electricsheepasia/asia-ilo-eip-wdis-sex-age-mts-nb-discouraged-job-seekers-by-sex-age-and-marital-sta

--- 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 marital status (thousands) | Asia (ILOSTAT)" --- # Discouraged job-seekers by sex, age and marital status (thousands) | Asia (ILOSTAT) 🌏 **15,953 observations** · **28 Asia countries** · **1999–2025** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **15,953 observations** of `Other measures of labour underutilization` data across **28 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_MTS_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_MTS_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 28 Asia countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `TUR` | 1,982 | 2000 | 2024 | | `IDN` | 1,846 | 2000 | 2023 | | `PHL` | 1,746 | 2001 | 2023 | | `KOR` | 1,074 | 2003 | 2025 | | `ISR` | 1,027 | 2012 | 2024 | | `VNM` | 995 | 2010 | 2024 | | `CYP` | 926 | 1999 | 2020 | | `PSE` | 881 | 2012 | 2025 | | `ARM` | 829 | 2008 | 2018 | | `MNG` | 763 | 2013 | 2024 | | `JOR` | 561 | 2017 | 2024 | | `LKA` | 549 | 2016 | 2024 | | `AFG` | 373 | 2012 | 2021 | | `BRN` | 354 | 2014 | 2024 | | `BGD` | 328 | 2010 | 2024 | | ... | _13 more countries_ | | | ## Indicators (sample) - `EIP_WDIS_SEX_AGE_MTS_NB` — Discouraged job-seekers by sex, age and marital status (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_MTS_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 | `MTS_AGGREGATE_TOTAL` | | `classif2.label` | `string` | — | `Marital status (Aggregate): 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` | — | `—` | | `note_classif.label` | `string` | — | `—` | | `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-mts-nb-discouraged-job-seekers-by-sex-age-and-marital-sta") 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_MTS_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EIP_WDIS_SEX_AGE_MTS_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EIP_WDIS_SEX_AGE_MTS_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_eip_wdis_sex_age_mts_nb_discouraged_job_seekers_by_sex_age_and_marital_sta_2025, title = {Discouraged job-seekers by sex, age and marital status (thousands) | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EIP_WDIS_SEX_AGE_MTS_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-eip-wdis-sex-age-mts-nb-discouraged-job-seekers-by-sex-age-and-marital-sta}} } ``` ## 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_MTS_NB_The dataset, titled Discouraged job-seekers by sex, age and marital status (thousands) | Asia (ILOSTAT), is a tabular dataset focusing on Other measures of labour underutilization indicators in Asia. It contains 15,953 observations across 28 Asian countries, spanning from 1999 to 2025, with the primary indicator being Discouraged job-seekers by sex, age and marital status (thousands), which measures the number of individuals who have given up job-seeking due to various reasons. The data is sourced from the International Labour Organizations (ILO) ILOSTAT database, retrieved via API and filtered for Asian countries, and harmonized for consistency. The dataset includes detailed disaggregation dimensions such as sex (total, male, female), age, and marital status, and features columns like country codes, data sources, observation years, observed values, and data quality flags. It is suitable for tasks like tabular classification, regression, and time-series forecasting, and is designed to support researchers and developers in analyzing trends and patterns in Asian labour markets.

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中央统计数据库，通过其REST API接口直接抽取指标代码为EIP_WDIS_SEX_AGE_MTS_NB的原始数据，并依据ISO 3166-1 alpha-3标准筛选出亚洲28个国家的观测记录。数据经过ILO基于国际劳动统计学家会议（ICLS）定义的系统性协调与整合，对不同国家劳动力调查的微观数据进行标准化处理，确保跨国家、跨年度的可比性。同时，数据集中保留了来源标注列，以便追溯每一条观测的原始调查来源，保障数据的可溯源性。

特点

该数据集包含15,953条观测记录，时间跨度从1999年至2025年，覆盖亚洲28个国家，聚焦于“沮丧求职者”这一劳动力利用不足的核心指标，并以千人为计量单位。数据集不仅按性别（总、男、女）进行细分，还融合了年龄与婚姻状况的交叉分类维度，提供了丰富的多层次分析视角。此外，数据附带了观测状态标志（如不可靠、临时性）及详细的注释信息，便于研究者评估数据质量，并识别因方法论修订导致的断点序列。

