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This dissertation consists of three separate research papers. Each paper uses a different econometric technique to analyze a problem relating to the social aspects of climate change. The first paper investigates a potential adaptive strategy to counteract warming stream waters through stream intervention projects. Using novel non-parametric matching estimation techniques it is shown that these intervention projects have positive effects on homeowners that are near to the stream but downstream of the project site. The second paper uses Bayesian econometric techniques to analyze survey data regarding the welfare losses experienced as a result of power outages across Europe. This paper shows how the severity and spatial distribution of these welfare losses will change as the climate warms, which enables the current electricity grid expansion taking place in Europe to account for these effects of climate change. The third paper uses Classical econometric techniques to estimate the effect of temperature on visitor recreation choices around Lake Tahoe. It is then shown that under climate scenarios the demand for beach and water access at Lake Tahoe will greatly increase, which suggests that lake managers begin to plan regulations and build infrastructure to account for this demand increase. / Ph. D.

The use of contingent valuation (CV) methods to estimate benefits has become increasingly common in project analysis. Ever since the NOAA Blue Ribbon Panel Report in 1993 (NOAA, 1993) recommended the use of the referendum form of CV, it seems to have become the method of choice in practical settings. Referendum-type questions are thought to be easier to answer than the open-ended variety. But there is a downside: econometric techniques must be applied to the referendum data in order to infer the mean or median willingness to pay (WTP) of the sample and, thus, of the population of potential beneficiaries. This is not, however, just a technical point. Its implications are demonstrated with data obtained from a referendum CV study done for a proposed sewer and wastewater treatment project designed to improve water quality in the Tietê River flowing through the city of São Paulo, Brazil. The results show that: A factor of 4 separates lowest from highest central tendency estimates of WTP, ignoring one implausible outlier that is 14 times larger than the largest of the other figures. This variation is ample enough to make a difference in the cost-benefit analysis results for the project under conservative assumptions. Analysts that use referendum CV data must be sensitive to the problems they buy into, and decide how to deal with the resulting benefits uncertainty in their project analysis. If the principal use of CV survey data is to produce a mean or median estimate of WTP for Cost-Benefit analysis rather than to test for the factors influencing referendum choice responses and, by implication, WTP, nonparametric approaches have the advantage of simplicity over parametric approaches. / Master of Arts

Vertical integration has received much attention in the last 25 years and there are well-known theories that explain this behavior. However, the one common thread that runs through virtually all of this research is the assumption of private property rights. Very little attention has been paid to how firms behave when the property rights structure under which they operate changes. The commercial fishing industry is a prime example of an industry where property rights have shifted. Due to problems of over-fishing and over-capitalization, economists have championed the conversion of fisheries from common property or open access resources, to private property through the use of quota programs. Research shows that quota management is effective in reducing capitalization in fisheries, yet there are questions about other effects the programs might have. Among these is a concern over increased vertical integration. Some argue that this is leading to a loss of the independent fisherman that is a part of U.S. history, much like family farms. There is also concern that increased vertical integration is in turn leading to decreased competition in these markets; by owning the quota that is required to fish, processors are increasing their power over the market for unprocessed fish. In response to these and other concerns, the United States Congress imposed a moratorium on the implementation of individual transferable quota-style (ITQ) programs in 1996. Speculation aside, however, there is no empirical evidence to confirm or refute that the use of quota management actually leads to increased vertical coordination. Three fisheries are used as case studies to analyze what affects the decision to vertically coordinate in commercial fisheries. The traditional reasons for vertical integration are to lower transaction costs or to foreclose a market. But now a new factor, shifting property rights, is also considered. Results indicate that the individual characteristics of the fishery are more important than the management or property rights regime itself. This gives some direction to designing management programs that meet our desire to reduce over-capitalization and over-fishing, yet avoid increasing vertical coordination, all the while minimizing the loss of resource rent in the fishery. / Ph. D.

