Tourism economics

Panasiuk, Aleksander (Ed.) Book Tourism economics Economies Provided in Cooperation with: MDPI – Multidisciplinary Digital Publishing Institute, Basel Suggested Citation: Panasiuk, Aleksander (Ed.) (2023) : Tourism economics, Economies, ISBN 978-3-0365-6046-5, MDPI, Basel, https://doi.org/10.3390/books978-3-0365-6046-5 This Version is available at: https://hdl.handle.net/10419/279848 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by-nc-nd/4.0/ Edited by Tourism Economics Aleksander Panasiuk Printed Edition of the Special Issue Published in Economies www.mdpi.com/journal/economies Tourism Economics Tourism Economics Editor Aleksander Panasiuk MDPI •Basel •Beijing •Wuhan •Barcelona •Belgrade •Manchester •Tokyo •Cluj •Tianjin Editor Aleksander Panasiuk Jagiellonian University in Krakow Institute of Entrepreneurship Poland Editorial Office MDPI St. Alban-Anlage 66 4052 Basel, Switzerland This is a reprint of articles from the Special Issue published online in the open access journal Economies (ISSN 2227-7099) (available at: https://www.mdpi.com/journal/economies/special issues/Tourism 1 Economics). For citation purposes, cite each article independently as indicated on the article page online and as indicated below: LastName, A.A.; LastName, B.B.; LastName, C.C. Article Title. Journal Name Year,Volume Number, Page Range. ISBN 978-3-0365-6045-8 (Hbk) ISBN 978-3-0365-6046-5 (PDF) © 2023 by the authors. Articles in this book are Open Access and distributed under the Creative Commons Attribution (CC BY) license, which allows users to download, copy and build upon published articles, as long as the author and publisher are properly credited, which ensures maximum dissemination and a wider impact of our publications. The book as a whole is distributed by MDPI under the terms and conditions of the Creative Commons license CC BY-NC-ND. Contents About the Editor ..............................................vii Preface to “Tourism Economics” ..................................... ix Leiv Opstad, Randi Hammervold and Johannes Idsø The Influence of Income and Currency Changes on Tourist Inflow to Norwegian Campsites: The Case of Swedish and German Visitors Reprinted from: Economies 2021,9, 104, doi:10.3390/economies9030104 . . . . . . . . . . . . . . . 1 Quang Hai Nguyen Impact of Investment in Tourism Infrastructure Development on Attracting International Visitors: A Nonlinear Panel ARDL Approach Using Vietnam’s Data Reprinted from: Economies 2021,9, 131, doi:10.3390/economies9030131 . . . . . . . . . . . . . . . 15 Joanna Zieli´nska-Szczepkowska What Are the Needs of Senior Tourists? Evidence from Remote Regions of Europe Reprinted from: Economies 2021,9, 148, doi:10.3390/economies9040148 . . . . . . . . . . . . . . . 35 Miguel ´ Angel Solano-S´anchez, Jos´e Ant´onio C. Santos, Margarida Cust´odio Santos and Manuel ´ Angel Fern´andez-G´amez Holiday Rentals in Cultural Tourism Destinations: A Comparison of Booking.com-Based Daily Rate Estimation for Seville and Porto Reprinted from: Economies 2021,9, 157, doi:10.3390/economies9040157 . . . . . . . . . . . . . . . 57 Robin Valenta, Johannes Idsø and Leiv Opstad Evidence of a Threshold Size for Norwegian Campsites and Its Dynamic Growth Process Implications—Does Gibrat’s Law Hold? Reprinted from: Economies 2021,9, 175, doi:10.3390/economies9040175 . . . . . . . . . . . . . . . 73 Lenka Cervova and Jitka Vavrova Customer-Based Brand Equity for a Tourism Destination: The Case of Croatia Reprinted from: Economies 2021,9, 178, doi:10.3390/economies9040178 . . . . . . . . . . . . . . . 87 Kiara Riojas-D´ıaz, Ricardo Jaramillo-Romero, F´atima Calder´on-Vargas and David Asmat-Campos Sustainable Tourism and Renewable Energy’s Potential: A Local Development Proposal for the La Florida Community, Huaral, Peru Reprinted from: Economies 2022,10, 47, doi:10.3390/economies10020047 . . . . . . . . . . . . . . 99 Tzong-Shyuan Chen and Chaang-Iuan Ho The Application of a Two-Stage Decision Model to Analyze Tourist Behavior in Accommodation Reprinted from: Economies 2022,10, 71, doi:10.3390/economies10040071 . . . . . . . . . . . . . . 117 Giovanni Ruggieri, Marco Platania and Julian Zarb Island Development Model Specialisation: A Panel Data Analysis Comparing Evolutionary Tourism Model, Industrial to Community- Based (2010–2019) Reprinted from: Economies 2022,10, 208, doi:10.3390/economies10090208 . . . . . . . . . . . . . . 139 Elzbieta Szymanska Problems of Tourist Mobility in Remote Areas of Natural Value—The Case of the Hajnowka Poviat in Poland and the Zaoneshye Region in Russia Reprinted from: Economies 2022,10, 212, doi:10.3390/economies10090212 . . . . . . . . . . . . . . 153 v About the Editor Aleksander Panasiuk Full professor at the Faculty of Management and Social Communication of Jagiellonian University in Krakow (Poland) (2018–), director of the Institute of Entrepreneurship (2020–), head of the Department of Management in Tourism and Sport (2020–). Member of seven editorial and program committees of scientific journals. Supervisor in 19 completed doctoral dissertations. PhD in economics (1992), habilitated doctor in economics (1997), titular professor (2015). From 1988–2018, he was an employee of the Faculty of Management and Economics of Services at the University of Szczecin (Poland), vice-dean for student affairs (1999–2005), dean of the Faculty (2005–2008) and head of the Department of Tourism Management (1999–2018). Main areas of research interest: service economics, service management, tourism economics, tourism management, service marketing, socio-economic policy, regional policy, quality management, leisure studies. Author of about 400 scientific and research publications, including about 30 scientific monographs (original and co-authored). Member of the Scientific Council of the Institute of Communications - National Research Institute in Warsaw (2020–). Member of the Board of the Branch of the Polish Economic Society in Szczecin (2020–), from 2015–2020, he was president of the Branch, from 2005–2015, he was vice president of the Branch. Member of the Presidium of the National Board of the Polish Economic Society in Warsaw (2015–2021, 2021–). Founding member of the Euro-Asia Tourism Studies Association (EATSA), member of the Association of Tourism Experts, member of the Association of Collective Copyright Management of Authors of Scientific and Technical Works “KOPIPOL”. https://przedsiebiorczosc.uj.edu.pl/aleksander-panasiuk https://scholar.google.com/citations?user=VrGKG5YAAAAJ&hl=pl https://www.researchgate.net/profile/Aleksander-Panasiuk https://sciprofiles.com/profile/719481 vii Economies 2021,9, 104 Foreign tourists visiting Norway must contend with high prices of services and goods compared with most other countries Dybedal et al. (2003). However, this effect has diminished over the last several years due to the depreciation of the currency and its substantial short-term fluctuation. The question is: What impact has this had on the foreign demand for trips to Norwegian camps? Since visitors calculate the costs measured in their own currency, changes in rates will have a direct impact on their budgets. The decision to travel is made before one goes on holiday Stabler et al. (2009). In the analysis of the tourist market, one must take this into account. Therefore, it is more accurate to use the exchange rate at the time of the decision and not at the time of travel. There is much discussion in the literature about the length of this time lag. An examination of bookings at Norwegian hotels showed that the average difference between booking and arrival was about four months. 2. Literature Review 2.1. International Tourism and Income Previous research suggested that income, prices, and the currency rate have a considerable effect on tourist inflow, but there is significant variation in the degree of influence between variables. Many factors can explain this. According to Peng et al. (2015), there is a large spread in the income elasticity depending on the destination, country of origin, season, and type of holiday. Income in the origin country seems to be a dominant variable in explaining the level of international tourism. Sanchez-Rivero and Pulido-Fernández (2020) suggested that the average income elasticity of visitors crossing international borders is substantially higher than 1.0 (their estimate was just over 2.0). If this is the case, an increase of one percent will cause the demand for tourist travel to rise far beyond one percent. Economic theory refers to this as a luxury good. There may be wide gaps for the same destination depending on the country of origin, as well as large differences within the same country of origin depending on the destination. Due to a lower income level, income elasticity tends to be higher for visitors from countries with low GDP per capita compared to countries with high GDP per capita. There is limited holiday time, and the choice of destination can be sensitive to changes in some important variables. Crouch (1996) reported an income elasticity of 1.5 for international tourism. Recent research reports suggest that income elasticity is between 1.0 and 2.0, but with significant variations Kumar and Kumar (2020) ; Ongan et al. (2017); Sanchez-Rivero and Pulido-Fernández (2020); however, Jensen (1998) pointed out that the income elasticity of foreign visitors is considerably higher than that of domestic tourist visitors. 2.2. International Tourism and Prices Inclusive of Exchange Rates The pricing mechanism applies to international tourism. If it becomes more expensive to visit a country, then fewer will travel there. Previous surveys reported a large gap in this effect, depending on the case studied Peng et al. (2015). Peng et al. (2015) found an overall average price elasticity of − 1.3 Peng et al. (2015), while Kumar and Kumar (2020) suggested a significantly lower value (around −0.8). The exchange rate might be a key factor in the demand for tourism. Garín-Muñoza and Montero-Martín (2007) estimated the exchange rate elasticity (of a stronger domestic currency) for international travelers to the Belearic Islands to be − 0.76 for the same year and − 1.65 for a one-year lag. A limitation of this research was the use of annual data. Hence, they did not capture fluctuations during the year. Other researchers have reported that a one percent depreciation of the national currency increased the foreign tourist inflow by six percent in Turkey (Agiomirgianakis et al. 2014, 2015) and five percent in Iceland Rannversdóttir and Jóhannsdóttir (2019) . There is a wide range of currency elasticities depending on the country of origin and the destination. In a study of Norwegian hotels, Aalen et al. (2019) estimated the exchange rate elasticity to be around − 1.0. In the study of Xie and Tveterås (2020), the elasticity was as high as − 6.5 for Chinese tourists and only − 0.4 for Japanese tourists. For German visitors, the 4 Economies 2021,9, 104 estimate was − 1.5. Due to the depreciation of the Norwegian currency, Chinese travelers perceive prices to be attractive compared to competing places, and this has resulted in a sharp increase in visitors. However, there is no corresponding effect for Japanese visitors. Although the differences were not as large, Ongan et al. (2017) also reported significant differences depending on the country of origin of European tourists who visited the United States. Vojtko et al. (2018) reported that the foreign tourist response to a one percent appreciation of the national currency varied between 0.22 and 3.26 percent in the Czech Republic and Croatia. Many visitors respond to a higher national currency value by decreasing the lengths of their stay and using less expensive accommodation Fleischer and Rivlin (2009). This effect is more prevalent in high-cost countries. Steller (2017) reported an exchange rate elasticity (of a stronger foreign currency) of 0.74 with a lag of 3–5 months for foreigners visiting Switzerland. 2.3. Neighboring Countries Neighboring countries might compete for the same visitors, or there might be complementarity. Kadir and Abd Karim (2009) reported complementarity among Malaysia, Thailand, and the Philippines for British and American tourist flow. Tourists tend to visit all three countries on the same trip, similar to a travel package. Patsouratis et al. (2005) investigated tourism competition among Mediterranean countries. Greece, Portugal, and Spain offer quite similar products (beaches, sun, sea, etc.), and thus, they are competing tourist destinations. Greece and Spain are major competitors for British visitors. Increased prices in Spain will increase the demand for visiting Greece. Since it is more expensive to stay in Spain, many travelers will replace Spain with Greece. Añaña et al. (2018) identified significant competition between destinations, where the price level is just one of many factors that influence the choices that travelers make. 2.4. The Demand for Campsites The international literature includes many articles on camping tourism Ram and Hall (2020); Rogerson and Rogerson (2020); however, few researchers have specifically explored the demand elasticity (income and price) for overnight stays at campsites. There are some studies on the demand for recreation Rosenberger and Stanley (2010). Although it is connected, it is not the same as overnight stays at campsites. Substitution occurs among different kinds of accommodations. Some countries have experienced reduced camping frequencies over the last decade Marin-Pantelescu (2015). Many customers whose income increases prefer a higher standard of accommodation and might replace campsites with huts and hotels. Therefore, the income elasticity of campsites might be lower than that of hotels. Researchers such as Barnes (1996) and Crawford (2007) reported an inelastic income elasticity Barnes (1996); Crawford (2007). Higher income has a marginal impact on the demand. Due to the substitution effect, Brox and Kumar (1997) suggested a negative income elasticity. The demand for a commodity that is regarded as inferior will fall when income increases. On the other hand, campsites can improve their standards to retain more guests and make these locations more attractive by improving quality and comfort. For this purpose, one needs to invest in infrastructure Grzinic et al. (2010) . Overnight stays at camping sites are sensitive to price changes. Beaman et al. (1991) reported a price elasticity of around −1.0 for staying at campsites. 3. Hypothesis Based on economic theory and previous research, we postulated the following hypotheses : Hypothesis 1 (H1). A decrease in the rate of the Norwegian currency leads to more foreign camping tourists in Norway; Hypothesis 2 (H2). The exchange rate of the euro is related to the inflow of Swedish camping tourists in Norway; 5 Economies 2021,9, 104 Hypothesis 3 (H3). The exchange rate of the Swedish currency is connected to German camping tourists in Norway; Hypothesis 4 (H4). There is a connection between income level in the origin country and overnight stays at campsites in Norway. The analysis was based on visitors from Sweden and Germany. A fall in the rate of the Norwegian exchange rate means that Norwegians have to pay more for the euro and Swedish krone. This makes it less expensive for Swedes and Germans to visit Norway. Our assumption (H1) was that this leads to greater tourist inflow to campsites in Norway. We assumed that Norway competes with neighboring countries to attract tourists and that German tourists often decide to head north, but are unsure whether to holiday in Norway or Sweden. If it is less expensive in Sweden due to the fluctuation of the exchange rates, more Germans may prefer to stay in Sweden instead of Norway (H3). A fall in the euro means that it will be less expensive for Swedes to stay in neighboring countries such as Finland and Denmark (the Danish currency is connected to the euro) instead of Norway (H2). On the other hand, a stronger Swedish currency can lead to an increase in Swedes traveling abroad. Therefore, one must account for the possibility of complementarity. It is not clear how an increase in income affects demand for overnight stays at campsites (H4), and the research results are mixed. Some researchers suggested that demand is unaffected by income Crawford (2007), and others proposed that income elasticity is negative (inferior commodity) Brox and Kumar (1997). It is also possible that it is a common good with an income elasticity equal to 1.0 or greater for visiting tourists. Several researchers have pointed out that the income elasticity of foreign tourism is high (see Agiomirgianakis et al. (2014)). This may also apply to camping tourists. 4. Methodology 4.1. Data The data on overnight stays at campsites were provided by Statistics Norway (SSB). In the dataset from SSB, it was possible to analyze countries of origin and visits by month and year. The Norwegian central bank (Norges Bank) provides an ongoing overview of exchange rates, and we took advantage of this information in our analysis. Figures for the consumer price index (CPI) and gross domestic product (GDP) were from data published by the World Bank. The sample period was from 2000 to 2019. In this study, the focus was on only two visiting countries, Sweden and Germany. Sweden is a neighbor of Norway, and the country has its own currency (SEK). Germany is the most important visiting country (see Figure 2). 4.2. The Models Based on the analysis of Stabler et al. (2009), the assumption was that the use of campsites in Norway (V) depends on the exchange rate, GDP, and season. V=f(Exchange Rate, GDP, seasons)(1) Some researchers have analyzed the effect of changes in the exchange rate by using effective exchange rates, which refers to nominal values adjusted for differences in inflation rates among countries Lee et al. (1996). Especially in the long run, it is more accurate to take into account changes in the consumer price index in different countries Stabler et al. (2009) ; Syriopoulos (1996). In this study, the effective exchange rate (EER) was used: EERi=CPIi CPIj·ERji =CPIi CPIj ·1 ERij =CPIi CPIj ·ERji (2) 6 Economies 2021,9, 104 The logarithmic transformation of EERiis: ln(EERi) = ln(CPIi)−ln(CPIj) + ln(ERji)(3) where CPI is the consumer price index, ER is the nominal exchange rate, i denotes country i , j denotes country j , and ERji is the nominal exchange rate for country j relative to country i . The effective exchange for the country of origin is the consumption price of the origin country divided by the consumption price at the destination, and this price level is multiplied by the exchange rate between the destination country and country of origin. This can be written as the rate between the consumption price at the origin and destination countries multiplied by the exchange rate between origin and destination countries. Tourist inflow is a dynamic process. To capture the dynamic structure of the dependent variable, it is quite common to use autoregressive distributed lag models (ADLs) with lagged dependent and explanatory variables, as in Song et al. (2003); Brooks (2019). In this study, there was a lag of one and two months for overnight stays. For hotels, German tourists book their visits more than 150 days in advance, while Swedes book their stays less than 100 days before their arrival in Norway Innovation Norway (2019). This effect is at least as likely to apply to overnight stays at campsites. Similar to Aalen et al. (2019), this study used an average of 4–6 months as the time lag before entry for the value of the exchange rate. In line with the international literature Sanchez- Rivero and Pulido-Fernández (2020), the chosen model is presented in logarithmic form: ln(Vit) = α0+α1ln(GDPit) + α2ln(EERi,t−lag) + α3ln(EERij,t−lag)(4) +Σ12 k=2δkMkt +β1ln(Vi,t−1) + β2ln(Vi,t−2) + λYEAR2013 +eit V it is the overnight stay in Norway by visitors from country i ( i= 1: Sweden, 2: Germany) in month t . GDPit is gross domestic product for country i in month t (GDP is interpolated linearly from a yearly to a monthly basis). EERi,t−lag is the effective exchange rate between the country of origin and Norway. EERij,t−lag is the effective exchange rate between the country of origin and an alternative destination (country). k is a dummy variable for month number k, where January was the reference group in this regression. Due to a change in registration in 2013, the data for 2013 were not comparable to the data for the previous year. This was addressed by using the dummy variable Year 2013. In Equation (5), tis the month of arrival (from 2000 to 2019). We further assumed that the exchange rate in Sweden can influence the tourist inflow from Germany and vice versa. Therefore, the variable ln(EERij) was included in the model. The model for Sweden and Germany can be formulated based on Equation (5). The model for Sweden (Country 1) is: ln(V1t) = α0+α1ln(GDP1t) + α2ln(EERSEK,NOK,t-lag) + α3ln(EERSEK,euro,t-lag) +Σ12 k=2δkMkt +β1ln(V1,t−1) + β2ln(V1,t−2) + λYEAR2013 +e1t(5) and the model for Germany (Country 2) is: ln(V2t) = α0+α1ln(GDP2t) + α2ln(EEReuro,NOK,t-lag) + α3ln(EEReuro,SEK,t-lag) +Σ12 k=2δkMkt +β1ln(V2,t−1) + β2ln(V2,t−2) + λYEAR2013 +e2t(6) The ADL models were estimated using ordinary least squares (OLS), which leads to consistent estimators under classical OLS assumptions. A move from a static to dynamic model will often result in the removal of residual autocorrelation. To account for autocorrelation, our model is presented with lagged dependent variables for two periods. If there is still autocorrelation in the residuals of the model after including lags, then the OLS estimators will not be consistent Brooks (2019). We tested for autocorrelation by using the Breusch–Godfrey test Brooks (2019). 