The surgical robot experienced rapid uptake throughout hospitals in the US despite lack of clinical evidence that it is superior to existing methods and undeterred by its high cost. This type of technology may be a “weapon” in the medical arms race hypothesis which asserts that competition among hospitals may be welfare reducing wherein it encourages resource use that is not commensurate with beneficial health outcomes. This paper is a case-study of the diffusion of the surgical robot among hospitals in Florida. We address the medical arms race hypothesis directly by investigating whether a hospital’s decision to adopt a robot is a function of the neighboring, competing hospitals’ decisions to do so. Using a spatial autoregressive probit model, we find that the spatial coefficient is significant and negative. That is, when neighboring hospitals operate a robot, a given hospital is less likely to operate one. Indeed, hospitals appear to consider the behavior of rival hospitals, but not in a way that would be consistent with a medical arms race. Support is lent to the hypothesis that as more hospitals become providers of robotic-assisted surgery, the less profitable it becomes to enter the market.

In the past few decades spatial econometric models have become a standard tool in empirical research. Nevertheless applications in binary-choice models remain scarce. This paper makes use of Bayesian Spatial Probit Models to model and estimate spatial interactions in location decisions. For this purpose, we focus on the Austrian retail gasoline market, which is going through a process of remarkable structural changes. A short analysis shows that, during the last decade 10.9% of the stations had left the market and a percentage of 29.6% had either left the market or had changed the brand. This paper aims at investigating this process. A special characteristic of this market is the local competition structure which is characterized by spatial dependencies along local competitors. To capture these spatial dependencies and since the dependent variable is binary in nature (an exit had taken place or not), we apply a Bayesian spatial probit model using MCMC estimation on station level data for the whole Austrian retail gasoline market. Our results suggest, that the decision to leave the market, does not only depend on own characteristics, but also on competitors. In particular, we find the exit decisions to exhibit a negative spatial correlation. Moreover, our model allows to quantify spatial spillover effects of this market. (authors' abstract) / Series: Department of Economics Working Paper Series

This dissertation considers three topics under the themes of wetland restoration, urban sprawl, and recreation demand employing spatial data and analysis. A key question addressed in the first essay is how we can identify priority areas for wetlands restoration along the Louisiana coast under the Coastal Wetlands Planning, Protection, and Restoration Act by estimating amenity values received by nearby residents from hypothetical wetlands restoration projects. The second essay evaluates the effectiveness of alternative land-use policy variables for controlling development in a sprawling metropolitan area during two extreme market conditions. The third essay estimates the effect on consumer welfare from improved satisfaction of recreation information availability.

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