Umbrella Summary

What is turnover?

The definition of turnover tends to somewhat vary in practice and research. The most common element is an employee leaving their job. Beyond that, turnover can be further defined as internal vs. external, voluntary vs. involuntary, functional vs. dysfunctional, and avoidable vs. unavoidable.

  • Internal vs. external: Internal turnover means that the employee moves to a different job within the organization, whereas external means that the employee leaves the organization altogether. In child welfare, however, the “organization” may be defined as all of state or county government, which means that a lot of movement is considered internal.
  • Voluntary vs. involuntary: Voluntary turnover is initiated by an employee, and involuntary turnover is initiated by an agency or organization, such as through terminations or layoffs (Mobley et al., 1979).
  • Functional vs. dysfunctional: Functional turnover is that which is beneficial to the organization (i.e., turnover of a poor performer), whereas dysfunctional turnover is that which is detrimental to the organization (i.e., turnover of a high performer; Dalton et al., 1981).
  • Avoidable vs. unavoidable: Avoidable turnover is that which could have been prevented by the organization, whereas unavoidable turnover is that which could not have been prevented (Abelson, 1987).

Many turnover researchers assert that when it comes to preventing turnover, the focus should be on turnover that is external, voluntary, dysfunctional, and avoidable—preventable loss of good employees. Organizations, however, may have additional interests, depending on the extent, nature, and impact of the turnover problem. For example, in child welfare, internal transfers can have a significant impact on children and families, so excessive internal movement would be valuable to explore. Likewise, a significant number of dismissals or reassignments may signal a need to better screen, train, or coach employees.

Turnover measurement begins at the level of individual employees, with significant variation in the amount and type of records available across organizations. Reasons for separation or movement typically involve a standard set of categories (e.g., resigned, dismissed, retired, promoted, demoted, transfer, deceased, laid off). To determine whether turnover is internal or external, personnel data need to include information about where the employee went. Optimally, this information is reflected in the reasons for separation. For example, transfers, promotions, and demotions are internal, and resignations, dismissals, and retirements are external. Sometimes separation reasons provide even further detail about where the person went inside or outside the organization (e.g., transferred to ____ department or job, resigned for job outside government), which allows for more nuanced analyses.

Voluntary turnover is primarily limited to resignations, though retirement is also sometimes included. One challenge with voluntariness is that many people resign ahead of an anticipated termination, which inflates the incidence of voluntary turnover. If these circumstances are captured in personnel records, measures will be more accurate. Optimally, this is a specific separation category (e.g., resigned to avoid dismissal), though this judgment will be made by the organization, rather than the employee, and therefore may not be entirely accurate. In lieu of or as a supplement to personnel data, surveys can be used to get additional perspectives or details. For example, departing or past employees can be asked to answer a series of questions about the extent to which it was their decision to leave the organization and whether they felt encouraged to leave or stay (see Campion, 1991 for suggestions). Honest answers are more likely to be provided when an outside party (such as a consultant or researcher) inquires, and responses are also sometimes different when they are provided at a later date (e.g., Campion, 1991; Hinrichs, 1975). Most organizations have sufficient records to calculate voluntary and involuntary turnover, and researchers have historically focused on voluntary turnover because of their interest in employee motivation and decision making (Hom et al., 2017).

Turnover functionality can be more difficult to readily measure. Most terminations are due to poor performance of some sort, so those tend to qualify as functional turnover, as would resignations to avoid dismissal. Among voluntary leavers, however, there are both good and poor performers, so additional information is needed to identify them. Performance indicators include past performance reviews, performance metrics, and any disciplinary actions. When performance information is available, it may not be connected to turnover data, so steps must be taken to connect the information. In lieu of these types of performance data, researchers have asked supervisors to rate the departing employee on overall performance and whether they would rehire the person (Dalton et al., 1981). Though organizations would be wise to do the same thing, it does not appear to be very common.

To assess turnover avoidability, one method involves learning about employees’ reasons for leaving and then categorizing these reasons as avoidable or unavoidable. The challenge is that beyond a small set of very clear-cut issues that are outside the agency’s control (e.g., medical issues, death), many of the reasons that have been proposed as unavoidable (e.g., pregnancy, spouse relocation, staying home to care for children, going to school; Abelson, 1987; Dalton et al., 1981) may very well be driven by job dissatisfaction of some sort. A potentially more accurate, but still imperfect, method is to ask departing or past employees to rate a series of questions about the extent to which they believe there is anything the agency could have done to prevent them from leaving (see Campion, 1991 for suggestions).

