Gad Allon

Electric Vehicle (EV) Fleet and Charging Infrastructure: Decision-Making by Drivers in the Gig Economy

Funded Research Proposal

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

High-Flying Analytics: Harnessing Wearable Sensors and AI to Safeguard Military Aviators

Funded Research Proposal

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

Electric Vehicle (EV) Fleet and Charging Infrastructure: Decision-Making by Drivers

Funded Research Proposal

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

Algorithmic Pricing and Transparency in the Gig Economy

Funded Research Proposal

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

Creative Disruption? Innovations in Supply Chain Management

A large number of shipping containers on the deck of a cargo ship at sea, representing global trade and logistics. 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

Optimizing Service using High Dimensional Panel Data

Funded Research Proposal

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