electricsheepasia/asia-ilo-eip-teip-sex-edu-cbr-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 - international-migrant-stock - ilo - labour - employment pretty_name: "Persons outside the labour force by sex, education and place of birth (thousands) | Asia (ILOSTAT)" --- # Persons outside the labour force by sex, education and place of birth (thousands) | Asia (ILOSTAT) 🌏 **5,278 observations** · **19 Asia countries** · **1999–2024** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **5,278 observations** of `International migrant stock` data across **19 Asia countries**, spanning **1999–2024**, 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_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_EDU_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 19 Asia countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `CYP` | 1,180 | 1999 | 2024 | | `TUR` | 774 | 2000 | 2024 | | `ISR` | 701 | 2012 | 2024 | | `ARM` | 426 | 2001 | 2023 | | `BRN` | 420 | 2014 | 2024 | | `TLS` | 268 | 2001 | 2022 | | `KHM` | 244 | 2009 | 2021 | | `IDN` | 180 | 2017 | 2022 | | `MNG` | 177 | 2019 | 2024 | | `MDV` | 168 | 2014 | 2019 | | `IRQ` | 149 | 2007 | 2021 | | `MMR` | 110 | 2014 | 2015 | | `ARE` | 98 | 2022 | 2023 | | `LAO` | 85 | 2017 | 2022 | | `TJK` | 80 | 2007 | 2009 | | ... | _4 more countries_ | | | ## Indicators (sample) - `EIP_TEIP_SEX_EDU_CBR_NB` — Persons outside the labour force by sex, education 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_EDU_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.) | `EDU_AGGREGATE_TOTAL` | | `classif1.label` | `string` | — | `Education (Aggregate levels): Total` | | `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` | — | `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` | | `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-edu-cbr-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_CBR_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EIP_TEIP_SEX_EDU_CBR_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EIP_TEIP_SEX_EDU_CBR_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_eip_teip_sex_edu_cbr_nb_persons_outside_the_labour_force_by_sex_education_2024, title = {Persons outside the labour force by sex, education and place of birth (thousands) | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2024}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EIP_TEIP_SEX_EDU_CBR_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-eip-teip-sex-edu-cbr-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_CBR_NB_This dataset contains 5,278 observations of Persons outside the labour force by sex, education and place of birth (thousands) data across 19 Asia countries, spanning 1999–2024. The data is sourced from the International Labour Organization (ILO)s ILOSTAT database, retrieved via API and filtered to Asia country codes. It covers one core indicator: EIP_TEIP_SEX_EDU_CBR_NB, which disaggregates persons outside the labour force by sex (total, male, female), education level (total), and place of birth (total). The schema includes columns for country code, country name, data source, indicator code, indicator name, sex classification, education classification, place of birth classification, observation year, observed value (in thousands), and data quality flags. The data is annual frequency, harmonized using International Conference of Labour Statisticians (ICLS) definitions, and suitable for machine learning tasks such as 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统计数据库，通过调用其REST API获取指标‘EIP_TEIP_SEX_EDU_CBR_NB’的原始数据，并依据亚洲ISO3国家代码进行地理筛选。数据经过ILO基于国际劳工统计学家会议（ICLS）定义的标准进行统一化处理，整合了来自各国劳动力调查、家庭收支调查及行政记录等多源信息。最终由Electric Sheep Asia团队重新封装为结构化表格数据集，确保每个观测值均标注了数据来源标签，便于追踪与验证。

特点

本数据集涵盖1999年至2024年间19个亚洲国家的5,278条观测记录，核心指标为非劳动力人口数量（按性别、教育程度及出生地细分，单位：千人）。数据以年度频率呈现，并包含三重维度的分类信息：性别（总、男、女）、教育聚合水平及出生地类型。此外，每行观测均附有观测状态标识（如临时性或不可靠值）及详细注释（如方法修订或非标准教育层级），为数据质量评估提供了透明依据。

使用方法

用户可通过HuggingFace Datasets库的load_dataset()函数一键加载该数据集，并转换为pandas DataFrame以便分析。典型应用包括：按国家筛选子集进行国别研究；对特定指标按时间序列排序并可视化趋势；或利用透视表功能构建国家×年份的矩阵，便于跨区域比较。数据集的年度频率与标准化结构使其特别适用于劳动力市场的时间序列分析、性别差异研究及教育与出生地因素的交叉探索。

