electricsheepasia/asia-ilo-eip-rcar-sex-rt-share-of-persons-outside-the-labour-force-due-to-c

--- license: cc-by-4.0 language: - en task_categories: - tabular-classification - tabular-regression - time-series-forecasting multilinguality: monolingual size_categories: - n<1K tags: - tabular - asia - ilostat - other-measures-of-labour-underutilization - ilo - labour - employment pretty_name: "Share of persons outside the labour force due to care responsibilities by sex (%) | Asia (ILOSTAT)" --- # Share of persons outside the labour force due to care responsibilities by sex (%) | Asia (ILOSTAT) 🌏 **409 observations** · **25 Asia countries** · **2000–2025** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **409 observations** of `Other measures of labour underutilization` data across **25 Asia countries**, spanning **2000–2025**, 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_RCAR_SEX_RT) - **Publisher:** International Labour Organization (ILO) - **License:** [cc-by-4.0](https://creativecommons.org/licenses/by/4.0/) - **Topic:** Other measures of labour underutilization ## Methodology Data pulled directly from the ILOSTAT REST API at `https://rplumber.ilo.org/data/indicator?id=EIP_RCAR_SEX_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 | |---------|-----:|-----------:|----------:| | `IRN` | 51 | 2008 | 2024 | | `KGZ` | 42 | 2010 | 2023 | | `KOR` | 33 | 2015 | 2025 | | `LKA` | 33 | 2014 | 2024 | | `ARM` | 27 | 2010 | 2023 | | `TUR` | 27 | 2012 | 2020 | | `JOR` | 24 | 2017 | 2024 | | `PSE` | 20 | 2015 | 2025 | | `THA` | 18 | 2000 | 2019 | | `IND` | 17 | 2021 | 2025 | | `MMR` | 15 | 2015 | 2020 | | `BGD` | 14 | 2017 | 2024 | | `VNM` | 12 | 2018 | 2024 | | `MNG` | 12 | 2014 | 2022 | | `SAU` | 11 | 2017 | 2021 | | ... | _10 more countries_ | | | ## Indicators (sample) - `EIP_RCAR_SEX_RT` — Share of persons outside the labour force due to care responsibilities by sex (%) ## 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) | `BA:15715` | | `source.label` | `string` | Source name in English | `LFS - Labour Force Survey` | | `indicator` | `string` | ILOSTAT indicator code | `EIP_RCAR_SEX_RT` | | `indicator.label` | `string` | Indicator name in English | `Share of persons outside the labour f…` | | `sex` | `string` | Disaggregation by sex (SEX_T = total, SEX_M = male, SEX_F = female) | `SEX_T` | | `sex.label` | `string` | — | `Total` | | `time` | `int64` | Observation year | `2020` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `44.36` | | `obs_status` | `string` | Observation status flag (e.g. provisional, unreliable) | `B` | | `obs_status.label` | `string` | — | `Break in series` | | `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`** (4 unique values): `SEX_T`, `SEX_M`, `SEX_F`, `SEX_O` ## 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-rcar-sex-rt-share-of-persons-outside-the-labour-force-due-to-c") 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_RCAR_SEX_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EIP_RCAR_SEX_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EIP_RCAR_SEX_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_eip_rcar_sex_rt_share_of_persons_outside_the_labour_force_due_to_c_2025, title = {Share of persons outside the labour force due to care responsibilities by sex (%) | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EIP_RCAR_SEX_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-eip-rcar-sex-rt-share-of-persons-outside-the-labour-force-due-to-c}} } ``` ## 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_RCAR_SEX_RT_This dataset contains 409 observations of Share of persons outside the labour force due to care responsibilities by sex (%) across 25 Asia countries, spanning 2000–2025. The data is sourced from the International Labour Organizations ILOSTAT database and repackaged by Electric Sheep Asia for machine learning readiness. It features a tabular structure with columns for country codes, years, sex disaggregation, observed values, and status flags, suitable for tabular classification, regression, and time-series forecasting tasks. The data is harmonized using ICLS definitions and includes source annotations and 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统计数据库，通过调用其REST API接口（https://rplumber.ilo.org/data/indicator?id=EIP_RCAR_SEX_RT）直接获取原始数据，并严格限定于亚洲国家（依据ISO 3166-1 alpha-3国家代码筛选）。ILOSTAT采用国际劳工统计学家会议（ICLS）定义对各国劳动力调查微观数据进行标准化处理，以确保跨国家与跨年度的可比性。随后，数据由Electric Sheep Asia团队进行重新封装，将异构源数据统一为整洁的表格化格式，并附有标准化的数据集卡片（Dataset Card），以提升在机器学习工作流中的可用性。这一流程确保了数据从官方统计源头到科研应用的透明且可复现的链路。

