Publications
Publications
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- All HBS Web
(3,325)
- Faculty Publications (1,067)
- 2023
- Working Paper
Mapping Organizational-Level Networks Using Individual-Level Connections: Evidence from Online Professional Networks
By: Shelley Xin Li, Frank Nagle and Aner Zhou
Organization-level networks facilitate the flow of information and business activities in the
economy. Prior research relies solely on high-level connections to measure these networks. Therefore, to
understand the role of employee connections at all job levels in... View Details
- 2024
- Working Paper
Migration, Climate Similarity, and the Consequences of Climate Mismatch
By: Marguerite Obolensky, Marco Tabellini and Charles Taylor
This paper examines the concept of “climate matching” in migration—the idea that migrants seek out destinations with familiar climates. Focusing on the US, we document that temperature distance between origin and destination predicts the distribution of migrants across... View Details
Keywords: Migration; Climate; Immigration; Residency; Weather; Ethnicity; Climate Change; Geographic Location; Policy; United States
- August 2023
- Article
Do Rating Agencies Behave Defensively for Higher Risk Issuers?
By: Samuel B. Bonsall IV, Kevin Koharki, Pepa Kraft, Karl A. Muller III and Anywhere Sikochi
We examine whether rating agencies act defensively toward issuers with a higher likelihood of default. We find that agencies' qualitative soft rating adjustments are more accurate as issuers' default risk grows, as evidenced by the adjustments leading to lower Type I... View Details
- August 2023
- Article
Explaining Machine Learning Models with Interactive Natural Language Conversations Using TalkToModel
By: Dylan Slack, Satyapriya Krishna, Himabindu Lakkaraju and Sameer Singh
Practitioners increasingly use machine learning (ML) models, yet models have become more complex and harder to understand. To understand complex models, researchers have proposed techniques to explain model predictions. However, practitioners struggle to use... View Details
- 2023
- Working Paper
How People Use Statistics
By: Pedro Bordalo, John J. Conlon, Nicola Gennaioli, Spencer Yongwook Kwon and Andrei Shleifer
We document two new facts about the distributions of answers in famous statistical problems: they are i) multi-modal and ii) unstable with respect to irrelevant changes in the problem. We offer a model in which, when solving a problem, people represent each hypothesis... View Details
- 2023
- Other Unpublished Work
If 3 Was 9
One determinant of where economies and markets are headed is how well we handle the climate crisis. Headlines on the climate tend to emphasize two points. First, average global temps are rising, with it being the hottest summer in 1,000 centuries per some estimates.... View Details
- July 2023
- Case
Crocs: Using Community-Centric Marketing to Make Ugly Iconic
By: Ayelet Israeli and Anne V. Wilson
In 2022, the Crocs Classic Clog was the best-selling item of clothing on Amazon, the brand was one of the fastest growing brands in the U.S., and global net revenue had increased to approximately $3.6 billion. By most accounts, Crocs had become the “it” shoe. Crocs... View Details
- July 2023 (Revised July 2023)
- Background Note
Generative AI Value Chain
By: Andy Wu and Matt Higgins
Generative AI refers to a type of artificial intelligence (AI) that can create new content (e.g., text, image, or audio) in response to a prompt from a user. ChatGPT, Bard, and Claude are examples of text generating AIs, and DALL-E, Midjourney, and Stable Diffusion are... View Details
- 2023
- Working Paper
Algorithm Failures and Consumers' Response: Evidence from Zillow
By: Isamar Troncoso, Runshan Fu, Nikhil Malik and Davide Proserpio
In November 2021, Zillow announced the closure of its iBuyer business. Popular media largely attributed this to a failure of its proprietary forecasting algorithm. We study the response of consumers to Zillow’s iBuyer business closure. We show that after the iBuyer... View Details
- July 2023
- Article
Before or After? The Effects of Payment Decision Timing in Pay-What-You-Want Contexts
By: Raghabendra P. KC, Vincent Mak and Elie Ofek
