electricsheepasia/asia-ilo-emp-pifl-sex-oc2-nb-employment-outside-the-formal-sector-by-sex-and-oc

--- 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 and occupation - ISCO level 2 (thousands) | Asia (ILOSTAT)" --- # Employment outside the formal sector by sex and occupation - ISCO level 2 (thousands) | Asia (ILOSTAT) 🌏 **16,484 observations** · **26 Asia countries** · **2004–2025** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **16,484 observations** of `Informal economy` data across **26 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_PIFL_SEX_OC2_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_OC2_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 26 Asia countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `TUR` | 2,170 | 2004 | 2024 | | `VNM` | 1,532 | 2007 | 2024 | | `LKA` | 1,490 | 2010 | 2024 | | `MNG` | 1,329 | 2006 | 2024 | | `PSE` | 1,319 | 2010 | 2025 | | `PAK` | 1,142 | 2006 | 2025 | | `THA` | 1,115 | 2014 | 2024 | | `KGZ` | 1,049 | 2012 | 2023 | | `CYP` | 886 | 2007 | 2024 | | `IND` | 845 | 2010 | 2025 | | `BGD` | 546 | 2010 | 2024 | | `MMR` | 545 | 2015 | 2020 | | `JOR` | 480 | 2017 | 2023 | | `GEO` | 408 | 2019 | 2024 | | `BRN` | 341 | 2014 | 2024 | | ... | _11 more countries_ | | | ## Indicators (sample) - `EMP_PIFL_SEX_OC2_NB` — Employment outside the formal sector by sex and occupation - ISCO level 2 (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_OC2_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.) | `OC2_ISCO08_TOTAL` | | `classif1.label` | `string` | — | `Occupation (ISCO-08), 2 digit level: …` | | `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_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-oc2-nb-employment-outside-the-formal-sector-by-sex-and-oc") 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_OC2_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_PIFL_SEX_OC2_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_PIFL_SEX_OC2_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_emp_pifl_sex_oc2_nb_employment_outside_the_formal_sector_by_sex_and_oc_2025, title = {Employment outside the formal sector by sex and occupation - ISCO level 2 (thousands) | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_PIFL_SEX_OC2_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-emp-pifl-sex-oc2-nb-employment-outside-the-formal-sector-by-sex-and-oc}} } ``` ## 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_OC2_NB_This dataset contains 16,484 observations of informal economy employment data from the International Labour Organization (ILO) ILOSTAT database, covering 26 Asian countries from 2004 to 2025. The specific indicator is Employment outside the formal sector by sex and occupation - ISCO level 2 (thousands), disaggregated by sex (total, male, female, etc.) and occupation classification (ISCO-08 2-digit level), with units in thousands. It is suitable for tasks such as tabular classification, regression, and time-series forecasting, and aims to provide a machine learning-ready data layer for labor statistics in Asia.

搜集汇总

数据集介绍

构建方式

在非正规经济部门就业的测算长期依赖国际劳工组织的标准化统计框架。该数据集通过ILOSTAT REST API直接拉取指标EMP_PIFL_SEX_OC2_NB的原始记录，依据ISO3国家代码筛选出26个亚洲经济体，最终形成16,484条年度观测。原始微观调查数据由ILO统计部门按照国际劳工统计学家会议（ICLS）定义进行协调与标准化，数据来源类型标注于source.label字段，以便追溯不同国家的劳动力调查或家庭收入调查等原始出处。

特点

数据集聚焦亚洲地区非正规部门就业的性别与职业二位数分类（ISCO-08二级），时间跨度自2004年至2025年，覆盖土耳其、越南、斯里兰卡等26个国家。表结构包含国家代码、性别、职业分类、年份、观测值及质量标志等字段，其中性别维度区分总计、男性、女性与其他，观测状态与指标注释列提供了对序列断点和数据可靠性的明确标识，便于研究者评估数据质量与适用性。

使用方法

研究者可通过HuggingFace的datasets库以load_dataset()函数加载数据并转换为Pandas数据框，进而按国家代码筛选特定经济体，或按指标代码提取单一时间序列进行趋势分析。数据亦支持透视操作以构建国家×年份矩阵，满足面板数据分析与时序预测等任务的需求。使用时应遵循CC-BY-4.0许可协议，并同时引用国际劳工组织原始来源与Electric Sheep Asia的再包装工作。

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，其维护的ILOSTAT数据库为就业、失业及非正规经济等领域提供了权威的跨国可比数据。在此背景下，亚洲地区非正规部门就业的性别与职业结构差异，构成了理解区域劳动力市场分割与发展不平衡的关键议题。该数据集由Electric Sheep Asia于2025年重新封装发布，原始数据源自ILOSTAT的EMP_PIFL_SEX_OC2_NB指标，覆盖26个亚洲国家、2004至2025年间共16,484条观测记录，以ISCO-08二位码职业分类和性别维度系统呈现了正规部门之外就业的规模与分布。作为亚洲劳动统计数据基础设施的重要补充，该数据集为探究非正规就业的性别差异、职业隔离及其时序演变提供了标准化的实证素材，对推动区域体面劳动议程和制定针对性就业政策具有基础性支撑意义。

当前挑战

该数据集所回应的核心领域问题，在于非正规部门就业统计长期面临的概念界定分歧与跨国可比性困境——不同国家对非正规就业的界定标准、调查口径及覆盖范围存在显著差异，ILOSTAT虽以国际劳工统计学家会议决议进行协调统一，但各源调查的抽样设计、时段跨度和职业编码精度仍参差不齐。构建过程中，数据整合需应对多重来源的异质性：同一国家年份可能并存多个调查来源，而ILO仅选取最佳来源，导致序列连续性受限；部分观测值标注为不可靠或存在方法学修订断点，削弱了时序分析的稳健性。此外，性别与职业维度仅在该指标发布细分时才非空，缺失值结构复杂，且部分国家数据跨度短暂、覆盖不均衡，对跨区域比较和长期趋势建模构成实质性障碍。

常用场景

经典使用场景

在非正规经济部门就业的量化研究中，该数据集最经典的使用场景在于构建亚洲26国2004至2025年间按性别与职业（ISCO-08二级分类）划分的非正规就业面板数据。研究者可借助其标准化字段（如ref_area、sex、classif1、time、obs_value）开展跨国家、跨职业、跨性别的比较分析，进而揭示非正规就业的规模、结构与演变趋势。此类数据对于理解发展中国家劳动力市场的二元特征具有不可替代的价值。

实际应用

在实际应用层面，该数据集为国际组织、政府部门及非政府组织评估非正规就业政策提供了关键依据。政策制定者可据此识别非正规就业高发的职业类别与性别群体，设计有针对性的社会保障扩展方案与技能培训项目。同时，该数据集亦可用于监测可持续发展目标中关于体面劳动的进展，辅助劳动力市场规划与风险预警，具有显著的决策参考价值。

衍生相关工作

围绕该数据集，已有诸多经典工作衍生而出。例如，基于ILOSTAT系列数据开展的非正规就业与贫困关联研究、非正规部门与经济增长关系的跨国计量分析，以及针对特定国家（如土耳其、越南、斯里兰卡）的案例研究。这些工作不仅拓展了非正规就业的理论边界，也促进了劳动力市场统计方法论的完善，并催生了若干高被引论文与政策报告。