Submitted by Renata Lopes (renatasil82@gmail.com) on 2017-04-03T18:25:50Z No. of bitstreams: 1 rejanecorreadeoliveira.pdf: 6128260 bytes, checksum: 15189c9967dc026ed861378d43cdbb89 (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2017-04-03T19:11:19Z (GMT) No. of bitstreams: 1 rejanecorreadeoliveira.pdf: 6128260 bytes, checksum: 15189c9967dc026ed861378d43cdbb89 (MD5) / Made available in DSpace on 2017-04-03T19:11:19Z (GMT). No. of bitstreams: 1 rejanecorreadeoliveira.pdf: 6128260 bytes, checksum: 15189c9967dc026ed861378d43cdbb89 (MD5) Previous issue date: 2009-12-16 / CNPq - Conselho Nacional de Desenvolvimento Científico e Tecnológico / O desmatamento das florestas tropicais é um elemento importante na questão das mudanças climáticas. No Brasil, o desmatamento provocado por focos de calor torna o país um grande emissor mundial de dióxido de carbono, um dos gases causadores do efeito estufa. Há a preocupação de que, com o avanço do desenvolvimento, a pressão sobre as florestas tropicais aumente. Dentro deste contexto, esse trabalho investigou a hipótese da Curva de Kuznets Ambiental (CKA) para a região da Amazônia Legal, isto é, se existe uma relação na forma de “U” invertido entre um índice de degradação ambiental (área desmatada anual) e o crescimento econômico (indicado pelo PIB per capita), em nível municipal, no período 20012006, utilizando um modelo para dados em painel com dependência espacial. Variáveis explicativas adicionais foram incluídas, tais como: rebanho bovino, culturas agrícolas (soja e cana-de-açúcar), extração vegetal e silvicultura, densidade populacional, crédito rural e área de floresta pré-existente. Devido ao histórico de ocupação, extensão da área e características geográficas, existe a indicação de diferenças intrarregionais importantes. A heterogeneidade espacial do parâmetros foi tratada em conjuntos de modelos com regimes espaciais distintos (macrozonas e estados). A heterogeneidade espacial extrema foi tratada pela estimação de regressões ponderadas geograficamente (RPG). Os resultados da Análise Exploratória de Dados Espaciais sugerem a existência de “clusters” em um padrão Alto-Alto (municípios com altos valores de desmatamento próximos a municípios com desmatamento também elevado) na região do Arco do Povoamento Adensado e Amazônia Central. Os resultados econométricos indicam a presença de efeitos não-observados, sendo mais adequada a estimação por efeitos fixos. O modelo global que melhor se ajusta aos dados é o modelo de erro espacial com transbordamentos espaciais; para este, foi verificada a relação da CKA na forma de “N” invertido, indicando que a área desmatada anual é maior para baixos níveis de PIB per capita, sendo decrescente à medida que o PIB aumenta, depois volta a crescer, e para níveis de renda mais elevados, torna-se decrescente. As variáveis relacionadas ao rebanho bovino, crédito rural e a existência de floresta anterior são consideradas significativas em 5%. Para os diferentes regimes espaciais, os seguintes resultados são encontrados: a) no conjunto de modelos para as três macrozonas (Amazônia Ocidental, Amazônia Central e Arco do Povoamento Adensado), a CKA não é verificada em nenhuma de suas formas; b) no conjunto de modelos que considera um regime espacial para cada estado, a hipótese da CKA é verificada na forma de “U” invertido para Mato Grosso, e na forma de “U” para o estado do Pará e monotônica crescente para o Maranhão; nos demais estados, a hipótese da CKA não é verificada. No nível extremo de heterogeneidade espacial, a estimação por regressões ponderadas geograficamente resulta em parâmetros calculados para cada município, sendo possível representar em mapa as diferentes formas encontradas para a CKA, concentradas nas porções central e nordeste da Amazônia. Os resultados mostram diferentes relações entre desmatamento e PIB per capita municipal, assim como diferentes relações do desmatamento com as demais variáveis explicativas, revelando a heterogeneidade do espaço amazônico. / The tropical deforestation is a main issue on the global climate change discussion. In Brazil, the deforestation caused by hot spots is responsible for large emissions of carbon dioxide, a greenhouse gas. There is always a concern about the increasing pressure over the forest as development advances. Within this context, this work aims to investigate the Environmental Kuznets Curve (EKC) hypothesis applied to Legal Amazon region: whether there is an inverted “U” relationship between an environmental degradation index (annually deforested area) and economic growth (GDP per capita) at municipal level from 2001 to 2006, using a panel data model with spatial dependence. Some other variables mentioned in the literature were considered to explain deforestation: cattle size, soybean and sugar cane crops, vegetal extraction and forestry products, population density, rural credit, and previous forest area. Additionally, there can be important intra-regional differences due to occupation history, large area and geographical aspects. The spatial heterogeneity of coefficients was considered by studying spatial regimes into two sets (macro-regions and states). The extreme coefficients spatial heterogeneity was considered by estimating the model using geographically weighted regressions (GWR). The Exploratory Spatial Data Analysis (ESDA) shows the presence of clusters in a High-High pattern (municipalities with high deforestation values near municipalities with high values for the deforested area also) in the Densely Populated Arch and Central Amazon. The econometric results reveal the presence of unobserved effects, being the fixed effects estimate the most appropriate one. The global model (considering the whole region) that fits the data well is the spatial error model with spillovers; for this model, the EKC