Projects and Labs

Ulrich Doraszelski, Joseph J. Aresty Professor, Professor of Business Economics and Public Policy, Economics, and Marketing, Jordi Jaumandreu, Senior Academic Researcher, Boston University

Artificial intelligence, machine learning, robots, and automation have fundamentally changed firms’ production processes over the years. Yet, the measurement of productivity traditionally assumes that these new technologies have scaled up existing production processes without substantially affecting how firms combine the various inputs to produce output. This project develops methods for the measurement of productivity that account for these (and other) new technologies with the overarching goal of ensuring that their impact is fully reflected in the aggregate productivity statistics.

Bingxin Zhao, Assistant Professor of Statistics and Data Science

How does the brain of a manager differ from that of other people? Are manager’s brains born or made? In this project, a half-million large-scale biomedical datasets will be analyzed in combination with advanced statistical learning methods to uncover inter-subject variations in brain structure and function related to being a manager. Researchers will analyze the genetic endowment of manager-related brain differences by integrating imaging, genetics, and environmental information, and investigate their links to social activity, lifestyle, mental health, and physical health.

Winston Dou, Assistant Professor of Finance, David Reibstein, William Stewart Woodside Professor of Marketing

Environmental, Social, and Governance (ESG) issues are playing an increasingly important role in firms’ strategic decision-making in financing, investment, marketing, and industry competition. This project aims to provide theoretical insights and empirical evidence on how corporate ESG activities could be motivated by firms’ considerations about product market competition and the potential feedback effect between ESG activities and competitive advantages in the product market.

Leon Musolff, Assistant Professor of Business, Economics, and Public Policy, James Brand, Senior Researcher, Microsoft, Juan Camilo Castillo, Assistant Professor of Economics, Will Wang, Chief Economist, Operate, Unity 

Access to ample computing resources has become a key concern for leading firms across many industries. To investigate this concern, researchers empirically study the market design problem faced by cloud providers, which need to determine how to price and allocate fixed computing capacity across firms with differing needs and volatile demand. This investigation focuses on the prevailing “quality differentiation” strategy and its impact on market outcomes.

Duncan Watts, Stevens University Professor, Xinlan Emily Hu, Ph.D. candidate, Operations, Information and Decisions

Which aspects of a team’s interaction (the “team process”) predict team success? And how might the answer change across different types of teams, tasks, and contexts? This project answers these questions by computationally modeling theories about team processes, then testing the theories head-to-head on a variety of real teams — from political deliberation groups to freelance software engineers. This model will uncover the boundary conditions of what makes an interaction successful, producing both precise social scientific theories for academics and data-backed insights for managers, executives, and team leaders.

Britta Glennon, Assistant Professor of Management, Saerom (Ronnie) Lee, Assistant Professor of Management, Saerom (Ronnie) Lee, Assistant Professor of Management

How do entrepreneurs choose the location of their startups? Other dimensions of startup formation have long received extensive attention from scholars, but location choice—particularly, across national borders—remains under-explored. This project explores questions such as: are entrepreneurs more likely to establish their startups in other countries in response to more restrictive immigration or trade policies in the US? What types of individuals are most or least responsive? Who are the winners and losers?

Ravi B. Parikh, Assistant Professor of Health Policy and Medicine, Ezekiel J. Emanuel, Vice Provost for Global Initiatives

The U.S. government pays Medicare Advantage (MA) insurers a set amount for each person who enrolls, with higher rates paid for patients assigned more co-morbidities via the current risk adjustment methodology. This incentivizes “upcoding”, systematic over-billing by MA insurers that results in at least $10 billion in excessive and unjustified costs each year. Leveraging applied machine learning methods, this “ML-Guided Risk Scoring” project aims to validate a more accurate risk score with wide adoption potential that can reduce gaming and upcoding. The outputs of this research approach will have relevance for policy adoption, leading to fairer payment to providers, incentives to care for medically vulnerable patients and parity between MA and traditional Medicare.

Amy Sepinwall, Associate Professor of Legal Studies and Business Ethics

This project seeks to gain clarity on whether AI can satisfy the requirements of moral agency and how this impacts corporations.

Duncan Watts, Stevens University Professor of Computer and Information Science, Communication, and Operations, Information and Decisions
Linnea Gandhi, Doctoral Candidate, OID

This project seeks to build a map of “nudge” or “choice architecture” interventions, enabling practitioners and academics alike to navigate the theoretical space easily and effectively. The map will be seeded with historical studies and enriched with data from new lab and field experiments to help validate what we, as a field, do and don’t yet “know” about the efficacy of these interventions across contexts.

