The objective of this thesis is to study the sources of discrepancy between the actual traffic in motorways under concession schemes and the traffic forecast ex-ante. The demand forecast for a specific project is the main variable influencing its realization. From a public sector perspective, socio-economic evaluations are driven by demand forecasts, which gives the basis for choose and hierarchy public projects in order to maximise social welfare. From a private sector perspective, traffic forecasts are the base of financial evaluation and toll setting.Despite its importance and the numerous and important developments in the field, the differences of forecast and ex-post traffic are usually very high. Some recent studies show that differences as big as 20% are much more the rule than the exception.A huge amount of uncertainty is associated with the forecasting exercise. First because transport is a derived demand and depends on many exogenous variables, also uncertain; because modelling is and simplification exercise, implies many assumptions and rely on field data, many times incomplete or of low quality; moreover, modelling human (in this case users) behaviour is always a dangerous enterprise.Although these arguments could explain at least the larger part of errors associated with forecasts, one can wonder whether the agents implicated in the forecast would or could use this uncertainty strategically in their favour. In a competition for the field scheme (bids), the bidder may overestimate the demand in order to reduce the toll included in the bid. This strategic behaviour can introduce a high bias in forecasts. Also, overoptimistic (or overpessimistic) forecasters may introduce a bias in the forecast.We propose to focus in turn on the three main groups of agents involved in the demand forecast process. The forecasters, the project promoters and the users. Study all the issues related to them would be a too ambitious (or more concretely impossible) task. We then focus on some particular issues related to the modelling of the actors' behaviour in the context of the demand forecast for toll roads.Regarding the forecaster behaviour, we present the results of the first large sample survey on forecasters' perceptions and opinions about forecasting demand for transport projects, based on an on-line survey. We first describe the main characteristics of forecasters. We then describe the last forecast forecasters prepared. We turn to the models forecasters apply, the errors they declare on past forecasts and the main sources of errors according to them. We then describe the forecast environment in terms of pressure forecasters receive. These unique results provide a picture of the world of forecasters and forecasts, allowing for a better understanding of them. We turn then to the study of the optimism and overconfidence in transport forecasts. Optimism and overconfidence in general are recognized human traits. We analyze the overoptimistic bias by comparing the distribution of stated errors with actual errors found in literature; we also compare the own skilful of subjects in doing forecasts with studies showing self-evaluations of a common skill - driving. We finally propose a regression of the competence, quality and errors on the main forecasters' and projects' specific variables.Results show that the distribution of errors transport forecasters state has a smaller average magnitude and a smaller variance than those found in literature. Comparing forecasters perception of their own competence with the results found in literature about drivers skill self-evaluation, however, we could not find a significant difference, meaning that the forecasters' overconfidence is in line with what could be viewed as a normal human overconfidence level.The pressure for results forecasters receive and the strategic manipulation they affirm exist merit a special attention. They imply that while forecasters' behavioural biases may exist and should be take in account when evaluation forecasts, the project promoter may influence forecasts by pressuring the forecasters to produce results which better fit his expectancies.We then study the bidders' strategic behaviour in auctions for road concessions. We address three questions in turn. First, we investigate the overall effects of the winner's curse on bidding behaviour in such auctions. Second, we examine the effects of the winner's curse on contract auctions with differing levels of common-value components. Third, we investigate how the winner's curse affects bidding behaviour in such auctions when we account for the possibility for bidders to renegotiate. Using a unique, self-constructed, dataset of 49 worldwide road concessions, we show that the winner's curse effect is particularly strong in toll road concession contract auctions. Thus, we show that bidders bid less aggressively in toll road concession auctions when they expect more competition. We observe that this winner's curse effect is even larger for projects where the