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- 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...
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Keywords:
Pay Transparency;
Online Labor Market;
Privacy;
Wage Gap;
Corporate Disclosure;
Wages;
Negotiation
Cullen, Zoë B., and Bobak Pakzad-Hurson. "Equilibrium Effects of Pay Transparency." Econometrica 91, no. 3 (May 2023): 765–802. (Lead Article.)
- 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...
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Keywords:
Forecasting and Prediction;
AI and Machine Learning;
Consumer Behavior;
Technology Adoption;
Competitive Advantage
Narayandas, Das, and Arijit Sengupta. "Using AI to Adjust Your Marketing and Sales in a Volatile World." Harvard Business Review Digital Articles (April 12, 2023).
- 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...
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Keywords:
Survey Validation;
Experiment;
Preference Measurement;
Surveys;
Economics;
Behavior;
Measurement and Metrics
Falk, Armin, Anke Becker, Thomas Dohmen, David B. Huffman, and Uwe Sunde. "The Preference Survey Module: A Validated Instrument for Measuring Risk, Time, and Social Preferences." Management Science 69, no. 4 (April 2023): 1935–1950.
- 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...
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Keywords:
Training;
Organizational Change and Adaptation;
Knowledge Management;
Production;
Product;
Organizational Structure;
Auto Industry;
Argentina
Adhvaryu, Achyuta, Vittorio Bassi, Anant Nyshadham, Jorge Tamayo, and Nicolas Torres. "Organizational Responses to Product Cycles." Harvard Business School Working Paper, No. 23-061, March 2023.
- 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,...
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Enke, Benjamin, Thomas Graeber, and Ryan Oprea. "Complexity and Time." NBER Working Paper Series, No. 31047, March 2023.
- 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...
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Keywords:
Decision Trees;
Computational Advertising;
Market Segmentation;
Analytics and Data Science;
E-commerce;
Consumer Behavior;
Marketplace Matching;
Marketing Channels;
Digital Marketing
Aouad, Ali, Adam Elmachtoub, Kris J. Ferreira, and Ryan McNellis. "Market Segmentation Trees." Manufacturing & Service Operations Management 25, no. 2 (March–April 2023): 648–667.
- 2023
- Working Paper
Nailing Prediction: Experimental Evidence on the Value of Tools in Predictive Model Development
Predictive model development is understudied despite its centrality in modern artificial
intelligence and machine learning business applications. Although prior discussions
highlight advances in methods (along the dimensions of data, computing power, and
algorithms)...
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Keywords:
Analytics and Data Science
Yue, Daniel, Paul Hamilton, and Iavor Bojinov. "Nailing Prediction: Experimental Evidence on the Value of Tools in Predictive Model Development." Harvard Business School Working Paper, No. 23-029, December 2022. (Revised April 2023.)
- 2022
- Working Paper
Demand-and-Supply Imbalance Risk and Long-Term Swap Spreads
By: Samuel G. Hanson, Aytek Malkhozov and Gyuri Venter
We develop and test a model in which swap spreads are determined by end users' demand for and constrained intermediaries' supply of long-term interest rate swaps. Swap spreads reflect compensation both for using scarce intermediary capital and for bearing convergence...
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Hanson, Samuel G., Aytek Malkhozov, and Gyuri Venter. "Demand-and-Supply Imbalance Risk and Long-Term Swap Spreads." Working Paper, December 2022.
- 2022
- Article
Efficiently Training Low-Curvature Neural Networks
By: Suraj Srinivas, Kyle Matoba, Himabindu Lakkaraju and Francois Fleuret
Standard deep neural networks often have excess non-linearity, making them susceptible to issues such as low adversarial robustness and gradient instability. Common methods to address these downstream issues, such as adversarial training, are expensive and often...
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Keywords:
AI and Machine Learning
Srinivas, Suraj, Kyle Matoba, Himabindu Lakkaraju, and Francois Fleuret. "Efficiently Training Low-Curvature Neural Networks." Advances in Neural Information Processing Systems (NeurIPS) (2022).