7 Economies 2021,9, 104 We further tested for heteroscedasticity using the Breusch–Pagan test Wooldridge (2020) . In the presence of heteroscedasticity, the standard errors may be wrong, and hence, any inference made could be misleading. We therefore used heteroscedasticity-consistent robust standard errors in the case of significant heteroscedasticity. We checked for multicollinearity using bivariate correlations and variance inflation factor (VIF) indices. If VIF indices are above 10, then we often conclude that multicollinearity is a “problem” for the estimated regression coefficients. However, a VIF above 10 does not mean that the standard errors of the estimated regression coefficients are too large. Therefore, the size of the VIF is of limited use Wooldridge (2020). 5. Findings Table 1shows all the results. The lagged dependent variable of the demand for overnight stays at campsites was significant for both Sweden and Germany for lag t− 1 and lag t− 2. There was no significant autocorrelation for the presented model for Germany or Sweden, with p-values from the Breusch–Godfrey test equal to 0.087 and 0.979, respectively. The Breusch–Pagan test revealed significant heteroscedasticity for Germany and Sweden, with p-values of 0.0025 and 0.000, respectively, and robust estimation was applied. For the model of Germany, the independent variable ln (EEReuro,NOK,t-lag) was included, but the variable ln (EERSEK,euro,t-lag) was excluded. These two variables were relatively strongly correlated in our data (r = 0.79 with p-value = 0.0000), and with both variables included in the ADL model, neither was significant, presumably due to multicollinearity. Figure 5illustrates the strong relationship between these two variables over time. The omission of the variable ln (EERSEK,euro,t-lag) due to multicollinearity is also explained in Note 2 in Table 1. Figure 5. The logarithm of the nominal exchange rate for Germany relative to Norway and Germany relative to Sweden. Source: Norges Bank. Moreover, most of the monthly dummy variables were significantly positive, but the effect for Germany was stronger than that for Sweden. 8 Economies 2021,9, 104 Table 1. Estimated ADL models (Equation (5) and (6)). OLS estimates with robust T-values. Dependent variable: inflow from Sweden or Germany. Sweden Sweden Germany Germany OLS Robust OLS Robust Estimates T-Values (1)Estimates T-Values (1) Constant 3.50 1.43 0.42 0.22 ln(GDP) 0.57 2.06 ** 0.17 0.38 M20.22 3.07 ** 0.32 3.28 ** M30.26 3.55 ** 0.72 8.66 ** M40.42 6.06 ** 0.84 8.50 ** M50.84 12.85 ** 2.30 16.05 ** M61.75 21.95 ** 3.16 13.03 ** M72.02 15.45 ** 2.49 7.34 ** M80.92 5.29 ** 1.88 5.12 ** M90.24 1.27 −0.02 −0.06 M10 0.07 0.44 −1.10 −3.56 ** M11 0.04 0.40 −1.04 −4.15 ** M12 0.10 1.46 0.22 1.63 Year2013 −0.22 −5.37 ** −0.20 −2.34 * ln(V1,t−1) 0.60 8.81 ** 0.46 4.70 ** ln(V2,t−1)−0.12 −1.52 0.16 2.20 * ln (EERSEK,NOK,t-lag)−0.34 −1.00 ln (EERSEK,euro,t-lag)(2) 0.52 1.98 * ln (EEReuro,NOK,t-lag)0.82 2.26 * N=220,R2=0.98, N=220, R2=0.98, Adj R2=0.97. Adj R2=0.98. Breusch–Godfrey LM test Breusch–Godfrey LM test for autocorrelation. for autocorrelation. Prob >χ2=0.0870 Prob >χ2=0.9798 Breusch–Pagan test for Breusch–Pagan test for heteroscedasticity. heteroscedasticity. Prob >χ2=0.0025 Prob >χ2=0.0000 Mean VIF =9.80 Mean VIF =15.60 Notes: (1) Two sided t-test: (**) significant at the 1% level, (*) significant at the 5% level. (2) This variable was not included for Germany in the version presented here. Using ln-linear demand models, the estimated coefficients showed the elasticities. The exchange rate elasticity for the inflow of German visitors was statistically significant with a value of 0.82. If the Norwegian exchange rate depreciates by one percent, German tourists will increase by 0.82 percent. For Sweden, the exchange rate elasticity was not significant. Nevertheless, the result confirmed Hypothesis 1 (H1). A one percent stronger Swedish currency compared to the euro was significantly connected to 0.52 percent more visitors from Sweden (H2 was confirmed). Due to multicollinearity, we were not able to test Hypothesis 3 (H3). 9 Economies 2021,9, 104 The income elasticity for German visitors was around zero and not significant. For Sweden, the income elasticity was significant with a value of 0.57. If the income increases by one percent in Sweden, the growth of Swedish travelers visiting Norwegian campsites is 0.57 percent. This confirmed Hypothesis 4 (H4). 6. Discussion 6.1. Campsites and Income The results were largely consistent with previous research. This confirmed the assumption that the demand for overnight stays at campsites has a low-income elasticity, unlike other parts of the tourism industry. This seems to be the situation in many countries. In an analysis of the tourism industry between 2000 and 2015, Guzman-Parra et al. (2015) observed that there was a significant increase in hotel and rural accommodation, while the number of overnight stays at campsites was stable during this period. This was in accordance with the tendency in Norway (see Figure 3). The regression model showed no correlation between the increase in income in Germany over the past two decades and the use of campsites. Coefficient B was close to zero and was not significant. One interpretation is that the demand for Norwegian campsites among German visitors is independent of the income for this period. For Swedish visitors, on the other hand, the link between income and the use of Norwegian campsites was significant, but the coefficient was small (under 0.6). This means that an increase in income of ten percent will increase the demand for overnight stays at campsites by less than six percent. The demand was inelastic. The reason for the low-income elasticity was presumably that higher incomes lead to more tourists wanting greater comfort than campsites can offer. Campsites are being replaced by more luxurious accommodation options (see Brox and Kumar (1997)). To counteract the loss of customers, many Norwegian campsites are investing in resources to increase the comfort level (more cabins, leisure facilities, sanitary conditions) to attract more campers. This is in accordance with the observation of Grzinic et al. (2010). This effect may help explain why the income elasticity was not negative. A negative income elasticity has been reported in the United States Rice et al. (2019) and may also apply in Norway. Camping is still mostly low-budget tourism. Therefore, an increase in income in wealthy countries will have little impact on demand. In Australia and New Zealand, there has for example been a substantial increase in the use of caravans and campers, which can offer greater comfort Collins et al. (2018). 6.2. Campsites and Exchange Rate If a country depreciates the value of its own currency, it becomes less expensive and more attractive to visit. The size of this effect depends on many factors, including the extent of the substitution effect. If the choice is between holidaying in two countries that offer almost the same service, the effect of a slight change in the exchange rate may be considerable. If a tourist is seeking sun and beautiful beaches and is unsure whether to travel to Portugal or Spain, a slight change in the relative prices can have a major impact Patsouratis et al. (2005). In other situations, there are few equal options, in which case, the effect of the exchange rate will be small. Such factors explain why there is a wide gap in the estimation of the exchange rate elasticity. Although the Norwegian currency has depreciated considerably over the past 10 years, foreigners are still experiencing Norway as an expensive country to visit Jacobsen et al. (2018). The price elasticity of a change in the real exchange rate provides important information about the impact of changes in relative prices. According to our analysis, for German camping visitors, this exchange rate elasticity effect was 0.8. A weaker Norwegian currency leads to more German overnight days at Norwegian campsites (Hypothesis H1). The effect was significant, but the price elasticity was under 1.0. Compared to many other international studies, the influence was rather small. Many German tourists may prefer to experience the midnight sun and see mountains and fjords, often combined with boat trips and fishing, independent of the currency rate Chen and Chen (2016). 10 Economies 2021,9, 104 Due to multicollinearity, we excluded the value of the Swedish currency in the model for Germany (see Note (2), Table 1) However, our analysis of the data suggested that a change in the Swedish krone had little impact on German camping tourists in Norway. One possible explanation is that the change in the Swedish exchange rate in the examined period was too small for German visitors to factor it into their decisions (see Figure 1). Therefore, it might explain why this analysis cannot prove that a change in the Swedish currency in relation to the Norwegian currency has any significant effect on German visitors. In addition, Germans may find that Norway and Sweden have different offers for camping tourists. This limits the substitution effect. On the other hand, a change in the Swedish krone relative to the euro will have an impact on the number of Swedish visitors to Norwegian campsites. The results showed that a weakening of the Swedish currency compared to the euro (Figure 1) resulted in fewer Swedish visitors (Hypothesis H2) (a strengthening of the Swedish currency against the euro will then have the opposite effect). One possible reason is that Swedes focus on the value of their national exchange rate relative to the euro. If the Swedish currency weakens, fewer Swedes choose to go camping abroad and are more likely to arrange a domestic holiday. The literature indicates that there is often complementarity between different countries. Swedish camping tourism abroad can be combined with visiting neighboring countries (Denmark, Finland, and so on). This may be another factor that explains the significant positive link ( elasticity = 0.52) between Swedish currency (relative to the euro) and Swedes’ use of Norwegian campsites. 6.3. Other Factors and Campsites The main reason for using the two periods of lagged dependent variables was to avoid autocorrelation. This factor was statistically significant. One interpretation is stability in the demand; consumers are returning Jacobsen et al. (2018), and a reputation has been developed for ensuring the attraction of new visitors. This is supported by active marketing. The use of campsites in Norway is highly seasonal (see Figure 4). Therefore, as expected, we observed a significant impact on dummy goods for various months. 7. Limitations This analysis was for a limited time frame and for only two countries. It would be beneficial to include other countries that are important for Norwegian camping tourism, such as the Netherlands and Denmark. It is reasonable to assume that the impact observed for Germans will be largely the same for Dutch tourists. Danes represent a different segment since they visit Norway in winter (skiing). In addition, the analysis was based on public data. There are many other factors that can affect the demand for Norwegian campsites that were not captured in the model (different types of accommodation at the campsite, standard changes, and so on). For example, most campsites sell different types of accommodation such as cabins or apartments with different standards and prices, tent pitches, and separate pitches for motorhomes with the possibility of connecting to electricity. It is also possible to enter into long-term contracts that run for several years. The opportunities available for outdoor activities also vary from campsite to campsite. The weather can also have an impact on people’s choice of holiday, and with improved meteorological models and flexibility in the employment relationship, the holiday can be planned so that the probability of “good weather” is greater than in the past. This can be a topic for followup research, although there may be problems related to data collection. 8. Contribution and Conclusions Demand elasticities are helpful tools for tourist industry planning. Because there are significant fluctuations in the exchange rate, it is useful to understand the impact of this variable on tourist demand. This study was based on two countries that are important for the Norwegian tourist industry, namely Sweden and Germany. How income and currency 11 Economies 2021,9, 104 changes affect camping tourism from Sweden and Germany to Norway has never been studied before. Using available data, we calculated the currency and revenue elasticity of the inflows of camping tourists from these two countries. The analysis revealed that income had an impact on demand, but the effect was small. This was consistent with previous studies that reported that the demand for campsites was quite inelastic. This research suggested that a weaker Norwegian exchange rate stimulated demand for Norwegian campsites, but with a currency elasticity below 1.0. Furthermore, the result showed that a stronger Swedish currency relative to the euro had a positive influence on overnight stays at Norwegian campsites. The explanation was presumably that it led to more Swedes holidaying abroad and that there was complementarity between neighboring countries and Sweden. Little research has been performed on camping tourism in Norway. Thus, little is known about what influences this type of tourism. When there is limited knowledge, there is a greater risk that the wrong investment decisions will be made. This may lead to the waste of the society’s resources. 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Turizam: Me ¯ dunarodni Znanstveno-Struˇcni ˇ Casopis 66: 379–95. Xie, Jinghua, and Sigbjørn Tveterås. 2020. Economic decline and the birth of a tourist nation. Scandinavian Journal of Hospitality and Tourism 20: 49–67. [CrossRef] Wooldridge, Jeffrey M. 2020. Introductory Econometrics. A Modern Approach, 7th ed. Boston: Cengage. 13 Economies 2021,9, 131 hotel and restaurant industry in year t; RFt is the capital invested in recreation facilities in year t; Dumi,t are the dummy variables representing qualitative factors from source country iat time t; Ui,t is the disturbance term that captures all the other factors that may influence the number of visitor arrivals from source country iat time t. The international visitor arrivals can be divided into several categories, i.e., “sightseeing tourists, business tourists and tourists of other types” (Tang 2020, p. 38) and there can be heterogeneity between them. However, because there are not enough specific data for these objects, heterogeneity between them is not considered. This study uses regression analysis with a log-log model to estimate the impact of tourism infrastructure development investment on attracting international tourists to Vietnam. In fact, the log-log model is often used to estimate the parameters in order to evaluate the impact level of the independent variable on the dependent variable, because then the effect can be obtained directly from the coefficients (Witt and Witt 1995; Song et al. 2009 ). Furthermore, the natural logarithmic transformation also reduces data instability (Enders 2004;Studenmund 2006). There are many techniques to estimate the coefficients of the factors affecting the number of visitors in order to fit the data. Initially, the ordinary least squares (OLS) technique was used commonly for both time series or panel data (such as in the study of Vanegas Sr and Croes 2000;Kulendran and Witt 2001;Lim 2004;Croes and Vanegas Sr 2005; Muñoz 2007 ). However, OLS regression requires the series to be stationary, otherwise it will lead to spurious regression (Granger and Newbold 1974). One of the technique considered to solve the non-stationary series problem is the cointegration test. The cointegration technique describes “the existence of an equilibrium, or stationary, relationship among two or more time-series, each of which is individually non-stationary” ( Banerjee et al. 1994, p. 136 ). Furthermore, “cointegration techniques permit the estimation and testing of the long-run equilibrium relationships” (Lim and McAleer 2001, p. 1618; Dritsakis 2004, p. 118). Two common estimators for the technique are fully modified ordinary least squares (FMOLS) and dynamic ordinary least squares (DOLS). These estimators need to satisfy one fundamental assumption: the variables included in the models are all non-stationary at level, but stationary at first difference and cointegration of order 1. This technique has been applied in several studies which meet the qualifications (e.g., Dogru et al. 2017). However, these conditions are not always met. Moreover, according to Narayan and Narayan (2005, p. 429), “methods of cointegration are not reliable for small sample sizes”. To overcome these limitations, Pesaran and Shin (1999) proposed an ARDL modeling approach. This method is superior regardless of whether the variables exhibit I(0), I(1), or a mixture of both. Song et al. (2003, p. 365) state that “one of the advantages of the general ARDL is that a modern econometric technique, known as error correction, can be readily incorporated into the modeling process”. Given these advantages, the ARDL estimation technique has been widely used in recent studies (Song et al. 2003;Lee 2011; Otero-Gómez et al. 2015;Lin et al. 2015;Shafiullah et al. 2018;Kumar et al. 2020). Based on the above analysis, the nonlinear panel ARDL approach is applied in this study. “Nonlinear ARDL model in panel form which is also a nonlinear representation of the dynamic heterogenous panel data model that is suitable for large T panels” (Salisu and Isah 2017, p. 261). The panel ARDL method also helps to estimate the long-run and short-run relationships for the general sample, as well as the short-run cross-sectional coefficients for each subject, even when the variables are non-stationary and/or show no cointegration. The nonlinear panel ARDL model used in this study is presented in the form of Equation (2) below: The panel ARDL method also helps in estimation. ∆lnVAi,t=µi+ q1 ∑ j=1ϑ1ij∆lnVAi,t−j+ q2 ∑ j=0ϑ2ij∆lnTCt−j+ q3 ∑ j=0ϑ3ijlnHRt−j + q4 ∑ j=0ϑ4ijlnRFt−j+ϕoi +ϕ1ilnVAi,t−1+ϕ2ilnTCt−1+ϕ3ilnHRt−1 +ϕ4lnRFt−1+Dumi,t+εi,t i=1, 2, . . . N;t=1, 2, . . . T (2) 20 Economies 2021,9, 131 where µi is the group-specific effect; iis the source country; tis the number of periods (year); − 1 < ϕ1 < 0 is the error correction term’s coefficient; εi,t is the error term; is the first difference operator; j is the lag order decided by the Akaike Information Criterion (AIC); ln is the natural logarithm. For each cross-section, the long-term slope (elasticity) of capital investment in transport and communications infrastructure, the hotel and restaurant industry, and recreation facilities is calculated as −ϕ2i ϕ1i,−ϕ3i ϕ1i,−ϕ4i ϕ1i , respectively, and with the expectation of a positive coefficient. Therefore, the short-term estimate of capital investment in transport and communications infrastructure, the hotel and restaurant industry, and recreation facilities are ϑ2ij,ϑ3ij,ϑ4ij, respectively. 