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

--- 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, education and disability status (thousands) | Asia (ILOSTAT)" --- # Persons outside the labour force by sex, education and disability status (thousands) | Asia (ILOSTAT) 🌏 **6,299 observations** · **20 Asia countries** · **1996–2024** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **6,299 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_EDU_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_EDU_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` | 978 | 2005 | 2024 | | `MNG` | 837 | 2006 | 2024 | | `ARM` | 744 | 2007 | 2023 | | `KHM` | 600 | 1996 | 2023 | | `ISR` | 432 | 2016 | 2023 | | `IDN` | 411 | 2010 | 2023 | | `LKA` | 375 | 2018 | 2024 | | `THA` | 270 | 2007 | 2019 | | `BGD` | 232 | 2011 | 2024 | | `PSE` | 225 | 2018 | 2022 | | `TLS` | 194 | 2015 | 2022 | | `IRQ` | 156 | 2007 | 2021 | | `AFG` | 153 | 2017 | 2021 | | `LAO` | 153 | 2015 | 2022 | | `TJK` | 135 | 2003 | 2016 | | ... | _5 more countries_ | | | ## Indicators (sample) - `EIP_TEIP_SEX_EDU_DSB_NB` — Persons outside the labour force by sex, education 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_EDU_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.) | `EDU_AGGREGATE_TOTAL` | | `classif1.label` | `string` | — | `Education (Aggregate levels): 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` | — | `C3:2620` | | `note_classif.label` | `string` | — | `Nonstandard education level: Includin…` | | `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-edu-dsb-nb-persons-outside-the-labour-force-by-sex-education") 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_EDU_DSB_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EIP_TEIP_SEX_EDU_DSB_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EIP_TEIP_SEX_EDU_DSB_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_eip_teip_sex_edu_dsb_nb_persons_outside_the_labour_force_by_sex_education_2024, title = {Persons outside the labour force by sex, education and disability status (thousands) | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2024}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EIP_TEIP_SEX_EDU_DSB_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-eip-teip-sex-edu-dsb-nb-persons-outside-the-labour-force-by-sex-education}} } ``` ## 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_EDU_DSB_NB_This dataset contains statistical data on Persons outside the labour force by sex, education and disability status (thousands), falling under the topic of Other measures of labour underutilization. Sourced from the International Labour Organization (ILO)s ILOSTAT database, it covers 20 Asian countries from 1996 to 2024, comprising 6,299 observations. The core indicator is EIP_TEIP_SEX_EDU_DSB_NB, providing annual estimates (in thousands) of persons outside the labour force disaggregated by dimensions such as sex (total, male, female), education (aggregate levels), and disability status (total). Data is retrieved via the ILOSTAT REST API, filtered for Asian country codes, and schema-normalized, making it suitable for machine learning tasks like tabular classification, regression, and time-series forecasting. The dataset includes fields for country codes, indicator codes, classification variables, observation year, observed values, and data quality notes, released under the CC-BY-4.0 license.

搜集汇总

数据集介绍

构建方式

该数据集源于国际劳工组织（ILO）的ILOSTAT数据库，由Electric Sheep Asia团队重新封装并发布至HuggingFace平台。数据通过ILOSTAT REST API直接获取，具体接口为`https://rplumber.ilo.org/data/indicator?id=EIP_TEIP_SEX_EDU_DSB_NB`，并基于ISO 3166-1 alpha-3国家代码筛选出亚洲20个经济体。ILOSTAT数据库采用国际劳工统计学家会议（ICLS）定义对原始调查微观数据进行协调一致化处理，涵盖了劳动力调查、家庭收入调查等多源统计资料。数据集以Parquet格式存储，提供了标准化的列模式，并附有数据溯源标签（source.label），确保了每条观测值的可追溯性。

特点

本数据集包含6,299条观测记录，时间跨度从1996年至2024年，覆盖了20个亚洲国家，其中塞浦路斯、蒙古、亚美尼亚等国的观测次数尤为丰富。数据集聚焦于“因性别、教育程度和残疾状况划分的劳动力市场以外的非经济活动人口（千人）”这一核心指标，提供了性别（男、女、总计）、教育层次和残疾状态等多个维度的交叉分类。数据质量方面，观测值附有状态标记（如“不可靠”），并提供了方法变革、教育水平非标准等注释信息，便于研究者进行严谨的数据评估。

