electricsheepasia/asia-ilo-emp-pifl-sex-age-jbt-rt-share-of-employment-outside-the-formal-sector-by-s

--- 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: "Share of employment outside the formal sector by sex, age and working time arrangement (%) | Asia (ILOSTAT)" --- # Share of employment outside the formal sector by sex, age and working time arrangement (%) | Asia (ILOSTAT) 🌏 **28,109 observations** · **27 Asia countries** · **2004–2025** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **28,109 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_PIFL_SEX_AGE_JBT_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_PIFL_SEX_AGE_JBT_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` | 2,835 | 2004 | 2024 | | `MNG` | 2,430 | 2006 | 2024 | | `PSE` | 2,350 | 2010 | 2025 | | `CYP` | 2,308 | 2007 | 2024 | | `VNM` | 2,263 | 2007 | 2024 | | `KGZ` | 2,062 | 2012 | 2023 | | `LKA` | 1,890 | 2010 | 2024 | | `PAK` | 1,620 | 2006 | 2021 | | `THA` | 1,350 | 2014 | 2024 | | `BRN` | 1,245 | 2014 | 2024 | | `JOR` | 1,048 | 2017 | 2024 | | `BGD` | 962 | 2010 | 2024 | | `IDN` | 810 | 2016 | 2023 | | `GEO` | 810 | 2019 | 2024 | | `MMR` | 710 | 2015 | 2020 | | ... | _12 more countries_ | | | ## Indicators (sample) - `EMP_PIFL_SEX_AGE_JBT_RT` — Share of employment outside the formal sector by sex, age and working time arrangement (%) ## 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_AGE_JBT_RT` | | `indicator.label` | `string` | Indicator name in English | `Share of employment outside the forma…` | | `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.) | `AGE_YTHADULT_YGE15` | | `classif1.label` | `string` | — | `Age (Youth, adults): 15+` | | `classif2` | `string` | Second classification variable where applicable | `JBT_TIME_TOTAL` | | `classif2.label` | `string` | — | `Working time arrangement: Total` | | `time` | `int64` | Observation year | `2021` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `77.474` | | `obs_status` | `string` | Observation status flag (e.g. provisional, unreliable) | `U` | | `obs_status.label` | `string` | — | `Unreliable` | | `note_classif` | `string` | — | `C6:1058` | | `note_classif.label` | `string` | — | `Nonstandard age group: Excluding age 15` | | `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-age-jbt-rt-share-of-employment-outside-the-formal-sector-by-s") 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_AGE_JBT_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_PIFL_SEX_AGE_JBT_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_PIFL_SEX_AGE_JBT_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_emp_pifl_sex_age_jbt_rt_share_of_employment_outside_the_formal_sector_by_s_2025, title = {Share of employment outside the formal sector by sex, age and working time arrangement (%) | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_PIFL_SEX_AGE_JBT_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-emp-pifl-sex-age-jbt-rt-share-of-employment-outside-the-formal-sector-by-s}} } ``` ## 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_AGE_JBT_RT_This dataset contains 28,109 observations of informal economy data across 27 Asia countries, spanning 2004–2025, covering 1 distinct indicator: Share of employment outside the formal sector by sex, age and working time arrangement (%). The data is sourced from the International Labour Organization (ILO) ILOSTAT database, retrieved via REST API and filtered to Asia ISO3 country codes. It includes dimensions such as country code, year, sex, age group, working time arrangement, along with observed values and status flags. Data is harmonized by ILO using International Conference of Labour Statisticians (ICLS) definitions and is suitable for tabular classification, regression, and time-series forecasting tasks.

