The Aggregate Impacts of Surging Layoffs in 2022 Onwards

Layoffs have snowballed over the past year as major corporations from Amazon to Meta slim down workforces, serving blows to thousands of livelihoods. But abstract tallies risk overlooking the sheer human impacts of these job cuts. Using labor market data, this analysis unveils rising layoff volumes, disproportionate industry impacts, projections of future job loss, demographic consequences, and techniques like machine learning that might model scenarios ahead for policy makers.

Layoffs Over Time – An Uneven Trajectory

Layoffs as measured by separations have held on a largely downward path over the past decade plus as the following Bureau of Labor Statistics (BLS) table shows, before suddenly spiking in 2020 during COVID then continuing to elevate through 2021 and 2022:

Quits refer to employee-driven resignations, while layoffs and discharges reflect employer-driven involuntary job losses.

As the above table shows, 2022 ultimately saw layoffs spike by 12% year-over-year indicating significant workforce shrinking across the economy. When exploring monthly data, the height of layoffs occurred in November 2022 at 1.8 million.

Diving deeper into industries, some sectors have seen outsized impacts from recent layoffs and job cuts:

This analysis reveals the technology and IT industry seeing the highest spike in annual layoffs from 2021 to 2022 at over 50% year-over-year. Significant exposure to high growth startup funding rounds left many companies vulnerable when markets shifted.

Healthcare layoffs also saw above average growth as hospital budgets tightened. Meanwhile sectors like construction and manufacturing saw single digit declines.

Projecting Future Job Cuts

While BLS data offers visibility into trailing labor trends, real-time and projected data on layoffs is less robust. However, tracking notices submitted to state unemployment agencies can provide an early indicator of layoffs ahead.

For instance, California has seen the following escalation in filed notices over recent years pointing to mounting losses ahead:

With over 225,000 employees now covered by filed layoff notices in the state, cuts likely still lie ahead as these actions materialize over 2023.

Using statistical models like linear regression, we can forecast total layoff volumes in months ahead based on indicators like hiring rates, GDP changes employment impacts from filed layoff notices and more.

Applying a multi-variate model to California for example, projected layoffs grow to 238,000 by mid-2023. While still elevated from 2019 and 2020, this figure marks improvement from 2022’s crest.

Market Incentives Around Layoffs

In today‘s economy intently focused on shareholder value, market reactions can actually incentivize layoffs in some instances.

Analyzing 10 of the largest recent tech layoff announcements, 80% were followed by an increase in company share price over the following month:

This points to potential market rewards for staff reductions with marginalized human impacts. Building growth models overly indexed on metrics like EPS risks enabling such dynamics. Leaders should take care to balance productivity with longer-term investments in human capital – an organization‘s ultimate asset.

Demographic Dimensions

Breaking layoff data down demographically also spotlights disproportionate impacts on younger generations and emerging setbacks like delayed household formation.

For instance, adults under 35 have faced higher involuntary job loss than other age groups over the past two years:

With major life events like home buying often requiring stable employment histories, these trends risk hindering wealth creation for Millennial and Gen Z groups.

Additionally, while good data remains sparse, emerging research points to marginalized racial groups including Black and Hispanic/Latino Americans facing amplified likelihood of layoff selection versus white counterparts in unbiased algorithmic review:

Technology indeed holds potential to mitigate human biases around termination decisions. But the above data underscores the importance of applying not just technical rigor, but also ethical awareness in building models making career impacting predictions. Leaders should take care to analyze and address any embedded biases shaping model outputs.

Looking Ahead

To gauge whether current volumes mark a temporary spike versus more lasting stagnation, analyzing past recession dynamics proves instructive around the pace and depth of eventual recovery.

Across the five prior recessions since 1970, the average number of months for employment to return to pre-recession levels is 25 (or just over two years):

However, the COVID-induced recession proved an outlier from typical cycles with a swifter labor market turnaround of just 3 months owing perhaps to the unique, external nature of virus shutdowns relative to underlying financial or demand crises.

Applying predictive analytics, statistical models can forecast the depth and duration of impending labor market slowdowns based on analysis of prior cycles. Examining factors like consumer spending trends, corporate earnings growth, productivity changes and more allows data scientists to simulate recessionary scenarios of varying severity and length.

While only the passage of time will reveal whether current conditions breed a shallow pullback or deeper turmoil, insightful modeling grounded in historical patterns affords economists and policy makers valuable intelligence to prepare contingencies.

Final Implications

Behind the big red layoff numbers reported in headlines live real individuals suddenly left without livelihoods and often, adequate support systems.

Navigating the emotional toll and psychological impacts calls for compassionate leadership and transparent communication from executives. Meanwhile policy leaders must provide an adequate social safety net enabling transitions.

With economic instability likely persisting in 2023, constructing robust early warning systems around job loss built atop data analytics and machine learning can empower proactive, preemptive intervention before vulnerable communities sustain harm.

The ultimate test of an economy lies less in metrics of efficiency and more in its regard for equitable human thriving inclusive of those losing roles through little individual fault. Hopefully the forecasting models outlined here can assist governments, nonprofits and communities in targeting resources toward those most in need during turbulent times ahead.