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

--- 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, education and rural / urban areas (thousands) | Asia (ILOSTAT)" --- # Discouraged job-seekers by sex, education and rural / urban areas (thousands) | Asia (ILOSTAT) 🌏 **7,145 observations** · **25 Asia countries** · **1999–2025** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **7,145 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_EDU_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_EDU_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` | 1,085 | 2000 | 2023 | | `MNG` | 662 | 2003 | 2024 | | `CYP` | 610 | 1999 | 2024 | | `TUR` | 601 | 2000 | 2013 | | `PSE` | 571 | 2012 | 2022 | | `VNM` | 568 | 2010 | 2024 | | `KOR` | 494 | 2015 | 2025 | | `JOR` | 358 | 2017 | 2024 | | `ARM` | 355 | 2008 | 2018 | | `LKA` | 262 | 2016 | 2024 | | `PHL` | 192 | 2017 | 2023 | | `GEO` | 182 | 2020 | 2024 | | `BRN` | 179 | 2014 | 2024 | | `BGD` | 174 | 2010 | 2024 | | `AFG` | 171 | 2014 | 2021 | | ... | _10 more countries_ | | | ## Indicators (sample) - `EIP_WDIS_SEX_EDU_GEO_NB` — Discouraged job-seekers by sex, education 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_EDU_GEO_NB` | | `indicator.label` | `string` | Indicator name in English | `Discouraged job-seekers by sex, educa…` | | `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.) | `EDU_AGGREGATE_TOTAL` | | `classif1.label` | `string` | — | `Education (Aggregate levels): Total` | | `classif2` | `string` | Second classification variable where applicable | `GEO_COV_NAT` | | `classif2.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) | `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-edu-geo-nb-discouraged-job-seekers-by-sex-education-and-rural") 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_EDU_GEO_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EIP_WDIS_SEX_EDU_GEO_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EIP_WDIS_SEX_EDU_GEO_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_eip_wdis_sex_edu_geo_nb_discouraged_job_seekers_by_sex_education_and_rural_2025, title = {Discouraged job-seekers by sex, education 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_EDU_GEO_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-eip-wdis-sex-edu-geo-nb-discouraged-job-seekers-by-sex-education-and-rural}} } ``` ## 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_EDU_GEO_NB_This dataset contains 7,145 observations of Other measures of labour underutilization data across 25 Asia countries, spanning 1999–2025, with the specific indicator Discouraged job-seekers by sex, education and rural / urban areas (thousands). The data is sourced from the International Labour Organization (ILO) ILOSTAT database, retrieved via API and filtered to Asia ISO3 country codes. It includes fields such as country code, year, observed value, disaggregation by sex (total, male, female), education classification, area classification, along with source information, observation status, and notes. The data is annual frequency and suitable for tabular classification, regression, and time-series forecasting tasks. The dataset is repackaged by Electric Sheep Asia as part of a unified, ML-ready data layer for Asia.

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数据库，聚焦于亚太地区劳动力市场中的‘沮丧求职者’群体。数据通过ILOSTAT官方REST API直接抽取，原始指标编码为EIP_WDIS_SEX_EDU_GEO_NB，代表按性别、教育程度及城乡地域划分的沮丧求职者数量（千人）。抽取后依据亚洲ISO3国家代码进行地理范围筛选，仅保留25个亚洲国家或地区的观测记录。ILOSTAT对各国劳动力调查的微观数据依据国际劳工统计学家会议（ICLS）标准进行了统一协调与标化处理，每一条观测均附有source.label字段以明确数据来源，确保可追溯性与权威性。最终数据集以Parquet格式封装，经由Electric Sheep Asia流水线完成标准化重打包，实现了从原始联合统计到机器学习就绪表格数据的平滑转化。

使用方法

数据集的加载极为便捷，依托HuggingFace Datasets库，用户仅需一行代码即可将完整的7,145条观测读入内存，并自然地转换为pandas DataFrame进行后续操作。针对国家层面的分析，可通过ref_area字段做条件筛选，快速锁定如印度尼西亚或蒙古等单一国家的子集。由于数据已按年排列，直接使用sort_values('time')即可构建连续时间序列，配合matplotlib或seaborn绘制趋势图十分顺手。常见的透视表操作亦得到完美支持：以time为行索引、ref_area为列名、obs_value为值，可瞬间张成国家×年份的宽表矩阵，适合面板数据建模或跨经济体比较。对于需要处理性别或地域分解的研究者，sex与classif1/classif2字段提供了天然的筛选与分组维度，整体设计体现出了‘解压即用、分析无缝’的工程化思维。

