Publications
Publications
Show Results For
-
All HBS Web
(3,288)
- Faculty Publications (154)
Page 1 of
154
Results
→
- 2021
- Working Paper
Equilibrium Effects of Pay Transparency
By: Zoë B. Cullen and Bobak Pakzad-Hurson
The public discourse around pay transparency has focused on the direct effect: how workers seek
to rectify newly-disclosed pay inequities through renegotiations. The question of how wage-setting
and hiring practices of the firm respond in equilibrium has received less...
View Details
- May 2021
- Article
Choice Architecture in Physician–patient Communication: A Mixed-methods Assessments of Physicians’ Competency
By: J. Hart, Yadav, K., Szymanski, S., Summer, A., Tannenbaum, A., Zlatev, J., Daniels, D. and Halpern, S.D.
Background: Clinicians’ use of choice architecture, or how they present options, systematically influences the choices made by patients and their surrogate decision makers. However, clinicians may incompletely understand this influence.
Objective:... View Details
Objective:... View Details
- 2021
- Working Paper
Property Rights and Urban Form
By: Simeon Djankov, Edward L. Glaeser, Valeria Perotti and Andrei Shleifer
How do the different elements in the standard bundle of property rights, including those of possession and transfer, influence the shape of cities? This paper incorporates insecure property rights into a standard model of urban land prices and density, and makes...
View Details
- 2021
- Working Paper
Time Dependence and Preference: Implications for Compensation Structure and Shift Scheduling
By: Doug J. Chung, Byungyeon Kim and Byoung G. Park
This study jointly examines agents’ time dependence—period effects within instantaneous utility—and time preference—behavior on discounting future utility. The study considers the start- and end-of-period effects for time dependence and exponential and hyperbolic...
View Details
- April 2021
- Case
Distinct Software
By: Das Narayandas, Arijit Sengupta and Jonathan Wray
Distinct Software (disguised name), a global enterprise software company, is at an important point in its growth trajectory where the luster of its mantra of “grow and win at any cost” has dimmed with increasing competition and margin pressures. To help navigate its...
View Details
- 2020
- Working Paper
Is Accounting Useful for Forecasting GDP Growth? A Machine Learning Perspective
By: Srikant Datar, Apurv Jain, Charles C.Y. Wang and Siyu Zhang
We provide a comprehensive examination of whether, to what extent, and which accounting variables are useful for improving the predictive accuracy of GDP growth forecasts. We leverage statistical models that accommodate a broad set of (341) variables—outnumbering the...
View Details
- April 2021
- Article
Homing and Platform Responses to Entry: Historical Evidence from the U.S. Newspaper Industry
By: K. Francis Park, Robert Seamans and Feng Zhu
We examine how heterogeneity in customers’ tendencies to single-home or multi-home affects a platform’s competitive responses to new entrants in the market. We first develop a formal model to generate predictions about how a platform will respond. We then empirically...
View Details
- February 2021
- Tutorial
Assessing Prediction Accuracy of Machine Learning Models
By: Michael Toffel and Natalie Epstein
This video describes how to assess the accuracy of machine learning prediction models, primarily in the context of machine learning models that predict binary outcomes, such as logistic regression, random forest, or nearest neighbor models. After introducing and...
View Details
- 2021
- Working Paper
FinTech Lending and Cashless Payments
By: Boris Vallée, Pulak Ghosh and Yao Zeng
This study provides a new perspective to understand the rise and future potential of FinTech lending by linking it to the informational role of cashless payments. We uncover both theoretically and empirically a synergy between FinTech lending and cashless payments....
View Details
Keywords:
Fintech;
Lending;
Payments;
Data Sharing;
Financing and Loans;
Technology;
Banks and Banking;
Business Model
- February 2021
- Case
Digital Manufacturing at Amgen
By: Shane Greenstein, Kyle Myers and Sarah Mehta
This case discusses efforts made by biotechnology (biotech) company Amgen to introduce digital technologies into its manufacturing processes. Doing so is complicated by the fact that the process for manufacturing biologics—or therapeutics made from living cells—is...