特点

数据集聚焦于亚洲地区因照料责任而退出劳动力市场的人口比例（按性别分），共包含409条观测记录，覆盖25个亚洲国家，时间跨度从2000年至2025年。其核心指标为`EIP_RCAR_SEX_RT`，并依据性别（总、男、女）进行了细致分解，为分析非经济活动人口中隐性劳动利用不足现象提供了宝贵视角。数据质量方面，来源均明确标识于`source.label`列以便追溯，并通过使用ILO筛选的“最佳来源”来处理同一国家同年份存在多个数据源的情形。此外，数据标注了因方法修订引发的时序断裂（`obs_status`字段），为严谨的时间序列分析提供了必要的先验知识。

使用方法

使用者可借助HuggingFace `datasets`库通过一行代码`load_dataset("electricsheepasia/asia-ilo-eip-rcar-sex-rt-share-of-persons-outside-the-labour-force-due-to-c")`将数据轻松加载至Python环境，并可直接转换为Pandas DataFrame以进行后续分析。典型操作包括：依据国家代码（`ref_area`）筛选特定国家的时间序列；针对单一指标（如`EIP_RCAR_SEX_RT`）按年份排序并可视化其变化趋势；利用透视表功能构建“国家×年份”的矩阵，用于面板数据建模或缺失值识别。该设计旨在降低从官方统计到分析建模的技术门槛，赋能社会科学与经济学领域的研究人员快速开展实证研究。

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整理发布，并由Electric Sheep Asia重新打包至HuggingFace平台。数据集聚焦于亚洲25个国家在2000至2025年间因照护责任而退出劳动力市场的人口比例，按性别细分，涵盖409条观测值。其核心研究问题在于量化性别化的照护负担对劳动力参与的影响，为理解劳动力利用不足的非传统维度提供关键数据支持。作为ILOSTAT体系中“劳动力利用不足的其他衡量指标”的重要组成部分，该数据集对劳动经济学、性别研究及社会政策分析具有显著价值，尤其为亚太地区可持续发展目标（SDG）中的体面工作与性别平等议题提供了实证基础。

当前挑战

该数据集所应对的领域核心挑战在于，传统的失业率指标未能充分捕捉因照护责任等非市场活动导致的劳动力边缘化现象，尤其是女性因无偿照护工作而退出劳动力市场的隐形状况。在构建过程中，数据集面临多重困难：首先，不同亚洲国家的劳动力调查在定义、时间跨度和数据质量上存在显著差异，需依赖ILOSTAT的ICLS标准进行协调统一。其次，部分国家（如泰国、印度）的观测年份稀疏或不连续，时间序列分析面临缺失值处理问题。此外，数据标注中存在的“序列中断”状态（如方法论修订或来源变更）要求使用者具备细致的清理与校正流程，以确保跨国家跨年份比较的有效性。

常用场景

经典使用场景

在劳动经济学与性别研究领域，该数据集的核心应用聚焦于量化分析亚洲地区因照料责任而退出劳动力市场的人口比例及其性别差异。研究者可借助其涵盖25个国家、跨度25年的面板数据，构建固定效应或随机效应模型，探究经济发展水平、社会保障政策、文化规范等因素对非劳动力人口中“照料退出”现象的影响。时间序列特征使其特别适用于评估重大政策变革或经济危机前后，不同性别群体劳动参与行为的动态演变模式。

衍生相关工作

该数据集衍生的典型工作包括：基于ILOSTAT数据构建的“劳动力利用不足多维度指标”研究，其将本数据集中的“照料退出率”与失业率、时间相关未充分就业率等指标结合，形成综合监测框架；利用该时间序列数据训练的时间序列预测模型，用于前瞻性模拟老龄化社会背景下照料负担对劳动力供给的长期冲击；以及结合微观调查数据的多层次分析，揭示国家政策环境如何调节照料责任与就业决策之间的因果路径。

数据集最近研究

最新研究方向

该数据集聚焦于亚洲地区因照顾责任而退出劳动力市场的人口比例，按性别分列，是衡量劳动力利用不足的重要指标。在研究前沿，该数据被广泛应用于性别经济学与劳动市场分析，尤其结合新冠疫情后全球照护经济（care economy）的议题，成为探讨女性劳动参与率低迷、性别平等与家庭照料负担关联的热点数据源。通过ILOSTAT标准化采集体系，数据覆盖2000至2025年间的25个亚洲国家，为跨时期、跨国别的比较研究提供了坚实基石。更重要的是，它支持时序预测与面板回归，助力学者揭示照护责任对劳动力市场弹性的影响，并为政策制定者优化社会保障与育儿支持体系提供实证依据，具有显著的学术与政策意义。