common uncertainty is greater. Moreover, we show that the winner's curse effect is weaker when the likelihood of renegotiation is higher. While the traditional implication would be that more competition is not always desirable when the winner's curse is particularly strong, we show that, in toll road concession contract auctions, more competition may be always desirable. Modelling aggregated users' behaviour, we study the long term traffic maturity. We argue that traffic maturity results from decreasing marginal utility of transport. The elasticity of individual mobility with respect to the revenue decreases after a certain level of mobility is reached. In order to find evidences of decreasing elasticity we analyse a cross-section time-series sample including 40 French motorways' sections. This analysis shows that decreasing elasticity can be observed in the long term. We then propose a decreasing function for the traffic elasticity with respect to the economic growth, which depends on the traffic level on the road. Although “unconditional” decreasing elasticities were already proposed in the literature, this is the first work, as far as we know, putting this idea in evidence and giving it a functional form. This model provides better interpretation of the coupling between traffic and economic growth, and a better long-term forecast. From the disaggregate perspective, we study the main individual modal choice variable, the value of time. The value of travel time savings is a fundamental concept in transport economics and its size strongly affects the socio-economic evaluation of transport schemes. Financial assessment of tolled roads rely upon the value of time as the main (or even the unique) willingness to pay measure. Values of time estimates, which primarily represent behavioural values, as then increasingly been used as measures of out-of-pocket money. In this setting, one of the main issues regarding the value of time is its distribution over the population. We apply the Logit, the Mixed Logit and the Bayesian Mixed Logit models to estimate the value of time in freight transport in France. Estimations with mixed logit faced many difficulties, as expected. These difficulties could be avoided using the Bayesian procedures, providing also the opportunity of properly integrating a priori beliefs. Results show that 1) using a single constant value of time, representative of an average, can lead to demand overestimation, 2) the estimated average value of time of freight transport in France is about 45 Euro, depending on the load/empty and hire/own account variables, which implies that 3) the standard value recommended in France should be reviewed upwards.

As an alternative renewable source for bioenergy, forest biomass has recently drawn more attention from the U.S. government and the general public. Woody biomass policies have been adopted to encourage the new bioenergy industry. A variety of state policy incentives attempt to create a desirable legal climate and lure new firms, imposing two important questions regarding state government policies and the sustainable use of forest resources. This dissertation sheds some light on these questions. The first paper constructs a woody biomass policy index through scoring each statute and weighting different categories of policies from the vantage point of renewable energy investment. It analyzes the disparity in the strength of state government incentives in the woody biomass utilization. The second paper employs a conditional logit model (CLM) to explore the effects of woody biomass policies on the siting decisions of new bioenergy projects. In addition, significant state attributes influencing the births of new bioenergy firms are identified such as resource availability, business tax climate, delivered pulpwood price, and the average wage rate. The third paper uses the Sub-Regional Timber Supply (SRTS) model to examine the regional aggregate forest biomass feedstock potential in Tennessee and to predict the impacts of additional pulpwood demand on the regional roundwood market through 2030. The fourth paper includes the benefits of thinning and logging residues in a dynamic optimization model to analyze how bioenergy policies will impact forest stock, harvest levels, optimal rotation, and silvicultural effort. The results may have substantial implications regarding woody biomass policies, the creation of a new bioenergy industry, and sustainable forest resource management. A lucrative state woody biomass policy support and tax climate can attract new bioenergy businesses. States endowed with abundant forest resources may choose to provide strong tax incentives to spur the birth of new plants. However, overuse of forest biomass can impact roundwood markets and traditional wood processing industries. How government incentives will affect the sustainability of natural resources can be diverse. These findings offer constructive insights in the enactment and implementation of new woody biomass legislation.

