electricsheepasia/asia-ilo-luu-xlux-sex-nb-jobs-gap-thousands

--- 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: "Jobs gap (thousands) | Asia (ILOSTAT)" --- # Jobs gap (thousands) | Asia (ILOSTAT) 🌏 **402 observations** · **24 Asia countries** · **1999–2025** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **402 observations** of `Other measures of labour underutilization` data across **24 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_XLUX_SEX_NB) - **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_XLUX_SEX_NB` 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 24 Asia countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `CYP` | 78 | 1999 | 2024 | | `PHL` | 51 | 2007 | 2023 | | `KGZ` | 39 | 2011 | 2023 | | `PSE` | 30 | 2015 | 2025 | | `BRN` | 27 | 2014 | 2024 | | `JOR` | 24 | 2017 | 2024 | | `GEO` | 18 | 2019 | 2024 | | `MNG` | 18 | 2019 | 2024 | | `ARM` | 15 | 2007 | 2017 | | `VNM` | 15 | 2020 | 2024 | | `MMR` | 15 | 2015 | 2020 | | `SGP` | 12 | 2021 | 2024 | | `TLS` | 9 | 2010 | 2021 | | `IDN` | 9 | 2018 | 2023 | | `BTN` | 6 | 2023 | 2024 | | ... | _9 more countries_ | | | ## Indicators (sample) - `LUU_XLUX_SEX_NB` — Jobs gap (thousands) ## 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_XLUX_SEX_NB` | | `indicator.label` | `string` | Indicator name in English | `Jobs gap (thousands)` | | `sex` | `string` | Disaggregation by sex (SEX_T = total, SEX_M = male, SEX_F = female) | `SEX_T` | | `sex.label` | `string` | — | `Total` | | `time` | `int64` | Observation year | `2021` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `1153.348` | | `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-xlux-sex-nb-jobs-gap-thousands") 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_XLUX_SEX_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="LUU_XLUX_SEX_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "LUU_XLUX_SEX_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_luu_xlux_sex_nb_jobs_gap_thousands_2025, title = {Jobs gap (thousands) | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=LUU_XLUX_SEX_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-luu-xlux-sex-nb-jobs-gap-thousands}} } ``` ## 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_XLUX_SEX_NB_This dataset contains Other measures of labour underutilization data from the International Labour Organization (ILO) ILOSTAT database, specifically focusing on Jobs gap (thousands) in Asia. It covers 24 Asian countries from 1999 to 2025, with 402 observations and 1 distinct indicator (LUU_XLUX_SEX_NB). The data is pulled from the ILOSTAT REST API and harmonized by ILO using International Conference of Labour Statisticians (ICLS) definitions. The dataset includes a detailed schema with columns such as country codes, sources, indicators, sex disaggregation, observation years, values, and quality flags, suitable for tabular classification, regression, and time-series forecasting tasks. Data is annual frequency and comes with caveats like the use of best sources and non-null constraints on disaggregation columns.

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）的ILOSTAT统计数据库，经由Electric Sheep Asia团队重新封装而成。构建过程通过调用ILOSTAT REST API，直接获取指标‘LUU_XLUX_SEX_NB’（就业缺口，单位：千）的原始数据，并依据亚洲ISO3国家代码进行地理范围过滤。ILOSTAT本身采用国际劳工统计学家会议（ICLS）定义对各国劳动力调查微观数据进行统一协调，数据来源在‘source.label’列中予以标注，确保了数据的可追溯性与标准化。最终数据集包含402条观测记录，覆盖24个亚洲国家，时间跨度从1999年至2025年，以Parquet格式发布，便于机器学习与时间序列分析场景的快速加载。

特点

该数据集的核心特点在于聚焦‘其他劳动利用不足衡量指标’中的就业缺口数据，为亚洲区域劳动力市场研究提供了高价值的量化视角。数据集维度丰富，包含国家代码、性别分列（男性、女性及总计）、观测年份、数值及状态注释等字段，其中性别分列提供了3种取值，能够支持性别差异分析。数据以年频记录，并标记了ILO选定的‘最佳来源’，同时附带了断点、方法修订等质量说明，便于用户审慎使用。其精炼的规模（402条记录）与专注的主题，使其成为探究亚洲国家长期就业缺口趋势的理想材料。

