The effects of advertising, previous experience and

recommendation in tourist´s length of stay: a hurdle count

data approach.

Authors and e-mails of them: David Boto Garcíauo224803@uniovi.es

Department: ECONOMICS

University: University of Oviedo

Subject area: Tourism and territory

Abstract: This article analyses tourist´s length of stay in a particular destination (Asturias) using a Hurdle Negative Binomial model which allow us to firstly separate hikers from proper tourists and then explain the length of stay of those who actually stay for more than a day. Apart from sociodemographic characteristics of the individual, we are interested in the effect of distance, mode of transportation, type of accommodation and party size. Moreover, one of the relevant features this paper addresses is how advertising, recommendations from relatives or friends and previous experience at the destination affects both the probability of an overnight stay and the length of stay at the destination, given that these factors minimize the risk of uncertainty. Another prominent issue is that we control from regional differences in preferences for tourism within Asturias depending on geographical characteristics. Our estimations are based on a conditional demand function for tourism using a time series of cross-sectional data. The results indicate that having seen advertising of Asturias increase the probability of stopping over but it has no effect on the length of stay, whereas recommendation from friends or relatives and previous experience both positively affect the decision to sleep at the destination and the number of days.

Keywords: length of stay, tourist´s decision-making, advertising, conditional demand,

Negative Binomial model

JEL codes: C35, D12, D81

1. Introduction

Length of stay in a tourist destination is one of the most relevant issues in tourist decision-making process (Decrop and Snelders, 2004). The more he stays, the more is the knowledge about products, services and places to visit (Davies and Mangal, 1992; Gokovali et al., 2007; Martínez and Raya, 2008) and, consequently, the more will be the expenditure. In fact, some studies have found evidence about a strong correlation between length of stay and total expenditure (Leones et al., 1998; Agarwal and Yochum, 1999; Thrane, 2002; Laesser and Crouch, 2006; Mehmetoglu, 2007; Fredman, 2008), even though short stays are usually associated with higher levels of average daily expenditure (Cannon and Ford, 2002; Downward and Lumsdom, 2000, 2003; Hsieh et al., 1997, among others). Given the importance for destinations to have long-stay tourism, it seems necessary to identify which factors determine the length of stay.

In this article, we analyze the determinants of length of stay in a particular tourist destination (Asturias) using a two-step count data model. Specifically, our main interest lies in the role that information about the destination plays in tourist decision regarding how long to stay. It is worth to identify how the different sources of information affect length of stay for the proper design of marketing policies as to promote longer stays, associated with higher occupancy rates and revenue streams.

There are several papers in the literature which investigate the effects of sociodemographic characteristics such as age, income or nationality, among others, (Gokovali et al., 2007; Barros and Machado, 2010) on length of stay. Besides, other articles analyze the relationship between the number of days the tourist stays at the destination and the mode of transport, the type of accommodation selected or the purpose of the trip (Martínez-García and Raya, 2008; Alen et al., 2014). In this article, apart from controlling for this sort of factors, our study purpose is to determine how having seen advertisements, previous experience at the destination and recommendation from friends or relatives (word of mouth effect) affect length of stay. Another issue of interest is how duration is connected with the most valued attribute of the destination for each tourist, so that we can identify the most important pull factor, ceteris paribus.

This paper employs a pooled cross-section data for a sample of 33.461 tourists visiting Asturias in the period 2010-2016. For analyzing the effects of the different sources of information on tourist´s length of stay, we estimate a Hurdle Negative Binomial count data model (Mullahy, 1986). This methodology allows us to; firstly, identify the factors which determine the decision to stop over and, afterwards, to analyze the effect of a set of variables on the length of stay for those who actually sleep at least one night at the destination. From a methodological point of view, we employ a Truncated at Zero Negative Binomial P count data model for modelling the positive outcomes in the second step, which is considered as the best alternative (Greene, 2008).

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The estimation results show that having seen advertising of Asturias increase the probability of stopping over but it has no effect on the length of stay. Recommendation from friends or relatives and previous experience both positively affect the decision to sleep at the destination and the number of days. Performing some active tourism activities or booking the trip through travel agencies also increase the length of stay. Moreover, the estimations also indicate that the relationship between distance to origin and length of stay is not linear. Foreign visitors tend to stay more than Spaniards whereas education is not significant for explaining the number of days a tourist stays. Climate, natural environment and looking for tranquility emerge as the main pull factors that increase length of stay at Asturias.

The paper is structured as follows. After this introductory section, we present a review of the economic literature regarding this topic. Later on, we present the theoretical model under which the estimations are based. Afterwards, in the fourth section we describe the database and the variables employed. Then, in the fifth section we present the empirical model. The sixth section outlines the main results. Last section concludes.

2. Literature Review

The economic relevance of tourism has aroused an increasing interest in analyzing the determinants of the length of stay (LOS) in the economic literature. Several studies have involved mainly descriptive analysis of differences in length of stay given tourist’s socio-demographic and/or trip-related characteristics, including Oppermann (1995, 1997), Seaton and Palmer (1997), Sung et al. (2001) and Lew and McKercher (2002), among others. These studies show how length of stay varies with nationality, age, occupation status, repeat visit behavior, stage in the family life cycle and physical distance between place of origin and destination, among other variables. However, their descriptive nature hinders formal inference tests on the causal relationships between individual socio-demographic profiles and actual trip experiences and length of stay. However, in the last decade, there has been a widespread empirical application of regression models for explaining length of stay, which allow the researcher to study the pure effect of a covariate on the dependent variable, ceteris paribus.

Alegre and Pou (2006) analyze German and Britain tourists’ length of stay at the Balearic Islands (Spain) in terms of a Logit model. Their empirical results show that tourists who are over 60 present a higher probability to stay for longer than the rest of age groups whereas the higher is the education level, the least is length of stay. Gokovali et al. (2007) investigate the determinants of length of stay for a sample of sun and beach tourists visiting Bodrum (Turkey) using survival models. They indicate that Russian tourists tend to stay for the longest duration, followed by tourists from Germany and the Netherlands. Income, party size, previous visits and tourist experience (taste for travelling) positively affect LOS meanwhile daily spending and educational level are negatively related with the probability of staying for longer.

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Martínez-García and Raya (2008) model the length of stay for low-cost travelers. Being over 50, having only primary education, travelling in the high peak season and staying in campsites or private accommodation are the factors which further increase the number of days stayed at the destination. Barros et al. (2008) analyze length of stay of Portuguese tourists travelling to South America on charter flights. Controlling for sociodemographic characteristics, budget and temporal constraints, their main interest relies on how brochures and the degree of advanced booking affect length of stay. The econometric estimations prove that the time spam a tourist stays at a destination is positively related to having booked in advance, having seen advertisements, previous visits and the frequency of travel. Barros and Machado (2010) examine the determinants of length of stay in the Madeira Island (Portugal) using a sample of foreign tourists departing from Funchal Airport. The main conclusions of their study are that age, gender, education and hotel quality increase length of stay but expenditure reduces it. Besides, Germans stay longer than British, Dutch and French tourists do. Gomes de Menezes and Moniz (2011) account for the connection between trip experiences and length of stay. Their main interest here is to disclose how travel motive, alternative destinations considered, repeat visitation rate, overall satisfaction and revisit intention condition the stay. Repeat visitors, taking charter flights and those who visit friends or relatives tend to exhibit longer stays.

With the purpose of distinguishing different groups of tourists with homogeneous preferences, Alegre et al. (2011) estimate a latent class count data model for length of stay which endogenously assigns individuals one of the two considered existing classes. Their estimations indicate that age, profession, nationality and the total tourist expenditure are all statistically significant in defining preferences. For both segments, the price per day´s stay has a negative effect on the length of stay, being the magnitude higher for the shorter-stay segment.

