electricsheepasia/asia-ilo-ees-tees-sex-est-mts-nb-employees-by-sex-establishment-size-and-marital-st

--- 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 - employees - ilo - labour - employment pretty_name: "Employees by sex, establishment size and marital status (thousands) | Asia (ILOSTAT)" --- # Employees by sex, establishment size and marital status (thousands) | Asia (ILOSTAT) 🌏 **28,887 observations** · **25 Asia countries** · **2000–2025** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **28,887 observations** of `Employees` 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_EST_MTS_NB) - **Publisher:** International Labour Organization (ILO) - **License:** [cc-by-4.0](https://creativecommons.org/licenses/by/4.0/) - **Topic:** Employees ## Methodology Data pulled directly from the ILOSTAT REST API at `https://rplumber.ilo.org/data/indicator?id=EES_TEES_SEX_EST_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 | |---------|-----:|-----------:|----------:| | `THA` | 4,114 | 2010 | 2024 | | `MNG` | 3,515 | 2013 | 2024 | | `PAK` | 3,171 | 2006 | 2025 | | `TUR` | 2,552 | 2000 | 2024 | | `JOR` | 2,419 | 2017 | 2024 | | `IRN` | 2,374 | 2005 | 2024 | | `LKA` | 1,680 | 2010 | 2024 | | `JPN` | 1,439 | 2000 | 2023 | | `PSE` | 1,366 | 2010 | 2025 | | `ARM` | 744 | 2009 | 2017 | | `TLS` | 647 | 2001 | 2013 | | `VNM` | 550 | 2019 | 2020 | | `KHM` | 546 | 2012 | 2019 | | `BGD` | 522 | 2010 | 2022 | | `TJK` | 506 | 2007 | 2009 | | ... | _10 more countries_ | | | ## Indicators (sample) - `EES_TEES_SEX_EST_MTS_NB` — Employees by sex, establishment size 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_EST_MTS_NB` | | `indicator.label` | `string` | Indicator name in English | `Employees by sex, establishment size …` | | `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.) | `EST_AGGREGATE_TOTAL` | | `classif1.label` | `string` | — | `Establishment size (Aggregate): 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`** (3 unique values): `SEX_T`, `SEX_M`, `SEX_F` ## 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-est-mts-nb-employees-by-sex-establishment-size-and-marital-st") 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_EST_MTS_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EES_TEES_SEX_EST_MTS_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EES_TEES_SEX_EST_MTS_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_ees_tees_sex_est_mts_nb_employees_by_sex_establishment_size_and_marital_st_2025, title = {Employees by sex, establishment size and marital status (thousands) | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EES_TEES_SEX_EST_MTS_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-ees-tees-sex-est-mts-nb-employees-by-sex-establishment-size-and-marital-st}} } ``` ## 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_EST_MTS_NB_This dataset contains 28,887 observations of Employees data across 25 Asia countries, spanning 2000–2025, covering 1 distinct indicators. The data is sourced from ILOSTAT, the ILOs central statistics database, and focuses on the topic of employees by sex, establishment size and marital status (thousands). It has been repackaged for machine learning use, with a tabular schema including columns such as country code, indicator, sex, establishment size, marital status, time, and observed value. The data is harmonized by the ILOs Department of Statistics and is suitable for tasks like tabular classification, regression, and time-series forecasting.

搜集汇总

数据集介绍

构建方式

在全球劳动统计体系内，国际劳工组织（ILO）长期承担着跨国劳动力市场数据的协调与标准化职责，ILOSTAT即为该机构面向全球发布的中央统计数据库。本数据集由Electric Sheep Asia团队基于ILOSTAT公开的REST API接口获取原始记录，具体针对指标EES_TEES_SEX_EST_MTS_NB进行抽取，并依据ISO 3166-1 alpha-3国家代码筛选出亚洲地区25个国家的观测数据。原始调查微数据经国际劳工统计学家会议（ICLS）定义体系进行统一协调，来源信息以source.label字段加以标注，以便追溯。最终以Parquet格式重新封装发布于HuggingFace平台，形成覆盖2000至2025年、共计28,887条记录的标准化数据集。

