electricsheepasia/asia-ilo-eip-xjob-sex-nb-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 (thousands) | Asia (ILOSTAT)" --- # Potential labour force and willing non-jobseekers (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=EIP_XJOB_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=EIP_XJOB_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) - `EIP_XJOB_SEX_NB` — Potential labour force and willing non-jobseekers (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 | `EIP_XJOB_SEX_NB` | | `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) | `690.937` | | `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-nb-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_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EIP_XJOB_SEX_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EIP_XJOB_SEX_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_eip_xjob_sex_nb_potential_labour_force_and_willing_non_jobseekers_2025, title = {Potential labour force and willing non-jobseekers (thousands) | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EIP_XJOB_SEX_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-eip-xjob-sex-nb-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_NB_The dataset is named Potential labour force and willing non-jobseekers (thousands) | Asia (ILOSTAT) and is a tabular dataset focusing on other measures of labour underutilization in Asia. It contains 402 observations across 24 Asian countries (e.g., Cyprus, Philippines, Kyrgyzstan), spanning the years 1999 to 2025. The primary indicator is Potential labour force and willing non-jobseekers (thousands) (ILOSTAT code: EIP_XJOB_SEX_NB), disaggregated by sex (total, male, female). Data is sourced from the International Labour Organizations (ILO) ILOSTAT database, a leading global repository for labour statistics that harmonizes data from national labour force surveys, household income surveys, and administrative records using International Conference of Labour Statisticians (ICLS) definitions. The dataset is provided at an annual frequency and includes columns such as country code, year, observed value, data source, and observation status. It is suitable for tabular classification, regression, and time-series forecasting tasks. Repackaged by Electric Sheep Asia in Parquet format for machine learning readiness, it is released under the CC-BY-4.0 license.

搜集汇总

数据集介绍

构建方式

该数据集源自国际劳工组织（ILO）的ILOSTAT统计数据库，聚焦于亚洲地区潜在劳动力与有意愿的非求职者数量（以千人为单位）。数据通过ILOSTAT提供的REST API接口直接获取，具体指标代码为EIP_XJOB_SEX_NB，并依据ISO 3166-1 alpha-3标准筛选出24个亚洲国家的观测记录。ILOSTAT本身整合了各国劳动力调查、家庭收入调查、企业调查及行政记录等多元来源的微观数据，并依据国际劳工统计学家会议（ICLS）定义进行标准化处理，确保跨国家与跨年份数据的可比性。最终数据集包含402条观测，时间跨度覆盖1999年至2025年，每条记录均附有原始数据源标签以便溯源。

使用方法

用户可通过HuggingFace Datasets库便捷加载该数据集，仅需一行代码`load_dataset("electricsheepasia/asia-ilo-eip-xjob-sex-nb-potential-labour-force-and-willing-non-jobseekers")`即可获取数据，并将其转换为pandas DataFrame以进行后续分析。典型的使用场景包括筛选特定国家（如印度尼西亚）的观测数据、绘制单一指标的年度时间序列图，或利用透视表构建国家×年份的观测值矩阵，便于面板数据分析。数据集以Parquet格式存储，兼顾了存储效率与读取速度，适用于机器学习、计量经济学建模以及劳动力政策研究等多种应用场景。

背景与挑战

背景概述

该数据集由国际劳工组织（ILO）统计司发布，后经Electric Sheep Asia于2025年重新整理并托管于HuggingFace平台，旨在提供亚洲地区潜在劳动力与自愿非求职者人数的标准化时间序列数据，覆盖1999至2025年间24个亚洲国家共计402条观测值。作为ILOSTAT数据库中衡量劳动力未充分利用状况的核心指标之一，该数据集聚焦于那些虽未积极求职但愿意工作的潜在劳动力群体，弥补了传统失业率对劳动力市场疲软状态刻画不足的缺陷。通过统一采用国际劳工统计学家会议（ICLS）定义进行数据协调，并依托ILO权威的劳动力调查与行政记录资源，该数据集为亚洲劳动力市场的跨国比较、结构性失业分析及政策评估提供了关键数据基础，在劳动经济学、发展经济学及国际统计领域具有重要参考价值。

当前挑战

该数据集面临的主要挑战在于如何准确界定和测度潜在劳动力这一边缘性群体。传统失业率仅捕捉积极求职者，而此指标需通过复杂的调查问卷设计识别“愿意工作但未求职”的个体，不同国家在劳动参与意愿的认定标准上存在差异，导致跨国可比性受限。数据构建过程中面临多重困难：ILO需从各国劳动力调查中提取原始微观数据，但各国调查频率、样本框架及数据质量参差不齐，部分国家存在指标缺失或时间序列不连续问题；数据标注中的“最优来源”选择机制虽可缓解多源冲突，但不同来源间的口径差异仍可能引入系统性偏差。此外，性别、年龄等细分维度的数据稀疏性制约了更深层次的结构性分析，而部分观测值被标记为“方法修订”或“中断”状态，进一步增加了时间序列建模的复杂度。

常用场景

经典使用场景

该数据集在劳动经济学与区域发展研究领域中被广泛用于分析亚洲各国的潜在劳动力规模与非自愿性失业现象。研究者常利用“潜在劳动力及有意愿的非求职者”这一核心指标，评估不同国家劳动力市场中的隐性失业程度，并通过性别维度（男、女、总计）的拆解，揭示劳动力参与率背后的结构性差异。借助该数据集涵盖1999至2025年间24个亚洲国家的面板数据，学者能够构建纵贯式的比较研究，追踪经济发展与劳动供需矛盾之间的动态关系。

解决学术问题

数据集有效填补了传统失业率指标在衡量劳动力市场健康度上的不足，为学术界提供了刻画隐性失业与劳动力资源错配的关键数据支持。传统失业统计往往忽略那些因失去信心而不再求职的潜在劳动者，该数据通过量化“非求职者”群体，帮助研究者更全面评估亚洲国家在经济增长转型期所面临的实际就业压力。其丰富的年度面板结构还支持宏观计量模型中的因果关系推断，例如考察教育政策、社会保障改革或产业结构调整如何影响劳动力闲置率，从而为发展经济学与人口经济学的交叉议题提供实证基础。

实际应用

在实际应用中，该数据集为国际组织、政府劳工部门以及政策研究机构提供了监测和预判劳动市场动态的基础工具。政策制定者可借助各国潜在劳动力规模的变化趋势，识别出需要重点干预的弱势群体，例如女性非求职者比例偏高的地区，从而设计更具针对性的职业技能培训与再就业激励措施。此外，数据集还常被用于构建劳动供需预测模型，支持短期人力资源规划和中长期发展战略的制定，尤其适用于亚洲新兴经济体在产业升级过程中对就业弹性与结构性失业风险的评估。

数据集最近研究

最新研究方向

在劳动经济学与可持续发展目标的交叉领域中，该数据集对亚洲24国潜在劳动力与愿意非求职群体的时序刻画，为后疫情时代劳动力利用不足问题提供了精准的量化工具。其跨年度、多国别的面板结构，使研究者能够深入剖析隐性失业与就业意愿的动态演变，尤其在性别维度（SEX_T/SEX_M/SEX_F）的分解，为探讨亚洲地区女性劳动参与意愿的结构性障碍及政策干预效果开辟了新路径。结合ILOSTAT的官方统计与机器学习就绪的格式，这一数据正成为构建劳动力市场预警模型、评估非正规就业转化效率以及验证宏观经济波动对弱势群体冲击的前沿热点资源。