electricsheepasia/asia-ilo-eip-dwap-sex-age-cbr-rt-inactivity-rate-by-sex-age-and-place-of-birth

--- 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: "Inactivity rate by sex, age and place of birth (%) | Asia (ILOSTAT)" --- # Inactivity rate by sex, age and place of birth (%) | 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_DWAP_SEX_AGE_CBR_RT) - **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_DWAP_SEX_AGE_CBR_RT` 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_DWAP_SEX_AGE_CBR_RT` — Inactivity rate by sex, age and place of birth (%) ## 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_DWAP_SEX_AGE_CBR_RT` | | `indicator.label` | `string` | Indicator name in English | `Inactivity rate by sex, age and place…` | | `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) | `48.275` | | `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-dwap-sex-age-cbr-rt-inactivity-rate-by-sex-age-and-place-of-birth") 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_DWAP_SEX_AGE_CBR_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EIP_DWAP_SEX_AGE_CBR_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EIP_DWAP_SEX_AGE_CBR_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_eip_dwap_sex_age_cbr_rt_inactivity_rate_by_sex_age_and_place_of_birth_2025, title = {Inactivity rate by sex, age and place of birth (%) | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EIP_DWAP_SEX_AGE_CBR_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-eip-dwap-sex-age-cbr-rt-inactivity-rate-by-sex-age-and-place-of-birth}} } ``` ## 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_DWAP_SEX_AGE_CBR_RT_This dataset contains the Inactivity rate by sex, age and place of birth (%) indicator data from the International Labour Organization (ILO) ILOSTAT database, specifically focused on Asia. It covers 25 Asian countries, spanning the years 1991 to 2025, with a total of 10,377 observations. The core indicator is EIP_DWAP_SEX_AGE_CBR_RT, which measures the inactivity rate (in percentage) across different sexes, age groups, and places of birth. Data is sourced via the ILOSTAT REST API and filtered to include only Asian countries. The dataset provides a detailed schema including country codes, country names, data sources, indicator codes, sex disaggregation (e.g., total, male, female), age classifications, place of birth classifications, observation years, observed values, and status flags. It is suitable for tasks such as tabular classification, regression, and time-series forecasting, aiming to offer machine learning-ready data for research on Asian labor markets, international migrant stock, and related socio-economic issues.

搜集汇总

数据集介绍

构建方式

本数据集源自国际劳工组织（ILO）的ILOSTAT数据库，通过其REST API接口直接提取指标代码为EIP_DWAP_SEX_AGE_CBR_RT的原始数据，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）定义对各国劳动力调查、家庭收支调查等微观数据进行标准化处理，数据来源在source.label列中标注以确保可追溯性。该数据集由Electric Sheep Asia重新打包为Parquet格式，使用HuggingFace Datasets库提供统一的加载接口，便于研究者直接调用。

特点

该数据集涵盖1991年至2025年间25个亚洲国家的10,377条观测记录，聚焦于按性别、年龄和出生地划分的不活动率指标。数据具备丰富的分类维度，包括性别（总计、男性、女性）、年龄组与出生地类型，可支持多维度交叉分析。数据质量经过ILO精选，当同一国家同年份存在多个来源时采用'最佳来源'，且观测状态标记（如临时、不可靠）为使用者提供质量预警。数据集以年度频率呈现，适合时间序列分析与面板数据建模。

使用方法

使用者可通过HuggingFace Datasets库的load_dataset函数一键加载数据集，返回的Dataset对象可直接转换为Pandas DataFrame进行后续分析。支持按国家代码筛选子集、对单一指标进行时间序列可视化，以及使用pivot_table构建国家×年份矩阵进行横向比较。数据集以表格形式存储，包含ref_area（国家代码）、time（年份）、obs_value（观测值）等核心字段，以及sex、classif1、classif2等分类变量，满足回归、分类与时间序列预测等任务需求。

背景与挑战

背景概述

该数据集由国际劳工组织（ILO）统计部门于2025年发布，经Electric Sheep Asia重新打包后托管于HuggingFace平台。其核心研究问题聚焦于亚洲地区按性别、年龄和出生地划分的劳动力不活跃率，旨在揭示移民群体在区域劳动力市场中的结构性差异。作为ILOSTAT数据库的衍生数据集，它整合了来自25个亚洲国家1991年至2025年间的10,377条观测值，覆盖劳动力调查、家庭收支调查等多源数据，为理解亚洲移民劳动力的社会经济融入提供了关键量化基础。该数据集的发布显著提升了区域劳动统计数据的可获取性与机器学习就绪程度，推动跨国比较分析与时间序列建模在劳动经济学领域的应用。

当前挑战

该数据集所解决的领域问题在于量化亚洲移民群体因性别、年龄及出生地差异导致的劳动力市场不活跃现象，挑战在于处理数据源的异质性——不同国家调查方法、分类标准及年度频率的差异使得跨时空一致性校准成为难点。构建过程中面临多重挑战：首先，需将ILOSTAT原始API中的多源微观数据进行标准化，过滤非亚洲地区数据并映射至ISO 3166-1 alpha-3编码体系；其次，处理观测值中因方法论修订（如中断序列标注）和质量标识（如不可靠标记）带来的数据噪声；最后，确保性别与年龄细分字段在缺失时仍保持维度完整性，同时平衡最佳来源选择策略与数据追溯性要求，最终形成适用于分类、回归及时间序列预测任务的清洁表格数据。

常用场景

经典使用场景

该数据集收录了1991年至2025年间亚洲25个国家的10,377条观测数据，核心指标为按性别、年龄和出生地划分的不活动率。在劳动经济学与人口学领域，研究者常利用此类面板数据开展跨国比较分析，探讨不同亚群在劳动力市场参与程度上的结构性差异。通过整合ILOSTAT统一规范的统计口径，该数据集支持构建时间序列模型，以追踪亚洲各国不活动率的长期演变趋势，并识别性别、年龄组及移民身份对就业参与的综合影响。其精细的分层维度也为研究代际流动、性别平等及迁移政策效果提供了可靠的数据基础。

实际应用

在实际政策制定与国际发展评估中，该数据集是监测亚洲各国实现联合国可持续发展目标8（体面工作和经济增长）进展的关键工具。国际组织与各国劳动部门可借用其中的分性别、分年龄段不活动率指标，精准定位劳动力边缘群体，从而设计差异化的就业促进与社会保障策略。例如，借助数据揭示的女性参与率短板与青年不活动高峰，政府可优化职业培训资源配置、完善育儿支持政策，或制定针对移民群体的定向帮扶措施。此外，该数据集的时间跨度为评估金融危机、疫情冲击等宏观事件对各地劳动力市场的非对称影响提供了历史参照。

衍生相关工作

该数据集衍生出了一系列聚焦亚洲劳动力市场动态的经典工作，其中最具代表性的是基于ILOSTAT调和数据的面板回归与结构分解研究。学者们常将其与世界经济展望、教育统计等外部数据库融合，构建多元因素模型，以解析经济增长、教育扩张与人口结构变迁对不活动率的驱动机制。部分工作专门研究了性别不平等在劳动参与中的持续性表现，并提出了去家务化政策与弹性工作制等对策框架。另有研究将出生地分类变量引申为迁移身份，探讨国际移民在接收国就业市场的融入陷阱，从而丰富了关于亚洲区域内南北流动与南南合作的经济学叙事。