relationship appears to have an inverted “N” shape, so the annual deforested area is higher for lower incomes, it decreases when GDP per capita increases, then it goes back to increase, and for higher income levels it decreases again. The variables related to cattle size, rural credit and previous forest area seems to affect the deforested area. When the spatial heterogeneity is considered by means of different spatial regimes, the following results are found: a) for the three macro-regions set of models (Occidental Amazon, Central Amazon and Densely Populated Arch), none of the shapes for the EKC relationship is verified; b) for the nine states set of models, the EKC hypothesis is found in an inverted “U” shape for Mato Grosso state, a “U” shape for Pará state and increasingly monotonic shape for Maranhão state, whereas for the other states the EKC is not found. For the extreme level of spatial heterogeneity, the geographically weighted regressions show local coefficients: it was possible to map the different shapes for EKC found mainly in central and northeastern portions of Amazon. The results show different local relationships between deforestation and GDP per capita at municipal level, and also different local relationships between deforestation and the additional variables.

Spatial interaction models of the gravity type are widely used to describe origin-destination flows. They draw attention to three types of variables to explain variation in spatial interactions across geographic space: variables that characterize the origin region of interaction, variables that characterize the destination region of interaction, and variables that measure the separation between origin and destination regions. A violation of standard minimal assumptions for least squares estimation may be associated with two problems: spatial autocorrelation within the residuals, and spatial autocorrelation within explanatory variables. This paper compares a spatial econometric solution with the spatial statistical Moran eigenvector spatial filtering solution to accounting for spatial autocorrelation within model residuals. An example using patent citation data that capture knowledge flows across 257 European regions serves to illustrate the application of the two approaches.

The objective of this study is to identify knowledge spillovers that spread across regions in Europe and vary in magnitude for different industries. The study uses a panel of 203 NUTS-2 regions covering the 15 pre-2004 EU-member-states to estimate the impact over the period 1998-2003, and distinguish between five major industries. The study implements a fixed effects panel data regression model with spatial autocorrelation to estimate effects using patent applications as a measure of R&D output to capture the contribution of R&D (direct and spilled-over) to regional productivity at the industry level. The results suggest that interregional knowledge spillovers and their productivity effects are to a substantial degree geographically localised and this finding is consistent with the localisation hypothesis of knowledge spillovers. There is a substantial amount of heterogeneity across industries with evidence that two industries (electronics, and chemical industries) produce interregional knowledge spillovers that have positive and highly significant productivity effects. The study, moreover, confirms the importance of spatial autoregressive disturbance in the fixed effects model for measuring the TFP impact of interregional knowledge spillovers at the industry level. (authors' abstract)

Entrepreneurship is increasingly recognised as an important component of the contemporary knowledge based economy and crucial to the attainment of economic growth and development. However, entrepreneurial activity varies significantly across space within countries. This thesis makes an original contribution by examining the determinants of spatial variations in entrepreneurship across sub-regions of Great Britain from 2008-2010. Through utilising newly available data on firm births and applying exploratory spatial data analysis and spatial econometric techniques, two prominent theories of entrepreneurship are examined. First, the Knowledge Spillover Theory of Entrepreneurship posits that underutilised knowledge by incumbent firms creates entrepreneurial opportunities. The appropriation of these opportunities through entrepreneurial activity, in the form of a new firm, leads to dynamic knowledge spillovers, which generate economic growth. The empirical analysis presented in this thesis concludes that more knowledge intensive regions exhibit significantly higher firm birth rates; however the composition of the regional knowledge stock is critical, as a diverse knowledge stock generates more entrepreneurial opportunities. Second, several theories emphasise the importance of idiosyncratic knowledge and human capital, in the form of entrepreneurial ability, on the discovery and exploitation of entrepreneurial opportunities. The results of this thesis suggest that human capital is vital to the entrepreneurial process, and that university education is a greater source of entrepreneurial ability than labour market experience. Furthermore, the results also suggest that the regulatory burden of the public sector, financial constraints, regional unemployment, and the absence of a local entrepreneurial culture can significantly detract from regional entrepreneurial activity. In light of these results, there are several implications for policy which include: emphasising the importance of effective policy towards intellectual property rights, targeting entrepreneurial education initiatives towards university students and graduates, and reducing unnecessary public sector regulation that can act as a ‘barrier’ to entrepreneurship.

