electricsheepasia/asia-ilo-luu-xlu3-sex-dsb-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: - n<1K 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 disability statu | Asia (ILOSTAT)" --- # Combined rate of unemployment and potential labour force (LU3) by sex and disability statu | Asia (ILOSTAT) 🌏 **509 observations** · **16 Asia countries** · **2007–2024** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **509 observations** of `Other measures of labour underutilization` data across **16 Asia countries**, spanning **2007–2024**, 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_DSB_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_DSB_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 16 Asia countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `ARM` | 108 | 2007 | 2018 | | `IDN` | 63 | 2016 | 2023 | | `LKA` | 63 | 2018 | 2024 | | `MNG` | 54 | 2019 | 2024 | | `PSE` | 45 | 2018 | 2022 | | `BGD` | 27 | 2022 | 2024 | | `IRQ` | 27 | 2007 | 2021 | | `AFG` | 26 | 2017 | 2021 | | `TLS` | 26 | 2016 | 2022 | | `KHM` | 15 | 2012 | 2019 | | `LAO` | 12 | 2017 | 2022 | | `LBN` | 9 | 2019 | 2019 | | `PAK` | 9 | 2021 | 2021 | | `MDV` | 9 | 2019 | 2019 | | `TJK` | 9 | 2016 | 2016 | | ... | _1 more countries_ | | | ## Indicators (sample) - `LUU_XLU3_SEX_DSB_RT` — Combined rate of unemployment and potential labour force (LU3) by sex and disability 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_XLU3_SEX_DSB_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.) | `DSB_STATUS_TOTAL` | | `classif1.label` | `string` | — | `Disability status: Total` | | `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) | `U` | | `obs_status.label` | `string` | — | `Unreliable` | | `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-dsb-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_DSB_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="LUU_XLU3_SEX_DSB_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "LUU_XLU3_SEX_DSB_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_luu_xlu3_sex_dsb_rt_combined_rate_of_unemployment_and_potential_labour_2024, title = {Combined rate of unemployment and potential labour force (LU3) by sex and disability statu | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2024}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=LUU_XLU3_SEX_DSB_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-luu-xlu3-sex-dsb-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_DSB_RT_This dataset contains 509 observations of the combined rate of unemployment and potential labour force (LU3) by sex and disability status across 16 Asia countries, spanning from 2007 to 2024, covering 1 distinct indicator. The data is sourced from the ILOSTAT database of the International Labour Organization (ILO), retrieved via REST API and filtered for Asian countries. It includes fields such as country code, country name, data source, indicator code, indicator name, sex disaggregation, disability status classification, observation year, observed value, observation status, and related notes. The data is harmonized by the ILOs Department of Statistics based on International Conference of Labour Statisticians (ICLS) definitions and is suitable for tasks like tabular classification, tabular regression, and time-series forecasting.

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ILOSTAT数据库，聚焦亚洲地区劳动力利用不足的复合指标。通过ILOSTAT REST API提取特定指标LUU_XLU3_SEX_DSB_RT的原始数据，依据ISO3国家代码筛选出16个亚洲国家，时间跨度为2007至2024年。数据经过ILO统计部门依据ICLS定义进行统一协调处理，并附有来源标识以确保可追溯性。数据集以Parquet格式打包，便于高效存储与读取，共包含509条观测记录，每条记录涵盖国家、年份、性别、残疾状况及观测值等关键字段。

特点

该数据集的核心特征在于其聚焦于劳动力利用不足的综合度量LU3，即失业与潜在劳动力合计比率，并按性别和残疾状态进行精细分层，提供SEX_T、SEX_M、SEX_F三种性别分类。覆盖16个亚洲国家，时间序列长达17年，为区域劳动力市场研究提供了丰富的时间维度。每条观测附带详细的元数据，如数据来源、观测状态（如不可靠标记）及备注，提升了数据的透明度和可用性。数据质量经过ILO严格把关，确保在不同国家间具有可比性和一致性。