- 2022
- Article
Which Explanation Should I Choose? A Function Approximation Perspective to Characterizing Post hoc Explanations
By: Tessa Han, Suraj Srinivas and Himabindu Lakkaraju
A critical problem in the field of post hoc explainability is the lack of a common foundational goal among methods. For example, some methods are motivated by function approximation, some by game theoretic notions, and some by obtaining clean visualizations. This...
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Han, Tessa, Suraj Srinivas, and Himabindu Lakkaraju. "Which Explanation Should I Choose? A Function Approximation Perspective to Characterizing Post hoc Explanations." Advances in Neural Information Processing Systems (NeurIPS) (2022). (Best Paper Award, International Conference on Machine Learning (ICML) Workshop on Interpretable ML in Healthcare.)
- 2022
- Article
A Human-Centric Take on Model Monitoring
By: Murtuza Shergadwala, Himabindu Lakkaraju and Krishnaram Kenthapadi
Predictive models are increasingly used to make various consequential decisions in high-stakes domains such as healthcare, finance, and policy. It becomes critical to ensure that these models make accurate predictions, are robust to shifts in the data, do not rely on...
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Shergadwala, Murtuza, Himabindu Lakkaraju, and Krishnaram Kenthapadi. "A Human-Centric Take on Model Monitoring." Proceedings of the AAAI Conference on Human Computation and Crowdsourcing (HCOMP) 10 (2022): 173–183.
- October–December 2022
- Article
Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem
By: Mochen Yang, Edward McFowland III, Gordon Burtch and Gediminas Adomavicius
Combining machine learning with econometric analysis is becoming increasingly prevalent in both research and practice. A common empirical strategy involves the application of predictive modeling techniques to "mine" variables of interest from available data, followed...
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Keywords:
Machine Learning;
Econometric Analysis;
Instrumental Variable;
Random Forest;
Causal Inference;
AI and Machine Learning;
Forecasting and Prediction
Yang, Mochen, Edward McFowland III, Gordon Burtch, and Gediminas Adomavicius. "Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem." INFORMS Journal on Data Science 1, no. 2 (October–December 2022): 138–155.
- Working Paper
Representation and Extrapolation: Evidence from Clinical Trials
By: Marcella Alsan, Maya Durvasula, Harsh Gupta, Joshua Schwartzstein and Heidi L. Williams
This article examines the consequences and causes of low enrollment of Black patients in clinical
trials. We develop a simple model of similarity-based extrapolation that predicts that evidence is
more relevant for decision-making by physicians and patients when it...
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Keywords:
Representation;
Racial Disparity;
Health Testing and Trials;
Race;
Equality and Inequality;
Innovation and Invention;
Pharmaceutical Industry
Alsan, Marcella, Maya Durvasula, Harsh Gupta, Joshua Schwartzstein, and Heidi L. Williams. "Representation and Extrapolation: Evidence from Clinical Trials." NBER Working Paper Series, No. 30575, October 2022. (Revise and resubmit, Quarterly Journal of Economics.)
- 2022
- Working Paper
What's My Employee Worth? The Effects of Salary Benchmarking
By: Zoë B. Cullen, Shengwu Li and Ricardo Perez-Truglia
While U.S. legislation prohibits employers from sharing information about their employees’
compensation with each other, companies are still allowed to acquire and use more aggregated
data provided by third parties. Most medium and large firms report using this type...
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Cullen, Zoë B., Shengwu Li, and Ricardo Perez-Truglia. "What's My Employee Worth? The Effects of Salary Benchmarking." NBER Working Paper Series, No. 30570, October 2022. (Revised January 2023.)
- 2022
- Working Paper
Perceptions about Monetary Policy
By: Michael D. Bauer, Carolin Pflueger and Adi Sunderam
We estimate perceptions about the Fed's monetary policy rule from micro data on professional forecasters. The perceived rule varies significantly over time, with important consequences for monetary policy and bond markets. Over the monetary policy cycle, easings are...
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Bauer, Michael D., Carolin Pflueger, and Adi Sunderam. "Perceptions about Monetary Policy." NBER Working Paper Series, No. 30480, September 2022.
