electricsheepasia/asia-ilo-eip-xplf-sex-geo-nb-potential-labour-force-by-sex-and-rural-urban-area

--- 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 - other-measures-of-labour-underutilization - ilo - labour - employment pretty_name: "Potential labour force by sex and rural / urban areas (thousands) | Asia (ILOSTAT)" --- # Potential labour force by sex and rural / urban areas (thousands) | Asia (ILOSTAT) 🌏 **1,843 observations** · **27 Asia countries** · **1999–2025** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **1,843 observations** of `Other measures of labour underutilization` data across **27 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_XPLF_SEX_GEO_NB) - **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_XPLF_SEX_GEO_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 27 Asia countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `CYP` | 233 | 1999 | 2024 | | `PHL` | 156 | 2007 | 2023 | | `VNM` | 156 | 2007 | 2024 | | `PSE` | 132 | 2012 | 2022 | | `TUR` | 126 | 2000 | 2013 | | `LKA` | 123 | 2010 | 2024 | | `THA` | 120 | 2010 | 2024 | | `ARM` | 105 | 2007 | 2018 | | `BRN` | 81 | 2014 | 2024 | | `JOR` | 72 | 2017 | 2024 | | `IDN` | 72 | 2015 | 2023 | | `MNG` | 54 | 2019 | 2024 | | `GEO` | 54 | 2019 | 2024 | | `TLS` | 45 | 2010 | 2022 | | `MMR` | 45 | 2015 | 2020 | | ... | _12 more countries_ | | | ## Indicators (sample) - `EIP_XPLF_SEX_GEO_NB` — Potential labour force by sex and rural / urban areas (thousands) ## 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_XPLF_SEX_GEO_NB` | | `indicator.label` | `string` | Indicator name in English | `Potential labour force by sex and rur…` | | `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.) | `GEO_COV_NAT` | | `classif1.label` | `string` | — | `Area type: National` | | `time` | `int64` | Observation year | `2021` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `637.031` | | `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_S3:8` | | `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-xplf-sex-geo-nb-potential-labour-force-by-sex-and-rural-urban-area") 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_XPLF_SEX_GEO_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EIP_XPLF_SEX_GEO_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EIP_XPLF_SEX_GEO_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_eip_xplf_sex_geo_nb_potential_labour_force_by_sex_and_rural_urban_area_2025, title = {Potential labour force by sex and rural / urban areas (thousands) | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EIP_XPLF_SEX_GEO_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-eip-xplf-sex-geo-nb-potential-labour-force-by-sex-and-rural-urban-area}} } ``` ## 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_XPLF_SEX_GEO_NB_This dataset contains potential labour force statistics for Asian countries from the International Labour Organization (ILO) ILOSTAT database, specifically the indicator Potential labour force by sex and rural / urban areas (thousands). It covers 27 Asian countries from 1999 to 2025, with 1,843 observations. Data is disaggregated by sex (total, male, female) and area type (e.g., national level), and includes fields such as country codes, data sources, observation years, observed values, and status flags. The data is sourced from ILO-harmonised official statistics like labour force surveys, published at annual frequency, and is suitable for tasks such as tabular classification, regression, and time-series forecasting.

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，经由Electric Sheep Asia重新打包并发布。构建过程首先通过ILOSTAT REST API直接提取“其他劳动力利用不足指标”中的特定代码（EIP_XPLF_SEX_GEO_NB），随后依据ISO3国家代码筛选出27个亚洲国家的数据。ILOSTAT本身对原始调查微观数据依据国际劳工统计学家会议（ICLS）定义进行了统一协调处理，而数据来源的标记列（source.label）则可追溯至各国家劳动力调查等原始资料。最终数据集包含1,843条观测值，覆盖1999年至2025年的时间跨度，以结构化表格形式呈现。

特点

该数据集聚焦于“潜在劳动力人口”这一关键劳动力利用不足指标，并按性别（总、男、女）以及城乡地域维度进行细致分解。横跨27个亚洲国家，时间序列长达27年，观测值数量超过1,800条，涵盖土耳其、菲律宾、越南等多样化的国家和地区。数据质量方面，采用年度频率，当同一国家与年份存在多个来源时，选用ILO甄别的“最佳来源”，且分解维度列仅在对应分类数据发布时方为非空。此外，每条记录均附有观测状态和备注信息，以注明如方法论修订等引发的序列中断，确保了数据使用的严谨性。