Grigolon et al. (2014) develop a dynamic model of choice of length of stay, discriminating among going on holidays for short, medium or long periods. They point out that the effect of a particular vacation length made in the past affects travelers’ choice of a future vacation with the same length. Oliveira-Santos et al. (2015) employ a shared heterogeneity duration model to Brazil visitors‘ length of stay. The estimations indicate that income does not have a significant effect on expected length of stay; tourists visiting two destinations stay shorter than those who visit a single destination; the effect of party size is negative following a non-monotic path; summer season is associated with the longest stays and those who lodge at hotels stay for short periods.

More recently, Thrane (2016) scrutinizes Norwegian students´ length of stay at summer vacation destinations. The novel aspect about this research is that the interest is focused on the differences between those who decide the return date before taking the trip and those who take the decision along the way. “Open-returners” have the longest length of stay, suggesting that “pre-fixed” returners may face more economic and available time constraints. Another salient result is that females tend to stay longer than males.

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Finally, Nicolau et al. (2016) conduct a research in which they assess the relation between distance and previous visits and length of stay. They employ a Truncated Negative Binomial model whose estimations indicate that as distance increases, length of stay increases too. This may be caused by the fact that tourists want to compensate for the spent time and costs to reach to the destination and spread the fixed costs.

As for the methodologies employed, different modelling strategies can be identified in the literature: duration models (Gokovali, et al. 2007; Martinez-Garcia and Raya 2008; Gomes de Menezes et al., 2008; Barros et al. 2010; Barros and Machado 2010; Machado, 2010; Peypoch et al. 2012; Thrane 2012; Wang et al., 2012), tobit (Mak and Moncur, 1979; Fleischer and Pizam, 2002), OLS regression (Mak and Nishimura, 1979; Thrane and Farstad, 2012, 2015), panel data (Martínez-Roget and Rodríguez, 2006; Grigolon et al., 2014), ordered logit (Ferrer-Rosell et al., 2014), binomial logit (Alegre and Pou, 2006), multinomial logit (Grigolon et al. 2014), count data models (Hellström, 2006; Brida et al., 2013; Nicolau et al., 2016), latent class (Alegre et al. 2011; Yang and Zhang, 2015) or nested logit model (Nicolau and Más, 2009). We will discuss and justify our count data approach for studying length of stay in section five.

Before ending this section, it is important to note that, as the different empirical studies regarding length of stay at a particular destination that we have presented above refer to different countries, temporal periods and type of tourists, conclusions should be drawn with caution. Nonetheless, a general conclusion is that length of stay at a destination can be explained by opportunity, possibility and preference. In other words, time and economic constraints (daily prices of accommodation, travel costs, income, etc.), travel characteristics (motive of the trip, mode of transportation, who you travel with, party size, etc.), sociodemographic characteristics (age, gender, labor status, etc.) and the level of information about the place emerge as the key determinants. By controlling for the specific circumstances of each tourist, an important source of heterogeneity among them can be accommodated (Heckman, 2001).

3. Theoretical framework

Lancaster´s consumer theory (1966) indicates that consumer´s utility is generated by certain attributes or characteristics which the consumption or possession of physical entities produce. However, a tourist (traveler) does not derive utility from possessing or consuming travel destinations but from being in the particular destination for a certain period of time (Rugg, 1983).

The theoretical framework of this paper is based on the discrete choice or random utility models proposed by McFadden (1974) and Manski (1977) – firstly applied to length of stay by Alegre and Pou (2006) – where consumers compare the utility of different alternatives and choose that which maximizes his utility subject to time and economic constraints, bearing in mind that they do not demand quantities but time (length of stay).

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Following Dubin and McFadden (1984), tourist´s length of stay is the result of a utility maximization process subject to budget restrictions and time constraints so that:

Max U (q, Z, ttrans, ttur, n, ε)q, z, ttrans, ttur,

s.a p´q + ptrans+ p tur t tur≤ Yt trans + t tur ≤ Tq, z, t trans, p, p trans, p tur ≥0 (1)

where q is a vector of consumer goods except tourist ones; Z refers to the characteristics of the trip (accommodation, transport, etc.,); t is the total length of the trip, disaggregated into the necessary travel time for reaching to the destination (t trans) and the properly length of stay there (ttur); n represents sociodemographic characteristics and preferences of the tourist and ε is a random error term for non-observable factors (McFadden, 1981). Moreover, p is the price vector of goods other than tourist, and ptrans

and ptur are the daily prices of transport and accommodation respectively.

The individual chooses a destination j among a choice set S under a utility maximization criterion. As the number of alternatives in the choice set is unlimited, our analysis is conditional on the election of the observable destination j. The information about the tourist good is restricted to the final election, not having information about the alternatives he might have considered. Consequently, assuming that the tourist has previously elected a particular destination j as it provides him the highest level of conditioned utility, the conditional demand function for length of stay at destination j given the characteristics of the trip can be expressed as:

t j-tur= ttur (p, pj-tur, zj, Y – pj-trans, T – tj-trans, n, ε) (2)

This conditional demand function (Pollak, 1969, 1971) allows us to estimate length of stay taking pre-fixed values of the selected destination and trip characteristics so that length of stay (ttur) explicitly depends on these arguments.

Under the assumption that the utility function of the individual is weakly separable1 the conditional demand function for length of stay can be written as:

t j-tur= ttur (pj-tur, zj, Y – pq– pj-trans, T – tj-trans, n, ε) (3)

1 This implies that “goods can be partitioned into different groups so that preferences within groups can be described independently of the quantities of other goods” (Deaton and Muellbauer, 1980, p.122). This means that the decision about how much tourism to consume (length of stay) can be econometrically modeled without having to know tourist´s preferences and quantities consumed of other goods. Thereby, it is only required to have information regarding tourist consumption, retaining the demand functions the desirable and usual properties.

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The theoretical model presented above states that the tourist´s choice of length of stay at a previously selected destination depends on the trip characteristics, the daily price, income, available time, consumer´s preferences and a random error term. Following this approach and as it is usual in the literature (see Oliveira-Santos et al., 2015 for a wide review of the determinants of length of stay), we can decompose tourist´s preferences among sociodemographic characteristics (Soc), destination attributes (Attrib), and trip-related characteristics (Travel).

Under the assumption that the individual´s utility also depends on the expected quality of the destination chosen, information availability must also be incorporated in the model. When deciding how long to stay – and especially for first-timers –, tourists face a high risk of making a bad decision as the specific characteristics of a destination are unknown until the individual reaches there (i.e., intangibility). Due to this uncertainty, tourist´s choice regarding the number of days to stay strongly depends on the available both external and internal information about the destination and its characteristics (Moutinho 1987). This “experience good” nature of tourism (Mill and Morrison, 2009) induces travelers to carry out extensive information search strategies (Klein, 1998; Roehl and Fesenmaier, 1992). Information search has been widely studied in tourism marketing (Fodness and Murray 1997, 1999; Vogt and Fesenmaier 1998, Gursoy and McCleary, 2004). Consequently, the selected LOS for each individual may be strongly related with the awareness of the destination´s characteristics in presence of risk aversion. We distinguish three main sources of information: advertisement, previous experience and worth-of-mouth effect.

Advertisement

Stigler (1961) identified that advertising reduces consumer's search costs as it provides proper, critical and useful information to potential and current consumers. In the same vein, Woodside and Dubelaar (2002) conclude that advertising helps the individual to get positive perceptions of the destination and then increases expenditures there. Nowadays, tourism advertising is regarded as one of the most influential information sources for prospective and current visitors (Gretzel et al., 2000; Woodside and King, 2001; Kim et al., 2005; Park and Nicolau, 2015).

