electricsheepasia/asia-ilo-eip-teip-sex-geo-dsb-nb-persons-outside-the-labour-force-by-sex-rural-urba

--- 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, rural / urban areas and disability status (thousa | Asia (ILOSTAT)" --- # Persons outside the labour force by sex, rural / urban areas and disability status (thousa | Asia (ILOSTAT) 🌏 **3,170 observations** · **17 Asia countries** · **1996–2024** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **3,170 observations** of `Other measures of labour underutilization` data across **17 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_GEO_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_GEO_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 17 Asia countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `CYP` | 540 | 2005 | 2024 | | `MNG` | 486 | 2006 | 2024 | | `ARM` | 459 | 2007 | 2023 | | `KHM` | 351 | 1996 | 2023 | | `IDN` | 243 | 2010 | 2023 | | `LKA` | 189 | 2018 | 2024 | | `PSE` | 180 | 2018 | 2022 | | `THA` | 135 | 2007 | 2019 | | `TLS` | 107 | 2015 | 2022 | | `BGD` | 93 | 2011 | 2024 | | `AFG` | 90 | 2017 | 2021 | | `LAO` | 81 | 2015 | 2022 | | `IRQ` | 54 | 2012 | 2021 | | `TJK` | 54 | 2003 | 2007 | | `PAK` | 54 | 2020 | 2021 | | ... | _2 more countries_ | | | ## Indicators (sample) - `EIP_TEIP_SEX_GEO_DSB_NB` — Persons outside the labour force by sex, rural / urban areas 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_GEO_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.) | `GEO_COV_NAT` | | `classif1.label` | `string` | — | `Area type: National` | | `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-geo-dsb-nb-persons-outside-the-labour-force-by-sex-rural-urba") 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_GEO_DSB_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EIP_TEIP_SEX_GEO_DSB_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EIP_TEIP_SEX_GEO_DSB_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_eip_teip_sex_geo_dsb_nb_persons_outside_the_labour_force_by_sex_rural_urba_2024, title = {Persons outside the labour force by sex, rural / urban areas and disability status (thousa | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2024}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EIP_TEIP_SEX_GEO_DSB_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-eip-teip-sex-geo-dsb-nb-persons-outside-the-labour-force-by-sex-rural-urba}} } ``` ## 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_GEO_DSB_NB_This dataset contains 3,170 observations across 17 Asian countries from 1996 to 2024, focusing on the indicator Persons outside the labour force by sex, rural / urban areas and disability status (thousands) (code EIP_TEIP_SEX_GEO_DSB_NB). Data is sourced from the International Labour Organization (ILO) ILOSTAT database, retrieved via API and filtered to Asian countries, covering fields such as country code, year, sex (total, male, female, etc.), area type (e.g., national), disability status (e.g., total), observed values, data status flags, and notes. The dataset is suitable for machine learning tasks like tabular classification, regression, and time-series forecasting, providing standardized data on labour underutilization in Asia.

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，通过直接调用其REST API接口获取指示符为EIP_TEIP_SEX_GEO_DSB_NB的原始数据，并依据亚洲ISO3国家代码进行过滤与区域筛选。ILOSTAT依据国际劳动统计学家会议（ICLS）的统一定义，对各国劳动力调查、家庭收入调查、企业调查及行政记录等微观数据进行协调与标准化处理，以确保跨国可比性。数据经Electric Sheep Asia团队重新打包为Parquet格式，保留了原始来源标记（source.label）以追踪数据溯源，最终形成了包含3170条观测的紧凑型表格数据集。

特点

该数据集横跨1996年至2024年，覆盖17个亚洲国家，聚焦于‘劳动力市场未充分利用的其他衡量指标’领域，具体追踪按性别、城乡区域及残疾状况划分的劳动力人口外群体数量（单位：千）。其核心特色在于丰富的维度解构：提供性别（男、女、总计及他类）的一级分类，并嵌入地域覆盖（全国或城乡）与残疾状态（总计或特定类别）等二级分类变量，同时包含观测值状态标记（如暂定、数据中断）及多层级注释字段（如指标定义变化、方法论修订）。数据以年频率呈现，确保时间序列分析的稳定性。

