electricsheepasia/asia-ilo-ees-tees-sex-ifl-mts-nb-employees-by-sex-informal-formal-job-and-marital-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: "Employees by sex, informal/formal job and marital status (thousands) | Asia (ILOSTAT)" --- # Employees by sex, informal/formal job and marital status (thousands) | Asia (ILOSTAT) 🌏 **11,724 observations** · **25 Asia countries** · **2000–2025** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **11,724 observations** of `Informal economy` data across **25 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_MTS_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_MTS_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 | |---------|-----:|-----------:|----------:| | `TUR` | 1,835 | 2000 | 2024 | | `LKA` | 1,008 | 2010 | 2024 | | `MNG` | 971 | 2013 | 2024 | | `VNM` | 946 | 2013 | 2024 | | `PAK` | 936 | 2006 | 2025 | | `IND` | 744 | 2010 | 2025 | | `THA` | 720 | 2014 | 2024 | | `BRN` | 646 | 2014 | 2024 | | `JOR` | 576 | 2017 | 2024 | | `PSE` | 491 | 2010 | 2025 | | `BGD` | 434 | 2010 | 2024 | | `IDN` | 432 | 2016 | 2023 | | `MMR` | 359 | 2015 | 2020 | | `ARM` | 358 | 2008 | 2017 | | `TLS` | 231 | 2010 | 2021 | | ... | _10 more countries_ | | | ## Indicators (sample) - `EES_TEES_SEX_IFL_MTS_NB` — Employees by sex, informal/formal job and marital status (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_MTS_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 | `MTS_AGGREGATE_TOTAL` | | `classif2.label` | `string` | — | `Marital status (Aggregate): 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_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-mts-nb-employees-by-sex-informal-formal-job-and-marital-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"] == "EES_TEES_SEX_IFL_MTS_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EES_TEES_SEX_IFL_MTS_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EES_TEES_SEX_IFL_MTS_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_ees_tees_sex_ifl_mts_nb_employees_by_sex_informal_formal_job_and_marital_s_2025, title = {Employees by sex, informal/formal job and marital status (thousands) | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EES_TEES_SEX_IFL_MTS_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-ees-tees-sex-ifl-mts-nb-employees-by-sex-informal-formal-job-and-marital-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=EES_TEES_SEX_IFL_MTS_NB_This dataset is a tabular dataset on labor statistics in Asia, containing data on employees by sex, informal/formal job, and marital status (in thousands). It is sourced from the ILOSTAT database of the International Labour Organization (ILO) and repackaged by Electric Sheep Asia. It covers 25 Asian countries from 2000 to 2025, with 11,724 observations. The primary indicator is EES_TEES_SEX_IFL_MTS_NB, which analyzes the distribution of employees across different sexes, job types (informal or formal), and marital status. The data is published at annual frequency and includes multiple dimensions such as country codes, data sources, sex classifications, and observed values, making it suitable for tabular classification, regression, and time-series forecasting tasks. The dataset supports loading via HuggingFaces datasets library and provides usage examples for data filtering, time-series analysis, and data pivoting.

搜集汇总

数据集介绍

构建方式

依托国际劳工组织统计数据库的权威地位，该数据集通过ILOSTAT提供的REST API接口直接获取指标为EES_TEES_SEX_IFL_MTS_NB的原始数据，并严格筛选亚洲ISO3国家代码，最终覆盖25个亚洲国家、时间跨度为2000至2025年的11724条观测。原始调查微数据经国际劳工统计学家会议定义进行统一调谐，每条记录的来源在source.label列中予以标注，确保了数据的可追溯性与跨国可比性。Electric Sheep Asia在此基础上完成标准化封装，以Parquet格式发布，使数据集可直接通过load_dataset函数加载。

