electricsheepasia/asia-ilo-eip-dwap-sex-age-cct-rt-inactivity-rate-by-sex-age-and-citizenship

--- 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 citizenship (%) | Asia (ILOSTAT)" --- # Inactivity rate by sex, age and citizenship (%) | Asia (ILOSTAT) 🌏 **11,066 observations** · **25 Asia countries** · **1999–2025** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **11,066 observations** of `International migrant stock` data across **25 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_DWAP_SEX_AGE_CCT_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_CCT_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 | | `IRN` | 1,620 | 2005 | 2024 | | `GEO` | 1,215 | 2009 | 2023 | | `ARM` | 798 | 2001 | 2023 | | `SAU` | 729 | 2017 | 2025 | | `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 | | ... | _10 more countries_ | | | ## Indicators (sample) - `EIP_DWAP_SEX_AGE_CCT_RT` — Inactivity rate by sex, age and citizenship (%) ## 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_DWAP_SEX_AGE_CCT_RT` | | `indicator.label` | `string` | Indicator name in English | `Inactivity rate by sex, age and citiz…` | | `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) | `34.706` | | `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-dwap-sex-age-cct-rt-inactivity-rate-by-sex-age-and-citizenship") 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_CCT_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EIP_DWAP_SEX_AGE_CCT_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EIP_DWAP_SEX_AGE_CCT_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_eip_dwap_sex_age_cct_rt_inactivity_rate_by_sex_age_and_citizenship_2025, title = {Inactivity rate by sex, age and citizenship (%) | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EIP_DWAP_SEX_AGE_CCT_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-eip-dwap-sex-age-cct-rt-inactivity-rate-by-sex-age-and-citizenship}} } ``` ## 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_CCT_RT_This dataset contains 11,066 observations of inactivity rate by sex, age and citizenship (%) across 25 Asia countries, spanning 1999 to 2025, covering 1 distinct indicator. The data is sourced from the ILOSTAT database of the International Labour Organization (ILO), with the topic of international migrant stock. It includes detailed disaggregation dimensions such as sex, age, and citizenship, along with information on data sources, observation status, and notes, suitable for tasks like tabular classification, regression, and time-series forecasting.

搜集汇总

数据集介绍

构建方式

本数据集源于国际劳工组织（ILO）旗下的ILOSTAT数据库，通过其REST API直接抽取原始指标数据，并依据ISO 3166-1 alpha-3标准过滤出覆盖25个亚洲国家的观测记录。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、家庭收支调查等微观数据进行标准化处理，确保跨国可比性。数据集中每条记录均附带来源标识（source.label），便于追溯原始调查来源，从而构建起一个结构严谨、来源清晰的亚洲劳动力市场数据集。

特点

该数据集包含11,066条观测，时间跨度从1999年至2025年，涵盖25个亚洲国家，聚焦于“按性别、年龄和公民身份划分的不活动率”这一核心指标。其显著特征在于提供多维度的分类变量，包括性别（总、男、女）以及年龄组和公民身份等细分维度，使得研究者能够深入剖析不同人口群体的劳动参与差异。此外，数据还标注了观测状态（如临时、不可靠）及方法变更注释，为数据质量的评估提供了透明依据。

使用方法

该数据集以HuggingFace Datasets格式发布，兼容Python环境中的简便调用。研究人员可通过`load_dataset()`函数快速加载数据，并利用`to_pandas()`方法将其转换为Pandas DataFrame以进行后续分析。典型用法包括按国家筛选子集、针对特定指标绘制时间序列图，以及通过数据透视表构建国家-年份矩阵。数据集采用cc-by-4.0许可协议，使用时需同时引用ILO原始数据来源和Electric Sheep Asia的再封装版本。

背景与挑战

背景概述

该数据集由国际劳工组织（ILO）于2025年通过其核心统计数据库ILOSTAT构建，并由Electric Sheep Asia重新打包发布，专注于亚洲地区按性别、年龄和公民身份划分的经济不活跃率。数据集覆盖25个亚洲国家，时间跨度从1999年至2025年，包含11,066条观测记录，旨在系统性地揭示劳动力市场中不同群体参与度的结构性差异。作为全球劳动统计的权威来源，ILOSTAT通过协调各国劳动力调查、家庭收支调查及行政记录，为评估可持续发展目标中体面工作指标提供了关键支撑。该数据集在劳动经济学、人口迁移研究及社会政策评估领域具有重要影响力，尤其为分析亚洲区域内移民与非移民群体的劳动力边缘化趋势奠定了量化基础。

当前挑战

该数据集面临的核心挑战在于劳动力市场非活跃状态的测量复杂性。首先，亚洲各国在劳动统计定义、调查频率及数据采集方法上存在显著差异，例如部分国家依赖劳动力调查，而另一些则采用行政记录，导致跨国家与跨时段的可比性受限。其次，数据按性别、年龄和公民身份进行精细分层，但细粒度分类常因样本量不足而产生不可靠估计（如观测状态标记为'Unreliable'），且部分国家缺乏连续年度序列，造成时间序列分析中的断点问题。构建过程中，ILO需从多元原始数据中统一按照国际劳工统计学家会议定义进行协调，并筛选最优来源，但来源变更与方法学修订（如指标注释中提及的序列断裂）仍是数据一致性的主要障碍，尤其在追踪长期趋势时需谨慎处理断点效应。

常用场景

经典使用场景

该数据集汇聚了1999年至2025年间亚洲25个国家按性别、年龄和公民身份划分的经济不活跃率数据，共计11,066条观测记录，是劳动经济学与人口统计学交叉领域开展跨国比较与纵向分析的重要资源。研究者可借助该数据深入剖析亚洲各国劳动市场中不同人群的参与模式差异，例如探索女性、青年或外籍劳工在特定国家面临的结构性就业障碍。其经典应用涵盖基于时间序列的长期趋势描摹、基于人口分组的异质性分析，以及宏观经济增长与劳动供给弹性间的关联建模，为理解亚洲区域劳动力市场的动态演变提供了坚实的数据支撑。

衍生相关工作

该数据集催生了一系列聚焦亚洲劳动力市场异质性的学术延伸工作。经典研究方向包括构建面板向量自回归模型以量化移民流入对本地居民劳动参与率的动态影响，以及利用分解方法（如Oaxaca-Blinder）度量性别间不活跃率差异的来源构成。部分研究整合该数据与ILOSTAT其他指标（如失业率、工资水平），用以训练机器学习模型预测国家层面的劳动市场韧性。另有工作将其作为基准数据，验证新型时变参数状态空间模型在多国家多群体时间序列中的预测性能，推动了贝叶斯统计方法在劳动经济学中的应用边界。

数据集最近研究

最新研究方向

在亚洲劳动力市场研究中，基于ILOSTAT数据集对特定人群经济不活跃率的性别、年龄及公民身份维度进行交叉分析已成为前沿热点。该数据集覆盖25个亚洲国家长达27年的观测，为探究后疫情时代劳动力参与结构性变化提供了关键实证基础。特别是结合移民存量与公民身份标签，研究者得以剖析非公民群体（如外籍劳工、难民）在亚洲经济体中的就业边缘化现象，以及女性与青年群体在技能转型浪潮中的隐性失业风险。该数据对评估各国社会保障体系包容性、预测区域劳动力供给弹性及设计精准就业干预政策具有重要启示，尤其在东南亚与西亚国家的非正规经济转型议题中扮演着数据基石角色。