Yes / Flow experience has been widely investigated in experiential activities such as sports, the performing arts, gaming and Internet usage. Most studies focus on the positive aspects of flow experience and its effect on performance. In stark contrast, gambling research focusing on the negative side of addiction lacks an in-depth investigation of gamblers’ (positive) flow encounters. This separation of research lines seems out of place given that recent research indicates connections between flow and addiction. Joining both constructs in a causal effects model helps to gain a better understanding of their relationship and its contingencies. This paper empirically investigates whether and how it is possible to observe a “Janus face” of flow with its various sub-dimensions in online gambling. Empirical data was collected from 500 online gamblers by applying a structured questionnaire with established scales. The data was analyzed with a confirmatory factor analysis and a double-hurdle model to separate casual gamblers who are unsusceptible to any addiction issues from gamblers affected by initiatory addiction issues. The findings indicate that online gambling addiction is negatively influenced by two sub-dimensions of flow experience, namely a sense of control and concentration on the task at hand, while enhanced by a transformation of time and autotelic experience.

Le travail présenté dans cette thèse prend pour cadre d'analyse les systèmes d'innovation et vise à étudier les effets et les déterminants des collaborations science-industrie. Notre démarche empirique repose sur la combinaison entre une analyse statistique et économétrique de données nationales, et la réalisation d'enquêtes exploratoires sur un territoire donné. Pour déterminer l'impact de ces collaborations sur l'innovation des firmes, nous considérons deux mesures de l'innovation : le dépôt de brevet et l'intensité d'innovation. En distinguant entre deux types de collaborations académiques que sont les collaborations avec les universités et établissements d'enseignement supérieur et les collaborations avec les organismes publics de recherche ou privés à but non-lucratif, il ressort de ce travail que ces collaborations ont un effet positif et significatif sur l'innovation. Les déterminants des collaborations science-industrie sont, quant à eux, analysés à travers deux enquêtes : l'une portant sur les entreprises d'un technopôle, et l'autre réalisée auprès d'enseignants-chercheurs d'une université. Les deux enquêtes révèlent que les entreprises collaborent avec des organismes académiques principalement pour rechercher des solutions aux problèmes qu'elles rencontrent, alors que les chercheurs s'engagent dans ces collaborations pour rester au courant des problématiques actuelles des acteurs économiques. Les résultats de la première enquête établissent en outre un caractère multiscalaire des collaborations science-industrie, ce qui relativise le poids de la proximité géographique permanente au profit d'une proximité géographique temporaire couplée à d'autres types de proximité. La seconde enquête, qui s'intéresse à la propension des chercheurs à collaborer montre que les déterminants de cet engagement diffèrent selon le type de collaboration. / The works presented in this thesis use systems of innovation as an analytical framework and aims to study the effects and determinants of science-industry collaborations. Our empirical approach relies on a combination of statistical and econometric analysis of national data, and exploratory surveys within a given territory. To determine the impact of these collaborations on firms' innovation, we consider two measures of innovation: patenting and innovative performance. Taking into accounts two types of academic collaboration, including collaborations with universities and establishments of higher education and public and nonprofit research organizations, it appears that these collaborations have a significant and positive effect on innovation. On the other hand, determinants of science-industry collaborations are analyzed through two surveys: one covering firms belonging to a technopole, and the other conducted among researchers from a university. Both surveys show that firms collaborate with academic organizations mainly to find solutions to problems they face, while researchers are involved in these collaborations to stay abreast of current issues of economic agents. The results of the first survey establish a mutliscalar nature of science-industry collaborations, which minimize the importance of permanent geographical proximity in favor of a temporary geographical proximity coupled with other types of proximity. The second survey, which focuses on the determinants of researchers' propensity to collaborate, shows that these determinants are different according to the type of collaboration.

