Detecting Free Riders in Collective Brands through a Hierarchical Choice Process

Free-riding behaviors exist in tourism and they should be analyzed from a comprehensive perspective; while the literature has mainly focused on free riders operating in a destination, the destinations themselves might also free ride when they are under the umbrella of a collective brand. The objective of this article is to detect potential free-riding destinations by estimating the contribution of the different individual destinations to their collective brands, from the point of view of consumer perception. We argue that these individual contributions can be better understood by reflecting the various stages that tourists follow to reach their final decision. A hierarchical choice process is proposed in which the following choices are nested (not independent): “whether to buy,” “what collective brand to buy,” and “what individual brand to buy.” A Mixed Logit model confirms this sequence, which permits estimation of individual contributions and detection of free riders.

A Bayesian approach to estimating the regression coefficients of a multinomial logit model

A Bayesian approach to estimation of the regression coefficients of a multinominal logit model with ordinal scale response categories is presented. A Monte Carlo method is used to construct the posterior distribution of the link function. The link function is treated as an arbitrary scalar function. Then the Gauss-Markov theorem is used to determine a function of the link which produces a random vector of coefficients. The posterior distribution of the random vector of coefficients is used to estimate the regression coefficients. The method described is referred to as a Bayesian generalized least square (BGLS) analysis. Two cases involving multinominal logit models are described. Case I involves a cumulative logit model and Case II involves a proportional-odds model. All inferences about the coefficients for both cases are described in terms of the posterior distribution of the regression coefficients. The results from the BGLS method are compared to maximum likelihood estimates of the regression coefficients. The BGLS method avoids the nonlinear problems encountered when estimating the regression coefficients of a generalized linear model. The method is not complex or computationally intensive. The BGLS method offers several advantages over Bayesian approaches. ^

Optimal designs for the prediction in hierarchical random coefficient regression models

Magdeburg, Univ., Fak. für Mathematik, Diss., 2015

Constant coefficient tests for random coefficient regression

Random coefficient regression models have been applied in differentfields and they constitute a unifying setup for many statisticalproblems. The nonparametric study of this model started with Beranand Hall (1992) and it has become a fruitful framework. In thispaper we propose and study statistics for testing a basic hypothesisconcerning this model: the constancy of coefficients. The asymptoticbehavior of the statistics is investigated and bootstrapapproximations are used in order to determine the critical values ofthe test statistics. A simulation study illustrates the performanceof the proposals.

Analysing Conjoint Analysis Data by a Random Coefficient Regression Model

2000 Mathematics Subject Classification: 62J12, 62K15, 91B42, 62H99.

Now You See it, Now You Don't: Why Do Real Estate Agents Withhold Available Houses from Black Customers?

Potential home buyers may initiate contact with a real estate agent by asking to see a particular advertised house. This paper asks whether an agent's response to such a request depends on the race of the potential buyer or on whether the house is located in an integrated neighborhood. We build on previous research about the causes of discrimination in housing by using data from fair housing audits, a matched-pair technique for comparing the treatment of equllay qualified black and white home buyers. However, we shift the focus from differences in the treatment of paired buyers to agent decisions concerning an individual housing unit using a sample of all houses seen during he 1989 Housing Discrimination study. We estimate a random effect, multinomial logit model to explain a real estate agent's joint decisions concerning whether to show each unit to a black auditor and to a white auditor. We find evidence that agents withhold houses in suburban, integrated neighborhoods from all customers (redlining), that agents' decisions to show houses in integrated neighborhoods are not the same for black and white customers (steering), and that the houses agents show are more likely to deviate from the initial request when the customeris black than when the customer is white. These deviations are consistent with the possibility that agents act upon the belief that some types of transactions are relatively unlikely for black customers (statistical discrimination).

Stochastic choice analysis of tourism destinations

The analysis of tourist destination choice, defined by intra-country administrative units and by product types “coastal/inland and village/city”, permits the characterisation of tourist flow behaviour, which is fundamental for public planning and business management. In this study, we analyse the determinant factors of tourist destination choice, proposing various research hypotheses relative to the impact of destination attributes and the personal characteristics of tourists. The methodology applied estimates Nested and Random Coefficients Multinomial Logit Models, which allow control over possible correlations among different destinations. The empirical application is realised in Spain on a sample of 3,781 individuals and allows us to conclude that prices, distance to the destination and personal motivations are determinants in destination choice.

