electricsheepasia/asia-ilo-luu-xlu3-sex-geo-rt-combined-rate-of-unemployment-and-potential-labour

--- 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: "Combined rate of unemployment and potential labour force (LU3) by sex and rural / urban ar | Asia (ILOSTAT)" --- # Combined rate of unemployment and potential labour force (LU3) by sex and rural / urban ar | Asia (ILOSTAT) 🌏 **1,854 observations** · **27 Asia countries** · **1999–2025** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **1,854 observations** of `Other measures of labour underutilization` data across **27 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_XLU3_SEX_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_XLU3_SEX_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 27 Asia countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `CYP` | 234 | 1999 | 2024 | | `PHL` | 156 | 2007 | 2023 | | `VNM` | 156 | 2007 | 2024 | | `PSE` | 132 | 2012 | 2022 | | `LKA` | 126 | 2010 | 2024 | | `TUR` | 126 | 2000 | 2013 | | `THA` | 120 | 2010 | 2024 | | `ARM` | 108 | 2007 | 2018 | | `BRN` | 81 | 2014 | 2024 | | `JOR` | 72 | 2017 | 2024 | | `IDN` | 72 | 2015 | 2023 | | `MNG` | 54 | 2019 | 2024 | | `GEO` | 54 | 2019 | 2024 | | `TLS` | 45 | 2010 | 2022 | | `MMR` | 45 | 2015 | 2020 | | ... | _12 more countries_ | | | ## Indicators (sample) - `LUU_XLU3_SEX_GEO_RT` — Combined rate of unemployment and potential labour force (LU3) by sex 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_XLU3_SEX_GEO_RT` | | `indicator.label` | `string` | Indicator name in English | `Combined rate of unemployment and pot…` | | `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` | | `time` | `int64` | Observation year | `2021` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `12.524` | | `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-xlu3-sex-geo-rt-combined-rate-of-unemployment-and-potential-labour") 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_XLU3_SEX_GEO_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="LUU_XLU3_SEX_GEO_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "LUU_XLU3_SEX_GEO_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_luu_xlu3_sex_geo_rt_combined_rate_of_unemployment_and_potential_labour_2025, title = {Combined rate of unemployment and potential labour force (LU3) by sex and rural / urban ar | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=LUU_XLU3_SEX_GEO_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-luu-xlu3-sex-geo-rt-combined-rate-of-unemployment-and-potential-labour}} } ``` ## 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_XLU3_SEX_GEO_RT_This dataset contains Combined rate of unemployment and potential labour force (LU3) data from the International Labour Organization (ILO) ILOSTAT database, disaggregated by sex and rural/urban areas, covering 27 Asian countries from 1999 to 2025. It includes 1,854 observations with one core indicator: LUU_XLU3_SEX_GEO_RT, representing the combined rate of unemployment and potential labour force by sex and rural/urban areas (%). The data is sourced from ILOs official statistics, harmonized according to International Conference of Labour Statisticians (ICLS) definitions. The dataset schema includes fields such as country code, indicator code, sex classification, year, and observed value, suitable for tabular classification, regression, and time-series forecasting tasks.

搜集汇总

数据集介绍

构建方式

本数据集源于国际劳工组织（ILO）的ILOSTAT核心统计数据库，聚焦亚洲地区劳动力利用不足的度量维度，整合了‘失业与潜在劳动力合计比率（LU3）’指标，并按性别及城乡地域进行细致划分。数据通过ILOSTAT REST API直接获取，依据ISO 3166-1 alpha-3标准筛选出27个亚洲国家，时间跨度自1999年至2025年，共涵盖1,854条观测记录。ILO依据国际劳工统计学家会议（ICLS）定义对原始调查微观数据进行统一协调处理，确保跨国家、跨时期数据的可比性，并附有来源标签以保障数据的可追溯性。

特点

该数据集在结构设计上体现出高度的规范性与细致性，囊括了参考区域、数据来源、指标代码、性别分类、地域分类及观测状态等多元字段。其独到之处在于深度融合了时间序列与横截面数据，支持按国家、性别与地域的多维度切分，便于研究者剖析劳动力市场的结构性差异。数据质量方面，库中仅采纳各年度‘最佳来源’，并标注了数据中断或方法修订等异常状态，为严谨的计量分析提供了坚实的数据基础。同时，数据以Parquet格式封装，与HuggingFace Datasets库无缝对接，实现了高效的数据读取与操作。

