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

--- 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 and age (thousands) | Asia (ILOSTAT)" --- # Persons outside the labour force by sex and age (thousands) | Asia (ILOSTAT) 🌏 **71,771 observations** · **48 Asia countries** · **1946–2025** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **71,771 observations** of `Other measures of labour underutilization` data across **48 Asia countries**, spanning **1946–2025**, 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_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_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 48 Asia countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `JPN` | 5,064 | 1947 | 2025 | | `TWN` | 3,612 | 1978 | 2020 | | `SGP` | 3,540 | 1957 | 2024 | | `PAK` | 3,279 | 1961 | 2025 | | `IDN` | 2,991 | 1961 | 2023 | | `TUR` | 2,898 | 1955 | 2025 | | `PHL` | 2,889 | 1960 | 2023 | | `CYP` | 2,857 | 1960 | 2025 | | `KOR` | 2,688 | 1960 | 2025 | | `LKA` | 2,577 | 1946 | 2024 | | `THA` | 2,493 | 1976 | 2024 | | `PSE` | 2,100 | 2000 | 2025 | | `IRN` | 2,073 | 1956 | 2024 | | `KHM` | 1,996 | 1962 | 2023 | | `GEO` | 1,956 | 1998 | 2024 | | ... | _33 more countries_ | | | ## Indicators (sample) - `EIP_TEIP_SEX_AGE_NB` — Persons outside the labour force by sex and age (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_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+` | | `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` | — | `C6:1633` | | `note_classif.label` | `string` | — | `Nonstandard age group: Including ages…` | | `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-nb-persons-outside-the-labour-force-by-sex-and-age-th") 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_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EIP_TEIP_SEX_AGE_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EIP_TEIP_SEX_AGE_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_eip_teip_sex_age_nb_persons_outside_the_labour_force_by_sex_and_age_th_2025, title = {Persons outside the labour force by sex and age (thousands) | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EIP_TEIP_SEX_AGE_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-eip-teip-sex-age-nb-persons-outside-the-labour-force-by-sex-and-age-th}} } ``` ## 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_NB_This dataset is a tabular dataset on persons outside the labour force by sex and age (in thousands) for Asian countries, sourced from the International Labour Organization (ILO) ILOSTAT database. It contains 71,771 observations across 48 Asian countries (e.g., Japan, Taiwan, Singapore, Pakistan), spanning the years 1946 to 2025, and covers one key indicator: EIP_TEIP_SEX_AGE_NB (persons outside the labour force by sex and age, in thousands). Data is extracted via the ILOSTAT REST API and filtered to Asian country codes, harmonized using International Conference of Labour Statisticians (ICLS) definitions. The dataset schema includes columns such as country code (ref_area), country name (ref_area.label), source code and label (source, source.label), indicator code and label (indicator, indicator.label), sex classification (sex, sex.label), age classification (classif1, classif1.label), year (time), observed value (obs_value), and data quality flags (e.g., obs_status). Data is published at annual frequency and includes sex disaggregation (total, male, female, other). It is suitable for NLP tasks like tabular classification, regression, and time-series forecasting, enabling analysis of labour market trends and non-labour force dynamics in Asia. The dataset is repackaged by Electric Sheep Asia and 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统计数据库，通过REST API接口直接获取原始指标数据，并依据亚洲ISO 3166-1 alpha-3国家代码进行地理筛选。数据经过ICLS国际劳工统计学家会议定义的标准化处理，对各国劳动力调查、家庭收入调查及行政记录等多元来源的微观数据进行统一整合与清洗，最终形成涵盖71,771条观测记录的表格型数据集，由Electric Sheep Asia团队完成重新封装和发布。

特点

数据集聚焦于亚洲地区劳动力市场之外的群体，覆盖1946年至2025年间48个亚洲国家的年度时间序列数据，核心指标为按性别与年龄分层的非劳动力人口数量（单位：千）。数据呈现多维分层结构，包含性别（总、男、女、其他）与年龄组等分类变量，并附带详尽的数据来源标签、观测状态标记及注释说明，便于研究者追溯数据质量与背景信息。

