Occupational classification refers to the process of categorizing social occupations with similar general or essential characteristics into a systematic framework based on defined rules and standards. With societal development and progress, occupations evolve rapidly. Beyond the obsolescence of old roles and emergence of new ones, the activities and methodologies within the same occupation also undergo transformations. Consequently, occupational classification inherently reflects its historical context. Broadly speaking, during periods with fewer occupations, the terms "occupation" and "industry" were often synonymous. However, these concepts now represent distinct yet interrelated classifications, where industries typically serve as broader categories for occupations. Spatially, occupational distributions exhibit variations across regions, urban-rural divides, industries, and nations.
Applications of occupational information for enterprises and human resources departments:
- Quickly and effortlessly develop effective job descriptions.
- Expand quality candidate pools for open positions.
- Define success factors for employee performance.
- Align organizational development with workplace demands.
- Refine recruitment and training objectives.
- Design competitive compensation and promotion systems.
Applications of occupational information for job seekers:
- Match jobs to their interests, skills, and experience.
- Explore growth occupations using up-to-date labor market data.
- Research pathways to securing ideal employment.
- Maximize income potential and job satisfaction.
- Understand determinants of success within their professional fields.
CnOpendata presents the O*NET Occupational Characteristics Data, covering over 1,000 occupations based on the 2018 International Standard Classification of Occupations (ISCO). This dataset encompasses occupation-related skills, abilities, knowledge, work activities, and interest information, facilitating occupational exploration, career counseling, and diverse human resources research.
Time Scope
Cross-sectional data: September 2025 (can be updated as needed)
Field Presentation
职业概括
具体职业描述
职业要求
工作经验要求
员工要求
员工特征
职业特征
其他信息
Sample Data
Given the dataset’s structural complexity with multiple nested tables, this introduction displays partial samples only. Detailed field indicators and complete sample data can be accessed via the module-specific branch pages on the right.
职业基本信息表
Relevant Literature
- Lena Hensvik, Thomas Le Barbanchon, Roland Rathelot, 2021, “Job search during the COVID-19 crisis”, Journal of Public Economics, Volume 194.
- Chen Lin, Gao Yuepeng, Yu Linhui, 2024: "How Does Artificial Intelligence Transform Labor Demand in Enterprises?—Evidence from Big Data on Recruitment Platforms" (人工智能如何改变企业对劳动力的需求？——来自招聘平台大数据的分析), Management World (管理世界), No. 6.
Data Update Frequency
Annual updates