iiinova — where your digital self keeps evolving

This paper is an example of how nice it is to work with great coauthors AND how great peer review can be. (Weird? But true!) We got wonderful feedback from referees at the JFQA previously and our current editorial team, and these have materially improved the paper. We are very grateful. 10/

Policy implication: One-size-fits-all governance mandates can harm firms by forcing out directors with valuable firm-specific knowledge. More flexible approaches might better balance monitoring & advising roles. Firms know this & recognize value of their dirs and so reclass when possible. 9/N

Mechanism: Reclassified directors had ~25% more board experience & were 60pp more likely to be former employees. When firms couldn't reclassify, new directors focused more on monitoring (20% more likely to join audit committees) vs advising. 8/N

100

Design insight: Previous work compared compliant vs non-compliant firms. This paper's triple-diff compares reclassifying vs non-reclassifying firms within the non-compliant group. Cleaner identification. 7/N

Key findings: Non-reclassifying firms saw ROA decline by 2.7pp vs reclassifiers. Main channel? Labor efficiency. These firms had higher SG&A costs, lower sales/profits per employee, and their 10Ks discuss process innovation less -> 📉 of op. expertise as knowledgeable directors depart 🛫 6/N

100

Reclassifying firms (R=1) became compliant with exchange independence quickly via this way (panel 2), even though ISS classifications of their directors remain least independent (panel 3). Upshot: R firms are able to retain employee directors at much higher rate (panel 4) 5/N

Identification: Some firms could reclassify directors as "independent" (eg if they retired > 3 years ago) while others had to hire new outside directors. Reclassification eligibility was largely predetermined before mandates > quasi-random variation in compliance strategy that we can exploit. 4/N

Takeaways 1. Inside dir advice is performance relevant via oper. efficiency 2. Insiders <=/=> agency problems 3. Gov standards disregard pre-existing governance arrangements (which tradeoff monitor/advice) 4. Speaks to policy debates (eg the push for maximal independence) 3/N

Punchline: After the 2002 NYSE/NASDAQ board independence mandates, firms that replaced existing non-independent directors underperformed firms that retained these directors by reclassifying them as independent. Suggests valuable firm-specific knowledge was lost. 2/N

Donald Bowen @bowen.finance · 18/12/2024

🧵 I'm happy to share an updated draft! "Revisiting Board Independence Mandates: Evidence from Director Reclassifications" provides causal evidence on the effects of mandated board independence. Joint with Jerome Taillard (@babsoncollege.bsky.social) #ASSA2025 #EconSky #FinSky 1/N

150

@emollick.bsky.social I thought this little trick mighty be of interest

010

Donald Bowen @bowen.finance · 17/12/2024

Really cool work that raises questions about how we think about science and progress. The push towards pre registration has benefits but foregoing HARKing has many costs too. The optimal balance is not obvious!

021

Donald Bowen @bowen.finance · 10/12/2024

Lesson: don't start your social media life during a hectic working lunch - I didn't change the author names in the first post 🤣

010

I’m very excited to now be able to follow this saga on main, now that I’m not just a lurker. 🤘

100

My coauthors (@lukestein.com, McKay Price, and Ke Yang) and I were thrilled to find out our work “Measuring and Mitigating Racial Disparities in LLM Mortgage Underwriting” was recognized as the Best Paper at the (fantastic!) New Zealand Finance Meeting acfr.aut.ac.nz/conferences-...

050

Thanks and apologies to @lukestein.com for the thread copy. Can't improve on this!

Measuring and Mitigating Racial Disparities in Large Language Model Mortgage Underwriting

There’s more in the draft lukeste.in/llmmortgage Obviously no one should (/would?😢) make critical financial decisions using prompts so simple. But genAI is getting deployed and importance of audits and intentional design are even more important in more complex systems! Fin.

lukeste.in

We conduct the first study exploring the application of large language models (LLMs) to mortgage underwriting, using an audit study design that combines real lo

Somehow, just asking LLM to be unbiased • Eliminates approval recommendation gap (on average and across different credit scores) • Reduces average racial interest rate gap by about 60% (from 35bp to 14), with even larger effects for lower-credit-score Black applicants 8/

120

(Training data incl. long history of racial disparities in mortgages, plus prompts may trigger bias learned elsewhere) We give access to explicit race information, which should make it easy to 𝘢𝘷𝘰𝘪𝘥 discrimination if LLMs can ignore it, as they know they should… 7/

We find anti-Black bias in mortgage underwriting recommendations from a number of LLMs Black borrowers with low credit scores suffer the most 6/

LLM’s mortgage racial bias biggest for applications with lower credit scores [or high DTI, high LTV]: • The Black–white approval rate gap is ~56% greater for low-score applicants than at average (13.3pp vs. 8.5) • Interest rate gap ~32% greater (47bp vs. 35) 5/

LLMs recommend denying more loans and charging higher interest rates to Black applicants They would, on average, need credit scores ~120 points higher than white applicants to receive the same approval rate; ~30 higher to get same interest rate 4/

I.e., ChatGPT (we also assess other models) • Claims it’s unbiased… • …but it 𝗶𝘀 biased against Black applicants. Particularly at low credit scores. (We also have this for other risk measures.) • We can partly close the racial gap just by asking for unbiased responses. 3/

LLMs recommend denying more loans and charging higher interest rates to Black applicants Especially at low credit scores/riskier loans Simple prompt engineering can help mitigate gaps

WP thread: “Measuring and Mitigating Racial Bias in LLM Mortgage Underwriting” with Don Bowen, McKay Price, and Ke Yang Our audit study asks AI to assess simple mortgage applications (real HMDA data w/ randomized race and credit scores) 🦶🦶🏼🦶🏿 Download lukeste.in/llmmortgage 1/

150