electricsheepasia/asia-ilo-eip-xplf-sex-dsb-nb-potential-labour-force-by-sex-and-disability-statu

--- 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: "Potential labour force by sex and disability status (thousands) | Asia (ILOSTAT)" --- # Potential labour force by sex and disability status (thousands) | Asia (ILOSTAT) 🌏 **491 observations** · **16 Asia countries** · **2007–2024** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **491 observations** of `Other measures of labour underutilization` data across **16 Asia countries**, spanning **2007–2024**, 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_DSB_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_DSB_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 16 Asia countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `ARM` | 104 | 2007 | 2018 | | `IDN` | 63 | 2016 | 2023 | | `MNG` | 54 | 2019 | 2024 | | `LKA` | 52 | 2018 | 2024 | | `PSE` | 45 | 2018 | 2022 | | `BGD` | 27 | 2022 | 2024 | | `IRQ` | 27 | 2007 | 2021 | | `AFG` | 26 | 2017 | 2021 | | `TLS` | 25 | 2016 | 2022 | | `KHM` | 15 | 2012 | 2019 | | `LAO` | 12 | 2017 | 2022 | | `LBN` | 9 | 2019 | 2019 | | `TJK` | 9 | 2016 | 2016 | | `MDV` | 9 | 2019 | 2019 | | `PAK` | 8 | 2021 | 2021 | | ... | _1 more countries_ | | | ## Indicators (sample) - `EIP_XPLF_SEX_DSB_NB` — Potential labour force by sex and disability 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_DSB_NB` | | `indicator.label` | `string` | Indicator name in English | `Potential labour force by sex and dis…` | | `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.) | `DSB_STATUS_TOTAL` | | `classif1.label` | `string` | — | `Disability status: 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_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-dsb-nb-potential-labour-force-by-sex-and-disability-statu") 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_DSB_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EIP_XPLF_SEX_DSB_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EIP_XPLF_SEX_DSB_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_eip_xplf_sex_dsb_nb_potential_labour_force_by_sex_and_disability_statu_2024, title = {Potential labour force by sex and disability status (thousands) | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2024}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EIP_XPLF_SEX_DSB_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-eip-xplf-sex-dsb-nb-potential-labour-force-by-sex-and-disability-statu}} } ``` ## 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_DSB_NB_This dataset contains 491 observations of Other measures of labour underutilization data across 16 Asia countries, spanning 2007–2024, covering 1 distinct indicator: Potential labour force by sex and disability status (thousands). It is sourced from the International Labour Organization (ILO) ILOSTAT database, repackaged and normalized for machine learning use, and provided in tabular format 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中央统计数据库，通过其REST API接口直接抽取了指标代码为EIP_XPLF_SEX_DSB_NB的原始数据。抽取后，数据依据亚洲ISO3国家代码进行地理范围过滤，并由Electric Sheep Asia团队进行重新封装与标准化处理。构建过程中，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协调，确保了跨国的可比性。最终形成了包含16个亚洲国家、2007至2024年间共491条观测记录的结构化表格数据集。

使用方法

用户可通过HuggingFace的datasets库便捷加载该数据集，一行代码即可将其转换为Pandas DataFrame进行后续分析。典型应用包括：按国家代码筛选特定国家的子集进行纵向分析；对单一指标进行时间序列排序与可视化；利用透视表功能构建以年份为行、国家为列的矩阵，便于进行跨国的面板数据比较。数据以cc-by-4.0许可证发布，使用时需同时引用ILO原始来源及Electric Sheep Asia的重新封装版本。

背景与挑战

背景概述

劳动力市场的边缘群体，尤其是残障人士，其潜在劳动参与状况长期缺乏系统性的量化分析，这严重制约了包容性就业政策的制定与评估。该数据集由国际劳工组织（ILO）统计司于2024年创建，并由Electric Sheep Asia重新整理发布，旨在填补亚洲地区残障群体潜在劳动力数据的关键空白。其核心研究问题聚焦于通过性别的交叉维度，精确测算十六个亚洲国家在2007年至2024年间未被传统失业统计充分反映的隐性劳动力资源。该数据集依托ILO的ILOSTAT权威数据库，通过标准化流程整合各国劳动力调查数据，为区域劳动经济学、残障社会学及可持续发展目标（SDGs）中体面工作指标的监测提供了坚实的数据基础，显著提升了学界对亚洲非正规及边缘劳动力市场的理解深度。

当前挑战

该数据集所解决的领域核心挑战在于，传统失业率指标无法捕捉“潜在劳动力”——即那些虽未积极求职但因残障等结构性障碍而退出市场的群体。构建过程中，首要挑战是应对十六国间迥异的劳动力调查口径与质量控制体系，ILO需依据国际劳工统计学家会议标准进行复杂的跨国产出协调。其次，数据碎片化严重，多数国家仅提供稀疏的时间序列，部分年份或国家的观测值存在大量缺失，且标记为“不可靠”的观测状态增加了模型拟合的难度。此外，性别与残障状态交叉分组后的样本量骤降至491条，分析维度受限，使得构建稳健的时序预测或因果推断模型面临严峻的小样本与高噪声挑战。

常用场景

经典使用场景

在劳动经济学与政策研究领域，该数据集最经典的使用场景是开展区域劳动力潜在供给的跨国比较分析。研究者能够基于亚洲16国2007至2024年的纵向观测数据，按性别和残疾状况两大维度对潜在劳动力群体进行精准量化。通过时间序列建模，可系统评估各国潜在劳动力规模的演变轨迹与结构性特征，揭示不同社会经济背景下残疾人群体的劳动参与潜力，为理解亚洲地区劳动力市场的异质性提供了坚实的数据基础。

解决学术问题

该数据集精准回应了学术界在劳动力资源测算中普遍面临的残疾群体数据缺失与维度单一的问题。传统的劳动力统计往往忽略潜在劳动力这一边缘群体，而本数据通过纳入'其他劳动力利用不足指标'，使研究得以深入探讨残疾状态与性别如何共同塑造个体进入劳动市场的障碍。其意义在于推动了包容性劳动统计指标的标准化，为测量可持续发展目标中体面工作的实现程度提供了关键证据，促进了劳动经济学向精细化与多元化研究范式的转型。

实际应用

在实际应用层面，该数据集为亚洲各国政府及国际组织制定精准就业政策提供了量化依据。决策者可依据分性别与残疾状况的潜在劳动力规模，识别就业服务覆盖的薄弱环节，优化职业培训资源配置，并设计针对性的社区融合方案。对于企业人力资源管理者而言，该数据可用于评估特定地区残疾求职者的潜在供应规模，从而完善多元化招聘策略。此外，非政府组织可借助这些长期记录监测权益保障政策的落地效果，推动更具包容性的劳动力市场生态建设。

数据集最近研究

最新研究方向

在全球劳动力市场包容性议题持续升温的背景下，该数据集聚焦于亚洲区域潜在劳动力人口中按性别与残疾状态划分的统计特征，为探究残疾群体在劳动力参与中的结构性障碍提供了量化基础。当前前沿研究倾向于利用此类细粒度时序数据，结合交叉性分析框架，揭示性别与残疾状态对劳动力边缘化程度的叠加效应，并跟踪各国在实现联合国可持续发展目标“体面工作”方面的差异化进展。该数据集涵盖16个亚洲国家近二十年的观察值，可支撑面板数据建模与政策干预效果评估，有助于推动从宏观就业总量向弱势群体劳动参与质量的精细化研究转向。