electricsheepasia/asia-ilo-eip-wdis-sex-geo-nb-discouraged-job-seekers-by-sex-and-rural-urban-are

--- 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: "Discouraged job-seekers by sex and rural / urban areas (thousands) | Asia (ILOSTAT)" --- # Discouraged job-seekers by sex and rural / urban areas (thousands) | Asia (ILOSTAT) 🌏 **1,842 observations** · **25 Asia countries** · **1999–2025** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **1,842 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_WDIS_SEX_GEO_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_WDIS_SEX_GEO_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 25 Asia countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `IDN` | 216 | 2000 | 2023 | | `CYP` | 183 | 1999 | 2024 | | `MNG` | 162 | 2003 | 2024 | | `PHL` | 156 | 2007 | 2023 | | `VNM` | 154 | 2007 | 2024 | | `PSE` | 131 | 2012 | 2022 | | `TUR` | 126 | 2000 | 2013 | | `KOR` | 99 | 2015 | 2025 | | `ARM` | 90 | 2008 | 2018 | | `LKA` | 80 | 2016 | 2024 | | `JOR` | 72 | 2017 | 2024 | | `BRN` | 62 | 2014 | 2024 | | `AFG` | 47 | 2008 | 2021 | | `GEO` | 45 | 2020 | 2024 | | `MMR` | 44 | 2015 | 2020 | | ... | _10 more countries_ | | | ## Indicators (sample) - `EIP_WDIS_SEX_GEO_NB` — Discouraged job-seekers by sex and rural / urban areas (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_WDIS_SEX_GEO_NB` | | `indicator.label` | `string` | Indicator name in English | `Discouraged job-seekers by sex and ru…` | | `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) | `135.254` | | `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-wdis-sex-geo-nb-discouraged-job-seekers-by-sex-and-rural-urban-are") 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_WDIS_SEX_GEO_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EIP_WDIS_SEX_GEO_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EIP_WDIS_SEX_GEO_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_eip_wdis_sex_geo_nb_discouraged_job_seekers_by_sex_and_rural_urban_are_2025, title = {Discouraged job-seekers by sex and rural / urban areas (thousands) | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EIP_WDIS_SEX_GEO_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-eip-wdis-sex-geo-nb-discouraged-job-seekers-by-sex-and-rural-urban-are}} } ``` ## 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_WDIS_SEX_GEO_NB_This dataset contains Other measures of labour underutilization data from the International Labour Organization (ILO) ILOSTAT database, specifically focusing on the Discouraged job-seekers by sex and rural / urban areas (thousands) indicator (code: EIP_WDIS_SEX_GEO_NB) for 25 Asian countries. It includes 1,842 observations spanning the years 1999 to 2025, providing disaggregated annual labour market data by dimensions such as sex (total, male, female) and area type (e.g., national level), for analyzing labour underutilization in Asia, particularly job-seekers who have given up searching for work due to various reasons. The data is harmonized by ILO, sourced from labour force surveys and other sources, and includes data quality notes and status flags.

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核心统计数据库，聚焦于亚洲地区因丧失信心而放弃求职的劳动力群体。数据通过ILOSTAT REST API直接提取，并依据ILO统计学家国际会议（ICLS）定义对原始调查微观数据进行统一协调。在构建过程中，研究团队筛选出25个亚洲国家的ISO3代码作为地理范围，最终整合为覆盖1999年至2025年、包含1,842条观测记录的结构化面板数据，旨在为亚洲劳动力市场的非充分就业研究提供可靠的数据基础。

使用方法

使用者可通过HuggingFace Datasets库的load_dataset()函数一键加载该数据，并直接转换为Pandas DataFrame进行后续分析。对于特定国家的研究，可利用'ref_area'列进行条件筛选，例如过滤出印度尼西亚（IDN）的子集。同时，支持按时间排序后绘制时间序列图，以观察特定指标的变化趋势。如需进行跨国家对比，建议通过pivot_table函数将数据透视成以年份为行、国家为列的矩阵格式，便于面板数据分析与可视化。

