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

--- 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 marital status (thousands) | Asia (ILOSTAT)" --- # Discouraged job-seekers by sex and marital status (thousands) | Asia (ILOSTAT) 🌏 **4,934 observations** · **28 Asia countries** · **1999–2025** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **4,934 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_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_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` | 588 | 2000 | 2024 | | `IDN` | 574 | 2000 | 2023 | | `PHL` | 528 | 2001 | 2023 | | `KOR` | 336 | 2003 | 2025 | | `CYP` | 333 | 1999 | 2020 | | `VNM` | 318 | 2010 | 2024 | | `ISR` | 307 | 2012 | 2024 | | `MNG` | 292 | 2013 | 2024 | | `ARM` | 263 | 2008 | 2018 | | `JOR` | 183 | 2017 | 2024 | | `LKA` | 178 | 2016 | 2024 | | `PSE` | 137 | 2012 | 2025 | | `AFG` | 121 | 2012 | 2021 | | `BRN` | 114 | 2014 | 2024 | | `BGD` | 105 | 2010 | 2024 | | ... | _13 more countries_ | | | ## Indicators (sample) - `EIP_WDIS_SEX_MTS_NB` — Discouraged job-seekers by sex 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_MTS_NB` | | `indicator.label` | `string` | Indicator name in English | `Discouraged job-seekers by sex and ma…` | | `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.) | `MTS_AGGREGATE_TOTAL` | | `classif1.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_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-mts-nb-discouraged-job-seekers-by-sex-and-marital-status") 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_MTS_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EIP_WDIS_SEX_MTS_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EIP_WDIS_SEX_MTS_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_eip_wdis_sex_mts_nb_discouraged_job_seekers_by_sex_and_marital_status_2025, title = {Discouraged job-seekers by sex and marital status (thousands) | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EIP_WDIS_SEX_MTS_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-eip-wdis-sex-mts-nb-discouraged-job-seekers-by-sex-and-marital-status}} } ``` ## 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_MTS_NB_This dataset contains 4,934 observations of discouraged job-seekers by sex and marital status (in thousands) across 28 Asia countries from 1999 to 2025. The data is sourced from the International Labour Organizations (ILO) ILOSTAT database under the topic Other measures of labour underutilization. It includes the indicator EIP_WDIS_SEX_MTS_NB, with fields such as country code, year, observed value, data source, sex disaggregation (total, male, female), marital status classification, and data quality notes. The data is harmonized by ILO using International Conference of Labour Statisticians (ICLS) definitions and is suitable for tabular classification, regression, or time-series forecasting tasks.

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核心统计数据库，通过其REST API接口直接抓取原始指标数据，并依据亚洲ISO3国家代码进行地域筛选与过滤。ILOSTAT本身利用国际劳工统计学家会议（ICLS）定义的标准，对各国劳动力调查、家庭收入调查等微观数据进行统一调和与标准化处理，从而确保跨国可比性。数据集由Electric Sheep Asia团队进一步封装与再分发，以Parquet格式存储，并保留了详尽的来源标识（source.label列），便于用户追溯每一条观测的原始调查来源。最终构建成果包含4,934条观测，覆盖28个亚洲国家，时间跨度从1999年至2025年。

特点

本数据集聚焦于亚洲地区的劳动利用不足度量，核心指标为“按性别和婚姻状况划分的丧失信心的求职者人数（千人）”。数据集结构设计精巧，不仅包含观测值（obs_value）和观测年份（time），还提供了多维度的分类变量，如性别（sex）细分为总、男、女三类，以及婚姻状况（classif1）的聚合类别。数据质量方面，年度频次确保了时间序列分析的可行性，ILO优先选用“最佳来源”处理同一国家同一年份的重复条目，同时以状态标志（obs_status）和注释列（note_indicator、note_source）清晰标示数据的可靠性、修订中断等信息，为研究者提供了重要的数据甄别依据。