Previous experience

Lower information search involvement is required when the individual has previously been to the destination and has first-hand information. In this situation, the tourist has more confidence in the decisions made and the perceived risk is quite less (Kerstetter and Cho, 2004). Previous studies have shown that repeat visitors display longer stays (Lehto et al., 2004; Gokovali et al., 2007; Gomes de Menezes et al., 2008). Indeed, Mill and Morrison (2009) argue that prior travel experience to the destination is one of the most relevant information sources. Nonetheless, more important than simply having previously been there is the total number of times the tourist has visited the destination.

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As the number of visits increase, the tourist acquires expertise, defined as ‘‘qualitatively higher levels of either knowledge or skill’’ (Jacoby et al., 1986, p. 469), which minimizes the uncertainty and lead to more efficient choices.

Recommendation from friends or relatives (Word-of-mouth)

When an individual is planning to visit an area for the first time and due to the mentioned uncertainty, he looks for information about the place such as the weather, where to eat, what to visit, etc. Thanks to new technologies, individuals have free and easy access to huge quantities of information. However, the problem lies on the credibility2 that the potential tourist gives to the different information he receives. For this reason, individuals tend to rely more on recommendations from friends and relatives as they perceived them as trustworthy (Gitelson and Kerstetter, 1994; Bieger and Laesser, 2004; Draper, 2016). The well-known “worth-of-mouth” effect is well-documented in the tourism industry3 (Cheng et al., 2006; Yeoh et al., 2013; Luo and Zhong, 2015).

Our empirical model for the LOS conditional demand function may be thus written as:

LOS= f(Info, Soc, Attrib, Travel, Price, Income, ε) (4)

4. Database

Our analysis of tourist´s length of stay employs a pooled time series cross-section database of individuals visiting Asturias in the period 2010-2016. The Asturian Tourist Information Service (SITA) conducts a detailed survey throughout the whole year to a representative sample of all the visitors to the Principality of Asturias over 18. Data were collected through personal interviews for 33,461 individuals4 using a quota random sampling5 procedure based on type of accommodation, geographical area, day of the week and month. Sample size was determined according to a 95% confidence level with a 5% error. Questionnaires were completed both on the street and in collective establishments all over the Asturian geography. They were available in Spanish, German, English and French.

The survey gathers microdata regarding tourist sociodemographic characteristics, travel motivation, places visited, total number of nights spent at Asturias, mode of transport, 2 Credibility or trustworthiness is related to how individuals perceive, interpret, and respond to information (Grewal et al., 1994).3 See Confente (2014) for a wide review of the most important studies regarding word of mouth related to tourist decisions. 4 The number of surveyed individuals for each of the seven years considered is a representative sample of the total visitors per year (4,926; 5,076; 5,150; 4,268; 4,510; 4,831; and 4,700 respectively). 5 As opposed to random sampling, quota sampling allows the sample to be properly representative of the total population under study, overcoming the possible selection bias that may arise with random sampling as respondents are self-selected. In this sense, quota random sampling guarantees that each type of tourist is proportionally represented in the sample.

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place of origin, expenditure or type of accommodation, among others. Those visitors who stayed in Asturias for more than 30 days were removed from the sample (Hellström and Nordström, 2008) as they should not be considered tourists being strictly. Moreover, given that not all the interviewed answered all the questions, we have some missing values for certain variables so our final sample consists of 12,594 individuals.

Tourism has faced a worldwide trend of decreasing length of stay as reported by the UNWTO (2006, 2007) and several other authors (e.g., Alegre and Pou 2006; Barros et al. 2008; Martinez-Garcia and Raya, 2008; Barros and Machado 2010). Average length of stay in Asturias continuously fell between 2010-2014, changing from 3.75 average overnights stays in 2010 to 2.95 in 2014. Nevertheless, both in 2015 and 2016 they have increased to 3.65 and 3.44 respectively. Over the whole period, the mean of stays is 3.32 nights. Almost 30% of the total visitors are hikers; 37% have seen some advertisement regarding Asturias; 28% declare they come to Asturias for the first time and 40% of the visitors consider novelty as the main reason for coming. The principal trip purpose is holiday/leisure (83%) and they mainly travel to Asturias by car (82%) and in a couple (50%). Most visit Asturias in the second trimester (46%) and have organized the trip themselves (92%). The preferred area is the central one (43%) which includes the three main cities (Oviedo-Gijón-Avilés) of the region. The mean distance to origin is 500 kilometers although only 6% come from foreign countries. About 28% live in Asturias (locals) whereas 66% do in the rest of Spain. The average expenditure per person and day is 61 € and the most chosen accommodation for those who spend at least one night in Asturias is hotels (43%).

The main objective of the present study is to estimate the effect of three different sources of information (advertising, previous experience and recommendation from friends and relatives) on length of stay. For reaching our study purpose in a proper way, it is necessary to take into account that tourists are not homogeneous and differ in some many ways. For truly isolate the effect of these three covariates on length of stay, we are not interested on the unconditional relationship but on the conditional one, controlling for the remaining trip and individual characteristics.

In the survey, individuals are asked whether they have seen any kind of advertisement of Asturias and if they have previously visited this region. In case they have come before, they report the total number of visits. Besides, they are asked about the main reason why they chose Asturias, being the recommendation from friends or relatives or a positive previous experience two of the possibilities. To test the impact of the degree of uncertainty on length of stay, we define the following dummy variables: adver (which takes the value 1 if the tourist has seen any kind of advertisement), first (in case is the first time the individual visits Asturias), num_vis (which account for the number of visits made), recommend (if the individual declares he visits Asturias due to recommendation) and exp (if the individual states a positive previous visit is the main reason for returning).

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According with our theoretical model, we consider the following groups of variables as controls:

Sociodemographic characteristics (Soc): gender, age (both in levels and in a quadratic form allowing for possible non-linearity), education level (primary studies, secondary studies and higher education), labor status (distinguishing among professional, executive, civil servant, businessman/businesswoman, employee, self-employed, student, housewife/househusband, unemployed and retired) and nationality (Spaniard versus foreign).

Destination attributes (Attrib): as stated before, in the survey tourists are asked about the main reason for having chosen Asturias. Apart from recommendation and previous experience, they can choose among the following alternatives: novelty, natural environment, heritage, tranquility, gastronomy and climate. All of them are defined as dummies.

Travel-related characteristics (Travel) : distance to origin (measured as the total number of kilometers from the tourist´s living place to Oviedo6, also considered in a square form as the relationship may not be linear), trip companions (couple, family, friends, workmates or in a big group), how the trip was organized (the individual did it himself, through a travel agency, through a club or through the company where he works), type of accommodation (hotel, rural house, hostel, camping or private accommodation), how the accommodation was booked (by phone, in person, through a travel agency, through the internet or through the company where the individual works), mode of transportation to reach there (by car, by bus, by train, by plane), party size (number of members in the travel group), if the individual only visits Asturias in this trip or not, if the purpose of the trip is leisure or another and if the tourist conducts active tourism activities or not.

We also take into account temporal factors (Temp) that may influence the decision regarding how long to stay in Asturias. They can also act as proxies of time availability and preferences. Specifically, we control for the year and the trimester which the visit to Asturias takes place and also differentiate between week and weekend. Additionally, we control for the regional area (Geo) where the tourist stays (West area, central area – separating Oviedo-Gijón-Avilés area from the rest –, East coast and East inner area) as another way of controlling observable heterogeneity in preferences among tourists.

