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Job training and organizational performance: Analyses from medical institutions in China

Yao Yao, Gordon G. Liu and Yujie Cui

China Economic Review, 2020, vol. 60, issue C

Abstract: The benefit of training on economic outcomes has been well-documented in previous literature, as training is generally recognized as one of the important approaches in human capital investment. However, the return of employee training on organizational level performance is not verified sufficiently, especially in the healthcare field. By taking advantage of a dataset with the information on all medical institutions in Sichuan, China over 2012–2016, this paper investigates the effects of employee training on medical institution’s performance through the Propensity Score Matching (PSM) method and the Event Study approach. The results show that training benefits medical institution performance in some indicators, such as total visits, outpatient visits, non-medical-insurance, and local competitiveness. These effects, however, vary across different types of medical institutions. Grassroot medical institutions in the county area that serve as a health gatekeeper (known as primary health care institution, PHCIs) gain most in hospital visits after training. In addition, training results in a 16.5% increase in the average performance wage for county-PHCIs, and a 52.8% increase in the employees’ performance wage for higher-level general hospitals in cities. In general, by participating in training, PHCIs in county region gain more in the overall revenue through the non-medical-insurance income.

Keywords: Human capital; Training; Medical institutions; Performance; Event study (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:chieco:v:60:y:2020:i:c:s1043951x19301579

DOI: 10.1016/j.chieco.2019.101396

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China Economic Review is currently edited by B.M. Fleisher, K. X. D. Huang, M.E. Lovely, Y. Wen, X. Zhang and X. Zhu

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