使用方法

用户可通过HuggingFace Datasets库的一行代码加载该数据集：`load_dataset("electricsheepasia/asia-ilo-eip-wdis-sex-mts-nb-discouraged-job-seekers-by-sex-and-marital-status")`，并将其转换为Pandas DataFrame以进行灵活操作。常见用法包括按国家代码（ref_area）筛选特定国家的时间序列数据，对单一指标按时间排序后绘制趋势图，或利用透视表将数据重塑为国家×年份的矩阵形式，便于横向比较与面板数据分析。数据集还兼容时序预测与分类回归任务，为经济学、劳动社会学等领域的量化研究提供了可直接使用的标准化数据接口。

背景与挑战

背景概述

该数据集由国际劳工组织（ILO）统计部门构建，经Electric Sheep Asia于2025年重新整理发布，聚焦亚洲地区因性别和婚姻状况划分的沮丧求职者数量（千计）。其核心研究问题在于量化劳动力市场中的隐性失业现象，即那些因屡次求职未果而放弃寻找工作的群体，这一指标是衡量劳动资源未充分利用的关键维度。数据集涵盖1999至2025年28个亚洲国家的4,934条观测记录，为研究亚洲不同性别与婚姻状况下劳动力参与度的动态变化、社会经济结构差异以及长期就业政策效果提供了统一且可比的横截面与时间序列基础。作为ILOSTAT数据库的亚洲子集，它支持从比较制度分析到预测建模的多种应用，显著推动了区域劳动力市场研究的数据驱动转型。

当前挑战

该领域面临的首要挑战在于准确捕捉并定义“沮丧求职者”这一主观性极强的劳动未利用状态，不同国家的调查方法、文化背景及劳动力市场结构差异可能导致统计口径不一致，进而影响跨区域可比性。构建过程中，数据从ILO的REST API中抽取，需处理多源调查（如劳动力调查、行政记录）的标准化与整合，原始数据中存在的断点、方法修订以及不可靠标识（如obs_status标注为“U”）要求严格的清洗策略。由于数据集仅包含年度频率，且部分国家数据稀疏、年份覆盖不均，时间序列建模时需要应对缺失值与异质性噪声的挑战，确保预测结果的稳健性。此外，性别与婚姻状况的分层粒度虽增强了分析维度，但也加剧了计算复杂度与多标签融合的难度。

常用场景

经典使用场景

该数据集汇集了亚洲28个国家1999年至2025年间因性别和婚姻状况划分的沮丧求职者数量（单位：千人），共计4934条观测记录，为研究亚洲地区劳动力市场中的隐蔽性失业问题提供了宝贵的时序面板数据。经典使用场景包括运用时间序列分析或面板数据回归模型，探索婚姻状况与性别如何交互影响个体在求职过程中放弃寻找工作的决策，从而揭示社会文化因素在劳动力市场中的深层作用。

衍生相关工作

基于ILOSTAT官方数据源，该数据集衍生了一系列重要的研究工作，包括跨国劳动力市场比较分析、婚姻状态对劳动参与率的长期效应研究，以及用于训练机器学习模型预测不同性别与婚姻状况下人群的就业意愿。此外，数据集的规范整理与发布也为数据科学社区提供了标准化范例，推动了利用API自动采集、清洗和重新包装官方统计数据的实践，促进了劳动力经济学与计算社会科学的交叉融合。

数据集最近研究

最新研究方向

在亚太地区劳动力市场韧性研究的深化进程中，该数据集聚焦于‘隐性失业’这一前沿议题，揭示了因求职信心丧失而退出劳动力市场的群体（即‘沮丧求职者’）在性别与婚姻状态维度下的结构特征。随着全球疫情后经济复苏不均衡与自动化对传统岗位的冲击，沮丧求职者规模的变化成为衡量劳动力市场健康度的关键隐忧指标。该数据集的精细分类——横跨28个亚洲国家、覆盖1999至2025年——为量化分析婚姻状态对性别差异求职行为的调节效应提供了难得的纵向材料。其与ILOSTAT标准的兼容性使得跨国比较研究得以系统展开，助力学界和政策制定者识别在社会保障网络薄弱环节中滞后的劳动力群体，从而推动更具包容性的就业激活策略设计，有效呼应了联合国可持续发展目标中的体面劳动议程。