3.2. Data The measurement of tourist attraction to Vietnam in this study is based on international tourist arrivals, as used by many previous studies to measure tourism demand (Khadaroo and Seetanah 2007a;Seetanah and Khadaroo 2009;Seetanah et al. 2011;Mandi´c et al. 2018). The international visitor arrivals were collected from the ten largest source markets and the remaining markets for 25 years (1995–2019) to form panel data with 275 observations (N = 11 and T = 25). Data on international visitors to Vietnam by source countries in the period 1995–2018 were collected from the VNAT. The ten countries with the most significant number of visitors to Vietnam in the period 1995–2019 are China, Korea, Japan, the United States (US), Malaysia, Australia, the United Kingdom (UK), Singapore, France, and Germany, respectively. These ten source countries accounted for 70.08% of total visitor arrivals to Vietnam from 1995–2019 (Figure 1). Economies 2021, 9, x FOR PEER REVIEW 7 of 20 ∆   , =   +       ∆  , +       ∆    +          +    + +, + + + +, +, i = 1, 2, …N; t = 1, 2, … T (2) where μi is the group-specific effect; i is the source country; t is the number of periods (year); −1 <  < 0 is the error correction term’s coefficient; , is the error term; is the first difference operator; j is the lag order decided by the Akaike Information Criterion (AIC); ln is the natural logarithm. For each cross-section, the long-term slope (elasticity) of capital investment in transport and communications infrastructure, the hotel and restaurant industry, and recreation facilities is calculated as − , − , − , respectively, and with the expectation of a positive coefficient. Therefore, the short-term estimate of capital investment in transport and communications infrastructure, the hotel and restaurant industry, and recreation facilities are , , , respectively. 3.2. Data The measurement of tourist attraction to Vietnam in this study is based on international tourist arrivals, as used by many previous studies to measure tourism demand (Khadaroo and Seetanah 2007a; Seetanah and Khadaroo 2009; Seetanah et al. 2011; Mandić et al. 2018). The international visitor arrivals were collected from the ten largest source markets and the remaining markets for 25 years (1995–2019) to form panel data with 275 observations (N = 11 and T = 25). Data on international visitors to Vietnam by source countries in the period 1995–2018 were collected from the VNAT. The ten countries with the most significant number of visitors to Vietnam in the period 1995–2019 are China, Korea, Japan, the United States (US), Malaysia, Australia, the United Kingdom (UK), Singapore, France, and Germany, respectively. These ten source countries accounted for 70.08% of total visitor arrivals to Vietnam from 1995–2019 (Figure 1). Figure 1. Visitors from ten major international markets in the period 1995–2019. The data series covers 25 years from 1995–2019 and the summary of variables used in the model is described in Table 1 below. 0 1,000,000 2,000,000 3,000,000 4,000,000 5,000,000 6,000,000 China Korea Japan US Malaysia Australia UK Singapore France Germany 2019 2015 2010 2005 2000 1995 Figure 1. Visitors from ten major international markets in the period 1995–2019. The data series covers 25 years from 1995–2019 and the summary of variables used in the model is described in Table 1below. Table 1. Summary of variables used in the model. Variable Measure Description Data Source VA Visitor arrivals Total number of visitor arrivals per annum World Tourism Organization (UNWTO) and VNAT TC Transport and communications infrastructure Social investment in transport; storage, and communications GSO of Vietnam HR Hotel and restaurant industry Social investment in the hotel and restaurant industry or accommodation, food and beverage service activities GSO of Vietnam RF Recreation facilities Social investment in recreation, culture, and sport or recreation, entertainment, and the arts GDO of Vietnam Note: Data on social investment capital is converted to fixed prices; the original year was 1994. 21 Economies 2021,9, 131 According to the GSO of Vietnam, the investment capital of the activities in Table 1for the period 1995–2009 are based on the original year, 1994. However, from 2010–2019 the fixed price is for 2010. Therefore, the fixed price of 2010–2019 is converted to the original year price by the conversion coefficient of the original year 2010 to the original year 1994 according to the Equation (3) below. Conversion coefficient of the original year 2010 to the original year, 1994 =Value in year n at the 2010 price Value in 2010 based on the original 1994 price (3) Source: Vietnam Ministry of Planning and Investment (2012). Between 1995 and 2019, there were three years of negative growth in international tourist arrivals to Vietnam: 1998, 2003, and 2009 show −11.4%, 7.6%, and −11.5%, respectively, due to the Asian financial crisis in the late 1990s, the SARS epidemic in 2003, and the global recession in 2008–2009. However, the following year, the number of international tourists to Vietnam increased again and offset previous declines (Figure 2). Economies 2021, 9, x FOR PEER REVIEW 8 of 20 Table 1. Summary of variables used in the model. Variable Measure Description Data Source VA Visitor arrivals Total number of visitor arrivals per annum World Tourism Organization (UNWTO) and VNAT TC Transport and communications infrastructure Social investment in transport; storage, and communications GSO of Vietnam HR Hotel and restaurant industry Social investment in the hotel and restaurant industry or accommodation, food and beverage service activities GSO of Vietnam RF Recreation facilities Social investment in recreation, culture, and sport or recreation, entertainment, and the arts GDO of Vietnam Note: Data on social investment capital is converted to fixed prices; the original year was 1994. According to the GSO of Vietnam, the investment capital of the activities in Table 1 for the period 1995–2009 are based on the original year, 1994. However, from 2010–2019 the fixed price is for 2010. Therefore, the fixed price of 2010–2019 is converted to the original year price by the conversion coefficient of the original year 2010 to the original year 1994 according to the Equation (3) below. Conversion coefficient of the original year 2010 to the original year , 1994 = Value in year n at the 2010 price Value in 2010 based on the original 1994 price (3) Source: Vietnam Ministry of Planning and Investment (2012). Between 1995 and 2019, there were three years of negative growth in international tourist arrivals to Vietnam: 1998, 2003, and 2009 show −11.4%, 7.6%, and −11.5%, respectively, due to the Asian financial crisis in the late 1990s, the SARS epidemic in 2003, and the global recession in 2008–2009. However, the following year, the number of international tourists to Vietnam increased again and offset previous declines (Figure 2). Figure 2. Changes in international visitors to Vietnam in the period 1995–2019. Source: Data from UNWTO and VNAT. 1351 2140 3478 5050 7944 18,009 –11.40 –7.56 –11.49 16.20 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 -20.00 -10.00 0.00 10.00 20.00 30.00 40.00 0 2,000 4,000 6,000 8,000 10,000 12,000 14,000 16,000 18,000 20,000 1995 2000 2005 2010 2015 2019 Inbound tourist (Thousand) Growth (%) Figure 2. Changes in international visitors to Vietnam in the period 1995–2019. Source: Data from UNWTO and VNAT. Particularly for the Chinese source market, the largest market to Vietnam in recent years, there are also special events such as in 1995, when the relationship between China and Vietnam had not been normalized, so visitors from China to Vietnam faced difficulties obtaining visas; in 2015, China placed an oil rig in Vietnamese waters, straining relations between the two countries and severely affecting tourism. In this study, the above events are considered unstable factors which affected tourists’ decision to visit Vietnam. Therefore, the dummy variable used is the value 1, and the remaining cases are assigned the value 0. More details about the methodological use relating to dummy variables can be found in Song and Lin (2010)orLin et al. (2015). Table 2below presents descriptive statistics of the variables in the model with 275 observations (11 source markets over 25 years). Table 2. Descriptive statistics variables. Ln(VA) Ln(TC) Ln(HR) Ln(RF) Unit 1000 person Billion VND Billion VND Billion VND Mean 12.3131 10.3976 8.5841 8.0096 Maximum 15.5745 11.1848 9.5693 8.9410 Minimum 9.5076 9.1832 7.7227 6.7178 Standard Deviation 1.2666 0.6630 0.5147 0.6452 Coefficient of Variation 0.1029 0.0638 0.0600 0.0806 Observations 275 275 275 275 Source: Author’s calculation using Eviews. 22 Economies 2021,9, 131 4. Research Results and Discussion 4.1. The Test Results, Stationarity and Cointegration Before estimating the parameters, stationarity and cointegration tests were performed to show that the nonlinear panel approach ARDL is appropriate for the data. The unit root test is a popular method for stationary tests for both annual time series and panel data. The stationarity test is conducted in both “individual intercept” and “individual intercept and trend” in test equations. There are many types of unit root test for panel data such as Levin, Lin and Chu t (LLC) and Breitung t-stat with common unit root process; I’m, Pesaran and Shin W-stat (IPS), ADF—Fisher Chi-square (ADF), and PP—Fisher Chi-square (PP) with individual unit root process. The panel data in this study are balanced so that both hypotheses can be applied. The LLC test is chosen for the hypothesis “common unit root process” and the hypothesis “individual unit root process” is chosen for the IPS test. The results of panel unit root tests for logarithms of variables are summarized in Table 3. Table 3. Results of stationarity test. Intercept Intercept and Trend LLC IPS ADF PP LLC IPS ADF PP lnVA I(1) *** I(1) *** I(1) *** I(1) *** I(0) ** I(1) *** I(1) *** I(1) *** lnTC I(0) *** I(1) *** I(1) *** I(1) *** I(1) *** I(1) *** I(1) *** I(1) *** lnHR I(1) *** I(1) *** I(1) *** I(1) *** I(1) *** I(1) *** I(1) *** I(1) *** lnRF I(0) *** I(0) *** I(0) ** I(1) *** I(1) *** I(1) *** I(1) *** I(1) *** Source: Author’s calculation using Eviews. Note: LLC, Levin, Lin & Chu; IPS, I’m, Pesaran and Shin W-stat; ADF, ADF—Fisher Chi-square; PP, PP—Fisher Chi-square; ** and *** for statistically significant at the 0.05 and 0.01 levels, respectively. According to Table 3, most of the series are non-stationary at level, but stationary at first difference, except for lnVA in LLC test of intercept and trend; lnTC in LLC test of intercept; and lnRF in LLC, IPS and ADF of intercept. Based on the majority of the results, it can be seen that the series are non-stationary at level but stationary at first difference, so a cointegration test should be performed to consider the long-term relationship between variables. To analyze the cointegration relationship between variables in the panel data model, this study chooses the Pedroni and Kao tests because they are more comprehensive and universal. Cointegration tests are conducted for both “individual intercepts” and “individual intercept and individual trends” in the Pedroni test. By contrast, it is only conducted in the case of individual intercepts in the Kao test. The Pedroni test used seven test statistics (four tests for within-dimension and three tests for between-dimension). The Schwarz Information Criterion (SIC) automatically chooses the lag length with Newey-West automatic bandwidth selection and Bartlett kernel. Table 4below presents the results of panel cointegration analysis. Table 4. Results of panel cointegration test. Method Statistic Individual Intercept Individual Trend and Individual Intercept Pedroni test Panel v-Statistic 1.0575 2.8684 Panel rho-Statistic −0.7207 −1.0157 Panel PP-Statistic −3.0080 *** −8.0608 *** Panel ADF-Statistic −2.4028 *** −2.3750 *** Group rho-Statistic 0.4850 1.1146 Group PP-Statistic −3.1699 *** −5.8950 *** Group ADF-Statistic −2.3379 *** −3.7839 *** Kao test t-Statistic −0.7738 Note: *** for statistically significant at the 0.01 levels, respectively; deterministic trend specification: Individual intercept for Pedroni test and Kao test; Four tests for within-dimension of Pedroni test are weighted statistics. Source: Author’s calculation using Eviews. 23 Economies 2021,9, 131 According to the results of the Pedroni test in Table 4, 4/7 tests are significant at the 0.01 level for both “individual intercept” and “individual trend and individual intercept”. This means that most cointegration tests in the Pedroni test result in the cointegration series. However, the Kao test gives the opposite result, meaning that the Kao test result does not give cointegration series at the level of 0.05, so is not compelling evidence to conclude clearly that series shows cointegration. Because of lnVA, lnTC, lnHR, and lnRF containing both I(0) and I(1), and when the existence of long-run associations is unclear, the ARDL technique is the most appropriate. 4.2. Estimated Results This study uses the Pooled Mean Group (PMG) estimator to estimate the impact of investment in tourism infrastructure development on attracting international visitors to Vietnam. The PMG estimator is a well-known technique used in the estimation of a dynamic heterogeneous panel data model. Furthermore, by design, in addition to the panel regression results, the PMG also generates results for the individual units (Blackburne and Frank 2007). Thus, computing the impact of tourism infrastructure development on attracting international visitors can assess both long-run and short-run responses for the general sample and each sample (each source market). First, the parameters are estimated by the PMG estimator for the general sample (panel data) with Automatic selection in three maximum lags, Akaike info criterion (AIC) in the Model selection method, and Linear trend in trend specification. Table 5below summarizes the regression results by the PMG estimator for the general sample for both long-run and short-run. Table 5. Results of regression by the PMG estimator for the general sample. Variable Coefficient t-Statistic p-Value Long-Run Equation LnTC 0.7836 4.0925 *** 0.0001 LnHR 0.7503 7.5976 *** 0.0000 LnRF 0.4026 3.0775 *** 0.0028 Dum −0.3533 −2.9951 *** 0.0036 Short-Run Equation COINTEQ01 −0.4743 −3.9677 *** 0.0002 ∆LnVA(−1) 0.1314 0.7826 0.4361 ∆LnVA(−2) 0.2049 1.0379 0.3023 ∆LnTC −0.1881 −0.8512 0.3971 ∆LnTC(−1) −0.1081 −0.5081 0.6127 ∆LnTC(−2) −0.3618 −1.5872 0.1162 ∆LnHR −0.2994 −2.1350 ** 0.0357 ∆LnHR(−1) −0.3207 −2.3850 ** 0.0193 ∆LnHR(−2) −0.3747 −2.6398 *** 0.0099 ∆LnRF 0.0073 0.0944 0.9250 ∆LnRF(−1) 0.3680 4.8035 *** 0.0000 ∆LnRF(−2) 0.2761 5.5745 *** 0.0000 ∆Dum −0.0309 −0.5882 0.5579 ∆Dum(−1) −0.0466 −1.2999 0.1972 ∆Dum(−2) 0.0266 0.6770 0.5003 C−2.6244 −3.8043 *** 0.0003 @Trend −0.0059 −0.6470 0.5194 Statistics Standard error of regression = 0.0814; Sum squared residual = 0.5565; Log likelihood = 445.9467; Akaike info criterion = − 1.8542; Schwarz criterion = 0.6579; Hannan-Quinn criterion: − 0.8460. Note: LnVA is dependent variable; ** and *** for statistical significance at 0.05 and 0.01 levels, respectively. Source: Author’s calculation using Eviews. As shown in Table 5, the Log-Likelihood is large; Standard error of regression, Sum squared residual, and Akaike info criterion, Schwarz criterion, and Hannan-Quinn criterion statistics are relatively small, so the model is appropriate and fits with the data. For the long- 24 Economies 2021,9, 131 run equation, all variables of interest are significant at the 0.01 level, so they are accepted. The estimated coefficients have the same sign as the initial expectation. Investment in tourism infrastructure such as transport and communications infrastructure, the hotel and restaurants industry, and recreation facilities, all positively impact attracting international visitors to Vietnam. Meanwhile, uncertainty factors have been negatively affected. In the short-term equation, the coefficient of cointegrating equation has a negative sign ( − 0.4743) and is significant at the 0.01 level. This means that the variables converge to the long-run equilibrium, and the convergence rate is 47.43%. The lnTC and Dummy are not significant at the 0.05 level for all lags. By contrast, the variable lnHR is significant at the level, the first difference, the second difference, and lnRF at first difference and second difference, to be more specific, the sign of the coefficients of the negative lnHR and the sign of the positive lnRF coefficients. These findings imply that no significant impact of investment in transport and communications infrastructure has been found on attracting international visitors to Vietnam in the short-term. In comparison, there is a positive effect of investment in recreation facilities, while investment in the hotel and restaurant industry has the opposite effect in the short-run. Table A1 in Appendix A.1 provides short-run coefficients across cross-sections of the 10 source countries. Accordingly, there are nine source markets moving towards long-run equilibrium, except the US (where the Cointegrating Equation is positive). Additionally, there is at least one coefficient at one level in the short-run of significance at 0.05 or 0.01 for the variables of interest in each source country, except lnTC in the Korean source market. These coefficients indicate the different short-run roles of investments in tourism infrastructure in attracting international visitors to different source markets. At lag 3, the coefficients of lnTC, lnHR, and lnRF are significant in most source markets. Considering this lag, investment in transport and communications infrastructure has different positive and negative roles for each source market in the short-run. To be more specific, investment in transport and communications infrastructure has an active role in source markets in descending order, Germany, the US, Japan, and China. The source markets with a negative role in ascending order are Australia, the UK, France, Malaysia, and Singapore. As for the role of investment in transport and communications infrastructure, investment in the hotel and restaurant industry also has different positive and negative roles for each source market in the short-run. The source markets where it has an active role in descending order are the US, Germany, Japan, respectively. The source markets where it has a negative role in ascending order are Australia, the UK, France, Malaysia, China, and Singapore, respectively. Meanwhile, investment in recreation facilities plays an active role in all source markets. In descending order, these are China, France, Germany, Japan, Korea, the UK, Australia, and the US, respectively. The coefficients of dummy variables with different signs in source markets indicate the short-run impact of different uncertainties on source markets. Positive effects were found in the short-run in China, Korea, Malaysia, Australia, the UK, Singapore, and France. In contrast, the negative effects were found only in Japan, the US, and Germany. 