Updates will usually be on the home page.

  • New paper: Measuring and Mitigating Racial Bias in Large Language Model Mortgage Underwriting
  • Coming fall 2024: Updated patent data


Rapidly Evolving Technologies and Startup Exits (with Gerard Hoberg and Laurent Fresard)

(SSRN Link) (Official link) (Replication kits: analysis and measure construction) (Plug and play data for researchers!)

Media: Tuck Forum on Private Equity and Venture Capital

Management Science (2023) 69:2: 940-967

Formerly titled “Technological Disruptiveness and the Evolution of IPOs and Sell-Outs”

This paper examines startups’ positioning within technological cycles. We use patent text to measure whether innovation pertains to a technological area that is rapidly evolving or stable. We show that innovation in rapidly evolving areas (i.e., early in the cycle) substitute for existing technologies, whereas innovation in stable areas (i.e., later in the cycle) complement them. Our new measure is distinct from existing characterizations of innovation and is economically important. We find that startups in rapidly evolving areas tend to exit via IPO, thus remaining independent, consistent with technological substitution. In contrast, startups in stable areas tend to sell-out, consistent with technological complementarity and synergies.

What’s your identification strategy? Innovation in corporate finance research (with Laurent Fresard and Jerome Taillard)

Management Science (2017) 63(8): 2529-2548

(SSRN link) (Official link) (Data and replication code/kit)

We study the diffusion of techniques designed to identify causal relationships in corporate finance research. We estimate the diffusion started in the mid-nineties, lags twenty years compared to economics, and is now used in the majority of corporate finance articles. Consistent with recent theories of technology diffusion, the adoption varies across researchers based on individuals’ expected net benefits of adoption. Younger scholars, holders of PhDs in economics, and those working at top institutions adopt faster. Adoption is accelerated through networks of colleagues and alumni and is also facilitated by straddlers who cross-over from economics to finance. Our findings highlight new forces that explain the diffusion of innovation and shape the norms of academic research.

Working Papers

Measuring and Mitigating Racial Bias in Large Language Model Mortgage Underwriting (with McKay Price, Luke Stein, and Ke Yang)

(SSRN link)

We conduct an audit study of loan approval and interest rate decisions suggested by large language models (LLMs). Using a dataset of real loan applications and experimentally manipulated race and credit scores, we find that LLMs recommend denying more loans and charging higher interest rates to Black applicants than otherwise-identical white applicants. This racial bias is largest for lower-credit-score applicants and riskier loans, but present across the credit spectrum. Surprisingly, simply instructing the LLM to make unbiased decisions eliminates the racial disparity in approvals and moderates the interest rate disparity. LLM recommendations correlate strongly with real lenders’ decisions, despite having no fine tuning or specialized training, no macroeconomic context, and access to only limited data from each loan application. A number of different leading LLMs produce racially biased recommendations, although the magnitudes and patterns vary. Our results highlight the critical importance of auditing LLMs and demonstrate that even basic prompt engineering can help reduce LLM bias.


Revisiting Board Independence Mandates: Evidence from Director Reclassifications

(with Jerome Taillard)

(SSRN link)

(Previously titled “Were non-independent boards really captured before SOX?”)

Media mentions: Columbia Law School’s Blue Sky Blog

We provide causal evidence on the effects of board independence mandates by exploiting quasi-exogenous variation in compliance strategies. We compare firms reclassifying existing directors as independent to firms replacing them with independent directors, with reclassification eligibility largely predating the mandates. Using a triple-difference framework, we find non-reclassifying firms perform worse post-mandate, consistent with optimal pre-mandate board composition. In particular, boards of non-reclassifying firms retain fewer former employees, highlighting the value of firm-specific knowledge and advisory services. We rule out alternative explanations, such as director co-option. Our study highlights the value of directors and the negative consequences of board mandates.

Patent acquisition, investment, and contracting

(SSRN link)

Best Paper in Innovation and Entrepreneurship from the Ed Snider Center for Entrepreneurship and Markets, 2017

Numerous works have examined the finance-related implications of intellectual property that is generated internally or acquired through M&A activity. The transfer of intellectual property via the secondary market for patents has received less attention. This paper fills that gap by asking how patent acquisitions interact with firm investment policy. I find that patent acquirers subsequently invest in more R&D, increase internal patenting, and eventually make new investments in CAPX. Firms with more technological expertise and investment opportunities acquire more patents. Patent sales are the dominant type of contract and maximize investment incentives; patent licenses frequently contain royalties, which induce underinvestment problems. Nevertheless, licensing can be explained in part by financial and strategic considerations. Licensing is more likely when buyers become financially constrained, when revenue can be shifted to low tax sellers, and when the buyer is a competitor acquiring rights to a valuable patent. Overall, these results suggest patent acquisitions are motivated by the pursuit of investment synergies, rather than innovation substitution, commercialization motives, or legal threats.