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

--- 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: "Employment outside the formal sector by sex, occupation and public/private sector (thousan | Asia (ILOSTAT)" --- # Employment outside the formal sector by sex, occupation and public/private sector (thousan | Asia (ILOSTAT) 🌏 **15,190 observations** · **25 Asia countries** · **2006–2025** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **15,190 observations** of `Informal economy` data across **25 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_OCU_INS_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_OCU_INS_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 25 Asia countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `MNG` | 1,564 | 2006 | 2024 | | `LKA` | 1,398 | 2010 | 2024 | | `VNM` | 1,362 | 2007 | 2024 | | `TUR` | 1,344 | 2009 | 2024 | | `PSE` | 1,286 | 2010 | 2025 | | `PAK` | 1,124 | 2006 | 2025 | | `KGZ` | 1,095 | 2012 | 2023 | | `THA` | 1,000 | 2014 | 2024 | | `BGD` | 733 | 2010 | 2024 | | `BRN` | 666 | 2014 | 2024 | | `JOR` | 660 | 2017 | 2024 | | `GEO` | 460 | 2019 | 2024 | | `MMR` | 436 | 2015 | 2020 | | `ARM` | 404 | 2008 | 2017 | | `IDN` | 294 | 2016 | 2023 | | ... | _10 more countries_ | | | ## Indicators (sample) - `EMP_PIFL_SEX_OCU_INS_NB` — Employment outside the formal sector by sex, occupation and public/private sector (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_OCU_INS_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.) | `OCU_SKILL_TOTAL` | | `classif1.label` | `string` | — | `Occupation (Skill level): Total` | | `classif2` | `string` | Second classification variable where applicable | `INS_SECTOR_TOTAL` | | `classif2.label` | `string` | — | `Institutional sector: Total` | | `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) | `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`** (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-ocu-ins-nb-employment-outside-the-formal-sector-by-sex-occupa") 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_OCU_INS_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_PIFL_SEX_OCU_INS_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_PIFL_SEX_OCU_INS_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_emp_pifl_sex_ocu_ins_nb_employment_outside_the_formal_sector_by_sex_occupa_2025, title = {Employment outside the formal sector by sex, occupation and public/private sector (thousan | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_PIFL_SEX_OCU_INS_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-emp-pifl-sex-ocu-ins-nb-employment-outside-the-formal-sector-by-sex-occupa}} } ``` ## 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_OCU_INS_NB_This dataset is named Employment outside the formal sector by sex, occupation and public/private sector (thousan | Asia (ILOSTAT) and contains 15,190 observations across 25 Asian countries, spanning from 2006 to 2025, covering 1 core indicator: EMP_PIFL_SEX_OCU_INS_NB, which represents employment outside the formal sector by sex, occupation and public/private sector (in thousands). The data is sourced from ILOSTAT, the central statistics database of the International Labour Organization (ILO), with a focus on the informal economy. The dataset was obtained via the ILOSTAT REST API and filtered to Asian country codes, with ILOSTAT harmonizing raw survey microdata using International Conference of Labour Statisticians (ICLS) definitions. The columns include country code, country name, data source, indicator code, sex classification, classification variables, time, observed value, observation status, etc., with the sex column having 4 unique values (total, male, female, other). The data is annual frequency, and when multiple sources exist for the same country and year, the ILO-selected best source is used. The dataset was repackaged by Electric Sheep Asia as part of a unified, ML-ready data layer for Asia.

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统计数据库，通过其REST API端点直接提取指标EMP_PIFL_SEX_OCU_INS_NB的原始数据，并依据ISO3国家代码筛选出亚洲地区记录。ILOSTAT对各国劳动力调查等微观数据进行统一协调，采用国际劳工统计学家会议（ICLS）定义确保跨国可比性。Electric Sheep Asia对抽取数据进行规范化重打包，生成包含15,190条观测的Parquet格式数据集，保留了来源标签以便追溯，最终发布于HuggingFace平台。