The classical Poisson, geometric and negative binomial regression models for count data belong to the family of generalized linear models and are available at the core of the statistics toolbox in the R system for statistical computing. After reviewing the conceptual and computational features of these methods, a new implementation of zero-inflated and hurdle regression models in the functions zeroinfl() and hurdle() from the package pscl is introduced. It re-uses design and functionality of the basic R functions just as the underlying conceptual tools extend the classical models. Both model classes are able to incorporate over-dispersion and excess zeros - two problems that typically occur in count data sets in economics and the social and political sciences - better than their classical counterparts. Using cross-section data on the demand for medical care, it is illustrated how the classical as well as the zero-augmented models can be fitted, inspected and tested in practice. (author's abstract) / Series: Research Report Series / Department of Statistics and Mathematics

The classical Poisson, geometric and negative binomial regression models for count data belong to the family of generalized linear models and are available at the core of the statistics toolbox in the R system for statistical computing. After reviewing the conceptual and computational features of these methods, a new implementation of hurdle and zero-inflated regression models in the functions hurdle() and zeroinfl() from the package pscl is introduced. It re-uses design and functionality of the basic R functions just as the underlying conceptual tools extend the classical models. Both hurdle and zero-inflated model, are able to incorporate over-dispersion and excess zeros-two problems that typically occur in count data sets in economics and the social sciences-better than their classical counterparts. Using cross-section data on the demand for medical care, it is illustrated how the classical as well as the zero-augmented models can be fitted, inspected and tested in practice. (authors' abstract)

The food-away-from-home (FAFH) sector in South Africa has continued to increase in popularity. This is illustrated by the increased presence of FAFH in the diets of the country’s citizens. However, the sector in South Africa remains un-researched with regard to understanding household preferences and the composition of consumer expenditure. This study analyses the effects of income and socio-demographic variables on FAFH expenditure for South Africa. These results will be useful to the foodservice sector and policy makers in order to identify potential customers, respond to current customers’ changing demands and develop marketing and operational strategies, and address important nutrition and health consequences, respectively. Data from Income and Expenditure Surveys (IESs) of 2005/2006 and 2010/2011 of StatsSA (Statistics South Africa) were used to estimate the responsiveness of household FAFH expenditure in South Africa to income and a number of socio-demographic variables. The IESs contain a large number of households with zero FAFH expenditure observations which means that the use of ordinary least squares (OLS) would result in biased and inconsistent results. Furthermore, omitting households with zero FAFH expenditure, and applying OLS reduces the sample size and consequently the efficiency of estimation. Previous studies made use of the univariate and multivariate an adjustment factor and a two-stage process where the second stage is a Generalised Method of Moments (GMM) Within-Group estimator. The majority of studies suggest that double-hurdle models are appropriate for applications where zero expenditure observations are due to abstention or economic factors. The double-hurdle model is more flexible than the tobit model because it allows for the possibility that zero and positive values are generated by different mechanisms. The model used assumes independence between the two hurdles. The first hurdle determines the probability of purchasing FAFH, while the second hurdle determines the amount spent on FAFH. The double-hurdle models estimated for the IESs of 2005/2006 and 2010/2011 illustrate that households headed by younger White females with a small household size and living in an urban settlement are most likely to purchase FAFH. However, households headed by younger White males with a small household size and living in an urban formal settlement are likely to have the highest expenditure on FAFH. An increase in income positively affects the decision to buy FAFH and the amount spent by participating households. The APE (average partial effect) was calculated for the income variable. The APE determines the probability of purchasing FAFH and the income elasticities (conditional and unconditional) of expenditure on FAFH by households. The estimated conditional income elasticity of expenditure is 0,27 and the unconditional income elasticity of expenditure is 0,611 for the IES of 2005/2006. While the estimated conditional income elasticity is 0,171 and the unconditional income elasticity is 0,472 for the IES of 2010/2011. The probability of purchasing FAFH is 0,0905 and 0,0568 for the IESs of 2005/2006 and 2010/2011 respectively. The income elasticity of expenditure on FAFH is inelastic and FAFH is a normal good for the average South African household. The small size of the participation elasticities mean that growth in the FAFH sector will be driven by households with existing expenditure. Future studies should focus on per capita FAFH expenditure, the effect of the lifestage of the individual, rather than age, on FAFH expenditure, FAFH expenditure for different meals (breakfast, lunch and dinner) and facility types (quick- and full-service restaurants) and the effect of income and socio-demographic factors on FAFH expenditure on different food types (for example beef, chicken, lamb, potatoes and salads). / Dissertation (MScAgric)--University of Pretoria, 2014. / tm2015 / Agricultural Economics, Extension and Rural Development / MScAgric / Unrestricted

