Marketplace Design

How has Wikipedia activity changed for articles with content similar to ChatGPT following its introduction? We estimate the impact using differences-in-differences models, with dissimilar Wikipedia articles as a baseline for comparison, to examine how changes in voluntary knowledge contributions and information-seeking behavior differ by article content. Our analysis reveals that newly created, popular articles whose content overlaps with ChatGPT 3.5 saw a greater decline in editing and viewership after the November 2022 launch of ChatGPT than dissimilar articles did.

One of the most crucial aspects and significant levers that gaming companies possess in designing digital games is setting the level of difficulty, which essentially regulates the user’s ability to progress within the game. This aspect is particularly significant in free-to-play (F2P) games, where the paid version often aims to enhance the player’s experience and to facilitate faster progression.

We recover valuations of time using detailed data from a large ride-hail platform, where drivers bid on trips and consumers choose between a set of rides with different prices and wait times. Leveraging a consumer panel, we estimate demand as a function of both prices and wait times and use the resulting estimates to recover heterogeneity in the value of time across consumers. We study the welfare implications of personalized pricing and its effect on the platform, drivers, and consumers.

In the months before the 2020 U.S. election, several political campaign websites added prechecked boxes (defaults), automatically making all donations into recurring weekly contributions unless donors unchecked them. Since these changes occurred at different times for different campaigns, we use a staggered difference-in-differences design to measure the causal effects of defaults on donors’ behavior. We estimate that defaults increased campaign donations by over $43 million while increasing requested refunds by almost $3 million.

Housing affordability has become the main policy challenge for most large cities in the world. Zoning, rent control, housing vouchers, and tax credits are the main levers employed by policy makers. We build a new dynamic stochastic spatial equilibrium model to evaluate the effect of these policies on house prices, rents, residential construction, labor supply, output, income and wealth inequality, as well as the location decision of households within the city. The analysis incorporates risk, wealth effects, and resident landlords.

When an inventory manager attempts to construct probabilistic models of demand based on past data, demand samples are almost never available: only sales data can be used. This limitation, referred to as demand censoring, introduces an exploration-exploitation trade-off as the ordering decisions impact the information collected. Much of the literature has sought to understand how operational decisions should be modified to incorporate this trade-off. We ask an even more basic question: when does the exploration-exploitation trade-off matter?

We propose a new valuation method for private equity investments. First, we construct a cash-flow replicating portfolio for the private investment, using cash-flows on various listed equity and fixed income instruments. The second step values the replicating portfolio using a flexible asset pricing model that accurately prices the systematic risk in listed equity and fixed income instruments of different horizons.

The initial phase of the COVID-19 pandemic was characterized by voluminous, highly negative news coverage. Markets overreacted to uninformative news, and reacted more to news during high volatility periods. News coverage responded to lagged market activity, and causally impacted contemporaneous returns. The early part of the pandemic was characterized by pronounced feedback between news and markets. I propose a structural break test to identify the presence and end of such feedback episodes. This one ended in March 2020, which was knowable by the end of April.