I am an Assistant Professor of Operations, Information and Decisions and a Claude Marion Endowed Faculty Scholar at the Wharton School, University of Pennsylvania. I use large datasets to study and optimize the organization of workers and of markets. My work applies mathematical modeling, causal analysis, and algorithms to improve the performance and treatment of workforces, the operations of online markets, and the design of policies and regulations for complex networks.
I earned my undergraduate degree in Mathematics and Economics from Stanford University, my JD from the Harvard Law School, and my PhD from the Stanford Graduate School of Business.
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Abstract: Gig economy workers make strategic decisions about where and when to work. These decisions are central to gig economy operations and are important policy targets both to firms that operate ridehail and delivery platforms and to regulators that oversee labor markets. We collaborate with a driver analytics company to empirically measure two types of strategic behavior: multihoming, an online change between platforms, and repositioning, a physical change between locations. Using a comprehensive dataset that tracks worker activity across platforms, we estimate a structural model to analyze how workers optimize their earnings and respond to earnings-based incentives to switch platforms or locations. We show that workers are highly heterogeneous in their preferences and find multihoming especially costly, both in absolute terms and relative to the cost of repositioning. Through counterfactual simulations, we show that firms and regulators can substantially improve system efficiency by enabling workers to freely multihome: workers' hourly earnings increase by 2.0% and service levels increase by 53.1%. In contrast, the existing equilibrium is similar to a system without multihoming, in which hourly earnings increase by 1.3% but service capacity decreases by 4.1%. Additionally, we show that policies to limit traffic congestion by increasing travel costs should include incentives to ensure that workers remain able to efficiently reposition. An increase to repositioning costs by $1 per mile increases hourly earnings by 2.3% but substantially decreases service capacity by 29.6%.
Jong Myeong Lim, Ken Moon, Sergei Savin (2023), Searching for the Best Yardstick: Cost of Quality Improvements in the U.S. Hospital Industry, Management Science, forthcoming.
Abstract: The Hospital Value-Based Purchasing (VBP) Program is Medicare’s implementation of yardstick incentives applied to hospitals in the U.S. Under the VBP Program, 2% of all Medicare payments to hospitals, estimated to be US$1.9B in fiscal year 2021, are withheld and redistributed based on their relative performance in the quality of delivered care. We develop a dynamic mean-field equilibrium model in which hospitals are engaged in a repeated competition under yardstick incentives. Using structural estimation methods, we recover key parameters that govern hospitals’ decisions to invest in quality improvement, including the financial and non-financial costs and uncertain outcomes of investment. By dynamically solving for hospitals’ individually optimal investment policies, we estimate the trajectory of quality improvements for each hospital, including its investment decisions and quality levels throughout the implementation of the VBP Program. Our counterfactual analyses explore the benefits, on the one hand, of modifying the overall size of the yardstick incentives and, on the other hand, of implementing a more focused program tailored to hospital type. We find that increasing the size of the incentives from 2% to 4% would have resulted in an additional quality investment of US$1.2B from 2011 to 2018, leading to a 3.3% reduction in the average rate of central line-associated bloodstream infections (CLABSI). Applying yardstick incentives to the tailored hospital peer groups, even without changing the size of the incentives, can lead to an average reduction of 1.4% in the rate of CLABSI among groups of hospitals associated with the highest costs of quality investment.
Abstract: Platforms have come under criticism from regulatory agencies, policymakers, and media scholars for the unfettered spread of fake news online. A key concern is that, as fake news becomes prevalent, individuals may fall into online "echo chambers" that predominantly expose them only to fake news. Using a dataset reporting 30,995 individual households’ online activity, we empirically examine the reach of false news content and whether echo chambers exist. We find that the population is widely exposed to online false news. However, echo chambers are minimal, and the most avid readers of false news content regularly expose themselves to mainstream news sources. Using a natural experiment occurring on a major social media platform, we find that being exposed to false news content causes households to increase their exposure to countervailing mainstream news (by 9.1% in the experiment). Hence, a naive intervention that reduces the supply of false news sources on a platform also reduces the overall consumption of news. Based on a structural model of household decisions whether to diversify their online news sources, we prescribe how platforms should moderate false news content. We find that platforms can further reduce the size of echo chambers (by 12-18%) by focusing their content moderation efforts on the households that are most susceptible to consuming predominantly false news, instead of the households most deeply exposed to false news.