使用方法

用户可通过HuggingFace的datasets库便捷加载该数据集，仅需执行一行Python命令：load_dataset(‘electricsheepasia/asia-ilo-luu-xlux-sex-nb-jobs-gap-thousands’)，数据即被自动转换为Pandas DataFrame格式以供分析。典型使用方式包括按国家（如Indonesia）筛选子集、对特定指标按时间排序以绘制时间序列图，或通过pivot_table构建国家×年份的数值矩阵。数据集支持分类、回归与时间序列预测等多种任务类型，适用于劳动经济学、国际发展等领域的定量研究与建模实践。

背景与挑战

背景概述

在全球劳动市场中，劳动力利用不足的度量是评估经济健康与社会福祉的关键指标。国际劳工组织（ILO）作为劳动统计的权威来源，通过ILOSTAT数据库提供了系统化的劳动数据。该数据集由Electric Sheep Asia于2025年整理发布，聚焦亚洲地区，包含24个国家从1999年至2025年间共402条关于“就业缺口（千人）”的观测记录。其核心研究问题在于量化亚洲各国在不同性别维度下的劳动力利用不足状况，以揭示隐性失业与就业机会短缺的结构性特征。该数据集不仅弥补了亚洲区域高分辨率劳动统计数据的空白，也为宏观经济学、劳动经济学以及可持续发展目标（SDG）框架下的政策评估提供了可靠数据基础，对推动区域劳动市场研究与跨国比较具有重要影响力。

当前挑战

该数据集所解决的核心领域挑战是劳动力利用不足的精细化度量问题。传统的失业率统计无法涵盖因劳动时间不足、技能不匹配或就业意愿下降而形成的隐蔽性失业缺口，而该数据集通过ILOSTAT统一指标“就业缺口”填补了此空白。在数据集构建过程中，面临的挑战包括多源调查数据的协调与标准化，例如各国家统计机构采用不同调查方法（如劳动力调查、行政记录）以及年度序列频率不统一。此外，数据存在时序断裂与质量标记（如“序列中断”“方法修订”），需要谨慎处理以保证一致性。性别与来源等维度信息的完整记录也增加了数据抽取与清洗的复杂性，要求对断点进行专门标注与处理。

常用场景

经典使用场景

在劳动经济学与区域发展研究的交汇处，亚洲劳动力市场的不充分就业问题一直是学界关注的焦点。该数据集基于国际劳工组织ILOSTAT官方统计体系，汇聚了1999至2025年间24个亚洲国家的岗位缺口数据，共计402条观测记录。研究者可借此构建面板数据模型，系统追踪亚洲各国劳动力利用不足的时空演变轨迹。其典型应用在于评估亚洲经济体在全球化浪潮与产业结构转型过程中的就业吸纳能力，为理解发展中国家的劳动力市场韧性提供量化支撑。

衍生相关工作

围绕该数据集已衍生出一系列具有影响力的研究工作。其中，基于ILOSTAT跨国家面板数据所构建的劳动力市场压力指数，成为评估经济危机冲击与政策响应效果的核心工具。部分研究将其与国民账户体系结合，分析劳动力闲置对潜在产出的拖累效应。另有一些工作聚焦于性别维度的比较分析，揭示女性在亚洲劳动力市场中承受的结构性就业障碍。该数据还催生了将岗位缺口与环境脆弱性指标进行交叉分析的新范式，探讨气候变化对不充分就业的放大效应。

数据集最近研究

最新研究方向

该数据集基于国际劳工组织ILOSTAT官方统计，聚焦亚洲24国1999–2025年就业缺口（千单位）这一劳动力未充分利用的核心指标。在全球经济复苏分化、后疫情时代劳动力市场结构性失衡加剧的背景下，该数据为研究亚洲地区非正规就业、性别差异与政策干预效果提供了高时间分辨率样本。前沿方向包括结合性别细分维度（SEX_T/M/F）构建时序预测模型，评估自动化与绿色转型对劳动力需求冲击；以及利用跨国产出面板数据，分析就业缺口与宏观经济韧性、社会保障覆盖率之间的非线性关联。数据经Harmonization流程处理，支持可复现的机器学习流水线，为可持续发展目标（SDG 8.5体面工作）的区域监测贡献了关键实证基础。