Occupational classification refers to the process of categorizing social occupations with similar general and essential characteristics into systematic taxonomies based on defined rules and standards. With social development and progress, occupational transformations have accelerated significantly. Beyond the obsolescence of old roles and emergence of new ones, the activities and methodologies within the same occupation also evolve continuously. Consequently, occupational categorization exhibits distinct temporal characteristics. Broadly speaking, occupations were synonymous with industries in eras with limited occupational diversity. However, contemporary distinctions reveal occupations and industries as interrelated yet distinct concepts, where industries generally serve as categorical frameworks for occupations in classification systems. Geographically, occupational distributions demonstrate variations across regions, urban-rural areas, industries, and national boundaries.
Applications of occupational information for enterprises and human resource departments:
- Rapidly develop effective job descriptions with ease.
- Expand high-quality candidate pools for vacant positions.
- Define success factors for employee performance.
- Align organizational development with workplace requirements.
- Refine recruitment and training objectives.
- Design competitive compensation and promotion systems.
Applications of occupational information for job seekers:
- Match positions aligned with their interests, skills, and experience.
- Explore emerging trends in growth occupations using current labor market data.
- Investigate strategies for securing ideal employment.
- Maximize earning potential and job satisfaction.
- Understand determinants of success within their professional fields.
CnOpendata presents the O*NET U.S. Occupational Characteristics Data, encompassing over 1,000 occupations based on the 2018 International Standard Classification of Occupations. This dataset covers occupation-related skills, abilities, knowledge domains, work activities, and interest profiles, serving as a valuable resource for career exploration, vocational counseling, and human resources research.
Temporal Coverage
Cross-sectional data: End of 2023
Field Specifications
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 sub-branch table scenarios, this introduction page displays partial examples only. Detailed field specifications and complete sample data can be accessed through dedicated module branch pages on the right.
职业基本信息表
Related 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 Corporate Labor Demand? Evidence from Recruitment Platform Big Data", Management World, No.6.
Data Update Frequency
Annual updates