Abstract: To maximize productivity, manufacturers must organize and equip their workforces to efficiently handle variable workloads. Their success depends on their ability to assign experienced and skilled workers to specialized tasks and coordinate work on production lines. Worker turnover may disrupt such efforts. We use staffing, productivity, and pay data from within a major consumer electronics manufacturer's supply chain to study how firms should manage worker turnover and its effects using production decisions, wages, and inventory. We find that worker turnover impedes coordination between assembly line co-workers by weakening knowledge sharing and relationships. Publicly available unit-cost estimates imply that worker turnover accounts for $206--274 million in added direct expenses alone from defectively assembled units failing the firm's stringent quality control. To evaluate managerial alternatives, we structurally estimate a dynamic equilibrium model (Experience-Based Equilibrium, Fershtman and Pakes 2012) encompassing (1) workers' endogenous turnover decisions and (2) the firm's weekly planning of its production scheduling and staffing in response. In counterfactual analyses, a less turnover-prone, hence more productive, workforce significantly benefits the firm, reducing its variable production costs by 4.5%, or an estimated $928 million for the studied product. Such benefits justify paying higher efficiency wages even to less skilled workforces; further, interestingly, rational inventory management policies incentivize self-interested firms to reduce, rather than tolerate, turnover.
Ken Moon, P. Loyalka, P. Bergemann, J. Cohen (2021), The Hidden Cost of Worker Turnover: Attributing Product Reliability to the Turnover of Factory Workers, Management Science, 68 (5), pp. 3755-3767.
Abstract: Product reliability is a key concern for manufacturers. We examine worker turnover as a significant but under-recognized determinant of product reliability. Our study collects and integrates (1) data reporting factory worker staffing and turnover from within a major consumer electronics producer's supply chain and (2) traceable data reporting the component quality and field failures---i.e., replacements and repairs---of nearly 50M consumer mobile devices over four years of customer usage. Devices are individually traced back to the factory conditions and staffing, down to the assembly line-week, under which they were produced. Despite the manufacturer's extensive quality-control efforts including stringent testing, each percentage-point increase in the weekly rate of workers quitting from an assembly line (its weekly worker turnover) is found to increase field failures by 0.74-0.79%. In the high-turnover weeks following paydays, eventual field failures are strikingly 10.2% more common than for devices produced during the lowest-turnover weeks immediately before paydays. In other weeks, the assembly lines experiencing higher turnover produce an estimated 2-3% more field failures on average. The associated costs amount to hundreds of millions USD. We demonstrate that staffing and retaining a stable factory workforce critically underlies product reliability and showcase the value of traceability coupled with connected workplace and product data in supply chain operations.
Gad Allon, Georgios Askalidis, Randall Berry, Nicole Immorlica, Ken Moon, Amandeep Singh (2021), When to Be Agile: Ratings and Version Updates in Mobile Apps, Management Science, 68 (6), pp. 4261-4278.
Abstract: Lean and agile models of product development organize flexible capacity to rapidly update individual products in response to customer feedback. While agile operations have been adopted across numerous industries, neither the benefits nor the factors explaining when firms choose to become agile are validated and understood. We study these questions using data on the development of mobile apps, which occurs through the dynamic release of new versions into the mobile app marketplace, in conjunction with customer ratings. We develop a structural model estimating the dependence of product versioning on (A) market feedback in the form of customer ratings, against (B) project and work-based considerations, such as development timelines, scale economies, and operational constraints. In contrast to when they actually benefit from operational agility, firms become agile when launching riskier products (in terms of uncertainty in initial customer reception) and less agile when able to exploit scale economies from coordinating development over a portfolio of apps. Agile operations increase firm payoffs by margins of 20-80%, and interestingly partial agility is often sufficient to capture the bulk of these returns. Finally turning to a question of marketplace design, we study how the mobile app marketplace should design the display of ratings to incentivize quality (increasing app categories’ average user satisfaction rates by as much as 26%).