Employment rate impacts of charging the Heavy Good Vehicle corridor (Madrid-Sevilla) across Spanish regions: a Random Utility Based Multiregional Input-Output approach

The implementation of a charging policy for heavy goods vehicles in European Union (EU) member countries has been imposed to reflect costs of construction and maintenance of infrastructure as well as externalities such as congestion, accidents and environmental impact. In this context, EU countries approved the Eurovignette directive (1999/62/EC) and its amending directive (2006 /38/EC) which established a legal framework to regulate the system of tolls. Even if that regulation seek s to increase the efficien cy of freight, it will trigger direct and indirect effects on Spain’s regional economies by increasing transport costs. This paper presents the development of a multiregional Input-Output methodology (MRIO) with elastic trade coefficients to predict in terregional trade, using transport attributes integrated in multinomial logit models. This method is highly useful to carry out an ex-ante evaluation of transport policies because it involves road freight transport cost sensitivity, and determine regional distributive and substitution economic effect s of countries like Spain, characterized by socio-demographic and economic attributes, differentiated region by region. It will thus be possible to determine cost-effective strategies, given different policy scenarios. MRIO mode l would then be used to determine the impact on the employment rate of imposing a charge in the Madrid-Sevilla corridor in Spain. This methodology is important for measuring the impact on the employment rate since it is one of the main macroeconomic indicators of Spain’s regional and national economic situation. A previous research developed (DESTINO) using a MRIO method estimated employment impacts of road pricing policy across Spanish regions considering a fuel tax charge (€/liter) in the entire shortest cost path network for freight transport. Actually, it found that the variation in employment is expected to be substantial for some regions, and negligible for others. For example, in this Spanish case study of regional employment has showed reductions between 16.1% (Rioja) and 1.4% (Madrid region). This variation range seems to be related to either the intensity of freight transport in each region or dependency of regions to transport intensive economic sect ors. In fact, regions with freight transport intensive sectors will lose more jobs while regions with a predominantly service economy undergo a fairly insignificant loss of employment. This paper is focused on evaluating a freight transport vehicle-kilometer charge (€/km) in a non-tolled motorway corridor (A-4) between Madrid-Sevilla (517 Km.). The consequences of the road pricing policy implementation show s that the employment reductions are not as high as the diminution stated in the previous research because this corridor does not affect the whole freight transport system of Spain.

Bias from ignoring price dispersion in demand estimation

Consumers often pay different prices for the same product bought in the same store at the same time. However, the demand estimation literature has ignored that fact using, instead, aggregate measures such as the “list” or average price. In this paper we show that this will lead to biased price coefficients. Furthermore, we perform simple comparative statics simulation exercises for the logit and random coefficient models. In the “list” price case we find that the bias is larger when discounts are higher, proportion of consumers facing discount prices is higher and when consumers are more unwilling to buy the product so that they almost only do it when facing discount. In the average price case we find that the bias is larger when discounts are higher, proportion of consumers that have access to discount are similar to the ones that do not have access and when consumers willingness to buy is very dependent on idiosyncratic shocks. Also bias is less problematic in the average price case in markets with a lot of bargain deals, so that prices are as good as individual. We conclude by proposing ways that the econometrician can reduce this bias using different information that he may have available.

Econometric models of financial risks

The goal of this dissertation is to use statistical tools to analyze specific financial risks that have played dominant roles in the US financial crisis of 2008-2009. The first risk relates to the level of aggregate stress in the financial markets. I estimate the impact of financial stress on economic activity and monetary policy using structural VAR analysis. The second set of risks concerns the US housing market. There are in fact two prominent risks associated with a US mortgage, as borrowers can both prepay or default on a mortgage. I test the existence of unobservable heterogeneity in the borrower's decision to default or prepay on his mortgage by estimating a multinomial logit model with borrower-specific random coefficients.

The negative effects of failed service recoveries

Research has shown that more than half of attempted recovery efforts fail, producing a ‘double deviation’ effect. Surprisingly, these double deviation effects have received little attention in marketing literature. This paper examines what happens after these critical encounters, which behavior or set of behaviors the customers are prone to follow and how customers’ perceptions of the firm’s recovery efforts influence these behaviors. For the analysis of choice of the type of response (complaining, exit, complaining and exit, and no-switching), we estimate multinomial Logit models with random coefficients (RCL). The results of our study show that magnitude of service failure, explanations, apologies, perceived justice, angry and frustration felt by the customer, and satisfaction with service recovery have a significant effect on customers’ choice of the type of response. Implications from the findings are offered.