This thesis is about the economic aspects of public procurement of services through auctionsin Sweden. It focuses on two different institutions, auctions used to find foste r-parents forboarded out children in the 19th century, and auctions of cleaning service contracts in the 20 thcentury. I n both cases, the contracting entity is the municipality or its representative, thelowest bidder wins the auction, and is paid in accordance with his/her bid.In the child auctions, c hildren were allotted to foster-parents by means of an open biddingprocess, the descending English auction, where the bids were given continuously to theauctioneer. In modern public procurement sealed bids ar e used in accordance with the firstprice,sealed bid auction. The first part of this thesis is about price formation in the childauctions and a study is made of the children who were auctioned several times, indicatingasymmetric information among bidders or adverse selection in this market. These subjects arestudied using field data on 601 child auctions held in Northern Sweden during the period 1863to 1889. The empirical findings in the first paper suggest that the foster-parents had a cleareconomic motive in these auctions. The child characteristics significantly affect the amount ofcompensation paid to the foster-parent. There is also evidence that farmers preferred older,more productive children. The second paper studies whether a re-auctioned child commandeda higher or lower price than a child that was not re-auctioned. The results indicate some formof asymmetric information because foster-parents demanded a higher level of compensationfor a re-auctioned child. The results also suggest that the probability that a child was reauctionedincreased if he/she was not healthy.The third paper in the thesis discusses a kind of "paradox". Although there is a competitiveeffect on the bids in the auction of cleaning service contracts, the contracting entity has anoption, given by law, to restrict the number of bidders. In th is paper, an implementation costis introduced fo r the contracting entity to justify such a restriction. The results, based onSwedish municipality data, indicate that contract and municipality characteristics, assumed toaffect the implementation cost, affect the volume of the procurement, and the number ofbidders, but not necessarily the choice of allocation mechanism. The final paper studiesregional differences in bids, costs, and competition in municipal procurement using the samedata as in paper [3]. The results show higher estimated costs for completing the contract, butlower estimated mean bids in the major city area of Stockholm compared with the res t of thecountry. This is explained by lower profit margins and higher operational costs in the majorcity area. An analysis is also carried out of why the lowest bidder is not always the contractedbidder. / <p>Härtill 4 delar.</p> / digitalisering@umu

The dynamics around managing urban infrastructure are changing dramatically. Today’s infrastructure management challenges – in the wake of shrinking coffers and stricter stakeholders’ requirements – include finding better condition assessment tools and prediction models, and effective and intelligent use of hard-earn data to ensure the sustainability of urban infrastructure systems. Wastewater collection networks – an important and critical component of urban infrastructure – have been neglected, and as a result, municipalities in North America and other parts of the world have accrued significant liabilities and infrastructure deficits. To reduce cost of ownership, to cope with heighten accountability, and to provide reliable and sustainable service, these systems need to be managed in an effective and intelligent manner. The overall objective of this research is to present a new strategic management framework and related tools to support multi-perspective maintenance, rehabilitation and replacement (M, R&R) planning for wastewater collection networks. The principal objectives of this research include: (1) Developing a comprehensive wastewater collection network asset database consisting of high quality condition assessment data to support the work presented in this thesis, as well as, the future research in this area. (2) Proposing a framework and related system to aggregate heterogeneous data from municipal wastewater collection networks to develop better understanding of their historical and future performance. (3) Developing statistical models to understand the deterioration of wastewater pipelines. (4) To investigate how strategic management principles and theories can be applied to effectively manage wastewater collection networks, and propose a new management framework and related system. (5) Demonstrating the application of strategic management framework and economic principles along with the proposed deterioration model to develop long-term financial sustainability plans for wastewater collection networks. A relational database application, WatBAMS (Waterloo Buried Asset Management System), consisting of high quality data from the City of Niagara Falls wastewater collection system is developed. The wastewater pipelines’ inspections were completed using a relatively new Side Scanner and Evaluation Technology camera that has advantages over the traditional Closed Circuit Television cameras. Appropriate quality assurance and quality control procedures were developed and adopted to capture, store and analyze the condition assessment data. To aggregate heterogeneous data from municipal wastewater collection systems, a data integration framework based on data warehousing approach is proposed. A prototype application, BAMS (Buried Asset Management System), based on XML technologies and specifications shows implementation of the proposed framework. Using wastewater pipelines condition assessment data from the City of Niagara Falls wastewater collection network, the limitations of ordinary and binary logistic regression methodologies for deterioration modeling of wastewater pipelines are demonstrated. Two new empirical models based on ordinal regression modeling technique are proposed. A new multi-perspective – that is, operational/technical, social/political, regulatory, and finance – strategic management framework based on modified balanced-scorecard model is developed. The proposed framework is based on the findings of the first Canadian National Asset Management workshop held in Hamilton, Ontario in 2007. The application of balanced-scorecard model along with additional management tools, such as strategy maps, dashboard reports and business intelligence applications, is presented using data from the City of Niagara Falls. Using economic principles and example management scenarios, application of Monte Carlo simulation technique along with the proposed deterioration model is presented to forecast financial requirements for long-term M, R&R plans for wastewater collection networks. A myriad of asset management systems and frameworks were found for transportation infrastructure. However, to date few efforts have been concentrated on understanding the performance behaviour of wastewater collection systems, and developing effective and intelligent M, R&R strategies. Incomplete inventories, and scarcity and poor quality of existing datasets on wastewater collection systems were found to be critical and limiting issues in conducting research in this field. It was found that the existing deterioration models either violated model assumptions or assumptions could not be verified due to limited and questionable quality data. The degradation of Reinforced Concrete pipes was found to be affected by age, whereas, for Vitrified Clay pipes, the degradation was not age dependent. The results of financial simulation model show that the City of Niagara Falls can save millions of dollars, in the long-term, by following a pro-active M, R&R strategy. The work presented in this thesis provides an insight into how an effective and intelligent management system can be developed for wastewater collection networks. The proposed framework and related system will lead to the sustainability of wastewater collection networks and assist municipal public works departments to proactively manage their wastewater collection networks.

