electricsheepasia/asia-ilo-ees-tees-sex-ifl-geo-nb-employees-by-sex-informal-formal-job-and-rural-urb

--- 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: "Employees by sex, informal/formal job and rural / urban areas (thousands) | Asia (ILOSTAT)" --- # Employees by sex, informal/formal job and rural / urban areas (thousands) | Asia (ILOSTAT) 🌏 **4,505 observations** · **22 Asia countries** · **2000–2025** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **4,505 observations** of `Informal economy` data across **22 Asia countries**, spanning **2000–2025**, 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=EES_TEES_SEX_IFL_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=EES_TEES_SEX_IFL_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 22 Asia countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `MNG` | 486 | 2006 | 2024 | | `PSE` | 468 | 2010 | 2022 | | `VNM` | 387 | 2007 | 2024 | | `LKA` | 378 | 2010 | 2024 | | `TUR` | 378 | 2000 | 2013 | | `PAK` | 351 | 2006 | 2025 | | `IND` | 274 | 2010 | 2025 | | `THA` | 270 | 2014 | 2024 | | `BRN` | 243 | 2014 | 2024 | | `JOR` | 216 | 2017 | 2024 | | `GEO` | 162 | 2019 | 2024 | | `IDN` | 162 | 2016 | 2023 | | `BGD` | 136 | 2010 | 2024 | | `ARM` | 135 | 2008 | 2017 | | `MMR` | 135 | 2015 | 2020 | | ... | _7 more countries_ | | | ## Indicators (sample) - `EES_TEES_SEX_IFL_GEO_NB` — Employees by sex, informal/formal job 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 | `EES_TEES_SEX_IFL_GEO_NB` | | `indicator.label` | `string` | Indicator name in English | `Employees by sex, informal/formal job…` | | `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.) | `IFL_NATURE_TOTAL` | | `classif1.label` | `string` | — | `Nature of job: 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) | `1709.649` | | `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-ees-tees-sex-ifl-geo-nb-employees-by-sex-informal-formal-job-and-rural-urb") 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"] == "EES_TEES_SEX_IFL_GEO_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EES_TEES_SEX_IFL_GEO_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EES_TEES_SEX_IFL_GEO_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_ees_tees_sex_ifl_geo_nb_employees_by_sex_informal_formal_job_and_rural_urb_2025, title = {Employees by sex, informal/formal job and rural / urban areas (thousands) | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EES_TEES_SEX_IFL_GEO_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-ees-tees-sex-ifl-geo-nb-employees-by-sex-informal-formal-job-and-rural-urb}} } ``` ## 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=EES_TEES_SEX_IFL_GEO_NB_This dataset contains informal economy data from the International Labour Organization (ILO) ILOSTAT database, specifically the indicator EES_TEES_SEX_IFL_GEO_NB, which measures employees by sex, informal/formal job, and rural/urban areas (in thousands). It covers 22 Asian countries from 2000 to 2025, with 4,505 observations. Data is sourced directly from the ILOSTAT REST API and filtered for Asian ISO3 country codes. The dataset includes disaggregation dimensions such as sex, job nature, and area type, along with source and quality flags. Repackaged by Electric Sheep Asia in Parquet format for ML-ready use, it supports 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）中央统计数据库ILOSTAT的权威数据资源，通过REST API接口直接提取指标EES_TEES_SEX_IFL_GEO_NB的原始记录，并依据ISO3国家代码筛选出22个亚洲国家，形成覆盖2000至2025年的44505条观测样本。原始调查微观数据经ILO统计部门依照国际劳工统计学家会议（ICLS）定义进行标准化调和，每条记录均标注来源标签以确保可追溯性，最终由Electric Sheep Asia统一架构、以Parquet格式封装发布，实现了从多源异构调查到机器学习就绪数据集的转化。

使用方法

研究者可通过HuggingFace的datasets库以一行代码加载数据集并转换为Pandas数据框，继而按国家代码、指标代码或年份进行灵活筛选与切片。典型操作包括提取单一国家的时序数据以观察趋势演变，或利用透视表将数据重塑为国家与年份的矩阵形式以支持横向比较。该数据集适用于表格分类、回归与时序预测等任务，但使用时应留意部分年份存在序列断裂或方法修订等状态标记，并遵循CC-BY-4.0许可协议，同时引用ILO原始来源与Electric Sheep Asia的再封装工作。

背景与挑战

背景概述

非正规经济就业的测度长期构成劳动统计领域的核心难题，其界定标准随国际劳工统计学家会议决议的修订而不断演化。国际劳工组织依托ILOSTAT平台，汇聚各国劳动力调查与住户收入调查等微观数据，经系统化协调后发布全球可比指标。为填补亚洲区域在性别与城乡维度交叉细化数据上的空白，Electric Sheep Asia将ILOSTAT原始数据重新封装为面向机器学习工作流的标准化数据集，覆盖22个亚洲国家、2000至2025年逾四千五百条观测，为劳动经济学与性别研究提供颗粒度更细的实证基础。

当前挑战

该数据集所回应的领域问题，在于非正规就业规模的精确认定本身即受制于各国统计口径与调查方法的异质性，国际劳工组织虽以国际劳工统计学家会议定义加以协调，但各国劳动力调查在非正规部门边界、农村地区覆盖及性别分类完整性上仍存在系统性偏差。构建过程中面临的挑战则来自三个方面：部分国家或年份数据缺失导致时序断裂；来源代码与断点标记需逐条追溯以保证可溯性；城乡维度与性别维度的交叉分类并非所有国家均予发布，使得面板数据结构不平衡，对建模方法与缺失值处理策略构成直接约束。

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地区非正规就业的性别结构与城乡差异。研究者常以年份为时序轴，将男女两性在非正规与正规部门中的就业人数作为核心变量，构建面板数据模型，从而揭示非正规就业比例随经济发展阶段的演变轨迹。借助城乡分类维度，该数据集亦被用于比较农村与城市劳动力市场在非正规化程度上的异质性，为理解发展中国家劳动力市场二元结构提供了量化基础。

解决学术问题

该数据集有效回应了非正规就业测度中长期存在的可比性难题。由于各国劳动力调查在非正规部门界定、抽样口径与统计年份上差异显著，跨国比较往往面临严重的数据断裂与口径不一致问题。ILOSTAT依据国际劳工统计学家会议标准对原始微观数据进行调和，使得亚洲22国的非正规就业数据得以在同一框架下对齐。这一处理为检验非正规就业与性别不平等、城乡收入差距、社会保障覆盖等学术命题提供了可靠的数据支撑，推动了跨国劳动比较研究的规范化。

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评估非正规就业规模、设计社会保障扩面政策提供了关键依据。通过分解性别与城乡维度，决策者可精准识别非正规就业中的脆弱群体，例如农村女性非正规从业者，从而制定更具靶向性的就业促进与社保覆盖策略。此外，该数据集亦可用于监测联合国可持续发展目标中关于体面工作的相关指标进展，辅助劳动力市场风险评估与预警系统的构建。

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22国非正规与正规就业的性别差异及城乡分布提供了2000至2025年的长时序观测，成为劳动经济学与发展研究的前沿支撑。近期研究聚焦于非正规就业的性别鸿沟、城乡分割及经济周期敏感性，利用该数据进行面板回归与时间序列预测，揭示女性在非正规部门中的过度代表及其与贫困脆弱性的关联。该数据集亦服务于SDG体面劳动目标的监测评估，为政策制定者设计针对性社会保障与就业正规化干预提供实证基础，推动了亚洲劳动力市场结构转型的量化研究。