使用方法

借助HuggingFace的datasets库，研究者可通过简洁的Python命令快速加载数据并转换为Pandas DataFrame，即刻开展探索性分析。针对特定国家，可运用条件筛选语句提取子集数据；对于单一指标，可按时序进行排序与可视化，以揭示劳动力市场的演变趋势；亦可利用透视表功能重构为国家×年份的矩阵，便于进行面板数据分析或横向对比研究。该数据集的设计兼顾了易用性与灵活性，为劳动经济学、区域发展研究等领域的学者提供了便捷、可靠的数据支撑。

背景与挑战

背景概述

该数据集由国际劳工组织（ILO）统计部门基于其核心统计数据库ILOSTAT构建，经Electric Sheep Asia于2025年重新封装并发布在HuggingFace平台，旨在为亚洲地区劳动力市场研究提供标准化、机器可读的失业与潜在劳动力综合率（LU3）数据。数据集涵盖了27个亚洲国家、1999至2025年的1,854条观测记录，按性别和城乡区域进行维度划分，为研究亚洲劳动力利用不足的结构性特征提供了珍贵的时空颗粒度。其核心研究问题聚焦于LU3指标在亚洲各国间的分布差异、时序演变及政策响应，因ILO采用国际劳工统计学家会议（ICLS）标准对各国调查微观数据进行统一协调，该数据集在跨国比较研究中具有基准性和权威性，为劳动经济学、发展经济学及区域政策分析提供了关键数据支撑，显著推动了亚洲劳动力市场相关实证研究的可复现性和交叉可比性。

当前挑战

该数据集所面临的挑战首先源于其统计口径的复杂性：LU3作为劳动力利用不足的综合度量，需整合失业与潜在劳动力两类群体，各国在数据收集方法、调查频率及统计口径上存在显著差异，即便有ILOSTAT的标准化协调，上述差异仍可能导致跨国数据的可比性受限。其次，构建过程中面临多重技术挑战：数据整合需从ILOSTAT REST API抓取原始数据，并依据ISO 3166-1 alpha-3编码过滤至亚洲区域；原始数据常存在序列断裂（break in series）、方法修订及数据来源不唯一等问题，需通过观测状态标记和最佳来源选择进行质量处理，这一环节对数据一致性和完整性构成考验。再者，由于数据以年度频率发布且部分国家样本较少，时序分析在短期波动捕捉和低频国家预测上存在固有限制，同时数据字段中非完整的不确定性标记（如观测状态标签）也为机器学习建模带来了噪声与误差传播的隐患。

常用场景

经典使用场景

该数据集以ILOSTAT为数据源，聚焦亚洲27国1999至2025年间综合失业与潜在劳动力（LU3）比率，并按性别及城乡地域维度细致划分，为时间序列预测、面板数据回归及多分类任务提供了结构化基准。研究者常借助该数据评估不同亚洲经济体在劳动力市场闲置维度上的动态演变，并运用季节调整或混合效应模型解析宏观劳动力供给弹性。

解决学术问题

在劳动经济学与发展研究中，该数据弥补了亚洲区域LU3指标长期碎片化的缺憾，为探讨正规就业不足、隐性失业与劳动力市场结构转型等学术议题提供了统一口径的实证基础。其国别与年度嵌套结构可支持固定效应回归，用于隔离政策干预、经济周期或城乡差异对劳动力利用不足的影响，促进了对非标准就业形态的区域比较研究。

衍生相关工作

围绕此数据集，涌现出诸多衍生工作，例如基于其时间序列维度构建亚洲各国劳动力闲置率预测模型，或结合性别维度探究劳动参与率的差异动因。此外，亦有研究将其与ILOSTAT其他指标整合，构建多维度劳动力市场不充分就业指数，或利用其地理覆盖范围刻画区域经济一体化下的劳动力流动特征，推动包容性增长相关学术议题的实微观审验。