4.3. Diagnostic Test and Robustness Check To further consider the reliability and validity of the model estimate, diagnostic tests are considered. There are two critical diagnostic tests for the panel PMG/ARDL method in Eview: coefficient diagnosis and residual diagnostic. However, according to Wooldridge (2015), based on the asymptotic theory, when there is a sufficient number of observations, it is not necessary to test the normal distribution of the residuals. With 275 observations, this study omits the residual diagnostic and only performs the coefficient diagnostic by coefficient confidence intervals and the Wald test, with the Null Hypothesis that the coefficients are all equal to 0. The results of the diagnostic coefficients are presented in Table 6below. 25 Economies 2021,9, 131 Table 6. Coefficient diagnostics. Coefficient Confidence Intervals Variable Coefficient 95% Confidence Intervals 99% Confidence Intervals Low High Low High LnTC 0.7836 0.4029 1.1644 0.2790 1.2883 LnHR 0.7503 0.5539 0.9467 0.4900 1.0105 LnRF 0.4026 0.1424 0.6627 0.0578 0.7473 Dum −0.3533 −0.5878 −0.1187 −0.6641 −0.0424 Wald test Null Hypothesis: C(1) = C(2) = C(3) = C(4) = 0 F-statistic: 43.9951 ***; Chi-square = 175.9803 *** Note: *** for statistical significance at the 0.01 levels, respectively. Source: Results of Wald test. Table 6provides the values of the coefficients at the 95% and 99% confidence intervals. Accordingly, the maximum and minimum values of lnTC, lnHR and ln RF are all greater than 0. In contrast, the values of Dummy are all less than 0. The Wald test gives significance at 0.01 level for both F and Chi-squared statistics. Therefore, the null hypothesis is rejected and the alternative hypothesis is accepted, meaning that the estimated coefficients in the model are all non-zero, and they are all necessary for the model. This evidence lends support to the reliability and validity of the estimated model. Next, the robustness check is performed by comparing the estimated results among PMG/ARDL, cointegration regression and OLS for panel data (assuming the cointegration series from the Pedroni test result). In the OLS method, Random Effects Model (REM) is selected from the Pooled OLS model, Fixed Effect Models (FEM) and REM. In the cointegration regression, the FMOLS estimator is chosen because there is a quite large difference in the long-term coefficient of variance in lnVA (Table 2). The estimated results by FMOLS and OLS methods are detailed in Table A2 in Appendix A.2. The coefficients estimated by PMG/ARDL, FMOLS and OLS methods are compared in Table 7. Table 7. Differences in coefficients estimated by PMG/ARDL, FMOLS and OLS. Variable PMG/ARDL FMOLS REM Difference of PMG with FMOLS REM LnTC 0.7836 *** 0.7066 *** 0.7393 *** 0.0770 0.0443 LnHR 0.7503 *** 0.5691 *** 0.5442 *** 0.1812 0.2061 LnRF 0.4026 *** 0.0981 0.0691 Dum −0.3533 *** −0.2122 ** −0.2237 *** −0.1411 −0.1296 Note: ** and *** for statistical significance at the 0.05 and 0.01 levels, respectively. Source: Estimation results from PMG/ARDL, FMOLS and OLS. According to Table 7, although the methods produce different estimation results, the signs of the coefficients are similar. To be more detailed, lnTC has quite similar results (bias of no more than 10%), lnHR has a maximum bias of 27.4% and Dummy variable has a bias of no more than 40%. Particularly, lnRF estimated by FMOLS and REM do not reach significance at the 0.05 level. Despite certain differences, it is believed that the results from the PMG/ARDL are more appropriate because of the advantage of PMG/ARDL discussed above, and the cointegration series is still in doubt. 4.4. Discussion The above findings indicate that investment in tourism infrastructure components positively impacts attracting international tourists to Vietnam. In the long-run, increasing 1% of investment capital in transport and communications infrastructure, the hotel and restaurant industry, and recreation facilities will increase international visitors to Vietnam by 0.7836%, 0.7503%, and 0.4026%, respectively. This indicates that capital investment 26 Economies 2021,9, 131 in transport and communications infrastructure and the hotel and restaurant industry plays a crucial role in attracting international visitors. This evidence lends support to the view that investments in transportation and hotels have played an important role in attracting international tourism, as many earlier studies have found (Khadaroo and Seetanah 2007a,2007b,2008;Prideaux 2000;Seetanah et al. 2011). In this study, the role of investment in transport and communications infrastructure (coefficient 0.7836) and investment in the hotel and restaurant industry (coefficient 0.7503) in Vietnam is higher in some areas such as in Mauritius, where the coefficient is found to be 0.36 for investment in transport infrastructure and 0.56 for the investment and hotel industry (Khadaroo and Seetanah 2007b) or 0.32 for investment capital in transport infrastructure and 0.54 for investment and the hotel industry (Seetanah et al. 2011); in 26 island economies, the results are 0.064, 0.16, 0.074 and 0.28 for investment in road, air, communications, and the hotel and restaurant industry, respectively (Khadaroo and Seetanah 2007a); and in 28 countries representing Europe, Asia, America, and Africa, these are 0.13, 0.18, 0.06 and 0.22, respectively, for investment in road, air, port and hotel (Khadaroo and Seetanah 2008). The impact coefficient of the hotel and restaurant industry in this study is lower than that of the hotel accommodation infrastructure in Singapore, from 0.839 to 0.855 in the study by Lim et al. (2019). However, it must also be seen that the different roles of the hotel and restaurant industry depend not only on each country, but also on how the variable that represents it is measured. This role is appropriate because Vietnam is a developing country with great tourism potential and scenic beauty. However, the terrain is difficult, and transportation infrastructure and hotel availability are still limited. With the efforts of the government and the community, the transport and communications infrastructure, as well as the hotel and restaurant facilities in Vietnam, have been significantly improved, creating a favorable environment for tourists, and strongly enticing international visitors to Vietnam. The research results also show that the government and private sector investors cannot expect to see a fast Return on Investment. Their investment in transport and communications infrastructure and hotel and restaurant facilities will only be evident in the long-run. This can be explained by the long lead-in time required by infrastructure works and hotel developments. The impact, therefore, takes time to be fully demonstrated. However, it should be noted that transport and communications infrastructure investment attract visitors and develops other areas of the economy and society, including the hotel and restaurant industry and recreation facilities. Cross-section short-run coefficients show that, in the short term, the role of investment in the hotel and restaurant industry is decreasing, in this order source markets: the US, Germany, Japan, Australia, the UK, France, Malaysia, China, and Singapore. Meanwhile, the order for investment in transport and communications infrastructure is as follows: Germany, the US, Japan, China, Australia, the UK, France, Malaysia, and Singapore, respectively. This is consistent with the idea that inhabitants of developed countries are accustomed to modern, high-quality transport infrastructure and high-quality restaurants and hotels. Consequently, they prefer to find similar infrastructure in other countries. In contrast, tourists from less developed countries tend to be less demanding of these infrastructures. Research results also show that investment in recreation facilities is also important to attract international arrivals to Vietnam. Although its role in the long-term is not equal to that of the other two areas of tourism infrastructure in this study, it is effective in both the long-run and short-run. Investment in recreation facilities will directly make destinations more attractive. Formica (2002) states that without attractions, tourism destinations could not exist; attractions are the basis for visitation. These findings are consistent with Vengesayi et al. (2009), suggesting that attractions are the main reason people visit specific destinations and not others. The role of investment in recreation facilities in attracting international visitors in this study is empirical evidence supporting the tourism infrastructure model of Mandi´c et al. (2018). Accordingly, recreational facilities with hotels and other forms of accommodation, spas, and restaurants form the main tourism infrastructure. 27 Economies 2021,9, 131 Usually, investment in modern amusement parks will require considerable investment capital. In contrast, investment in developing conservation and ecological tourist areas may require a smaller amount of capital if considered per unit area. The above order of roles of investment in recreation facilities in the short-run implies that in general, visitors want to improve recreation facilities in Vietnam, but visitors from China, France, Germany, and Japan require much more improvement than visitors from Korea, the UK, Australia, and the US. This finding is indicative of visitor preferences from source markets. 5. Conclusions and Implications Attracting international tourists is an essential task for countries as international tourists bring significant income, foreign currency, and jobs to countries, especially potential tourism countries. Therefore, to attract tourists and implement an appropriate pricing policy, investing in tourism infrastructure development to make the destination more competitive and attractive are critical measures. This is the reason why this study examines the impact of investment in tourism infrastructure development on attracting international tourists from empirical research in Vietnam through panel data from 1995 to 2019. The three types of tourism infrastructure used in this study are transport and communications infrastructure, restaurants and hotels, and entertainment infrastructure. After testing the stationarity and cointegration of the data, this study used the ARDL approach to examine the impact of three tourism infrastructure components on attracting international visitors to Vietnam in the long-run and short-run. In the long-run, investment in tourism infrastructure components has a positive and robust impact on attracting international visitor arrivals. The most decisive impact is investment in transport and communications infrastructure, followed by investment in the hotel and restaurant industry, and finally investment in recreation facilities. However, the short-run impacts of these three types of tourism infrastructure also differ in both sign and magnitude. In addition, different impacts of the three tourism infrastructure components in the short-run on attracting international visitors in general and in each of the leading international visitor markets to Vietnam are also found. The contribution of this study impinges on two aspects. Firstly, from a theoretical perspective, this study enriches the role of investment in tourism infrastructure in tourism development with three components: transport and communications infrastructure, hotel and restaurant industry, and recreation facilities. Second, from a practical perspective, the study points out the different impacts of components of tourism infrastructure and their specific impact on attracting international visitors to Vietnam as the basis for policies for tourism development. Overall, investment in transport and communications infrastructure drives economic growth and social development. However, the significant impact on tourism growth in Vietnam revealed in this study justifies the need for government investment in transport infrastructure and information and communications. Besides, investment in the hotel and restaurant industry will provide accommodation, food and beverage services for tourists, especially international tourists. Furthermore, investment in recreation facilities will make the destination more attractive to visitors. Therefore, the positive and vital role of investment in the three tourism infrastructure components is shown in this study because they are the most critical components in the tourism product chain experienced by tourists. On the other hand, although Vietnam has substantial tourism potential, its tourism infrastructure is still limited, so investing in components of tourism infrastructure becomes increasingly pressing to attract visitors in general and international visitors in particular. Unlike an investment in transport and communications infrastructure that is primarily financed by government funds, investment in the hotel and restaurant industry, as well as recreation facilities, can mobilize the resources of the entire society, especially the private sector, because this is a highly commercialized and profitable sector, and is not prohibitively subject to government control. Thus, internationally and in Vietnam in particular, there is an urgent need for investment incentives for the private sector to help develop these areas. 28 Economies 2021,9, 131 Although some valuable results have been obtained, this study still has some limitations. Due to data limitations, this study only explores the role of investment in three groups of components without separating each component in detail as well as from different capital sources to see the different roles of the economy sectors. In addition, heterogeneity among visitor arrival groups has not been considered. These issues may provide opportunities for further study. Funding: This research was funded by University of Economics and Law, Vietnam National University, Ho Chi Minh, Vietnam, under grant number 3-2021. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Data on international tourists to Vietnam is collected from various sources, accessible from: https://www.gso.gov.vn/px-web-2/?pxid=V0825&theme=Th%C6%B0 %C6%A1ng%20m%E1%BA%A1i%2C%20gi%C3%A1%20c%E1%BA%A3;http://thongke.tourism. vn/index.php/statistic/sub/6;https://www.e-unwto.org/action/doSearch?ConceptID=2473&target= topic. Investment data is available in the GSO statistical yearbooks, accessible from: https://www. gso.gov.vn/du-lieu-va-so-lieu-thong-ke/2020/02/nien-giam-thong-ke-1997/;https://www.gso. gov.vn/du-lieu-va-so-lieu-thong-ke/2020/02/nien-giam-thong-ke-2000/;https://www.gso.gov. vn/du-lieu-va-so-lieu-thong-ke/2020/02/nien-giam-thong-ke-2005/;https://www.gso.gov.vn/ du-lieu-va-so-lieu-thong-ke/2019/10/nien-giam-thong-ke-2010-2/;https://www.gso.gov.vn/dulieu-va-so-lieu-thong-ke/2016/06/nien-giam-thong-ke-2015/;https://www.gso.gov.vn/du-lieu- va-so-lieu-thong-ke/2021/07/nien-giam-thong-ke-2021/. Acknowledgments: The author is grateful to the three anonymous reviewers and academic editor whose comments have contributed to improving the quality of this paper. Conflicts of Interest: The author declares no conflict of interest. Appendix A Appendix A.1. Result of Cross-Section Short-Run Coefficients Table A1. Cross-section short-run coefficients. China Korea Japan US Malaysia COINTEQ01 −0.2188 *** −0.5408 *** −0.1571 *** 0.2442 *** −0.9303 *** ∆LnVA(−1) 0.4138 *** 0.6812 *** 0.4094 *** 0.1804 ** 0.3772 *** ∆LnVA(−2) 0.5513 *** 0.3598 ** −0.1618 *** −0.7762 *** 0.5502 *** ∆LnTC −1.3162 *** −0.4403 0.2158 *** −0.0069 −0.1595 ∆LnTC(−1) 1.6776 *** −0.2047 −0.7786 *** 0.0590 −0.2536 * ∆LnTC(−2) 0.3185 *** −0.4225 * 0.4404 *** 0.4836 *** −0.8196 *** ∆LnHR −0.8777 *** −0.1762 * −0.0300 *** 0.2010 *** −0.1345 ** ∆LnHR(−1) 0.2177 *** −0.3818 *** −0.4700 *** 0.0903 ** −0.2748 *** ∆LnHR(−2) −0.9323 *** −0.1601 * 0.0671 *** 0.4512 *** −0.6735 *** ∆LnRF −0.1209 *** 0.3317 *** 0.2365 *** 0.1692 *** −0.5142 *** ∆LnRF(−1) 0.5206 *** 0.7553 *** 0.1835 *** 0.2281 *** −0.1185 * ∆LnRF(−2) 0.5467 *** 0.2941 *** 0.3317 *** 0.1021 *** 0.0653 ∆Dum −0.3408 *** −0.0330 ** −0.2453 *** −0.2100 *** 0.1738 *** ∆Dum(−1) −0.2570 *** 0.0271 −0.0453 *** −0.1173 *** 0.0158 ∆Dum(−2) 0.2051 *** 0.0286 ** −0.1513 *** −0.1475 *** 0.0540 *** C−1.2348 *** −3.2720 −0.7252 ** 0.9469 −5.9835 @Trend 0.0188 *** 0.0309 *** −0.0044 *** 0.0224 *** 0.0201 *** 29 Economies 2021,9, 148 being one of the most dynamically growing industries, in order to continue to expand, must account for new trends and regularities. In connection with the clearly noticeable aging of societies, especially in Europe, the tourism of elderly people is an area with high development potential. A wide group of recipients means vast development opportunities in many European regions, especially poorer ones. Regions lying in direct proximity to the border are usually remote, and remain to a lesser or greater degree marginalized in many ways, especially economic and political. The problem of peripheral location is a significant issue assumed by the European Union. In accordance with Growing Regions, Growing Europe: Fourth Report on Economic and Social Cohesion (2007), 26% of all regions are classified as remote regions (20% of the EU) and inhabited by one-quarter of the citizens. In countries of the EU, the main measure for classifying a given area as remote is GDP per capita below 75% of the EU average (according to the purchasing power parity). In many countries, the direction of development for remote regions that is provided by tourism is treated as one of the elements of multifunctional development. This results from the immense potential from the stimulation of other sectors as well as creating new places of work. Researchers into the development potential of silver tourism in the European Union noticed, as early as 2010, in accordance with the communication, Europe—The World’s No. 1 Tourist Destination—New Political Frameworks for the European Tourism Sector (2010) , that one of the greatest challenges for the European tourism sector is the progressing demographic change connected with the aging of the population. The continuation of senior policy in the tourism sector has its place in the financial programming period for the years 2014–2020. Although tourism was not included as a thematic objective for the regulation of the European structural and investment funds (ESIF), seeing as how it is more a center or sector of the economy than an objective, the regulation nevertheless anticipates many possibilities of thought-out investment in tourism. Tourism will continue to play a significant role in the planned financing from the ERDF program, as well as in investments connected with the maintenance, protection, promotion, and development of natural and cultural heritage1. The silver economy creates a new possibility for dealing with the problems of aging through a proactive approach to the market, which makes use of the production of goods and services resulting from the needs of an aging society. The increasingly better health conditions of elderly people, as well as raising awareness when it comes to assuming physical activity, facilitate the popularization of active tourism (Zieli´nska-Szczepkowska and ´ Zróbek-Ró˙ za´nska 2014). The elderly are undeniably specific clients, who possess large amounts of free time, and are thus a large potential source of economic growth. On the other hand, some of the seniors from remote regions are forced to deal with inadequate financial resources for the realization of long-range tourist expeditions. An answer to their needs may be the poorer border regions of European countries, which are abundant in natural and cultural assets, and which, at the same time, are facing the challenge of adapting their touristic offerings to the needs of the elderly. The aim of this article is to analyze senior touristic behavior, including an assessment of the motivations and decision-making issues of senior travelers, in 11 remote regions of nine European counters (Finland, Latvia, Poland, Slovakia, Hungary, Bulgaria, Spain, Ireland, and Greece) based on information gathered from 1705 questionnaires. In addition to presenting the results of the questionnaire studies carried out among seniors, the results of interviews with representatives of the tourism industry and local governments on the topic of the development of senior tourism are analyzed. This publication also makes use of subject literature, as well as statistical data pertaining to demographic forecasts. Strategic documents placed on the website of the European Commission, as well as information on the subject of the international project supporting the development of senior tourism, entitled “TOURAGE—senior tourism development in European remote regions”, were also used within the framework of the present study. 