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

--- 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, age and rural / urban areas (thousands) | Asia (ILOSTAT)" --- # Persons outside the labour force by sex, age and rural / urban areas (thousands) | Asia (ILOSTAT) 🌏 **55,236 observations** · **35 Asia countries** · **1970–2025** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **55,236 observations** of `Other measures of labour underutilization` data across **35 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_GEO_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_GEO_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 35 Asia countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `IDN` | 4,752 | 1990 | 2023 | | `PSE` | 4,416 | 2000 | 2022 | | `CYP` | 3,878 | 1999 | 2025 | | `KHM` | 2,891 | 1996 | 2023 | | `MNG` | 2,736 | 2003 | 2024 | | `ARM` | 2,592 | 2001 | 2023 | | `PHL` | 2,496 | 2007 | 2023 | | `VNM` | 2,496 | 2007 | 2024 | | `PAK` | 2,385 | 2005 | 2025 | | `KOR` | 2,304 | 2000 | 2025 | | `GEO` | 2,304 | 2009 | 2024 | | `THA` | 2,082 | 2007 | 2024 | | `IND` | 2,034 | 1994 | 2025 | | `LKA` | 2,026 | 2009 | 2024 | | `TUR` | 2,016 | 2000 | 2013 | | ... | _20 more countries_ | | | ## Indicators (sample) - `EIP_TEIP_SEX_AGE_GEO_NB` — Persons outside the labour force by sex, age and rural / urban areas (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_GEO_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 | `GEO_COV_NAT` | | `classif2.label` | `string` | — | `Area type: National` | | `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` | — | `C12:2951` | | `note_classif.label` | `string` | — | `Rural / urban areas coverage definiti…` | | `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-geo-nb-persons-outside-the-labour-force-by-sex-age-and-ru") 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_GEO_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EIP_TEIP_SEX_AGE_GEO_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EIP_TEIP_SEX_AGE_GEO_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_eip_teip_sex_age_geo_nb_persons_outside_the_labour_force_by_sex_age_and_ru_2025, title = {Persons outside the labour force by sex, age and rural / urban areas (thousands) | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EIP_TEIP_SEX_AGE_GEO_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-eip-teip-sex-age-geo-nb-persons-outside-the-labour-force-by-sex-age-and-ru}} } ``` ## 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_GEO_NB_This dataset contains 55,236 observations of non-labour force population data across 35 Asia countries, spanning from 1970 to 2025. It is sourced from the International Labour Organizations (ILO) ILOSTAT database, with the core indicator being EIP_TEIP_SEX_AGE_GEO_NB—Persons outside the labour force by sex, age and rural/urban areas (in thousands). The dataset includes multi-dimensional disaggregation such as country codes, data sources, sex (total, male, female, other), age groups, rural/urban area types, observation years, and observed values. Data is published at annual frequency and harmonized using ICLS (International Conference of Labour Statisticians) definitions by ILO. It also features data quality flags like observation status and series break notes for traceability and reliability. This dataset is suitable for tabular classification, regression, and time-series forecasting tasks, designed to provide machine learning-ready labour market data for Asia.

搜集汇总

数据集介绍

构建方式

该数据集由Electric Sheep Asia基于国际劳工组织（ILO）的ILOSTAT统计数据库重新打包构建而成。数据通过ILOSTAT的REST API接口直接获取，原始数据源自各国劳动力调查、家庭收支调查及行政记录等官方来源，并遵循国际劳工统计学家会议（ICLS）定义进行标准化处理。构建过程中，数据被筛选至亚洲35个国家的范围，并保留了来源列的追踪标识，以确保数据溯源清晰。最终呈现为具有55,236条观测的表格数据集，覆盖1970年至2025年的年度时间序列。

使用方法

数据集以HuggingFace Datasets库的标准格式发布，用户可通过`load_dataset`函数一行代码快速加载。加载后，数据可轻松转换为Pandas DataFrame进行后续分析，如按国家进行过滤以获取特定国家的子集，或绘制特定指标的时间序列曲线。数据集亦支持透视操作，可构建国家×年份的观测值矩阵，便于进行跨国的面板数据分析。推荐将数据用于亚洲劳动力市场的学术研究、政策评估及计量经济学建模。