For researchers, turnover measurement usually involves analyses of the individual employee data just described. Organizations, however, often focus on aggregating individual data to arrive at turnover rates, which are calculated by dividing the number of individuals who have left by the total number of employees (in that same time period) and then multiplying by 100 to get a rate. The number of employees is typically known as a headcount, and there are variations in how this is calculated. In general, it is best to use an average headcount across time to account for fluctuations.

Turnover rates are typically calculated at least annually and more often (e.g., monthly or quarterly) if turnover is problematic and needs to be monitored more closely. Unless the number of employees (headcount) changes drastically, the turnover rate will naturally increase across time as more people leave. For example, assuming steady conditions, a turnover rate over a 6-month span is likely to be about half the turnover rate over a 12-month span. It is therefore important to understand the associated time frames when comparing rates. Turnover rates for time periods that are less than one year can be annualized, as a means of standardizing or forecasting.

To better understand turnover, turnover rates can be segmented, or disaggregated, by a variety of variables, to explore group differences that provide insights on important dynamics. These variables include not only the different types of turnover described above, but also other factors such as job, role, geography, supervisor or manager, education, or other demographics.

What are turnover intentions?

Because of the challenges and time associated with tracking turnover in an organization, researchers and organizations often use turnover intentions or other withdrawal cognitions as either an early indicator of or a proxy for actual turnover. Withdrawal cognitions typically include thoughts about leaving, thoughts about searching for a job, intentions to search, and intentions to leave. In child welfare workforce research, intentions to stay are occasionally used, though they are often about staying in the profession (e.g., Ellett, 2009), which is more akin to occupational commitment than intentions related to a job or organization. Despite the possible conceptual differences among all of these withdrawal cognitions, they often get used interchangeably and without acknowledgement of the potential for distinction. Measures of withdrawal cognitions vary widely, and though most focus on leaving a job, some use language about leaving the organization. They are generally short, often consisting of one to a few items, such as “How often do you think about quitting your job?,” “How often do you think about searching for a new job?,” “I intend to search for a position with another employer in the next year” (Bentein et al., 2005), and “I intend to quit my job within the next six months” (Hom & Griffeth, 1995).

Why is turnover important?

Turnover is important to consider because it can have important practical implications and financial repercussions for an organization. Employee turnover can cause disruptions in the workplace, including affecting coworkers, supervisors, and the people the employee had
served. Greater turnover is modestly related to lower organizational performance overall, as well as lower financial performance and workforce productivity. Higher turnover rates are also moderately associated with lower customer satisfaction (Park & Shaw, 2013). Thus, it is important to try to limit turnover because of its association with detrimental organizational outcomes.

What contributes to turnover and turnover intentions?

The standard research design for studying turnover has involved first measuring a myriad of employee characteristics and perceptions and then examining the extent to which they predict subsequent (typically voluntary) turnover. Thus, the bulk of meta-analytic research has thus far focused on assessing factors that are merely associated with turnover, not on causal relationships. As two of the earliest hypothesized predictors of turnover, job satisfaction and organizational commitment are the most commonly researched job attitudes. Despite their central role in turnover theory, however, they are only moderately related to turnover (Rubenstein et al., 2017). Other noteworthy factors include coping skills, rewards, fit, job embeddedness, occupational commitment, perceived organizational support, employee engagement, stress and burnout, leadership, job security, job alternatives, and tenure, all of which are also moderately related to turnover (Rubenstein et al., 2017).

The strongest predictors of turnover are withdrawal cognitions and job search behavior, which are seen as more proximal precursors, rather than underlying causal factors, of turnover. It should be noted that though the connections with turnover are strong, they are not perfect, and the two should not be treated as the same thing (Rubenstein et al., 2017). Withdrawal cognitions and behaviors may nonetheless have value in their own right, in that it is undesirable to have employees be in a state of withdrawal, even if it doesn’t ultimately lead to turnover. Measuring these warning signs can advance our understanding and possibly be used to intervene with employees before it’s too late.
A variety of other individual attributes, job attitudes, job characteristics, contextual factors, and employee behaviors in the workplace may contribute to experiencing turnover intentions and ultimately leaving a job. For an in-depth look at other potential contributing factors, please refer to the QIC-WD Workforce Research Catalog.

What research is needed?

Though there is a lot of research on turnover in child welfare, there is significant variety in what is studied, how, and why, which makes it difficult to identify patterns of findings. Child welfare workforce research could advance in a more efficient and unified way by capitalizing on previous research outside of child welfare described in this summary. Taking advantage of existing theories, methods, measures, and findings would cultivate a more robust body of evidence and would allow for identification of similar and unique aspects of turnover in this profession.

