Toxic Culture Is Driving the Great Resignation

Research using employee data reveals the top five predictors of attrition and four actions managers can take in the short term to reduce attrition.

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Measuring Culture

This series includes the MIT SMR/Glassdoor Culture 500, an annual index and research project that uses over 1.4 million employee reviews to analyze culture in leading companies, along with new research focused on measuring organizational culture using a scientific approach.
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More than 40% of all employees were thinking about leaving their jobs at the beginning of 2021, and as the year went on, workers quit in unprecedented numbers.1 Between April and September 2021, more than 24 million American employees left their jobs, an all-time record.2 As the Great Resignation rolls on, business leaders are struggling to make sense of the factors driving the mass exodus. More importantly, they are looking for ways to hold on to valued employees.

To better understand the sources of the Great Resignation and help leaders respond effectively, we analyzed 34 million online employee profiles to identify U.S. workers who left their employer for any reason (including quitting, retiring, or being laid off) between April and September 2021.3 The data, from Revelio Labs, where one of us (Ben) is the CEO, enabled us to estimate company-level attrition rates for the Culture 500, a sample of large, mainly for-profit companies that together employ nearly one-quarter of the private-sector workforce in the United States.4

While resignation rates are high on average, they are not uniform across companies. Attrition rates for the six months we studied ranged from less than 2% to more than 30% across companies. Industry explains part of this variation. The graph below shows the estimated attrition rate for 38 industries from April through September, and the spread across industries is striking. (See “Industry Average Attrition Rate in the Great Resignation.”) Apparel retailers, on average, lost employees at three times the rate of airlines, medical device makers, and health insurers.

The Great Resignation is affecting blue-collar and white-collar sectors with equal force. Some of the hardest hit industries — apparel retail, fast food, and specialty retail — employ the highest percentage of blue-collar workers among all industries we studied. Management consulting, in contrast, had the second-highest attrition rate but also employs the largest percentage of white-collar professionals of any Culture 500 industry. Enterprise software, which also suffered high churn, employs the highest percentage of engineering and technical employees.

Industry explains some of the variation in attrition rates across companies but not all of it. Even within the same industry, we observed significant differences in attrition rates. The figure below compares competitors with high and low attrition rates within their industries. (See “How Culture 500 Company Attrition Rates Compare Within Industries.”) Workers are 3.8 times more likely to leave Tesla than Ford, for example, and more than twice as likely to quit JetBlue than Southwest Airlines.

Not surprisingly, companies with a reputation for a healthy culture, including Southwest Airlines, Johnson & Johnson, Enterprise Rent-A-Car, and LinkedIn, experienced lower-than-average turnover during the first six months of the Great Resignation.

Although the sample is small, these pairs hint at another, more intriguing pattern. More-innovative companies, including SpaceX, Tesla, Nvidia, and Netflix, are experiencing higher attrition rates than their more staid competitors. The pattern is not limited to technology-intensive industries, since innovative companies like Goldman Sachs and Red Bull have suffered higher turnover as well.

To dig deeper into the drivers of intra-industry turnover, we calculated how each Culture 500 company’s attrition rate compared with the average of its industry as a whole. This measure, which we call industry-adjusted attrition, translates each company’s attrition rate into standard deviations above or below the average for its industry.5

We also analyzed the free text of more than 1.4 million Glassdoor reviews, using the Natural Employee Language Understanding platform developed by CultureX, a company two of us (Donald and Charles) cofounded. For each Culture 500 company, we measured how frequently employees mentioned 172 topics and how positively they talked about each topic. We then analyzed which topics best predicted a company’s industry-adjusted attrition rate.

Top Predictors of Employee Turnover During the Great Resignation

Much of the media discussion about the Great Resignation has focused on employee dissatisfaction with wages. How frequently and positively employees mentioned compensation, however, ranks 16th among all topics in terms of predicting employee turnover. This result is consistent with a large body of evidence that pay has only a moderate impact on employee turnover.6 (Compensation can, however, be an important predictor of attrition in certain settings, such as nurses in large health care systems).

