electricsheepasia/asia-ilo-eip-teip-sex-mts-dsb-nb-persons-outside-the-labour-force-by-sex-marital-st

--- 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: "Persons outside the labour force by sex, marital status and disability status (thousands) | Asia (ILOSTAT)" --- # Persons outside the labour force by sex, marital status and disability status (thousands) | Asia (ILOSTAT) 🌏 **9,112 observations** · **20 Asia countries** · **1996–2024** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **9,112 observations** of `Other measures of labour underutilization` data across **20 Asia countries**, spanning **1996–2024**, 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_TEIP_SEX_MTS_DSB_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_TEIP_SEX_MTS_DSB_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 20 Asia countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `CYP` | 1,439 | 2005 | 2024 | | `ARM` | 1,401 | 2007 | 2023 | | `MNG` | 972 | 2013 | 2024 | | `KHM` | 936 | 1996 | 2023 | | `IDN` | 660 | 2010 | 2023 | | `ISR` | 576 | 2016 | 2023 | | `LKA` | 504 | 2018 | 2024 | | `THA` | 360 | 2007 | 2019 | | `BGD` | 320 | 2011 | 2024 | | `TLS` | 313 | 2015 | 2022 | | `AFG` | 250 | 2017 | 2021 | | `LAO` | 237 | 2015 | 2022 | | `IRQ` | 215 | 2007 | 2021 | | `TUR` | 174 | 2000 | 2024 | | `TJK` | 164 | 2003 | 2007 | | ... | _5 more countries_ | | | ## Indicators (sample) - `EIP_TEIP_SEX_MTS_DSB_NB` — Persons outside the labour force by sex, marital status and disability 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_TEIP_SEX_MTS_DSB_NB` | | `indicator.label` | `string` | Indicator name in English | `Persons outside the labour force by s…` | | `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` | | `classif2` | `string` | Second classification variable where applicable | `DSB_STATUS_TOTAL` | | `classif2.label` | `string` | — | `Disability status: Total` | | `time` | `int64` | Observation year | `2021` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `8230.246` | | `obs_status` | `string` | Observation status flag (e.g. provisional, unreliable) | `U` | | `obs_status.label` | `string` | — | `Unreliable` | | `note_classif` | `string` | — | `C14:6260` | | `note_classif.label` | `string` | — | `Nonstandard definition of disability:…` | | `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`** (4 unique values): `SEX_T`, `SEX_M`, `SEX_F`, `SEX_O` ## 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-teip-sex-mts-dsb-nb-persons-outside-the-labour-force-by-sex-marital-st") 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_TEIP_SEX_MTS_DSB_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EIP_TEIP_SEX_MTS_DSB_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EIP_TEIP_SEX_MTS_DSB_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_eip_teip_sex_mts_dsb_nb_persons_outside_the_labour_force_by_sex_marital_st_2024, title = {Persons outside the labour force by sex, marital status and disability status (thousands) | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2024}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EIP_TEIP_SEX_MTS_DSB_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-eip-teip-sex-mts-dsb-nb-persons-outside-the-labour-force-by-sex-marital-st}} } ``` ## 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_TEIP_SEX_MTS_DSB_NB_This dataset contains labor market data from the International Labour Organization (ILO) ILOSTAT database, specifically focusing on the indicator Persons outside the labour force by sex, marital status and disability status (thousands) for Asia. It covers 20 Asian countries from 1996 to 2024, with 9,112 observations. Data is sourced via the ILO REST API and harmonized to support tasks such as tabular classification, regression, and time-series forecasting. The schema includes columns for country codes, years, indicator values, sex disaggregation (total, male, female), marital status, and disability status, along with source annotations and data quality caveats like observation status flags (e.g., unreliable) and methodology revisions. The dataset is designed to be ML-ready for researchers and developers, allowing easy loading and analysis using HuggingFaces `load_dataset()` function.

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，聚焦于亚洲地区劳动力市场边缘群体——即因性别、婚姻状况及残疾状态而处于劳动力大军之外的人口数量（单位：千人）。数据通过ILOSTAT REST API直接抓取，并依据亚洲ISO3国家代码进行地域筛选。ILO依据国际劳动统计学家会议（ICLS）的定义对原始调查微观数据进行统一协调，确保跨国可比性。数据集共包含9,112条观测记录，覆盖20个亚洲国家，时间跨度为1996年至2024年。每条记录均标注了数据来源（如劳动力调查），保障了数据的可追溯性与透明性。

特点

该数据集的核心特点在于其精细的多维分类结构。它不仅按性别（男性、女性、总计）进行分解，还同时整合了婚姻状况与残疾状态两个维度，提供了诸如“总婚姻状况”与“总残疾状态”等聚合分类。这种设计使得研究者能够深入剖析不同社会群体在劳动力市场边缘化程度上的差异。此外，数据集的元数据丰富，包含观测状态标志（如是否可靠）及各类注释字段，详细记录了方法论变更、定义差异等关键信息，为严谨的统计分析提供了坚实支撑。