The increasing availability of richer, high-dimensional, home sales data-sets, as well as spatially geocoded data, allows for the use of new econometric and computational methods to explore novel research questions. This dissertation consists of three separate research papers which aim to leverage this trend to answer empirical inferential questions, propose new computational approaches in environmental valuation, and address future challenges. The first research chapter estimates the effect on home values of 10 large-scale urban stream restoration projects situated near the project sites. The study area is the Johnson Creek Watershed in Portland, Oregon. The research design incorporates four matching model approaches that vary based on the temporal bands' width, a narrow and a wider band, and two spatial zoning buffers, a smaller and larger that account for the affected homes' distances. Estimated effects tend to be positive for six projects when the restoration projects' distance is smaller, and the temporal bands are narrow, while two restoration projects have positive effects on home values across all four modeling approaches. The second research chapter focuses on the underlying statistical and computational properties of matching methods for causal treatment effects. The prevailing notion in the literature is that there is a tradeoff between bias and variance linked to the number of matched control observations for each treatment unit. In addition, in the era of Big Data, there is a paucity of research addressing the tradeoffs between inferential accuracy and computational time across different matching methods. Is it worth employing computationally costly matching methods if the gains in bias reduction and efficiency are negligible? We revisit the notion of bias-variance tradeoff and address the subject of computational time considerations. We conduct a simulation study and evaluate 160 models and 320 estimands. The results suggest that the conventional notion of a bias-variance tradeoff, with bias increasing and variance decreasing with the number of matched controls, does not hold under the bias-corrected matching estimator (BCME), developed by Abadie and Imbens (2011). Specifically, for the BCME, the trend of bias decreases as the number of matches per treated unit increases. Moreover, when the pre-matching balance's quality is already good, choosing only one match results in a significantly larger bias under all methods and estimators. In addition, the genetic search matching algorithm, GenMatch, is superior compared to the baseline Greedy Method by achieving a better balance between the observed covariate distributions of the treated and matched control groups. On the down side, GenMatch is 408 times slower compared to a greedy matching method. However, when we employ the BCME on matched data, there is a negligible difference in bias reduction between the two matching methods. Traditionally, environmental valuation methods using residential property transactions follow two approaches, hedonic price functions and Random Utility sorting models. An alternative approach is the Iterated Bidding Algorithm (IBA), introduced by Kuminoff and Jarrah (2010). This third chapter aims to improve the IBA approach to property and environmental valuation compared to its early applications. We implement this approach in an artificially simulated residential housing market, maintaining full control over the data generating mechanism. We implement the Mesh Adaptive Direct Search Algorithm (MADS) and introduce a convergence criterion that leverages the knowledge of individuals' actual pairing to homes. We proceed to estimate the preference parameters of the distribution of an underlying artificially simulated housing market. We estimate with significantly higher precision than the original baseline Nelder-Mead optimization that relied only on a price discrepancy convergence criterion, as implemented during the IBAs earlier applications. / Doctor of Philosophy / The increasing availability of richer, high-dimensional, home sales data sets enables us to employ new methods to explore novel research questions involving housing markets. This dissertation consists of three separate research papers which leverage this trend. The first research paper estimates the effects on home values of 10 large-scale urban stream restoration projects in Portland, Oregon. These homes are located near the project sites. The results show that the distance of the homes from the project sites and the duration of the construction cause different effects on home values. However, two restorations have positive effects regardless of the distance and the duration period. The second research study is focused on the issue of causality. The study demonstrates that a traditional notion concerning causality known as the ``bias-variance tradeoff" is not always valid. In addition, the research shows that sophisticated but time-consuming algorithms have negligible effects in improving the accuracy of estimating the causal effects when we account for the required computational time. The third research study improves an environmental evaluation method that relies on residential property transactions. The methodology leverages the features of more informative residential data sets in conjunction with a more efficient optimization method, leading to significant improvements. The study concludes that due to these improvements, this alternative method can be employed to elicit the true preferences of homeowners over housing and locational characteristics by avoiding the shortcomings of existing techniques.

Zugl.: Diss. Univ. Freiburg, 2005.

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