electricsheepasia/asia-ilo-emp-pifl-sex-ins-geo-nb-employment-outside-the-formal-sector-by-sex-public

--- 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 - informal-economy - ilo - labour - employment pretty_name: "Employment outside the formal sector by sex, public/private sector and rural/urban areas ( | Asia (ILOSTAT)" --- # Employment outside the formal sector by sex, public/private sector and rural/urban areas ( | Asia (ILOSTAT) 🌏 **2,748 observations** · **21 Asia countries** · **2006–2025** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **2,748 observations** of `Informal economy` data across **21 Asia countries**, spanning **2006–2025**, 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_PIFL_SEX_INS_GEO_NB) - **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_PIFL_SEX_INS_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 21 Asia countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `MNG` | 333 | 2006 | 2024 | | `PSE` | 312 | 2010 | 2022 | | `VNM` | 295 | 2007 | 2024 | | `LKA` | 263 | 2010 | 2024 | | `PAK` | 243 | 2006 | 2025 | | `THA` | 207 | 2014 | 2024 | | `BRN` | 162 | 2014 | 2024 | | `JOR` | 144 | 2017 | 2024 | | `BGD` | 133 | 2010 | 2024 | | `GEO` | 108 | 2019 | 2024 | | `ARM` | 90 | 2008 | 2017 | | `TUR` | 90 | 2009 | 2013 | | `MMR` | 90 | 2015 | 2020 | | `IDN` | 72 | 2016 | 2023 | | `TLS` | 62 | 2010 | 2021 | | ... | _6 more countries_ | | | ## Indicators (sample) - `EMP_PIFL_SEX_INS_GEO_NB` — Employment outside the formal sector by sex, public/private sector 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 | `EMP_PIFL_SEX_INS_GEO_NB` | | `indicator.label` | `string` | Indicator name in English | `Employment outside the formal sector …` | | `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.) | `INS_SECTOR_TOTAL` | | `classif1.label` | `string` | — | `Institutional sector: Total` | | `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) | `5949.625` | | `obs_status` | `string` | Observation status flag (e.g. provisional, unreliable) | `B` | | `obs_status.label` | `string` | — | `Break in series` | | `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`** (4 unique values): `SEX_T`, `SEX_M`, `SEX_F`, `SEX_O` ## 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-pifl-sex-ins-geo-nb-employment-outside-the-formal-sector-by-sex-public") 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_PIFL_SEX_INS_GEO_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_PIFL_SEX_INS_GEO_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_PIFL_SEX_INS_GEO_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_emp_pifl_sex_ins_geo_nb_employment_outside_the_formal_sector_by_sex_public_2025, title = {Employment outside the formal sector by sex, public/private sector and rural/urban areas ( | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_PIFL_SEX_INS_GEO_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-emp-pifl-sex-ins-geo-nb-employment-outside-the-formal-sector-by-sex-public}} } ``` ## 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_PIFL_SEX_INS_GEO_NB_This dataset contains 2,748 observations of informal economy employment data across 21 Asia countries, spanning from 2006 to 2025, with the indicator Employment outside the formal sector by sex, public/private sector and rural/urban areas (thousands). The data is sourced from ILOSTAT, the International Labour Organizations central statistics database, which covers areas such as employment, unemployment, wages, working time, child labour, informal economy, social protection, occupational injuries, and SDG decent work targets. The dataset is harmonized by the ILOs Department of Statistics using International Conference of Labour Statisticians (ICLS) definitions and repackaged by Electric Sheep Asia to provide a unified, ML-ready data layer for Asia. It includes columns such as country code, country name, data source, indicator code, indicator name, sex disaggregation, classification variables, observation year, observed value, observation status, and related notes, enabling filtering and analysis by country, year, and indicator.

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）的中央统计数据库ILOSTAT，通过其REST API直接获取指标EMP_PIFL_SEX_INS_GEO_NB的原始数据，并依据亚洲ISO3国家代码进行筛选。ILOSTAT依据国际劳工统计学家会议（ICLS）定义对各国劳动力调查等微观数据进行协调，数据经Electric Sheep Asia重新打包为Parquet格式，并标注来源以确保可追溯性。

特点

数据集涵盖21个亚洲国家，时间跨度为2006至2025年，包含2748条观测记录，聚焦于非正规部门就业的性别、公私部门及城乡分布。数据以年度频率呈现，提供多维度分解变量，如性别（总数、男性、女性、其他）和分类变量，并包含观测状态与注释字段，便于评估数据质量。

使用方法

使用者可通过Hugging Face的datasets库加载数据集，转换为Pandas数据框进行灵活操作。例如，可筛选特定国家（如印度尼西亚）的数据，或针对单一指标绘制时间序列图，亦可透视生成国家×年份矩阵，以支持表格分类、回归及时间序列预测等任务。

背景与挑战

背景概述

国际劳工组织长期致力于全球劳动力市场统计监测，ILOSTAT数据库为其核心统计平台，汇集涵盖非正规经济、就业与失业等多维度指标。该数据集由Electric Sheep Asia于2025年基于ILOSTAT国家劳动力调查等微数据重新封装而成，覆盖亚洲21国2006至2025年非正规部门就业观察值共2748条，按性别、公私部门及城乡维度细分。其核心研究问题在于揭示亚洲非正规就业的性别差异与空间异质性，为体面劳动政策评估提供量化基准，对发展经济学与劳动社会学领域具有实证参考价值。

当前挑战

非正规就业测度长期面临概念界定与跨国可比性难题，国际劳工统计学家会议定义虽提供框架，但各国调查口径与执行能力差异导致数据断点频繁，部分国家年均观测值缺失或不连续。构建过程中，非正规经济活动的隐蔽性易引致漏报，且不同分类维度下样本量骤减，削弱统计效力。该数据集还需应对源数据年度更新迟滞与元数据标注不一致等挑战，如何在保持原真性的同时提升时序可分析性，构成后续应用的关键瓶颈。

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各国非正规部门就业的性别差异与城乡分布格局。研究者依托2,748条年度观测记录，以性别、公私部门属性及城乡地域为分层维度，构建跨国面板数据，系统比较不同制度环境下非正规就业的规模与结构变迁。时间序列建模与横截面分类任务均可基于此数据集展开，从而揭示2006至2025年间亚洲劳动力市场非正规化的动态轨迹。

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监测非正规就业风险提供了关键依据。社会保障扩面、劳动权益保护以及城乡就业促进等政策的制定与评估，均可借助该数据集识别脆弱群体集中分布的区域与人群。私营部门亦可将之用于评估目标市场的劳动力供给特征，辅助投资决策与供应链劳工合规审查，具有明确的现实可操作性。

衍生相关工作

围绕该数据集，已衍生出一系列聚焦亚洲非正规经济的经典研究。学者们在此基础上开展性别就业差距分解、非正规就业与贫困关联分析以及非正规部门周期性波动研究，并发展出跨数据库融合方法，将之与世界银行发展指标或各国劳动力调查微数据对接。这些工作拓展了非正规就业研究的分析边界，也为Electric Sheep Asia系列数据集的后续开发奠定了方法论参照。