背景与挑战

背景概述

在劳动经济学与社会政策研究领域，劳动力市场的隐性失业现象，尤其是“沮丧求职者”群体，长期受到国际组织与学术界的广泛关注。该数据集由国际劳工组织（ILO）通过其ILOSTAT数据库创建，并经Electric Sheep Asia于2025年重新整理后发布于HuggingFace平台，聚焦亚洲地区按性别和城乡划分的沮丧求职者数量（单位：千人）。核心研究问题在于，如何利用标准化、可纵向比较的统计数据，揭示亚洲25个国家从1999年至2025年间，不同性别与地理背景下劳动力闲置的结构性特征。该数据集作为ILO其他劳动力利用不足指标体系的一部分，为评估亚洲劳动力市场的性别差异、城乡分化及宏观经济波动下的就业韧性提供了关键定量基础。

当前挑战

该数据集所应对的领域挑战在于，传统的失业率指标往往低估了劳动力市场的真实闲置程度，沮丧求职者——即因认为无合适工作而放弃求职的人群——这一隐性维度长期缺乏系统性的跨国可比数据支持。在构建过程中，数据整合面临多重困难：不同国家劳动调查的问卷设计、抽样框架与统计口径差异显著，ILO虽依据国际劳动统计学家会议（ICLS）定义进行协调，但数据来源的连续性和质量参差不齐，如数据集中已标注的方法变更（Break in series）与可靠性状态等元数据字段即反映了此问题。此外，部分国家观测年份稀疏，覆盖时间不连续，性别与城乡分组的细粒度数据在早期年份尤显缺失，这为高信度的时间序列建模与跨区域比较带来了显著障碍。

常用场景

经典使用场景

该数据集收录了1999年至2025年间亚洲25个国家关于‘丧失信心的求职者’的年度观测数据，总计1842条记录，按性别与城乡地域进行了细致划分。研究者常将其作为时间序列分析与面板数据建模的核心素材，用以追踪劳动力市场中的隐性失业状态。通过将观测值作为因变量，并引入宏观经济社会指标作为协变量，可构建回归模型或分类模型，从而系统性地揭示不同性别群体在城乡二元结构下求职意愿消退的演化规律与差异性特征。

解决学术问题

在劳动经济学与社会学研究中，传统失业率指标往往无法完整刻画劳动力市场的真实困境。该数据集聚焦于‘丧失信心的求职者’这一边缘但关键的群体，有效弥补了官方失业统计的盲区。它为探究性别不平等与城乡发展差距如何转化为隐性劳动参与率的下降提供了量化基础，支持学者检验劳动力市场理论中关于‘就业悲观情绪’的假设。通过对长期趋势的挖掘，研究者得以辨析经济周期冲击与结构性变迁对求职行为的异质性影响，从而深化对劳动力资源错配与潜在人力资本沉没现象的理论认知。

实际应用

在实际应用中，该数据集为国际劳工组织、亚洲各国劳动部门以及发展机构提供了可操作的决策支持工具。通过实时监测不同性别与地域间求职者信心的波动，政策制定者能够精准识别出亟待干预的地区与群体，进而设计差异化的就业促进方案，如针对农村女性的职业培训项目或城市中针对失业绝望群体的心理与就业援助计划。此外，该数据还服务于非政府组织的项目评估，通过对历史数据的反事实分析，衡量已有干预措施在提振求职信心方面的实际成效。

数据集最近研究

最新研究方向

该数据集聚焦于亚洲地区因性别和城乡差异而放弃求职的群体规模，为劳动经济学中‘隐性失业’的量化研究提供了关键支撑。当前前沿方向正从传统失业率统计转向对劳动力市场边缘群体（如沮丧求职者）的精细刻画，尤其在ILO所倡导的‘体面劳动’与联合国可持续发展目标（SDGs）框架下，该数据可揭示亚洲快速城镇化进程中结构性就业矛盾的性别分化。结合新冠疫情后多国劳动力回流乡村的热点现象，研究者可利用此时间序列数据追溯‘沮丧效应’的演变轨迹，识别政策干预的敏感窗口，从而为区域就业促进战略的靶向设计提供实证依据。