使用方法

研究者可通过HuggingFace的datasets库直接加载该数据集，使用`load_dataset()`函数即可快速获取训练集并转换为Pandas DataFrame进行探索。典型应用包括：按国家代码（如ref_area == 'IDN'）过滤以聚焦特定区域分析；针对单一指示符（如EIP_TEIP_SEX_GEO_DSB_NB）按时间排序后绘制折线图以观察劳动力外人口演变趋势；或利用透视表将数据重塑为国家×年份矩阵，便于进行面板数据回归与跨国比较。数据已预设为年度频率，且ILO已选定‘最佳来源’处理重复观测，用户可直接使用而无需额外清洗。

背景与挑战

背景概述

在全球劳动统计领域，国际劳工组织（ILO）的ILOSTAT数据库是衡量各国劳动力市场状况的权威来源。该数据集由Electric Sheep Asia于2024年重新包装并发布于HuggingFace，聚焦于亚洲17个国家中1996年至2024年间因性别、城乡区域及残疾状态而处于劳动力市场之外的个体数量（以千计）。其核心研究问题在于量化亚洲地区劳动力利用不足的隐蔽维度——即那些既非就业亦非失业的群体，这一指标对于揭示社会包容性、残疾政策效果及性别平等程度具有关键意义。数据集提供了3,170条观测值，涵盖劳动力调查、行政记录等多源数据，经ILO依据国际劳动统计学家会议（ICLS）标准进行统一化处理，为亚洲劳动力市场的跨国比较与时间序列分析奠定了坚实基础，推动了区域发展研究从宏观就业率向更精细的劳动力利用质量视角演进。

当前挑战

该数据集所应对的领域挑战在于，传统失业率指标难以捕捉劳动力市场的隐性闲置——例如因残疾、家庭责任或歧视而放弃求职的群体，导致政策制定者低估社会边缘化程度。具体挑战包括：1) 跨国数据协调难题：17个亚洲国家的原始调查在定义“劳动力之外人口”、残疾分类及城乡边界上存在显著差异，ILO需通过标准化流程调和这些概念歧义以保障可比性；2) 数据稀疏性与时间断层：部分国家如塔吉克斯坦仅有2003–2007年的有限记录，而阿富汗数据则受限于2017–2021年，长期缺失值削弱了趋势分析的稳健性；3) 观测状态标记的复杂性：数据集中包含“序列断裂”（Break in series）等状态标识，反映了因方法论修订或调查框架调整引发的历史数据不可直接拼接问题，研究者需谨慎处理此类结构断点以避免推论偏差。

常用场景

经典使用场景

该数据集汇集了国际劳工组织ILOSTAT数据库中关于亚太地区17个国家1996年至2024年间劳动力市场边缘群体的核心指标——即因性别、城乡地域及残疾状况而游离于劳动力市场之外的个体数量。研究者常将其用于构建多元化的分类与回归模型，以探讨劳动力市场参与率的驱动因素，或利用时间序列预测方法分析区域就业结构的演变趋势。对于劳动经济学而言，这一数据集为精准刻画亚洲弱势劳动群体的规模与分布提供了宝贵的数据基础。

实际应用

在实际应用层面，该数据集为国际组织、政府机构及非营利部门提供了可操作的决策工具。政策制定者可以利用其中的分类信息设计更具针对性的就业促进计划，例如在城乡结合部开展针对女性与残疾人的职业培训项目。劳动市场分析师则能借助时间序列数据监测经济危机或公共卫生事件对弱势群体的冲击程度，从而及时调整社会保障制度。此外，该数据也为跨国就业比较研究提供了标准化接口，使得亚洲各国之间的劳动力政策效果评估更加透明和可比。

衍生相关工作

该数据集衍生了一系列具有影响力的相关研究工作。许多学者利用其结构化特征开展劳动力供给弹性测算与区域异质性分析，揭示了残疾状态与就业参与之间的非线性关系。同时，基于该数据集的预测模型被广泛应用于劳动市场早期预警系统，以识别就业收缩的先行信号。在数据科学领域，研究者进一步围绕该数据开发了可复现的标准化处理管线，推动了ILOSTAT数据在HuggingFace生态中的机器学习就绪化进程，使得跨学科团队能够快速在此基础上构建更复杂的因果推断与政策模拟模型。