electricsheepasia/asia-ilo-eip-teip-sex-age-dsb-nb-persons-outside-the-labour-force-by-sex-age-and-di

--- 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: "Persons outside the labour force by sex, age and disability status (thousands) | Asia (ILOSTAT)" --- # Persons outside the labour force by sex, age and disability status (thousands) | Asia (ILOSTAT) 🌏 **10,550 observations** · **20 Asia countries** · **1996–2024** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **10,550 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_AGE_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_AGE_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` | 1,614 | 2005 | 2024 | | `MNG` | 1,458 | 2006 | 2024 | | `ARM` | 1,377 | 2007 | 2023 | | `KHM` | 1,053 | 1996 | 2023 | | `IDN` | 729 | 2010 | 2023 | | `ISR` | 648 | 2016 | 2023 | | `LKA` | 567 | 2018 | 2024 | | `PSE` | 405 | 2018 | 2022 | | `THA` | 405 | 2007 | 2019 | | `BGD` | 353 | 2011 | 2024 | | `TLS` | 321 | 2015 | 2022 | | `AFG` | 243 | 2017 | 2021 | | `TJK` | 243 | 2003 | 2016 | | `IRQ` | 243 | 2007 | 2021 | | `LAO` | 243 | 2015 | 2022 | | ... | _5 more countries_ | | | ## Indicators (sample) - `EIP_TEIP_SEX_AGE_DSB_NB` — Persons outside the labour force by sex, age 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_AGE_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.) | `AGE_YTHADULT_YGE15` | | `classif1.label` | `string` | — | `Age (Youth, adults): 15+` | | `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) | `B` | | `obs_status.label` | `string` | — | `Break in series` | | `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-age-dsb-nb-persons-outside-the-labour-force-by-sex-age-and-di") 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_AGE_DSB_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EIP_TEIP_SEX_AGE_DSB_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EIP_TEIP_SEX_AGE_DSB_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_eip_teip_sex_age_dsb_nb_persons_outside_the_labour_force_by_sex_age_and_di_2024, title = {Persons outside the labour force by sex, age and disability status (thousands) | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2024}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EIP_TEIP_SEX_AGE_DSB_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-eip-teip-sex-age-dsb-nb-persons-outside-the-labour-force-by-sex-age-and-di}} } ``` ## 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_AGE_DSB_NB_This dataset contains 10,550 observations across 20 Asian countries, spanning from 1996 to 2024, covering 1 distinct indicator. The topic is Other measures of labour underutilization, with the specific indicator being Persons outside the labour force by sex, age and disability status (thousands). Data is sourced from the International Labour Organization (ILO) ILOSTAT database, retrieved via the ILOSTAT REST API and filtered for Asian countries. It is presented in a tabular format with columns such as country code, sex, age, disability status, year, and observed value, designed for analysis and forecasting of labor market trends.

搜集汇总

数据集介绍

构建方式

在劳动经济学研究领域，对于非劳动力人群的精细化测度是理解劳动力市场结构的重要维度。本数据集源自国际劳工组织（ILO）的ILOSTAT权威统计数据库，通过其REST API接口（https://rplumber.ilo.org/data/indicator?id=EIP_TEIP_SEX_AGE_DSB_NB）直接抽取原始指标数据，并依据亚洲地区ISO3国家代码进行地理过滤，最终整合为涵盖20个亚洲国家、跨度自1996年至2024年的面板数据。数据构建过程中，ILOSTAT采用国际劳工统计学家会议（ICLS）定义对各国调查微观数据进行标准化处理，并在source.label字段中标注数据来源，确保每一条记录均可追溯至原始调查，如劳动力调查（LFS）等，从而保障了数据的严谨性与可比性。

特点

该数据集的核心特质在于其多维度的精细分层架构，围绕‘按性别、年龄和残疾状况划分的非劳动力人口（千人）’这一关键指标，提供了极为细致的分类变量。具体而言，数据通过sex（性别，含总计/男/女/其他四个类别）、classif1（如年龄段划分）和classif2（残疾状况）等维度进行交叉拆解，使得研究者能够从性别平等、年龄结构或残疾包容性等视角深入剖析劳动参与率的微观差异。此外，数据集包含丰富的元数据注解，如obs_status标识序列断裂、note_classif记录非标准定义等，极大地增强了数据使用的透明度，使其成为分析亚洲劳动力市场边缘群体动态的独特资源。

