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

--- 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 and disability status (thousands) | Asia (ILOSTAT)" --- # Persons outside the labour force by sex and disability status (thousands) | Asia (ILOSTAT) 🌏 **1,174 observations** · **20 Asia countries** · **1996–2024** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **1,174 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_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_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` | 180 | 2005 | 2024 | | `MNG` | 162 | 2006 | 2024 | | `ARM` | 153 | 2007 | 2023 | | `KHM` | 117 | 1996 | 2023 | | `IDN` | 81 | 2010 | 2023 | | `ISR` | 72 | 2016 | 2023 | | `LKA` | 63 | 2018 | 2024 | | `PSE` | 45 | 2018 | 2022 | | `THA` | 45 | 2007 | 2019 | | `BGD` | 40 | 2011 | 2024 | | `TLS` | 36 | 2015 | 2022 | | `AFG` | 27 | 2017 | 2021 | | `TJK` | 27 | 2003 | 2016 | | `IRQ` | 27 | 2007 | 2021 | | `LAO` | 27 | 2015 | 2022 | | ... | _5 more countries_ | | | ## Indicators (sample) - `EIP_TEIP_SEX_DSB_NB` — Persons outside the labour force by sex 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_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.) | `DSB_STATUS_TOTAL` | | `classif1.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-dsb-nb-persons-outside-the-labour-force-by-sex-and-disabi") 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_DSB_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EIP_TEIP_SEX_DSB_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EIP_TEIP_SEX_DSB_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_eip_teip_sex_dsb_nb_persons_outside_the_labour_force_by_sex_and_disabi_2024, title = {Persons outside the labour force by sex and disability status (thousands) | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2024}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EIP_TEIP_SEX_DSB_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-eip-teip-sex-dsb-nb-persons-outside-the-labour-force-by-sex-and-disabi}} } ``` ## 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_DSB_NB_This dataset contains 1,174 observations of Other measures of labour underutilization data across 20 Asia countries, spanning 1996–2024, covering 1 distinct indicator, specifically Persons outside the labour force by sex and disability status (thousands). It is sourced from the ILOSTAT database of the International Labour Organization (ILO), retrieved via API and filtered to Asian country codes, and includes columns such as country code, source, indicator, sex disaggregation, time, and observed values, suitable for tabular classification, regression, and time-series forecasting tasks.

搜集汇总

数据集介绍

构建方式

该数据集由国际劳工组织（ILO）统计数据库ILOSTAT的REST API直接拉取原始数据而来，针对亚洲地区ISO3国家代码进行过滤与整合。数据源涵盖各国劳动力调查、家庭收入调查、机构调查及行政记录，并依据国际劳工统计学家会议（ICLS）定义进行统一化处理。Electric Sheep Asia团队对原始数据进行了重封装与标准化处理，以确保数据结构的一致性，最终形成包含1,174条观测记录、覆盖20个亚洲国家、时间跨度从1996年至2024年的公开数据集。

特点

数据集聚焦于'按性别和残疾状况划分的劳动力以外人口（千人）'这一核心指标，是衡量劳动力未充分利用情况的重要补充。其独特性在于提供了性别（男性、女性、总计、其他）与残疾状况的交叉分层，使得研究者能够深入剖析劳动力市场边缘人群的结构特征。此外，数据集附带了详尽的数据来源标注、观测状态标识（如序列中断、方法论修订）以及注释信息，为数据质量追溯与分析提供了坚实的透明性保障。

使用方法

用户可通过HuggingFace Datasets库的load_dataset()函数快速加载数据，并将训练集转换为Pandas DataFrame进行后续分析。数据支持按国家代码筛选特定地区的观测值，亦可针对唯一指标'EIP_TEIP_SEX_DSB_NB'进行时间序列的可视化与建模。对于跨国家比较，可利用透视表功能将数据重塑为国家×年份的矩阵形式，便于开展面板数据分析或时序预测任务。数据集内嵌的'obs_status'与'note_*'字段更可辅助用户识别并处理数据中的质量警告与序列断裂问题。

背景与挑战

背景概述

该数据集由国际劳工组织（ILO）统计部门于2024年发布，并经Electric Sheep Asia重新整理后托管于HuggingFace平台，聚焦于亚洲地区20个国家1996年至2024年间因性别与残疾状态而脱离劳动力市场的人数观测。其核心研究问题在于量化劳动力利用不足现象中的结构性差异，尤其关注残疾人群体的就业排斥程度，从而为包容性劳动政策制定提供数据基石。作为ILOSTAT数据库的衍生品，该数据集依托于国际劳工统计学家会议（ICLS）定义，整合了劳动力调查、住户收入调查等官方统计来源，在劳动经济学、社会保障及可持续发展目标（SDGs）监测领域具有重要参考价值，推动了亚洲区域劳动力统计的标准化与可比性研究。

当前挑战

该数据集主要面临两大领域挑战：首先，劳动力市场中残疾人群体的边缘化问题长期被忽视，传统统计多以总体就业率掩盖分布不均，而该数据集通过性别与残疾状态的交叉分析，揭示了劳动参与率背后更深层的社会包容性障碍。在构建过程中，数据整合面临多重困难，包括各国调查年份跨度大（1996–2024）、残疾定义不统一（如“常规残疾定义”与“非标准定义”差异）、及数据质量标注（如“序列断裂”和“方法论修订”）需精细处理。此外，多源数据的最佳来源选择机制与年度频率限制了高维时间序列分析，而分类维度（如性别、残疾状态）的非完全覆盖亦增加了跨群体比较的复杂性与不确定性。

常用场景

经典使用场景

该数据集记录了1996年至2024年间20个亚洲国家中，按性别和残疾状况划分的劳动参与边缘人口数量（单位：千人）。作为国际劳工组织ILOSTAT数据库的亚洲子集，其经典使用场景集中于劳动经济学中的劳动参与率分析、劳动力市场弱势群体识别以及时间序列预测。研究者常借助该数据构建面板数据模型，探究区域间劳动未被充分利用的态势差异，或结合性别与残疾交叉维度，评估社会保障政策对特殊群体的覆盖效果。

实际应用

在实际应用中，该数据集为国际组织、国家劳动部门及非政府机构制定精准就业帮扶政策提供了数据支撑。政策制定者可基于不同年份、国家及残疾类型的劳动边缘人口波动，评估经济危机、公共卫生事件或法规调整对脆弱群体劳动参与意愿的冲击。例如，通过分析印度尼西亚与柬埔寨的序列差异，优化区域性职业康复项目资源配置；或利用性别分组数据，设计促进残疾女性重返劳动市场的专项培训计划。

衍生相关工作

该数据集衍生的相关工作主要围绕劳动边缘化指标的跨领域融合与模型创新。经典延伸包括将时序数据与宏观经济变量（如GDP增长率、教育投入）结合，构建劳动参与率动态预测模型；或利用性别与残疾的交互分类，发展劳动力市场歧视的多层次计量分析框架。此外，数据集的标准化格式推动了基于机器学习的劳动边缘人口聚类研究，以及ILOSTAT全球数据库与亚洲区域子集的一致性校验工作，为劳动统计的自动清洗与整合提供了基准参考。