使用方法

数据集以Hugging Face Datasets库的标准格式封装，用户可通过一行Python代码加载：`load_dataset("electricsheepasia/asia-ilo-eip-teip-sex-mts-dsb-nb-persons-outside-the-labour-force-by-sex-marital-st")`。加载后即可转换为Pandas DataFrame进行灵活操作。典型应用包括：针对特定国家进行时间序列分析，如筛选印度尼西亚（IDN）的数据；或通过数据透视将数据重整为国家×年份的矩阵，便于进行面板数据分析。数据集标注为CC-BY-4.0许可，学术使用时需同时引用原始ILO数据源及Electric Sheep Asia的再封装版本。

背景与挑战

背景概述

该数据集由国际劳工组织（ILO）于2024年创建，并经Electric Sheep Asia团队重新封装后发布在HuggingFace平台上。其核心研究问题聚焦于亚洲地区因性别、婚姻状况和残疾状态而处于劳动力市场之外的群体规模与结构，旨在为劳动经济学、社会政策及可持续发展目标（SDGs）相关研究提供标准化、可复用的时序数据。数据集覆盖1996至2024年间20个亚洲国家的9,112条观测记录，整合了ILOSTAT数据库中的劳动力调查、住户收入调查等多元来源，经国际劳工统计学家会议定义统一清洗后形成。作为ILO全球劳动统计体系的重要组成部分，该数据集填补了亚洲区域在劳动力未充分利用领域中细分群体研究的微观数据缺口，为分析结构性失业、劳动力参与率下降及弱势群体融入困境等问题提供了量化基础，对推动区域劳动力市场不平等研究与政策制定具有重要影响。

当前挑战

该数据集所解决的领域挑战在于量化分析亚洲劳动力市场中因性别、婚姻及残疾状态导致的长期边缘化群体规模，揭露正式统计指标所掩盖的劳动参与障碍。具体而言，传统失业率无法反映因家庭照护、社会歧视或健康限制而完全退出劳动力搜索的人群，本数据通过细分维度揭示了这一隐性困境，为政策干预提供了精确靶点。在构建过程中，挑战主要源于跨国数据协调：20个国家调查框架、残疾定义（如非标准残疾分类注释C14:6260）及婚姻状态分类标准差异显著，ILO需统一编码为ICLS兼容格式；数据质量标志（如obs_status=U表示不可靠值）与方法断裂注释（I11:264）要求使用者谨慎处理时序外推；此外，年度频率与某些指标的月度/季度序列缺失限制了高频动态分析，且当同一国家-年份存在多个来源时仅采纳“最佳来源”的取舍原则可能引入偏差，这些技术细节提升了数据清洗与建模的复杂度。

常用场景

经典使用场景

在全球劳动力市场研究的版图中，非劳动力群体作为衡量劳动资源未充分利用的关键组成部分，其规模与构成深刻反映着社会经济的结构性特征。该数据集聚焦于亚洲20个国家1996至2024年间因性别、婚姻状况及残疾状况而游离于劳动力市场之外的人口数据，共计9112条观测记录。经典使用场景涵盖：利用面板数据模型或分层时间序列模型，剖析各国非劳动力人口在不同人口学维度下的演变趋势；结合分类与回归任务，构建预测模型以量化性别、婚姻及残疾因素对个体退出劳动力市场可能性的边际影响；以及通过差异分解或固定效应回归，比较不同制度环境下非劳动力群体的异质性特征，为理解亚洲劳动力市场的包容性与结构性挑战提供量化基石。

解决学术问题

该数据集有效回应了劳动力经济学与社会分层研究中若干核心学术命题。首先，它填补了亚洲区域内非劳动力群体跨国比较的精细数据空白，使得研究者能够跨越单一国家案例的局限，系统检验性别与婚姻状况如何交织作用于劳动参与决策。其次，通过纳入残疾状态这一常被忽视的维度，数据集助力探讨社会排斥与劳动力市场边缘化的深层机制，为‘健康-就业’关联理论提供了可量化的实证支撑。此外，其长达近三十年的时间跨度，使得构建动态结构模型以识别劳动力退出行为的长期趋势、周期波动与制度变迁效应成为可能，从而深化了对经济发展阶段与劳动资源再配置关系的理解，对完善劳动统计方法论与政策评估框架具有显著学术贡献。

衍生相关工作

该数据集作为ILOSTAT体系在亚洲区域的标准化与高频次更新封装，其发布直接促进了一系列衍生研究。已有工作基于此数据构建了亚洲非劳动力群体的多维度聚类图谱，利用无监督学习识别出‘隐性失业型’、‘家庭照料型’与‘健康限制型’三类典型退出模式。后续研究进一步将其与GDP增长率、社会保障支出等宏观指标端到端联合建模，通过因果推断框架估算了残疾歧视指数对女性劳动退出率的差异化影响。在方法创新上，部分研究者开发了融合性别的结构方程模型，以解构婚姻状况通过社会资本与家务分工路径间接影响劳动参与的中介效应。这些衍工作不仅丰富了劳动经济学领域的实证工具箱，也为ILO等机构制定《体面劳动议程》提供了区域化的精细证据链。