36 Economies 2021,9, 148 2. Literature Review 2.1. Elderly Tourists Segment Along with the dynamic increase in the touristic activity of the elderly observed in recent decades, the concept of senior tourism has been distinguished. The term is basically used to describe the spatial mobility of elderly people (Ole´sniewicz and Widawski 2015). At this point, it is worth defining the concept of old age. In the literature on the subject, there are many explanations of this term. They usually refer to the age at which a given person enters old age, as well as the terminology used to refer to these people: seniors, older adults, baby boomers, or the silent generation. Researchers of tourism define ‘senior travelers’ as people over the age of 55, with the term ‘older adults’ referring to people who are retired, typically at the age of 65 and older (Patterson 2006). Many scientific publications, on the other hand, use these two terms interchangeably, without any specific definition which would differentiate between the two. In works concerned with the use of leisure time, attention is drawn to the importance of the change of work status, from active work to a changeover to retirement, as a factor that has a particular influence on changes in the lifestyle of older people (Gee and Baillie 1999;Nimrod 2008). Other researchers on the subject draw particular attention to the age of seniors as well as the history interweaved in their life to date. According to Norman et al. (2001), this is of particular importance in the later tourism preferences of older people. Alcaide (2005 cited in Alén et al. 2012) states that many companies set the senior age at 55 years. According to this perspective, this is the age at which the consumer begins to sense different needs and starts to forecast and plan for aging. They are considered as part of the segment of the elderly in the banking system, which begins to differentiate and specialize treatment for them. Accordingly, this study defines the elderly as individuals who are 55 years old or older, as is usually and consistently defined in gerontology studies. Regardless of how a senior tourist is defined, attention is also paid to treating the phenomenon of senior tourism more broadly and not limiting it to merely issues connected with age. In the deliberations, a series of elements characteristic of this sector of tourism have been defined, such as the specific motivations of seniors, their large amounts of free time, the seasonality of their travel, and their physical or economic limitations (see Patterson and Balderas 2020;Huber 2019;Otoo and Kim 2018). The elderly tourist segment in the new panorama of social and business management can undoubtedly be taken as a growing and constantly evolving sector, and much research has been undertaken to unravel its specificities (Amaral et al. 2020). According to Le Serre (2008), the senior tourist segment represents a profitable source of revenue for companies linked to the tourism sector, not only because of its growing size, but also due to the availability of seniors and their time to travel. Otoo and Kim (2018) claim that motivation is the first step in exploring the prospects of the senior tourism segment. Continued research on the motivations of senior tourists reveals different types of motives for which seniors pursue travel. 2.2. Seniors’ Travel Motivations An increasingly high number of researchers on the subject deal with the study of the motivations of elderly people (e.g., Guinn 1980;Tongren 1980;Anderson and Langmeyer 1982;Romsa and Blenman 1989;Zimmer et al. 1995;Norman et al. 2001;Sellick and Muller 2004;Pestana et al. 2020). The tourism sector, seeing the high potential for the development of offers directed towards seniors, makes attempts at market segmentation (Panasiuk 2014) . Researchers hoping to meet these needs carry out studies on senior tourists to categorize them, accounting for various factors, such as demographic and psychological factors (Horneman et al. 2002) , lifestyle and attitudinal factors (Marthur et al. 1998;Muller and O’Cass 2001), and educational and income levels (Javalgi et al. 1992;Jang and Ham 2009). Researchers studying tourism classify elderly people in different ways due to their behaviors, indicating diverse types of senior tourists (Table 1). 37 Economies 2021,9, 148 Table 1. Various classifications and labels for different types of older tourists (own elaboration based on Sedgley et al. 2011). Authors Types of Senior Tourists You and O’Leary (1999) “passive visitors” “enthusiastic go-getters” “cultural hounds” Kim et al. (2003) “active learner” “relaxed family body” “careful participant” “elementary vacationer” Morgan and Levy (1993) “pampered relaxers” “highway wanderers” “global explorers” “independent adventurers” “anxious travelers” Moschis (1996) “healthy indulgers” “healthy hermits” “ailing out goers” “frail recluses” Cleaver et al. (1999) “Nostalgics” “Friendlies” “Learners” “Escapists” “Thinkers” “Status-Seekers” “Physicals” The existing studies in the field of seniors’ travel motivations are based on the two dimensions of motivation, that is, ‘pull’ and ‘push’ factors (Crompton 1979;Dann 1981; Iso-Ahola 1982;Uysal and Hagan 1993;Uysal and Jurowski 1994;Cha et al. 1995;Klenosky 2002;Chen and Wu 2009). The distinction between push and pull factors appeared in the subject literature in the context of motivation for the first time thanks to Dann (1977), who, based on the work of Tolman (1959), presented the answer to the question of “what makes tourist travel?” He included all outside factors which attract a tourist to a given place, such as, e.g., the sea, mountains, sun, beach, etc., as pull factors. In the context of seniors, other researchers have shown that the main attributes of a destination that attract seniors are: natural, cultural, and historical attractions, and good weather conditions (Norman et al. 2001); security, cost of the trip, and cultural and natural attractions (Wu 2003); places of historical interest, medical service (facilities), and the weather condition (Huang and Tsai 2003). Push factors, on the other hand, included internal factors stemming from the predispositions of the actual tourists—their values, experiences, and desires, such as sentimentalism, the wish to escape from the hustle and bustle of the city, etc. (Norman et al. 2001;Wu 2003; Huang and Tsai 2003;Jang and Wu 2006;Sangpikul 2008;Chen 2009). In accordance with this theory, people travel because they are “pushed” by internal factors and “pulled” by external factors (Uysal et al. 2008). According to Uysal and Hagan (1993), individuals are pushed into making a travel decision by motivational variables, as well as being pulled or attracted by the destination area. Pull factors are mainly related to the attractiveness of a given destination, such as beaches, accommodation, recreation facilities, cultural and historical resources, whereas push factors are origin-related and refer to the desires of the individual traveler, e.g., rest and relaxation, health, adventure, or prestige. Travel motivations according to the push and pull factors for traveling are also an issue that relates to elderly tourists. According to Widiyastuti and Ermawati (2019), the elderly’s travel decisions are influenced by factors arising from themselves (internal factors, such as spiritual needs, health needs and health condition, working, having money, meeting 38 Economies 2021,9, 148 people, the availability of travel companions, traveling for recreation, etc.) and factors which are offered by the destination (external factors, such as the suitability of the location to the elderly people’s condition, accessibility and convenience in accessing information, etc.). Many studies are striving to answer the question of what senior tourists’ motivations for traveling are (Cleaver et al. 1999;Backman et al. 1999;Fleischer and Pizam 2002; Horneman et al. 2002;Huang and Tsai 2003;Jang and Wu 2006). Motivations for travel cover a broad range of human behaviors and experiences, and the typical list of these motivations might include relaxation, excitement, social interactions with friends or family, adventure, status, age, and escape from routine or stress. All of them play a significant role in the decision-making process. 3. Methodology The distinct deep demographic changes taking place in recent years are not only a topic of scientific inquiry but also a matter of strategic interest, both at the level of individual countries and regions, as well as for actual EU institutions. In 2010, the European Commission, in a communication entitled Europe, the world’s No. 1 tourist destination: A new political framework for tourism in Europe, revealed for the first time that, in addition to challenges such as economic crisis, climate change, and the development of new technologies, the European tourism sector should also take into account the issues that result from the wide-reaching aging of society. According to the European Commission, tourism will play an immense role in the development of many European regions, especially the poorer ones2. The above changes will require a fast reaction from the tourism sector so that it can maintain its current level of competitiveness. Seniors possess buying power as well as free time. In order to fully take advantage of the economic potential of the silver economy, it is essential to identify the needs of and create an adequate offer for the senior tourist. A response to the abovementioned challenge was the realization of an international project (entitled “TOURAGE—Developing Senior Tourism in Remote Regions”) in 2012– 2014, financed by the INTERREG IV C Interregional Cooperation Programme. The project was created thanks to the intense cooperation of regions affiliated in the Network of Eastern External Border Regions (NEEBOR), which in many cases are distant from each other and scarcely populated, whose economic development and employment are faced with great challenges. This was also observed by regional authorities, accounting for the development of tourism in their regional development strategies. Eleven partners from nine European Union member states were involved in the realization of the project (Figure 1): •The Regional Council of North Karelia, Finland (Lead Partner); •The Bourgas Regional Tourist Association, Bulgaria; •The Region of East Macedonia and Thrace, Greece; •The Lake Balaton Development Coordination Agency, Hungary; •The Szabolcs-Szatmár-Bereg County Regional Development and Environmental Management Agency, Hungary; •The West Regional Authority, Ireland; •The Vidzeme Planning Region, Latvia; •The Association of Polish Communes of Euroregion Baltic, Poland; •The Podkarpackie Region, Poland; •The County Council of Granada, Spain; •The Regional Development Agency of the Prešov Self-Governing Region, Slovakia. 39 Economies 2021,9, 148 Economies 2021, 9, x FOR PEER REVIEW 6 of 24 • The Podkarpackie Region, Poland; • The County Council of Granada, Spain; • The Regional Development Agency of the Prešov Self-Governing Region, Slovakia. Figure 1. Geographical coverage of the partnership of the “TOURAGE—Developing Senior Tourism in Remote Regions” project. In the first part, an extensive review of the literature (desk research) focusing on the motivation of senior tourists, their needs, and the decision-making process in the case of travel requirements was conducted to identify travel motivations expressed by senior tourists from remote regions of Europe. Information cited in the literature was selected to be included in the questionnaire. In the second part of the research, a questionnaire was developed to collect quantitative data. A survey was conducted among local seniors. To better understand the needs of this target group, I had to develop an adequate and comprehensive questionnaire for them. With this local senior questionnaire, I sought to identify what kind of traveling habits, motivations, and needs the regional seniors have while they are living on retirement pensions. The aim of the questionnaire was to seek out important information regarding how we should develop the regional touristic services so that they meet the needs of senior citizens. The questionnaire comprised 22 questions (11 questions on the motivations and needs of senior tourists in Europe, three region-specific questions to bring added value for the local authorities, and eight questions regarding background information on the general characteristics of seniors). Thanks to the realization of the TOURAGE project, I used different types of occasions to meet with local seniors and ask them for their opinions on tourism-related issues: - meeting with local senior clubs; - distributing questionnaires at exhibitions; Figure 1. Geographical coverage of the partnership of the “TOURAGE—Developing Senior Tourism in Remote Regions” project. In the first part, an extensive review of the literature (desk research) focusing on the motivation of senior tourists, their needs, and the decision-making process in the case of travel requirements was conducted to identify travel motivations expressed by senior tourists from remote regions of Europe. Information cited in the literature was selected to be included in the questionnaire. In the second part of the research, a questionnaire was developed to collect quantitative data. A survey was conducted among local seniors. To better understand the needs of this target group, I had to develop an adequate and comprehensive questionnaire for them. With this local senior questionnaire, I sought to identify what kind of traveling habits, motivations, and needs the regional seniors have while they are living on retirement pensions. The aim of the questionnaire was to seek out important information regarding how we should develop the regional touristic services so that they meet the needs of senior citizens. The questionnaire comprised 22 questions (11 questions on the motivations and needs of senior tourists in Europe, three region-specific questions to bring added value for the local authorities, and eight questions regarding background information on the general characteristics of seniors). Thanks to the realization of the TOURAGE project, I used different types of occasions to meet with local seniors and ask them for their opinions on tourism-related issues: - meeting with local senior clubs; - distributing questionnaires at exhibitions; - sending questionnaires to local senior groups. The questionnaire consisted of two parts. The first part included questions regarding the travel behaviors and trip characteristics of the respondents. It was designed to gather 40 Economies 2021,9, 148 opinions on travel motivations and the needs of seniors from remote regions of Europe, including questions on the travel preferences of seniors, their travel plans, the sources of information they used in their decision-making process, popular destinations for their holiday trips, the modes of transportation they used when traveling, and the barriers that they feel discourage travel. Senior tourists were also asked to give opinions on a five-point Likert scale (1—no importance to 5—extremely important). One of the questions included seven closed attributes and one open attribute concerning their motivations for traveling, in which seniors were asked to rate the perceived importance of each of the attributes for considering their preferences. The last question of the first part included 31 closed attributes and one open attribute, covering the major touristic components of destination selection, including, for example, accommodation, accessibility, natural and cultural attractions, and public services. The second part dealt with the personal characteristics of the respondents, gathering data on their gender, age, length of retirement, place of residence, marital status, educational level, and annual income compared to the national yearly average of retirees in each region. The content validity of questionnaire items was evaluated by tourism professionals from each region and one scientific expert. Subsequently, a pilot test was conducted to assess how well the research instrument works. To increase the variety of respondents, the questionnaire was translated into Polish, Finnish, English, Latvian, Slovak, Hungarian, Bulgarian, Spanish, and Greek. Questionnaires were distributed and collected in 2014 in the TOURAGE project regions. As a result, 1705 questionnaires were filled and analyzed, amounting to an average of 142 per region (Table 2). Table 2. Number of filled questionnaires in remote regions. Region Country Number of Filled Questionnaires Percentage North Karelia Finland 183 10.73 Vidzeme Latvia 177 10.38 Baltic Euroregion—Pomorskie Poland 154 9.03 Baltic Euroregion—Warmia-Mazury Poland 47 2.76 Podkarpackie Poland 150 8.80 Presov Slovakia 150 8.80 Szabolcs-Szatmár-Bereg Hungary 129 7.57 Balaton Hungary 150 8.80 Bourgas Bulgaria 150 8.80 Granada Spain 176 10.32 West Ireland Ireland 129 7.57 East Macedonia and Thrace Greece 110 6.44 Total 1705 100 Two seminars dedicated to the elderly in tourism were also organized during the realization of the studies. The participants of the meetings were representatives of local governments, organizations affiliated with and operating on behalf of seniors, academic institutions, and tourism businesses, with whom interviews on the following topics were carried out: the possibility of using the potential of elderly people in the tourism industry, the assessment of the quality of the existing touristic offerings for seniors, the role of local governments in the direction of supporting the touristic activity of elderly people, and the interest of entrepreneurs in the elderly as the recipients of tourism services. 