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

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

🧵 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

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-...

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/

We find anti-Black bias in mortgage underwriting recommendations from a number of LLMs

Black borrowers with low credit scores suffer the most

6/

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/