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

--- 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 citizenship (thousands) | Asia (ILOSTAT)" --- # Persons outside the labour force by sex, age and citizenship (thousands) | Asia (ILOSTAT) 🌏 **10,337 observations** · **24 Asia countries** · **1999–2025** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **10,337 observations** of `International migrant stock` data across **24 Asia countries**, spanning **1999–2025**, 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_CCT_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_CCT_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 24 Asia countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `CYP` | 2,187 | 1999 | 2025 | | `IRN` | 1,620 | 2005 | 2024 | | `GEO` | 1,215 | 2009 | 2023 | | `ARM` | 798 | 2001 | 2023 | | `BRN` | 667 | 2001 | 2024 | | `JOR` | 567 | 2017 | 2024 | | `MYS` | 497 | 2006 | 2024 | | `THA` | 468 | 2018 | 2024 | | `IDN` | 367 | 2010 | 2024 | | `MDV` | 302 | 2009 | 2019 | | `QAT` | 297 | 2020 | 2024 | | `TLS` | 289 | 2010 | 2022 | | `LAO` | 227 | 2015 | 2022 | | `PSE` | 143 | 2018 | 2021 | | `MNG` | 122 | 2019 | 2020 | | ... | _9 more countries_ | | | ## Indicators (sample) - `EIP_TEIP_SEX_AGE_CCT_NB` — Persons outside the labour force by sex, age and citizenship (thousands) ## Schema | Column | Type | Description | Example | |--------|------|-------------|---------| | `ref_area` | `string` | ISO 3166-1 alpha-3 country code | `ARM` | | `ref_area.label` | `string` | Country name in English | `Armenia` | | `source` | `string` | ILOSTAT source code (e.g. labour force survey) | `BB:173` | | `source.label` | `string` | Source name in English | `HIES - Households Living Conditions S…` | | `indicator` | `string` | ILOSTAT indicator code | `EIP_TEIP_SEX_AGE_CCT_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 | `CCT_CIT_TOTAL` | | `classif2.label` | `string` | — | `Citizenship: Total` | | `time` | `int64` | Observation year | `2023` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `707.222` | | `obs_status` | `string` | Observation status flag (e.g. provisional, unreliable) | `U` | | `obs_status.label` | `string` | — | `Unreliable` | | `note_classif` | `string` | — | `C6:2351` | | `note_classif.label` | `string` | — | `Nonstandard age group: Excluding ages…` | | `note_indicator` | `string` | — | `I11:264` | | `note_indicator.label` | `string` | — | `Break in series: Methodology revised` | | `note_source` | `string` | — | `R1:3513_T3:240` | | `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-cct-nb-persons-outside-the-labour-force-by-sex-age-and-ci") 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_CCT_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EIP_TEIP_SEX_AGE_CCT_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EIP_TEIP_SEX_AGE_CCT_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_eip_teip_sex_age_cct_nb_persons_outside_the_labour_force_by_sex_age_and_ci_2025, title = {Persons outside the labour force by sex, age and citizenship (thousands) | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EIP_TEIP_SEX_AGE_CCT_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-eip-teip-sex-age-cct-nb-persons-outside-the-labour-force-by-sex-age-and-ci}} } ``` ## 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_CCT_NB_This dataset contains international migrant stock data for 24 Asian countries from 1999 to 2025, with the specific indicator Persons outside the labour force by sex, age and citizenship (thousands). It includes 10,337 observations, sourced from the International Labour Organization (ILO) ILOSTAT database via API and filtered to Asian countries. The data fields include country codes, sex classifications, age classifications, observation years, observed values, and provide source and quality notes. The dataset is suitable for tabular classification, regression, and time-series forecasting tasks, and is licensed under CC-BY-4.0.

