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

--- 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 rural / urban areas (thousands) | Asia (ILOSTAT)" --- # Potential labour force by sex, age and rural / urban areas (thousands) | Asia (ILOSTAT) 🌏 **15,464 observations** · **27 Asia countries** · **1999–2025** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **15,464 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_AGE_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_AGE_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` | 1,884 | 1999 | 2024 | | `PHL` | 1,404 | 2007 | 2023 | | `VNM` | 1,393 | 2007 | 2024 | | `TUR` | 1,125 | 2000 | 2013 | | `PSE` | 1,068 | 2012 | 2022 | | `THA` | 1,059 | 2010 | 2024 | | `LKA` | 886 | 2010 | 2024 | | `ARM` | 876 | 2007 | 2018 | | `BRN` | 667 | 2014 | 2024 | | `IDN` | 648 | 2015 | 2023 | | `JOR` | 607 | 2017 | 2024 | | `GEO` | 486 | 2019 | 2024 | | `MNG` | 456 | 2019 | 2024 | | `MMR` | 353 | 2015 | 2020 | | `TLS` | 346 | 2010 | 2022 | | ... | _12 more countries_ | | | ## Indicators (sample) - `EIP_XPLF_SEX_AGE_GEO_NB` — Potential labour force by sex, age 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_AGE_GEO_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 | `GEO_COV_NAT` | | `classif2.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) | `U` | | `obs_status.label` | `string` | — | `Unreliable` | | `note_classif` | `string` | — | `—` | | `note_classif.label` | `string` | — | `—` | | `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-geo-nb-potential-labour-force-by-sex-age-and-rural-urban") 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_GEO_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EIP_XPLF_SEX_AGE_GEO_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EIP_XPLF_SEX_AGE_GEO_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_eip_xplf_sex_age_geo_nb_potential_labour_force_by_sex_age_and_rural_urban_2025, title = {Potential labour force by sex, age 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_AGE_GEO_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-eip-xplf-sex-age-geo-nb-potential-labour-force-by-sex-age-and-rural-urban}} } ``` ## 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_GEO_NB_This dataset contains 15,464 observations of Other measures of labour underutilization data across 27 Asia countries, spanning 1999–2025, with a focus on the indicator Potential labour force by sex, age and rural / urban areas (thousands). The data is sourced from the International Labour Organization (ILO) ILOSTAT database, retrieved via API and filtered for Asian countries. It is structured in tabular format with columns for country codes, year, sex, age classification, rural/urban area, observed values, and data quality flags, suitable for tasks such as tabular classification, regression, or 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权威统计数据库，由Electric Sheep Asia团队精心整合与再封装而成。研究人员通过ILOSTAT的REST API直接提取原始指标数据，并依据亚洲地区的ISO3国家代码进行精准筛选，最终汇聚了来自27个亚洲国家1999年至2025年间的观测记录。ILOSTAT自身遵循国际劳工统计学家会议（ICLS）的定义标准，对各国劳动力调查原始微观数据进行严格的统一协调处理，并在数据集中通过'source.label'列明确标注数据来源，以保障数据的可追溯性与可靠性。最终生成的结构化数据集包含15,464条观测值，以高效且易于处理的格式呈现。

使用方法

研究人员可通过HuggingFace的`datasets`库极为便捷地加载此数据集，仅需一行代码`load_dataset()`即可将数据读入内存。数据以Pandas DataFrame形式呈现，支持灵活的数据操作，例如可通过`ref_area`列按特定国家（如印度尼西亚）进行筛选，用于聚焦国别分析。对于时间序列分析，研究者可使用`sort_values`按年份排序数据，并利用`plot`方法直观展示特定指标随时间的变化趋势。此外，通过`pivot_table`功能，能够轻松将数据重塑为国家×年份的宽表矩阵，便于进行面板数据回归或横向比较研究，极大提升了从数据获取到分析转化的工作效率。