In general, corporate culture is a much more reliable predictor of industry-adjusted attrition than how employees assess their compensation. The figure below displays the five predictors of relative attrition. (See “Top Predictors of Attrition During Great Resignation.”) To give a sense of their relative importance, we’ve benchmarked each element relative to the predictive power of compensation.7 A toxic corporate culture, for example, is 10.4 times more powerful than compensation in predicting a company’s attrition rate compared with its industry.

Let’s take a closer look at each of the top five predictors of employee turnover.

Toxic corporate culture. A toxic corporate culture is by far the strongest predictor of industry-adjusted attrition and is 10 times more important than compensation in predicting turnover. Our analysis found that the leading elements contributing to toxic cultures include failure to promote diversity, equity, and inclusion; workers feeling disrespected; and unethical behavior. In an upcoming article, we will dive deeper into each of these factors and examine different ways managers and employees can spot signals of toxic culture.8 For now, the important point is that a toxic culture is the biggest factor pushing employees out the door during the Great Resignation.

Job insecurity and reorganization. In a previous article, we reported that job insecurity and reorganizations are important predictors of how employees rate a company’s overall culture. So it’s not surprising that employment instability and restructurings influence employee turnover.9 Managers frequently resort to layoffs and reorganizations when their company’s prospects are bleak. Previous research has found that employees’ negative assessments of their company’s future outlook is a strong predictor of attrition.10 When a company is struggling, employees are more likely to jump ship in search of more job security and professional opportunities. Past layoffs, moreover, typically leave surviving employees with heavier workloads, which may increase their odds of leaving.

Another reason job insecurity could predict turnover is related to our measure of employee attrition, which incorporates job changes for all causes — including layoffs and involuntary terminations. We would expect frequent mentions of reorganizations and layoffs to predict involuntary turnover. According to the U.S. Bureau of Labor Statistics, however, involuntary separations have accounted for less than one-quarter of all employee exits among large companies during the Great Resignation.11 So it’s likely that poor career prospects and job insecurity contributed significantly to employees leaving on their own accord as well.

High levels of innovation. It’s not surprising that workers leave companies with toxic cultures or frequent layoffs. But it is surprising that employees are more likely to exit from innovative companies. In the Culture 500 sample, we found that the more positively employees talked about innovation at their company, the more likely they were to quit. The attrition rates of the three most innovative Culture 500 companies — Nvidia, Tesla, and SpaceX — are three standard deviations higher than those in their respective industries.

Staying at the bleeding edge of innovation typically requires employees to put in longer hours, work at a faster pace, and endure more stress than they would in a slower-moving company. The work may be exciting and satisfying but also difficult to sustain in the long term. When employees rate their company’s innovation positively, they are more likely to speak negatively about work-life balance and a manageable workload. During the Great Resignation, employees may be reconsidering the personal toll that relentless innovation takes.

Failure to recognize performance. Employees are more likely to leave companies that fail to distinguish between high performers and laggards when it comes to recognition and rewards. Companies that fail to recognize and reward strong performers have higher rates of attrition, and the same is true for employers that tolerate underperformance. The issue is not compensation below market rates, but rather recognition — both informal and financial — that is not linked to effort and results. High-performing employees are the most likely to resent a lack of recognition for their results, which means that companies may be losing some of their most productive workers during the Great Resignation.

Poor response to COVID-19. Employees who mentioned COVID-19 more frequently in their reviews or talked about their company’s response to the pandemic in negative terms were more likely to quit. The same pattern holds true when employees talk more generally about their company’s policies for protecting their health and well-being.

Short-Term Actions to Boost Retention

The powerful predictors of attrition listed above are not easy to change. A weak future outlook that spurs restructuring and layoffs may be difficult to reverse; it is too late to fix a poor response to the pandemic; and a toxic corporate culture cannot be improved overnight. Relentless innovation provides companies like Tesla or Nvidia with a competitive advantage, so they must find ways to retain employees without sacrificing their innovation edge.

