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

--- 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 - other-measures-of-labour-underutilization - ilo - labour - employment pretty_name: "Potential labour force by sex, age and education (thousands) | Asia (ILOSTAT)" --- # Potential labour force by sex, age and education (thousands) | Asia (ILOSTAT) 🌏 **24,606 observations** · **32 Asia countries** · **1999–2025** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **24,606 observations** of `Other measures of labour underutilization` data across **32 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_AGE_EDU_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_AGE_EDU_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 32 Asia countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `CYP` | 2,509 | 1999 | 2024 | | `VNM` | 1,942 | 2010 | 2024 | | `TUR` | 1,826 | 2000 | 2013 | | `KOR` | 1,682 | 2000 | 2019 | | `THA` | 1,613 | 2010 | 2024 | | `PSE` | 1,519 | 2012 | 2025 | | `ARM` | 1,135 | 2007 | 2018 | | `LKA` | 1,133 | 2010 | 2024 | | `IDN` | 1,072 | 2015 | 2023 | | `BRN` | 956 | 2014 | 2024 | | `JOR` | 945 | 2017 | 2024 | | `PHL` | 669 | 2003 | 2023 | | `GEO` | 668 | 2019 | 2024 | | `MNG` | 641 | 2019 | 2024 | | `BGD` | 634 | 2013 | 2024 | | ... | _17 more countries_ | | | ## Indicators (sample) - `EIP_XPLF_SEX_AGE_EDU_NB` — Potential labour force by sex, age and education (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_AGE_EDU_NB` | | `indicator.label` | `string` | Indicator name in English | `Potential labour force by sex, age an…` | | `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 | `EDU_AGGREGATE_TOTAL` | | `classif2.label` | `string` | — | `Education (Aggregate levels): 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-age-edu-nb-potential-labour-force-by-sex-age-and-education-th") 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_AGE_EDU_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EIP_XPLF_SEX_AGE_EDU_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EIP_XPLF_SEX_AGE_EDU_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_eip_xplf_sex_age_edu_nb_potential_labour_force_by_sex_age_and_education_th_2025, title = {Potential labour force by sex, age and education (thousands) | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EIP_XPLF_SEX_AGE_EDU_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-eip-xplf-sex-age-edu-nb-potential-labour-force-by-sex-age-and-education-th}} } ``` ## 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_AGE_EDU_NB_This dataset, titled Potential labour force by sex, age and education (thousands) | Asia (ILOSTAT), contains 24,606 observations across 32 Asia countries, spanning the years 1999 to 2025. It focuses on the Other measures of labour underutilization indicator, specifically EIP_XPLF_SEX_AGE_EDU_NB, which measures the potential labour force by sex, age, and education in thousands. The data is sourced from the International Labour Organization (ILO)s ILOSTAT database, retrieved via API and filtered for Asia country codes, and repackaged for machine learning readiness. The dataset is in tabular format with columns including country code, country name, data source, indicator, sex, age classification, education classification, observation year, observed value, observation status, and related notes, supporting tasks like tabular classification, regression, and time-series forecasting. Data quality notes: it is annual frequency, uses ILO-selected best source for multiple sources, and disaggregation columns are non-null only when the indicator publishes breakdowns. It is suitable for labour market analysis, economic research, and predictive modeling, and can be loaded using the HuggingFace datasets library.

搜集汇总

数据集介绍

构建方式

本数据集源自国际劳工组织（ILO）的核心统计数据库ILOSTAT，聚焦亚洲地区劳动力市场中的潜在劳动力群体。研究者通过ILOSTAT的REST API直接抓取了指标代码为EIP_XPLF_SEX_AGE_EDU_NB的原始数据，并依据ISO 3166-1 alpha-3标准，将地理范围限定至32个亚洲国家。数据采集后，经由Electric Sheep Asia团队进行标准化清洗与整合，遵循ILO依据国际劳工统计学家会议（ICLS）定义对微观调查数据进行协调的方法论，确保了各来源数据的可比性与一致性。最终，该数据集以Parquet格式封装，并托管于HuggingFace平台，便于学者与开发者直接调用。