背景与挑战

背景概述

该数据集由国际劳工组织（ILO）统计司基于ILOSTAT数据库构建，经Electric Sheep Asia于2025年重新打包并发布至HuggingFace平台，聚焦于亚洲地区潜在劳动力人口按性别、年龄及城乡区域划分的统计数据（单位：千人）。核心研究问题在于揭示亚洲27个国家1999至2025年间劳动力未充分利用的潜在规模与结构性特征，为劳动经济学、人口迁移及可持续发展目标（SDG）相关研究提供基础数据支撑。作为ILO官方统计体系的延伸，该数据集凭借其权威来源、跨年度面板结构及细粒度分类标签，已成为亚洲区域劳动力市场分析的重要参考资源，尤其在评估隐性失业、就业不足等非标准劳动力指标方面具有显著影响力。

当前挑战

数据集所应对的领域挑战在于传统失业率指标难以全面捕捉劳动力市场的潜在闲置状态，而潜在劳动力人口作为衡量劳动未充分利用的关键维度，其统计口径的跨国一致性、分类粒度以及时序数据的断裂问题亟需解决。构建过程中，ILO需协调来自各国劳动力调查、家庭收入调查等多源异构数据的归一化处理，依据国际劳工统计学家会议（ICLS）定义进行标准转化，同时面对亚洲国家间统计能力差异导致的数据缺失、方法论修订引发的序列断裂（如指标标签中标注的“Break in series”），以及城乡分类与年龄分组的可比性约束，这些均对数据的精确建模与跨时期比较构成严峻挑战。

常用场景

经典使用场景

在劳动经济学与人口统计学交汇的学术疆域中，该数据集作为国际劳工组织（ILO）权威统计数据的亚洲子集，最经典的应用场景是构建潜在劳动力规模的时空演化图谱。研究者可依托其27个国家、1999至2025年的年度观测值，结合性别、年龄与城乡地域的三重分类维度，精准刻画亚洲不同区域劳动力蓄水池的动态变迁。该数据尤其适用于时间序列分析与面板数据回归，用以揭示经济发展周期、人口结构转型与潜在劳动力供给之间的内在关联，为劳动参与率预测模型提供坚实的实证基底。

解决学术问题

该数据集精准回应了亚洲劳动力市场中‘隐性失业’与‘潜在劳动力’量化测度的学术难题。通过整合ILO统一规范的时序指标‘按性别、年龄和城乡分列的潜在劳动力人数’，研究可突破传统失业率统计的局限性，深入探讨劳动力闲置的多维形态——即那些虽有就业意愿却因各种障碍未积极求职的群体。此举不仅丰富了劳动力利用不充分的理论框架，更使跨国比较与纵向追踪成为可能，为制定包容性就业政策提供了关键的数据锚点与实证依据。

实际应用

在宏观经济决策与社会治理层面，该数据集展现出显著的实践价值。政府部门与国际组织可据此评估不同性别、年龄层及城乡区域的劳动力储备状况，进而优化就业促进战略与技能培训资源的区域配置。例如，通过监测城乡潜在劳动力的分布差异，可为乡村振兴中的人力资本开发提供量化支持；基于性别维度的数据分析则有助于识别女性劳动参与的制度性障碍，推动性别平等的职场政策落地。数据集还可嵌入经济预警系统，作为研判就业市场韧性的辅助指标。

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人口的多维解构，涵盖1999至2025年间27个国家的性别、年龄及城乡分布信息，为后疫情时代亚洲劳动力市场的结构性变迁研究提供了关键数据支撑。当前，结合ILOSTAT统计标准与高频时间序列特征，前沿研究正深入探讨数字经济转型下非正规就业与隐性失业的测量方法，并将该数据与家庭调查、卫星夜间灯光数据融合，构建劳动力利用不足的预测模型，以回应区域经济绿色转型与人口老龄化叠加带来的就业质量评估挑战。此类跨学科分析不仅助力完善国际劳工组织定义的劳动力闲置指标，更为亚洲发展中经济体制定精准的积极劳动力市场政策提供了实证依据。