Our analysis identified four actions that managers can take in the short term to reduce attrition. (See “Short-Term Steps for Companies to Increase Retention.”) As in the graph above, each bar represents the topic’s predictive power relative to compensation. This time, the topics predict a company’s ability to retain employees compared with industry peers. Providing employees with lateral career opportunities, for example, is 2.5 times more powerful as a predictor of a company’s relative retention rate compared with compensation.

Provide opportunities for lateral job moves. Not all employees want to climb the corporate ladder or take on additional work or responsibilities. Many workers simply want a change of pace or the opportunity to try something new. When employees talk positively about lateral opportunities — new jobs offering fresh challenges without a promotion — they are less likely to quit. Lateral career opportunities are 12 times more predictive of employee retention than promotions. We observed the same pattern in multinationals: The more frequently employees discussed the possibility of international postings, the more likely they were to stick with their current employer.

Sponsor corporate social events. Company-organized social events, including happy hours, team-building excursions, potluck dinners, and other activities outside the workplace are a key element of a healthy corporate culture, so it’s no surprise that they are also associated with higher rates of retention.12 Organizing fun social events is a low-cost way to reinforce corporate culture as employees return to the office, and it strengthens employees’ personal connections to their team members.

Offer remote work options. Much of the media coverage of the Great Resignation has focused on the importance of remote work in retaining employees. Unsurprisingly, when employees discussed remote work options in more positive terms, they were less likely to quit. What you might not have expected is the relatively modest impact of remote work on retention — just a bit more powerful than compensation in predicting lower attrition. Remote work options may have a modest effect on employee turnover because most companies in an industry converge on similar policies. If companies cannot differentiate themselves based on remote work options, they may need to look elsewhere — providing lateral job opportunities, for instance, or making schedules more predictable — to retain employees.

Make schedules more predictable for front-line employees. When blue-collar employees describe their schedules as predictable, they are less likely to quit. Having a predictable schedule is six times more powerful in predicting front-line employee retention than having a flexible schedule. (A predictable schedule has no predictive power for white-collar employees.)

This finding is consistent with a study of 28 Gap stores, in which employees at randomly-assigned locations received their work schedules two weeks in advance, and their managers were barred from canceling their shifts at the last minute. Employees in the control stores were subject to the usual scheduling practices.13 The stores with predictable schedules increased retention among their most experienced associates. Compared with the workers at the control stores, the employees with fixed schedules had a 7% improvement in their quality of sleep. The benefits were especially pronounced for workers with children, who reported a 15% reduction in stress.

Much of the media coverage of the Great Resignation focuses on high turnover among burned-out knowledge workers who are dissatisfied with their stagnant wages. Our findings are broadly consistent with this narrative. Industries that employ large numbers of professional and technical employees, like management consulting and enterprise software, have experienced high turnover. We found indirect evidence that burnout may contribute to higher levels of attrition among companies that excel at innovation. It’s worth noting, however, that our direct measures of burnout, workload, and work-life balance do not emerge as key predictors of industry-adjusted turnover.

The simplistic narrative of white-collar burnout misses other critical realities of the Great Resignation. Our findings reinforce recent government statistics showing that blue-collar intensive industries like retail and fast food are experiencing unprecedented levels of attrition.14

More fundamentally, we found that corporate culture is more important than burnout or compensation in predicting which companies lost employees at a higher rate than their industries as a whole. A toxic corporate culture is the single best predictor of which companies suffered from high attrition in the first six months of the Great Resignation. The failure to appreciate high performers, through formal and informal recognition, is another element of culture that predicts attrition. A failure to recognize performance is likely to drive out a company’s most productive employees. This is not to argue that compensation and burnout don’t influence attrition — of course they do. The important point is that other aspects of culture appear to matter even more.

