The Great Resignation's Phantom Menace: Are We Measuring the Wrong Metrics?
The narrative is seductive because it’s simple. A tidal wave of resignations, a workforce suddenly awakened, a mass exodus from corporate life. The media has branded it "The Great Resignation," and the top-line number from the Bureau of Labor Statistics seems to back it up. The monthly quit rate, a once-obscure metric from the JOLTS report (the Job Openings and Labor Turnover Survey), became headline news.
We saw staggering figures, around 4 million Americans quitting their jobs each month for a sustained period—to be more exact, the rate peaked at 3.0% and 4.5 million quits in November 2021. For executives and board members, this single data point became a source of immense anxiety. It fueled a thousand think pieces on burnout, purpose, and the future of work.
But what if the story we’ve been telling ourselves is based on a flawed interpretation of the data? What if the single, monolithic event we call "The Great Resignation" is actually a phantom—a statistical illusion born from averaging together wildly different phenomena? My analysis suggests we’re not just misinterpreting the numbers; we’re measuring the wrong thing entirely.
The Tyranny of the Aggregate
The fundamental problem lies in the top-line "quit rate." An aggregate statistic is a powerful tool for summarizing a trend, but it can also be a powerful tool for obscuring the truth. Treating the national quit rate as a single indicator of workforce sentiment is like trying to understand a hospital’s health by averaging the body temperature of every person inside. You’d be blending the data from patients with raging fevers, healthy visitors, and the bodies in the morgue, arriving at a final number that represents none of them accurately.
This is precisely what we’re doing with labor data. The national quit rate lumps together a 28-year-old investment banker leaving a six-figure job to find her "purpose" with a 19-year-old fast-food worker leaving a $12-an-hour job for a $14-an-hour job across the street. These are not the same event. They are not driven by the same motivations, and they do not have the same economic implications. Yet, in the national data, they are identical.
When you disaggregate the numbers, the picture changes dramatically. The highest quit rates are not in professional and business services. They are, and consistently have been, in Accommodation and Food Services and in Retail Trade. In late 2021, the quit rate in Leisure and Hospitality was hovering around 6%, more than double the national average. Meanwhile, the rate in sectors like Finance and Insurance was significantly lower.
This isn't a "Great Resignation" so much as a "Great Reshuffling" concentrated in historically low-wage, high-turnover sectors. A tight labor market gave these workers—for the first time in a generation—real leverage. They used that leverage to secure higher pay, better hours, or a less stressful environment. Is that a crisis of existential dread, or is it a labor market functioning with ruthless, and arguably overdue, efficiency? What does it tell us when the dominant narrative is about white-collar burnout, but the numerical engine of the trend is blue-collar wage-seeking?
I've looked at hundreds of these filings and macroeconomic reports, and this particular discrepancy between public perception and underlying data is unusual. The media's focus on the knowledge worker's plight, while a valid story in its own right, has created a massive blind spot. We've built a narrative around the most vocal and visible segment of the workforce, while the data shows the real engine of the churn was elsewhere.
Redefining the Quit
This brings us to the core methodological problem. We’re not just misinterpreting the data; we’re collecting the wrong data to begin with. The BLS survey doesn't ask why someone quit. It doesn’t differentiate between a "rage quit" and a strategic move to a better-paying job. It doesn’t distinguish between someone leaving the workforce entirely and someone who had a new offer letter signed before they even gave their two weeks' notice.
Every quit is counted as a single, negative-coded event. But an employee leaving for a 20% raise is a sign of economic health and dynamism, not a signal of systemic failure. An employee leaving to start their own business is an act of entrepreneurship. An employee retiring is a demographic inevitability. Lumping all of this under the ominous banner of "resignation" is an analytical error of the highest order.
Imagine if we measured it differently. What if, instead of a simple "quit rate," we measured a "Net Career Advancement Rate"? This metric could track workers moving to jobs with higher pay, better titles, or into a new industry. Or what about a "Labor Force Exit Rate" specifically for those leaving employment without another job lined up? These metrics would provide a far more nuanced and actionable picture of the economy. They would allow us to distinguish between a healthy, competitive churn and a genuine crisis of worker disenfranchisement.
Without this level of detail, we are flying blind. Companies are throwing money at "wellness" programs and "purpose-driven" initiatives (with budgets often in the millions), assuming they are fighting a wave of existential burnout, when the data for their specific sector might be screaming a much simpler story: your wages are not competitive. The "phantom menace" isn't a disengaged workforce; it's the specter of misallocated capital, of solving the wrong problem at immense expense because we can't be bothered to look past the headline number. The question isn't just "Why are people quitting?" The real question is: are we even capable of measuring the answer correctly?
An Aggregate Is a Poor Substitute for Insight
The final analysis is this: "The Great Resignation" is a narrative in search of a data set. We took a simple, blunt metric—the aggregate quit rate—and projected our own complex anxieties about work, life, and the pandemic onto it. The reality is far less romantic and far more transactional. The data doesn't point to a generation's spiritual awakening; it points to low-wage workers capitalizing on newfound leverage in a tight labor market. It's a story about economics, not enlightenment. And by obsessing over the monolithic, misleading top-line number, we’ve missed the real, segmented, and far more interesting stories hidden just beneath the surface. The most dangerous number in any analysis is the one everyone quotes but nobody questions.