Abstract: Customer decision-making is difficult to study empirically when choice sets are combinatorially complex. For example, in service networks such as physical marketplaces, shopping centers, and amusement parks, customers can choose from combinatorially many network paths that reflect their motivations to visit the stations they like while dynamically avoiding congestion (e.g., by delaying visiting a temporarily crowded store or attraction). Existing methods suffer serious issues when estimating the preferences regarding consumption and waiting that drive customers' network paths. We address this problem by leveraging seminal developments from discrete optimization and machine learning. We prove that customers need only make a small number of "local" choice comparisons in order to confirm that their choices are optimal; moreover, we need only analyze such comparisons in order to maximally learn customer preferences from data reporting their network paths. Because neural networks excel at identifying such hidden low-dimensional structure, we leverage them to build estimators that discriminate between, hence identify, customer types from their choices of paths. By consistently and tractably estimating customer preferences, our empirical methods enable deeper analyses of service network or marketplace design, capacity management, and customer targeting.
Pia Ramchandani, Hamsa Bastani, Ken Moon (Under Revision), Responsible Sourcing: The First Step Is the Hardest.
Abstract: Responsible sourcing is a priority for companies and consumers concerned with corporate social responsibility (CSR) in global supply chains. Most brands' product lines contain just a few products certified by third parties- which suggests that brands limit their efforts at ensuring that suppliers behave responsibly. In this paper, we examine a previously under-appreciated role of certifications: that certifications enable brands to learn about how to source responsibly. By successfully certifying even a single product, the certifying brand may enjoy positive, knowledge-based spillovers encouraging responsible sourcing throughout its product line. Using data on the responsible sourcing decisions of coffee brands in the $48B US consumer market, we find that certifying brands' rates of CSR violations (adjusted for disparities in production volume and detection) are similarly low regardless of whether the brand's portfolio is 3% certified or 100% certified- consistent with learning-based spillover effects. Certifying brands' violation rates are an estimated 61-78% lower than for comparable brands that make no CSR claims. While we find that brands making their own uncertified, on-packaging CSR claims also exhibit low CSR violation rates, their low violation rates are nearly entirely explained by the countries from which they source. In contrast, certifying brands appear uniquely able to source responsibly even from within "high-risk" countries. Our work novelly suggests that prevalent dual-sourcing may surprisingly amplify, rather than limit, responsible sourcing in supply chains, and that certified sourcing valuably develops the pool of responsible suppliers in high-risk countries.
Abstract: We explore marketplace design in the context of a business-to-business platform specializing in liquidation auctions. Even when the platform’s aggregate levels of supply and demand remain fixed, we establish that the platform’s ability to use its design levers to manage the availability of supply over time yields significant value. We study two such levers, each using the platform’s availability of supply as a means to incentivize participation from buyers who decide strategically when/how often to participate. First, the platform’s listing policy sets the ending times of incoming auctions (hence, the frequency of market clearing). Exploiting a natural experiment, we illustrate that consolidating auctions’ ending times to certain weekdays increases the platform’s revenues by 7.3% mainly by inducing a higher level of bidder participation. The second lever is a recommendation system that can be used to reveal information about real-time market thickness to potential bidders. The optimization of these levers highlights a novel trade-off. Namely, when the platform consolidates auctions’ ending times, more bidders may participate in the marketplace (demand-side competition); but ultimately auctions for substitutable goods cannibalize one another (supply-side competition). To optimize these design decisions, we estimate a structural model that endogenizes bidders’ dynamic behavior, that is, their decisions on whether/how often to participate in the marketplace and how much to bid. We find that appropriately designing a recommendation system yields an additional revenue increase (on top of the benefits obtained by optimizing the platform’s listing policy) by reducing supply-side cannibalization and altering the composition of participating bidders.
Haim Mendelson, Ken Moon, Yuanyuan Shen (Working), Behavioral and Social Motivations in a Crowdfunding Marketplace.
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.
Matching supply with demand is an enormous challenge for firms: excess supply is too costly, inadequate supply irritates customers. In the course, we will explore how firms can better organize their operations so that they more effectively align their supply with the demand for their products and services. Throughout the course, we illustrate mathematical analysis applied to real operational challenges--we seek rigor and relevance. Our aim is to provide both tactical knowledge and high-level insights needed by general managers and management consultants. We will demonstrate that companies can use (and have used) the principles from this course to significantly enhance their competitiveness.
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 framework 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.
New research from Wharton’s Ken Moon and Senthil Veeraraghavan recommends a data-driven solution for social media platforms to deal with fake news.…Read MoreKnowledge at Wharton - 8/9/2022