Nas últimas décadas, o setor de pecuária bovina leiteira vem passando por diversas modificações influenciadas por redução de número de produtores, mudanças de políticas macroeconômicas e agrícolas e abertura econômica do Brasil. A produção de leite aumentou significativamente ao longo dos anos. Entretanto, mesmo o Brasil sendo um dos maiores produtores do mundo de leite bovino, sua produtividade (medida em litros de leite por vaca) está bem abaixo da dos principais países que operam no mercado. A produtividade da atividade ganha destaque para viabilizar o aumento da produção, suprir a demanda interna e dar maior competitividade ao setor no mercado externo. Diante disso, objetiva-se, através dessa tese, analisar a evolução diferenciada, interestadual e intraestadual, da produtividade da pecuária bovina leiteira nos Estados de Goiás, Minas Gerais, Paraná, Rio Grande do Sul, Santa Catarina e São Paulo, no período de 1974 a 2016, com base em áreas mínimas comparáveis (AMC). Esses Estados representaram 77,5% da produção de leite no Brasil em 2016. Através da análise exploratória de dados espaciais foi identificada a existência de autocorrelação espacial, sendo que a produtividade da pecuária bovina leiteira de uma AMC sofre influência da produtividade das AMC vizinhas. Foram identificados clusters espaciais de produtividade dos tipos Alto-Alto, Baixo-Baixo, Alto-Baixo e Baixo-Alto em todos os Estados considerados ao longo do período em análise. A configuração e localização geográficas desses clusters sofreram alterações nos Estados, refletindo os deslocamentos da produção ocorridos. Posteriomente, passou-se para a análise de convergência no intuito de identificar se está ocorrendo redução na diferença entre as produtividades da pecuária bovina leiteira entre as AMC e se os efeitos espaciais contribuem para as convergências absoluta e condicional. Para tanto, fez-se uso da econometria espacial. Para a análise de convergência condicional foram incorporadas variáveis de primeira e segunda natureza, propostas na Nova Geografia Econômica. Tanto a análise da convergência absoluta quanto a da convergência condicional confirmam a hipótese de existência de convergência e demonstram o efeito de transbordamento, ou seja, os choques ocorridos em uma AMC refletem nas AMC vizinhas. Entretanto, a velocidade de convergência foi baixa nas duas situações, indicando que a redução das diferenças de produtividade está ocorrendo de forma muito lenta. A análise de convergência condicional mostrou que as características iniciais das AMC influenciam para qual ponto estacionário a produtividade da pecuária bovina leiteira irá convergir, sendo que as variáveis distância da capital, pluviosidade, população, Produto Interno Bruto, crédito rural de investimento para pecuária e área com culturas tiveram influência diferenciada nos Estados no processo de convergência da produtividade tanto no período analisado como um todo como nos subperíodos considerados na tese. / In recent decades, the dairy cattle sector has undergone changes influenced by decline in the number of producers, changes in government macroeconomic and agricultural policies, and the country\'s economic opening. Milk production has increased significantly over the years. Brazil is one of the world\'s largest producers of bovine milk; however, its productivity (measured in liters of milk per cow) is still lower than that found in other major milk producing countries. The productivity of the activity is important to enable the increase of production, supplying domestic demand and giving greater competitiveness in the external market. The objective of this thesis is to analyze the differentiated interstate and intrastate evolution of dairy cattle productivity in the Brazilian states of Goiás, Minas Gerais, Paraná, Rio Grande do Sul, Santa Catarina and São Paulo from 1974 through 2016 based on minimum comparable areas (MCA). These states accounted for 77.5% of Brazil\'s milk production in 2016. Exploratory spatial data analysis confirmed the existence of spatial autocorrelation indicting that dairy cattle productivity in an MCA is influenced by the productivity in neighboring MCA. Over the analyzed period, High-Low, Low-Low, High-Low, and Low-High productivity spatial clusters were identified in all studied states. The configuration and geographic location of these clusters underwent changes during the study period, reflecting production displacement. Convergence analysis using spatial econometrics was carried out to determine if the differences in dairy cattle productiveness among MCA were reduced over the period and if spatial effects contributed to any absolute or conditional convergence. First and second nature variables were employed for the analysis of conditional convergence, as proposed by the New Economic Geography. Both convergence analyses, absolute and conditional, confirmed the convergence hypothesis and demonstrated the overflow effect, in that shocks occurring in one MCA were reflected in neighboring MCA. However, the speed of convergence was low in both situations, indicating that productivity differences among the MCA were being reduced very slowly. The analysis of conditional convergence showed that the productivity of dairy farming in different MCA will tend to converge at the same stationary point if the MCA show similar initial values for selected variables. The selected variables are average yearly rainfall, population size, gross domestic product, and investment credit for livestock and rangeland acquisitions and are intended to represent conditions in each MCA and state at a specific time. Each variable had a differentiated influence on the process of productivity convergence over the period and subperiods considered in this thesis.

