波动性(金融)
人事变更率
业务
经济
地铁列车时刻表
人口经济学
计量经济学
管理
作者
Alon Bergman,Guy David,Hummy Song
标识
DOI:10.1287/msom.2023.1205
摘要
Problem definition: Employers across many sectors of the economy have been fast to adopt variable work scheduling policies. The cost of this flexibility for employers is usually borne by employees, for whom unstable work schedules create several disruptions. In the context of home healthcare, we examine how employer-driven volatility in nurses’ schedules impacts their decision to voluntarily leave their job. Methodology/results: Using an instrumental variables approach, we causally identify the effect of schedule volatility on nurses’ voluntary turnover. We begin by constructing an operational measure of schedule volatility using time-stamped work log data from one of the largest home health agencies in the United States. Because this measure may be endogenous to the worker’s decision to quit, we instrument for schedule volatility using paid days off taken by other nurses in the same branch. We find that higher levels of schedule volatility substantially increase a worker’s likelihood of quitting. Specifically, a one-standard-deviation increase in schedule volatility increases the average worker’s propensity to quit on a given day by more than threefold. Translated into yearly terms, 30 days of high schedule volatility over the course of the year increases the average worker’s probability of quitting that year by 20%. Our policy simulations of counterfactual scheduling policies suggest that excess schedule volatility can explain a significant portion of voluntary turnover, and some interventions have the potential to substantially reduce workers’ daily propensity to quit. Managerial implications: This work contributes to the understanding of the extent to which employees value control over their own work schedules and are averse to volatile work schedules that are dictated by employers. Especially in the current environment where there is a growing emphasis on work-life balance and employee-driven flexibility, finding a way to support stable schedules could be important for employers to attract and retain workers. Funding: This work was supported by the National Research Service Award Postdoctoral Fellowship, the Wharton Dean's Research Fund, the Agency for Healthcare Research and Quality [T32 Grant 5T32HS26116], and the Claude Marion Endowed Faculty Scholar Award. Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2023.1205 .
科研通智能强力驱动
Strongly Powered by AbleSci AI