COVID-19 Makes Job Gains Difficult to Predict, Count
Since the start of COVID-19, economists have had a hard time predicting the number of jobs added in the monthly U.S. jobs reports, according to The Wall Street Journal (subscription).
Why? There are two main reasons for the prediction difficulty: changing consumer behavior in the face of stimulus checks and pandemic fears and a decline in payroll data collected by the government from employers.
- “Congress sent households trillions of dollars in stimulus payments and enhanced unemployment benefits. Forecasters were caught off guard by how quickly consumers spent that money, much of it on goods. Economists also struggled to ascertain how quickly businesses would reopen and consumers would return to restaurants and stores. This summer’s delta variant added uncertainty about whether employers would cut jobs.”
Significantly off the mark: Since the beginning of the pandemic, economists’ job-growth estimates have been off, sometimes by hundreds of thousands. But such predictions are no easy task even in normal times, relying on voluntary Bureau of Labor Statistics surveys, to which employers may respond “late or not at all.”
- “The response rate fell from 59% in February 2020 to 45% last month, BLS data show.”
- When businesses don’t respond, the government must guess their payroll sizes.
Other changes: The pandemic disrupted typical seasonal-adjustment patterns and purchasing habits. Retailers probably cut staff at brick-and-mortar stores to account for the shift to more online ordering, which “may have affected retail employment in November, typically a big month for hiring.”
The NAM says: “Even in a tight labor market, predicting monthly job gains can be tough,” said NAM Chief Economist Chad Moutray. “Manufacturing job growth has been solid, with the sector adding 315,000 workers year to date, the best since 1994. The economic environment remains uncertain, with lingering challenges, including the pandemic, supply-chain bottlenecks and soaring costs. This has created more volatility in the data than we might prefer, even if the longer-term trend remains favorable.”