electricsheepasia/asia-ilo-eip-xplf-sex-edu-mts-nb-potential-labour-force-by-sex-education-and-marita

--- 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, education and marital status (thousands) | Asia (ILOSTAT)" --- # Potential labour force by sex, education and marital status (thousands) | Asia (ILOSTAT) 🌏 **8,591 observations** · **28 Asia countries** · **1999–2025** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **8,591 observations** of `Other measures of labour underutilization` data across **28 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_EDU_MTS_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_EDU_MTS_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 28 Asia countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `CYP` | 801 | 1999 | 2020 | | `VNM` | 697 | 2010 | 2024 | | `THA` | 660 | 2010 | 2024 | | `TUR` | 658 | 2000 | 2013 | | `KOR` | 636 | 2000 | 2019 | | `PSE` | 574 | 2012 | 2025 | | `LKA` | 482 | 2010 | 2024 | | `ARM` | 409 | 2007 | 2018 | | `BRN` | 389 | 2014 | 2024 | | `IDN` | 360 | 2015 | 2023 | | `JOR` | 360 | 2017 | 2024 | | `MNG` | 249 | 2019 | 2024 | | `BGD` | 246 | 2013 | 2024 | | `PHL` | 242 | 2003 | 2023 | | `AFG` | 227 | 2014 | 2021 | | ... | _13 more countries_ | | | ## Indicators (sample) - `EIP_XPLF_SEX_EDU_MTS_NB` — Potential labour force by sex, education and marital status (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_EDU_MTS_NB` | | `indicator.label` | `string` | Indicator name in English | `Potential labour force by sex, educat…` | | `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 | `MTS_AGGREGATE_TOTAL` | | `classif2.label` | `string` | — | `Marital status (Aggregate): Total` | | `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) | `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_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-edu-mts-nb-potential-labour-force-by-sex-education-and-marita") 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_EDU_MTS_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EIP_XPLF_SEX_EDU_MTS_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EIP_XPLF_SEX_EDU_MTS_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_eip_xplf_sex_edu_mts_nb_potential_labour_force_by_sex_education_and_marita_2025, title = {Potential labour force by sex, education and marital status (thousands) | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EIP_XPLF_SEX_EDU_MTS_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-eip-xplf-sex-edu-mts-nb-potential-labour-force-by-sex-education-and-marita}} } ``` ## 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_EDU_MTS_NB_This dataset contains 8,591 observations of Potential labour force by sex, education and marital status (thousands) data across 28 Asia countries, spanning 1999–2025, covering 1 distinct indicator. It is sourced from ILOSTAT, the ILOs central statistics database, with harmonized data based on International Conference of Labour Statisticians definitions. The dataset includes columns such as country code, sex disaggregation, education and marital status classifications, year, and observed values, and is designed for tabular classification, regression, and time-series forecasting tasks.

搜集汇总

数据集介绍

构建方式

该数据集依托国际劳工组织（ILO）的ILOSTAT REST API，通过调取指标代码为EIP_XPLF_SEX_EDU_MTS_NB的原始数据，并依据ISO 3166-1 alpha-3国家代码筛选出亚洲28个国家的观测记录构建而成。ILOSTAT依据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协调统一，数据来源均标注于source.label字段中，确保了数据的可追溯性。最终数据集包含8,591条观测，时间跨度覆盖1999年至2025年，由Electric Sheep Asia进行整理与封装。

特点

该数据集聚焦于亚洲区域潜在劳动力这一劳动利用不足的测度指标，按性别、教育程度及婚姻状况进行详细分层，提供了SEX_T（总计）、SEX_M（男性）、SEX_F（女性）三种性别维度以及多种教育与婚姻状态的分类组合。数据以年度频率呈现，并采用ILO优选的最佳数据源，确保同一国家与年份下数据的一致性。此外，数据集还涵盖了观测状态标志（如不可靠、暂定）及系列中断、非标准教育层级等注释信息，有助于用户评估数据质量。

