electricsheepasia/asia-ilo-emp-nifl-age-ec2-rt-informal-employment-rate-by-age-and-economic-activ

--- 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 - informal-economy - ilo - labour - employment pretty_name: "Informal employment rate by age and economic activity - ISIC level 2 (%) | Asia (ILOSTAT)" --- # Informal employment rate by age and economic activity - ISIC level 2 (%) | Asia (ILOSTAT) 🌏 **30,465 observations** · **27 Asia countries** · **2004–2025** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **30,465 observations** of `Informal economy` data across **27 Asia countries**, spanning **2004–2025**, 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=EMP_NIFL_AGE_EC2_RT) - **Publisher:** International Labour Organization (ILO) - **License:** [cc-by-4.0](https://creativecommons.org/licenses/by/4.0/) - **Topic:** Informal economy ## Methodology Data pulled directly from the ILOSTAT REST API at `https://rplumber.ilo.org/data/indicator?id=EMP_NIFL_AGE_EC2_RT` 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 | |---------|-----:|-----------:|----------:| | `TUR` | 4,478 | 2004 | 2024 | | `VNM` | 3,314 | 2007 | 2024 | | `IND` | 2,572 | 2005 | 2025 | | `LKA` | 2,568 | 2010 | 2024 | | `PSE` | 2,523 | 2010 | 2025 | | `THA` | 2,094 | 2014 | 2024 | | `MNG` | 1,980 | 2006 | 2024 | | `KGZ` | 1,979 | 2012 | 2023 | | `PAK` | 1,901 | 2009 | 2025 | | `BGD` | 1,323 | 2010 | 2024 | | `JOR` | 977 | 2017 | 2023 | | `MMR` | 977 | 2015 | 2020 | | `BRN` | 768 | 2014 | 2024 | | `GEO` | 747 | 2019 | 2024 | | `KHM` | 337 | 2012 | 2019 | | ... | _12 more countries_ | | | ## Indicators (sample) - `EMP_NIFL_AGE_EC2_RT` — Informal employment rate by age and economic activity - ISIC level 2 (%) ## 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 | `EMP_NIFL_AGE_EC2_RT` | | `indicator.label` | `string` | Indicator name in English | `Informal employment rate by age and e…` | | `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 | `EC2_ISIC4_TOTAL` | | `classif2.label` | `string` | — | `Economic activity (ISIC-Rev.4), 2 dig…` | | `time` | `int64` | Observation year | `2021` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `86.135` | | `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…` | ## 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-emp-nifl-age-ec2-rt-informal-employment-rate-by-age-and-economic-activ") 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"] == "EMP_NIFL_AGE_EC2_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_NIFL_AGE_EC2_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_NIFL_AGE_EC2_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_emp_nifl_age_ec2_rt_informal_employment_rate_by_age_and_economic_activ_2025, title = {Informal employment rate by age and economic activity - ISIC level 2 (%) | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_NIFL_AGE_EC2_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-emp-nifl-age-ec2-rt-informal-employment-rate-by-age-and-economic-activ}} } ``` ## 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 2026-05-26 via the Electric Sheep pipeline. Source URL: https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_NIFL_AGE_EC2_RT_This dataset contains 30,465 observations of informal economy data across 27 Asia countries, spanning 2004–2025, covering 1 distinct indicator: Informal employment rate by age and economic activity - ISIC level 2 (%). It is sourced from ILOSTAT, the ILOs central statistics database, via API and filtered to Asia ISO3 country codes, focusing on the informal economy topic. The dataset includes columns such as country code, country name, source, indicator, classification variables (e.g., age and economic activity), time, observed value, etc., and is suitable for tabular classification, regression, or time-series forecasting tasks.

