electricsheepasia/asia-ilo-ees-tees-sex-ifl-edu-nb-employees-by-sex-informal-formal-job-and-education

--- 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: "Employees by sex, informal/formal job and education (thousands) | Asia (ILOSTAT)" --- # Employees by sex, informal/formal job and education (thousands) | Asia (ILOSTAT) 🌏 **20,987 observations** · **26 Asia countries** · **2000–2025** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **20,987 observations** of `Informal economy` data across **26 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_EDU_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_EDU_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 | |---------|-----:|-----------:|----------:| | `MNG` | 2,120 | 2006 | 2024 | | `PSE` | 1,983 | 2010 | 2025 | | `LKA` | 1,739 | 2010 | 2024 | | `VNM` | 1,623 | 2013 | 2024 | | `PAK` | 1,565 | 2006 | 2025 | | `TUR` | 1,449 | 2000 | 2024 | | `THA` | 1,432 | 2014 | 2024 | | `BRN` | 1,293 | 2014 | 2024 | | `IND` | 1,274 | 2010 | 2025 | | `JOR` | 998 | 2017 | 2024 | | `BGD` | 864 | 2010 | 2024 | | `IDN` | 804 | 2016 | 2023 | | `GEO` | 655 | 2019 | 2024 | | `MMR` | 533 | 2015 | 2020 | | `ARM` | 448 | 2008 | 2017 | | ... | _11 more countries_ | | | ## Indicators (sample) - `EES_TEES_SEX_IFL_EDU_NB` — Employees by sex, informal/formal job and education (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_EDU_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 | `EDU_AGGREGATE_TOTAL` | | `classif2.label` | `string` | — | `Education (Aggregate levels): Total` | | `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) | `U` | | `obs_status.label` | `string` | — | `Unreliable` | | `note_classif` | `string` | — | `C3:2620` | | `note_classif.label` | `string` | — | `Nonstandard education level: Includin…` | | `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-edu-nb-employees-by-sex-informal-formal-job-and-education") 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_EDU_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EES_TEES_SEX_IFL_EDU_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EES_TEES_SEX_IFL_EDU_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_ees_tees_sex_ifl_edu_nb_employees_by_sex_informal_formal_job_and_education_2025, title = {Employees by sex, informal/formal job and education (thousands) | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EES_TEES_SEX_IFL_EDU_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-ees-tees-sex-ifl-edu-nb-employees-by-sex-informal-formal-job-and-education}} } ``` ## 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_EDU_NB_This dataset contains 20,987 observations of informal economy data across 26 Asia countries, spanning 2000–2025, covering 1 distinct indicator: Employees by sex, informal/formal job and education (thousands) (code: EES_TEES_SEX_IFL_EDU_NB). The data is sourced from ILOSTAT, the ILOs central statistics database, which harmonizes raw survey microdata using International Conference of Labour Statisticians (ICLS) definitions. It includes columns such as country code, source, indicator, sex disaggregation, education classification, year, and observed value, with notes on data quality and usage examples. The dataset is repackaged by Electric Sheep Asia for machine learning applications in tabular classification, regression, and time-series forecasting.

搜集汇总

数据集介绍

构建方式

在国际劳工统计领域，非正规经济就业的性别与教育维度长期缺乏可比性强的跨国面板数据。该数据集由Electric Sheep Asia团队对国际劳工组织（ILO）ILOSTAT数据库进行系统性再封装而成，其构建过程依托ILOSTAT REST API接口直接拉取指标EES_TEES_SEX_IFL_EDU_NB的原始记录，并依据ISO 3166-1 alpha-3标准筛选出26个亚洲国家。ILOSTAT本身采用国际劳工统计学家会议（ICLS）的定义对各国劳动力调查、家庭收入调查及行政记录等微观数据进行标准化调和，来源信息以source.label字段加以标注，从而保证了从原始调查到跨国可比指标的完整可追溯性。

特点

该数据集汇聚了2000年至2025年间亚洲地区20,987条观测记录，覆盖26个国家，以非正规/正规就业、性别与教育程度的交叉分类为核心指标。数据结构上，除ref_area、time、obs_value等基础字段外，还包含sex、classif1、classif2等多重分解维度，每个分类变量均配有英文标签与注释字段，用以标识非标准教育层级、序列断裂及数据可靠性等质量信息。观测值单位为千人，年度频率，ILO在存在多源数据的国家×年份组合中选取其认定的最佳来源，从而在广度与一致性之间取得平衡。

使用方法

研究者可借助HuggingFace datasets库以一行代码加载该数据集，并通过to_pandas()方法转换为数据框进行后续分析。常见操作包括按ref_area字段筛选单一国家、按indicator字段提取目标指标并依时间排序以绘制时序趋势，以及利用pivot_table构建国家×年份矩阵以便开展跨国比较或面板回归。该数据集适用于表格分类、回归与时间序列预测等任务，使用时应遵循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，并留意obs_status与note系列字段所提示的数据质量警示。

背景与挑战

背景概述

非正规经济就业的性别与教育维度长期是劳动经济学与发展研究的核心议题。国际劳工组织（ILO）依托ILOSTAT数据库，整合各国劳动力调查与住户调查微观数据，按国际劳工统计学家会议（ICLS）标准进行跨国协调，构建了覆盖亚洲26国、2000至2025年的就业者非正规/正规就业与教育程度数据集。该数据集经Electric Sheep Asia重新打包，以标准化Schema与Parquet格式发布，为亚洲非正规就业的性别差异与教育分层研究提供了跨时空比较基础，对监测体面劳动与SDG目标具有重要参考价值。

当前挑战

该数据集所回应的领域问题，在于如何以跨国可比口径刻画非正规就业中性别与教育结构的动态演变，其难点源于非正规就业定义在不同国家统计体系中的异质性。构建过程中，ILOSTAT虽以ICLS标准协调原始调查微数据，但各国劳动力调查的抽样设计、教育分类与就业界定差异显著，导致序列断裂、观测状态标记为不可靠或临时值等情况频现；部分国家年份覆盖不连续，性别分类中SEX_O样本稀疏，进一步制约了时序建模与跨国面板分析的稳健性。

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，该数据集最为经典的应用是构建亚洲地区非正规就业的性别与教育分层时间序列面板。研究者借助其2000至2025年间26个亚洲国家的两万余条观测记录，可系统刻画不同受教育程度劳动者在正规与非正规部门间的分布变迁，进而揭示性别差异在就业形态中的演化轨迹。借助时间序列预测与面板回归方法，该数据集亦常用于检验非正规就业随经济周期与教育扩张而变动的动态规律。

解决学术问题

针对非正规经济规模测度口径不一、跨國可比性匮乏的长期学术困境，该数据集依托国际劳工组织统一协调的ICLS定义体系，为研究者提供了标准化、可追溯的跨国比较基础。其价值在于消解了以往因各国调查方法差异所导致的数据碎片化问题，使学者得以在统一框架下探讨教育禀赋、性别结构与就业正规化之间的因果关联，为非正规部门的理论建模与跨国实证提供了可靠的经验支撑。

衍生相关工作

围绕该数据集，学界与数据科学社区衍生出一系列具有影响力的后续工作。研究者将其与ILOSTAT其他指标库进行横向融合，构建了涵盖工资、工时与社会保护的多维非正规就业分析框架；机器学习社区则利用其表格结构开展就业形态分类与回归建模，推动了劳动统计数据的自动化预测研究。Electric Sheep Asia在HuggingFace上的标准化封装，进一步催生了面向亚洲劳动力市场的可复现数据管道与基准评测工作。