Publications
Publications
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(2,957)
- Faculty Publications (967)
- 2023
- Article
Conduit Incentives: Eliciting Cooperation from Workers Outside of Managers' Control
By: Susanna Gallani
Can managers use monetary incentives to elicit cooperation from workers they cannot reward for their efforts? I study “conduit incentives,” an innovative incentive design, whereby managers influence bonus-ineligible workers’ effort by offering bonus-eligible employees... View Details
Keywords: Organizational Behavior Modification; Peer Monitoring; Persistence Of Performance Improvements; Crowding Out; Implicit Incentives; Compensation; Healthcare; Social Pressure; Image Motivation; Incentives; Motivation; Performance; Behavior; Motivation and Incentives; Compensation and Benefits; Governing Rules, Regulations, and Reforms; Organizational Culture; Health Industry; California
- May 2023
- Article
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... View Details
Keywords: Pay Transparency; Online Labor Market; Privacy; Wage Gap; Corporate Disclosure; Wages; Negotiation
- 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
- 2023
- Article
Probabilistically Robust Recourse: Navigating the Trade-offs between Costs and Robustness in Algorithmic Recourse
By: Martin Pawelczyk, Teresa Datta, Johannes van-den-Heuvel, Gjergji Kasneci and Himabindu Lakkaraju
As machine learning models are increasingly being employed to make consequential decisions in real-world settings, it becomes critical to ensure that individuals who are adversely impacted (e.g., loan denied) by the predictions of these models are provided with a means... View Details
- April 21, 2023
- Article
When Scenario Planning Fails
By: Kalle Heikkinen, William R. Kerr, Mika Malin, Panu Routila and Eemil Rupponen
How can organizations perform scenario planning when they are hit by shocks outside of leaders’ field of vision? Interviews with Nordic executives, who experienced both the Covid-19 pandemic and were in close proximity to Russia as the country invaded Ukraine, can... 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
- 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
- April 2023
- Article
Learning Down to Train Up: Mentors Are More Effective When They Value Insights from Below
By: Ting Zhang, Dan Wang and Adam D. Galinsky
Although mentorship is vital for individual success, potential mentors often view it as a costly burden. To understand what motivates mentors to overcome this barrier and more fully engage with their mentees, we introduce a new construct, learning direction, which... 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
- 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
- 2023
- Working Paper
The Limits of Algorithmic Measures of Race in Studies of Outcome Disparities
By: David S. Scharfstein and Sergey Chernenko
We show that the use of algorithms to predict race has significant limitations in measuring and understanding the sources of racial disparities in finance, economics, and other contexts. First, we derive theoretically the direction and magnitude of measurement bias in... View Details
- April 2023
- Article
The Preference Survey Module: A Validated Instrument for Measuring Risk, Time, and Social Preferences
By: Armin Falk, Anke Becker, Thomas Dohmen, David B. Huffman and Uwe Sunde
Incentivized choice experiments are a key approach to measuring preferences in economics but are also costly. Survey measures are a low-cost alternative but can suffer from additional forms of measurement error due to their hypothetical nature. This paper seeks to... View Details
- 2023
- Working Paper
Organizational Responses to Product Cycles
By: Achyuta Adhvaryu, Vittorio Bassi, Anant Nyshadham, Jorge Tamayo and Nicolas Torres
Product cycles entail the mass production of new—and often increasingly complex—products on a regular basis. How do firms manage these changes? We use granular daily data from a leading automobile manufacturer to study the organizational impacts of introducing new... View Details
- March 2023
- Supplement
Allianz Türkiye (C): Managing the 2017 Hail Storm
By: John D. Macomber and Fares Khrais
Allianz Turkey is a property casualty insurance company operating in a region experiencing increasing losses from natural catastrophe events related to climate change, for example hail, wildfire, and flooding. There are also substantial other natural catastrophe... View Details
- March 2023 (Revised April 2024)
- Case
Allianz Türkiye: Adapting to Climate Change
By: John D. Macomber and Fares Khrais
Allianz Turkey is a property casualty insurance company operating in a region experiencing increasing losses from natural catastrophe events related to climate change, for example hail, wildfire, and flooding. There are also substantial other natural catastrophe... View Details
- 2023
- Working Paper
Complexity and Time
By: Benjamin Enke, Thomas Graeber and Ryan Oprea
We provide experimental evidence that core intertemporal choice anomalies -- including extreme short-run impatience, structural estimates of present bias, hyperbolicity and transitivity violations -- are driven by complexity rather than time or risk preferences. First,... View Details
- March–April 2023
- Article
Market Segmentation Trees
By: Ali Aouad, Adam Elmachtoub, Kris J. Ferreira and Ryan McNellis
Problem definition: We seek to provide an interpretable framework for segmenting users in a population for personalized decision making. Methodology/results: We propose a general methodology, market segmentation trees (MSTs), for learning market... View Details
- January–February 2023
- Article
Forecasting COVID-19 and Analyzing the Effect of Government Interventions
By: Michael Lingzhi Li, Hamza Tazi Bouardi, Omar Skali Lami, Thomas Trikalinos, Nikolaos Trichakis and Dimitris Bertsimas
We developed DELPHI, a novel epidemiological model for predicting detected cases and deaths in the prevaccination era of the COVID-19 pandemic. The model allows for underdetection of infections and effects of government interventions. We have applied DELPHI across more... View Details
- February 2023
- Article
The Effect of Systems of Management Controls on Honesty in Managerial Reporting
By: Aishwarrya Deore, Susanna Gallani and Ranjani Krishnan
While budgetary controls with capital rationing are optimal in theory and widespread in practice, empirical research documents their association with higher employee dishonesty compared to budgetary controls without rationing. In this study, we examine whether... View Details
- January–February 2023
- Article
Data-Driven COVID-19 Vaccine Development for Janssen
By: Dimitris Bertsimas, Michael Lingzhi Li, Xinggang Liu, Jennings Xu and Najat Khan
The COVID-19 pandemic has spurred extensive vaccine research worldwide. One crucial part of vaccine development is the phase III clinical trial that assesses the vaccine for safety and efficacy in the prevention of COVID-19. In this work, we enumerate the first... View Details