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

--- 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 - international-migrant-stock - ilo - labour - employment pretty_name: "Persons outside the labour force by sex, age and place of birth (thousands) | Asia (ILOSTAT)" --- # Persons outside the labour force by sex, age and place of birth (thousands) | Asia (ILOSTAT) 🌏 **10,377 observations** · **25 Asia countries** · **1991–2025** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **10,377 observations** of `International migrant stock` data across **25 Asia countries**, spanning **1991–2025**, 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_CBR_NB) - **Publisher:** International Labour Organization (ILO) - **License:** [cc-by-4.0](https://creativecommons.org/licenses/by/4.0/) - **Topic:** International migrant stock ## Methodology Data pulled directly from the ILOSTAT REST API at `https://rplumber.ilo.org/data/indicator?id=EIP_TEIP_SEX_AGE_CBR_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 25 Asia countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `CYP` | 2,187 | 1999 | 2025 | | `TUR` | 1,458 | 2000 | 2025 | | `ISR` | 1,053 | 2012 | 2024 | | `ARM` | 788 | 2001 | 2023 | | `BRN` | 729 | 2014 | 2024 | | `IDN` | 576 | 2017 | 2024 | | `TLS` | 457 | 2001 | 2022 | | `KHM` | 433 | 2008 | 2021 | | `THA` | 342 | 2018 | 2024 | | `MNG` | 336 | 2019 | 2024 | | `ARE` | 324 | 2018 | 2024 | | `MDV` | 284 | 2014 | 2019 | | `IRQ` | 236 | 2007 | 2021 | | `IRN` | 216 | 2006 | 2011 | | `MMR` | 167 | 2014 | 2015 | | ... | _10 more countries_ | | | ## Indicators (sample) - `EIP_TEIP_SEX_AGE_CBR_NB` — Persons outside the labour force by sex, age and place of birth (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) | `BB:6361` | | `source.label` | `string` | Source name in English | `HIES - Households Living Conditions S…` | | `indicator` | `string` | ILOSTAT indicator code | `EIP_TEIP_SEX_AGE_CBR_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 | `CBR_BIR_TOTAL` | | `classif2.label` | `string` | — | `Place of birth: Total` | | `time` | `int64` | Observation year | `2014` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `7097.621` | | `obs_status` | `string` | Observation status flag (e.g. provisional, unreliable) | `U` | | `obs_status.label` | `string` | — | `Unreliable` | | `note_classif` | `string` | — | `—` | | `note_classif.label` | `string` | — | `—` | | `note_indicator` | `string` | — | `I11:264` | | `note_indicator.label` | `string` | — | `Break in series: Methodology revised` | | `note_source` | `string` | — | `R1:3513` | | `note_source.label` | `string` | — | `Repository: ILO-STATISTICS - Micro da…` | ## Disaggregation dimensions The following columns provide disaggregation dimensions: - **`sex`** (3 unique values): `SEX_T`, `SEX_M`, `SEX_F` ## 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-cbr-nb-persons-outside-the-labour-force-by-sex-age-and-pl") 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_CBR_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EIP_TEIP_SEX_AGE_CBR_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EIP_TEIP_SEX_AGE_CBR_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_eip_teip_sex_age_cbr_nb_persons_outside_the_labour_force_by_sex_age_and_pl_2025, title = {Persons outside the labour force by sex, age and place of birth (thousands) | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EIP_TEIP_SEX_AGE_CBR_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-eip-teip-sex-age-cbr-nb-persons-outside-the-labour-force-by-sex-age-and-pl}} } ``` ## 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_CBR_NB_This dataset contains 10,377 observations of persons outside the labour force by sex, age and place of birth (in thousands) across 25 Asia countries from 1991 to 2025. Sourced from the International Labour Organizations (ILO) ILOSTAT database, it includes fields such as country codes, data sources, indicator codes, sex and age classifications, observation years, values, and quality flags. The dataset is designed for tabular classification, regression, and time-series forecasting tasks, focusing on international migrant stock statistics.