使用方法

研究者可通过HuggingFace的`datasets`库直接加载数据集，使用`load_dataset()`函数即可获取训练集并转换为Pandas DataFrame进行后续分析。典型操作包括按国家代码筛选特定国家的子集、对单一指标进行时间序列可视化，或利用透视表将数据重塑为国家×年份的矩阵格式，适用于劳动力市场动态比较、性别差异分析及宏观经济建模等研究场景。

背景与挑战

背景概述

该数据集由国际劳工组织（ILO）于2025年通过其核心统计数据库ILOSTAT创建，并由Electric Sheep Asia重新打包发布，聚焦于亚洲48个国家的劳动力市场边缘群体——即因性别和年龄划分的劳动力外人口（以千人为单位）。数据集覆盖1946年至2025年间71,771条观测值，旨在捕捉劳动力利用不足的多维指标，为劳动经济学、人口统计学及区域可持续发展研究提供量化基础。作为ILO在亚太地区劳动力统计领域的重要贡献，该数据填补了亚洲各国劳动力外人群长期序列数据的空白，尤其支持性别与年龄交叉视角下的劳动力参与率分析，推动了非传统劳动力度量（如潜在劳动力、未充分就业）的实证研究，对政策制定者评估社会保障、就业促进及老龄化影响具有显著参考价值。

当前挑战

该数据集面临的核心挑战包括：领域问题层面，劳动力外人群的界定标准在各国统计实践中存在差异，如ILO与国际劳工统计学家会议（ICLS）定义的整合需平衡不同国家的调查习惯，导致指标可比性受限于分类一致性；构建过程中，数据来源涵盖劳动力调查、家庭收入调查、行政记录等多源异构数据，需经ILO统一清洗、插补及标记断点，但部分国家存在历史序列断裂、年龄段非标准划分（如含非标准年龄组）及数据标注变更等问题，需通过`obs_status`和`note_classif`等列进行追踪，增加了跨时期分析的数据质量权衡。

常用场景

经典使用场景

在劳动经济学与人口统计学研究中，该数据集常被用于分析亚洲地区劳动力市场外人群的规模与结构特征。研究者可以基于性别与年龄的细分维度，系统探讨不同群体退出劳动力市场的动态趋势，例如女性因家庭照料责任而长期游离于劳动市场之外的现象，或老年人口因退休制度与社会保障体系而逐步脱离就业的路径。该数据集提供的长时序观测值，尤其适合构建面板数据模型，以识别社会经济政策、产业结构变迁或文化规范对劳动力参与率的异质性影响。

实际应用

该数据集在实际政策制定与国际比较中扮演着关键角色。国际劳工组织及其成员国可依据此数据监测《2030年可持续发展议程》中体面劳动目标的实现进展，特别是针对亚洲地区女性劳动参与率低迷与人口老龄化加剧的双重挑战。各国劳动部门能够通过性别与年龄的交叉分析，识别出最需要政策干预的弱势群体，进而设计精准的职业培训计划、灵活就业支持措施或社会保障制度改革方案。此外，非政府组织亦可利用该数据评估经济危机或公共卫生事件（如疫情）对边缘劳动群体的冲击程度。

衍生相关工作

基于此数据集，研究社区已衍生出多项经典学术工作。其中，有多篇论文运用该数据构建了亚洲国家劳动力市场外人群的时序预测模型，结合经济周期与人口结构变量，预测未来二十年劳动供给的演变路径。另有研究者将其与ILOSTAT的其他指标（如失业率、非正规就业比例）进行联合分析，提出了劳动力利用不足的综合评估框架，并发表在劳动经济学顶级期刊上。此外，该数据常被用于跨国比较研究，揭示东亚、东南亚与南亚地区在性别劳动参与模式上的结构性差异，有力佐证了经济发展阶段与文化规范对劳动市场行为的交互作用。