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Publications
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- May 2023
- Teaching Note
TikTok and National Security: Investment in an Age of Data Sovereignty?
By: Jeremy Friedman
Teaching Note for HBS Case No. 722-020. View Details
- 2023
- Working Paper
Setting Gendered Expectations? Recruiter Outreach Bias in Online Tech Training Programs
By: Jacqueline N. Lane, Karim R. Lakhani and Roberto Fernandez
Competence development in digital technologies, analytics, and artificial intelligence is increasingly important to all types of organizations and their workforce. Universities and corporations are investing heavily in developing training programs, at all tenure... View Details
- 2023
- Working Paper
Causes and Consequences of State Violence against Civilians: The Rohingya of Myanmar
By: C. Austin Davis, Paula Lopez-Pena, A. Mushfiq Mobarak and Jaya Wen
The Rohingya crisis is a severe, ongoing conflict involving large-scale violence and forced displacement, yet its causes are contested and its consequences lack systematic documentation. We marshal a variety of existing and original data to shed light on its drivers,... View Details
- 2023
- Working Paper
Culture as a Signal: Evidence from a Natural Field Experiment
By: Wei Cai, Dennis Campbell and Jiehang Yu
The importance of culture as an informal management control system is increasingly acknowledged in academia. While prior research mainly focuses on the value of culture on internal stakeholders (e.g., employees), we examine whether culture serves as a credible signal... View Details
- May 2023
- Article
Do Internal Control Weaknesses Affect Firms' Demand for Financial Skills? Evidence from U.S. Job Postings
By: Janet Gao, Kenneth J. Merkley, Joseph Pacelli and Joseph H. Schroeder
Ineffective internal controls over financial reporting often relates to a lack of qualified personnel with sufficient accounting and technical expertise. In this study, we examine whether firms respond to internal control failures by increasing their demand for... View Details
- May 2023
- Article
How Do Campaigns Shape Vote Choice? Multi-Country Evidence from 62 Elections and 56 TV Debates
By: Caroline Le Pennec and Vincent Pons
We use two-round survey data from 62 elections in 10 countries since 1952 to study the formation of vote choice, beliefs, and policy preferences and assess how televised debates contribute to this process. Our data include 253,000 observations. We compare the... View Details
- May 2023
- Article
Incentive Effects of Subjective Allocations of Rewards and Penalties
By: Wei Cai, Susanna Gallani and Jee-Eun Shin
We examine the incentive effects of subjectivity in allocating tournament-based rewards and punishments. We use data from a company where reward and punishment decisions are based on a combination of objective metrics and subjective performance assessments. Rankings... View Details
- May–June 2023
- Article
Need for Speed: The Impact of In-Process Delays on Customer Behavior in Online Retail
By: Santiago Gallino, Nil Karacaoglu and Antonio Moreno
The impact of delays has been widely studied in various offline services. The focus of this study is online services, and we explore the impact of in-process delays—measured by website speed—on customer behavior. We leverage novel retail and website speed data to... View Details
- May 2023
- Article
Self-Preferencing at Amazon: Evidence from Search Rankings
By: Chiara Farronato, Andrey Fradkin and Alexander MacKay
We study whether Amazon engages in self-preferencing on its marketplace by favoring its own brands (e.g., Amazon Basics) in search. To address this question, we collect new micro-level consumer search data using a custom browser extension installed by a panel of study... View Details
- 2025
- Working Paper
Turning Points in Inflation: A Structural Breaks Approach with Micro Data
By: Alberto Cavallo and Gastón García Zavaleta
We introduce a novel methodology for detecting inflation turning points that combines high-frequency, disaggregated price data with standard structural break techniques to provide policymakers with more precise and timely signals of inflation dynamics. The methodology... View Details
- April 12, 2023
- Article
Using AI to Adjust Your Marketing and Sales in a Volatile World
By: Das Narayandas and Arijit Sengupta
Why are some firms better and faster than others at adapting their use of customer data to respond to changing or uncertain marketing conditions? A common thread across faster-acting firms is the use of AI models to predict outcomes at various stages of the customer... View Details
- 2024
- Working Paper
Using LLMs for Market Research
By: James Brand, Ayelet Israeli and Donald Ngwe
Large language models (LLMs) have rapidly gained popularity as labor-augmenting
tools for programming, writing, and many other processes that benefit from quick text
generation. In this paper we explore the uses and benefits of LLMs for researchers and
practitioners... View Details
- April 6, 2023
- Article
A New NFT Launch Strategy: The Wave Mint
By: Scott Duke Kominers and 1337 Skulls Sers
In an NFT project, the mint—the process by which tokens are initially allocated—largely determines who your community is and how they and the broader market view the project going forward. In this piece, we review a new minting strategy recently introduced by 1337... View Details
- 2023
- Working Paper
Corporate Website-based Measures of Firms' Value Drivers
By: Wei Cai, Dennis Campbell and Patrick Ferguson
We develop and validate new text-based measures of firms’ financial and non-financial value drivers. Using the Wayback Machine to access public US firms’ archived websites from 1995-2020, we scrape text from corporate homepages. We use Kaplan and Norton’s (1992)... View Details
- 2023
- Article
Estimating Causal Peer Influence in Homophilous Social Networks by Inferring Latent Locations.
By: Edward McFowland III and Cosma Rohilla Shalizi
Social influence cannot be identified from purely observational data on social networks, because such influence is generically confounded with latent homophily, that is, with a node’s network partners being informative about the node’s attributes and therefore its... View Details
- 2023
- Working Paper
Feature Importance Disparities for Data Bias Investigations
By: Peter W. Chang, Leor Fishman and Seth Neel
It is widely held that one cause of downstream bias in classifiers is bias present in the training data. Rectifying such biases may involve context-dependent interventions such as training separate models on subgroups, removing features with bias in the collection... View Details
- Spring 2023
- Article
Incentive Contract Design and Employee-Initiated Innovation: Evidence from the Field
By: Wei Cai, Susanna Gallani and Jee-Eun Shin
This study examines how the design of incentive contracts for tasks defined as workers’ official responsibilities (i.e., standard tasks) influences workers’ propensity to engage in employee-initiated innovation (EII). EII corresponds to innovation activities that are... View Details
- April 2023
- Article
On the Privacy Risks of Algorithmic Recourse
By: Martin Pawelczyk, Himabindu Lakkaraju and Seth Neel
As predictive models are increasingly being employed to make consequential decisions, there is a growing emphasis on developing techniques that can provide algorithmic recourse to affected individuals. While such recourses can be immensely beneficial to affected... View Details
- March–April 2023
- Article
Pricing for Heterogeneous Products: Analytics for Ticket Reselling
By: Michael Alley, Max Biggs, Rim Hariss, Charles Herrmann, Michael Lingzhi Li and Georgia Perakis
Problem definition: We present a data-driven study of the secondary ticket market. In particular, we are primarily concerned with accurately estimating price sensitivity for listed tickets. In this setting, there are many issues including endogeneity, heterogeneity in... View Details
- 2023
- Working Paper
PRIMO: Private Regression in Multiple Outcomes
By: Seth Neel
We introduce a new differentially private regression setting we call Private Regression in Multiple Outcomes (PRIMO), inspired the common situation where a data analyst wants to perform a set of l regressions while preserving privacy, where the covariates... View Details