In the Canary Islands (Spain), the tourism boom has been paralleled by sharp growth in the car rental sector. However, this economic activity is associated with problems such as rising levels of vehicle emissions. In this article, we discuss, on the one hand, the introduction of a tax to internalise the costs of emissions from car rental fleets and, on the other, the measures to reward users who rent environmentally-friendly cars. For this purpose, we propose a model based on statistical decision theory, from which a Bayesian rule is derived. According to this model, the tax increases with the number of days the car is rented but decreases in line with the environmental efficiency of the vehicle. A data sample of visitors to the Canary Islands is used to compare the covariates involved in computing the number of car rental days and the corresponding tax payable.
Many factors are involved in tourist decision expenses. Such circumstances may give rise to some asymmetry in the distribution of tourism expenditure. We propose in this paper a reparameterization of the three-parameter log-skew normal distribution for modelling the expenditure at the country of origin, at destination, and total expenditure in a tourism setting. This distribution seems to fit the expenditure data satisfactorily in all the parts of the empirical distribution. In particular, the proposed model is well suited to capture the skewness and kurtosis that may be present and the long tail to the right that the three variables mentioned above tend to present in practice.
Abstract:The target of this paper is to study the relevant factors affecting the victories away from home of football teams in order to fit the probability of winning an away match. The paper addressed the following research issues: (a) Is the identification of the significant variables underlying the results plausible? (b) Can information of these factors increase the probability of winning away from home and assist coaches in their decisions? Empirically, it is shown that there are more home victories and draws than away victories in the professional football leagues in Europe and this fact has to be taken into account. Thus, the classical logistic and Bayesian regression models do not seem to be adequate in this case and an asymmetric logistic regression model is therefore considered. This paper analyses 380 games played in the First Division of the Spanish Football League during the 2013-2014 season. Asymmetric logistic regression from a Bayesian point of view is chosen as the best model. This model detects new relevant factors undetected by standard logistic regressions. In view of the paper's findings, various practical recommendations were made in order to improve decision-making in this field. The Asymmetric logit link is a helpful device that can assist coaches in their game strategies.
The number of fatalities in Spain due to gender-based violence has increased in recent years, with a new rise in 2019, reaching the highest figure since 2015, a year that registered a peak with 60 victims. This article analyzes a database obtained from a survey on gender violence conducted by the Spanish Centre for Sociological Research. The survey, prepared by the Government Delegation for Gender Violence, consisted of interviews with women aged over 15 years living in 858 municipalities distributed over 50 provinces in Spain. The data reveal that most of the women interviewed have not suffered any type of physical, sexual, or psychological abuse. Hence, the application of standard logistic methodologies which suppose symmetric responses, can lead to a poor specification of the model, a misinterpretation of marginal effects and unidentified predictors. It seems more appropriate to consider an asymmetric link function to explain the probability of abuse (physical, sexual, or psychological). The Bayesian methodology allows the incorporation of such an asymmetric function improving the specification of the model. In this article, we compare both methodologies and prove that Bayesian asymmetric performs better results by considering several diagnostic criteria. Furthermore, this methodology detects some significative factors that are not revealed by the classical method, e.g., the partner’s nationality for sexual abuse or the women’s total number of intimate partners for psychological abuse. Bayesian asymmetric estimations reveal no significance concerning to the lowest partner’s level of education for physical abuse but if the intimate partner is currently studying this reduces the probability of sexual abuse. The woman’s level of education is not relevant to the physical, sexual, or psychological abuses suffered. Therefore, the findings may help identify economic and sociological factors not previously considered in this area and highlight policies that may be adopted or revised to help overcome this social problem.
This paper estimates the technical efficiency of Olympic disciplines in which Spanish athletes participate, taking into account the results obtained in the last three Olympic Games. A stochastic production frontier model (normal-exponential), using two control variables linked to economic factors such as budget and sports scholarships, is estimated in order to obtain different Olympic sports’ efficiencies distinguished by gender, using data from 2005 to 2016. The results detect some differences among the considered disciplines. In all the cases, athletics, canoeing, cycling, swimming, and tennis, depending on the gender, reach better values. This paper’s novelty lies in the efficiency analysis carried out on the Olympic disciplines and athletes of a country and not on the country’s efficiency, which allows managers and stakeholders to decide about investments concerning disciplines and athletes.
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