As for the daily prices, we consider the daily paid price of accommodation per person (denoted as d_exp_per) in euros. Tourists´ lodging expenditures per day are a good proxy of the minimum cost each day spent at Asturias represents7 (Mak et al., 1977;

6 In the case of locals – those who live in Asturias but they visit or stay in a different area for a certain period – the distance considered is nil.7 Using daily accommodation expenditure as a measure of the price for each day spent in the destination can be seriously criticized since different expenditures may arise from different qualities and amounts of goods and services consumed depending on the tourist´s preferences (Oliveira-Santos et al., 2015). However, as we also

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Silberman, 1985; Gokovali et al., 2007; Alegre et al., 2011). Regarding income, unfortunately we lack this variable in our dataset. We are aware of its critical importance from an economic point of view as the economic budget is a basic determinant of LOS. Given that we have information about age, education level and labor status and according to the well-known “Mincer earnings function” (Mincer, 1974) we proxy income with these three variables.

Fictitious variables were created for each categorical variable. Annex 1 presents the descriptive statistics of all the variables employed in our analysis, its acronym and its complete definition.

5. Model

There are several econometric models to analyze length of stay at a particular destination as a function of a set of explanatory variables. The choice of one or another depends on the assumptions about the dependent variable. If length of stay is considered to be a continuous variable, a linear regression model can be estimated by Ordinary Least Squares (OLS), under the assumption that its stochastic component has a normal distribution (Mak et al., 1977; Paul and Rimmawi, 1992). However, if the dependent variable is considered to be an integer value, then we can employ count data models (Smith, 1988; Hellerstein, 1991; Hellström, 2006). Here we assume the interest variable (total number of days at a given destination) as discrete and necessarily non-negative so that LOS . Its modeling as a function of a set of regressors should be done using count data models (Hellerstein and Mendelsohn, 1993), widely used in the literature (Palmer et al., 2007; Alegre et al., 2011; Salmasi et al., 2012; Brida et al. 2013; Alen et al., 2014; Nicolau et al., 2016).

One of the basic assumptions of the standard count data models is that both zeros and positive values of the dependent variable come from the same Data Generating Process for the whole sample. However, in our study case, it makes sense to think that there are two type of tourists: those who spend the night at the destination (proper tourists) and those who do not (hikers). Mullahy (1986) suggested that the effect of the different covariates on the probability of participation and on the intensity (number of positive counts) should not be restricted to be equal. To do so, it seems necessary to account for the excess proportion of zeros (corner solutions) in the sample, firstly separating participants from non-participants through a binary model and then in a second step model the number of days they stay – once they have previously decided to participate – using a count data model. There are two alternatives for this purpose: zero-inflated models8 and hurdle models9. The main difference between them is that a zero-inflated

control for a vast number of features, the daily price of lodging per person represents the price of the tourism good (each overnight stay) conditional on tourist´s preferences and characteristics. 8 See, e.g., Heilbron (1994) and Lambert (1992) on industrial processes and Greene (1994) on credit defaults. 9 This model is called “hurdle” model due to the fact that for observing a positive value of the dependent variable it is necessary to cross the first hurdle (Participation equation). See, e.g., Mullahy (1986), Rose et al.

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model assumes two processes as sources of zeros and combines a count distribution with a discrete point mass as a mixture, while the hurdle model separately handles zero observations and positive counts, where then a truncated-at-zero count distribution is used for the non-zero state. In our study case, hurdle model seems to be more convenient as the nature of true tourists (those who spend the night at the destination) and hikers (those who visit it but then return home in the same day) is clearly different. As we see it, individuals who visit Asturias participate or not in the overnight stay decision so that zeros can be regarded as genuine zeros.

The hurdle model can be constructed as follows:

a) Participation equation: we define a latent participation variable (di*) which is given by a set of explanatory variables Zi.

di*= Ziγ + ui (5)

where ui is a random error term which follows a logistic distribution, which results in the Logit model. The observation mechanism assigns d i=1 if di*>0 and di=0 otherwise. The probabilities for each alternative are given by:

P(di=1|Zi)= P(di*>0)=

P(di=0|Zi)= P(di*≤0)= 1 –

b) Intensity equation: the positive values of the dependent variable come from a Zero Truncated count data model10.

Hurdle model allows us to model length of stay as a two-step decision process which involves firstly deciding whether to stop over and, if so, the number of days. What is more, the covariates that models the decision to sleep at the destination can be different from those who appear in the truncated part.

The hurdle model is estimated by maximum likelihood. As both processes are assumed to be independent, the log likelihood function is the sum of the log L of the binary model defined in the first stage and the truncated count data model specified in the second one. Maximizing the hurdle log L is equivalent of maximizing both log L functions separately.

Iniatially, we assume that our dependent variable “total number of nights spent at the destination (denoted as LOS)” follows a Poisson distribution whose conditional mean and variance are given by:(2006) and Yen and Adamowicz (1994) on separately modeling participation and usage.10 Truncated count models are the discrete counterparts of truncated and censored models for continuous variables. Left-truncation at zero is the most common form (see Gurmu, 1991 and Gurmu and Trivedi, 1992).

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E(LOSi|Xi)= Var (LOSi|Xi)= = λi , (6)

where Xi is a vector of covariates which explain length of stay and, i = 1,...,N, indexes the N observations in the sample. We explicitly assume that there is a constant term in the model.

However, one of the main limitations of the Poisson regression model is that it imposes the conditional mean and variance to be equal (equidispersion property). This assumption is quite restrictive and commonly violated in applied work, generating the overdispersion problem where the conditional variance exceeds the conditional mean. Under these circumstances, inefficiencies arise11 and the inference based on standard errors is not valid (Wang and Famoye, 1997). Since observed data will almost always display pronounced overdispersion, analysts typically seek alternatives to the Poisson model. Mullany (1997) and Cameron and Trivedi (2009) indicate that the overdispersion problem arise due to the presence of unobserved heterogeneity, suggesting the need for a new specification in which the error term adequately represent unobservable or omitted variables. The econometric literature has proposed several alternatives to accommodate unobservable heterogeneity. The most commonly way when working with counts is to introduce multiplicative randomness (v) in the model so that the dependent variable follows a Negative Binomial (NB)12 model in the particular case that v ~ Gamma13 (1, α). The introduction of latent heterogeneity induces overdispersion while preserving the conditional mean as E(v)=1:

E(LOSi|Xi) = λi (13)Var(LOSi|Xi)= λi(1+ αλi) (14)

The Negative Binomial distribution can be seen as a more general case which collapses to the Poisson model if α=0. An overdispersion test consists on testing the null of α=0 (equidispersion) against the alternative α≠0.

Cameron and Trivedi (1986) suggested a reparametrization of the conditional variance so that it depends on a parameter P:

Var (LOS|Xi)= λi (1+αλiP-1) (15)

11 In the presence of the overdispersion problem, the Poisson model tends to underpredict the current frequency of zeros (Cameron and Trivedi, 2009) and inferences based on the standard errors are not valid because t-statistics are grossly inflated. Moreover, in data setting which involves truncation or censoring, overdispersion leads to inconsistency. 12 The regression model is developed in detail in a vast number of standard references such as Hausman et al., 1984; Cameron and Trivedi, 1986, 1998, 2005; Winkelmann, 1997 and Greene, 2008, so we will refer the reader to these authors for further details. 13 The choice of the gamma for the mixing distribution is quite arbitrary. There are other alternatives for modelling the underlying heterogeneity such as the lognormal and the inverse-Gaussian distribution (Willmot, 1987; Guo and Trivedi, 2002).