使用方法

研究者可通过HuggingFace Datasets库的load_dataset()函数一键加载该数据集，并直接转换为pandas DataFrame进行后续分析。推荐用法包括：按国家代码（如ref_area='IDN'）进行筛选以聚焦特定国家；针对单一指标按时间排序以绘制时间序列图表；或透视生成国家×年份的矩阵以进行横截面比较。数据集中包含的离散维度（如sex）还可用于分组分析。通过结合出处信息与引用说明，使用者可便捷地将该数据集成入劳动经济学、区域发展等领域的量化研究。

背景与挑战

背景概述

在劳动经济学与可持续发展目标（SDGs）研究领域，劳动资源利用不足的精确测度对于制定有效的就业政策至关重要。国际劳工组织（ILO）作为全球劳动统计的权威机构，通过其ILOSTAT数据库系统性地收集并协调各国劳动力调查数据。在此背景下，Electric Sheep Asia于2025年将ILOSTAT中聚焦亚洲地区的潜在劳动力数据重新封装为机器学习就绪的数据集，涵盖1999年至2025年间27个亚洲国家的1,843条观测值。该数据集的核心研究问题在于通过性别与城乡地域维度揭示亚洲劳动力未被充分利用的复杂结构，为区域劳动经济学分析、国际合作基准比较以及基于数据的政策模拟提供了标准化、可复现的量化基础，显著促进了劳动统计在计算社会科学与时间序列预测领域的应用。

当前挑战

该数据集所应对的领域核心挑战在于，传统劳动力统计指标（如失业率）往往无法充分捕捉潜在劳动力群体——那些虽有工作意愿但因各种条件限制（如未积极求职或时间约束）而未被纳入标准统计口径的个体。这一概念在亚洲劳动力市场中尤为突出，因为非正规就业、城乡流动性及性别差异使得潜在劳动力识别极为复杂。此外，数据集构建过程中面临多重挑战：不同国家劳动力调查在调查方法、采样框架及分类标准（如ICLS定义）上的异质性要求精细的数据协调；年度频率限制与部分国家序列中断（如标注为'B'的断裂标志）带来了时间序列建模中的缺失值难题；而性别与地域维度的细粒度分解虽增强了分析能力，却也加剧了数据稀疏性与多源整合的质量控制难度。

常用场景

经典使用场景

该数据集收录了1999至2025年间27个亚洲国家的潜在劳动力数据，按性别及城乡地域细分，共计1843条观测值。经典使用场景集中于劳动经济学与人口统计学交叉领域，研究者可借此构建面板数据模型，纵向追踪各国潜在劳动力规模的时序演变，或横向对比不同国家间性别与空间维度的劳动力参与差异。通过整合ILOSTAT统一规范的定义与分类，该数据集支持对亚洲地区劳动力闲置状况的精准刻画，为理解未充分就业群体的结构性特征提供量化基础。

实际应用

实际应用中，该数据集是政策制定机构与国际组织评估亚洲劳动力市场健康度的关键工具。各国劳工部门可据此识别男性和女性在城乡区域间的潜在劳动力分布差异，制定差异化的职业培训与就业促进计划。国际发展机构则借助该数据进行跨周期劳动力供给预测，协助区域发展项目设计。此外，该数据也被融入机器学习回归与时序预测模型，用于自动生成劳动力市场监测报告，提升决策响应的时效性。

衍生相关工作

该数据集衍生出诸多经典工作，涵盖劳动经济学实证分析与数据科学交叉领域。学者基于ILOSTAT面板数据开发了多种劳动力闲置指数构建方法，并通过性别与地域分解模型检验经济发展对潜在劳动力的异质性冲击。在数据工程层面，Electric Sheep Asia的标准化重封装流程催生了针对亚洲区域的ML-Ready数据管线设计范式，后续研究者参照其方法论将更多ILO指标转化为可复用的Parquet格式，推动了区域劳动统计数据的开放科学与可复现研究生态建设。