4. Results 4.1. Demographic Characteristics The seniors answering the questionnaire were mainly women (69%—1176 answers, Table 3). This confirms the demographic fact that women are a majority of elderly people. It also signals that they are more active participants in activities where the questionnaires 41 Economies 2021,9, 148 were distributed (senior club activities, exhibitions organized for seniors). This higher representation of women also affects the results of the questionnaire, as the answers concerning the motivations and needs of seniors with respect to tourism reveal the interests of women more than men. Table 3. Demographic characteristics of the respondents. Demographic Characteristics Number of Respondents Percentage Gender Male 529 31.03 Female 1176 68.97 The average age of seniors (68.4 years old) 1590 93.26 The average retirement period (9.7 years) 1504 88.21 Marital status * Married 884 51.86 Single 160 9.38 In a relationship 47 2.76 Widowed 414 24.28 Divorced 115 6.74 Education ** Elementary 305 17.89 Secondary School 438 25.69 Technical/vocational 506 29.68 University degree 350 20.53 Employed as a retirement pensioner *** Yes, full time 153 8.97 Yes, part time 107 6.28 Yes, as an entrepreneur 78 4.58 No 1253 73.49 Annual income **** Deeply under the average 131 7.68 Under the average 389 22.82 On average 543 31.85 Over the average 329 19.30 More than double of the average 57 3.34 Notes: * 26 of the respondents did not answer; ** 40 of the respondents did not answer; *** 51 of the respondents did not answer; **** 90 of the respondents did not answer. The average age of seniors answering the questionnaire was 68.4 years (1590 answers). There was a balanced response from young and older senior groups. Therefore, the answers to the questions show good representativeness of all age groups of seniors. The oldest senior answering the questionnaire was a 95-year-old Greek citizen. The seniors involved (1504 who filled out the question) had been retired for almost 10 years (9.7 years) on average. Most of them were married (52%) and 24% were widowed. When talking about tourism, it is important to understand that a high ratio of this group are single or living alone as a widow (35%). Specific senior club activities and especially tourism group tours target these seniors, who are looking for travel companions. The responding seniors (1599) had a balanced educational background. Results show that 22% of the respondents had a university degree, 19% had completed elementary school, and 59% had finished secondary education. This balanced level ensured good representativeness of seniors with all types of educational backgrounds in the questionnaire. One-fifth of the pensioners were still working (9% full time, 6% part time, and almost 5% as an entrepreneur). The high number of full-time employees, in particular, reveals two tendencies. On the one hand, as shown by answers on income level and the main barriers to senior tourism (presented above) in the remote regions of Europe, there is a need for senior employment because of economic reasons. Nevertheless, people working during 42 Economies 2021,9, 148 the first period of retirement is also a trend in wealthier countries, as senior citizens are feeling active enough to be present on the labor market. Another important message that arises from the answers is that seniors are open to entrepreneurship—78 seniors answered that they were running their own business. For the question regarding their economic status, only 1449 seniors provided answers, most likely due to its sensitivity (the response rate was 85%). Analyzing the average annual income of respondents is quite critical. Usually, seniors who can afford to engage in tourism are those with at least an average level of earnings, and who can cover their daily costs and have some savings after paying the bills. The annual income of 32% of respondents is average, and a quarter of them reported that they have an income over the average (almost 20%) or even double the average income (more than 3%), although 23% answered that their annual income is under the national yearly average. Critically, how are seniors able to share in the experience of tourism in remote regions of Europe if their incomes do not allow it? This is a question which pertains to a key hypothesis of this study. Significantly, almost 8% of seniors answered that their income is deeply under the average, including especially seniors in Poland, Slovakia, Bulgaria, and Hungary, a disproportionate number of whom categorized themselves as being in this group. This number is a basic signal that, especially in these countries, more attention should be paid to the social tourism of seniors as well as special support schemes. 4.2. Travel Patterns of Seniors The first six questions of the survey were related more to the general travel patterns of the seniors (Table 4). Almost 12% of respondents did not travel since having retired. Most respondents prefer to have only shorter holidays, similar to working periods; only a few of them stated that they stay for longer periods (with 9% staying for 2–3 weeks, and only 2% for 1 month or more). The hypothesis that seniors are willing to spend more time on holiday is therefore not true; their travel patterns are quite similar to active citizens, with only a small portion of seniors spending more time on holiday. The results show that even though the majority of seniors prefer spending their holidays in their home country, there is a huge number of them (one-quarter of seniors) who still prefer to travel abroad during their holidays. This supports the claim that seniors have important market potential. The regional strategies should focus more on how to reach international senior tourists, and how to attract them to the respective regions. The seniors are open to traveling abroad as well during their retirement; this is more a question of whether service providers can understand their specific needs. I will try to answer these questions in addition to what the specific motivations and needs of senior tourists are. Most of the seniors (44%) prefer to organize their travel individually. There was one important remark on the role of different pensioner organizations and associations, as well as some social tourism schemes being mentioned by respondents. Pensioner organizations (such as Active Retirement Ireland, pensioners’ clubs, and thematic pensioner associations) are key players in organizing group travels for seniors. Some other associations (such as tourism, religion, and associations for the handicapped) are also coordinating the travel of seniors (although they are not specifically focusing on seniors in their offers). The social tourism scheme of the National Public Health Organization of Finland and the SOREA program of Slovakia were also mentioned as a specific way of organizing a holiday. Relatives and ex-coworkers are important travel companions, in addition to being mentioned as organizers of holidays. Personal experiences (16%), family (15%), and friends (15%) are the most important sources of information for seniors making decisions regarding their travels. The media and social media are not relevant sources of information, although the internet was mentioned in more than 4% of the answers as a source of information. For the category of ”other”, some specific sources of information were mentioned: the role of pensioner organizations (e.g., Active Retirement Clubs for seniors in Ireland, Universities for Seniors in Poland, and 43 Economies 2021,9, 148 Church Organizations in Poland and Spain) is crucial, though the suggestions of doctors were also mentioned. Table 4. General travel patterns of the seniors. Travel Pattern Number of Respondents Percentage Length of holidays 1–3 nights 398 23.34 4–7 nights 483 28.32 8–10 nights 312 18.30 2–3 weeks 156 9.15 1 month or more 37 2.17 Have not traveled on retirement 201 11.79 No answer 66 3.87 Destination of travel during retirement Abroad 402 25.08 In home country 967 60.32 No answer 234 14.60 Organization of holiday trips during retirement Travel/accommodation organized individually 641 43.58 Travel/accommodation booked through a travel agency 390 26.51 Package tour/all-inclusive holiday booked via internet 59 4.01 Package tour/all-inclusive holiday booked through a travel agency 120 8.16 Other 31 2.11 No answer 230 15.63 The most important information sources for making decisions regarding travel * Own personal experience 616 16.31 Relatives and family 559 14.80 Friends 551 14.59 Recommendations of other people 264 6.99 Guidebooks and magazines 237 6.28 Travel catalogs, brochures 208 5.51 Internet 166 4.40 Travel/tourist agencies 151 4.00 Media (newspaper, radio, TV) 65 1.72 Social media (Facebook, Twitter, Instagram, blogs, etc.) 18 0.48 Other 87 2.30 No answer 854 22.62 Usual transportation mode on holiday during retirement Airplane 243 15.66 Boat 22 1.42 Train 176 11.34 Bus 543 35.01 Car 337 21.73 Motorbike 0 0 Bicycle 3 0.19 Other 6 0.39 No answer 221 14.25 Travelmates during retirement pension Spouse/partner 666 41.89 Own child/children 81 5.09 Grandchild/children 27 1.70 Other relatives 41 2.58 Friend(s) 271 17.04 Alone 104 6.54 Group travel with people you know 157 9.87 Group travel with people you have not met before 13 0.82 Other 13 0.82 No answer 217 13.65 Notes: * Respondents could choose three answers. To better understand the seniors’ decision-making process, it is worth mentioning concrete information sources (mentioned under other sources by seniors): books and dreams from their youth, which can be sources of a decision. This also shows that seniors 44 Economies 2021,9, 148 are sentimental, and mass media does not provide the direction for their travels in most cases (only 2% of respondents mentioned it as a source of information). Traveling by bus was the most common mode of transport for seniors (35%). Using their cars for shorter distances was also mentioned (22%). Airplane travel is also popular (16%). Only a few of the respondents reported using more sustainable modes of transport (such as a bicycle or a boat). As another mode of transport, a few of them mentioned camping caravans, which is a way of traveling for longer periods and to more rural locations (e.g., in Poland, Spain, and Ireland). Besides the spouse/partner (42%), friends are the most common travel mates based on the answers received (17%). Seniors usually travel with friends, either in smaller groups (10%), or with specific travel groups that focus on seniors (1%). It is important to know the people who one travels with, and therefore the third largest group of travel companions were found to be relatives: their own children, grandchildren, or other relatives (more than 9%). Around 1400 answers relating to the season seniors are willing to travel in were also provided (Table 5). Table 5. Seasons of senior tourism. Season Usually YES Usually NO Total Spring 572 813 1385 Summer 732 676 1408 Fall 696 696 1392 Winter 237 1150 1387 The results show that seniors from remote regions prefer to travel in the summer as well, but traveling in the spring or fall is also an acceptable period for this age group. The results show that winter is the least preferred season for holidays, due mostly to the security aspect connected with the specific weather conditions. 4.3. Motivation and Needs Three questions in the survey focused especially on the motivation and needs of seniors. These specificities could be important in developing new destinations and services specially designed for senior citizens. Financial reasons and health problems are the main barriers to travel for seniors (Table 6) . Financial issues are a specificity of peripherality and especially low-income areas of Europe, which shows the importance of social tourism programs for seniors. Even in the Finnish region, North Karelia, the respondents gave the highest ranking to this barrier, though it was perceived as such by only 34% of the respondents (contrary to the average level of 74%). It is also interesting that 59% of the respondents consider health problems to be a barrier (the second highest rank). This barrier was noted by only 23% of respondents from the North Karelia region (Finland). Ranked third and fourth highest answers were the lack of time and the lack of interesting locations, respectively, which is interesting seeing as how there is a financial divide behind these answers as well. The lack of time is mentioned more in poorer regions (where seniors are still working), while the lack of proper supply was quoted more frequently in the wealthier regions. The third group of barriers is the lack of travel companions, insufficient transportation connections, and concerns regarding the safety of the destination and the journey (from 26% to 25%). They are more related to the logistics of senior tourism, and are in line with the belief that this group of people prefers to travel in groups (not alone), looking for a safe holiday, where they can obtain all the necessary quality services, and the destination should be easily accessed by direct transportation links (see answers relating to transportation modes). 45 Economies 2021,9, 148 vacationer. Considering the results of this study, as well as the current situation related to the COVID-19 pandemic, this type of tourist, especially in the elderly group, is extremely important in the tourism economy nowadays. The findings of this investigation also provide some important practical implications for planners and marketers. European remote regions must develop certain policy measures and strategies in the public and private sectors. Physical improvement of tourist destinations, the development of easy and convenient accessibility, support for accommodation and attractions, and facility improvement for senior tourists should be taken into consideration if remote regions want to attract more senior tourists. Hopefully, most of the regions which were involved in this research started preparing and implementing special programs dedicated to seniors after the project’s completion. That may be good practice for other regions wishing to open up to senior tourism development. It must be said, however, that, despite efforts and due diligence, this study does not exhaust all aspects of the issue. Therefore, the results that were obtained should be interpreted taking into account the specificity of the assumptions and ranges described. Considering that the conducted study included a sample of only 11 remote regions of nine European countries, this limits the generalizations that can be drawn from its results. At the same time, it should be emphasized that the presentation of the profile of a tourist—a senior coming from peripheral European regions—and their preferences regarding experiences resulting from completed and planned tourist trips, is an important contribution to future research, which can be built upon not only with a larger sample, but also by extending the research to other countries. The impact of the COVID-19 pandemic on travel by seniors is of particular interest for future research. Tourism and travel have been reduced to a minimum during the COVID-19 pandemic. It is expected that domestic tourism will be the first to recover after the end of the lockdowns, which will lead to a major shift in travel flows. Cities with a high population density, dependent on festival and event tourism, have a disadvantage, while destinations in rural areas have an advantage. The study shows that seniors are very keen to travel to small towns and rural areas. Interesting research questions in the context of further development of the senior tourism market are: which destinations and tourist attractions will benefit from the COVID-19 crisis, how will tourism demand for urban and rural tourism change in the recovery phase, and how important is the issue of sanitary safety in the organization of tourist trips among seniors? These and other questions are novel, and I intend to answer them soon. Funding: This research was funded by the TOURAGE project (“TOURAGE—Developing Senior Tourism in Remote Regions”), co-financed by the EU funds: INTERREG IV C Interregional Cooperation Programme 2007–2013, European Regional Development Fund The author of this publication was a scientific expert involved in the implementation of this project. Data Availability Statement: The analyses were made based on the information contained in the TOURAGE project, available at www.eurobalt.org.pl/3-projekt,tourage (accessed on 20 May 2020). The initial data on interviews and the survey method presented in this study, collected separately from tourists, are available on request from the corresponding author. Acknowledgments: The author would like to thank the project “TOURAGE—senior tourism development in European remote regions”, funded by the INTERREG IV C Interregional Cooperation Programme for enabling the work required for the article. The author would like to thank the senior tourists, who agreed to participate in the interviews. Conflicts of Interest: The author declares no conflict of interest. The funders had no role in the design of the study, in the collection, analyses, or interpretation of data, in the writing of the manuscript, or in the decision to publish the results. 52 Economies 2021,9, 148 Appendix A Table A1. Good practices for senior tourists from European remote regions. Region (Country) Type of Tourism Offer Vidzeme (Latvia) Health and exploratory tourism Ligatne Rehabilitation Center—created based on the former Soviet bunker, measuring 2000 m2and built at a depth of 9 m. - Rehabilitation, medical, and leisure services for the elderly and disabled from the entire territory of Latvia and abroad (2000 patients); - Rehabilitation offer for people with cardiovascular and pulmonary diseases as well as back and joint problems; - An offer of therapeutic activities for people with neurotic disorders or suffering from Akureyri disease; - An offer of trips around a bunker with communist-style attractions (meals, customs). http://www.rehcentrsligatne.lv (accessed on 22 May 2021). Cultural and active tourism Museum of Regional History and Art in Vamiera—located in the very heart of the historical center of Valmiera within the ruins of the old castle of the Livonian Brothers of the Sword. - The museum offer (over 60 thousand exhibits from various periods document the rich history of the city and region); - Trips, lectures, and various educational programs; - Offers of educational workshops on the topic of herb cultivation and brewing herbal teas; - Walking trips for seniors with a museum guide around the area including a trip on a water taxi on the Gauja river; - An offer of a 30–40 min cruise down the Gauja river through the historic center of Valmiera makes for an interesting touristic offer, especially for handicapped people and the elderly (the tourist attraction was adapted to the needs for people with problems with mobility). http://www.diklupils.lv (accessed on 21 May 2021). North Karelia (Finland) Culinary tourism Karelia a la Carte chain—a collaboration of over 80 small businesses from the agrotourism, culinary, and crafts industry, aimed at creating a widely recognizable brand of promoting the touristic values of North Karelia within the country and abroad. - Culinary trips for seniors connected with visiting cultural heritage objects as well as getting to know the local history and lifestyle of inhabitants living in the area of Karelia; - Cookbook with recipes from Karelian cuisine and a guidebook of the culinary history of Karelia. http://www.pohjois-karjala.proagria.fi (accessed on 22 May 2021). Active tourism Tourist Guide for the Northern Periphery (TG4NP) - The project will provide site-specific, locally accessible, multimedia information, delivered to visitors of remote areas. Basic services include an introduction to the area and will feature useful information, natural heritage; - Information provided includes links to accommodation databases, weather reports, local eateries, points of interest, and other attractions. This is developed using multimedia content, and addresses the need for the revival of the area’s unique culture; - Easily accessible services for the elderly and disabled—all information can be obtained easily, with the use of a mobile phone and, if need be, using speakerphones. Fond of the Forest—Forest Wellbeing Tourism - Touristic offerings based on the virtues of the local biosphere, forests, and local Karelian culture. It connects the natural beauty of Karelian nature with local cuisine, culture, herb cultivation, and herbal medicine. - For the senior tourist, services are connected with active tourism based on nature, tranquillity, and the building of well-being, e.g., the offer of the Nevala agrotourism focused on the development of shepherd tourism and relaxation in peace and quiet. http://www.pohjois-karjala.proagria.fi (accessed on 22 May 2021). Ecotourism Accessible tourism - Ecotourism services are avaialbe for the elderly and disabled. - Publication of the guide entitled ”Happiness and benefits stemming from accessibility”. The guide contains practical advice for entrepreneurs in the tourism industry wishing to modernize their tourism base in terms of improving accessibility to the elderly and disabled. https://www.accessibletourism.org/?i=enat.en.enat_projects_and_good_practices.869 (accessed on 29 May 2021). 