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）旗下的ILOSTAT数据库精心构建，聚焦于亚洲地区劳动力市场以外人口的统计。数据直接通过ILOSTAT的REST API获取，筛选出亚洲25个国家的ISO3代码区域，并严格遵循国际劳工统计学家会议（ICLS）的定义标准进行数据规范与统一。数据集涵盖了从1991年至2025年共计10,377条观测记录，以千人为单位，呈现了按性别、年龄和出生地划分的非劳动力人口规模。数据的整合与标准化处理由Electric Sheep Asia完成，旨在为研究人员提供即用型的高质量结构化数据。

特点

该数据集兼具多维度的细致分类与广泛的时空覆盖性。其核心特色在于同时提供性别（男女及总计）、年龄分段（如15岁及以上青年与成人）及出生地（如国内外出生人口）三重维度下的交叉细分数据，使得对非劳动力人口结构的剖析更加立体。数据来源可追溯至各国劳动力调查、家庭收支调查等官方数据源，并在字段中嵌入来源标签以确保透明度。此外，数据集包含观测值状态标志与注释信息，如数据是否可靠性标记或系列中断说明，为严谨的学术分析提供了质量评估依据。

使用方法

用户可通过HuggingFace的`datasets`库便捷加载该数据集，仅需调用`load_dataset()`函数即可获得包含全部观测的DataFrame。借助Python生态，可基于`ref_area`字段进行国家层面的子集筛选，例如提取印度尼西亚的数据。针对时间序列分析，可按`time`字段排序并绘制`obs_value`的变化趋势。研究人员亦可将数据透视为国家×年份的矩阵，便于进行跨国比较或面板数据建模。该数据集兼容表格分类、回归及时间序列预测等多种机器学习任务，极大简化了劳动经济学领域的数据准备工作。

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团队重新打包整理，聚焦于1991年至2025年间亚洲25个国家中按性别、年龄及出生地划分的劳动力外人口（以千人为单位）。作为全球劳动统计的权威来源，ILOSTAT通过整合各国劳动力调查、家庭收入调查及行政记录，为研究亚洲地区劳动力市场的非参与群体提供了标准化、多维度的时序数据。数据集包含10,377条观测记录，覆盖塞浦路斯、土耳其、以色列等代表性国家，弥补了区域劳动统计中关于非经济活动人口细分信息的空白，为政策制定者、经济学家及社会科学研究者分析人口结构变化、移民影响及劳动力供给弹性提供了坚实基础，推动了亚洲劳动经济学与人口学交叉领域的实证研究发展。

当前挑战

该数据集所解决的领域问题在于，传统的劳动力统计多聚焦于就业与失业人口，而忽视了因家庭责任、教育或健康等原因游离于劳动力市场之外的群体，这一群体的规模与特征对理解国家经济潜力与社会福利需求至关重要。在构建过程中，数据集面临多重挑战：首先，来自不同国家的原始数据在调查方法、分类标准及时间跨度上存在显著差异，ILOSTAT需依据国际劳动统计学家会议定义进行协调统一，但部分数据仍标注为“不可靠”状态；其次，数据整合时需处理多个来源针对同一国家年份的重复指标，并以“最佳来源”原则做出取舍；此外，细粒度分类（如出生地、年龄分组）的缺失值问题要求严格的非空校验，同时确保跨国家-年份序列的连续性与可比性，这些技术细节直接影响了数据的质量与可用性。

常用场景

经典使用场景

该数据集记录了亚洲25个国家在1991至2025年间，按性别、年龄和出生地划分的劳动市场外人口数量（单位：千人），是劳动经济学与人口统计学交叉研究中的宝贵资源。经典使用场景包括构建时间序列预测模型，对各国非劳动力人口的长期趋势进行建模与推估；亦可利用其多维分类信息开展面板数据分析，探究不同人口亚群在经济活动参与上的结构性差异。

实际应用

在实际应用中，该数据集服务于国际组织与各国劳工部门的政策评估与制定。决策者可据此识别哪些群体长期处于劳动力市场之外，进而设计精准的就业促进或社会保障方案。同时，数据集也为劳动力市场监测系统的构建提供基准数据，支持对突发经济冲击（如疫情、贸易波动）下非劳动力人口变化的快速响应与动态调整。

衍生相关工作

基于该数据集，学术界已衍生出一系列富有影响力的后续研究。相关工作包括利用机器学习方法对非劳动力人口的演变路径进行聚类分析，揭示不同亚洲国家的发展模式；也有研究将其与宏观经济变量（如GDP增长率、移民政策指数）关联，构建因果推断模型以评估制度环境对劳动市场外群体的影响。此外，数据集还常被用作基准，以验证新型时间序列分解算法在劳动统计中的性能表现。