Our research identified four steps — offering lateral career opportunities, remote work, social events, and more predictable schedules — that may boost retention in the short term. Leaders who are serious about winning the war for talent during the Great Resignation and beyond, however, must do more. They should understand and address the elements of their culture that are causing employees to disengage and leave. And above all else, they must root out issues that contribute to a toxic culture. Our next article will explore, empirically, what constitutes a toxic culture and how organizations can address this challenge.

Topics

Measuring Culture

This series includes the MIT SMR/Glassdoor Culture 500, an annual index and research project that uses over 1.4 million employee reviews to analyze culture in leading companies, along with new research focused on measuring organizational culture using a scientific approach.
More in this series

References

1. Microsoft sponsored a survey of over 30,000 employees across 31 markets in January 2021 for its Work Trend Index. See “The Next Great Disruption Is Hybrid Work — Are We Ready?” Microsoft, March 22, 2021, www.microsoft.com.

2.Job Openings and Labor Turnover Survey,” U.S. Bureau of Labor Statistics, accessed Dec. 6, 2021, www.bls.gov. The data represents seasonally adjusted quits for total nonfarm employers in the U.S. from April through September 2021.

3. To test the accuracy of our estimates of employee attrition, we compared them with the November U.S. Bureau of Labor Statistics (BLS) Job Openings and Labor Turnover Survey (JOLTS) for total separations (including employee resignations, layoffs, and other sources of job separations) for private companies with more than 5,000 employees. The BLS total separation rate was 10.8% for April through September 2021, and our estimates were 10.1% for the same period.

4. To estimate turnover at the company level, we identified all job transitions where a user left their current employer for any reason, including quitting, retiring, or being laid off, and divided these by corporate head count. Job transitions were measured for April through September 2021 for 538 Culture 500 companies. The attrition rates are adjusted for sampling bias related to who has an online profile and for lags in when users reported transitions on their profiles. To test the robustness of our estimates of attrition, we separately estimated the hiring rate for each company for the same period. Hiring rate is defined as employees who joined the company divided by corporate head count. We would expect hiring and attrition rates to be correlated, as companies replace employees who leave. The Spearman correlation coefficient was 0.81 between the hiring and attrition rates.

5. We assigned each Culture 500 company to a primary industry and calculated how many standard deviations the focal company’s attrition rate was above or below the industry mean. We used the resulting industry-normalized attrition rate as the dependent variable in our subsequent models. To identify which factors were most important in predicting each company’s normalized attrition rate, we used an XGBoost model and calculated the SHAP (Shapley additive explanations) value for 172 topics measured by the CultureX Natural Employee Language Understanding platform. The SHAP value approach analyzes all possible combinations of features in a predictive model to estimate the marginal impact that each feature has on the outcome — in our case, which cultural elements have the biggest impact in predicting a company’s industry-normalized attrition rate. For an overview of SHAP models, see S.M. Lundberg, G. Erion, H. Chen, et al., “From Local Explanations to Global Understanding With Explainable AI for Trees,” Nature Machine Intelligence 2, no. 1 (January 2020): 56-67.

6. A.L. Rubenstein, M.B. Eberly, T.W. Lee, et al., “Surveying the Forest: A Meta-Analysis, Moderator Investigation, and Future-Oriented Discussion of the Antecedents of Voluntary Employee Turnover,” Personnel Psychology 71, no. 1 (spring 2018): 23-65; and D.G. Allen, P.C. Bryant, and J.M. Vardaman, “Retaining Talent: Replacing Misconceptions With Evidence-Based Strategies,” Academy of Management Perspectives 24, no. 2 (May 2010): 48-64.

7. Relative importance is calculated by dividing each topic’s SHAP value by the SHAP value for the compensation topic. When highly predictive features are closely related (such as job insecurity and restructuring), we report their combined predictive impact.