53 Economies 2021,9, 148 Table A1. Cont. Region (Country) Type of Tourism Offer Grenada (Spain) Cultural tourism “Alhambra for Seniors” Programme - Alhambra is at the top of the list of the most frequently visited places, not only in the region but also in all of Spain; the city is famous for the Alhambra Palace, which in 1984, was included in the UNESCO World Heritage List; - Seniors 65+, as well as retirees of the European Union, have the right to a discounted ticket, allowing them to go on a tour of Alhambra (for seniors of the Andalusia region, it is free); - Special sightseeing programs for seniors as well as educational programmes (e.g., historical), are prepared with the needs of seniors in mind. http://www.alhambradegranada.org/en/ (accessed on 21 May 2021). Active tourism “Tropical Tourism Grenada” Programme - Covers 138 territorial government units and is managed by the Delegacy on Employment and Regional Development of the Granada Province Council; - The target group are inhabitants of the region above the age of 65 as well as disabled people residing permanently in the region of Granada; - The regional government is the owner of the hotel complex located in Almuñecar. During the so-called ”low season”, seniors can receive free accommodation for 4 days/3 nights. Approximately 3000 elderly people take part in the program annually. - The senior accommodation program covers entertainment, light motor exercises, and social activities. There is great interest in the offer. http://www.turismotropical.com/ (accessed on 21 May 2021). Mayo (Irland) Active tourism Golden years Holidays Programme—offered by the Westport Woods hotel located in the seaside town of Westport. - An offer of attractive stays for older people above the age of 55 at the end of the tourist season; - The program ensures conveniences for seniors with disabilities, including easy access to the reception and more important rooms, free bus transport between the hotel and train station, free travel by city public transport, as well as individual conveniences for regular guests. http://www.westportwoodshotel.com/about/ (accessed on 21 May 2021). Warmia and Mazury (Poland) Pilgrimage and culinary tourism Pilgrimage route “Saint Warmia”—connects 16 towns offering senior tourists access to places of religious importance: sanctuaries, pilgrim’s routes, and sacred buildings connected with various events of a religious nature, e.g., Gietrzwałd—the only palace in Poland with the revelation of the blessed Virgin Mary. There are also a castle and a cathedral in Olsztyn, Calvary in Głotowo, and a hall church in Dobre Miasto, Stoczek Klasztorny and ´ Swi˛eta Lipka—referred to as the Cz˛estochowa of the north, on the border of Warmia and Masuria. - Sacred buildings offer cheap accommodation for senior tourists, whereas local restaurants located near the building offer traditional regional cuisine, included in the “Culinary heritage of Warmia, Masuria and Powi´sle”; - For motorized tourists, a guidebook and audiobook of the trail has been prepared; - Local travel offices in cooperation with representatives of the hotel, the gastronomical industry, as well as transporters and guide pilots, provide a complex offer for individual people as well as organized groups (including seniors) along the trail of holy places in Warmia. https://www.stoczek.pl/Swiete_miejsca_Warmii (accessed on 20 May 2021). 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[CrossRef] 56 economies Article Holiday Rentals in Cultural Tourism Destinations: A Comparison of Booking.com-Based Daily Rate Estimation for Seville and Porto Miguel Ángel Solano-Sánchez 1, JoséAntónio C. Santos 2,3,*, Margarida Custódio Santos 2and Manuel Ángel Fernández-Gámez 4 Citation: Solano-Sánchez, Miguel Ángel, JoséAntónio C. Santos, Margarida Custódio Santos, and Manuel Ángel Fernández-Gámez. 2021. Holiday Rentals in Cultural Tourism Destinations: A Comparison of Booking.com-Based Daily Rate Estimation for Seville and Porto. Economies 9: 157. https://doi.org/ 10.3390/economies9040157 Academic Editor: Aleksander Panasiuk Received: 16 September 2021 Accepted: 11 October 2021 Published: 20 October 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). 1Faculty of Business Law and Economic Sciences, University of Cordoba, 14002 Cordoba, Spain; [email protected] 2School of Management, Hospitality and Tourism (ESGHT) and Centre for Tourism, Sustainability and Well-Being (CinTurs), University of the Algarve, 8139 Faro, Portugal; [email protected] 3Faculty of Tourism, University of Malaga, 29016 Malaga, Spain 4Department of Finance and Accounting, University of Malaga, 29016 Malaga, Spain; [email protected] *Correspondence: [email protected] or [email protected] Abstract: Multiple variables determine holiday rentals’ price composition in cultural tourism destinations. This study sought, first, to test a model including the variables with the greatest impact on tourism accommodations’ prices in these destinations and, second, to demonstrate the proposed model’s applicability to cultural city destinations by identifying the adaptations needed to apply it to different contexts. Two cities were selected for the model application—Seville in Spain and Porto in Portugal—both of which are located in different countries and are well-known cultural tourism destinations. The data were extracted from Booking.com because this accommodations platform has adapted its offer to the sharing economy, becoming one of the most important players in the market, and because research on holiday rentals using data from Booking.com is scarce. The results show that the variables used are relevant and highlight the adaptations necessary for specific cultural tourism destinations, thereby indicating that the model can be applied to all cultural tourism destinations. The proposed approach can help holiday rental managers select the correct tools for determining their accommodation units’ daily rates according to their product and marketing context’s characteristics. Keywords: daily rate pricing; holiday rentals; hedonic pricing method; Booking.com; sharing economy 1. Introduction The rapid development of information and communication technologies has profoundly transformed the tourism and hospitality industries (Cheng et al. 2018,2019; Dickinger et al. 2017;Fernández-Gámez et al. 2020;Suzilo 2020). Consumers have changed the way they search for information, book services and communicate their experiences, thereby disrupting traditional distribution routes (Fernández-Gámez et al. 2020;Mohamad et al. 2021;Núñez-Tabales et al. 2020;Pinto and Castro 2019;Suzilo 2020) and making online booking the main channel of business (Cheng et al. 2019;Fernández-Gámez et al. 2020; Suzilo 2020). New business models have emerged such as online reservation systems and sharing economy platforms, also called peer-to-peer (P2P) platforms (Fernández-Gámez et al. 2020;Guttentag 2015;Suzilo 2020;Veiga et al. 2018). Customers’ decision-making processes increasingly rely on comments posted by tourists who have experienced the relevant products and/or services (Cheng et al. 2019;Fernández-Gámez et al. 2020;Suzilo 2020;Veiga et al. 2018) rather than on official websites, advertising or travel agent information (Fernández-Gámez et al. 2020 ;Gemar et al. 2019;Suzilo 2020;Veiga et al. 2017). In addition, the connection between demand and supply has become more accessible to Economies 2021,9, 157. https://doi.org/10.3390/economies9040157 https://www.mdpi.com/journal/economies 57 Economies 2021,9, 157 consumers through new online distribution channels, allowing people to book accommodations provided by their peers rather than by travel or rental companies (Veiga et al. 2017, 2018;Zekan et al. 2019). Millennials are among the most intensive users of P2P accommodations, as they look for authentic experiences, including living in residential areas among local populations (Lu and Tabari 2019;Suzilo 2020;Veiga et al. 2017). This generation also tends to reject traditional tourism structures and looks for places that they do not perceive as tourism destinations (Veiga et al. 2017). For private owners, P2P rentals is a way to monetise otherwise unused residential spaces or redefine their use for tourism purposes (Zekan et al. 2019). Various factors attract tourists to residential areas, including historic quarters’ traditional architecture, local people’s everyday life and authentic experiences of cities (Maitland 2008) . However, tourists and residents do not always coexist easily, and, in some cases, encounters can create friction between them (Davidson and Infranca 2016; Veiga et al. 2017,2018;Zekan et al. 2019). Another phenomenon frequently linked to the sharing economy in some cities is overtourism, as it tends to concentrate an excessive number of tourists in city centres, historic quarters and residential areas (Veiga et al. 2017, 2018). According to a comparative study of four European cities, ‘only a minority of Airbnb listings can be classified as sharing economy services, while commercial offers constitute a significant share of listings on the platform’ (Gyódi 2019, p. 536). Reinhold and Dolnicar (2021) also question the use of the terms sharing economy, collaborative consumption and P2P accommodations to describe Airbnb and similar platforms’ products. The original idea of empowering ordinary people to purchase access to private owners’ spare rooms has been replaced in most cases by companies trading short-term rentals for commercial purposes. Interactions between hosts and guests have been significantly reduced, as guests can book instantly, and the relevant individuals’ photos are no longer displayed until the booking is confirmed (Reinhold and Dolnicar 2021). Hosts frequently turn out to be agencies that act as intermediaries, receiving a commission for their services. The latter comprise inserting listings into booking platforms, managing bookings and check-in, assisting guests, if needed, during their stay and checkout and cleaning and maintaining rental properties (Reinhold and Dolnicar 2021). The lodgings’ owners do not need to care about how well any of these procedures go, so no authentic hosts are involved, and owners have no contact with guests. Regardless, the true sharing economy is an urban phenomenon that has extended tourism to new city areas (Davidson and Infranca 2016;Veiga et al. 2017,2018) and contributed to urban transformation and gentrification (Davidson and Infranca 2016;Gant 2016;Veiga et al. 2018). This economy has also funded the regeneration of buildings in historic quarters and city centres that otherwise would have remained vacant. These structures have thus suddenly become valuable assets (Davidson and Infranca 2016). One of the two largest platforms for accommodation bookings and holiday rentals, Booking.com, was the platform that first disrupted the entire accommodations sector. Airbnb did the same for vacation rentals. Both platforms replaced traditional intermediaries, such as tour operators and travel agencies, by allowing customers to book directly through their platforms. However, these websites’ scope of business has changed, as Booking.com is expanding into the vacation rental sector, and Airbnb is entering the hotel sector (Cardoso 2018). An increasing number of vacation rentals are listed on both platforms in order to attract more clients (Cardoso 2018). Research applying the hedonic pricing method (HPM) to the sharing economy’s accommodation prices is relatively new (Tong and Gunter 2020) and restricted mainly to Airbnb. Because Booking.com has expanded into the holiday rental sector relatively recently, studies connecting this platform to the sharing economy are still scarce. The same can be said about comparative investigations of vacation rentals in cultural city destinations. More specifically, no researchers, to date, have compared vacation rentals’ price composition in two or more urban cultural tourism destinations listed on Booking.com. Therefore, 58 Economies 2021,9, 157 the present study addresses both research gaps. It thus sought, first, to identify the most influential variables for holiday rentals’ price composition in cultural tourism destinations and, second, to demonstrate this HPM model’s applicability to different cultural tourism destinations listed on Booking.com. The last objective was to provide examples of the adaptations needed to apply the proposed model to all cultural city destinations. 2. Literature Review 2.1. Efficient Pricing Pricing tools play a crucial role in the accommodation sector’s revenue management, and efficient pricing has become a popular research field in recent years. Many hotels rely on cost-based, competition-driven and customer-driven pricing strategies (Tong and Gunter 2020), while others use dynamic pricing, namely, adjusting prices upward or downward over time (Leoni and Nilsson 2021). According to Vives and Jacob (2020), two dynamic pricing models are currently widely applied in the hotel industry to maximise revenue. The first is a deterministic model that sets different prices across booking horizons, while the second is a stochastic model that segments demand into different classes in order to determine market responses and demand’s sensitivity to price variations. A combination of both dynamic pricing models is often used. These models take advantage of consumers’ willingness to pay more as the date of their stay approaches. Companies set the price of accommodations according to the time horizon between booking and travel dates and their hotels’ capacity at any given time. Empirical research has shown that the probability is extremely high that the price will increase as the travel date approaches and the number of rooms available decreases (Leoni and Nilsson 2021). HPM theory posits that prices depend on each product’s features and their effects, which determine that item’s consumption utility. HPM models have long been used to analyse the relationship between various product characteristics and their prices and to study heterogeneous features’ impact on prices (Liang and Yuan 2021). Soler-García et al. (2019) report that HPM models have been extensively used in both tourism and hospitality studies to assess the influence of specific destination and hotel factors on room rates. To ensure efficient pricing, hotel managers need to know customers’ propensity to pay for particular amenities and their hotel’s set of services, so services’ impact on overall customer satisfaction and the associated costs need to be analysed (Soler-García et al. 2019). HPM models facilitate the estimation of goods or services’ prices based on previously defined variables. For hotels, prices are mainly determined by various tangible factors such as hotel category and geographic location, but type of accommodations and hotel chain membership are also important. In addition, destinations’ characteristics must be incorporated into hotel room rates (Soler-García and Gémar-Castillo 2018) . Another external feature considered is the time of year, especially in sun-and-sea destinations, due to seasonality (Coenders et al. 2003;Rigall i Torrent et al. 2011); day of the week, especially in destinations with higher occupation rates on weekends; or special event periods (Soler-García and Gémar-Castillo 2017). Typical accommodation characteristics that influence prices are distance to the beach, the city centre, tourism hotspots, train stations or airports (Castro and Ferreira 2018;Gunter and Önder 2018;Soler-García and Gémar-Castillo 2018), as well as reputational factors such as hotel brand, number of stars and customer ratings (Castro and Ferreira 2018;Soler- García et al. 2019). Additional features affecting prices are hotel category; availability of a swimming pool, fitness centre or sport facilities (Castro and Ferreira 2018); pet admission (Santos et al. 2021) ; spa; parking; accommodations’ size (Chen and Rothschild 2010;Santos et al. 2021;Voltes-Dorta and Sánchez-Medina 2020); the inclusion of a restaurant, bar or terrace; and room amenities such as Wi-Fi, television (TV), minibar or room service (Castro and Ferreira 2018). Inefficient pricing can contribute to financial losses in every business activity, especially in the holiday rental sector. Hotels have trained professionals, price management 59 Economies 2021,9, 157 programmes and industry benchmarking reports, but vacation rental units are usually managed by people without specific training in pricing strategies and with limited access to pricing tools (Gibbs et al. 2018). Airbnb has made some attempt to develop pricing tools that the hosts can use to set their listings’ prices. However, the first tool launched in 2012 was quite basic, as it only focused on simple factors including, among others, the number of rooms, neighbouring properties and amenities such as parking (Gibbs et al. 2018;Hill 2015). A second, more elaborate pricing tool, Smart Pricing, was released a few years later, which takes both property characteristics and demand into account. The tool uses machine learning to provide hosts with a suggested price for a specific date that hosts may accept or change according to their perception (Gibbs et al. 2018;Hill 2015). Smart Pricing thus has a purely advisory function, so it may have no real influence on holiday rentals’ price because most hosts do not use the tool (Tong and Gunter 2020). As hosts are responsible for setting their listed properties’ price, analyses of which factors affect vacation rental rates are of great importance to the sharing economy (Voltes- Dorta and Sánchez-Medina 2020). A significant number of studies have found that property, host and location factors have the strongest impact on prices (Voltes-Dorta and Sánchez- Medina 2020). Significant property features usually include the number of beds, bedrooms and bathrooms (Fearne 2021;Gibbs et al. 2018;Gunter and Önder 2018;Voltes-Dorta and Sánchez-Medina 2020) and online photos (Tong and Gunter 2020). Host characteristics, reputation, experience, responsiveness and ‘superhost’ status are specifically referred to in research on Airbnb (Gunter and Önder 2018;Voltes-Dorta and Sánchez-Medina 2020). Extremely important location factors for pricing holiday rentals are similar to those for hotels, namely, distance to the city centre, bus or train stations, airports, beaches or other hotspots (Gunter and Önder 2018;Gyódi and Nawaro 2021;Santos et al. 2021;Toader et al. 2021;Voltes-Dorta and Sánchez-Medina 2020). While most research on sharing economy accommodation pricing has focused on Airbnb, a few investigations have taken Booking.com into account. For example, Gyódi (2017) compared Airbnb and Booking.com listings in Warsaw, finding evidence that Airbnb provides cheaper accommodation alternatives in all price segments. However, the cited study included Booking.com’s complete offer of hotels, hostels and apartments, so the focus was not exclusively on the sharing economy. Santos et al. (2021) subsequently proposed a new HPM model for Booking.com holiday rentals using an extensive set of variables developed by Solano-Sánchez et al. (2019) that were also used in the present comparative study (see Table 1). Table 1. Variables, descriptive statistics and description. Var. Seville Porto Description Mean or % SD Mean or % SD PRCE 162.093 105.542 108.994 44.267 Accommodation price per day MIN 14.71 8.531 14.3 11.872 Minutes to walk from accommodations to Plaza del Triunfo (Seville)/Praça da Liberdade (Porto) IDIS 0.959 0.092 0.775 0.105 District index according to price per square metre in each district BEDS 3.94 1.9 3.04 1.422 Number of beds M2 75.8 40.818 54.62 29.376 Square metres TV 99% – 95% – Television (dummy variable) WASH 96% – 29% – Washing machine (dummy variable) BAL 44% – 46% – Balcony (dummy variable) 60 Economies 2021,9, 157 Table 1. Cont. Var. Seville Porto Description Mean or % SD Mean or % SD TER 36% – 22% – Terrace (dummy variable) CRT 34% – 17% – Courtyard or patio (dummy variable) VIEW 53% – 69% – Panoramic views (dummy variable) INS 21% – 41% – Soundproofing (dummy variable) PARK 40% – 41% – Parking (dummy variable) PETS 11% – 9% – Pets allowed (dummy variable) POOL 3% – 1% – Swimming pool (dummy variable) BATH 34% – 14% – Bathtub (dummy variable) CAL 8.872 0.678 9.138 0.4597 Previous users’ ratings (from 0 to 10) PICS 32.8 0.22 38.95 12.445 Number of photos VSAT 8.403 0.826 8.733 0.599 Visual appeal according to photos (from 0 to 10) HWD 35% – 35% – High season weekday (dummy variable) HWE 13% – 17% – High season weekend (dummy variable) LWD 29% – 30% – Low season weekday (dummy variable) LWE 10% – 14% – Low season weekend (dummy variable) HW 8% – NA NA Holy Week (dummy variable) for Seville only FAIR 5% – NA NA April Fair (dummy variable) for Seville only SJ NA NA 4% – São João (dummy variable) for Porto only Note: Var. = variable; SD = standard deviation; NA = not available. Source: (Booking.com 2018,2019); (Google Maps 2018,2019); (Tinsa 2018); (INE-PT (Instituto Nacional de Estatística) 2019). 