財務危機模型的研究一般納入財務正常公司與財務危機公司兩者當樣本，探討區分危機與正常公司的因素，本研究則進一步以財務危機公司為樣本，探討在財務危機公司中區分舞弊公司與正常經營公司的基本因素。 本研究從財務危機公司中，分出財務舞弊公司與正常經營公司，因此研究樣本包含發生舞弊的財務危機公司與正常經營而發生財務危機的公司。研究變數則從文獻篩選23個財務解釋變數，以及13個公司治理解釋變數，運用羅吉斯迴歸法進行實證，結果顯示3個財務變數和1個公司治理變數在區分財務危機公司中的財務舞弊公司與正常經營公司有顯著的區別能力，公司治理變數的董監事持股比率尤其顯著。 / Financial distress prediction is usually based on both financial distressed firms and non-distressed firms. Based on financial distressed firms, this study further investigates the factors distinguishing financial fraud firms from non-fraud firms. The sample includes fraud and no-fraud firms while both are financial distressed. Twenty-three financial and thirteen corporate governance variables are surveyed from literature. The empirical result of logit regression shows that three financial variables and one corporate governance variable are significant factors in distinguishing fraud from no-fraud firms in distressed companies. Especially, the percentage of holding stocks of board of directors is the most significant variable.

This thesis deals with the development, implementation and application of statistical modeling techniques which can be employed in the analysis of credit ratings. Credit ratings are one of the most widely used measures of credit risk and are relevant for a wide array of financial market participants, from investors, as part of their investment decision process, to regulators and legislators as a means of measuring and limiting risk. The majority of credit ratings is produced by the "Big Three" credit rating agencies Standard & Poors', Moody's and Fitch. Especially in the light of the 2007-2009 financial crisis, these rating agencies have been strongly criticized for failing to assess risk accurately and for the lack of transparency in their rating methodology. However, they continue to maintain a powerful role as financial market participants and have a huge impact on the cost of funding. These points of criticism call for the development of modeling techniques that can 1) facilitate an understanding of the factors that drive the rating agencies' evaluations, 2) generate insights into the rating patterns that these agencies exhibit. This dissertation consists of three research articles. The first one focuses on variable selection and assessment of variable importance in accounting-based models of credit risk. The credit risk measure employed in the study is derived from credit ratings assigned by ratings agencies Standard & Poors' and Moody's. To deal with the lack of theoretical foundation specific to this type of models, state-of-the-art statistical methods are employed. Different models are compared based on a predictive criterion and model uncertainty is accounted for in a Bayesian setting. Parsimonious models are identified after applying the proposed techniques. The second paper proposes the class of multivariate ordinal regression models for the modeling of credit ratings. The model class is motivated by the fact that correlated ordinal data arises naturally in the context of credit ratings. From a methodological point of view, we extend existing model specifications in several directions by allowing, among others, for a flexible covariate dependent correlation structure between the continuous variables underlying the ordinal credit ratings. The estimation of the proposed models is performed using composite likelihood methods. Insights into the heterogeneity among the "Big Three" are gained when applying this model class to the multiple credit ratings dataset. A comprehensive simulation study on the performance of the estimators is provided. The third research paper deals with the implementation and application of the model class introduced in the second article. In order to make the class of multivariate ordinal regression models more accessible, the R package mvord and the complementary paper included in this dissertation have been developed. The mvord package is available on the "Comprehensive R Archive Network" (CRAN) for free download and enhances the available ready-to-use statistical software for the analysis of correlated ordinal data. In the creation of the package a strong emphasis has been put on developing a user-friendly and flexible design. The user-friendly design allows end users to estimate in an easy way sophisticated models from the implemented model class. The end users the package appeals to are practitioners and researchers who deal with correlated ordinal data in various areas of application, ranging from credit risk to medicine or psychology.

