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

--- license: cc-by-4.0 language: - en task_categories: - tabular-classification - tabular-regression - time-series-forecasting multilinguality: monolingual size_categories: - 100K<n<1M tags: - tabular - asia - ilostat - other-measures-of-labour-underutilization - ilo - labour - employment pretty_name: "Persons outside the labour force by sex, age and education (thousands) | Asia (ILOSTAT)" --- # Persons outside the labour force by sex, age and education (thousands) | Asia (ILOSTAT) 🌏 **282,110 observations** · **41 Asia countries** · **1970–2025** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **282,110 observations** of `Other measures of labour underutilization` data across **41 Asia countries**, spanning **1970–2025**, 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_EDU_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_EDU_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 41 Asia countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `IDN` | 19,652 | 1990 | 2023 | | `PSE` | 16,800 | 2000 | 2025 | | `IRN` | 15,890 | 2005 | 2024 | | `CYP` | 15,862 | 1999 | 2025 | | `KOR` | 15,462 | 2000 | 2025 | | `TUR` | 13,700 | 2000 | 2025 | | `THA` | 12,561 | 2000 | 2024 | | `KHM` | 12,216 | 1996 | 2023 | | `MNG` | 11,927 | 2003 | 2024 | | `PAK` | 11,317 | 2005 | 2025 | | `VNM` | 11,217 | 2010 | 2024 | | `ISR` | 10,532 | 2009 | 2024 | | `GEO` | 9,855 | 2009 | 2024 | | `ARM` | 9,745 | 2001 | 2023 | | `IND` | 9,414 | 1994 | 2025 | | ... | _26 more countries_ | | | ## Indicators (sample) - `EIP_TEIP_SEX_AGE_EDU_NB` — Persons outside the labour force by sex, age and education (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_EDU_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 | `EDU_AGGREGATE_TOTAL` | | `classif2.label` | `string` | — | `Education (Aggregate levels): 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-age-edu-nb-persons-outside-the-labour-force-by-sex-age-and-ed") 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_EDU_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EIP_TEIP_SEX_AGE_EDU_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EIP_TEIP_SEX_AGE_EDU_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_eip_teip_sex_age_edu_nb_persons_outside_the_labour_force_by_sex_age_and_ed_2025, title = {Persons outside the labour force by sex, age and education (thousands) | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EIP_TEIP_SEX_AGE_EDU_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-eip-teip-sex-age-edu-nb-persons-outside-the-labour-force-by-sex-age-and-ed}} } ``` ## 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_EDU_NB_This dataset contains statistics from the International Labour Organization (ILO) ILOSTAT database on Persons outside the labour force by sex, age and education (thousands) across 41 Asian countries from 1970 to 2025. It includes 282,110 observations covering one core indicator (EIP_TEIP_SEX_AGE_EDU_NB), organized in tabular format with columns such as country code, year, sex disaggregation (total, male, female, etc.), age and education classifications, observed values, and status flags. The data is sourced from ILO-harmonized official statistics like labour force surveys, repackaged by Electric Sheep Asia into a machine-learning-ready format, suitable for tasks like tabular classification, regression, and time-series forecasting.

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数据库，通过其REST API接口直接抽取原始数据，并依据亚洲ISO3国家代码进行筛选与整合。在数据预处理过程中，采用了国际劳工统计学家会议（ICLS）的定义框架对原始调查微观数据进行标准化处理，确保跨国家、跨年份的数据具有可比性和一致性。最终数据以Parquet格式重新打包，并发布至HuggingFace平台，以便于机器学习研究者和开发者直接通过`load_dataset()`接口调用。

特点

该数据集收录了1970年至2025年间亚洲41个国家的282,110条观测记录，涵盖劳动力市场以外人口按性别、年龄及教育程度划分的统计指标。数据具有多维度拆解特性，包含性别（男性、女性、总计）、年龄分组及教育层次等分类变量。每个观测值均附有来源标注和观测状态标志，便于识别数据质量与修订历史。此外，数据集的年度频率设计使其天然支持时间序列分析与面板数据建模。