特点

该数据集以亚洲发展中与发达经济体并存的区域视角为特色，覆盖25个国家、时间跨度达四分之一世纪，观测粒度细分为性别、机构规模与婚姻状况三重维度，能够支撑多维度的劳动力结构分析。数据结构采用统一的长表模式，包含国家代码、来源标识、指标编码、分类变量及观测值等字段，并配有观测状态标志与注释信息，便于识别数据质量与序列断点。性别维度涵盖总计、男性与女性三类取值，机构规模与婚姻状况作为分类变量提供聚合与细分层级。数据以年为频率发布，反映年度劳动力就业状况的变迁趋势。

使用方法

研究者可通过HuggingFace的datasets库直接加载该数据集，使用load_dataset函数获取后转换为Pandas数据框进行后续分析。典型操作包括按ref_area字段筛选特定国家、按indicator字段提取目标指标并依时间排序以构建时间序列、以及利用pivot_table方法将数据重塑为国家与年份的交叉矩阵。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，亦可服务于劳动经济学、发展研究与社会政策评估等领域的实证分析。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的再封装工作。

背景与挑战

背景概述

国际劳工组织（ILO）自1919年成立以来，始终致力于全球劳动统计的标准化与传播。ILOSTAT作为其核心统计数据库，汇集了200余个经济体的劳动力调查、住户收入调查及行政记录，为监测就业、失业、工资和体面劳动等可持续发展目标提供了权威基准。本数据集由Electric Sheep Asia于2026年从ILOSTAT REST API中提取并重新封装，聚焦亚洲25个国家2000至2025年间按性别、企业规模与婚姻状况分列的雇员人数（千人），共包含28,887条观测。该数据集填补了亚洲地区细分劳动统计的空白，为探究性别差异、企业结构对就业的影响以及婚姻状况与劳动参与的关系提供了关键数据支撑，对劳动经济学、社会政策评估及区域发展研究具有重要参考价值。

当前挑战

该数据集所应对的领域问题在于，传统劳动统计往往缺乏按性别、企业规模及婚姻状况交叉分类的雇员数据，难以揭示不同人口群体在劳动市场中的结构性差异，而此类细分数据对于制定精准就业政策、评估性别平等进展至关重要。构建过程中的主要挑战包括：原始调查数据来自不同国家且年份跨度大，各国统计能力与调查频率参差不齐，导致数据在时间与空间上的可比性受限；ILOSTAT虽采用国际劳工统计学家会议（ICLS）定义进行协调，但源数据仍存在方法修订、数值不可靠等标记，需借助观测状态与注释字段进行甄别；部分国家数据年份不连续或覆盖不全，影响时间序列分析的稳健性；此外，将多维分类变量整合为统一模式并保持语义一致，亦对数据清洗与元数据管理提出了较高要求。

常用场景

经典使用场景

在劳动经济学与人口统计学的交叉领域，探究企业规模、性别构成与婚姻状态如何交织影响就业格局，始终是核心议题。该数据集汇聚亚洲25国2000至2025年间的雇员观测记录，天然适配面板数据回归、时间序列预测及跨国比较分析。研究者常以“国家—年份—性别—企业规模—婚姻状况”为分层维度，构建固定效应模型或差分自回归模型，用以刻画不同婚姻状态下的男女雇员在各类规模企业中的分布动态，进而揭示亚洲劳动力市场的结构性变迁。

实际应用

在政策制定与商业决策层面，该数据集为劳工部门评估就业政策成效、国际组织监测体面劳动目标进展提供了量化依据。例如，通过追踪不同规模企业中已婚与未婚女性雇员比例的时序变化，可识别家庭友好型政策可能惠及的群体；企业亦可借助跨国比较，优化在亚洲不同市场的用工策略与福利设计。数据经HuggingFace平台封装后，可快速载入Python环境，支撑从探索性可视化到机器学习建模的各类实务分析。

衍生相关工作

基于该数据集，已衍生出若干围绕亚洲劳动力市场性别差异与就业结构的研究工作。部分学者利用其构建跨国面板，检验企业规模对性别工资差距的异质性影响；亦有研究将其与ILOSTAT其他指标（如工时、工资）链接，探讨婚姻状况通过工作时长渠道对就业参与的作用。在数据科学社区，该数据集被用于时间序列预测竞赛与分类任务基准，推动了针对亚洲地区劳动统计的机器学习模型开发，并启发了对国际劳工组织其他指标集的类似重封装实践。