Profiling Tourists for Balanced Utilization of Tourism-Based Resources in Kenya

Kenya is predominantly a nature-based tourism destination with wildlife (concentrated in the southern part of the country) and beaches (along the Indian Ocean) accounting for over 85% of the international tourists visiting the country. Other attractions are based on the physical landscape of the country and the culture of the people. Unfortunately, the full potential of culture-based attractions has not been exploited. The over-concentration of tourism activities in wildlife protected areas and on the coastal zone has had inherent problems that include severe environmental degradation. The less visited attractions stand the risk of neglect and could be eroded from the nation’s heritage with time. There is need to diversify tourism activities and spread them to other parts of the country by putting more emphasis on non-traditional ones such as cultural excursions. This research profiles tourists based on their preferences as assessed from the number of days they spend at different attraction sites. By associating the characteristics of tourists with various attractions, consumer preference profiles were established. Length of stay, presence of children, travel party size and gender are some of the significant factors that determined the profiles. Profiles can be used in encouraging proportionately more tourists with greater affinity for non-traditional attractions. Besides gender, other factors such as socio-economic status and whether one is travelling as a couple or not, turned out to be significant variables in influencing the resulting expenditure levels.Tourist profiles, Attractions, Culture, Expenditure, LISREL, Kenya

A Structural Equation Approach to Spatial Dependence Models

A strong increase in the availability of space-time data has occurred during the past decades. This has led to the development of a substantial literature dealing with the two particular problems inherent to this kind of data, i.e. serial dependence between the observations on each spatial unit over time, and spatial dependence between the observations on the spatial units at each point in time (e.g. Elhorst, 2001, 2003). Typical for spatial panel data models is that the causal direction cannot be based on instantaneous relationships between simultaneously measured variables. Rather the so-called cross-lagged panel design studies compare the effects of variables on each other across time. Although they circumvent the difficult problem of assessing causal direction in cross-sectional research, the cross-lagged panel design studies are usually performed in discrete time (Oud, 2002). Because of different discrete time observation intervals within and between studies, outcomes are often incomparable or appear to be contradictory (Gollob & Reichardt, 1987). This paper will describe the problems of cross-lagged space-time models in discrete time and propose how these problems can be solved through a continuous time approach. In this regard special attention will be paid to structural equation modelling (SEM). In addition, we shall describe how space-time dependence can he handled in a SEM framework

Linking of Repeated Games. When Does It Lead to More Cooperation and Pareto Improvements?

Linking of repeated games and exchange of concessions in fields of relative strength may lead to more cooperation and to Pareto improvements relative to the situation where each game is played separately. In this paper we formalize these statements, provide some general results concerning the conditions for more cooperation and Pareto improvements to materialize or not and analyze the relation between both. Special attention is paid to the role of asymmetries.Environmental Policy, Linking, Folk Theorem, Tensor Game, Prsioners' Dilemma, Full Cooperation, Pareto Efficiency, Minkowski Sum, Vector Maximum, Convex Analysis

Regional Fishery Management Organization as Games in Coalitional Form

This paper examines how a Regional Fisheries Management Organization (RFMO) might successfully achieve effective control of a high seas fishery in the context of partial cooperation. We analyse the feasible allocations of property rights among members of a given RFMO and coalitions of potential entrants. We demonstrate that the modified Shapley value is an appropriate device for the division of the gains from both partial and full cooperations.international fisheries, overexploitation, partial cooperation, games in partition function form, competitive equilibrium, modified Shapley value, Agribusiness, Crop Production/Industries, Environmental Economics and Policy, Food Consumption/Nutrition/Food Safety, Resource /Energy Economics and Policy,

Bayesian estimation of linear and nonlinear mixed models of fertilizer dosing with independent normally distributed random components

Many linear and nonlinear mixed response models are proposed to predict the optimum dose of fertilizer. However, a major restriction of this class of models is the normality assumption of the random parameter component. The purpose of this paper is to analyze the performance of linear and nonlinear mixed models of fertilizer dosing with independent normally distributed random parameter components. We compare the Linear Plateau, Spillman-Mitscherlich, and Quadratic random parameter models with different random effects distribution assumption, i.e. the normal, Student-t, slash, and contaminated normal distributions and the random errors following their symmetric normal independent distributions. The method is applied to datasets of multi-location trials of potassium fertilization of soybeans. The results show that the Student-t Spillman-Mitscherlich Response Model is the best model for soybean yield prediction

Bayesian Estimation of Spatial Regression Models with Skew-normally Covariates Measured with Errors: Evidence from Monte Carlo Simulations

Spatial data are susceptible to covariates measured with errors. However, the errorprone covariates and the random errors are usually assumed to be symmetrically, normally distribution. The purpose of this paper is to analyze Bayesian inference of spatial regression models with a covariate measured with Skew-normal error by way of Monte Carlo simulation. We consider the spatial regression models with different degree of spatial correlation in the covariate of interest and measurement error variance. The simulation examines the performance of Bayesian estimators in the case of (i) Naive models without measurement error correction; (ii) Normal distribution for the error-prone covariate and random errors; (iii) Skew-normal distribution (SN) for the error-prone covariate and normal distribution for random errors. We use the relative bias (RelBias) and Root Mean Squared Error (RMSE) as valuation criteria. The main result is that the Skew-normal prior estimator outperform the normal, symmetrical prior distribution and the Naive models without measurement error correction.     Keywords: Spatial regression, measurement error, Bayesian analysis, Skew-normal distributio