this was such a great read! concise and practical at the same time
i have one question regarding the first graphic (labor transmission chain): between steps 2 and 3, what impact do you think the k shaped economy has on labor market issues showing up in spending? not talking about it not mattering at all, more thinking along the lines of longer delays. or any additional metrics one should monitor because of it?
Thank you very much! I’m glad you enjoyed. Thank you for the thoughtful question as well. This two way dialogue only helps us make the product better. My answer, below:
The K-shaped economy absolutely affects transmission timing, but not in the way most people think.
The bifurcation doesn’t create uniform delays. It creates differential speeds. The bottom 80% transmits fast because they have no buffer. The top 20% has runway, so their labor stress takes longer to show up in their spending.
This is why we track distributional measures, not just aggregates. Headline PCE misses the split. You need to decompose:
Savings by cohort: Bottom 80% already underwater by $437B. Top 20% still holding $470B buffer. When their quits rate drops, the bottom cohort cuts spending within weeks. The top cohort can coast for quarters.
Retail by category: Luxury retail up 17%, value stores down 9%. Same labor market, different transmission speeds.
Credit stress by FICO band: Subprime delinquencies lead prime by 6-9 months. Labor fragility hits the bottom first, migrates up.
The framework doesn’t break. It just means you can’t rely on aggregate spending to validate labor stress anymore. You have to track where the stress accumulates. That’s why the Labor Fragility Index includes temp help and quits, not just unemployment. Flows lead, and they lead differently by cohort now.
Wow, thank you. That’s exactly what I was hoping this series would do. Making complex frameworks accessible without dumbing them down is the whole point.
You nailed the asymmetric information angle. Workers see company-level stress (slowing orders, inventory adjustments, overtime cuts) months before it shows up in BLS headlines. That’s the entire reason the quits rate works as a leading indicator. They’re not being cautious. They’re seeing something management hasn’t admitted yet.
Your sectoral observation is sharp. We actually had the same thought and have begun modeling it out. I felt like that may be getting a bit too deep, so I left it out of this piece. But if the pattern holds, I’d definitely do another report specifically focused on that. The microstructure could be exploitable.
Really appreciate the close read. This is the kind of feedback that surfaces the next question to answer.
this was such a great read! concise and practical at the same time
i have one question regarding the first graphic (labor transmission chain): between steps 2 and 3, what impact do you think the k shaped economy has on labor market issues showing up in spending? not talking about it not mattering at all, more thinking along the lines of longer delays. or any additional metrics one should monitor because of it?
Thank you very much! I’m glad you enjoyed. Thank you for the thoughtful question as well. This two way dialogue only helps us make the product better. My answer, below:
The K-shaped economy absolutely affects transmission timing, but not in the way most people think.
The bifurcation doesn’t create uniform delays. It creates differential speeds. The bottom 80% transmits fast because they have no buffer. The top 20% has runway, so their labor stress takes longer to show up in their spending.
This is why we track distributional measures, not just aggregates. Headline PCE misses the split. You need to decompose:
Savings by cohort: Bottom 80% already underwater by $437B. Top 20% still holding $470B buffer. When their quits rate drops, the bottom cohort cuts spending within weeks. The top cohort can coast for quarters.
Retail by category: Luxury retail up 17%, value stores down 9%. Same labor market, different transmission speeds.
Credit stress by FICO band: Subprime delinquencies lead prime by 6-9 months. Labor fragility hits the bottom first, migrates up.
The framework doesn’t break. It just means you can’t rely on aggregate spending to validate labor stress anymore. You have to track where the stress accumulates. That’s why the Labor Fragility Index includes temp help and quits, not just unemployment. Flows lead, and they lead differently by cohort now.
thank you for the thorough answer. makes a lot of sense!
essentially the differences between the top 20% and bottom 80% are so significant that their effects have to be modeled independently
Wow, thank you. That’s exactly what I was hoping this series would do. Making complex frameworks accessible without dumbing them down is the whole point.
You nailed the asymmetric information angle. Workers see company-level stress (slowing orders, inventory adjustments, overtime cuts) months before it shows up in BLS headlines. That’s the entire reason the quits rate works as a leading indicator. They’re not being cautious. They’re seeing something management hasn’t admitted yet.
Your sectoral observation is sharp. We actually had the same thought and have begun modeling it out. I felt like that may be getting a bit too deep, so I left it out of this piece. But if the pattern holds, I’d definitely do another report specifically focused on that. The microstructure could be exploitable.
Really appreciate the close read. This is the kind of feedback that surfaces the next question to answer.