UC Santa Barbara

This dissertation consists of three works which consider estimation of economic

variables when a spatial component exists. Each essay utilizes different techniques

and methodology for working with data that can be grouped into spatial clusters.

In the first essay, I estimate the impact of air pollution events caused by wildfire

smoke on respiratory and circulatory health outcomes. Utilizing a combination

of California health data and NOAA wildfire smoke data I can estimate the impact

of exposure to wildfire smoke on health outcomes for all individuals in California.

Using inpatient data I am able to construct a measure of exposure to wildfire smoke

prior to the hospital visit, this allows for the identification of the impact of wildfire

smoke exposure on different health outcomes. I find that an additional day of smoke

exposure in a month leads to on average 11.38 additional hospital admissions for respiratory

diagnoses and an additional 3 hospital admissions for circulatory diagnoses.

This translates to an annual cost of wildfire smoke exposure in California due to respiratory

and circulatory hospital admissions of $192,316,498.

The second essay, joint with Travis Cyronek, asks the question: 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 whom would otherwise occupy hotels, affecting traditional, longterm

renters. We find that Airbnbs decrease hotel prices by about $24 while they

increase average rents by $39 per month.

In the third essay, joint with Douglas G. Steigerwald, we study the behavior of

cluster-robust test statistics in models with instrumental variables when cluster heterogeneity

is present. Inference in a large number of papers using two-stage least

squares regressions published in American Economics Association journals are driven

by the presence of one or two influential clusters. We link a measure of cluster heterogeneity,

the feasible effective number of clusters, to measures of influence. Using

simulations, we demonstrate that high levels of cluster heterogeneity lead to coverage

of less than 95% for 95% confidence intervals when using instrumental variables with

panel data or with data that can be grouped into clusters. Using data from papers with

two-stage least squares regressions published in American Economic Association journals,

we show that the feasible effective number of clusters can be used as a pre-test

to the sensitivity of two-stage least squares inference to influential clusters. We further

show that when the feasible effective number of clusters is small, even when the

number of clusters is large, the distribution of the test statistic in non-normal. When

this severe cluster heterogeneity is present, the restricted wild cluster bootstrap can

be used to return coverage to the appropriate level.