搜集汇总

数据集介绍

构建方式

该数据集依托国际劳工组织（ILO）的ILOSTAT中央统计数据库，通过其REST API接口直接提取指标EMP_PIFL_SEX_AGE_JBT_RT的原始数据，并依据ISO3国家代码筛选出亚洲地区。源数据由ILO统计部门依据国际劳工统计学家会议（ICLS）定义对各国劳动力调查、家庭收入调查等微观数据进行标准化调和，确保跨国可比性。随后由Electric Sheep Asia进行重新打包，保留原始来源标签，并以Parquet格式发布，最终形成覆盖27个亚洲国家、2004至2025年的28109条观测记录。

使用方法

研究者可通过HuggingFace datasets库以一行代码加载数据集，并转换为Pandas数据框进行灵活操作。典型用法包括：筛选特定国家（如df[df['ref_area']=='IDN']）以聚焦国别分析；提取单一指标的时间序列并排序绘图，观察非正规就业份额的演变趋势；或利用pivot_table将数据重塑为国家×年份矩阵，便于跨国比较与计量建模。数据集亦支持直接接入机器学习流程，用于分类、回归或时间序列预测任务，为劳动经济学与政策评估提供便捷的数据支撑。

背景与挑战

背景概述

非正规经济就业的量化测度长期以来构成劳动经济学与发展研究的核心议题。国际劳工组织（ILO）依托其统计部门，基于各国劳动力调查等微观数据，经国际劳工统计学家会议（ICLS）定义统一调和后，构建了ILOSTAT这一全球权威劳动统计数据库。本数据集由Electric Sheep Asia于2026年再包装发布，收录2004至2025年间27个亚洲国家28109条观测，系统刻画按性别、年龄与工作时间安排分组的非正规部门就业占比，为亚洲非正规就业的跨国比较与历时演变研究提供了标准化、可直接加载的数据基础，对非正规经济监测及体面劳动目标评估具有重要支撑意义。

当前挑战

该数据集所回应的领域问题在于非正规就业的识别与跨境可比：各国对非正规部门的界定长期存在差异，工作场所、雇佣关系与法律登记状态的异质性使指标口径难以统一。构建层面的挑战同样突出，原始数据源自不同年份、不同抽样设计的劳动力调查与住户调查，年龄分组与工作时间分类标准参差不齐，需经ICLS框架调和；部分国家年份缺失或序列中断，观测状态标记为暂定或不可靠，加之同一国家年份存在多源冲突需依循ILO最优源甄别，这些因素共同制约了数据的完整性与一致性。

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，衡量非正规部门就业占比是刻画劳动力市场结构的关键维度。该数据集最经典的使用场景在于构建亚洲二十七国非正规就业占比的面板数据，按性别、年龄组与工作时间安排进行三重分解，从而支撑跨国比较研究与趋势分析。研究者可借此观察不同人口群体在非正规经济中的分布差异，并利用时间序列建模方法捕捉二〇〇四年至二〇二五年间的演变轨迹。

解决学术问题

该数据集直面的核心学术问题是：非正规就业在不同性别、年龄与工作时长群体间呈现何种异质性，以及这种异质性如何随经济发展与制度变迁而演化。它提供了标准化、可追溯的跨国观测值，使研究者得以检验非正规部门规模与经济增长、社会保障覆盖及劳动力市场规制之间的理论假说，从而为发展经济学与劳动社会学中的非正规性理论提供实证基础，其意义在于将分散的国别调查数据整合为可比的分析资源。

实际应用

在政策实践层面，该数据集服务于国际组织与各国劳工部门的监测与评估需求。社会保障机构可据此识别非正规就业集中的年龄与性别群体，以设计更具针对性的参保激励与覆盖扩展策略；劳动监察部门则能借助工作时长维度评估非正规就业者的劳动条件风险。此外，发展金融机构与智库亦将该数据用于国别劳动力市场诊断，支持包容性增长与体面劳动相关项目的规划与绩效衡量。

数据集最近研究

最新研究方向

在全球非正规经济研究持续深化的背景下，该数据集为亚洲地区非正规就业的性别与年龄维度分析提供了独特的长时段面板数据。当前前沿研究聚焦于非正规就业的性别差异与生命周期效应，探讨不同年龄群体在非正规部门中的分布动态及其与工作时长安排的交互作用。该数据集覆盖2004至2025年27个亚洲国家，支持时间序列预测与跨国比较，有助于揭示劳动力市场正规化进程中的结构性不平等。相关研究热点包括非正规就业对社会保障覆盖的影响、性别差距的演变趋势以及疫情后非正规部门的复苏路径，对实现体面劳动与可持续发展目标具有重要政策意义。