背景与挑战

背景概述

该数据集由国际劳工组织（ILO）于2024年通过其核心统计数据库ILOSTAT发布，经Electric Sheep Asia重新整理并托管于HuggingFace平台，旨在系统记录亚洲19个国家中1999年至2024年间按性别、教育程度和出生地划分的非劳动力人口数量（单位：千人）。作为全球劳动统计的权威来源，ILO长期致力于整合各国劳动力调查与行政记录数据，以支持可持续发展目标中体面劳动指标的监测。该数据集聚焦于亚太地区劳动市场边缘群体——即未参与就业或失业搜寻的“非劳动力”人群，其规模与结构直接反映区域经济活力、教育水平与人口流动的交互影响，为研究性别不平等、移民融合及人力资本配置提供了关键实证基础。通过标准化分类维度（如性别、教育层级与出生地），该数据集填补了亚洲跨国比较研究中精细化劳动统计的空白，尤其适合用于时间序列分析、面板回归及机器学习分类任务。

当前挑战

该数据集所面临的挑战首先在于领域问题本身：非劳动力人群的定义与测量高度依赖各国统计方法的一致性，不同国家在“潜在劳动力”与“非经济活动”的界定上存在分歧，导致跨国比较时需谨慎处理分类标准差异。构建过程中，数据来源涵盖家庭调查（如HIES）、行政登记及劳动力调查，各来源的抽样框架、调查周期与质量控制标准不一，易引入系统性偏差；ILO虽通过“最佳来源”选择机制进行协调，但同一国家不同年份的数据源切换仍可能造成时间序列断点（如注释中标记的方法修正）。此外，教育程度分类的非标准化（如非主流教育水平的纳入）与部分观测值的不可靠性标记（如obs_status字段中的“U”标志）进一步增加了数据清洗与插补的复杂性。对于机器学习应用而言，时间跨度的非平衡性（如缅甸仅覆盖2014–2015年）与少量国家（总计19个）的样本限制，削弱了模型泛化能力的验证基础。

常用场景

经典使用场景

该数据集源自ILOSTAT权威劳动统计数据库，聚焦于亚洲19国1999至2024年间按性别、教育程度及出生地划分的劳动力市场外人口规模（以千人为单位）。其经典使用场景在于为劳动经济学与人口统计学研究提供细粒度的面板数据，研究非劳动力群体的结构性变迁，例如分析女性因教育水平差异而退出劳动力市场的趋势，或比较本土出生与移民人口在非经济活动中的分布特征。研究者可通过时间序列分析或面板回归模型，揭示经济发展阶段、教育扩张政策与劳动力参与率之间的动态关联。

解决学术问题

该数据集有效解决了学术界在非劳动力人口研究中长期面临的数据稀缺与异质性难题。在既往文献中，由于缺乏统一标准且覆盖多国的微观数据，难以系统探讨教育分层如何影响不同性别及出生背景人群的非经济活动模式。借助该数据集，学者能够实证检验人力资本理论在亚洲语境下的适用性，量化教育提升对女性重新进入劳动力市场的抑制作用，并区分结构性失业与自愿退出的边界。其意义在于为可持续发展目标（SDG）中关于体面工作与性别平等的国际比较提供了可靠的数据基石，推动了非正规经济与劳动力边缘化群体的理论建模。

衍生相关工作

基于该数据集衍生的经典工作涵盖了劳动力市场研究的多个前沿方向。一类工作聚焦于构建亚洲国家非劳动力人口的教育回报率模型，通过将数据集与宏观经济指标（如GDP增长率、女性劳动参与率）融合，检验教育扩张是否导致了‘高学历低就业’现象的区域分化。另一类代表性研究利用该数据集的出生地分类信息，探究国际移民在目的地国的劳动力市场融入困境，例如移民女性因学历不被认可而被迫退出正规经济的社会机制。此外，该数据集还催生了针对数据质量评估的方法论创新，研究者开发出处理ILOSTAT中‘不可靠’标记观测值的插补算法，提升了面板数据分析的稳健性。