使用方法

用户可通过HuggingFace Datasets库的load_dataset()函数一步加载数据，并将其转换为Pandas DataFrame进行后续分析。示例代码展示了如何按国家（如印度尼西亚）进行筛选，或对特定指标进行时间序列可视化。此外，利用pivot_table方法可将数据重塑为国家×年份的矩阵形式，便于开展跨国家、跨时期的比较研究。数据集字段设计清晰，ref_area、sex、classif1等列提供了灵活的过滤与分组操作入口。

背景与挑战

背景概述

在全球劳动力市场动态日益复杂的背景下，准确衡量劳动力未被充分利用的程度成为劳动经济学研究的核心议题。该数据集由国际劳工组织（ILO）于2025年构建，并由Electric Sheep Asia团队重新整理发布，聚焦于亚洲地区按性别、教育程度和婚姻状况分层的潜在劳动力规模（以千人为单位），涵盖28个亚洲国家及地区、1999年至2025年的8,591条观测记录。数据源自ILOSTAT——全球劳动统计的权威数据库，采用国际劳工统计学家会议（ICLS）的统一定义，整合了各国劳动力调查、家庭收入调查等多元数据源。该数据集通过精细的人口统计分层，为研究亚洲劳动力市场中的结构性失业、就业不足及隐性劳动力储备提供了关键实证基础，对评估区域发展政策、性别平等及教育回报等议题具有重要影响力。

当前挑战

该数据集所应对的领域挑战在于：传统失业率指标往往低估劳动力市场真实闲置程度，而潜在劳动力（包括那些虽未积极求职但愿意工作的人群）的度量能更全面地反映劳动资源错配问题，尤其对于亚洲新兴经济体与转型国家中非正规就业普遍、女性劳动参与波动显著的现象，按性别、教育和婚姻状况的分层分析尤为必要。在构建过程中，面临多重数据挑战：各来源国调查方法与问卷设计差异导致数据可比性需通过ILO的标准化流程协调；教育水平与婚姻状况的分类体系因国家而异，需通过注释字段记录非标准类别；部分年度观测值因调查覆盖不全或方法修订而被标记为不可靠，数据质量需依赖状态标记字段（如'U'）进行甄别；多源数据在相同国家与年份冲突时需采用'最佳来源'遴选规则，但这一过程可能引入主观偏差；此外，年度频率数据无法捕捉季度或月度的短期波动，限制了对劳动力市场实时动态的解析能力。

常用场景

经典使用场景

该数据集聚焦于亚洲地区潜在劳动力人口按性别、教育程度及婚姻状况的细分统计，为劳动经济学与人口学领域的经典研究提供了坚实的数据基石。研究者常将其用于分析亚洲不同国家在劳动力市场边缘群体的结构性特征，量化性别与教育背景对潜在劳动力参与率的影响，并追踪1999至2025年间此指标的时空演变规律。通过时序与面板数据建模，可系统揭示婚姻状况与教育分层如何共同塑造劳动供给弹性，为理解亚洲经济体未充分利用劳动力资源的动态格局奠定基础。

实际应用

在实际应用中，该数据集为国际组织及亚洲各国政府制定包容性就业政策提供了量化支撑。劳动统计部门可据此识别教育性别差距对潜在劳动力池的制约，婚姻法律改革影响下女性隐性失业的暗涌，以及青年群体在转型经济体中的边缘化风险。企业人力资源规划亦可借此前瞻性预判特定教育背景与婚姻状态人群的劳动力供给弹性，优化区域人才布局。其对亚洲28国的跨时期覆盖，使跨国比较成为可能，助力决策者瞄准最脆弱的劳动力群体。

衍生相关工作

该数据集催生了多项具有影响力的衍生研究，包括基于ILOSTAT潜在劳动力指标构建的亚洲劳动力闲置程度综合指数，以及性别-教育-婚姻三维交互下的马尔可夫状态转移模型。后续工作有利用该数据训练稀疏时间序列预测模型，预测后疫情时代亚洲各国潜在劳动力规模的路径依赖效应；也有研究将其与GDP、教育支出等宏观变量融合，构建劳动力市场韧性评价框架。此外，该数据集的规范化处理流程为其他地区类似数据的机器学习兼容性重整提供了可参照的范式。