In an incrementally discouraging development for US policymakers, the first estimate of Q1 labor productivity undershot expectations and unit labor costs rose more than expected.
Thursday’s BLS release, which included recent revisions, won’t get top billing across the financial mediascape, but it’s relevant all the same.
Higher productivity is one way (the best way) to sustain robust growth outcomes without unacceptably high inflation. Jerome Powell talked at some length about that during Wednesday’s press conference.
Thursday’s update saw productivity rise just 0.3% in Q1, less than half the rate consensus expected headed into the week (the estimate might’ve come down by Thursday, though).
Employee hours rose 1% in Q1 and output 1.3%. You net output and hours worked to get productivity.
Compensation per hour rose 5%, brisker than the prior two quarters. It’s the juxtaposition between lackluster productivity and surging wages that pushes unit labor costs higher.
The 4.7% ULC print counted as the fastest in a year. Consensus was looking for a 4% increase.
Unit labor costs were subdued over the prior two quarters. Revisions erased a Q3 2023 decline.
To reiterate: It’d be a mistake to make too much of the data. It’s preliminary, the series (all of them) are volatile and, more germane, it’s not a top-tier release. Or at least not in traders’ minds.
Caveats aside, the numbers add to the (by now extensive) body of evidence arguing against Fed easing. As of this week, that evidence includes a very warm read on a key measure of quarterly comp costs and a disconcerting jump in ISM’s gauge of factory price pressures.
During the May press conference, Powell said it isn’t clear whether recent productivity gains will prove sustainable. Suffice to say Thursday’s data won’t do anything to “increase the level of confidence” on the Committee in that regard.
Perhaps AI will come to the rescue. But . . . if an AI agent replaces a human employee, does the agent’s output get counted toward employee productivity? For example, “Multifamily rental marketplace Apartment List announced that it would provide its advanced GenAI leasing agent Lea Pro to property management partners at no cost”. https://www.globest.com/2024/05/02/what-happens-when-an-advanced-leasing-agent-becomes-free If a property manager uses Lea Pro instead of human leasing agents, how does that show up in BLS data?
Probably. Isn’t it simply aggregated data of output/number of employees?
I wish I understood better how the BLS methodology will handle a technological innovation that 1) increases revenue with fewer employees, or 2) leaves revenue unchanged but decreases expense thus increases profits with fewer employees.
I don’t think that profits are accounted for. Simple output/# employees. Yes, I should verify that…
So, Investopedia says “Economic productivity is calculated as a ratio of gross domestic product (GDP) to hours worked.”
So hours worked, not the number of people working as I suggested.