Peer-to-Peer Rental Markets in the Sharing Economy

Опубликовано 27 июня 2016, 21:39

In my talk, I will discuss three distinct studies I have worked on, spending a little more time on the first one. The first study presents a new dynamic model of peer-to-peer Internet-enabled rental markets for durable goods in which consumers may also trade their durable assets in (traditional) secondary markets, transaction costs and depreciation rates may vary with usage intensity, and consumers are heterogeneous in their price sensitivity and asset utilization rates. The study characterizes the stationary equilibrium of the model. It analyzes the welfare and distributional effects of introducing these rental markets by calibrating the model with US automobile industry data and 2 years of transaction-level data obtained from Getaround, a large peer-to-peer car rental marketplace. Counterfactual analyses vary marketplace access levels and matching frictions, showing that peer-to-peer rental markets change the allocation of goods significantly, substituting rental for ownership and lowering used-good prices while increasing consumer surplus. Consumption shifts are significantly more pronounced for below-median income users, who also provide a majority of rental supply. Our results also suggest that these below-median income consumers will enjoy a disproportionate fraction of eventual welfare gains from this kind of 'sharing economy' through broader inclusion, higher quality rental-based consumption, and new ownership facilitated by rental supply revenues. The second study analyzes over 178,000 five-factor personality profiles of users of an online reputation provider and their Facebook social network. It provides evidence of friendship based on similarities in personality traits, shows how personality similarity is related to network embeddedness, and suggests a model of tie formation based on matching opportunities created by shared friends. The final study uses a dataset of over 536,000 news articles from Reuters about 16 countries over the period 1988 to 2013. It constructs a sentiment measure using the fraction of positive and negative words in the text, and demonstrates that this measure can improve predictions of macroeconomic and financial variables. This measure also improves the forecast of the economy compared to the consensus forecast, and supports a model where forecasters do properly incorporate all the available information in forming their expectations. (The first and second studies are in collaboration with Arun Sundararajan, and the second study is also in collaboration with Carlos Herrera and Toni Prada.)