electricsheepasia/asia-ilo-eip-xjob-sex-rt-potential-labour-force-and-willing-non-jobseekers

--- 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: "Potential labour force and willing non-jobseekers rate (%) | Asia (ILOSTAT)" --- # Potential labour force and willing non-jobseekers rate (%) | Asia (ILOSTAT) 🌏 **414 observations** · **25 Asia countries** · **1999–2025** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **414 observations** of `Other measures of labour underutilization` data across **25 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=EIP_XJOB_SEX_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=EIP_XJOB_SEX_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 25 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 | | `MNG` | 18 | 2019 | 2024 | | `GEO` | 18 | 2019 | 2024 | | `ARM` | 15 | 2007 | 2017 | | `MMR` | 15 | 2015 | 2020 | | `VNM` | 15 | 2020 | 2024 | | `SAU` | 12 | 2018 | 2022 | | `SGP` | 12 | 2021 | 2024 | | `TLS` | 9 | 2010 | 2021 | | `IDN` | 9 | 2018 | 2023 | | ... | _10 more countries_ | | | ## Indicators (sample) - `EIP_XJOB_SEX_RT` — Potential labour force and willing non-jobseekers rate (%) ## 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 | `EIP_XJOB_SEX_RT` | | `indicator.label` | `string` | Indicator name in English | `Potential labour force and willing no…` | | `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) | `7.8` | | `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-eip-xjob-sex-rt-potential-labour-force-and-willing-non-jobseekers") 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"] == "EIP_XJOB_SEX_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EIP_XJOB_SEX_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EIP_XJOB_SEX_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_eip_xjob_sex_rt_potential_labour_force_and_willing_non_jobseekers_2025, title = {Potential labour force and willing non-jobseekers rate (%) | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EIP_XJOB_SEX_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-eip-xjob-sex-rt-potential-labour-force-and-willing-non-jobseekers}} } ``` ## 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=EIP_XJOB_SEX_RT_This dataset contains 414 observations of Other measures of labour underutilization data across 25 Asia countries, spanning 1999–2025, covering 1 distinct indicator (Potential labour force and willing non-jobseekers rate (%)). The data is sourced from ILOSTAT, the ILOs central statistics database, pulled via API and filtered to Asia ISO3 country codes, harmonized using ICLS definitions. It is provided in tabular format with columns such as country code, year, indicator value, and sex disaggregation, suitable for tasks like tabular classification, regression, and time-series forecasting.

搜集汇总

数据集介绍

构建方式

该数据集的构建依托于国际劳工组织（ILO）旗下的ILOSTAT统计数据库，通过REST API直接获取指标代码为EIP_XJOB_SEX_RT的原始数据，并依据亚洲ISO3国家代码进行地理范围筛选。ILOSTAT采用国际劳工统计学家会议（ICLS）定义对各国劳动力调查的微观数据进行标准化处理，以确保跨国可比性。数据集收录了1999年至2025年间25个亚洲国家的414条观测记录，涵盖潜在劳动力与愿意就业的非求职者比率这一核心指标，并在source.label列中标注了数据来源，便于追溯与验证。

特点

该数据集具备多维度的结构化特征，不仅包含ISO 3166-1 alpha-3国家代码与对应的英文国名，还提供了按性别（总人口、男性、女性）进行的分组统计字段，支持细粒度的劳动力市场分析。每条观测记录均附有观测状态标志与详细的注释信息，如数据中断及方法修订说明，增强了数据使用的透明度。作为专注于亚洲区域的劳动利用率指标集合，其时间跨度覆盖二十余年，且来源统一于ILO官方统计体系，兼具权威性与时效性。

使用方法

用户可通过HuggingFace的datasets库，以一行代码load_dataset加载该数据集并转换为Pandas DataFrame进行后续分析。典型应用包括：按国家代码筛选特定地区的观测数据，生成时间序列以观察指标演变趋势，以及利用透视表构建国家-年份矩阵以开展横截面比较。数据集以Parquet格式存储，设计上兼容机器学习工作流，研究者可快速将其纳入分类、回归或时间序列预测任务，无需繁琐的格式转换与预处理。

背景与挑战

背景概述

在全球劳动统计领域，非自愿失业者以外的劳动力闲置群体长期缺乏系统性测度，国际劳工组织（ILO）于1999年至2025年间构建的亚洲潜在劳动力与愿意非求职者率数据集，由Electric Sheep Asia团队基于ILOSTAT数据库重新打包发布。该数据集聚焦于“其他劳动力未充分利用指标”，覆盖25个亚洲经济体的414条年度观测值，旨在捕捉那些有意愿工作但因各种原因未积极求职的潜在劳动力规模。作为ILO统计司统一的劳动统计框架下的衍生数据产品，它填补了传统失业率指标对亚洲地区劳动力市场结构性闲置的刻画空白，为发展经济学、劳动经济学及区域劳动力政策评估提供了关键的量化依据，尤其对监测可持续发展目标（SDGs）中的体面劳动进展具有显著推动力。

当前挑战

该数据集面临的核心挑战在于如何界定和测度“潜在劳动力”这一灰色群体。传统失业统计仅反映积极求职却未能就业的人员，而本指标需纳入因家庭责任、教育障碍、疾病或丧失信心等放弃求职的劳动力，其定义边界依赖于各国劳动力调查问卷设计的细微差异，导致跨国可比性存在先天障碍。此外，构建过程中，ILOSTAT需从200多个经济体的不同调查类型（劳动力调查、住户收入调查等）中提取数据，并通过ICLS国际劳工统计学家会议标准进行协调，但亚洲国家间调查频率、抽样框架及中断序列标记（如方法修订引发的断点）的异质性，使得同一国家多年份数据的时序一致性难以保障，多来源冲突时仅能依赖“最佳来源”选择，可能引入隐性偏差。

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点上，该数据集因其聚焦亚洲25国潜在劳动力与有意愿非求职者比率而备受瞩目。研究者常将其作为量化劳动力利用不足的核心指标，通过横跨1999至2025年的年度观测，揭示亚洲各国在就业边缘群体的动态变化。该数据集的经典用法在于构建面板数据模型，分析性别维度下的劳动市场闲置状况，从而刻画亚洲经济体在结构性转型中劳动力资源的真实配置效率。

解决学术问题

该数据集精准回应了传统失业率无法捕捉的隐性劳动力闲置问题。在学术界，它为解决“灰心丧气工人”效应与潜在劳动力测度难题提供了标准化的跨国可比框架。通过引入ILO国际劳工统计学家会议定义的统一口径，研究者得以突破单一失业指标的局限，深入探讨劳动参与率之外的经济脆弱性，进而为评估劳动力市场政策的社会包容性贡献了关键证据。

衍生相关工作

基于此数据集衍生了一系列影响深远的研究工作，尤其在劳动利用不足指标的国际比较领域。学者们利用其跨年度观测，构建了亚洲区域劳动力市场韧性指数，或将数据与微观调查结合，剖析非正规就业与潜在劳动力之间的转化机制。同时，该数据集的标准化Schema为后续的自动化数据管道与机器学习就绪数据层建设树立了典范，推动了ILOSTAT数据在计算社会科学中的系统化再利用。