13

When P takes the values 1 and 2 we obtain the well-known NB1 and NB214 models (Gurmu and Trivedi, 1996). The former specifies a linear variance function whereas the latter considers a quadratic variance function. Both variants of the model are easily estimated by maximum likelihood. The main inconvenience of these models is that the conditional variance is imposed exogenously by the researcher. Cameron and Trivedi (1998) also note that other exponents apart from 1 and 2 in the conditional variance would be possible (p.73). In a discussion paper regarding the different functional forms of the Negative Binomial model, Greene (2008) suggests that as the NBP model estimates the parameter P endogenously, this model is likely to be the preferable alternative, which he then demonstrates empirically. Unlike the Poisson maximum likelihood estimator, NB2 is not consistent if the variance specification is incorrect. Although a quadratic conditional variance (NB2) often works well in empirical works and is the most commonly used, it may be badly specified in case the true P is higher than 2.

As for these reasons, in our empirical model we estimate the Negative Binomial P model for modelling the number of nights spent at the destination in the second step conditional on being a true tourist (participant). As LOS is necessarily a positive variable, it is necessary truncate the distribution of the dependent variable. Therefore, we model the intensity equation in terms of a Zero Truncated Negative Binomial Model (in the following ZTNBP).

LOS*|Xi ~ ZTNBP (18)

Estimation of the Zero Truncated Negative Binomial P model is easily conducted by maximum likelihood. As the log-likelihood function to maximize is not globally concave and there is no certainty of a unique maximum. For this reason, during the estimation the estimates of the truncated NB2 model were used as starting points.

6. Results

Table 1 shows the estimation results of the proposed Hurdle count data model for explaining LOS. The first column shows the estimates of the binary Logit model for the participation decision whereas the second one refer to the Zero Truncated Negative Binomial P model for modelling the intensity (ZTNBP). The coefficient estimates are computed using robust standard errors. The estimated value of the parameter P in the ZTNBP model is 3.71 and is statistically significant. This value is quite far from the imposed 1 and 2 corresponding to the NB1 and NB2 alternatives so it seems that the NBP model would be the best choice. Remember that the consistency of the estimates relies on the proper specification of the conditional variance.

14 We adopt the same terminology as Cameron and Trivedi (1998). The NB1 model is also known as the constant dispersion model while NB2 is called the mean dispersion model. For further details of these model see Cameron and Trivedi (2013, p.80-89).

14

(Insert Table 1 here)

The α parameter, which accounts for the overdispersion phenomenon in the conditional variance due to unobserved heterogeneity, is statistically significant at the 1 percent level, proving evidence of the better adequacy of the Negative Binomial model for modelling length of stay in our sample in comparison to the restrictive Poisson model (Cameron and Trivedi, 1998).

Starting with sociodemographic characteristics, gender (man) is not significant, neither in the participation nor in the intensity equations, as it is well-documented in the previous literature (Martínez-García and Raya, 2008; Barros et al., 2010; Machado, 2010; Gomes de Menezes et al., 2008; Brida et al., 2013)15. With reference to nationality, we differentiate between people who live in Spain and those who do not by the dummy variable foreign. Individuals from further origins display a lower likelihood of stopping over Asturias whereas, conditional on having decided to stay, they tend to stay for longer periods. This may imply that foreign are not really attracted to overnight stay in Asturias compared to Spaniards. Nonetheless, those who decide to spend at least a night there stay for more days than other tourists who live in Spain. We also find that age is positively related both with the likelihood of sleeping at Asturias and with the number of days spent, although in a decreasing rate according to the negative coefficient of the squared term16. This is in line with the results of Fleischer and Pizam (2002) who obtained a concave relationship between age and length of stay17. Compared to primary education (reference category), having completed secondary studies (secondary) reduces the probability of sleeping at Asturias meanwhile higher education (high) is not significant (only at 10 percent). The effect of education on length of stay is not clear in the literature. Some authors argue that there is a positive relation (Peypoch et al., 2012; Ferrer-Rosell et al., 2014) whereas others claim the opposite (Gokovali et al., 2007; Martínez-García and Raya, 2008; Oliveira-Santos et al., 2015). Ongoing with labor status, we set unemployed as the reference category. Retired people, professionals, self-employed and businessmen/businesswomen are found to exert greater positive significant effects on the probability of staying at Asturias for more than just a day. Curiously, employed and self-employed people spend less time at Asturias than their unemployed peers do. This may be due to time constraints. It is important to highlight here that these last three variables (age, educational level and labor status) may also account for income differences among individuals, so their effects on the dependent variable can be overestimated.

No significant effects are detected in the intensity equation with regard to the daily price of accommodation per person (daily_ac_exp). This variable acts here as the minimum 15 However, Barros and Machado (2010) and Peypoch et al. (2012) found significant higher length of stay for male tourists meanwhile Oliveira-Santos et al. (2015) obtained that females stayed for longer. 16 The assumption of a linear relation between age and tourism demand seems to be excessively simplistic and unrepresentative of real behavior.17 Yang et al. (2011) and Oliveira-Santos et al. (2015) came upon evidence of the opposite path.

15

cost for each overnight stay. One possible explanation of the non-significance of the daily price relies on the fact that, for some tourists, high prices may be interpreted as signals of high quality (Lee, 1993; Keane, 1996, 1997; Chapman and Wahlers, 1999; Moutinho, 2000; Alegre and Juaneda, 2006) so that they will not necessarily reduce their stay but even increase. Regarding the transportation mode, assuming a diminishing marginal utility for the destination attributes, Rugg (1973) indicates that those who come in slow means of transport will stay for longer than those who do in faster ones. The reason behind is that if the marginal utility decreases with the number of overnight stays and given that the marginal cost for each mode is constant, those who select a speed transport mode will stay for few days in order to compensate the high marginal cost with high marginal utility. However, as we argue in our theoretical framework, given a fixed period of time for travelling, the more the tourist spends on reaching to the destination, the less time he can then allocate to staying there. As our analysis is conditional on the remaining trip characteristics (distance), tourists travelling by plane will arrive sooner and, consequently, will stay for longer. Setting car as the reference category, this double reasoning justifies why travelling by plane is not statistically significant in the intensity equation. The trade-off between monetary and time savings nullifies the differences across transportation modes as both effects act in opposite directions. Nonetheless, travelling by train or by plane increase the likelihood of stopping over Asturias, ceteris paribus.

In the same vein, distance between tourists’ origin and the destination is another key factor in tourism demand (Bell and Leeworthy, 1990; Silberman, 1985). As Nicolau et al. (2016) state “the literature shows little consensus about the effects of this variable on length of stay at the destination”. On the one hand, Taylor and Knudson (1976) argue that distance reduces utility as entails physical, temporal and financial effort, acting as a dissuasive factor. For a fixed available time for the journey, the more time it requires to arrive at the destination because of being distant, the less time the tourist will then stay there. Moreover, self-drivers or train rides may prefer to stop at different places on the way (Prideaux and Carson, 2003; Zillinger, 2007; Larsen, 2011). On the other hand, Wolfe (1970, 1972) indicates that the friction derived from distance disappears after passing a certain threshold. As travel costs are fixed and independent of the number of days spent at the destination, longer stays allow tourists to spread the costs over a longer period (Nicolau et al., 2016). In this line, a positive relationship between distance and length of stay has been found by Gronau (1970), Paul and Rimmawi (1992) and Blaine et al. (1993). In our empirical estimations, distance is significant both in the participation and intensity equations, revealing a positive relationship between distance and length of stay, although in a decreasing path. The negative and significant coefficient of the squared term in both equations indicates that after a certain threshold, additional kilometers from the origin does not increase the stay but reduce it.