使用方法

使用者可直接通过HuggingFace的`datasets`库以`load_dataset()`函数一行加载数据，并转换为Pandas DataFrame进行灵活操作。例如，可按国际标准国家代码（如'IDN'代表印度尼西亚）筛选特定国家的子集，或针对单一指标按年份排序，绘制时间序列趋势图。对于跨国家比较，可利用数据透视表功能将数据重塑为国家与年份的矩阵，便于进行面板数据分析或回归建模。研究中若引用该数据集，需同时标注ILO原始出处及Electric Sheep Asia的再封装版权信息，遵守CC-BY-4.0许可协议。

背景与挑战

背景概述

该数据集由国际劳工组织（ILO）统计司维护，经由Electric Sheep Asia于2025年重新打包并发布在HuggingFace平台上，聚焦亚洲32个国家1999至2025年间按性别、年龄和教育程度划分的潜在劳动力规模（单位：千人）。潜在劳动力是衡量劳动力未充分利用的关键指标，涵盖那些虽未积极求职但有就业意愿或受限于其他因素的适龄人群。该数据集为研究亚洲地区劳动力市场结构性失衡、教育回报率差异以及性别不平等提供了标准化、跨国的时序统计数据，成为劳动经济学、发展经济学与公共政策分析领域的重要基础资源。

当前挑战

该数据集面临多重挑战。首先，在领域问题层面，潜在劳动力的界定依赖于ICLS标准，但不同国家在调查问卷设计与数据采集方法上存在差异，导致跨国比较的可比性受限。其次，构建过程中需应对数据来源多样性的问题，包括劳动力调查、家庭收入调查与行政记录等，这些来源在抽样框、调查频率与时间序列连贯性上不统一。此外，部分观测值被标记为“不可靠”或存在序列中断，反映出原始数据质量控制的复杂性。对研究人员而言，处理这些质量标识并合理进行数据清洗与插补是开展稳健分析的前提。

常用场景

经典使用场景

该数据集记录了1999年至2025年间亚洲32个国家潜在劳动力人口按性别、年龄与教育程度分层的年度观测数据，总计24,606条记录。在劳动经济学与人口学领域，研究者常将其用于构建多维度劳动力供给模型，通过时间序列分析揭示不同教育群体在劳动力市场边缘的动态变化。数据集的结构化特性使其成为分类与回归任务的理想素材，例如预测特定性别或年龄组的潜在劳动力规模，或评估教育水平对劳动参与意愿的调节效应。此外，时空面板数据的特性支持跨国家比较研究，便于探索亚洲各国在劳动力利用不足模式上的异同。

解决学术问题

该数据集的核心学术价值在于填补了亚洲区域‘潜在劳动力’这一被忽视的劳动力利用不足指标的量化空白。传统劳动力统计多聚焦于失业率，而潜在劳动力——即有工作意愿但因各种原因未积极求职的人群——常被排除在标准劳动力之外。这一数据集通过提供按性别、年龄与教育程度精细分层的数据，使得研究者能够从更广阔的视角审视劳动力市场的结构性摩擦。它帮助学者识别哪些群体更容易被边缘化，例如低教育女性或青年，从而为劳动力市场制度、教育改革与包容性增长策略提供实证基础。其对亚洲32国的覆盖范围更是推动了区域间比较研究的深化。

实际应用

在实际政策制定中，该数据集为国际组织与各国劳动部门提供了监测和评估劳动力市场包容性的关键工具。通过分析潜在劳动力的组成与演变趋势，政府部门可以识别出社会保护体系未能充分覆盖的脆弱群体，进而优化职业培训计划、就业服务资源配置以及社会保障制度设计。例如，若数据揭示高教育青年女性在潜在劳动力中占比上升，决策者可据此调整职业中介服务或推广灵活就业模式。对于企业而言，这一数据集有助于洞察区域人力资本储备的动态，支持人力资源规划与投资选址决策；非政府组织亦可利用它倡导针对性的就业支持议程。

数据集最近研究

最新研究方向

该数据集聚焦于亚洲地区潜在劳动力市场，通过性别、年龄与教育程度的三维解构，为劳动经济学与可持续发展研究提供了高分辨率的时间序列数据。在人工智能与宏观经济预测交叉的前沿领域，研究者正利用此类细粒度指标构建劳动力参与率动态模型，以捕捉隐性失业与就业不足的复杂演化规律。结合ILO对劳动力利用不足指标的统一方法论，该数据能支持针对亚洲新兴经济体劳动市场韧性的多国比较分析，并为联合国2030年议程中体面工作目标的量化评估奠定实证基础。