An increasing volume of empirical literature demonstrates the possibility of spatial autocorrelation in land value models. A number of objections regarding the methodology followed in those empirical studies have been raised. This thesis examines three propositions. The first proposition states that there is spatial dependence in the land value model in Roanoke County. The second proposition is that mechanical construction of neighborhood effects, or grouping nearby land parcels into neighborhoods, is not always the best way to capture spatial effects. Finally, the third and most important proposition states that by implementing a comprehensive set of individual and joint misspecification tests, one can better identify misspecification error sources and establish a more statistically sound and reliable model than models based on existing spatial econometric practices. The findings of this dissertation basically confirm the validity of those three propositions. In addition, we conclude that based on their development status prices of land parcels in Roanoke County may follow different stochastic processes. Changes in the values of hedonic variables have different implications for different groups of land parcels. / Ph. D.

Across the U.S., wildfires have become increasingly destructive and costly over the past few decades, with impacts particularly severe in the State of Florida. Because of an increase in wildfire frequency and severity and the number of people living in fire-prone areas the issue of wildfire risk management is of growing significance. One of the most important wildfire risk reduction tools is prescribed fire to reduce fuel loads, thereby reducing wildfire intensity and resulting damages. Because fire moves across a landscape and ownership boundaries, the spatial pattern of fuel load reduction may influence individual landowners' decisions about fire risk management on their own property. We develop and empirically test a spatial econometric model to study the interaction between Florida landowners in their wildfire risk management decisions. / Master of Science

While political scientists have traditionally examined social influence through social network or contextual studies, this dissertation argues for the use of spatial econometrics as an alternative approach. While spatial econometrics is not new to political science, the dissertation attempts to broaden its application by exploring spaces based on geography, demographic characteristics, and ideology. Social influence can be understood as a form of spatial interdependence among individuals in these spaces and can be analyzed as spatial autocorrelation. In the dissertation, I discuss the dimensions of the three spaces, what might account for mutual influence in these spaces, how to measure distances in these spaces, and how to use these distances for estimating social influence in models of political attitudes using ANES data. By taking a broader approach to space, I show that spatial econometrics can offer many advantages over more conventional approaches. / text

This dissertation consists of three empirical essays of which contributions consist, first, in developing spatial weight matrices based on more than just pure geographical proximity for the modeling of interregional externalities. Second, my essays propose different approaches to discover spatial heterogeneity in the data generating processes, including the interregional externalities, under investigation. This dissertation provides Economic Geographers and Regional Scientists interested in the modeling and measurement of spatial externalities a set of practical examples based on new datasets and state-of-the-art spatial econometric techniques to consider for their own work. I hope my dissertation will provide them with some guidance on how various aspects of spatial externalities can be incorporated in traditional spatial weight matrices and of how much the impact of externalities can be spatially heterogeneous. The results of the dissertation should help spatial and regional policy makers to understand better various aspects of interregional dependence in regional economic systems and to devise locally effective and place-tailored spatial and regional policies. The first essay investigates the negative spatial externalities of irrigation on corn production. The spatial externalities of irrigation water are well known but have never been examined in a spatial econometric framework so far. We investigate their role in a theoretical model of profit-maximizing farming and verify our predictions empirically in a crop production function measured across US Corn Belt counties. The interregional groundwater and surface water externalities are modeled based on actual aquifer and river stream network characteristics. The second essay examines the positive spatial externalities of academic and private R&D spending in the frame of a regional knowledge production function measured across US counties. It distinguishes the role of local knowledge spillovers that are determined by geographical proximity from distant spillovers that we choose to capture through a matrix of patent creation-citation flows. The advantage of the latter matrix is its capacity to capture the technological proximity between counties as well as the direction of knowledge spillovers. These two elements have been missed in the literature so far. The last essay highlights and measures the presence of spatial heterogeneity in the marginal effect of the innovation inputs, more especially of the interregional knowledge spillovers. The literature of knowledge production function has adopted geographically aggregated units and controlled for region-specific conditions to highlight the presence of spatial heterogeneity in regional knowledge creation. However, most empirical studies have relied on a global modeling approach that measures spatially homogenous marginal effects of knowledge inputs. This essay explains the source of the heterogeneity in innovation and then measures the spatial heterogeneity in the marginal effects of knowledge spillovers as well as of other knowledge input factors across US counties. For this purpose, the nonparametric local modeling approaches of Geographically Weighted Regression (GWR) and Mixed GWR are used.

