Online recruitment refers to the practice where companies publish job requirements, work locations, qualifications, and compensation information on the internet for job seekers' reference, facilitating digital hiring processes. Online recruitment platforms utilize internet technologies to aggregate these job postings, creating network portals that connect employers with potential candidates.
From the demand perspective, recruitment information reflects talent demand variations across industries and provides insights into supply-demand matching in the U.S. labor market. From the supply side, such data offers valuable references for higher education reform and employment research, potentially enhancing graduate employment efficiency, alleviating structural contradictions, and improving job quality. More importantly, scholars can analyze comprehensive recruitment data to investigate regional and industrial income levels across the United States.
The CnOpenData team presents U.S. Company Recruitment Data, collected from tens of thousands of public sources including online recruitment platforms and corporate websites. This dataset employs natural language processing (NLP) for standardized categorization of positions, job responsibilities, and industries, containing fields such as Job Title (职位名称), Professional Skills (专业技能), Discipline Classification (学科分类), Industry Classification (产业分类), Company Name (公司名称), Address (地址), Salary (薪资), Experience Requirements (经验要求), Education Requirements (学历要求), Data Source (数据来源), and Posting Date (发布日期).
Time Coverage
2010-2022
Field Display
Sample Data
Relevant Literature
- Abebe, Girum, A. Stefano Caria, and Esteban Ortiz-Ospina. 2021. "The Selection of Talent: Experimental and Structural Evidence from Ethiopia." American Economic Review, 111 (6): 1757–1806.
Update Frequency
Updated irregularly