特点

数据集覆盖亚洲25个国家，时间跨度为2006至2025年，聚焦于非正规部门就业的性别、职业及公私部门分布。观测值以千人为单位，包含性别、职业技能水平和机构部门等多维分类变量，但部分分类维度仅在特定指标下非空。数据质量标志列提供了观测状态和序列断裂说明，增强了透明度。数据集规模适中，适用于表格分类、回归及时间序列预测任务，且采用CC-BY-4.0许可，便于学术与商业应用。

使用方法

研究者可通过HuggingFace的datasets库直接加载数据集，并转换为Pandas DataFrame进行灵活操作。典型用法包括按国家代码筛选子集，例如提取印度尼西亚的记录；或针对特定指标绘制时间序列趋势图。此外，可利用透视表将数据重塑为国家与年份的矩阵形式，便于跨区域比较。数据集支持分类、回归及时序预测建模，使用时应遵循CC-BY-4.0许可，同时引用ILO原始来源及Electric Sheep Asia的再分发工作。

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的标准化与可比性建设，其核心数据库ILOSTAT汇集了涵盖就业、失业、工资及非正规经济等维度的跨国劳动统计指标。在亚洲地区，非正规部门就业占据劳动力市场的显著份额，然而关于性别、职业及公私部门交叉分类的量化信息长期分散且口径不一，制约了相关实证研究的深入。该数据集由Electric Sheep Asia于2025年基于ILOSTAT API重新封装发布，涵盖25个亚洲国家、2006至2025年间共15,190条观测，聚焦非正规部门外就业的性别与职业分布。其核心研究问题在于揭示亚洲非正规就业的结构性差异与演变趋势，为劳动经济学、发展研究及社会政策评估提供高颗粒度的面板数据基础，对推动区域劳动力市场比较研究具有重要参考价值。

当前挑战

该数据集所服务的领域问题——非正规部门就业的精确测度与跨国比较——本身即面临概念界定与统计口径的深层挑战。国际劳工统计学家会议（ICLS）虽已建立非正规就业的标准定义框架，但各国劳动力调查在问卷设计、抽样方法及非正规部门操作化定义上仍存在显著异质性，导致跨国可比性受限。数据集构建过程中，原始微观调查数据的缺失值处理、非正规就业分类的国别映射、以及职业与公私部门等多维交叉分类下样本量的稀疏性，均构成数据质量的潜在风险。此外，部分观测值附有“不可靠”或“系列中断”等状态标记，提示数据时序一致性的脆弱。如何在保留国别特征的同时实现有效的统计调和，仍是该数据集应用与扩展的核心挑战。

常用场景

经典使用场景

在劳动经济学与非正规经济研究的交叉领域，该数据集最经典的使用场景在于刻画亚洲25国非正规部门就业的性别差异与职业结构分化。研究者可借助其涵盖2006至2025年的15,190条观测，按性别、技能水平职业及公共/私营部门维度进行面板分析，从而揭示女性在非正规就业中的过度代表现象及其随时间演变的轨迹。时间序列建模与跨国比较亦是常见用法，用以检验非正规就业规模与经济周期、制度变迁之间的动态关联。

解决学术问题

该数据集有效回应了非正规就业测量中长期存在的可比性难题。通过ILO统一协调的ICLS定义与标准化分类，它化解了各国劳动力调查在部门归属、职业编码与性别分层上的异质性，使跨国比较与趋势分析成为可能。其学术意义在于为检验非正规部门与正规部门之间的劳动力流动假说、性别职业隔离理论以及制度环境对非正规性的塑造效应提供了可靠的经验基础，推动了发展经济学与劳动社会学中相关理论的实证检验。

衍生相关工作

围绕该数据集及其同源ILOSTAT指标，已衍生出一系列经典工作。国际劳工组织定期发布的《世界非正规就业报告》即以其为核心数据源，系统评估全球及区域非正规就业格局。学术界则据此产出了关于非正规部门与贫困动态、性别工资差距以及非正规就业与宏观经济波动的跨国实证研究。Electric Sheep Asia的标准化封装亦催生了面向机器学习就绪的数据层建设，便利了预测建模与政策模拟等衍生分析。