Yes / Gambling research often refers to attitude and belief measurements to distinguish between problem and non-problem gamblers. Past studies also indicated that problem gamblers have a tendency to steeply discount rewards. We join both research streams and investigate the relationships between attitudes and beliefs on gambling addiction with the moderating effects of delay discounting using a novel methodological approach of double-hurdle model. We hereby differentiate the five subdimensions of the Gambling Attitude and Belief Scale (GABS): emotions, chasing, luck, attitudes and strategies. Findings show that emotional predispositions and chasing tendencies are positively related to the severity of online gambling addiction, independent of gamblers´ impulsivity. In contrast hereto, gambling attitudes act as inhibitor for gamblers willing to wait for some time to receive higher reward. Findings show that money-related impulsiveness influences the relationship between sub-dimensions of GABS and gambling addiction: Gambling attitudes and beliefs do not necessarily harm online gamblers but that their positive or negative relationship to addiction depends on online gamblers’ impulsivity.

In this thesis, I conducted an analysis of the consumption patterns associated with demographic and socio-economic characteristics, using Tobit and double-hurdle models. Data were collected for 11,574 households from the US Bureau of Labor Statistics for the year of 2014. Specific determinants included household size, age, income, gender, education, race, region, marital status, and whether the household lived in a coastal state. The results reveal that seafood expenditures are sequential decisions. Asian racial groups, households headed by married couples, a large number of members in households, higher income households, and households residing in the Atlantic and Gulf Coasts were variables that significantly impacted seafood expenditures.

This thesis consists of four papers concerning modelling of count data and tourism demand. For three of the papers the focus is on the integer-valued autoregressive moving average model class (INARMA), and especially on the ENAR(l) model. The fourth paper studies the interaction between households' choice of number of leisure trips and number of overnight stays within a bivariate count data modelling framework. Paper [I] extends the basic INAR(1) model to enable more flexible and realistic empirical economic applications. The model is generalized by relaxing some of the model's basic independence assumptions. Results are given in terms of first and second conditional and unconditional order moments. Extensions to general INAR(p), time-varying, multivariate and threshold models are also considered. Estimation by conditional least squares and generalized method of moments techniques is feasible. Monte Carlo simulations for two of the extended models indicate reasonable estimation and testing properties. An illustration based on the number of Swedish mechanical paper and pulp mills is considered. Paper[II] considers the robustness of a conventional Dickey-Fuller (DF) test for the testing of a unit root in the INAR(1) model. Finite sample distributions for a model with Poisson distributed disturbance terms are obtained by Monte Carlo simulation. These distributions are wider than those of AR(1) models with normal distributed error terms. As the drift and sample size, respectively, increase the distributions appear to tend to T-2) and standard normal distributions. The main results are summarized by an approximating equation that also enables calculation of critical values for any sample and drift size. Paper[III] utilizes the INAR(l) model to model the day-to-day movements in the number of guest nights in hotels. By cross-sectional and temporal aggregation an INARMA(1,1) model for monthly data is obtained. The approach enables easy interpretation and econometric modelling of the parameters, in terms of daily mean check-in and check-out probability. Empirically approaches accounting for seasonality by dummies and using differenced series, as well as forecasting, are studied for a series of Norwegian guest nights in Swedish hotels. In a forecast evaluation the improvements by introducing economic variables is minute. Paper[IV] empirically studies household's joint choice of the number of leisure trips and the total night to stay on these trips. The paper introduces a bivariate count hurdle model to account for the relative high frequencies of zeros. A truncated bivariate mixed Poisson lognormal distribution, allowing for both positive as well as negative correlation between the count variables, is utilized. Inflation techniques are used to account for clustering of leisure time to weekends. Simulated maximum likelihood is used as estimation method. A small policy study indicates that households substitute trips for nights as the travel costs increase. / <p>Härtill 4 uppsatser.</p> / digitalisering@umu