背景与挑战

背景概述

该数据集由国际劳工组织（ILO）于2025年通过其ILOSTAT数据库构建，并由Electric Sheep Asia进行重新封装，专注于亚洲地区因就业前景黯淡而放弃求职的群体。核心研究问题在于量化性别、教育程度及城乡地理差异如何影响劳动力市场的隐性闲置，进而揭示传统失业统计所掩盖的劳动力资源错配现象。作为ILO“其他劳动力利用不足指标”的重要组成部分，该数据集为发展经济学与劳动社会学提供了细分至亚国家层面的实证基础，其影响力体现在对亚洲25个国家、涵盖1999至2025年的7,145条观测记录中，尤其适用于评估可持续发展目标（SDG）中体面工作的进展，并为政策制定者识别特定弱势群体提供了精确的数据锚点。

当前挑战

该数据集面临的首要挑战在于解决劳动力利用不足测量中的维度复杂性，即如何将性别、教育背景与城乡区域三个交互影响的社会分层变量纳入同一分析框架，以揭示隐蔽失业的异质性结构。数据构建过程中遇到的挑战包括跨国调查数据的协调性难题，不同国家的劳动力调查在时间跨度与统计口径上存在差异（如部分年份数据标注为“不可靠”或“方法修订导致序列中断”），且亚洲地区部分国家数据稀疏性显著（如阿富汗仅覆盖2014至2021年）。此外，多源数据融合时的“最佳来源”选择机制可能导致特定子群体的观测缺失，而年度频率的限制更削弱了对短期经济冲击下失业意愿动态变化的捕捉能力，从而对时间序列模型的外推有效性构成约束。

常用场景

经典使用场景

在劳动力经济学与区域发展研究中，该数据集被广泛应用于分析亚太地区因就业前景黯淡而放弃求职的劳动者（即“沮丧失业者”）的时空分布异质性。研究者可依托其提供的性别、教育程度和城乡地理三维分类信息，构建多层线性模型或面板数据回归，探讨社会经济转型背景下隐性失业的驱动机制。例如，通过对比不同教育水平群体在城镇与农村的沮丧规模，揭示人力资本错配对劳动参与率的压制效应，为理解非典型失业形态提供了量化实证基础。

解决学术问题

该数据集的核心学术价值在于破解传统失业率指标的局限性，将“沮丧失业者”这一隐性劳动未充分利用群体纳入分析框架。它使学者能够量化衡量被失业统计遗漏的劳动力闲置程度，并检验教育回报率、城乡经济差距与劳动参与意愿之间的交互关系。通过跨性别、教育及地理维度的区分，研究者可识别出易受经济周期冲击的脆弱亚群体，从而推动劳动力市场制度研究从总量分析转向结构性诊断，为完善国际劳工组织（ILO）的“劳动未充分利用”指标体系贡献实证证据。

实际应用

在实际应用层面，该数据集为亚洲各国劳动政策制定者提供了精准干预的决策依据。例如，城乡规划的决策者可利用性别与教育分层数据，识别出农村低学历女性或城镇青年等沮丧失业高危人群，进而设计定向职业培训或就业信息服务。国际发展组织可借助跨年度时间序列，评估宏观危机（如疫情或金融危机）对特定群体求职信心的冲击强度，优化社会保障网覆盖范围。此外，商业机构也可结合该数据预测区域消费潜力，辅助市场进入策略的制定。

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、教育程度及城乡差异而丧失求职信心的群体规模，为劳动力市场中的隐性失业问题提供了精细化的量化视角。当前研究前沿正借助此类数据，结合机器学习模型探讨后疫情时代非正规就业的韧性、数字鸿沟对劳动参与率的结构性影响，以及教育回报率在城乡间的非对称性变迁。与ILO所倡导的体面劳动目标相呼应，该数据源能够揭示被传统失业率指标所掩盖的社会排斥现象，尤其为亚洲快速城镇化进程中弱势群体的就业政策制定提供了方法论支撑与实证依据。