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

--- 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: "Inactivity rate by sex, education and citizenship (%) | Asia (ILOSTAT)" --- # Inactivity rate by sex, education and citizenship (%) | Asia (ILOSTAT) 🌏 **5,565 observations** · **20 Asia countries** · **1999–2025** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **5,565 observations** of `International migrant stock` data across **20 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_EDU_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_EDU_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 20 Asia countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `CYP` | 1,174 | 1999 | 2024 | | `IRN` | 1,059 | 2005 | 2024 | | `GEO` | 674 | 2009 | 2023 | | `ARM` | 432 | 2001 | 2023 | | `SAU` | 415 | 2017 | 2025 | | `BRN` | 375 | 2014 | 2024 | | `JOR` | 315 | 2017 | 2024 | | `MDV` | 191 | 2009 | 2019 | | `TLS` | 173 | 2010 | 2022 | | `LAO` | 145 | 2015 | 2022 | | `IDN` | 122 | 2010 | 2023 | | `THA` | 108 | 2023 | 2024 | | `MNG` | 65 | 2019 | 2020 | | `LBN` | 54 | 2019 | 2019 | | `NPL` | 50 | 2008 | 2008 | | ... | _5 more countries_ | | | ## Indicators (sample) - `EIP_DWAP_SEX_EDU_CCT_RT` — Inactivity rate by sex, education 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_EDU_CCT_RT` | | `indicator.label` | `string` | Indicator name in English | `Inactivity rate by sex, education and…` | | `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 | `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` | — | `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_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-edu-cct-rt-inactivity-rate-by-sex-education-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_EDU_CCT_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EIP_DWAP_SEX_EDU_CCT_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EIP_DWAP_SEX_EDU_CCT_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_eip_dwap_sex_edu_cct_rt_inactivity_rate_by_sex_education_and_citizenship_2025, title = {Inactivity rate by sex, education and citizenship (%) | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EIP_DWAP_SEX_EDU_CCT_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-eip-dwap-sex-edu-cct-rt-inactivity-rate-by-sex-education-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_EDU_CCT_RT_This dataset contains 5,565 observations of the Inactivity rate by sex, education and citizenship (%) indicator from the International Labour Organization (ILO) ILOSTAT database, covering 20 Asia countries (including Cyprus, Iran, Georgia, Armenia, Saudi Arabia, Brunei, Jordan, Maldives, Timor-Leste, Laos, Indonesia, Thailand, Mongolia, Lebanon, Nepal, and others) from 1999 to 2025. Data is pulled directly from the ILOSTAT REST API and filtered to Asia ISO3 country codes, harmonized by the ILOs Department of Statistics using International Conference of Labour Statisticians (ICLS) definitions. The dataset includes columns such as country code, country name, source, indicator code, sex disaggregation, education classification, citizenship classification, observation year, observed value, observation status, and more, suitable for tabular classification, regression, and time-series forecasting tasks. Data is published at annual frequency, with some observations flagged as provisional or unreliable, and users should note data quality caveats.

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数据库，通过其REST API接口直接获取核心指标代码为EIP_DWAP_SEX_EDU_CCT_RT的失业率数据。原始数据基于各国劳动力调查、家庭收入调查及行政记录等多元来源，并依据国际劳工统计学家会议（ICLS）定义进行标准化处理。数据在提取后，进一步筛选出亚洲20个国家的ISO3代码，最终汇聚为涵盖1999年至2025年间共5,565条观测记录的表格型数据集。每条记录均包含来源标注（source.label），以确保数据可追溯性，并由Electric Sheep Asia团队完成重新打包与发布。

特点

本数据集聚焦于亚洲区域，覆盖20个国家，时间跨度长达27年，提供了按性别、教育程度和公民身份细分的经济活动人口失业率百分比。其核心特点在于丰富的分类维度，包括性别（总、男、女）、教育水平（含总计与细分类别）以及公民身份（总计与细分），能够支持多层次的交叉分析。数据质量方面，设有观测状态标记（obs_status）以标识数据可靠性，并包含方法论变更、非标准分类等注释信息（note_*字段），便于使用者评估数据适用性。此外，数据以年频率呈现，并优先采用ILO认定的最佳来源，确保了跨国家与跨年度的可比性。