背景与挑战

背景概述

该数据集由国际劳工组织（ILO）统计数据库ILOSTAT于2025年发布，经Electric Sheep Asia重新打包整理，汇聚了1970年至2025年间亚洲35个国家的劳动力市场微观数据。核心研究问题聚焦于量化亚洲地区因性别、年龄和城乡地域差异而处于劳动力市场之外的群体规模，旨在揭示劳动力未充分利用的深层次结构性特征。作为ILO在劳动统计领域的重要数据产品，该数据集为跨国比较劳动力参与率、识别弱势群体及评估就业政策效果提供了标准化基准，对发展经济学、人口统计学及区域劳动力市场研究具有显著推动作用。

当前挑战

该数据集致力于解决劳动力未充分利用多维指标的可比性难题。不同国家的调查框架、人口分类标准（如年龄分组、城乡定义）及数据收集方法差异显著，导致跨国间的细微比较仍需谨慎处理数据口径的异质性。构建过程中，ILO需从各国劳动力调查、行政记录等碎片化源数据中提取统一指标，并通过ICLS定义进行协调与标记，这面临数据质量参差不齐、时序断裂及分类变量缺失等挑战，需依赖专业统计处理以确保分析结果的稳健性。

常用场景

经典使用场景

该数据集的核心应用场景在于为劳动经济学与人口统计学研究提供亚洲区域非劳动力人口的结构性画像。基于国际劳工组织ILOSTAT权威数据，它收录了1970年至2025年间35个亚洲国家按性别、年龄及城乡划分的劳动力市场以外人口规模（单位：千人）。研究者能够利用该数据集进行面板数据分析，揭示不同国家及地区在非劳动力群体构成上的长期演变趋势，并服务于劳动力参与率的跨国比较与时空动态建模。

解决学术问题

在学术研究层面，该数据集有效回应了亚洲劳动力市场研究中数据碎片化与跨国可比性不足的长期困境。它精准量化了经济非活跃人口的分布特征，使学者得以深入剖析教育扩张、人口老龄化、城镇化进程以及性别角色转变对劳动力退出行为的影响。该数据的公开可用性极大推动了关于劳动力利用不足、隐性失业以及社会保障覆盖缺口等前沿议题的实证检验，为完善劳动市场理论与政策评估提供了坚实的数据基础。

实际应用

实际应用场景广泛分布于国际发展机构与政府决策部门。国际劳工组织、亚洲开发银行等机构可借此评估各国在体面劳动及可持续发展目标（SDGs）上的进展。国家统计局与劳动部门能够依据分性别、分城乡的非劳动力数据，精准识别弱势群体，调整就业促进与社会保护政策。此外，该数据集也支持企业人力资源战略规划，帮助跨国公司理解不同区域潜在劳动力供给的结构性变化，优化用工布局。

数据集最近研究

最新研究方向

该数据集聚焦于亚洲地区劳动力市场边缘群体的结构性特征，为理解非经济活动人口在性别、年龄及城乡维度的分布格局提供了高精度的时间序列证据。当前，伴随全球劳动力市场的深刻变革，尤其是后疫情时代就业形态的多元化与零工经济的崛起，研究重点已从传统的失业率统计转向对‘劳动力市场依附度’的精细化刻画。该数据覆盖35个亚洲国家、横跨半个世纪（1970-2025年）的观测，使得学者能够深入剖析亚洲不同发展阶段经济体在劳动参与率持续走低背景下的制度性成因与社会福利影响，并揭示女性、青年以及农村人口在劳动力市场排斥中的脆弱性叠加效应。其独特的ILO标准化分类体系与城乡划分维度，为构建跨国比较的劳动力闲置预警模型、评估社会保护政策的有效性以及预测未来劳动力供给结构演变提供了不可替代的量化基础。