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Economic integration and mobility of capital have set the ground for a significant competition over resources. Tax competition for internationally mobile tax bases such as foreign direct investments has become an important matter of study. Nevertheless, literature has focused on a regional or geographical neighboring condition competition through taxes. This dissertation aims to test whether tax competition for foreign direct investment has changed its regional characteristic towards a global or world-wide competition. Global or world-wide tax competition can be thought of as uncooperative tax policy reactions between governments of different countries of the world not necessarily near each other geographically, but in similar economic conditions and with the purpose to influence the allocation of mobile tax bases world-wide. For the purpose of this study, export oriented foreign capital investment was referred to as the internationally mobile tax base. A theoretical model was constructed allowing for three countries, geographical distance, transportation costs, labor and technology skills, as well as four types of individuals: workers, capitalists, and two types of entrepreneurs. Optimal corporate statutory and average effective tax rates were obtained in order to serve as reaction functions between governments and evaluate the presence of tax competition. A spatial econometric model was used to estimate the empirical approximation of the theoretical model. Four types of weight matrixes were computed: homogeneous weights, similar economic conditions, similar transportation costs from the FDI host country to the FDI home country, and neighboring conditions of FDI host countries. The sample covered 53 countries from different areas of the world from 1984 to 2002. Regarding the data, several variables were constructed, among those: the corporate average effective tax rate. The statutory corporate tax rate was discarded since it misses important factors for capital investment such as tax holidays and depreciation schedules. The principal result suggests that countries from the sample appear to behave in a tax competitive way not only in geographical neighboring terms but also in a global or world-wide approach. In fact, countries appear to compete in a stronger way in global or world-wide terms than when assuming a regional or neighboring condition.

Unordered choice models are commonly used in the field of transportation and several other fields to analyze discrete choice behavior. In the past decade, there have been substantial advances in specifying and estimating such models to allow unobserved taste variations and flexible error covariance structures. However, the current estimation methods are still computationally intensive and often break down when spatial dependence structures are introduced (due to the resulting high dimensionality of integration in the likelihood function). But a recently proposed method, the Maximum Approximate Composite Marginal Likelihood (MACML) method, offers an effective approach to estimate such models. The MACML approach combines a composite marginal likelihood (CML) estimation approach with an approximation method to evaluate the multivariate standard normal cumulative distribution (MVNCD) function. The composite likelihood approach replaces the likelihood function with a surrogate likelihood function of substantially lower dimensionality, which is then subsequently evaluated using an analytic approximation method rather than simulation techniques. This combination of the CML with the specific analytic approximation for the MVNCD function is effective because it involves only univariate and bivariate cumulative normal distribution function evaluations, regardless of the dimensionality of the problem. For my dissertation, I have four objectives. The first is to evaluate the performance of the MACML method to estimate unordered response models by undertaking a Monte Carlo simulation exercise. The second is to formulate and estimate a spatial and temporal unordered discrete choice model and apply this model to a land use change context and to the mode choice decision of school children. The third objective is to formulate a random coefficient model with non-normal mixing distributions on model parameters which can be estimated using the MACML approach. Finally, the fourth objective us to propose an improvement to the MACML method by incorporating a second order MVNCD function that is more accurate and evaluate its performance in estimating parameters for a variety of model structures. / text

This thesis proposes a new spatial multivariate count model to jointly analyze the traffic crash-related counts of pedestrians and bicyclists by injury severity. The modeling framework is applied to predict injury counts at a Census tract level, based on crash data from Manhattan, New York. The results highlight the need to use a multivariate modeling system for the analysis of injury counts by road-user type and injury severity level, while also accommodating spatial dependence effects in injury counts. / text