A number of articles only present hurdle models for count data. we are motivated to present hurdle models for semi-continuous data. Because semi- continuous data is also commonly seen in non-life insurance. The thesis deals with the parameterization of various hurdle models for semi-continuous data besides for count data in non-life insurance. Two components of a hurdle model are modeled separately. A hurdle component is modeled by a logistic regression. For a semi-continuous data, a continuous component is modeled by several various regressions. Parameters of each component are estimated through maximum likelihood estimation. Model selection is mentioned before theoretical approaches are applied on the vehicle insurance data. Finally, we get some predicted values based on the fitted models. The prediction gives insurance companies a general idea on setting premium but not accurate. 1

This dissertation consists of three chapters that address two major resource challenges faced by smallholder farmers in Sub-Saharan Africa: (i) weather shocks and (ii) limited land access for agricultural production. The first chapter looks at how weather shocks affect millet production and millet market price seasonality in Niger. In this paper, we use district-level longitudinal production and price data, along with high-resolution rainfall data to investigate the distinct impacts of positive and negative rainfall shocks on millet production and millet price seasonality in Niger. We find that a one standard deviation decrease in seasonal rainfall from historical averages is associated with declines in millet market price initially after harvest, but strong upward pressure on market prices 6 months after harvest. As a result, drought exacerbates existing price seasonality, which in turn can amplify negative impacts on households. Social protection programs need to account for potential increases in seasonal price variability in the design of programs to enhance household resilience to weather shocks. To better understand the household behavior that gives rise to the price responses observed in the first chapter, we explore weather shock impacts on household millet market participation in Niger in the second chapter. We merge a nationally representative household panel data with high-resolution spatially disaggregated rainfall data. We find that households are more likely to participate in the market as net sellers with negative rainfall shocks, but marketed quantity for net sellers decreases with negative rainfall shocks. Diversification into non-agricultural activities can mediate the impacts of negative rainfall shocks on market participation and lead to increases in volume of sales. Policies that support household involvement in the rural nonfarm economy through training and access to credit to help expand businesses may also stimulate millet market participation. In the third chapter, we use a rich dataset of 1,123 households to examine the determinants of individual household member access to groundnut fields, the predominant cash-crop in the Groundnut Basin of Senegal. The analysis also explores the implications of limited land access on groundnut productivity of young adult and female field managers. We find that young adults and females have fewer opportunities to access land compared to older and male household members. Further, we show that higher productivity may not be driving differential access to fields among older adults. Results suggest that with equal access, young adults may be as or more productive groundnut cultivators than older adults. Programs to increase young adult and female economic opportunities should focus on closing gaps in access to resources for production rather than decreasing observed production disparities. / Doctor of Philosophy / This dissertation addresses two major challenges that small farmers face in Sub-Saharan Africa: (i) erratic changes in weather patterns and (ii) land access for agricultural production. We divide the dissertation in three chapters. The first two chapters focus on weather shocks, while the third chapter focuses on land access. In the first chapter, we discuss how low and high rainfall affect the seasonal variation of market prices for the most important staple grain (millet) in Niger (West Africa). We find that lower rainfall than usual makes households sell their millet in the post-harvest period when market prices are generally low, and makes them buy back millet in the lean season when market prices are often high. As a result, policies that aim support household resilience to climate shocks should design programs that account for potential increases in seasonal price variability. In the second chapter, we study how low rainfall levels affect Niger millet farmers' decision to sell or not sell their harvest, as well as the association between low rainfall and the quantity of millet sold and bought. We distinguish three groups of farmers: (i) net buyers who have higher millet purchases than sales, (ii) autarkic who have zero millet purchases and millet sales, and (iii) net sellers who have higher sales than purchases. Our findings show that lower rainfall increases net sellers' probability to sell their millet, whereas it decreases the quantity they sell. Our results also reveal that households who diversify their sources of income into non-agricultural activities increase millet net sales even with low rainfall levels. Policies that support household involvement in these non-agricultural activities may also stimulate millet market participation. In the third chapter, we study the factors that affect household members' access to a groundnut field in Senegal with a particular focus on young adults and females. We show that females and young adults are less likely to access a field compared to older and male household members. Our results also suggest that with equal access, young adults may be as or more productive groundnut cultivators than older adults. Programs to increase young adult and female economic opportunities should focus on closing gaps in access to resources for production.