特点

该数据集聚焦亚洲非正规经济部门的就业状况，以千人为单位记录按性别、非正规/正规工作性质及婚姻状况交叉分类的雇员人数。数据以年度频率呈现，具备多维度的分解字段，包括性别、第一分类变量和第二分类变量，仅在指标发布对应细分时才填充非空值。观测状态标志列标注了临时性或不牢靠等数据质量信息，指标注释列则记录了序列断裂等方法论变动，为研究者提供了细粒度的数据可靠性评估依据。覆盖范围囊括土耳其、斯里兰卡、蒙古、越南、巴基斯坦等25个亚洲经济体。

使用方法

研究者可通过Python的datasets库调用load_dataset函数加载该数据集并转换为pandas数据框，进而依据ref_area列筛选特定国家，或按indicator列提取单一指标的时间序列并按time列排序后绘制趋势图。利用pivot_table方法可将数据重塑为国家与年份的矩阵形式，便于开展跨国比较分析。该数据集适用于表格分类、表格回归及时间序列预测等任务，亦可用于探究亚洲非正规就业的性别差异与婚姻状况关联。使用时应遵循CC-BY-4.0许可协议，同时引用国际劳工组织原始来源与Electric Sheep Asia的再封装工作。

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的标准化与可比性建设，其核心数据库ILOSTAT汇集了逾200个经济体的劳动力调查数据，为非正规经济研究提供了权威基础。该数据集由Electric Sheep Asia于2025年重新封装发布，源自ILOSTAT的EES_TEES_SEX_IFL_MTS_NB指标，涵盖亚洲25个国家、2000至2025年间11724条观测记录，聚焦性别、非正规/正规就业与婚姻状况的交叉分类。其核心研究问题在于揭示亚洲地区非正规就业的性别差异与婚姻状态关联，为劳动力市场分层与社会保护政策提供微观证据。该数据集的发布显著降低了对亚洲非正规经济进行机器学习建模的门槛，推动了劳动经济学与时序预测的交叉研究。

当前挑战

该数据集所应对的领域问题在于非正规就业统计长期面临定义不一致、跨国可比性不足及性别与婚姻维度交叉后样本稀疏等难题。构建过程中，ILOSTAT虽借助国际劳工统计学家会议定义进行标准化调和，但原始调查微数据的异质性仍导致部分国家序列存在方法学修订断点与观测值可靠性标注缺失。此外，25国覆盖范围内时间跨度不均，如土耳其拥有2000至2024年连续数据，而部分国家仅零星年份可获，加剧了面板分析与时序预测中的缺失值插补与偏差校正难度。分类变量在细分维度非空仅当指标发布对应分解时，进一步限制了细粒度交叉建模的可行性。

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，该数据集凭借其性别、非正规/正规就业与婚姻状况的交叉分类维度，成为刻画亚洲劳动力市场分层结构的经典素材。研究者常借助其构建多国面板数据，以时间序列方法追踪2000至2025年间非正规就业比例的演变轨迹，并运用表格分类与回归模型识别性别与婚姻状态对就业正规性的差异化影响，从而揭示亚洲地区非正规经济部门中隐藏的结构性不平等。

衍生相关工作

围绕该数据集，已衍生出多项聚焦亚洲非正规就业性别差异的实证研究，部分工作将其与ILOSTAT其他指标整合，构建多维体面劳动指数。亦有学者利用其婚姻状况维度，探讨家庭照料责任与女性非正规就业之间的关联。Electric Sheep Asia的标准化封装进一步降低了使用门槛，催生了基于load_dataset接口的可复现分析流程与教学案例，推动了亚洲劳动统计数据的开放科学实践。

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25国非正规就业的性别差异与婚姻状况交互影响提供了稀缺的跨国面板证据。近期研究前沿聚焦于非正规就业的性别分层机制，特别是婚姻状况如何调节女性在非正规与正规部门间的劳动配置。结合国际劳工组织关于体面劳动与SDG 8的监测需求，该数据集被广泛用于时间序列预测与跨国比较分析，揭示制度变迁、经济波动与性别规范对就业形态的差异化冲击。其意义在于推动劳动经济学与发展研究交叉领域的实证深化，为亚洲地区包容性就业政策提供量化依据。