Susanna Berkouwer, Assistant Professor of Business, Economics and Public Policy
Arthur van Benthem, Associate Professor of Business, Economics and Public Policy

This project builds a research portfolio that gathers large data sets from the U.S. and across the world and uses sophisticated econometric tools to analyze this data with the goal of quantifying the inefficiency and unintended consequences from inefficient regulations, and to propose improved energy policy.

Matthew Bloomfield, Assistant Professor of Accounting
Christina Zhu, Assistant Professor of Accounting

This project seeks to provide novel evidence regarding the link between firms’ public financial disclosures and their product market pricing decisions.

Saerom (Ronnie) Lee, Assistant Professor of Management
Exequiel (Zeke) Hernandez, Associate Professor of Management

Using multiple large-scale datasets on the U.S. labor market, this project will examine the firm-level drivers of hiring immigrant workers.

Aviv Nevo, George A. Weiss and Lydia Bravo Weiss University Professor; Professor of Marketing; Professor of Economics
Yihao Yuan, Doctoral Candidate, Marketing

This project seeks to understand the role that exclusive contracts play in shaping market structure, consumer demand, and innovation. This project will develop a structural model and use data-driven methods to quantify the impact of vertical contracts on consumer welfare and profits of studios and platforms in the video streaming market, a fast-growing market that already accounts for more than a quarter of all time spent on television sets by Americans.

Lindsey Cameron, Assistant Professor of Management

This project is an inductive two-part multi-sourced qualitative study that focuses on the practices and community around the developers that write bots, scripts and automated programs that are designed to override algorithmic controls and how workers use these technologies to resist and counter algorithmic control.

Marius Guenzel, Assistant Professor of Finance

The goal of this project is to empirically study the effect of workplace and economic stress on health outcomes including aging and mortality.

Natalie Carlson, Assistant Professor of Management

This project supports the launch of the initial Wharton/Columbia Management, Analytics, and Data (M.A.D.) Conference. The conference is created to bring together academics working to understand the role of data and analytics in shaping managerial practice and the determinants of firm performance.

Robert P. Inman, Richard King Mellon Professor Emeritus of Finance; Professor Emeritus of Business Economics & Public Policy

This project uses data collection for a student-led evaluation of the health and economic consequences of the Covid-19 pandemic and the effectiveness of national and state-wide policy responses to contain the coronavirus and to mitigate its health and economic consequences. The format for this evaluation will be a Capstone Course (BEPP 401) entitled, Federal and State Management of the Pandemic: Money, Messages, Vaccinations, and State Policies.

Dean Knox, Assistant Professor of Operations, Information, and Decisions
Rachel Mariman, Research Project Manager 

The goal of this project is to create a tool to automate causal inference from incomplete or imperfect data. This tool will reach a broad audience of applied researchers across the social and medical sciences by developing an easy-to-use front-end interface and implement more efficient back-end optimizations. In addition, the project will create a series of data applications to illustrate its ease of use. This project is funded by AI for Business.

Benjamin Lockwood, Assistant Professor of Business Economics and Public Policy
Santosh Anagol, Associate Professor of Business, Economics and Public Policy

This project seeks to characterize and advertise a new statistical method for estimating the elasticity of taxable income in the presence of optimization frictions.

Philip Tetlock, Professor of Management
Dillon Bowen, Doctoral Candidate of Operations, Information, and Decisions

This project will compare the effectiveness of many judgmental forecasting techniques on a standard set of forecasting tasks, delivering a concise list of highly effective techniques and recommended best practices.

Raghu Iyengar, Miers-Busch, W’1885 Professor; Professor of Marketing; Faculty Director, Wharton Customer Analytics
Nicole Wang Trexler, Associate Director of Data Science and Research

This project conducts a quasi-experimental research study to gain an in-depth look at how an experiential learning pathway impacts students’ informal learning journey. This information will help WCA design and deploy better experiential learning pathways to better serve the students at Wharton and within the Penn community.

Duncan Watts, Stevens University Professor of Computer and Information Science, Communication, and Operations, Information and Decisions
Baird Howland, PhD Student, Annenberg School of Communication, Computational Social Science Lab at Penn,
Valery Yakubovich, Executive Director, Computational Social Science Lab at Penn

The goal of this project is to study our understanding of the COVID-19 pandemic – the remarkably varied conceptions of what is happening, why it is happening, and what should be done in response, with a rare combination of quantitative rigor and qualitative depth.

Hamsa Bastani, Assistant Professor of Operations, Information, and Decisions
Pia Ramchandani, Doctoral Candidate of Operations, Information, and Decisions

In collaboration with the Tellfinder Alliance for Global Counter-Human Trafficking, this project will leverage unstructured, massive deep web data from leading adult-services websites using a novel machine learning framework to construct the first global network view of sex trafficking supply chains.