In addition, there is a need to bridge the gap between research and practice and to develop and test interventions to improve turnover, rather than merely predict it. Practitioners can enhance their workforce practices by using research evidence to drive decision making in the workplace, and practitioners and researchers can work together to develop evidence that is grounded in the practical realities, needs, and interests of agencies and the workforce. For summaries of previous research, see Allen et al. (2014), Hom et al. (2017), and Bolt et al. (2022). Also see the QIC-Take on measuring turnover for additional guidance

QIC-WD Takeaways

  • Turnover occurs when an employee leaves their job.
  • Turnover can be further defined as internal vs. external, voluntary vs. involuntary, functional vs. dysfunctional, and avoidable vs. unavoidable.
  • Turnover is generally measured using personnel records detailing whether an employee has left and should ideally include information on the employee’s reason for separation.
  • Additionally, surveys given to supervisors and departing employees can be used to supplement personnel data and get further information about different types of turnover.
  • Organizations often measure turnover rates at least annually, by dividing the number of individuals who have left the organization by the total number of employees and then multiplying by 100 to get a percentage.
  • Withdrawal cognitions include thoughts about leaving, thoughts about searching for a job, intentions to search, and intentions to leave.
  • Turnover has both performance-related and finance-related repercussions for organizations, including lower financial performance, workforce productivity, and customer satisfaction.
  • Withdrawal cognitions and job search behavior are the strongest predictors of turnover.
  • Certain employee attributes, job attitudes, job characteristics, contextual factors, and employee behaviors in the workplace may make one more likely to have turnover intentions and choose to leave an organization. For further details, see the QIC-WD Workforce Research Catalog.
  • Future research on child welfare turnover should capitalize on decades of turnover research outside of child welfare and should include evaluation of interventions to improve turnover.


Abelson, M. A. (1987). Examination of avoidable and unavoidable turnover. Journal of Applied Psychology, 72(3), 382–386.

Allen, D. G., Hancock, J. I., Vardaman, J. M., & Mckee, D. N. (2014). Analytical mindsets in turnover research. Journal of Organizational Behavior, 35(1), 61–86.

Bentein, K., Vandenberghe, C., Vandenberg, R., & Stinglhamber, F. (2005). The role of change in the relationship between commitment and turnover: A latent growth modeling approach. Journal of Applied Psychology, 90(3), 468–482.

Bolt, E. E. T., Winterton, J., & Cafferkey, K. (2022). A century of labour turnover research: A systematic review. International Journal of Management Reviews, 24(4), 555–576.

Campion, M. A. (1991). Meaning and measurement of turnover: Comparison of alternative measures and recommendations for research. Journal of Applied Psychology, 76(2), 199–212.

Dalton, D. R., Krackhardt, D. M., & Porter, L. W. (1981). Functional turnover: An empirical assessment. Journal of Applied Psychology, 66(6), 716–721.

Ellett, A. J., (2009). Intentions to remain employed in child welfare: The role of human caring, self-efficacy beliefs, and professional organizational culture. Children and Youth Services Review, 31, 78–79.

Hinrichs, J. R. (1975). Measurement of reasons for resignation of professionals: Questionnaire versus company and consultant exit interviews. Journal of Applied Psychology, 60(4), 530–532.

Hom, P. W., & Griffeth, R. W. (1995). Employee turnover. Southwestern College Publishing.

Hom, P. W., Lee, T. W., Shaw, J. D., & Hausknecht, J. P. (2017). One hundred years of employee turnover theory and research. Journal of Applied Psychology, 102(3), 530–545.

Park, T.-Y., & Shaw, J. D. (2013). Turnover rates and organizational performance: A meta-analysis. Journal of Applied Psychology, 98(2), 268–309.

Rubenstein, A. L., Eberly, M. B., Lee, T. W., & Mitchell, T. R. (2017). Surveying the forest: A meta-analysis, moderator investigation, and future-oriented discussion of the antecedents of voluntary employee turnover. Personnel Psychology, 71(1), 23–65.


Megan Paul, PhD, University of Nebraska-Lincoln

Sarah Stepanek, MA, University of Nebraska at Omaha

Suggested Citation

Paul, M., & Stepanek, S. (2023, September 29). Umbrella summary: Turnover. Quality Improvement Center for Workforce Development. /umbrella-summary/turnover

For general information about Umbrella Summaries, visit https://www.qic-wd.org/umbrella-summaries-faq