electricsheepasia/asia-ilo-eip-wdis-sex-age-edu-nb-discouraged-job-seekers-by-sex-age-and-education-t

--- license: cc-by-4.0 language: - en task_categories: - tabular-classification - tabular-regression - time-series-forecasting multilinguality: monolingual size_categories: - 10K<n<100K tags: - tabular - asia - ilostat - other-measures-of-labour-underutilization - ilo - labour - employment pretty_name: "Discouraged job-seekers by sex, age and education (thousands) | Asia (ILOSTAT)" --- # Discouraged job-seekers by sex, age and education (thousands) | Asia (ILOSTAT) 🌏 **22,545 observations** · **31 Asia countries** · **1999–2025** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **22,545 observations** of `Other measures of labour underutilization` data across **31 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_AGE_EDU_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_AGE_EDU_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 31 Asia countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `TUR` | 3,127 | 2000 | 2024 | | `IDN` | 2,935 | 2000 | 2023 | | `MNG` | 1,572 | 2003 | 2024 | | `KOR` | 1,566 | 2003 | 2025 | | `ISR` | 1,516 | 2012 | 2024 | | `VNM` | 1,485 | 2010 | 2024 | | `CYP` | 1,395 | 1999 | 2024 | | `PSE` | 1,395 | 2012 | 2025 | | `ARM` | 1,025 | 2008 | 2018 | | `JOR` | 882 | 2017 | 2024 | | `LKA` | 650 | 2016 | 2024 | | `PHL` | 608 | 2003 | 2023 | | `GEO` | 495 | 2020 | 2024 | | `BGD` | 487 | 2010 | 2024 | | `BRN` | 428 | 2014 | 2024 | | ... | _16 more countries_ | | | ## Indicators (sample) - `EIP_WDIS_SEX_AGE_EDU_NB` — Discouraged job-seekers by sex, age and education (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_AGE_EDU_NB` | | `indicator.label` | `string` | Indicator name in English | `Discouraged job-seekers by sex, age a…` | | `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.) | `AGE_YTHADULT_YGE15` | | `classif1.label` | `string` | — | `Age (Youth, adults): 15+` | | `classif2` | `string` | Second classification variable where applicable | `EDU_AGGREGATE_TOTAL` | | `classif2.label` | `string` | — | `Education (Aggregate levels): Total` | | `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) | `U` | | `obs_status.label` | `string` | — | `Unreliable` | | `note_classif` | `string` | — | `C3:2620` | | `note_classif.label` | `string` | — | `Nonstandard education level: Includin…` | | `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-age-edu-nb-discouraged-job-seekers-by-sex-age-and-education-t") 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_AGE_EDU_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EIP_WDIS_SEX_AGE_EDU_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EIP_WDIS_SEX_AGE_EDU_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_eip_wdis_sex_age_edu_nb_discouraged_job_seekers_by_sex_age_and_education_t_2025, title = {Discouraged job-seekers by sex, age and education (thousands) | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EIP_WDIS_SEX_AGE_EDU_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-eip-wdis-sex-age-edu-nb-discouraged-job-seekers-by-sex-age-and-education-t}} } ``` ## 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_AGE_EDU_NB_This dataset contains 22,545 observations of Other measures of labour underutilization data across 31 Asia countries, spanning 1999–2025, covering 1 distinct indicator: Discouraged job-seekers by sex, age and education (thousands). The data is sourced from the International Labour Organization (ILO) ILOSTAT database and repackaged by Electric Sheep Asia for tasks such as tabular classification, regression, and time-series forecasting. It includes a detailed schema with columns for country codes, indicators, sex, age, education, year, observed values, and data quality notes, along with usage examples.

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数据库，通过调用其REST API接口，筛选出亚洲地区的ISO3国家代码，提取了指标'EIP_WDIS_SEX_AGE_EDU_NB'（按性别、年龄和教育程度划分的沮丧求职者人数，单位：千人）的相关观测数据。数据在获取过程中遵循了ICLS（国际劳工统计学家会议）的定义标准，并在'source.label'字段中标注了原始数据来源，以确保数据溯源清晰可靠。最终将22,545条观测值整理为结构化表格，涵盖了31个亚洲国家从1999年至2025年的年度信息。

使用方法

研究人员可便捷地通过HuggingFace Datasets库的`load_dataset()`函数一键加载数据，并将其转换为Pandas DataFrame进行后续分析。典型的使用场景包括：对特定国家（如印度尼西亚）进行过滤，以观察该国沮丧求职者的时间序列趋势；或利用`pivot_table`方法将数据重塑为国家×年份的矩阵，进行跨国面板数据分析。所有数据列均以标准化的电羊亚洲（Electric Sheep Asia）模式命名，'ref_area'、'time'和'obs_value'等关键字段使得分类汇总与统计建模能够无缝衔接，极大降低了数据清理的预处理成本。

