We study e-commerce across 47 economies and 26 industries during the COVID-19 pandemic using aggregated and anonymized transaction-level data from Mastercard, scaled to represent total consumer spending. The share of online transactions in total consumption increased more in economies with...

Matched product data is collected from the leading online grocers in the U.S. The same exact products are identified in scanner data. The paper documents pricing strategies within and across online (and offline) retailers. First, online retailers exhibit substantially less uniform pricing than...

We use data from a large web-based job platform to study how the price of remote work is determined in a globalized labor market. In the platform, workers from around the world compete for jobs that can be done remotely. We document that, despite the global nature of the marketplace, the...

Using data from a prominent online platform for launching new digital products, we document that the composition of the platform's 'beta testers' on the day a new product is launched has a systematic and persistent impact on the venture's success. Specifically, we use word embedding methods to...

In the world of omnichannel retail, some brands open a flagship store at online marketplaces, while others avert it. Focusing on a large e-commerce platform, we empirically study how flagship entry affects consumers, the platform, and various sellers on the platform

This study provides a structural analysis of detailed, alternating-offer bargaining data from eBay, deriving bounds on buyers and sellers private value distributions using a range of assumptions on behavior. These assumptions range from very weak (assuming only that acceptance and rejection...

Using an administrative payroll dataset for 2.6 million retail workers, we find that the staggered rollout of a major e-commerce firm's fulfillment centers reduces traditional retail workers' income in geographically proximate counties by 2.4%. Wages of hourly workers, especially part-time...

Using bi-weekly snapshots of Zillow in three US cities, we document how home sellers and buyers interact with Zillow's Zestimate algorithm during the sales cycle of residential properties. We find that listing and selling outcomes respond significantly to Zestimate, and Zestimate is quickly...

This paper estimates the magnitude and distribution of surplus from the knowledge worker gig economy using data from an online labor market. Labor demand elasticities determine workers' wages, and buyers' past market experience shapes both their job posting frequency and hiring rates. We find...

This paper illustrates the usefulness of sequential Monte Carlo (SMC) methods in approximating DSGE model posterior distributions. We show how the tempering schedule can be chosen adaptively, document the accuracy and runtime benefits of generalized data tempering for "online" estimation (that...