Job preferences for medical students in China: A discrete choice experiment

Although the number of medical workers has increased rapidly, its scarcity in rural areas remains a serious problem in China. This study aimed to investigate medical students’ stated preferences when choosing a job, so as to assist policy makers with designing alternative interventions to address the unbalanced distribution of the health workforce in China. A discrete choice experiment (DCE) was conducted to elicit the job preferences of final year medical students. Attributes include work location, hospital type, monthly income, bianzhi (which can be loosely regarded as state administrative staffing), work environment, Training and career development opportunity. This study was carried out during April to June 2017 in 4 medical universities in Shandong Province, China. Mixed logit models were used to analyze the relative importance of job attributes. A total of 519 medical students participated in the survey. All 6 attributes were statistically significant with the expected sign and demonstrated the existence of preference heterogeneity. In the main effects mixed logit model, working in the city and a superior working environment were most strongly associated with job preference. A relatively unexpected finding was the relatively lower utility of offering bianzhi in job preferences. Subgroup analysis showed that females and those who have an urban background were significantly willing to pay more for working in the city. The most preferred scenario for medical students was to select a better work environment job in a tertiary hospital in the city, which could offer 9000 CNY monthly, with sufficient training and career development opportunities and bianzhi. Both monetary and nonmonetary intervention could be considered by policy makers to attract medical students to work in rural areas in China. There exists preference heterogeneity on medical students’ job preferences, which should also be taken into account in developing more effective policy incentive packages.