背景与挑战

背景概述

该数据集由国际劳工组织（ILO）统计数据库ILOSTAT提供，经Electric Sheep Asia于2025年重新打包并发布于HuggingFace平台，聚焦于亚洲31个国家1999年至2025年间因性别、年龄和教育程度划分的沮丧求职者数量（单位：千人）。作为衡量劳动力利用不足的重要指标，沮丧求职者数据揭示了潜在劳动力市场的隐性失业问题，为劳动经济学、社会政策及可持续发展目标（SDGs）研究提供了关键数据支撑。该数据集涵盖22,545条观测值，依托ILO对各国劳动力调查数据的标准化整合，成为亚洲区域跨国家、跨时间比较劳动力市场健康状况的稀缺资源。其对理解亚洲经济转型中结构性失业、性别不平等及教育鸿沟的量化影响具有重要学术与实践价值。

当前挑战

该数据集面临的挑战首先源于领域问题：沮丧求职者的界定依赖于ICLS定义，但各国调查方法与问卷设计差异可能导致观测值可比性下降，尤其当数据源自不同劳动调查或行政记录时（如source.label列所示）。此外，数据质量标注（如obs_status字段中的“不可靠”标识）与断点注释（note_indicator.label记录的方法论修订）要求研究者审慎处理时间序列一致性。在构建过程中，挑战包括：跨31国异构数据源的API自动抓取与清洗（如多来源冲突时采用ILO优选策略），以及按性别、年龄、教育等维度分列的稀疏性——某些国家年份的分组条目缺失，增加了纵向分析与面板构建的复杂性与不确定性。

常用场景

经典使用场景

在劳动经济学与社会统计研究中，该数据集为分析亚洲地区‘沮丧求职者’（Discouraged Job-seekers）的规模与结构提供了标准化、跨国的精细数据。其核心使用场景在于利用性别、年龄与教育程度的多维分类，揭示劳动力市场中隐性失业群体的分布特点。研究者通过观测1999年至2025年间31个亚洲国家的时间序列数据，能够追踪经济波动背景下沮丧求职者群体的动态演化，并横向对比不同发展水平国家间该指标的差异。该数据集特别适用于构建面板回归模型，考察社会保障制度、劳动力市场政策及产业结构转型对潜在劳动力退出市场行为的影响。

解决学术问题

该数据集有效填补了亚洲地区在‘劳动力利用不足’新兴统计领域的高质量数据空白。在传统失业率指标无法全面反映劳动力市场压力的情境下，它助力学术界深入探索‘沮丧工作者效应’的跨时空规律，解决了长期困扰学者的因数据碎片化而导致比较研究难以开展的困境。借助ILOSTAT统一口径下的标准化定义，研究得以规避各国调查方法差异带来的偏误，从而严谨地检验劳动力市场制度、人口结构与教育回报率等因素如何影响边缘劳动群体的退出决策。这一数据资源显著推动了关于非正规就业、隐性失业与区域劳动力韧性等议题的定量分析，为完善发展经济学中的劳动力市场理论提供了坚实依据。

实际应用

在公共政策制定与国际组织评估领域，该数据集为劳动部门及研究机构提供决策支撑工具，用于识别哪些国家、性别或教育群体中‘沮丧情绪’最为突出，从而精准设计就业激励与技能培训计划。国际劳工组织可借此监测亚洲地区体面劳动指标（SDG 8）的进展，评估经济危机或疫情冲击后劳动力市场的修复程度。开发者与数据科学家运用这批结构化数据，能够训练机器学习模型以预测沮丧求职者数量的未来趋势，或构建交互式仪表板，实时展示亚洲各国劳动力‘隐性闲置’的全景图谱，辅助企业、政府与学术界洞察人力资本配置的潜在风险。

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、年龄与教育程度而灰心丧气的求职者规模，为劳动经济学中的劳动力未充分利用研究提供了精细化的纵向数据支撑。当前前沿方向集中于利用时间序列模型与面板数据分析不同国家灰心求职者数量的动态演变，尤其关注1999年至2025年间亚洲各国经济波动、结构性转型与劳动政策调整对这一脆弱群体的影响。结合近年来新冠疫情对全球就业市场的深刻冲击以及亚洲新兴经济体劳动力市场的结构性变化，该数据集为评估非标准就业形态、隐性失业以及性别教育差异下的劳动力退出行为提供了宝贵证据。其跨国家、跨维度的解构特征，使其成为探究社会保护体系脆弱性、青年就业困境与终身学习效能等热点议题的关键资源，对助力实现体面劳动与可持续经济增长的全球目标具有重要实证意义。