"Family firms" talks about various non-monetary benefits that owners can derive from firms - which presumably mean they don't optimize prices if those conflict with their other objectives. I guess Holmstrom multi-tasking + multiple objectives imply similar things?

These findings have policy implications - groups that have a greater affinity for homeownership will be more likely to be benefited / harmed by credit or housing policy changes. Heterogeneity in take-up can result from group affinity!

Link to paper is here: papers.ssrn.com/sol3/papers....

#Econsky

We find non-RE wealth increases of HHs with high HO affinity limited to those in the right housing markets at the right time & who happen to see high price increases, NOT a general consequence of homeownership - so housing policy is limited if housing booms are not guaranteed!

We obtain restricted HRS data to see if affinity for HO impacts the portfolios of foreign-born retirees: as expected, they are more likely to own a home & own more RE in their portfolios. Total non-RE retirement wealth is also higher for those who have high HO affinity in origin country! But why?...

We show that AFFINITY matters for housing cycles and effects of credit supply shocks. High HO affinity households enter more into homeownership during 2000s housing boom, default less during the GFC - see paper for causal evidence on greater response to credit supply shocks.

It's hard to find exogenous changes in homeownership (HO) to study effects on HH finance. We build on literature looking at role of experiences/origins driving financial choices and show that HO in origin countries (HOCO) drives HO of foreign-born in the US! (15% passthrough)

To me this is one reason that good UX for LLM-based applications is important - users need to be guided by the designer to be able to quickly figure out "what is this good at" and "what is this not good at" - there is no time to validate all use cases for each chatbot encountered in the wild!

This thread was triggered by this great paper by @keyonv.bsky.social , Ashesh Rambachan, and @sendhil.bsky.social about how humans become overoptimistic about model capabilities after seeing performance on a small number of tasks.

That is, it is equally problematic when users (including researchers!) overestimate how trustworthy these models are on new tasks, as it is to not trust them ever.

It's necessary to constantly validate the quality of model output at different stages - "we asked the model to do X" is not enough!

Only after repeated use and exploration for where the weaknesses and pitfalls lie, and in which cases the LLM output can (with guardrails!) be trusted, does the user's expectation for LLM capabilities reach the "Plateau of Pragmatism".

BOTH the "Valley" and the "Mountain" are problematic places!

...but after some experimenting (@emollick.bsky.social suggests 10+ hours), many people find amazing abilities in some areas where LLMs exceed humans, reaching bedazzlement on "Magic Box Mountain".

However, their "jagged frontier" nature means that LLMs would fail on many other "easy" use cases.

With regard to whether our firm-level Generative AI exposure measure predicts ACTUAL adoption - my forthcoming research shows it does!

See below - our exposure measure with 2022 data strongly predicts whether firms mention Gen AI skills in their job postings in 2024. 2/2