16

With regard to accommodation type, staying at a private dwelling (mainly second residences or at friends´ or relatives´, but it could also be a rented apartment) leads to the longest stays. Focusing on proper market-based accommodations, tourists who lodge at a rural house (rural), a hostel or a hotel stay for shorter periods than those who determine on campings (the reference category), everything else being equal. This is consistent with previous findings (Mak and Nishimura, 1979; Silberman, 1985; Alegre and Pou, 2006; Alegre et al., 2011), which agree to note that cheap lodgings are linked with longer stays. Another issue of interest if how the trip and the selected accommodation was booked. Being the tourist himself the omitted category, we find that hiring the trip though a club (club) reduces the probability of spending the night at the destination whilst booking it though a travel agency (travel_agency) increases the length of stay. In regards to accommodation reservation, tourists who delegate it to travel agencies (r_trav_agen) or friends or relatives (r_friends_fam) stay for longer compared with those whose company books it for them (reference category).

Party size and its composition matters for explaining length of stay as tourism consumption is usually a social activity where activities are mainly group-based (Thornton et al., 1997). In fact, only 7% of the tourists in our sample travel alone but in a group. Because of this, deciding how long to stay is not necessarily the result of the initial individual´s desires but a balance between his personal preferences and the other members of the group ones. The decisions are normally taken jointly rather than separately and strongly depend on the relative negotiating power and the group size. Compared to travelling with work mates (workmates), those who come to Asturias alone, in a couple, with the family or with friends display a higher probability of spending at least a night there. This implies that those who visit Asturias due to labor related issues have little probability of staying and tend to come back to their origin on the same day, possibly because of time availability constraints. Regarding the length of the stay, there are no statistical differences among travelling alone, with friends or with work-mates. However, those who come in a couple or with their family stay for longer according to their corresponding positive and significant estimated coefficients. As for party size, the more individuals in the travel group, the more probable is for them to end up spending the night at Asturias, although this variable is not significant in the intensity equation.

Those who only visit Asturias in the current trip stay for longer periods than those who do not, as it could be expected. Conditional on the remaining characteristics of the journey, if Asturias is the only region visited then tourists spend all their available time there.

Now we move to the core of our analysis: the effect of the degree of information about the destination on the overnight stay probability and on the length of stay. First-visitors (first) display a positive coefficient in both equations, implying that those who have never been to Asturias stay for longer in comparison with repeaters. This finding contradicts Lehto et al., (2004), Alegre and Pou (2006), Gomes de Menezes et al.,

17

(2008), Barros and Machado (2010), Alegre et al., (2011) and Wang et al., (2012), whose empirical results indicate that repeaters are associated with longer stays. They argue that familiarity with the destination reduces uncertainty so that they choose to spend longer periods there as a need for stability and continuity. Nonetheless, for our study case, the positive relation between visiting Asturias for the first time and the number of days spent there, ceteris paribus, may be explained in terms of variety and novelty seeking behavior (Nicolau et al., 2016). Tourists might be prone to visit Asturias extensively and become acquainted of the different monuments, natural areas and villages to visit so that, when they go there for the first time, they decide to remain the required time to meet the destination properly. Indeed, novelty seeking18 has been proved to be a crucial element which strongly influences the tourist´s decision-making (Crompton, 1979; Petrick, 2002). The number of visits along the year (num_year_vis) is negatively related with the likelihood of stopping over Asturias whereas is not significant in the intensity equation. Conversely, those who declare that a positive previous experience at the destination is the main reason of coming back (exp) exhibit positive coefficients in the participation and intensity equations. For some tourists, if their previous experiences at a particular destination have been satisfactory and have led them high levels of utility, a good risk reduction method is to return to the same destination and stay for long periods.

As for the effect of advertisements on length of stay, those who state that they have seen any kind of advertisement (advert) –no matter if it was on the internet, a brochure or a TV spot – show a higher tendency to stop over Asturias. Moreover, these individuals tend to stay for more days. This is not surprising given that the experience nature of tourism induces people to build indirect experience from advertising contents such as texts, images or videos (Park and Nicolau, 2015). In fact, tourism advertising is considered one of the main external information and communication source as it both consciously and unconsciously affect consumer decision-making (Wicks and Schuett, 1991; Woodside and King, 2001; Martin 2010). In the same way, recommendation of the destination from friends or relatives (recommend) also increases the probability of spending the night and the number of days the tourist stays, ceteris paribus. This result seems to provide more evidence on the heavy reliance on friends and relatives opinions and advices regarding the characteristics of the destination tourists have (Fodness and Murray, 1997).

Given that coefficient estimates are not easy to interpret19, Table 3 reports the average marginal effects on the probability of stopping over Asturias (1) and on the conditional

18 It refers to the “inclination of consumers to shift from a choice they made on the most recent occasion” (Ratner et al., 1999). It also includes boredom alleviation, surprise, thrill and adventure (Lee and Crompton, 1992). 19 In the participation equation (Logit model), each coefficient indicates the log of the odds ratio, being this the ratio of the probability of observing a positive value of LOS divided by the probability of the opposite. In the intensity equation (ZTNBP model), each coefficient indicates the change in the log of LOS if the regressor Xk

changes in one unit.

18

expected number of nights spent at the destination for those who overnight stay (2) for the variables first, num_year_vis, exp, advert, and recommend.

Variable (1) (2)first 0.0408*** 0.0845***num_year_vis -0.0017*** 0.0041exp 0.0515*** 0.1912***advert 0.0196*** 0.0292**recommend 0.0302*** 0.1620***

Table 3. - Average marginal effects on the participation and intensityequations in the ZTNBP model.*** p<0.01, ** p<0.05, * p<0.1

Having seen any type of advertisement slightly increases the expected number of overnight stays in 0.029 units. Previous experience and recommendation as the main reason for visiting Asturias increase the stay in 0.19 and 0.16 nights respectively. First visitors display 0.040 higher probability of stopping over and a expected stay of 0.084 nights higher than repeaters. For a one-unit change in the number of visits along the year the probability of becoming a proper tourist is on average 0.0017 units lower. From the average marginal effects over the sample reported above, we can conclude that previous experience (exp) is the information source which give rise to the highest probability of an overnight stay and has the major impact on the length of stay, keeping everything else constant. This result implies that there is no better source of information than having a positive experience in the past.

Ongoing with the purpose of the trip, we only differentiate between visiting Asturias for leisure and entertainment20 and the rest of possibilities21 through the dummy variable leisure. Contrary to what could be expected, leisure tourists show a higher probability of being a hiker than a tourist, ceteris paribus. Besides, despite some authors have found that this group is the one with longer expected stays (Gomes de Menezes et al., 2008; Hellstrom, 2006), this variable is not significant in the intensity equation.

The underlying motivations to visit the selected destination are also important elements to take into account when explaining the length of the stay. They act as push factors which lead to the realization of the tourist travel (Moutinho, 1987; Gartner, 1993; Sirakaya et al., 1996; Kim and Lee, 2002). Our empirical estimations evidence that the appealing attributes Asturias provide such as tranquility (tranqui), the natural environment (natural) and its oceanic weather – with abundant rainfalls and moderate temperatures – (climate) positively influence the probability of sleeping at Asturias and the length of stay. Surprisingly, Asturian gastronomy (gastrono) is not significant in spite of its good reputation. Besides, those who point out that the main attraction for 20 We consider a tourist comes for leisure purposes if he declares the main reason of the current visit is one of the following: i) holidays/leisure, ii) religious issues (peregrination), iii) health, iv) sport; v) purchases. 21 Basically, visiting friends or relatives or something related to their job or studies (a course, a conference, a meeting, etc.).