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，原始数据通过ILOSTAT REST API接口获取，并依据亚洲ISO3国家代码进行筛选。ILOSTAT采用国际劳工统计学家会议（ICLS）标准定义对各国劳动力调查、家庭收入调查等微观数据进行统一协调，数据经过Electric Sheep Asia重新打包，以Parquet格式发布，涵盖27个亚洲国家2004至2025年间的30465条观测记录，每条记录均标注数据来源以便追溯。

特点

数据集聚焦于亚洲地区非正规就业率，以年龄和ISIC二级经济活动为分类维度，包含27个国家跨越二十余年的面板数据。其特点在于以表格形式呈现，兼具时间序列与截面维度，支持分类、回归及时间序列预测任务。数据包含丰富的元信息列，如来源标签、观测状态、指标注释等，便于识别数据质量与口径变动。数据集以英语单语发布，采用CC-BY-4.0许可，规模适中，适用于劳动经济学与区域发展研究。

使用方法

研究者可通过HuggingFace的datasets库以load_dataset()函数直接加载数据集，并转换为Pandas数据框进行分析。常见操作包括按国家代码筛选特定经济体的时间序列、针对单一指标绘制趋势图，或通过透视表构建国家与年份的矩阵视图。数据集亦支持直接用于机器学习建模，如非正规就业率的分类预测或回归分析。使用时应留意观测状态标志及指标注释，以识别数据修订或方法变更带来的序列断裂。

背景与挑战

背景概述

国际劳工组织（ILO）长期致力于全球劳动力市场统计监测，其核心数据库ILOSTAT为衡量非正规经济提供了权威基准。该数据集由Electric Sheep Asia于2025年从ILOSTAT REST API抽取并重新封装，覆盖27个亚洲国家、2004至2025年间30,465条观察记录，按年龄与ISIC Rev.4第二级经济活动分类呈现非正规就业率。其核心研究问题在于揭示亚洲地区非正规就业的年龄与行业异质性，为劳动经济学、社会保护政策及可持续发展目标（SDG 8）的进展评估提供量化基础。该数据集对追踪亚洲非正规经济结构变迁、支撑跨国比较研究具有重要参考价值。

当前挑战

亚洲非正规就业的测度面临定义多元与数据稀疏的双重掣肘，不同国家劳动年龄界定、非正规部门划分标准的差异导致跨国可比性受损。构建过程中，ILOSTAT对原始调查微数据的国际标准协调虽提升了规范化程度，但部分国家观测年份断续、来源更迭引发序列断裂（如`note_indicator`所标记的方法论修订），且观测状态标注为“不可靠”的样本需谨慎处理。此外，第二级经济活动分类下细分行业数据稀疏，年龄组与行业交叉维度存在大量缺失，对时间序列建模与面板分析构成实质性挑战。

常用场景

经典使用场景

在劳动经济学与非正规经济研究领域，该数据集最经典的使用场景在于刻画亚洲27国非正规就业率的年龄与经济活动二维分布特征。研究者常以年龄组为横轴、ISIC二级行业为纵轴，构建非正规就业率的热力图或趋势面板，揭示青年与成年劳动者在不同产业部门中的非正规就业脆弱性差异，并据此进行跨国比较与时间序列分解，成为洞察亚洲劳动力市场二元结构的基准工具。

实际应用

在政策实践层面，该数据集为国际组织与各国劳工部门提供了监测非正规就业风险群体的实证依据。社会保障机构可据此识别非正规就业高发的年龄—行业组合，精准设计扩面征缴与技能培训干预；发展金融机构可将其纳入国别劳动市场评估框架，辅助制定包容性增长战略；企业与人资研究机构亦可参照行业非正规就业率 benchmark，优化用工合规与供应链社会责任管理。

衍生相关工作

围绕该数据集已衍生出一系列经典研究工作。比较劳动制度分析文献利用其面板结构探讨非正规就业与劳动法规严格度的关联；结构转型研究将其与行业增加值数据匹配，检验非正规就业随产业升级的演变路径；计量建模工作则基于其时间序列特征开发了非正规就业率的预测与情景模拟方法，部分成果进一步整合进ILO全球就业趋势模型与SDG体面劳动指标监测体系。