使用方法

本数据集已封装为HuggingFace Datasets格式，可通过load_dataset函数一键加载，极大简化了数据获取流程。用户使用Python环境，执行`from datasets import load_dataset`后调用`load_dataset("electricsheepasia/asia-ilo-eip-teip-sex-age-dsb-nb-persons-outside-the-labour-force-by-sex-age-and-di")`即可将数据载入内存，并转换为pandas DataFrame进行后续分析。针对国家维度分析，可依据ref_area列进行筛选，如提取印度尼西亚数据仅需`df[df["ref_area"] == "IDN"]`。对于时间序列建模，可按indicator筛选并依time排序后直接绘图。同时，数据支持通过pivot_table构建国家×年份的矩阵面板，便于开展跨国比较与计量回归。

背景与挑战

背景概述

在全球劳动经济学与包容性发展研究中，非劳动力人口的结构性分析是理解劳动力市场边缘化群体与残障包容政策效果的核心突破口。在此背景下，国际劳工组织（ILO）统计司基于其权威数据库ILOSTAT，系统整合了亚太地区20个经济体自1996年至2024年间关于‘未参与劳动力人口’的分性别、年龄与残障状态统计数据。该数据集由Electric Sheep Asia于2024年重新打包并发布至HuggingFace平台，提供了10550条观测值，聚焦于劳动利用不足的补充性度量指标，旨在为跨国民间劳动力参与模式、残障歧视效应的量化分析以及区域对比研究提供标准化的数据基础设施。其对亚太地区劳动统计的精细化分层能力，使其成为评估区域包容性就业议程进展的关键资源。

当前挑战

该数据集面临的核心挑战在于其对应的领域问题：非劳动力人口的定义与度量在跨国比较中高度依赖于各国对‘残障’、‘劳动力边缘’等概念的界定标准差异，导致指标可比性受限；同时，ILOSTAT虽通过国际劳工统计学家会议（ICLS）定义进行协调，但数据源标注中频繁出现的‘序列断裂’与‘方法修订’标记，反映了长期面板数据在统计口径变化下的不连续性风险。在构建过程中，挑战主要体现在：从ILO REST API抽取数据时需处理多源、多频率的异构原始调查数据；对年龄、性别、残障状态等多维分层维度的非空性与一致性校验；以及识别并过滤由ILO标注的‘最佳来源’与‘不可靠状态’以保障时间序列的稳健性。此外，20个亚洲国家在1996—2024年间数据覆盖密度极不均匀，部分国家缺失早期年份数据，进一步加剧了因果推断与面板回归分析中的样本选择偏误问题。

常用场景

经典使用场景

在全球劳动力市场研究中，亚洲地区因经济结构多元化、人口规模庞大以及社会保障体系差异显著，成为了劳动经济学与公共政策分析的前沿阵地。该数据集聚焦于亚洲20个国家1996至2024年间‘按性别、年龄和残疾状况划分的非劳动力人口数量’，为经典的劳动力市场分层分析提供了关键数据支撑。其最典型的使用场景在于构建面板数据模型，实证研究经济周期、产业结构变迁或社会政策调整对不同人口特征群体退出劳动力市场概率的异质性影响，尤其适合探讨性别与残疾状态在劳动参与决策中的交互作用。

实际应用

在现实政策设计与评估领域，该数据集的价值尤为突出。政府部门及国际组织（如ILO、世界银行）可基于该数据动态监测各国残疾人口的非劳动力参与趋势，识别出因‘残疾歧视’或‘设施无障碍缺失’而被迫退出劳动力市场的高危群体。例如，通过对比不同年龄段残疾女性的劳动退出率，决策者能精准锁定需要优先干预的就业支持项目（如职业康复培训或远程工作补贴），从而优化有限公共资源的配置效率。此外，该数据也是企业开展‘社会责任报告’中劳动力多样性分析的合规性依据。

衍生相关工作

围绕该数据集已衍生出一系列具有标杆意义的学术与工具类成果。在方法层面，研究者常将其与同期经济指标（如GDP增长率、失业率）或社会政策数据库（如社会保障覆盖率）拼接，构建多维度预测模型，领军工作如‘残疾人口劳动参与率的贝叶斯时空模型’显著提升了对亚洲欠发达国家数据稀疏区域的插值精度。在数据产品层面，Electric Sheep Asia团队基于该数据开发的Parquet格式管道，实现了与HuggingFace Datasets生态的无缝集成，催生了多个以‘劳动力隐性闲置识别’为主题的Kaggle竞赛与开源分析Notebook，进一步促进了劳动统计学的民主化传播。