Submitted by Fabio Sobreira Campos da Costa (fabio.sobreira@ufpe.br) on 2017-04-27T13:35:51Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Dissertação de Economia_Marina Barbosa_2015.pdf: 1173994 bytes, checksum: 56ba2ed2359e0d8717bcb98803a45d70 (MD5) / Made available in DSpace on 2017-04-27T13:35:51Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Dissertação de Economia_Marina Barbosa_2015.pdf: 1173994 bytes, checksum: 56ba2ed2359e0d8717bcb98803a45d70 (MD5) Previous issue date: 2015-03-02 / FACEPE / A despeito de sua relevância para vida urbana brasileira, há um número bastante reduzido de estudos que tratam dos determinantes do tempo de commuting no Brasil, e ainda menos para a RMRs. Este trabalho fornece, pois,evidênciasa respeito dos condicionantes da mobilidade urbana nos municípios da Região Metropolitana do Recife (RMR), através do estudo do tempo de deslocamento casa-trabalho dos ocupados (tempo de commuting). Para tal, utiliza o instrumental fornecido pela Econometria Espacial aplicado a dados do Censo Demográfico 2010 do IBGE, com corte para Áreas de Ponderação. Considerando a forte dependência espacial associada ao deslocamento nos centros urbanos, foram considerados o Spatial Durbin Model(SDM), que fornece estimativas para dados que apresentam dependência espacial na variável dependente e nas variáveis explicativas, através da inclusão de um termo de defasagem espacial e o Spatial Error Model (SEM), que considera situações em que há autocorrelação espacial no termo de erro. A análise dos testes de dependência espacial mostrou que para a região estudada a dependência espacial ocorre via resíduos e não via defasagem espacial, de modo que os resultados considerados são os do modelo SEM. As evidências obtidas indicam que a distância ao centro e a densidade populacional exercem influência positiva no tempo de commuting, enquanto a renda domiciliar per capita e o percentual de imóveis alugados de cada área têm influência negativa. / Despite its relevance for the brazilian urban life, there are only a few studies which deal with the determinants of the commuting time in Brazil, and even fewer for the metropolitan region of Recife. The present work provides, then, evidence about the urban mobility conditioning for the municipalities of the metropolitan region of Recife (RMR), by studying the homework translation time of the employed (commuting time). To accomplish that, it uses a spatial econometrics framework applied to data provided by the 2010 IBGE Demographic Census, focused on weighting areas. Considering the strong spatial dependence related to the translation in urban centers, we considered the Spatial Durbing Model (SDM), which provides estimates for data that present spatial dependence in the dependent and explanatory variables, through the inclusion of a spatial lag term and the Spatial Error model (SEM), which considers situations in which there are spatial autocorrelation in the error term. The analysis of the spatial dependence tests showed that for the studied region the spatial dependence occurs through the disturbances and not through spatial lag, so that the presented results are from the SEM Model. The evidences obtained indicate that the distance to downtown and the population density positively influence the commuting time, while the median household income and the rented property percentage in each area has a negative influence.

Since Woodrow Wilson's (1885) analysis of Congress, researchers assumed that members of Congress look to one another for information, cues, and advice on unfamiliar policy areas. The amount of time and effort that each legislator and their staffers would have to put in to make all of these voting decisions would be insurmountable. Fellow legislators are a resource to turn to for guidance or assistance. Legislators are able to influence their colleagues above and beyond each of their individual preferences. The members of Congress that are most influential will not necessarily be the same for every bill. The significant legislators may be one's co-partisans and the party leadership or they may be a group of legislators with whom they share a common interest. Spatial analysis allows researchers to look more explicitly at the relationships between legislators and their colleagues. I use spatial probit and a spatial duration model to study these issues by examining the factors that influence voting decisions and the timing of position announcements. I look at a variety of different policy areas, including foreign policy, education, and agriculture, over an extensive time period (1933-2014) to test which relationships are most influential on their decisions. I study the interdependence between three different relationships, same party, state delegation, and ideological similarity, and hypothesize that these ties will lead legislators to behave more similarly. The use of the spatial analysis provides an opportunity to test these relationships and see if even after controlling for other influences there is dependence between legislators. In my research, I find that legislators are interdependent regardless of their individual characteristics. When I analyze voting behavior, legislators' behave similarly from one another across all three relationships above and beyond what we would expect given their personal preferences. These positive findings do not hold when I study the timing of position announcements where legislators behave dissimilarly from one another when interdependence exists. The study, overall, suggests that legislative ties are especially important in explaining voting behavior and that it is critical to account for these relationships.