) | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=LUU_XLU4_SEX_EDU_MTS_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-luu-xlu4-sex-edu-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_EDU_MTS_RT_This dataset is a tabular dataset on the composite rate of labour underutilization (LU4) for Asian countries, disaggregated by sex, education, and marital status (%). It contains 7,654 observations across 28 Asia countries, spanning the years 1999 to 2025, covering 1 distinct indicator: LUU_XLU4_SEX_EDU_MTS_RT (composite rate of labour underutilization by sex, education and marital status). The data is sourced from ILOSTAT, the International Labour Organizations (ILO) central statistics database, which harmonizes raw survey microdata using International Conference of Labour Statisticians (ICLS) definitions from sources such as national labour force surveys, household income surveys, establishment surveys, and administrative records. The dataset includes columns for country code, country name, data source, indicator code, sex disaggregation, education classification, marital status classification, observation year, observed value, observation status, and more, with data quality notes such as annual frequency and use of ILO-selected best source. Repackaged by Electric Sheep Asia, it is part of a unified, ML-ready data layer for Asia on HuggingFace.

搜集汇总

数据集介绍

构建方式

在全球劳动统计领域，国际劳工组织（ILO）的ILOSTAT数据库是衡量劳动力市场状况的权威来源。本数据集由Electric Sheep Asia团队从ILOSTAT REST API直接拉取，并过滤至亚洲28个国家的ISO3国家代码，最终整合了覆盖1999年至2025年的7,654条观测记录。数据源自各国劳动力调查、家庭收入调查等官方统计，ILO通过国际劳工统计学家会议（ICLS）定义对原始微观数据进行标准化处理，确保跨国可比性。每个观测值均附带来源标识（source.label），便于追溯数据质量与出处。

特点

该数据集聚焦于劳动力利用不足的综合率（LU4），按性别、教育程度和婚姻状况进行细分，展现了亚洲劳动力市场的多维结构。其独特性在于提供了三个分类维度的交互分析：性别（总、男、女）、教育水平（含合计及非标准教育层级标注）以及婚姻状况（含合计分类），总计28个国家构成了丰富的地理覆盖。数据还标注了观测状态（如不可靠、临时性）和系列中断等元注释，增强了使用的审慎性，而年度频率则适用于宏观趋势与跨时期比较研究。

使用方法

用户可通过HuggingFace Datasets库一键加载数据，使用`load_dataset`函数即可将Parquet格式的表格数据转化为Pandas DataFrame，便于后续分析。例如，可按国家代码（ref_area）筛选特定国家的子集，或针对单一指标（LUU_XLU4_SEX_EDU_MTS_RT）按年份排序进行时间序列可视化。高级应用支持透视表操作，以年份为行、国家为列构建国家-年份矩阵，适合面板数据回归或跨国产出缺口比较。数据列包含丰富分类标签，可直接用于机器学习中的分类或回归任务，亦便于构建劳动经济学预测模型。

背景与挑战

背景概述

在劳动经济学与可持续发展目标（SDGs）的研究中，劳动力利用不足的测量长期依赖失业率这一单一指标，难以全面反映隐性失业、时间相关就业不足及劳动力市场附着松弛等复杂现象。为填补这一缺口，国际劳工组织（ILO）于2025年发布了名为“Composite rate of labour underutilization (LU4) by sex, education and marital status (%) | Asia (ILOSTAT)”的数据集，由Electric Sheep Asia重新打包并托管于HuggingFace平台。该数据集聚焦亚洲地区，涵盖28个国家1999至2025年间7,654条观测，核心指标LU4综合衡量了性别、教育水平及婚姻状况对劳动力利用不足的影响。其数据源自ILOSTAT——全球劳动统计的权威数据库——通过统一ILO统计学家大会定义并整合各国劳动力调查微观数据。该数据集为监测SDG 8（体面工作和经济增长）中非充分就业的亚洲区域差异提供了标准化、可复现的分析基础，对发展经济学、公共政策评估及跨国比较研究具有显著推动作用。

当前挑战

该数据集所解决的领域挑战在于：传统失业率仅捕捉积极求职的失业人口，而忽略因经济原因减少工时、丧失求职信心或被制度性排斥于劳动力市场之外的群体，导致政策制定者低估劳动市场的真实松弛程度。LU4综合率通过纳入时间相关就业不足、潜在劳动力及失业人口，构建了更完整的劳动力利用图景。构建过程中面临的技术挑战包括：各来源国调查方法与统计口径存在异质性，需通过ILO标准化流程进行跨年度、跨国别的数据协调；分类维度复杂，需同时处理性别、教育水平及婚姻状况三个交叉分组，确保高维稀疏数据间的一致性；此外，部分观测被标记为“不可靠”，且存在因方法论修订造成的序列断裂，要求在发布时明确标注质量标志以供用户审慎使用。

常用场景

经典使用场景

该数据集广泛应用于劳动力市场分析领域，尤其聚焦于亚洲地区劳动力利用不足的综合度量。研究者可依据性别、教育程度和婚姻状况等维度，对LU4指标进行精细化的时间序列分析与跨国比较。通过加载该数据集，学者能够构建面板数据模型，系统评估不同人口细分群体在劳动力市场中的弱势地位及长期演变趋势。

衍生相关工作

该数据集衍生了多项围绕ILOSTAT指标体系展开的实证研究。经典工作包括利用该数据构建劳动力市场脆弱性的综合指数，或将LU4指标与经济增长、技能培训等宏观变量进行关联分析。部分研究将其作为基准数据，比较不同聚类算法在识别亚洲国家劳动力市场分化模式时的表现，从而在劳动经济学与计算社会科学之间架起了桥梁。

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率（LU4）研究，涵盖28个国家1999至2025年的观测数据，并按性别、教育程度和婚姻状况进行细分。在劳动经济学的前沿领域中，这一指标被广泛用于揭示隐性失业、就业不足和劳动力市场结构性矛盾，尤其是在后疫情时代经济复苏与劳动力转型的背景下，研究者借助该数据探索亚洲各国劳动力配置效率与政策干预效果。随着国际劳工组织（ILO）对体面劳动目标的持续推进，此类精细化、多维度的劳动力利用数据为精准识别弱势群体（如低教育女性或特定婚姻状态劳动者）的就业壁垒提供了实证基础，并推动了跨国比较研究与区域劳动力市场治理策略的优化。