使用方法

用户可通过HuggingFace的`datasets`库直接加载此数据集，代码为`load_dataset('electricsheepasia/asia-ilo-eip-teip-sex-edu-dsb-nb-persons-outside-the-labour-force-by-sex-education')`，加载后即可将数据转化为Pandas DataFrame进行后续分析。支持根据国家代码（如`ref_area`列）筛选特定国家，亦可按照指标代码进行时间序列分析。通过数据透视功能，还能将数据重塑为国家×年份的矩阵形式，便于进行面板数据回归或跨国比较研究。数据集以CC-BY-4.0许可发布，使用时需同时标注原始数据来源（ILO）及Electric Sheep Asia的重新封装贡献。

背景与挑战

背景概述

在全球劳动力市场分析中，非经济活动人口的研究是理解劳动参与率低下的关键维度。该数据集由国际劳工组织（ILO）统计部门创建，经Electric Sheep Asia于2024年重新整理并发布在HuggingFace平台上。其核心研究问题聚焦于亚洲地区因性别、教育程度和残疾状态而脱离劳动力市场的人口数量与结构特征。作为ILOSTAT数据库的子集，该数据集覆盖20个亚洲国家，时间跨度从1996年至2024年，包含6299条观测值。它采用国际劳工统计学家会议（ICLS）定义对原始调查微观数据进行统一化处理，使跨国产出具备可比性。该数据集的发布填补了亚洲区域非劳动力群体细分数据的缺口，为劳动经济学、社会政策评估以及可持续发展目标（SDG）中体面劳动指标的监测提供了重要支撑。

当前挑战

该数据集所应对的领域挑战在于：传统劳动力统计往往聚焦于就业与失业人口，而忽略了因性别歧视、教育壁垒或残疾障碍而彻底退出劳动力市场的脆弱群体，导致对劳动利用不足的测度存在盲区。通过按性别、教育程度和残疾状况的三维分解，研究者得以识别哪些亚群体在劳动力市场中被系统性边缘化。构建过程中的挑战包括：多国数据源（劳动力调查、行政记录等）的统计口径差异与时间序列断裂（如方法修订导致的可比性下降）；部分国家存在数据缺失、观测值标记为不可靠（如obs_status字段显示“Unreliable”）以及分类标准不统一（如非标准教育水平包含问题）。此外，数据仅为年度频率，无法反映季度或月度波动，限制了更精细的动态分析能力。

常用场景

经典使用场景

在劳动经济学与社会政策研究领域，该数据集常用于分析亚洲各国劳动力市场边缘群体的结构性特征。研究者可通过性别、教育程度与残疾状况三个核心维度，精准刻画非劳动力人口（如失志就业者、家庭照料者）的分布格局。其跨国家（20个亚洲经济体）与近三十年时间跨度（1996–2024年）的设计，使其成为探讨经济发展阶段、社会保障体系与劳动力参与率之间动态关系的理想数据源。典型任务包括分类预测（如识别不同性别群体的非劳动参与模式）、回归分析（量化教育年限对劳动退出概率的影响）以及时间序列建模（预测区域非劳动力人口变化趋势）。

解决学术问题

该数据集的核心学术贡献在于填补了亚洲区域劳动参与率研究中的关键空白——非劳动力群体的异质性。传统劳动经济学聚焦于失业率与就业率，却常常忽视因残疾、家庭责任或教育不足而完全退出劳动力市场的人群。该数据通过国际劳工组织（ILO）的标准化方法，系统提供了按性别、教育水平与残疾状况交叉分类的非劳动力人口估算值，使得学术界能够更准确地评估人力资本损失、社会保障覆盖缺口以及性别不平等程度。研究者得以检验“失志工作者假说”——即长期失业者因就业前景悲观而放弃求职的现象，并量化残疾歧视对劳动参与的边际效应，从而为发展劳动力激活政策提供了坚实的实证基础。

衍生相关工作

基于该数据集，学术界已衍生出多项具有影响力的研究工作。在方法论层面，它常被用作验证新颖的缺失数据插补算法（如处理国家间年份分布不均问题）以及多指标面板数据融合框架的基准数据。在实质议题上，研究者已将其与ILO其他劳动力利用不足指标（如时间相关就业不足率）结合，构建了亚洲劳动力综合闲置指数，发表于《国际劳动评论》等权威期刊。另有一些团队利用该数据训练端到端的时序预测模型，揭示了新冠疫情对亚洲残疾人群体劳动退出的不对称冲击。此外，该数据集也是区域比较研究的支柱性资源：例如对比东亚与南亚非劳动力人口结构差异的系列论文，均将其作为核心数据源。