使用方法

用户可通过HuggingFace的`datasets`库直接加载该数据集，并转换为Pandas DataFrame以进行后续分析。典型操作包括按国家代码过滤子集（如印度尼西亚）、对特定指标进行时间序列可视化，以及创建国家×年份的透视矩阵。该数据集适用于劳动力市场特征分类、不充分就业预测，以及宏观经济时间序列回归等任务。引用时需标注国际劳工组织原始数据来源及Electric Sheep Asia的重打包版本。

背景与挑战

背景概述

该数据集源于国际劳工组织（ILO）的ILOSTAT统计数据库，由Electric Sheep Asia于2025年重新打包发布，聚焦亚洲地区劳动力市场边缘群体的量化分析。核心研究问题在于揭示亚洲41个国家中，因性别、年龄及教育程度差异而处于劳动力队伍之外的人口分布特征，时间跨度覆盖1970年至2025年，包含超过28万条观测记录。作为劳动力未充分利用指标的重要组成部分，该数据为区域劳动经济学、发展政策评估及可持续发展目标（SDGs）中体面工作指标的监测提供了标准化、可复用的时序基础，极大促进了跨国比较研究与机器学习模型在劳动经济领域的应用。

当前挑战

该数据集所应对的领域挑战主要源于劳动力未充分利用现象的复杂性与多维性，传统劳动统计往往仅关注失业率，而忽视因家庭、教育或社会结构性因素放弃求职的隐性劳动力群体，数据集通过精细化分性别、年龄与教育层次的架构，填补了这一测量盲区。构建过程中面临多重困难：首先，各国劳动力调查的采集频率、分类口径与数据质量参差不齐，ILO虽通过国际劳工统计学家会议（ICLS）定义进行协调，但源数据的调查方法与修订导致的序列中断仍需标注处理；其次，跨41国长时序数据的整合需解决来源异质性与元数据差异，数据集通过保留source、obs_status及note等注释字段来显式记录可信度与修复注释，确保使用者能够准确评估数据局限从而做出审慎分析决策。

常用场景

经典使用场景

该数据集源自国际劳工组织（ILO）的ILOSTAT统计数据库，聚焦于亚洲地区因性别、年龄和受教育程度分层而处于劳动力市场之外的人口规模。作为劳动经济学与人口学交叉领域的基础性数据资源，它最经典的用途在于支撑时间序列分析与面板数据建模，研究者可基于1970至2025年间41个亚洲国家的近30万条观测记录，追踪非劳动力人口的动态演变轨迹。借助数据集中细粒度的分类维度（如性别、青年/成年年龄组、不同教育层级），学者能够系统刻画不同社会群体退出劳动力市场的结构性差异，从而为区域劳动力市场分析提供量化依据。

实际应用

在实际应用层面，此数据集为亚洲各国的劳动政策制定与国际组织评估提供了直接的数据支撑。政府部门可利用其按性别和教育程度细分的历年数据，精准识别需要职业培训和再就业服务的重点人群，从而优化公共就业服务的资源投放。国际发展机构则能借助该数据监控联合国可持续发展目标（SDG）中关于体面劳动与经济增长的进展状况，特别在评估青年和女性群体被劳动力市场边缘化的风险方面具有不可替代的作用。此外，数据集中详尽的数据来源标识（如劳动力调查的来源编码）能够帮助数据使用者追溯元数据，确保政策研究与经济预测的信息质量。

衍生相关工作

该数据集衍生了多类具有典型性的学术研究工作。首先，基于其时间序列与面板结构，催生了大量关于亚洲劳动力市场行为建模的计量经济学论文，例如运用固定效应模型或工具变量法，探究教育扩张和性别平等政策对非劳动力人口构成的因果效应。其次，结合数据中的多重分类标签，有学者构建了机器学习预测模型，利用历史宏观指标预测未来特定国家或年龄组的劳动力退出概率。此外，该数据集也是若干大型劳动力统计整合项目（如ILOSTAT的微数据协调工作）的输入部分，被用于生成跨国非劳动力人口的交互式可视化仪表盘，帮助流行病学家和社会学家评估非就业人口与公共健康及社会福祉之间的关联路径。