We study how payment decision timing—before versus after product delivery—influences consumer payment under pay-what-you-want pricing. We focus on situations where there is minimal change in consumer uncertainty regarding the product before versus after receiving it.... View Details
- July 2023
- Article
Takahashi-Alexander Revisited: Modeling Private Equity Portfolio Outcomes Using Historical Simulations
By: Dawson Beutler, Alex Billias, Sam Holt, Josh Lerner and TzuHwan Seet
In 2001, Dean Takahashi and Seth Alexander of the Yale University Investments Office developed a deterministic model for estimating future cash flows and valuations for the Yale endowment’s private equity portfolio. Their model, which is simple and intuitive, is still... View Details
- 2023
- Working Paper
The Complexity of Economic Decisions
By: Xavier Gabaix and Thomas Graeber
We propose a theory of the complexity of economic decisions. Leveraging a macroeconomic framework of production functions, we conceptualize the mind as a cognitive economy, where a task’s complexity is determined by its composition of cognitive operations. Complexity... View Details
- June 2023 (Revised May 2024)
- Case
Optimalen Capital
A new client portfolio manager at a quantitative investment management firm must explain why her firm, Optimalen Capital, has rebalanced a client portfolio with a set of trades that seem unintuitive. In particular, Optimalen has added to its position of Walmart (ticker... View Details
- 2023
- Working Paper
Insufficiently Justified Disparate Impact: A New Criterion for Subgroup Fairness
By: Neil Menghani, Edward McFowland III and Daniel B. Neill
In this paper, we develop a new criterion, "insufficiently justified disparate impact" (IJDI), for assessing whether recommendations (binarized predictions) made by an algorithmic decision support tool are fair. Our novel, utility-based IJDI criterion evaluates false... View Details
- June 2023
- Case
Tractor Supply Co
By: David L. Ager and Michael A. Roberto
In February 2023, Hal Lawton, CEO of Tractor Supply Co, the largest farm and ranch retailer in the United States reflected on the company’s 70% growth between 2019 and 2022. Economists had begun to predict an economic downturn and experts were predicting softening... View Details
- 2023
- Working Paper
Evaluation and Learning in R&D Investment
By: Alexander P. Frankel, Joshua L. Krieger, Danielle Li and Dimitris Papanikolaou
We examine the role of spillover learning in shaping the value of exploratory versus incremental
R&D. Using data from drug development, we show that novel drug candidates generate more
knowledge spillovers than incremental ones. Despite being less likely to reach... View Details
- June 2023
- Case
Accounting for Loan Losses at JPMorgan Chase: Predicting Credit Costs
By: Jonas Heese, Jung Koo Kang and James Weber
The case examines the accounting for loan losses at a large bank, how a bank sets its Allowance for Loan and Lease Losses (ALLL) on its financial statements. ALLL, and the rules that set them, determine when banks would and would not extend loans, which significantly... View Details
- 2023
- Working Paper
Auditing Predictive Models for Intersectional Biases
By: Kate S. Boxer, Edward McFowland III and Daniel B. Neill
Predictive models that satisfy group fairness criteria in aggregate for members of a protected class, but do not guarantee subgroup fairness, could produce biased predictions for individuals at the intersection of two or more protected classes. To address this risk, we... View Details
- 2023
- Article
Provable Detection of Propagating Sampling Bias in Prediction Models
By: Pavan Ravishankar, Qingyu Mo, Edward McFowland III and Daniel B. Neill
With an increased focus on incorporating fairness in machine learning models, it becomes imperative not only to assess and mitigate bias at each stage of the machine learning pipeline but also to understand the downstream impacts of bias across stages. Here we consider... View Details
- June 2023
- Article
When Does Uncertainty Matter? Understanding the Impact of Predictive Uncertainty in ML Assisted Decision Making
By: Sean McGrath, Parth Mehta, Alexandra Zytek, Isaac Lage and Himabindu Lakkaraju
As machine learning (ML) models are increasingly being employed to assist human decision
makers, it becomes critical to provide these decision makers with relevant inputs which can
help them decide if and how to incorporate model predictions into their decision... View Details