19

travelling to the destination was novelty (novelty) have a higher probability of spending the night at Asturias and exhibit longer stays. In the same way, performing active tourism22 (act_tour) activities have a positive impact both on the decision to overnight stay and on the number of days to remain at the destination. This suggests that this kind of tourists are an important segment for the tourism market as their interest in carrying out different outdoor activities require time and, consequently, imply longer stays.

Finally, in our regression framework we also control for temporal and geographical variables. The first ones mainly control for income differences throughout the business cycle23 meanwhile the second ones refer to differences in preferences across the territory. Everything else being equal and in comparison with 2010, tourists display lower stays during the period 2011-2014. However, there are no statistical differences between 2010 and the years 2015 and 2016. This may be associated with the fact that during the economic crisis people faced more economic constraints when travelling (Smeral and Song, 2015). Even if they did not, uncertainties and fears about the near future and the labor stability might have urged them to spend more on necessities and less on luxuries (tourism) in order to save money (Gunter and Smeral, 2016). As for seasonal effects along the year, we included two dummies for the second and third trimester, being the first the omitted one. The estimations indicate that people both exhibit higher likelihood of stopping over and longer stays in the second and third trimesters rather than in the first, ceteris paribus. Martínez-García and Raya (2008), Ferrer-Rosell et al. (2014) and Oliveira-Santos (2015), among others, have also indicated that the summer season is the preferred one for tourism. Low temperatures and bad weather discourages people to travel. From its part, weekend tourists tend to stay for shorter, suggesting that this kind of tourists may just stay for a couple of days because of time availability. As for territorial preferences, visitors exhibit a higher probability of sleeping at any accommodation of the central area rather than in the east or the west. However, the opposite pattern is observed when it comes to analyze the number of overnight stays. In this case, everything else being equal, tourists in the central area stay for shorter periods than those who choose the east or the west. This may imply that the central area of Asturias is related with short visits whereas the peripheral zones of the region lead to much more sustained stays.

In sum, as far as the results of the model are concerned, we observe that the different explanatory variables considered do not have the same effect on the probability of stopping over Asturias (participation equation) and on the number of days spent there (intensity equation). This highlights the relevance of distinguishing between tourists and hikers and its determinants, being this one of the novel aspects of this paper. Furthermore, our regression framework controls for a wide range of sources of

22 It is a style or philosophy of leisure travel that combines elements of adventure, nature and cultural tourism with an emphasis on low-impact and sustainable tourism and the use of local guides. It involves a wide range of activities such as trekking, horse routes, climbing and cycling, among others. 23 During the different phases of the business cycle, different income levels and prices may occur.

20

observable heterogeneity among tourists, allowing us to properly isolate the effect of the different information sources about the destination on the length of stay.

7. Conclusion

In this paper we model the number of overnight stays in a destination (Asturias) for a sample of national and international visitors in terms of a hurdle count data model. Our main interest is to analyse the effect that the information about the destination has on the number of days to stay there, once having controlled for a wide set of characteristics of the individual and the trip itself. In our empirical model, we take into account different sources of observable heterogeneity so that we can isolate the effect that previous experience, recommendation from friends and relatives and advertising has on length of stay, ceteris paribus. From a methodological point of view, the novel aspect of our approach is the fact that we first separate hikers from proper tourists using a binary Logit model and then model the number of overnight stays using a Zero Truncated Negative Binomial P model which endogenously estimated the parameter P of the conditional variance.

Our empirical results show that those who have never been to Asturias stay for longer in comparison with repeaters. On the other hand, those who state that they have seen any kind of advertisement (advert) –no matter if it was on the internet, a brochure or a TV spot – show a higher tendency to stop over Asturias. Moreover, these individuals tend to stay for more days. In the same way, recommendation of the destination from friends or relatives (recommend) also increases the probability of spending the night and the number of days the tourist stays, ceteris paribus. Finally, previous experience (exp) is the information source which give rise to the highest probability of an overnight stay and has the major impact on the length of stay.

[Further conclusions are still under development]

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Participation IntensityIndependent variables Logit

Truncated NBP

 y11 0.6070*** -0.0953***

(0.1280) (0.0209)y12 0.1440 -0.0954***

(0.1130) (0.0220)y13 0.1140 -0.1080***

(0.1140) (0.0253)y14 0.3130*** -0.1070***

(0.1160) (0.0227)y15 0.0808 0.0024

(0.1140) (0.0227)y16 0.4590*** -0.0239

(0.1340) (0.0271)t2 0.4920*** 0.3630***

(0.0800) (0.0147)t3 0.5610*** 0.0885***

(0.0834) (0.0167)daily_ac_exp -3.93e-05

(0.0003)distance 0.0008** 0.0003***

(0.0003) (4.87e-05)distance^2 -7.43e-08* -3.20e-08***

(4.41e-08) (5.67e-09)weekend -0.0143 -0.181*0**

(0.0720) (0.0124)man -0.0382 -0.0033

(0.0646) (0.0121)age 0.0449** 0.0114**

(0.0198) (0.0044)age^2 -0.0005** -6.18e-05

(0.0002) (5.26e-05)west -1.6970*** 0.1180***

(0.0917) (0.0178)centralr 0.0522 0.0308

(0.2010) (0.0292)east_inner -1.1120*** 0.1170***

(0.0990) (0.0181)east_coast -0.6960*** 0.1470***

(0.1010) (0.0185)alone 1.8260*** 0.1510

(0.4850) (0.0960)couple 1.4150*** 0.1900**

(0.4020) (0.0782)family 1.1220*** 0.2750***

(0.3960) (0.0767)

friends 0.8870** 0.1170(0.3920) (0.0766)

party_size 0.0261*** 0.0014(0.0084) (0.0014)

act_tour 1.7120*** 0.1570***(0.2420) (0.0218)

num_year_vis -0.0286*** 0.0041(0.0046) (0.0027)

bus -0.4410 -0.0274(0.3560) (0.0436)

train 0.8870** 0.1270**(0.4400) (0.0495)

plane 0.6400*** 0.0277(0.2390) (0.0313)

travel_agency 0.3280 0.0662**(0.3000) (0.0317)

club -0.9580** -0.0617(0.3900) (0.0734)

company 0.2360 -0.1250*(0.5550) (0.0691)

first 0.6520*** 0.0845***(0.0938) (0.0152)

professional 0.7060*** -0.0727(0.2620) (0.0622)

housewife 0.0957 -0.0040(0.2340) (0.0664)

student 0.3260 -0.0825(0.2240) (0.0630)

manager 1.0820 -0.1340*(0.7450) (0.0812)

businessman 1.0130*** -0.0877(0.3210) (0.0624)

self_empl 0.4330** -0.1430**(0.2140) (0.059)

employed 0.3280* -0.1500***(0.1910) (0.0566)

civil_servant 0.3650* -0.0677(0.2010) (0.0572)

retired 0.5760** -0.0233(0.2600) (0.0690)

secondary -0.3260*** 0.0071(0.0929) (0.0216)

high 0.1800* 0.0223(0.1070) (0.0252)

foreign -0.4590*** 0.1510***(0.1500) (0.0338)

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novelty 0.6940*** 0.1530***(0.1100) (0.0333)

recommend 0.4840*** 0.1620***(0.1260) (0.0355)

exp 0.8240*** 0.1910***(0.1030) (0.0339)

tranqui 0.6880** 0.2590***(0.3200) (0.0715)

climate 0.8610** 0.2970***(0.4350) (0.0751)

natural 0.8180*** 0.1990***(0.1210) (0.0364)

gastrono 0.1470 0.0898(0.2270) (0.0980)

leisure -0.4280*** 0.0193(0.1440) (0.0505)

advert 0.3140*** 0.0292**(0.0709) (0.0128)

hotel -0.3450***(0.0301)

hostel -0.0812**(0.0370)

rural -0.1700***(0.0312)

private 0.3740***(0.0621)

only_ast 0.1070***(0.0188)

r_phone 0.0414(0.0297)

r_person -0.00524(0.0395)

r_trav_agen 0.0814**(0.0354)

r_internet 0.0159(0.0291)

r_friends_fam 0.1240**(0.0542)

Constant -0.7420 0.5630***(0.6410) (0.1520)

ln alpha -5.4160***(0.4240)

alpha 0.0044***(0.0013)

P 3.7140***(0.2240)

Log L -3866.77 -26251.769Pseudo R^2 0.1497 0.0783Observations 16,887 12,594

Table 1.- Estimated coefficients of the hurdle model (Robust standard errors in parentheses).