8. Jason Sockin finds that “respect/abuse,” a topic that overlaps with our definition of toxic culture, is the single best predictor of employee satisfaction. See J. Sockin, “Show Me the Amenity: Are Higher-Paying Firms Better All Around?” SSRN, Nov. 18, 2021, https://papers.ssrn.com.

9. D. Sull and C. Sull, “10 Things Your Corporate Culture Needs to Get Right,” MIT Sloan Management Review, Sept. 16, 2021, https://sloanreview.mit.edu.

10. B. Zweig and D. Zhao, “Looking for Greener Pastures: What Workplace Factors Drive Attrition?” PDF file (Mill Valley, California: Glassdoor, 2021), www.glassdoor.com. A recent meta-analysis also found that job insecurity was a strong predictor of voluntary employee turnover. See A.L. Rubenstein et al., “Surveying the Forest,” 23-65.

11. In the November BLS JOLTS, seasonally adjusted layoffs and discharges for all private companies with more than 5,000 employees represented 24% of total separations for April through September 2021.

12. D. Sull and C. Sull, “10 Things.”

13. J.C. Williams, S.J. Lambert, S. Kesavan, et al., “Stable Scheduling Increases Productivity and Sales: The Stable Scheduling Study,” PDF file (San Francisco: Center for WorkLife Law, 2018), https://worklifelaw.org.

14.Job Openings and Labor Turnover Summary,” U.S. Bureau of Labor Statistics, Dec. 8, 2021, www.bls.gov.

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Comments (3)
Donald Sull
@Dan Holdsworth we're not making any assumptions about composition of employees. In this paper, we are simply predicting each company's attrition rate relative to its primary industry.  And we find that companies that are more innovative are more likely to have higher attrition. 

The question then becomes how do we interpret this finding. Great point that the composition of employees who self select to work in very innovative companies (SpaceX, Tesla, Nvidia) very likely differs from those who choose to work in more staid competitors (Boeing, Ford, Intel). 

Your hypothesis is very plausible that employees who choose to work in bleeding-edge innovators are more likely to jump ship. We know, for instance, that the median tenure of employees in start-ups is about half the average of employees in more established companies. 

But it's possible that the converse is true. Somebody who works at SpaceX could not stand the bureaucracy and boredom of a more traditional competitor. This would make them less likely to quit. 

The matching of types of employees to extremely innovative firms is a super interesting question, but we'd need a different study to shed light on the mechanisms that underlie higher attrition in bleeding-edge firms. 

Thanks for raising the issue!
Anonymous
Dan Holdsworth we're not making any assumptions about composition of employees. In this paper, we are simply predicting each company's attrition rate relative to its primary industry.  And we find that companies that are more innovative are more likely to have higher attrition. 

The question then becomes how do we interpret this finding. Great point that the composition of employees who self select to work in very innovative companies (SpaceX, Tesla, Nvidia) very likely differs from those who choose to work in more staid competitors (Boeing, Ford, Intel). 

Your hypothesis is very plausible that employees who choose to work in bleeding-edge innovators are more likely to jump ship. We know, for instance, that the median tenure of employees in start-ups is about half the average of employees in more established companies. 

But it's possible that the converse is true. Somebody who works at SpaceX could not stand the bureaucracy and boredom of a more traditional competitor. This would make them less likely to quit. 

The matching of types of employees to extremely innovative firms is a super interesting question, but we'd need a different study to shed light on the mechanisms that underlie higher attrition in bleeding-edge firms. 

Thanks for raising the issue!
Dan Holdsworth
Sirs, 
      I think that in writing this article, you have made an error in your initial assumptions about employees. You are assuming that the employees of all companies are all the same to start with; I don't think that this assumption is true.

If you instead assume that people who join riskier-seeming, more innovative companies are themselves more inclined to be risk-takers and more inclined to be flighty then much of the observed variation between companies is removed. The employees who jumped ship were already more likely to do this before they joined these companies.