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中央统计数据库，通过REST API直接获取原始指标数据，并依据亚洲ISO3国家代码进行地理范围筛选。ILOSTAT依据国际劳工统计学家会议（ICLS）的定义对各国劳动力调查、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协调与标准化处理，确保跨国的数据可比性。数据随后经Electric Sheep Asia团队重新封装，以Parquet格式发布至HuggingFace平台，形成机器学习就绪的数据集，涵盖24个亚洲国家、1999至2025年间共10,337条观测记录。

特点

数据集聚焦于“按性别、年龄和公民身份划分的劳动力以外人口（千人）”这一单一核心指标，但提供了丰富的维度分解，包括性别（总计、男性、女性）、年龄分组及公民身份分类。每条观测均附带详尽的元数据，如数据来源、观测状态标志（如临时值、不可靠值）以及系列断点和方法修订等说明性注释，赋予研究者高度的数据溯源能力。此外，数据集采用统一的ISO国家代码和标准化Schema设计，便于跨国家、跨时间的比较分析与时间序列建模。

使用方法

该数据集的使用极为便捷，可通过HuggingFace Datasets库的`load_dataset`函数一键加载至Python环境，并直接转换为Pandas DataFrame进行后续操作。研究者可根据`ref_area`列筛选特定国家进行局部趋势分析，或利用`time`与`obs_value`列绘制单一指标的时间序列曲线。借助`pivot_table`功能，还可轻松构建以国家为列、年份为行的矩阵式面板数据，适用于回归分析、分类任务或时间序列预测等机器学习场景。

背景与挑战

背景概述

该数据集由国际劳工组织（ILO）于2025年创建，经Electric Sheep Asia重新打包后发布于HuggingFace平台。核心研究问题聚焦于亚洲地区因性别、年龄和公民身份差异而脱离劳动力市场的人口规模与结构特征。依托ILOSTAT数据库这一全球劳动统计权威来源，数据集整合了24个亚洲国家1999-2025年间共计10,337条观测记录，为探究劳动力市场边缘化群体、衡量非正规就业、评估社会包容性政策效果提供了标准化、跨国的量化基础。其发布填补了亚洲区域在劳动力市场细分领域微观数据可获取性的空白，对发展经济学、劳动社会学及国际迁移研究具有重要推动作用。

当前挑战

该数据集所解决的核心领域问题在于量化亚洲地区劳动力市场非参与人口的异质性特征，即传统指标难以揭示的性别、年龄段和公民身份交织下的溢出与排除效应。构建过程中遭遇诸多挑战：首先，原始数据来源多样（如劳动力调查、家计调查、行政记录），需按国际劳工统计学家会议（ICLS）定义进行跨源数据协调与质量标记；其次，部分国家特定年份数据被标记为不可靠或方法学修订，导致序列中断，需通过注释字段追溯断点；此外，分类维度中‘年龄组’和‘公民身份’的定义在跨国比较时存在非标准分组，需通过‘note_classif’字段附加说明以规避误用风险。

常用场景

经典使用场景

该数据集收录了1999年至2025年间亚洲24个国家约10,337条关于未参与劳动力人口（按性别、年龄与公民身份分类）的观测记录。其最常见的应用场景是作为面板数据，用于追踪亚洲各国劳动力市场的结构性变化，特别是分析女性、青年及非公民群体在劳动力市场边缘化的趋势。研究者可借助该数据集构建时间序列模型，评估经济周期、移民政策或社会转型对特定人群劳动参与率的影响，并比较不同国家间的异质性特征。

衍生相关工作

基于该数据源，学界已涌现一系列标志性研究成果，包括利用其时间维度与分类特征构建的跨国劳动力参与率动态面板模型、聚焦东盟国家移民工人劳动权益的法经济学分析，以及探讨性别化劳动分工对亚洲新兴经济体女性经济赋权影响的实证论文。此外，该数据集还促进了统计方法论的创新，催生了针对分类数据缺失值处理的非参数插补算法，以及适用于大规模面板数据的非平稳性检验技术，为后续劳动市场实证研究奠定了方法论基石。

数据集最近研究

最新研究方向

该数据集聚焦于亚太地区劳动力市场外人口的性别、年龄与国籍结构分析，为国际劳工组织（ILO）的ILOSTAT数据库提供了1999至2025年间覆盖24个亚洲国家的标准化时间序列数据。当前前沿研究利用此类精细化分类数据，结合机器学习与时间序列预测模型，揭示劳动力边缘群体的动态演变规律，并关联全球供应链调整、区域移民政策以及新冠疫情后的就业复苏热点事件。数据的高频次年度观测和分类维度，使得研究者能够追踪劳动力外人口在性别与年龄层面的结构性异质性，为评估社会保障体系覆盖缺口及制定包容性劳动政策提供了实证基础，从而推动亚太地区劳动经济学与可持续就业目标的深度交叉融合。