*** p<0.01, ** p<0.05, * p<0.1

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ANNEX 1. - Descriptive statistics

Type of variable Variables N Mean Standard deviation Min Max Definition

TYPE OF TRAVELER tourist 33133 .7090 .4542 0 1 The individual sleeps in the destination at least 1 night

TYPE OF TRAVELER hiker 33133 .2909 .4542 0 1 The individual does not spend the night at the destination

DEPENDENT VARIABLE LOS 33133 3.324 3.987 0 30 Number of overnight stays

PRICES daily_ac_exp 33133 21.714 22.787 0 575 Daily expenditure per person (€) on accommodation

SOCIODEMOGRAPHICS age 30329 39.828 12.266 18 91 Age

SOCIODEMOGRAPHICS manager 32847 .0054 .0736 0 1 Manager

SOCIODEMOGRAPHICS civil_servant 32847 .1598 .3664 0 1 Civil servant

SOCIODEMOGRAPHICS businessman 32847 .0284 .1662 0 1 Business man/woman

SOCIODEMOGRAPHICS employed 32847 .4449 .4969 0 1 Employed

SOCIODEMOGRAPHICS self-empl 32847 .0714 .2575 0 1 Self-employed

SOCIODEMOGRAPHICS student 32847 .0960 .2946 0 1 Student

SOCIODEMOGRAPHICS housewife 32847 .0539 .2258 0 1 Housewife/ househusband

SOCIODEMOGRAPHICS unempl 32847 .0250 .1562 0 1 Unemployed

SOCIODEMOGRAPHICS retired 32847 .0590 .2357 0 1 Retired

SOCIODEMOGRAPHICS professional 32847 .0520 .2220 0 1 Professional

SOCIODEMOGRAPHICS primary 28496 .0825 .2752 0 1 Primary studies

SOCIODEMOGRAPHICS secondary 28496 .3313 .4648 0 1 Secondary studies

SOCIODEMOGRAPHICS high 28496 .5862 .4004 0 1 Higher education

SOCIODEMOGRAPHICS man 33133 .5364 .4986 0 1 Man

SOCIODEMOGRAPHICS foreign 33133 .0583 .2343 0 1 The individual lives in another country

GEOGRAPHICAL distance 33133 498.00 1076.6 0 17713 Distance between origin and Oviedo (km)

GEOGRAPHICAL west 33133 .1410 .3480 0 1 West area

GEOGRAPHICAL centraly 33133 .4287 .4949 0 1 Central area (Oviedo-Gijon-Avilés)

GEOGRAPHICAL centralr 33133 .1002 .3003 0 1 The rest of the central area

GEOGRAPHICAL east_coast 33133 .1791 .3834 0 1 East coast

GEOGRAPHICAL east_inner 33133 .1507 .3578 0 1 East inner area

INFO recommend 21228 .1003 .3004 0 1 The individual visits Asturias due to recommendation

INFO exp 21228 .2429 .4288 0 1 The individual visits Asturias due to previous experience

INFO advert 29893 .3728 .4835 0 1 The individual has seen advertising of Asturias.

INFO first 33133 .2834 .4506 0 1 First time the individual visits Asturias

INFO num_year_vis 33133 .5904 4.847 0 100 Number of previous visits to Asturias.

ATTRIBUTES natural 21228 .1281 .3342 0 1The individual visits Asturias due to its natural environment

ATTRIBUTES heritage 21228 .0154 .1231 0 1 The individual visits Asturias due to its heritage

ATTRIBUTES tranqui 21228 .0111 .1050 0 1 The individual visits Asturias looking for tranquility

ATTRIBUTES gastrono 21228 .0211 .1437 0 1 The individual visits Asturias due to its gastronomy

ATTRIBUTES climate 21228 .0086 .0926 0 1 The individual visits Asturias due to its climate

TEMPORAL t1 33133 .2084 .4061 0 1 First trimester (January-February-March-April)

TEMPORAL t2 33133 .4661 .4988 0 1 Second trimester (May-June-July-August)

TEMPORAL t3 33133 .3168 .4652 0 1Third trimester (September-October-November-December)

TEMPORAL weekend 33133 .5815 .4933 0 1 Weekend

TRANSPORT car 23930 .8209 .3834 0 1 Car

TRANSPORT bus 23930 .0277 .1643 0 1 Bus

TRANSPORT train 23930 .0293 .1687 0 1 Train

31

TRANSPORT plane 23930 .0815 .2736 0 1 Plane

TRIP alone 33133 .0711 .2571 0 1 The individual comes alone

TRIP couple 33133 .4969 .4999 0 1 The individual comes in a couple

TRIP family 33133 .2361 .4247 0 1 The individual comes with his family

TRIP friends 33133 .1561 .3629 0 1 The individual comes with friends

TRIP group 33133 .0243 .1541 0 1 The individual comes in a group

TRIP workmates 33133 .0152 .1225 0 1 The individual comes with workmates

TRIP only_ast 23492 .8563 .3507 0 1 The individual only visits Asturias.

TRIP party_size 33133 3.777 7.077 1 250 Party size

TRIP act_tour 33133 .0589 .2355 0 1 The individual makes active tourism

TRIP himself 33133 .9221 .2679 0 1 The individual organized the trip himself

TRIP company 33133 .0280 .1652 0 1 The company organized the trip

TRIP travel_agency 33133 .0357 .1855 0 1 The trip was organized by a travel agency

TRIP club 33133 .0140 .1176 0 1 The trip was organized by a club

TRIP leisure 33133 .8626 .3442 0 1The individual comes for leisure, sport, holidays, religious purposes or purchases.

TRIP novelty 21228 .3905 .4878 0 1 The individual visits Asturias due to novelty

TRIP r_phone 19031 .3350 .4720 0 1 The individual has booked the accommodation by phone

TRIP r_person 19031 .0785 .2690 0 1The individual has booked the accommodation personally

TRIP r_trav_agen 19031 .0703 .2556 0 1The individual has booked the accommodation through a travel agency

TRIP r_internet 19031 .3970 .4893 0 1The individual has booked the accommodation through the internet

TRIP r_company 19031 .0365 .1877 0 1The accommodation has been booked by the individual´s company.

TRIP r_friends_fam 19031 .0151 .1220 0 1 Friends or relative have booked the accommodation.

TRIP hotel 33133 .4296 .4950 0 1 The individual stays at a hotel

TRIP rural 33133 .0890 .2847 0 1 The individual stays at a rural house

TRIP hostel 33133 .0276 .1640 0 1 The individual stays at a hostel

TRIP camping 33133 .0507 .2194 0 1 The individual stays at a camping

TRIP private 33133 .1120 .3153 0 1 The individual stays at a private accommodation

32

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