使用方法

用户可通过HuggingFace Datasets库中的load_dataset函数一键加载该数据集，并将其转换为Pandas DataFrame进行灵活操作。例如，可以依据国家代码快速筛选特定国家的子集，或针对单一指标按时间排序绘制时间序列图。同时，利用pivot_table功能可将数据重塑为国家×年份的矩阵形式，便于进行面板数据分析或跨国家比较。数据集以Parquet格式打包，加载高效，适合开展劳动经济学领域的学术研究与政策评估工作。

背景与挑战

背景概述

在全球劳动力市场研究领域，劳动利用不足的非标准形态日益受到学界与政策制定者的关注。由国际劳工组织（ILO）统计部于2025年正式发布的“亚洲地区丧失信心的求职者”数据集，由Electric Sheep Asia重新封装，收录了涵盖28个亚洲国家、跨越1999至2025年间的15,953条观测记录。该数据集聚焦于按性别、年龄及婚姻状况划分的丧失信心求职者数量（单位：千），旨在量化那些因认为无合适工作而放弃主动求职的隐性失业群体。其核心研究问题在于揭示亚洲地区劳动利用不足的结构性特征与人口学差异，为评估劳动力市场的韧性与包容性提供数据支撑。作为ILOSTAT数据库的重要分支，该数据集已被广泛应用于国际比较、劳动经济学建模及可持续发展目标（SDG）监测，显著提升了亚洲区域劳动统计的精细化水平。

当前挑战

该数据集所应对的核心领域挑战在于刻画隐性失业的复杂测度难题。丧失信心求职者作为劳动利用不足的次生指标，其统计口径易受经济周期、文化观念及社会保障制度的影响，传统失业率指标难以完整揭示劳动力市场的真实闲置状况。在构建过程中，数据面临多重技术难点：首先，来源数据涉及28国劳动调查的异构问卷设计，ILO虽基于国际劳动统计学家会议（ICLS）定义进行协调，但各国在调查频率、年龄段分类及婚姻状态编码上仍存显著差异，可能引入测量误差。其次，部分观测值标记为“不可靠”或存在“方法修订断点”，如指标注释所示，历史数据的一致性维护成为关键瓶颈。此外，约15%的国家数据年份覆盖不连续（如阿富汗、孟加拉国），时间序列的稀疏性对趋势分析构成制约，需谨慎处理插值与外推的偏误风险。

常用场景

经典使用场景

该数据集作为国际劳工组织ILOSTAT数据库中关于亚洲地区劳动就业状况的核心数据源，广泛应用于劳动力市场分析领域。研究者常利用其包含的性别、年龄、婚姻状况等多维分类信息，追踪灰心丧气求职者（Discouraged job-seekers）这一特殊劳动未充分利用群体的规模与结构特征。通过时序数据建模，可系统考察亚洲各国劳动力参与率低迷的深层成因，为量化劳动市场的结构性矛盾提供坚实的基础数据支撑。

解决学术问题

数据集有效回应了劳动经济学中关于“隐性失业”的测量难题，弥补了传统失业率指标对潜在劳动力群体覆盖不足的缺陷。学者借助该数据能够深入探讨性别与婚姻状况如何交互影响个人退出劳动力市场的决策机制，并比较不同经济发展阶段国家的劳动参与弹性。其高分辨率的分层结构为分析劳动力市场韧性与制度回应性提供了可靠的数据基石，推动了劳动未充分利用模式的跨国实证研究。

衍生相关工作

该数据集衍生出了多项具有影响力的研究工作，包括围绕ILOSTAT数据开发的劳动力市场自动化分析管道以及基于机器学习的劳动未充分利用状态预测模型。部分研究以此为基础构建了亚洲地区灰心丧气求职者趋势的可视化仪表盘，另有学术工作将其与时序预测方法结合，用于评估经济波动对劳动力退出行为的影响。这些工作共同丰富了隐性就业问题的研究工具库，推动了数据驱动劳动政策分析范式的发展。