electricsheepasia/asia-ilo-luu-xlu4-sex-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 and marital status (%) | Asia (ILOSTAT)" --- # Composite rate of labour underutilization (LU4) by sex and marital status (%) | Asia (ILOSTAT) 🌏 **3,948 observations** · **28 Asia countries** · **1999–2025** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **3,948 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_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_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` | 514 | 1999 | 2020 | | `VNM` | 390 | 2010 | 2024 | | `THA` | 342 | 2010 | 2024 | | `LKA` | 335 | 2010 | 2024 | | `TUR` | 252 | 2004 | 2013 | | `BRN` | 213 | 2014 | 2024 | | `PHL` | 206 | 2017 | 2023 | | `JOR` | 192 | 2017 | 2024 | | `IDN` | 168 | 2016 | 2023 | | `MNG` | 162 | 2019 | 2024 | | `AFG` | 133 | 2012 | 2021 | | `BGD` | 124 | 2013 | 2024 | | `KHM` | 122 | 2003 | 2019 | | `PSE` | 115 | 2015 | 2025 | | `ARM` | 96 | 2014 | 2017 | | ... | _13 more countries_ | | | ## Indicators (sample) - `LUU_XLU4_SEX_MTS_RT` — Composite rate of labour underutilization (LU4) by sex 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_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.) | `MTS_AGGREGATE_TOTAL` | | `classif1.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-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_MTS_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="LUU_XLU4_SEX_MTS_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "LUU_XLU4_SEX_MTS_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_luu_xlu4_sex_mts_rt_composite_rate_of_labour_underutilization_lu4_by_s_2025, title = {Composite rate of labour underutilization (LU4) by sex and marital status (%) | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=LUU_XLU4_SEX_MTS_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-luu-xlu4-sex-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_MTS_RT_This dataset contains observations of the composite rate of labour underutilization (LU4) by sex and marital status for Asia, with 3,948 data points across 28 Asian countries from 1999 to 2025, covering 1 distinct indicator. The data is sourced from the ILOSTAT database of the International Labour Organization (ILO), retrieved via API and filtered to Asian ISO3 country codes. It includes annual frequency data with columns such as country code, indicator, sex disaggregation, marital status classification, year, and observed values, aimed at analyzing labour underutilization in the market. The data is harmonized by ILO and includes source and quality flags.

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）旗下的ILOSTAT中央统计数据库，经由Electric Sheep Asia团队从ILOSTAT REST API直接抓取原始数据，并精心过滤至亚洲地区28个国家的ISO3国家代码范围。ILOSTAT依据国际劳工统计学家会议（ICLS）的定义，对各国劳动力调查微观数据进行标准化处理，确保指标定义的一致性。数据集中包含了用于回溯来源的`source.label`字段，每个观测值的生成过程均可追溯至具体调查源，体现了严谨的溯源设计。

特点

该数据集聚焦于劳动利用不足的综合率（LU4），按性别与婚姻状况进行细致分解，涵盖1999至2025年间亚洲28个国家的3,948条观测记录，形成了丰富的时空面板数据。其核心变量采用ILOSTAT标准代码体系，除指标值与年份外，还携带观测状态标志及详尽注释，如实记录数据中断与方法论修订等元信息。数据来源涵盖劳动力调查、住户收入调查及行政记录等多类型渠道，且ILO优先选取同一国家年份下的最佳来源，保障了数据质量的高水准。

使用方法

用户可通过HuggingFace的`datasets`库以`load_dataset()`函数一行加载完整数据集，并利用Python的pandas工具将之转换为DataFrame进行灵活操作。支持按国家代码（如`ref_area`为'IDN'）筛选特定国家的时间序列，也可基于`indicator`列过滤单个指标并绘制指标值随年份变化的趋势图。借助pivot_table功能，能够快速将数据重塑为国家×年份的横截面矩阵，满足跨区域比较与面板数据回归分析的常见需求。

背景与挑战

背景概述

在全球劳动统计领域，国际劳工组织（ILO）的ILOSTAT数据库是衡量各国劳动力市场状况的权威来源。在此框架下，由Electric Sheep Asia于2025年重新打包并发布的数据集“Composite rate of labour underutilization (LU4) by sex and marital status (%) | Asia (ILOSTAT)”，聚焦亚洲28个国家1999年至2025年间的劳动力利用不足综合率（LU4）指标。该数据集包含3948条观测记录，按性别和婚姻状况进行细分，旨在揭示亚洲地区劳动力市场中的隐性失业和就业不足问题。作为ILOSTAT官方数据的高效整合版本，该数据集为区域劳动经济学研究、政策制定及社会分层分析提供了标准化的时间序列基础，显著提升了亚洲劳动力数据的可获取性与可复现性。

当前挑战

该数据集所应对的领域挑战在于，传统的失业率指标往往低估了劳动力市场的真实闲置程度，而LU4作为ILO提出的综合指标，能够捕捉包括失业、时间相关就业不足和潜在劳动力在内的多重维度，但各国统计口径与调查方法的差异导致跨国可比性脆弱。在构建过程中，核心挑战包括：需从ILOSTAT的REST API中精准提取并过滤亚洲国家的数据，同时处理因调查方法修订（如Break in series标记）引发的时序断裂问题；此外，部分国家存在数据稀疏性，例如阿富汗仅覆盖2012至2021年，而塞浦路斯虽时间跨度长但数据点分布不均，这要求研究者谨慎对待插补与模型选择。综合而言，数据质量与完备性的平衡是推动该数据集广泛应用的关键障碍。

常用场景

经典使用场景

该数据集收录了1999年至2025年间28个亚洲国家的劳动力利用不足综合率（LU4）数据，按性别和婚姻状况进行细分，总计3948条观测记录。其经典使用场景聚焦于时序预测与面板数据分析，研究者可借助该数据集构建时间序列模型，预测劳动力市场的动态演变趋势，或利用多元回归方法探究性别、婚姻状况等人口学特征与劳动力利用不足之间的量化关系。

衍生相关工作

围绕该数据，已衍生出多项经典工作。ILO基于此类数据定期发布全球与区域劳动趋势报告，成为劳动力统计的权威基准。学术界据此建立了亚洲多国面板数据模型，用于研究经济周期与劳动利用不足的联动机制。此外，该数据集常被用于验证机器学习和计量经济学方法在劳动指标预测中的效能，推动了跨学科合作的深入发展。

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综合率（LU4）的性别与婚姻状况维度，为劳动经济学中的隐性失业、就业质量评估及性别平等议题提供了关键面板数据支撑。当前前沿方向集中于利用时间序列模型与面板计量方法，剖析婚姻状态如何调节劳动力市场韧性与性别就业缺口，尤其是在后疫情时代亚洲非正规就业扩张的背景下。结合ILO倡导的体面劳动议程，该数据助力研究者量化婚姻对女性劳动参与率的“双刃剑”效应，并为亚太区域产业升级中的包容性劳动力政策设计提供实证依据，推动从传统失业率向多维劳动利用不足指标的范式转型。