- 2022
- Working Paper
Imagining the Future: Memory, Simulation and Beliefs about COVID
By: Pedro Bordalo, Giovanni Burro, Katherine B. Coffman, Nicola Gennaioli and Andrei Shleifer
How do people form beliefs about novel risks, with which they have little or no experience? A 2020 U.S. survey of beliefs about the lethality of COVID reveals that the elderly underestimate, and the young overestimate, their own risks, and that people with more health...
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Keywords:
Expectations;
Memory;
COVID-19 Pandemic;
Perception;
Behavior;
Decision Choices and Conditions;
Values and Beliefs
Bordalo, Pedro, Giovanni Burro, Katherine B. Coffman, Nicola Gennaioli, and Andrei Shleifer. "Imagining the Future: Memory, Simulation and Beliefs about COVID." NBER Working Paper Series, No. 30353, August 2022.
- 2022
- Working Paper
Machine Learning Models for Prediction of Scope 3 Carbon Emissions
By: George Serafeim and Gladys Vélez Caicedo
For most organizations, the vast amount of carbon emissions occur in their supply chain and in the post-sale processing, usage, and end of life treatment of a product, collectively labelled scope 3 emissions. In this paper, we train machine learning algorithms on 15...
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Keywords:
Carbon Emissions;
Climate Change;
Environment;
Carbon Accounting;
Machine Learning;
Artificial Intelligence;
Digital;
Data Science;
Environmental Sustainability;
Environmental Management;
Environmental Accounting
Serafeim, George, and Gladys Vélez Caicedo. "Machine Learning Models for Prediction of Scope 3 Carbon Emissions." Harvard Business School Working Paper, No. 22-080, June 2022.
- March 2022 (Revised July 2022)
- Module Note
Prediction & Machine Learning
This note provides an introduction to machine learning for an introductory data science course. The note begins with a description of supervised, unsupervised, and reinforcement learning. Then, the note provides a brief explanation of the difference between traditional...
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Keywords:
Machine Learning;
Data Science;
Learning;
Analytics and Data Science;
Performance Evaluation
Bojinov, Iavor I., Michael Parzen, and Paul Hamilton. "Prediction & Machine Learning." Harvard Business School Module Note 622-101, March 2022. (Revised July 2022.)
- February 2022 (Revised September 2022)
- Case
Lilium: Preparing for Takeoff
By: Navid Mojir, Vincent Dessain, Mette Fuglsang Hjortshoej and Emer Moloney
Lilium is a German company focused on developing electric vertical takeoff and landing vehicles (eVTOLs) that can be used to offer air taxi services. The company went public in September 2021 through a special purpose acquisition company (SPAC) deal, raising more than...
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Keywords:
SPACs;
Business Model;
Forecasting and Prediction;
Green Technology;
Capital Markets;
Venture Capital;
Initial Public Offering;
Rural Scope;
Urban Scope;
City;
Disruptive Innovation;
Growth and Development Strategy;
Technological Innovation;
Demand and Consumers;
Market Timing;
Industry Growth;
Infrastructure;
Logistics;
Product Design;
Product Development;
Production;
Service Delivery;
Service Operations;
Strategic Planning;
Partners and Partnerships;
Risk and Uncertainty;
Urban Development;
Sustainable Cities;
Business Strategy;
Competitive Strategy;
Competitive Advantage;
Air Transportation;
Aerospace Industry;
Air Transportation Industry;
Green Technology Industry;
Transportation Industry;
Travel Industry;
Germany;
Munich;
Brazil;
United States;
Florida
Mojir, Navid, Vincent Dessain, Mette Fuglsang Hjortshoej, and Emer Moloney. "Lilium: Preparing for Takeoff." Harvard Business School Case 522-084, February 2022. (Revised September 2022.)
- 2022
- Working Paper
The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective
By: Satyapriya Krishna, Tessa Han, Alex Gu, Javin Pombra, Shahin Jabbari, Steven Wu and Himabindu Lakkaraju
As various post hoc explanation methods are increasingly being leveraged to explain complex models in high-stakes settings, it becomes critical to develop a deeper understanding of if and when the explanations output by these methods disagree with each other, and how...
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Krishna, Satyapriya, Tessa Han, Alex Gu, Javin Pombra, Shahin Jabbari, Steven Wu, and Himabindu Lakkaraju. "The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective." Working Paper, 2022.