Submitted by Andre Maitan (andremaitan@yahoo.com.br) on 2011-07-25T18:57:36Z No. of bitstreams: 1 Tese MPFE - André Raj Maitan - 93280.pdf: 894819 bytes, checksum: cd191610788a5e242005e02d0e272ee3 (MD5) / Approved for entry into archive by Gisele Isaura Hannickel (gisele.hannickel@fgv.br) on 2011-07-25T19:18:50Z (GMT) No. of bitstreams: 1 Tese MPFE - André Raj Maitan - 93280.pdf: 894819 bytes, checksum: cd191610788a5e242005e02d0e272ee3 (MD5) / Approved for entry into archive by Gisele Isaura Hannickel (gisele.hannickel@fgv.br) on 2011-07-25T19:20:01Z (GMT) No. of bitstreams: 1 Tese MPFE - André Raj Maitan - 93280.pdf: 894819 bytes, checksum: cd191610788a5e242005e02d0e272ee3 (MD5) / Made available in DSpace on 2011-07-25T19:28:19Z (GMT). No. of bitstreams: 1 Tese MPFE - André Raj Maitan - 93280.pdf: 894819 bytes, checksum: cd191610788a5e242005e02d0e272ee3 (MD5) Previous issue date: 2011-06-27 / Our target here is to understand how non observables factors on water heating choice can affect electricity usage by individual consumers. In order to understand this, we will apply a discrete/continuous model in order to understand the impact of household water heating system discrete choice on household electricity consumption, continuous choice. First it was used a LOGIT model as a way to understand which variables influence consumer discrete choice. Hereafter with Discrete model results we verified if consumer choice is relevant on electricity consumption intensity. Hausman method results demonstrated that if we do not specify discrete choice at continuous model, it will bring biased estimators to our model, this occurs due to the fact that discrete choice variable is significant and it helps to explain continuous model. Results show that we have roughly 10% differences on estimators when we ignore discrete choice. It’s important to note that this kind of miscalculation can bring a important difference on government investment decision. This work and the results obtained reinforced Dubbin and McFadden (1984) work that it was used as principal reference to this work. / O objetivo deste trabalho passa por entender o impacto dos fatores não observáveis na escolha do sistema de aquecimento de água na intensidade do consumo de eletricidade domiciliar. Para isto será aplicado um modelo discreto-contínuo para entender o efeito da escolha discreta do tipo de aquecimento de água domiciliar sobre o consumo de energia elétrica do mesmo domicílio, escolha contínua. Primeiramente foi usado um modelo LOGIT e por meio deste foram entendidos os fatores que influenciam a escolha discreta do consumidor. Com os resultados encontrados na escolha discreta, temos em um segundo momento que constatar se esta escolha é significante para a intensidade do consumo de energia elétrica. Os resultados obtidos usando o método de Hausmann demonstraram que ignorar este fator (escolha discreta) pode levar a estimadores viesados para a parte contínua do modelo, ou seja, a escolha discreta é significante e ajuda a explicar o consumo de energia elétrica domiciliar. Os resultados mostraram que temos diferenças de até 10% no estimador obtido quando ignoramos este fator. É importante notar que este tipo de falha no cálculo, pode trazer problemas na decisão e na quantidade de investimento de um país. Além disso, os resultados reforçaram e ratificaram o trabalho desenvolvido por Dubbin e McFadden (1984) que foram usados como base para o estudo.

