Xufei Liu, PhD Candidate in Operations, Information and Decisions, The Wharton School; Gad Allon, Operations, Information and Decisions, The Wharton School; Ken Moon, Operations, Information and Decisions, The Wharton School Abstract: Long-term memory is key to deeper learning, yet students differ drastically in how they acquire and retain knowledge. Current…Read More
The “value chain smile” posits that firms capture the most value at the upstream (R&D/design) and downstream (marketing/customer-facing) ends of the supply chain, yet empirical evidence remains mixed. We argue that this ambiguity arises because existing studies rely on globally anchored, industry-level measures of upstreamness that obscure firm-specific supply network structure. This project proposes to develop and empirically test a firm-centric, local value chain framework that maps how value is created and captured across a firm’s actual suppliers and customers. Leveraging detailed supply network data and firm-level financial information, we will construct local value chain curves, characterize their shapes, and study how these configurations evolve over time. The analysis will focus on identifying the role of firm-level investments—particularly R&D and marketing—in enabling firms to attain high-value positions without changing their structural network location. By deepening measurement and expanding data coverage, this project aims to reconcile conflicting evidence on the value chain smile and provide a micro-founded understanding of value creation in supply networks.…Read More
The rapid integration of electric vehicles (EVs) into gig economy platforms like Uber and Lyft presents unique challenges, particularly in driver decision-making, earnings, and operational efficiency. This study explores how EV-specific constraints, such as charging infrastructure and fleet size, influence the behavior of gig economy drivers. We analyze the role of algorithms in shaping driver earnings, pricing, and trip allocations, addressing concerns about transparency, bias, and geographic disparities. Using a proprietary dataset combined with public data on charging station locations, our research employs descriptive analysis, regression models, and simulation to examine the impact of charging accessibility on driver efficiency and service levels. The findings aim to inform algorithmic design improvements and policy interventions, fostering more equitable and efficient EV integration in gig platforms.…Read More
For military aviators, functioning without fail within a high-stress work environment is a necessity. In order to complete critical missions, they make split-second decisions while piloting 44,000 lbs of hurtling machinery accelerating at up to 9G’s in high-altitude conditions. For less experienced pilots, managing the associated physical and mental fatigue is an integral and challenging component of executing flights safely and well. We collaborate with a pioneering company that innovatively outfits aviators at 22 USAF bases with wearable sensors that record both the physical stresses of sorties (flights) and the biophysical states and reactions of pilots in real time. Then, we develop analytical methods to make timely and accurate use of such novel wearable…Read More
The gig economy is rapidly integrating electric vehicles (EVs) into its infrastructure, particularly within ride-hailing services such as Uber and Lyft. Algorithms play a crucial role in this ecosystem, determining customer pricing, driver compensation, and matching drivers with customers. While these algorithms have enabled efficient matching of supply and demand, they also face criticisms, including a lack of transparency, potential bias, and inefficiency. This study investigates the decision-making processes of EV drivers within the gig economy, focusing on how charging infrastructure and fleet size impact…Read More
Algorithms control pricing and match customers and workers in the gig economy. However, algorithms face several critiques: they lack transparency, can be biased, and can be inefficient. We empirically analyze these issues and show that algorithms lose efficiency from two sources: competition between platforms and misaligned worker incentives. We model workers’ strategic responses to variation in pricing and estimate counterfactuals on the effects of minimum wage and transparent pricing policies.…Read More
Executive Summary: Our Spring ‘23 conference, “Creative Disruption? Innovations in Supply Chain Management,” highlighted the challenges and opportunities faced by global supply chains in the wake of unprecedented disruptions. Speakers from academia and industry discussed such themes as the balance between efficiency and resilience, the role of advanced technologies in…Read More
The main questions we analyze in this project are, what are the key engagement drivers in online discussion platforms, and how can a platform leverage these drivers to adjust the user feed to maximize user engagement throughout the path of the discussion.…Read More
The topic of the Gig economy has received considerable attention recently. Once a small minority of low-skilled workers with relatively low income, the gig economy now attracts high-skilled workers who are opting to join a flexible workforce.…Read More
Gig economy firms benefit from labor flexibility by hiring independent self-scheduling workers. Such flexibility poses a great challenge in terms of planning and committing to a service capacity. Understanding the motivations that drive gig economy workers is thus of great importance.…Read More
We study how retail stores’ multi-dimensioned service levels affect consumers’ buying behavior in a spatial setting. To this end, we propose the Double Block-Lasso BLP estimator, which combines the double selection procedure introduced in Belloni, Chernozhukov, and Hansen (2014), with demand estimation methods set forth in Berry, Levinsohn and Pakes (1995).…Read More