View Details
Keywords:
Digital Technologies;
Change;
Change Management;
Decision Making;
Cost vs Benefits;
Decisions;
Information;
Data and Data Sets;
Innovation and Invention;
Innovation and Management;
Innovation Leadership;
Innovation Strategy;
Technological Innovation;
Jobs and Positions;
Knowledge;
Leadership;
Organizational Culture;
Science;
Strategy;
Technology;
Technology Adoption;
Biotechnology Industry;
Pharmaceutical Industry;
United States;
California;
Puerto Rico;
Rhode Island
- 2021
- Working Paper
Real Credit Cycles
By: Pedro Bordalo, Nicola Gennaioli, Andrei Shleifer and Stephen J. Terry
We incorporate diagnostic expectations, a psychologically founded model of overreaction to news, into a workhorse business cycle model with heterogeneous firms and risky debt. A realistic degree of diagnosticity, estimated from the forecast errors of managers of U.S....
View Details
- January 2021
- Article
A Model of Relative Thinking
By: Benjamin Bushong, Matthew Rabin and Joshua Schwartzstein
Fixed differences loom smaller when compared to large differences. We propose a model of relative thinking where a person weighs a given change along a consumption dimension by less when it is compared to bigger changes along that dimension. In deterministic settings,...
View Details
- January 2021
- Article
Using Models to Persuade
By: Joshua Schwartzstein and Adi Sunderam
We present a framework where "model persuaders" influence receivers’ beliefs by proposing models that organize past data to make predictions. Receivers are assumed to find models more compelling when they better explain the data, fixing receivers’ prior beliefs. Model...
View Details
- Article
Soul and Machine (Learning)
By: Davide Proserpio, John R. Hauser, Xiao Liu, Tomomichi Amano, Burnap Alex, Tong Guo, Dokyun (DK) Lee, Randall Lewis, Kanishka Misra, Eric Schwarz, Artem Timoshenko, Lilei Xu and Hema Yoganarasimhan
Machine learning is bringing us self-driving cars, medical diagnoses, and language translation, but how can machine learning help marketers improve marketing decisions? Machine learning models predict extremely well, are scalable to “big data,” and are a natural fit to...
View Details
- 2020
- Working Paper
The Market for Healthcare in Low Income Countries
By: Reshmaan Hussam, Abhijit Banerjee, Jishnu Das, Jeffrey Hammer and Aakash Mohpal
New research clearly identifies trust as an important driver of the demand for healthcare. However, doctors who realize that patients may not trust them may also alter their behavior in response. We assemble a large dataset that assesses clinical performance using...
View Details
- 2020
- Working Paper
Translating Information into Action: A Public Health Experiment in Bangladesh
By: Reshmaan Hussam, Kailash Pandey, Abu Shonchoy and Chikako Yamauchi
Standard models of technology adoption posit learning as the basis of adoption. However, hygiene campaigns centered around information provision have been overwhelmingly unsuccessful in changing behavior and improving child health across the developing world. We design...
View Details
- 2020
- Article
Beyond Individualized Recourse: Interpretable and Interactive Summaries of Actionable Recourses
By: Kaivalya Rawal and Himabindu Lakkaraju
As predictive models are increasingly being deployed in high-stakes decision-making, there has been a lot of interest in developing algorithms which can provide recourses to affected individuals. While developing such tools is important, it is even more critical to...
View Details
- September–October 2020
- Article
Managing Churn to Maximize Profits
By: Aurelie Lemmens and Sunil Gupta
Customer defection threatens many industries, prompting companies to deploy targeted, proactive customer retention programs and offers. A conventional approach has been to target customers either based on their predicted churn probability or their responsiveness to a...
View Details
- 2020
- Working Paper
Time and the Value of Data
By: Ehsan Valavi, Joel Hestness, Newsha Ardalani and Marco Iansiti
This paper investigates the effectiveness of time-dependent data in improving the quality of AI-based products and services. Time-dependency means that data loses its relevance to problems over time. This loss causes deterioration in the algorithm's performance and,...
View Details
- August 2020 (Revised September 2020)
- Technical Note
Assessing Prediction Accuracy of Machine Learning Models
The note introduces a variety of methods to assess the accuracy of machine learning prediction models. The note begins by briefly introducing machine learning, overfitting, training versus test datasets, and cross validation. The following accuracy metrics and tools...
View Details