UC Santa Barbara

Search theory has proven useful for describing and modeling many different Economic interactions. More broadly, the frictions it can account for are key to understanding many of the outcomes in observational data. This dissertation explores how such frictions affect the choices of economic agents, and what this implies for measurement and aggregate behavior. In doing so I am interested in the essential connection between theory and empirics: models help the researcher think about the interpretation of data, and data offers a ruler with which to assess the performance of models.

The first chapter explores how worker perceptions about job finding affect where unemployed searchers choose to apply for jobs and how this impacts the behavior of key labor market variables. Motivated by the observed prevalence of optimistic bias in searcher expectations about job finding, I develop a model of directed search where workers are uncertain about the matching technology, but can learn about it with experience searching for employment. I find that misperceptions dampen the volatility of labor market variables. For example, the standard deviation of the unemployment rate decreases by 10% when accounting for this uncertainty, while its correlation with labor productivity decreases by 12%. I show that optimistically biased job finding expectations increase wages by 0.3%, but also increase the average unemployment spell length by 1.5 weeks and the unemployment rate by 0.6pp.

In the second chapter, joint with Christine Braun and Peter Rupert, we study how the presence of on-the-job leisure, that is, non-work at work, drives a wedge between measured hours of work and actual hours of work. If actual hours of work are lower than measured hours, productivity and wages are actually higher than those calculated by the Bureau of Labor Statistics, for example. Technological innovations, while making an hours of work more valuable, may also make it easier to engage in on-the-job leisure. We document the extent of on-the-job leisure and embed it into a model of technological change with imperfect monitoring to examine its effect on productivity and wages. Using the American Time Use Survey we show that for those workers who engage in OJL spend about 50 minutes per day doing so. We use the model to create a time series of actual hours of work and calculate actual output per hour.

In the third chapter, joint with Daniel Cullen, we ask, "How does the sharing economy affect traditional lodging markets?" The advent of platforms such as Airbnb in 2008 has introduced a new channel of market interaction between those with space and those who seek it. This allows for transactions of lodging services that might otherwise be underutilized. This paper develops a framework to help think about how peer-to-peer transactions interact with traditional rental markets, and what this means for property managers and tenants. Specifically, we examine how the introduction of sharing platforms (e.g. Airbnb) affect the listing decisions of vacant property managers and the lodging choices of dwelling seekers. The model features landlords who choose where to list vacant properties and renters who search for lodging. Renters can be either short or long-term, referencing how long they wish to occupy the property. Sharing platforms give landlords the option of accessing these short-term renters who would otherwise occupy hotels, affecting traditional, long-term renters. We find that Airbnbs decrease hotel prices by about $24 while they increase average rents by $39 per month.