Ce mémoire de thèse concerne l’identification des facteurs de risque d’une maladie spécifique présentant une hétérogénéité spatiale au sein d’une région donnée. Plus particulièrement,nous nous sommes intéressés aux cancers des voies aéro-digestives supérieures(VADS) dans la région Nord-Pas-de-Calais (NPDC) en France. Pour cela, une étude cas témoins a d’abord été réalisée à partir de la création d’un échantillon d’individus sains qui n’étaient pas affectés par des tumeurs cancéreuses (les témoins) et d’un échantillon d’individus atteints du cancer (les cas - ou patients), recrutés dans les centres de soins dans le cadre des projets de recherche DEREDIA et NOFARIS. Concernant la méthodologie,des modèles binaires spatiaux répondant à l’objectif ont été développés à partir de travaux issus des domaines de recherche en statistique/économétrie des comportements(analyse des décisions) ainsi qu’en statistique spatiale. Un des apports méthodologiques de la thèse repose sur la combinaison de techniques provenant de ces deux champs de recherche. Dans une première partie, nous avons utilisé un modèle spatial binaire paramétrique contenant une variable spatiale latente de choix dans le cadre d’un échantillonnage des données. Ce problème est connu sous le nom de "Choice-Based Sampling" (CBS) dans les modèles discrets. Contrairement à l’échantillon aléatoire où tous les éléments de la population ont la même probabilité d’être choisi, l’échantillonnage CBS dans le modèle discret est un type d’échantillonnage dans lequel la classification de la population est faite sous forme de sous-ensembles (strates) basés sur des choix alternatifs. Dans ce contexte,l’utilisation de la procédure d’estimation par maximum de vraisemblance standard (MLE)dans le CBS pourrait mener à des estimations incohérentes (asymptotiquement biaisées).Nous avons adopté ainsi le principe du maximum de vraisemblance auprès de l’étude de scas-témoins spatiaux. Nous avons également fourni un estimateur des moments généralisés(GMM), basé sur les résidus généralisés. Dans une seconde partie, un modèle spatial binaire semi-paramétrique a été considéré. Nous présentons dans ces parties, une simulation de Monte Carlo pour étudier la performance des méthodes d’estimation au sein d’un échantillon final, que nous avons ensuite appliqué aux données du cancer VADS dans la région Nord-Pas-de-Calais. La troisième partie est consacrée à l’étude d’une fonction de risque spatiale en présence de données entachées d’erreurs. En effet, dans le cadre des données cas-témoins considérées, nous supposons que certaines données de type déclarative ne soient pas correctes. Une application de cette méthode à la cartographie du risque de développer un cancer VADS dans la région Nord-Pas-de-Calais a été étudiée. La dernière partie est consacrée à un modèle de durée spatial et son application aux données considérées. / This thesis concerns the identification of risk factors for a certain type of diseasepresenting a certain spatial heterogeneity in a given region.. More specifically, we are interested in cancers of the upper aerodigestive tract (UADT) cancers in the Nord-Pasde-Calais region (NPDC), France. For this, a case-control study was first carried out bycreating a sample of healthy individuals who are not affected by cancerous tumors (thecontrols) and a sample of individuals with cancer (Cases or patients), recruited in healthcenters as part of DEREDIA and NOFARIS research projects. From a methodologicalpoint of view, spatial binary models which meet the objective have been developed onthe basis of studies in statistical/behavioral econometrics (decision analysis) and spatialstatistics. One of the methodological contributions of the thesis on this plan is the combinationof techniques from these two fields of research.In the first part, we used a spatial binary parametric models containing spatial latentchoice variable in a context of sampling data. This problem is known as Choice-BasedSampling (CBS) in discrete choice model. Unlike the random sample where all items in the population have the same probability of being chosen, the Choice-Based Sampling indiscrete choice model is a type of sampling where the classification of the population intosubsets to be sampled is based on the choices or outcomes. In this context, the use ofstandard Maximum likelihood estimation (MLE) procedure in CBS could lead to an inconsistent(asymptotically biased) estimation. Thus, we adapt the principle of maximumlikelihood in our context of spatial case-control studies. We also provide a GMM estimatorbased on the generalized residuals.In the second part, a spatial semi-parametric binary model was considered. We present inthese parts a Monte Carlo experiment to investigate the finite sample performance of theseestimation methods, then we apply to the (UADT) cancer data in the Nord-Pas-de-Calaisregion.The third part is devoted to the study of a spatial risk function in the presence of datacontaminated by measurement errors. Indeed, in the context of the considered case-controlstudy, it is very likely that certain data transmitted by the patients is not correct. Anapplication of this method to the mapping of the risk of having UADT cancer in the Nord-Pas-de-Calais region was studied. The last part is devoted to a spatial duration modeland its application to the real data was considered.