使用方法

该数据集完美契合HuggingFace Datasets生态，可通过一行代码load_dataset()加载为DataFrame，便于进行时间序列分析、分类或回归任务。用户可根据ref_area列轻松筛选特定国家数据，或利用indicator列聚焦失业率指标。借助pandas库，可快速实现数据透视，将长格式转换为以年份为行、国家为列的矩阵，便于跨区域比较。数据集还支持按性别、教育程度等维度进行分组聚合，探索不同群体间的失业差异。对于时间序列预测，可直接以time为索引、obs_value为目标变量建模。所有数据均为结构化表格格式，非常适合机器学习领域中的表格分类与回归任务。

背景与挑战

背景概述

该数据集由国际劳工组织（ILO）与Electric Sheep Asia于2025年联合创建，聚焦亚洲20个国家1999至2025年间按性别、教育程度和公民身份划分的经济不活动率（%）。ILOSTAT作为全球劳动统计核心数据库，整合自各国劳动力调查及行政记录，通过国际劳工统计学家会议（ICLS）标准进行数据协调。此数据集为研究亚洲劳动力市场结构性特征、性别差异及移民融入困境提供了高度细粒度的面板数据，支撑劳动经济学、人口迁移及社会保障领域的实证分析。其开放发布（CC-BY-4.0）与HuggingFace原生加载能力显著降低了跨国比较研究的技术门槛。

当前挑战

领域层面，经济不活动率受文化规范、教育体系差异及非正规就业界定模糊等因素干扰，准确揭示性别与公民身份对劳动力退出机制的独立影响极为困难。亚洲各国教育分类标准（如classif1字段中的‘非标准教育水平’）与国籍统计口径（classif2）的不一致加剧了跨国可比性挑战。构建过程中，原始微观数据来自不同年份的断面调查，ILO虽标注源数据质量（如obs_status字段的‘不可靠’标记），但时间序列中由方法论修订（如note_indicator.label中的序列断裂）导致的系统性偏差仍需谨慎处理，多源整合中的‘最佳来源’遴选规则也可能引入选择偏误。

常用场景

经典使用场景

该数据集来源于国际劳工组织（ILO）的ILOSTAT数据库，聚焦亚洲20个国家1999至2025年间按性别、教育程度与公民身份分层的不活动率观测值。其最经典的用途在于构建面板数据模型，以探究宏观劳动力市场中结构性非参与行为的演变规律。研究者可通过该数据跨年、跨国的标准化时序特征，精准刻画不同社会群体在劳动市场边缘化的动态模式，并为后续计量分析提供高质量的基准数据源。

解决学术问题

该数据集有效回应了劳动经济学与社会分层领域两个核心难题：其一，如何在跨国比较中解析公民身份差异如何调节教育回报与性别角色对劳动供给的影响；其二，如何在缺乏统一统计口径的亚洲地区实现长时间序列的非活动率可比性。基于ILO采纳的国际劳动统计学家会议（ICLS）定义，该数据为检验人力资本理论、性别隔离假说以及移民融合效应提供了实证依据，其分层粒度使研究者得以剥离群体间异质性，推动对亚洲劳动力市场边缘化机制的更深入理解。

衍生相关工作

围绕该数据集衍生了一系列经典工作，其中最具代表性的是利用其分层维度构建的分解分析框架，例如通过Blinder-Oaxaca分解量化性别与公民身份对不活动率的贡献差异。此外，时间序列计量模型如固定效应面板回归被广泛用于检验贸易自由化或社会保障制度改革对经济不活跃人群的冲击效应。近年亦有研究将其与移民存量数据结合，采用多层次模型揭示跨国流动对目的地劳动力场域的结构性影响，推动了对亚洲非正规就业与隐性失业的全新学术讨论。