Papel moderador de las motivaciones en el efecto de los precios de los destinos turísticos

La literatura de elección de destinos turísticos ha dedicado una gran atención al impacto directo del atributo “precio del destino”, pero no ha alcanzado un consenso en torno al mismo. Alternativamente, nuestro trabajo toma como punto de partida la relación entre las motivaciones turísticas y los beneficios buscados del turista en un destino, lo que lleva a proponer que el efecto del precio viene moderado por las motivaciones del turista a la hora de elegir un destino. Para ello, se argumentan diversas hipótesis de investigación que explican esta decisión a través de la interacción entre dicho atributo del destino y las motivaciones personales de los individuos. La metodología aplicada estima Modelos Logit con Coeficientes Aleatorios que permiten controlar posibles correlaciones entre los distintos destinos y recoger la heterogeneidad de los turistas. La aplicación empírica realizada en España sobre una muestra de 2.127 individuos evidencia que las motivaciones moderan el efecto de los precios en la elección de los destinos turísticos intrapaís.

Testing reference dependence, loss aversion and diminishing sensitivity in Spanish tourism

Based on Tversky and Kahneman’s Prospect Theory, we test the existence of reference dependence, loss aversion and diminishing sensitivity in Spanish tourism. To do this, we incorporate the reference-dependent model into a Multinomial Logit Model with Random Parameters -which controls for heterogeneity- and apply it to a sample of vacation choices made by Spaniards. We find that the difference between reference price and actual price is considered to make decisions, confirming that reference dependence exists; that people react more strongly to price increases than to price decreases relative to their reference price, which represents evidence in favor of the loss aversion phenomenon; and that there is diminishing sensitivity for losses only, showing convexity for these negative values.

Assessing advertising in a hierarchical decision model

Many destination marketing organizations in the United States and elsewhere are facing budget retrenchment for tourism marketing, especially for advertising. This study evaluates a three-stage model using Random Coefficient Logit (RCL) approach which controls for correlations between different non-independent alternatives and considers heterogeneity within individual’s responses to advertising. The results of this study indicate that the proposed RCL model results in a significantly better fit as compared to traditional logit models, and indicates that tourism advertising significantly influences tourist decisions with several variables (age, income, distance and Internet access) moderating these decisions differently depending on decision stage and product type. These findings suggest that this approach provides a better foundation for assessing, and in turn, designing more effective advertising campaigns.

Direct versus indirect channels: Differentiated loss aversion in a high‐involvement, non‐frequently purchased hedonic product

Purpose – This article aims to investigate whether intermediaries reduce loss aversion in the context of a high-involvement non-frequently purchased hedonic product (tourism packages). Design/methodology/approach – The study incorporates the reference-dependent model into a multinomial logit model with random parameters, which controls for heterogeneity and allows representation of different correlation patterns between non-independent alternatives. Findings – Differentiated loss aversion is found: consumers buying high-involvement non-frequently purchased hedonic products are less loss averse when using an intermediary than when dealing with each provider separately and booking their services independently. This result can be taken as identifying consumer-based added value provided by the intermediaries. Practical implications – Knowing the effect of an increase in their prices is crucial for tourism collective brands (e.g. “sun and sea”, “inland”, “green destinations”, “World Heritage destinations”). This is especially applicable nowadays on account of the fact that many destinations have lowered prices to attract tourists (although, in the future, they will have to put prices back up to their normal levels). The negative effect of raising prices can be absorbed more easily via indirect channels when compared to individual providers, as the influence of loss aversion is lower for the former than the latter. The key implication is that intermediaries can – and should – add value in competition with direct e-tailing. Originality/value – Research on loss aversion in retailing has been prolific, exclusively focused on low-involvement and frequently purchased products without distinguishing the direct or indirect character of the distribution channel. However, less is known about other types of products such as high-involvement non-frequently purchased hedonic products. This article focuses on the latter and analyzes different patterns of loss aversion in direct and indirect channels.