Civil wars, insecurity, and ethnic disputes have imposed a high human and economic toll in Africa. In this dissertation, I examine the destructive impacts of war on agricultural productivity growth across the continent. Poor agricultural sector performance is more likely to be present around or during times of conflict. Using a panel of 51 countries from 1962-2009 I find that war impedes agricultural productivity growth. But a decline in productivity growth is not associated with the onset of civil war. Results show that low per capita income, stagnant economic growth, a large population, and lack of political freedom correspond to higher incidence of war, while conflict and lack of rainfall are associated with low agricultural productivity growth. I find that armed conflict reduces agricultural productivity growth by 0.76 percent per year and a major armed conflict reduces TFP growth by 1.16 percent. The incidence of a major armed conflict is associated with an efficiency decline in the year by 1.24 percent, substantial setback, for more than three-quarters of countries. This dissertation extends the discussion from productivity and efficiency analysis to the inclusion of the spatial dimension by applying exploratory and confirmatory spatial data analysis and capitalizing on successful spatial techniques and analytical tools proven in geospatial science. The exploratory spatial data analysis provides evidence of spatial autocorrelation in agricultural TFP growth rates in Africa. The results of hot spot analysis reveal that Algeria, Tunisia, Libya in the northern region and Nigeria and Benin in the western region constitute hot spots of agricultural performance and the cold spot, which includes areas of meager productivity, Rwanda and Burundi in central Africa. Africa suffers substantial losses in agricultural productivity when certain countries experience major armed conflict. The dissertation shows that a war may reduce productivity in a given country, but its real effects are larger because it impacts surrounding countries. Overall African TFP declined by 0.0572 percent per year as a result of conflict in Sudan. A war in the Democratic Republic of Congo caused African TFP growth to decline by 0.0285 percent per year.

Waldo Tobler, con la sua prima legge della geografia, afferma “Ogni cosa è correlata con qualsiasi altra, ma le cose vicine sono più relazionate di quelle lontane" (Tobler, 1970). Se questo era certamente vero nel 1970, tale convinzione è stata messa in discussione con l’avvento delle Tecnologie dell’Informazione e della Comunicazione (ICT). Nel dibattito riguardo al processo di globalizzazione molti studiosi e giornalisti sostengono infatti che, con la velocizzazione delle telecomunicazioni, la distanza fisica è destinata a perdere il proprio potere esplicativo relativamente a molti fenomeni socio-economici (Cairncross, 2001; Friedman, 2005). Questa dissertazione vuole contribuire al dibattito rispondendo, seppure parzialmente, alla domanda “La distanza importa ancora?” e definire alcune possibili implicazioni di policy. L’obiettivo è quello di mostrare il ruolo della distanza geografica in tre diversi contesti economici caratterizzati da differenti dimensioni dell’unità di analisi. I risultati suggeriscono che, anche se su scala globale lo sviluppo delle nuove tecnologie ha modificato la percezione individuale della distanza come deterrente alle interazioni, lo spazio geografico mantiene ancora la sua rilevanza del definire le relazioni socio-economiche locali, aumentando il ruolo di città e regioni quali centri della maggioranza delle attività economiche. / Waldo Tobler, with his first law of geography, stated “Everything is related to everything else, but near things are more related than distant things" (Tobler, 1970). If it was certainly true in 1970, this belief is called into question in an era of development of Information and Communication Technologies (ICTs). In the debate over globalization processes, several scholars and journalists argue indeed that, with the increasing speed of telecommunications, physical distance is losing its explanatory power as determinant of socio-economical relationships (Cairncross, 2001; Friedman, 2005). This dissertation aims to give a contribution to this debate, partially answering to the broad question “Does distance still matter?" and to draw possible policy implications. The purpose is to show the role of geographical distance in three different economic environments, characterized by diversified size of the unit of analysis. Results suggest that, even if at a global scale improvements in ICTs have changed the individual perception of the distance as deterrent in interactions, geographical space still maintains its relevance in defining local socio-economic relationships, increasing the role of cities and regions as the core of most of economic activities.