使用方法

用户可通过HuggingFace的datasets库轻松加载数据，调用load_dataset函数即可获取训练集并转换为Pandas DataFrame进行探索。支持灵活的筛选操作，例如按国家代码提取特定国家的时间序列，或按指标排序后进行可视化分析。数据透视功能可方便地构建国家×年份矩阵，便于跨国家比较。建议用户在分析时留意观测状态字段，以排除不可靠数据，并引用原始ILO数据及Electric Sheep Asia的重打包版本，确保学术合规。

背景与挑战

背景概述

该数据集由国际劳工组织（ILO）统计部门创建，经Electric Sheep Asia于2024年重新整理并发布在HuggingFace平台，聚焦亚洲地区劳动力利用不足的综合衡量指标——失业与潜在劳动力合计比率（LU3），并按性别与残疾状况进行细分。数据涵盖2007至2024年间16个亚洲国家的509条观测记录，其核心研究问题在于揭示亚洲区域劳动力市场在包容性就业方面的结构性差异，特别是残疾群体面临的就业壁垒。作为ILOSTAT数据库的子集，该数据集继承了ILO在劳动力统计领域的权威性与标准化方法论，为比较劳动政策研究、可持续发展目标（SDG）监测及跨国面板数据分析提供了珍贵的基础数据资源，尤其在推动亚洲区域劳动力市场包容性研究方面具有重要的学术与实践价值。

当前挑战

该数据集面临多重挑战。首先，在领域层面，劳动力利用不足的传统指标（如失业率）往往低估了潜在劳动力（包括隐性失业与求职受挫者）的规模，LU3指标虽整合了失业与潜在劳动力，但其定义与测算依赖于国际劳工统计学家会议（ICLS）标准，各国统计口径与数据收集能力差异大，导致跨国产出可比性不足。其次，在构建过程中，数据整合面临显著障碍：亚洲各国调查频率、变量分类不一致，且部分国家的数据年限稀疏（如马尔代夫仅2019年），时间序列不完整；数据质量标记（如“不可靠”）提示部分观测值存在统计误差；此外，按残疾状态细分的数据在多数国家严重缺失，限制了对这一关键弱势群体的深入分析，增加了建模与推断的难度。

常用场景

经典使用场景

该数据集汇聚了国际劳工组织ILOSTAT数据库中关于亚洲16国2007至2024年间失业与潜在劳动力综合比率（LU3）的年度观测数据，按性别与残疾状态进行了精细划分。其经典应用场景涵盖基于表格的分类与回归任务，例如构建预测模型以识别影响劳动力未充分利用率的关键社会经济因素，或利用时间序列分析方法探究亚洲各国劳动力市场态势的演变轨迹，为劳动经济学领域的研究提供了坚实的数据基础。

解决学术问题

此数据集有效解决了区域劳动力市场研究中长期存在的数据碎片化与口径不统一问题。通过整合多国官方调查数据并采用国际劳工统计学家会议（ICLS）标准进行统一规范化，研究学者得以克服跨国比较的统计障碍，精确剖析性别差异与残疾状态在劳动力未充分利用方面的结构性不公。该数据集促进了关于劳动力市场包容性、残疾人就业障碍及性别平等课题的实证探索，为制定更具针对性的就业政策提供了科学依据。

衍生相关工作

依托这一数据集，衍生出了一系列颇具影响力的学术研究与政策分析工作。其中包括构建亚洲劳动力市场脆弱性指数，用于衡量各国在应对经济冲击时的韧性；开发基于机器学习的失业率预测模型，为短期劳动力市场走向提供预测；以及开展多维度不平等分解研究，量化性别、残疾状态等因素对劳动力未充分利用率的边际效应。这些工作不仅丰富了劳动经济学的理论框架，也为数据驱动的社会治理提供了范式参考。