Dan Taylor, Associate Professor of Accounting

This five-part case series will highlight recent accounting frauds (e.g., Wirecard, Luckin Coffee, etc.) and how each of the frauds could have been detected using business analytics.

Lorin Hitt, Zhang Jindong Professor; Professor of Operations, Information and Decisions
Lynn Wu, Associate Professor of Operations, Information and Decisions

This research explores how AI facilitates innovation by documenting specific cases and mechanisms on when AI technologies should be used to innovate and when they should not, and their implications on demand for different types of labor and productivity. This project is co-sponsored by AI for Business and Analytics at Wharton.

Michael Platt, James S. Riepe University Professor of Marketing, Neuroscience, and Psychology; Faculty Director, Wharton Neuroscience Initiative
Elizabeth Johnson, Executive Director, Wharton Neuroscience Initiative

Wharton Neuroscience Initiative will support two underrepresented undergraduate summer students focused specifically on Applied Neuroscience and Business Analytics for a 10-week summer internship program. These students will be part of a new, larger applied brain and cognitive science summer undergraduate internship program which aims to combat systemic inequalities and a lack of diversity that plague neuroscience, brain and behavioral science, analytics, and data science careers.

Susanna Berkouwer, Assistant Professor of Business Economics and Public Policy
Arthur van Benthem, Associate Professor of Business Economics and Public Policy

This project aims to understand and quantify inefficiencies in government regulations related to the environment and to provide tangible recommendations for designing smarter policies that address environmental concerns while driving economic growth.

Maurice E. Schweitzer, Cecilia Yen Koo Professor; Professor of Operations, Information and Decisions
T. Bradford Bitterly, Assistant Professor of Management, HKUST Business School
Alex Hirsch, Research Coordinator, Operations, Information and Decisions

This project will support the development and use of an AI-powered chatbot platform for negotiations. This project is co-sponsored by AI for Business and Analytics at Wharton.

Shing-Yi Wang, Associate Professor of Business Economics and Public Policy
Jing Cai, Assistant Professor at University of Maryland

This research explores how firms can improve their hiring decisions by employing state-of-the-art machine learning methods that use observable characteristics of applicants to predict their probability of staying in the job and their performance.

Dean Knox, Assistant Professor of Operations, Information, and Decisions
Rachel Mariman, Senior Research Project Manager, Analytics at Wharton

Using unique access to archives of administrative records on civilian complaints against police, this experimental study seeks to assess how city residents in Philadelphia, New York, and Chicago understand and perceive the current civilian complaint process, and systematically evaluates the impact of transparency initiatives on civic engagement and public trust in police.

J. Daniel Kim, Assistant Professor of Management
Saerom (Ronnie) Lee, Assistant Professor of Management

This project examines the scaling of startups by applying cutting-edge machine learning and econometric tools to assess a novel big dataset of more than 200 million jobs.

Eugene Katsevich, Assistant Professor of Statistics

Single cell CRISPR screen technology, proposed a few years ago, offers unprecedented opportunities to unravel the molecular mechanisms of human disease and guide drug development. The objective of this project is to produce a high-quality software implementation of Dr. Katsevich’s SCEPTRE methodology, with the goal of broad adoption among the genomics community.

Ryan Dew, Assistant Professor of Marketing

This research seeks to enhance our understanding of the role that aesthetics play in consumer decision-making and perceptions of companies and to develop effective tools that help companies craft data-driven visual brands, products, and platforms.

Robert P. Inman, Richard King Mellon Professor Emeritus of Finance; Professor Emeritus of Business Economics & Public Policy

This project uses data collection for a student-led evaluation of the health and economic consequences of the Covid-19 pandemic and the effectiveness of national and state-wide policy responses to contain the coronavirus and to mitigate its health and economic consequences. The format for this evaluation will be a Capstone Course (BEPP 401) entitled, Managing the Pandemic: Messages and Money.

Susan Wachter, Albert Sussman Professor of Real Estate, Professor of Finance
Shane Jensen, Professor of Statistics, Department of Statistics

This project seeks to identify the neighborhood amenity value of transforming blighted and vacant lots into maintained green open space by deploying spatial techniques and integrating multiple data sources to improve our understanding of the dynamics of urban change and identify how residents value “greener” neighborhoods.

Kartik Hosanagar, John C. Hower Professor of Technology & Digital Business; Professor of Marketing

This project seeks to conduct original academic research to better understand how real-world firms use A/B testing software and how consumers respond to online experiments.

