Ken Moon

Ken Moon
  • Assistant Professor of Operations, Information and Decisions

Contact Information

  • office Address:

    3730 Walnut Street
    559 Jon M. Huntsman Hall
    Philadelphia, PA 19104

Research Interests: Empirical operations management, Data analytics, Online marketplaces, Revenue management, Workforce planning, Econometrics and structural estimation

Links: CV


Ken Moon is an Assistant Professor of Operations, Information and Decisions at the Wharton School.  His research combines empirical methods and analytics with theory to study problems in operations management.  In collaboration with industry partners, his recent work emphasizes challenges in marketplace design and workforce planning faced by online platforms and markets, retailers, and manufacturers.

Ken completed his undergraduate studies in mathematics and economics at Stanford University, his JD at the Harvard Law School, and his PhD at the Stanford Graduate School of Business.

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  • Haim Mendelson, Ken Moon, Yuanyuan Shen (Working), Behavioral and Social Motivations in a Crowdfunding Marketplace.

  • Haim Mendelson and Ken Moon (2018), Modeling Success and Engagement for the App Economy, ACM The Web Conference (WWW'18).

  • Elena Belavina, Karan Girotra, Ken Moon, Jiding Zhang (Working), Matching Uncertain Talents to Jobs in Online Marketplaces.

    Abstract: Problem definition: We study the problem of how marketplaces should match workers to jobs. In settings where the relevant skills are observable (e.g., Uber), it is obvious that the marketplace platform should prioritize matches of the workers with the best attributes. However, in many important and growing settings, components of key skills and attributes (e.g., adaptability or ability to manage non-routine tasks) are difficult to measure at scale. Academic/practical relevance: We focus on matching in the presence of high bandwidth information, which is typically unobservable to the marketplace designer and is perceived by marketplace participants only through effort and cost (e.g., interviewing). Further, our method of estimating employers' demand is novel, rigorous, and scalable. We expect this type of marketplace to become more relevant as the gig economy expands from its current niche, at 0.5-1% of the overall US labor force. Methodology: We use data covering millions of job postings and transactions on a major online platform for sourcing freelance labor across diverse sectors (e.g., data entry, graphic design, translation, and web development). We first conduct a structural estimation regarding employers' demand preferences on observable skills versus on high bandwidth information. We then simulate counterfactual methods of allocating freelancers into employers' hiring consideration sets, and compare prioritization policies based relationships, observable skills and exploration. Results: High bandwidth information consistently explains the majority of variation in observed hiring decisions, while the value of observed attributes (e.g., skills, relationships) varies across markets. Further, in markets with high setup costs, the platform should prioritize initial matches and retention; in markets with low setup costs, exploration should be prioritized. Matching based on observed skills is demonstrably less efficient. Managerial implications: We point out the importance of the high bandwidth information, and the efficacy of exploiting such information in designing allocation policies. We further propose a framework to characterize markets: the setup cost determines whether exploration or relationships should be prioritized. Our study provides guidance to practitioners on how to match workers to jobs in given markets.

  • Ken Moon, P. Bergemann, D. Brown, A. Chen, J. Chu, E. Eisen, G. Fischer, P. Loyalka, S. Rho, J. Cohen (Under Revision), Manufacturing Productivity with Worker Turnover.

  • Kostas Bimpikis, Wedad J. Elmaghraby, Ken Moon, Wenchang Zhang (Under Revision), Managing Market Thickness in Online B2B Markets.

  • Ken Moon, Kostas Bimpikis, Haim Mendelson (2017), Randomized Markdowns and Online Monitoring, Management Science, 64 (3), pp. 1271-1290.


Past Courses


    OIDD 101 explores a variety of common quantitative modeling problems that arise frequently in business settings, and discusses how they can be formally modeled and solved with a combination of business insight and computer-based tools. The key topics covered include capacity management, service operations, inventory control, structured decision making, constrained optimization and simulation. This course teaches how to model complex business situations and how to master tools to improve business performance. The goal is to provide a set of foundational skills useful for future coursework atWharton as well as providing an overview of problems and techniques that characterize disciplines that comprise Operations and Information Management.


    Operations strategy is about organizing people and resources to gain a competitive advantage in the delivery of products (both goods and services) to customers. This course approaches this challenge primarily from two perspectives: 1) how should a firm design their products so that they can be profitably offered; 2) how can a firm best organize and acquire resources to deliver its portfolio of products to customers. To be able to make intelligent decisions regarding these high-level choices, this course also provides a foundation of analytical methods. These methods give students a conceptual framekwork for understanding the linkage between how a firm manages its supply and how well that supply matches the firm's resulting demand. Specific course topics include designing service systems, managing inventory and product variety, capacity planning, approaches to sourcing and supplier management, constructing global supply chains, managing sustainability initiatives, and revenue management. This course emphasizes both quantitative tools and qualitative frameworks. Neither is more important than the other.

In the News

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Latest Research

Haim Mendelson, Ken Moon, Yuanyuan Shen (Working), Behavioral and Social Motivations in a Crowdfunding Marketplace.
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In the News

Exit Line: The Effects of Employee Turnover on Manufacturing

High worker turnover rates in manufacturing can cost companies hundreds of millions of dollars. A new paper co-authored by Wharton’s Ken Moon looks at how firms can keep employees on the job longer.

Knowledge @ Wharton - 2018/12/4
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