Both traffic congestion and road accidents impose a burden on society, and it is therefore important for transport policy makers to reduce their impact. An ideal scenario would be that traffic congestion and accidents are reduced simultaneously, however, this may not be possible since it has been speculated that increased traffic congestion may be beneficial in terms of road safety. This is based on the premise that there would be fewer fatal accidents and the accidents that occurred would tend to be less severe due to the low average speed when congestion is present. If this is confirmed then it poses a potential dilemma for transport policy makers: the benefit of reducing congestion might be off-set by more severe accidents. It is therefore important to fully understand the relationship between traffic congestion and road accidents while controlling for other factors affecting road traffic accidents. The relationship between traffic congestion and road accidents appears to be an under researched area. Previous studies often lack a suitable congestion measurement and an appropriate econometric model using real-world data. This thesis aims to explore the relationship between traffic congestion and road accidents by using an econometric and GIS approach. The analysis is based on the data from the M25 motorway and its surrounding major roads for the period 2003-2007. A series of econometric models have been employed to investigate the effect of traffic congestion on both accident frequency (such as classical Negative Binomial and Bayesian spatial models) and accident severity (such as ordered logit and mixed logit models). The Bayesian spatial model and the mixed logit model are the best models estimated for accident frequency and accident severity analyses respectively. The model estimation results suggest that traffic congestion is positively associated with the frequency of fatal and serious injury accidents and negatively (i.e. inversely) associated with the severity of accidents that have occurred. Traffic congestion is found to have little impact on the frequency of slight injury accidents. Other contributing factors have also been controlled for and produced results consistent with previous studies. It is concluded that traffic congestion overall has a negative impact on road safety. This may be partially due to higher speed variance among vehicles within and between lanes and erratic driving behaviour in the presence of congestion. The results indicate that mobility and safety can be improved simultaneously, and therefore there is significant additional benefit of reducing traffic congestion in terms of road safety. Several policy implications have been identified in order to optimise the traffic flow and improve driving behaviour, which would be beneficial to both congestion and accident reduction. This includes: reinforcing electronic warning signs and the Active Traffic Management, enforcing average speed on a stretch of a roadway and introducing minimum speed limits in the UK. This thesis contributes to knowledge in terms of the relationship between traffic congestion and road accidents, showing that mobility and safety can be improved simultaneously. A new hypothesis is proposed that traffic congestion on major roads may increase the occurrence of serious injury accidents. This thesis also proposes a new map-matching technique so as to assign accidents to the correct road segments, and shows how a two-stage modelling process which combines both accident frequency and severity models can be used in site ranking with the objective of identifying hazardous accident hotspots for further safety examination and treatment.

This paper presents a theoretical neoclassical growth model with two kinds of capital, and technological interdependence among regions. Technological interdependence is assumed to operate through spatial externalities caused by disembodied knowledge diffusion between technologically similar regions. The transition from theory to econometrics yields a reduced-form empirical model that in the spatial econometrics literature is known as spatial Durbin model. Technological dependence between regions is formulated by a connectivity matrix that measures closeness of regions in a technological space spanned by 120 distinct technological fields. We use a system of 158 regions across 14 European countries over the period from 1995 to 2004 to empirically test the model. The paper illustrates the importance of an impact-based model interpretation, in terms of the LeSage and Pace (2009) approach, to correctly quantify the magnitude of spillover effects that avoid incorrect inferences about the presence or absence of significant capital externalities among technologically similar regions. (author's abstract)