Ken Moon, Assistant Professor in Operations, Information and Decisions

In collaboration with three highly sophisticated, intensive-care clinics operating within the University of Pennsylvania hospital system, this project equips medical providers with individually worn biometric sensors that closely track the workplace demands and stresses they experience while navigating swings in high acuity patients’ urgent needs.

Benjamin Keys, Rowan Family Foundation Professor; Professor of Real Estate; Professor of Finance
Maisy Wong, James T. Riady Associate Professor of Real Estate; Assistant Director, Grayken Program in International Real Estate at the Zell/Lurie Real Estate Center

This project procures access to the The Corelogic Multiple Listing Services (MLS) database and includes 10 million observations of property listing data, and more than 600 variables that describe listing details. Professor Ben Keys will use the dataset for his research agenda: How is Climate Change Reshaping Housing and Mortgage Markets? Professor Maisy Wong will use the dataset to study the returns to scale of MLS platforms.

Corinne Low, Assistant Professor of Business Economics and Public Policy
Judd B. Kessler, Associate Professor of Business Economics and Public Policy

This project expands upon the recently published paper in American Economic Review, Incentivized Resume Rating: Eliciting Employer Preferences without Deception, by building two platforms to disseminate the research tools and insights from the IRR method targeted at researchers/policymakers and firms.

Wharton Customer Analytics

This project will develop real-world case studies for teaching data analytics and data science in the classroom based on current datasets from Wharton Customer Analytics partners. In addition, Wharton Customer Analytics will develop a series of in-person workshops that would provide an overview of the analytics basics that are necessary to understand data science, data management, and data visualization. In addition, these workshops will be supplemented by online modules and professional video recorded content for use by students, faculty, and alumni.

Edgar Dobriban, Assistant Professor of Statistics
Eric J. Tchetgen Tchetgen, Luddy Family President’s Distinguished Professor, Professor of Statistics
Steven O. Kimbrough, Professor of Operations, Information and Decisions

Wharton Energy Analytics Lab brings to bear cutting-edge applications of machine learning techniques to position Wharton as an undisputed leader in connecting data analytics and energy markets to face societal challenges. The lab will develop research, teaching expertise, knowledge dissemination and outreach efforts to diverse audiences.

Michael R. Roberts, William H. Lawrence Professor of Finance

This funding supports the development of a new course: Data Science for Finance. The course will introduce students to data science for financial applications using the Python programming language and its ecosystem of packages (e.g., Dask, Matplotlib, Numpy, Numba, Pandas, SciPy, Scikit-Learn, StatsModels). To do so, students will investigate a variety of empirical questions from different areas within finance including: FinTech, investment management, corporate finance, corporate governance, venture capital, private equity, and entrepreneurial finance. The course will highlight how big data and data analytics shape the way finance is practiced.

Hamsa Bastani, Assistant Professor of Operations, Information, and Decisions

While modern data analytics are incredibly effective at extracting valuable insight from text data (e.g., product reviews, nurses’ notes, etc.), they require an enormous amount of data; this research seeks to increase the applicability of state-of-the-art text analytics algorithms by transferring word embeddings for large-scale data to domains with small- and medium-scale datasets.

Witold Henisz, Deloitte & Touche Professor of Management in Honor of Russell E. Palmer, former Managing Partner and Director, Wharton Political Risk Lab

This project focuses on analyzing the materiality of businesses’ environmental, social and governance risks and opportunities and promotes faculty research, teaching, and student learning in this area.

Laura Zarrow, Executive Director of Wharton People Analytics

People Analytics will produce a slate of instructive, engaging, 5-15-minute videos from previous conferences and events that illuminate aspects of people analytics for students, industry professionals, and alumni.

Prasanna Tambe, Associate Professor of Operations, Information, and Decisions

This research project conducts an empirical exploration of the relative costs and benefits of using machine learning based tools on video job application data during the hiring process.

Etan A. Green, Assistant Professor of Operations, Information, and Decisions

This research project trains an artificial intelligence to make optimal offers in negotiations on eBay.

Daniel Taylor, Associate Professor of Accounting

This project will develop research and teaching expertise on the application of Big Data and predictive analytics to issues related to insider trading, financial irregularities, and fraud. The Lab will aim to create new tools and technologies, academic research, and teaching and educational materials.

Mary Purk, Executive Director of Wharton Customer Analytics
Linda Zhao, Professor of Statistics

This conference, for Penn students, aims to inspire and educate data scientists, regardless of gender, and support women in analytics and data science-related careers. Planned for February 14, 2020, on Penn’s campus, this event is part of the larger Women in Data Science (WiDS) initiative originated at Stanford in November 2015 and includes a global conference, 150+ regional events, a datathon, and numerous podcasts.