Science and technology can help feed the ever-growing human population. Green Revolution, still under critique, helped to certain extent, to reduce poverty in Asia. Biotechnology is not a solution to all problems but could be used in conjunction with other new technologies, to feed Africa. The adoption of biotechnology by farmers throughout the world, and by African farmers in particular, could help Africans to farm successfully without asking for food aid from other continents. The study analysed the adoption of maize biotechnology by developing maize farmers of Gauteng Province, South Africa. Primary data was collected through a survey (using a structured questionnaire) from 121 maize farmers from 2011 to 2014 maize production seasons. Data was analysed using SPSS computer software for descriptive statistics, rate of adoption and logit function to determine factors influencing adoption of Bt maize by farmers. The results of the descriptive analysis showed that 54% of the farmers adopted Bt maize during the 2011 to 2014 maize production seasons in the Gauteng Province. Results of the Logit model analysis indicated that farm size, gender, age, education level, off-farm employment, extension visits and farm neighbour had positive significant impact on the adoption of Bt maize by farmers. Visits by sales representatives of companies selling maize seeds, affiliation to farmer organisations and farmers speaking about Bt maize during meetings had negative significant impact on adoption of Bt maize by farmers. Developing farmers need regular visits of extension officers and their knowledge in order to achieve a high rate of Bt maize adoption. Well planted demonstration plots should be encouraged for farmers to adopt Bt maize as farmers believe in seeing to copy. Representatives from seed suppliers need to improve their sales approach in order to encourage adoption of Bt maize. Female developing farmers should be encouraged to get involved in maize farming by example making exclusive financing model for women farmers. Government can create legislations to commit financial institutions to provide women farmers with low interests or zero interest on farm loans to women farmers. Rigorous training should be encouraged so that farmers could successfully adopt Bt maize / Agriculture, Animal Health and Human Ecology / M. Sc. (Agriculture and Animal Health)

Land is a major factor in agricultural production, so agricultural land allocated to smallholder farmers through Land Reform Program or by traditional leader need to be actively utilised for enhancement of agricultural business. The study assessed land utilisation by small and emerging farmers in the Greater Tzaneen Municipality. Data was collected from 86 farms and analysed using SPSS Version 23. The results indicate that 74% of the farmers fully utilised their farm lands. Results of Logit model revealed that, the amount received from leasing, value adding to products, annual farm income and savings had positive significant impact on the area of cultivation, while skills pertaining to farming activities and the proportion of farm inputs purchased with the farmer’s own money had negative impact. The significant variables should be considered to influence full farmland utilisation by small and emerging farmers in the study area; farmers need production inputs, affordable loans and other forms of funding to improve farmland utilisation / Agriculture, Animal Health and Human Ecology / M. Sc. (Agriculture)

Made available in DSpace on 2016-06-02T20:06:01Z (GMT). No. of bitstreams: 1 2049.pdf: 560086 bytes, checksum: 32b955d6a93e81457f49b0418b1e9514 (MD5) Previous issue date: 2008-06-06 / Financiadora de Estudos e Projetos / A summary of the state of the art relative to regression models for binary response variable and panel data is presented in this work. Those models may include efects from several sources: specific variables of interest, heterogeneity between individuals and lagged values of the response variable. The original contributions of the author are simulation studies to compare two diferent approaches to maximum likelihood estimation of parameters of dynamic models with all three kinds of efects, and also a study of properties of such estimators in group sequential analysis, using the bootstrap methodology. Original codes were developed in R for implementation of simulation studies. The relevance of the subject and the non availability of appropriate codes in commercial software for fitting dynamic models for binary response justify the choice of the theme. / Neste trabalho é apresentado inicialmente um levantamento da literatura referente a modelos de regressão não lineares quando a variável resposta é binária e as observações são um painel de dados. Tais modelos podem incluir efeitos de várias fontes: variáveis específicas de interesse, heterogeneidade não observável dos indivíduos e valores defasados da variável resposta. A parte original do trabalho consiste nos estudos por simulação usando programação criada para esse fim no software R, visando comparar duas propostas recentes da literatura para ajustar, por máxima verossimilhança condicional, modelos dinâmicos que incluem os três tipos de efeitos mencionados. Também é original o estudo empírico, usando a metodologia de reamostragem \bootstrap", de características da distribuição conjunta dos estimadores dos parâmetros em análises intermediárias dos dados. A justificativa do trabalho é a atualidade do tema e a inexistência de programas de ajuste de modelos dinâmicos de resposta binária na maioria dos softwares comerciais.