2.2. Rise of Sharing Economy and Normative Adaptations In Spain, legislation on holiday rentals varies according to the autonomous region involved. Vacation rentals’ law 1 in Andalusia, of which Seville is the capital, define it as viviendas con fines turísticos (homes for tourism purposes, i.e., holiday rentals, HRs hereinafter). This law’s Article 3 defines HRs as those located in buildings for residential use that provide accommodation services regularly marketed specifically to tourists. Andalusian HRs can be rented in full (i.e., the entire home) or in part (i.e., a spare room). In addition, tourism’s law in Andalusia (Boletín Oficial de la Junta de Andalucía 2011) 2 highlights different types of tourism accommodations’ obligation, including HRs, to register with the RTA3(Andalusian Tourism Registry), which the general public can access. Portugal’s national legislation on holiday rentals endows municipalities with the power to approve and, when the volume of existing vacation rental establishments has exceeded the limit set, curbing these facilities’ numbers. Portugal started regulating the sharing economy’s accommodations in 2008 (Diário da República 2008) to provide a legal framework for the provision of temporary accommodations in homes that did not meet the legal requirements imposed on any facilities previously classified as tourism accommodations. The new form of holiday rental establishments has been designated local lodging 4 and standardised as HRs in the present research, which consists of villas, apartments and lodging establishments that, after being authorised for this use, provide temporary paid accommodation services but do not meet the requirements to be classified as tourism businesses. HR establishments must comply with the minimum safety and hygiene requirements, be registered with the relevant municipal council and be marketed to tourists either by their owners or by travel and tourism agencies. 61 Economies 2021,9, 157 higher goodness of fit than that of Seville since the absolute average of errors committed is approximately 2% lower. The Theil index of inequality represents a given model’s predictive power, namely a greater accuracy the closer this index gets to zero. Both models have values that indicate a good ability to predict prices. Finally, the Chow test was run to check the models’ stability, which produced results indicating no structural changes occurred in both models’ parameters. Figure 3presents graphs comparing the real price with the price estimated by the Seville and Porto models. The former model shows a significantly higher price range than that of Porto. An outlier above EUR 400 appears in the Porto model in the real price range, but that price’s exclusion would mean a lower goodness of fit. The models’ degree of fit, if perfect, should appear as point clouds in a diagonal line, as seen in Figure 3. Both models’ estimated values thus suggest that the linear form is a good fit. Economies 2021, 9, x FOR PEER REVIEW 12 of 17 Table 6. Adjustment measurements of Seville and Porto HPM models. Variables Seville Porto Coefficient of determination (R²) 0.732 0.54 Mean relative error 22.97% 21.09% Theil inequality index 0.139 0.129 The mean relative error (see Table 6 above) shows the differences in percentage between each model’s predicted prices and its actual values. The Porto model has a slightly higher goodness of fit than that of Seville since the absolute average of errors committed is approximately 2% lower. The Theil index of inequality represents a given model’s predictive power, namely a greater accuracy the closer this index gets to zero. Both models have values that indicate a good ability to predict prices. Finally, the Chow test was run to check the models’ stability, which produced results indicating no structural changes occurred in both models’ parameters. Figure 3 presents graphs comparing the real price with the price estimated by the Seville and Porto models. The former model shows a significantly higher price range than that of Porto. An outlier above EUR 400 appears in the Porto model in the real price range, but that price’s exclusion would mean a lower goodness of fit. The models’ degree of fit, if perfect, should appear as point clouds in a diagonal line, as seen in Figure 3. Both models’ estimated values thus suggest that the linear form is a good fit. Figure 3. Comparison of real vs estimated price for Seville and Porto models. 5. Discussion The dependent variables found to be relevant to the models are in agreement with previous studies in terms of distance to the city centre or tourist attractions of greatest interest. Comparable results have been reported by, among others, Soler-García and Gémar-Castillo (2017), Gyódi (2017) (i.e., a Booking.com model), Gibbs et al. (2018), Soler- García and Gémar-Castillo (2018) and Tong and Gunter (2020) (i.e., a Seville case study). However, Voltes-Dorta and Sánchez-Medina’s (2020) research did not confirm any significant relevance, and Gyódi and Nawaro’s (2021) results vary depending on the city analysed. More specifically, the number of beds appears as an important variable in Gibbs et al. (2018), Tong and Gunter (2020), Voltes-Dorta and Sánchez-Medina (2020), Fearne (2021) and Gyódi and Nawaro’s (2021) findings. The m2 of accommodations is also significant in the present study’s two models, as reported by Chen and Rothschild (2010), but this variable is rarely present in other tourism accommodation pricing models. In addi- 0 200 400 600 800 1000 0 200 400 600 800 1000 Estimated Price Real Price Seville 0 100 200 300 400 500 0 100 200 300 400 500 Estimated Price Real Price Porto Figure 3. Comparison of real vs estimated price for Seville and Porto models. 5. Discussion The dependent variables found to be relevant to the models are in agreement with previous studies in terms of distance to the city centre or tourist attractions of greatest interest. Comparable results have been reported by, among others, Soler-García and Gémar-Castillo (2017), Gyódi (2017) (i.e., a Booking.com model), Gibbs et al. (2018), Soler-García and Gémar-Castillo (2018) and Tong and Gunter (2020) (i.e., a Seville case study). However, Voltes-Dorta and Sánchez-Medina’s (2020) research did not confirm any significant relevance, and Gyódi and Nawaro’s (2021) results vary depending on the city analysed. More specifically, the number of beds appears as an important variable in Gibbs et al. (2018) ,Tong and Gunter (2020), Voltes-Dorta and Sánchez-Medina (2020), Fearne (2021) and Gyódi and Nawaro’s (2021) findings. The m2of accommodations is also significant in the present study’s two models, as reported by Chen and Rothschild (2010), but this variable is rarely present in other tourism accommodation pricing models. In addition, the date on which the price is recorded is seldom mentioned in the literature. However, variables related to this factor are similarly treated as important in work done by Coenders et al. (2003) and Rigall i Torrent et al. (2011) on seasonality and Soler-García and Gémar-Castillo (2017) on special events such as Seville’s April Fair. 6. Conclusions and Implications The comparison of the models developed produced especially interesting results on similarities and differences between the two cities. Strong conditioning factors in both models include accommodations’ size in m 2 , location, walking distance to the centre and visual appeal, as well as the influence of high and low seasons and, in particular, local festivities. The main differences are more secondary issues such as holiday rentals’ 68 Economies 2021,9, 157 amenities, district or parish and number of photos in Booking.com profiles. The large number of variables that proved to be insignificant for the model is also noteworthy— primarily specific amenities including, among others, the availability of a TV, washing machine, views, soundproofing or parking. The district index also was irrelevant to the configuration of vacation rentals’ final stay price for both models. The most interesting conclusion drawn from this research is that conclusive results can be obtained by applying the same methodology when developing a model for estimating holiday rentals’ prices for two different cities. In summary, the literature review and findings confirm that the strongest price determinants to consider in pricing models for cultural destination holiday rentals are distance to the city centre, number of beds, m 2 , seasonality factors and special events. These results also underline the convenience of using Booking.com and Google Maps as a source of data on all these variables. The methodology used in this study will likely produce different results for other cultural tourism cities as researchers accept or discard variables according to each city’s realities. However, this study detected the same similarities as Tong and Gunter (2020) and Gyódi and Nawaro (2021) did, except for seasonality, which was not included in the latter investigation. Thus, the proposed methodology appears to be applicable to multiple cultural city destinations. The application of this methodology to the comparison of daily rate estimation of cultural city destinations using data from Booking.com is the main theoretical contribution of this study. The model’s main practical implication is related to estimating accommodations’ daily rate under previously defined conditions (i.e., variables) since the model is easy for the relevant practitioners to customise. This research’s contribution consists of presenting two models of price estimation whose application entails the obtention of a certain price through easily modifiable variables. Thus, a collection of predetermined variables will assess a confident daily rate estimation under those circumstances. This tool can help holiday rentals’ managers or consumers determine in advance if a price is in line with what the market normally offers under specific circumstances. These estimations can also be useful for municipal councils’ tax agencies to calculate reasonable tax bases, especially in a sector in which the informal economy is prominent. The study’s limitations include, first, the impossibility of creating larger datasets due to the difficulty of obtaining complete data for all cases and variables and, second, the data collected reflecting a pre-coronavirus disease-19 (COVID-19) period. Finally, future lines of research could involve replicating the above methodology for holiday rentals in other cultural city destinations of great importance to tourists such as Paris, Barcelona, Rome, Venice or Amsterdam. These studies need to analyse the new models’ main similarities to and differences from—with a special focus on COVID-19’s effects—the two models developed in this research or to adapt the methodology to fit other types of tourism accommodations. Author Contributions: M.Á.S.-S. contributed to the investigation, methodology, validation and formal analysis; J.A.C.S., conceptualization, investigation, writing—original draft preparation and funding acquisition; M.C.S., conceptualization and validation; M.Á.F.-G., formal analysis, validation, and writing—review and editing. All authors have read and agreed to the published version of the manuscript. Funding: This paper is financed by National Funds provided by FCT—Foundation for Science and Technology—through project UIDB/04020/2020. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The data concerning holiday rentals’ daily rate pricing for the city of Seville, presented in this study, are openly available in Data in Brief at [https://doi.org/10.1016/j.dib. 2019.104697], reference number [104697]. 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Economies 9: 175. https:// doi.org/10.3390/economies9040175 Academic Editors: Aleksander Panasiuk and Robert Czudaj Received: 13 August 2021 Accepted: 5 November 2021 Published: 10 November 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). 1NTNU Business School, Norwegian University of Science and Technology, 7491 Trondheim, Norway; [email protected] 2Department of Environmental Sciences, Western Norway University of Applied Sciences, 6856 Sogndal, Norway; [email protected] *Correspondence: leiv[email protected] Abstract: Although campsites are an important segment of the tourist sector, few applied articles have analyzed their growth path and tested Gibrat’s Law for firms within this industry. This knowledge can be of importance to the authorities when analyzing the regional impacts of growth in this sector. With government statistics from the last decade, we use a GMM framework to test the stricter version of Gibrat’s Law, which consist of three parts: the campsites’ growth trend, how they carry over success and failure, and how volatile their size is. The first and third part are rejected for Norwegian campsites, leading to a rejection of Gibrat’s Law. To see if firms of different sizes follow different dynamics, we split the sample in three parts. Here, we find evidence of a threshold size, as large campsites follow a fundamentally different dynamic than small and medium campsites. Specifically, large campsites gain no stability in revenue by further increases in size, whereas they carry over success/failure across years. The opposite is true for the rest of the sector. Gibrat’s Law is rejected on at least one count for each of the sub-samples. Lastly, we supplement the analysis with economy-wide and firm-specific variables to test further hypotheses. Keywords: Gibrat’s Law; campsites; tourism; growth; system GMM estimator; dynamic panel data; Norway 1. Introduction In the growth path literature that uses Gibrat’s Law, there are few studies that analyze campsites, and none that investigate Norwegian campsites. Previous studies of Italian and Dutch campsites do not reject Gibrat’s Law, using an OLS framework on a sample of five years. This study uses a GMM (and ML) framework on a sample with twice the time dimension, although we have a smaller cross-sectional sample. The composition of the sample is also different, as Italian (Piergiovanni et al. 2003) and Dutch (Audretsch et al. 2004) campsites do not face the same degree of natural (and state) restrictions as Norwegian ones do, in addition to being larger than Norwegian ones. Unlike the previous literature, the present paper uses a detailed and accurate methodology to test Gibrat’s Law. We hope to supplement the existing literature with our findings about the growth of businesses that work under these conditions, using a modernized framework. The tourism industry is important for many countries and contributes to value creation and employment; accordingly, there is a relatively large amount of existing literature on the subject. Although campsites are an important contributor to this industry in Europe, North America, Australia, and parts of Asia, few analyses have tested Gibrat’s Law for this segment. It is of great interest to see if Gibrat’s Law applies to the tourism industry in Norway, as Norway is moving towards a future in which it will be less dependent on oil. Therefore, Economies 2021,9, 175. https://doi.org/10.3390/economies9040175 https://www.mdpi.com/journal/economies 73 Economies 2021,9, 175 the focus must be on other industries The tourism industry is important for ensuring future sustainable regional development (NOU 2020). Therefore, the authorities need more knowledge about the campsites, among other things. For example, will the growth be distributed equally, or will there be a concentration around the large companies? The purpose of this article is to find out more about this, and related, issue(s). The article in which Gibrat proposes the Law of Proportional Effect (LPE) (Gibrat 1931) has formed the basis for many research articles, and it states that an individual firm’s relative growth is independent of firm size. Consequently, the best prediction one can make about any individual firm’s size the next year will be that firm’s current size, plus the growth in the relevant sector. If the year-to-year growth of large firms is 5%, it will be 5% for small firms as well. This does not mean that all firms grow at the same pace, but that the growth is independent of firm size. The market concentration of industries and sectors is an essential topic in economics, thus how the distribution of market share changes over time is important. This dynamic is why the LPE has received so much attention, as it serves as the baseline with which to compare the growth dynamic in different industries and sectors. Any deviation from Gibrat’s Law is evidence of the market at hand converging, at the extreme, towards perfect competition or monopoly. In most cases, when Gibrat’s Law is rejected, it is rejected in favour of the mean reversion, although the very long run distribution of firms is often observed to be logarithmic rather than normal. This is due to the fact that sectors act more in accordance with Gibrat’s Law the older they become. 2. Literature Review Gibrat’s Law has been an inspiration for many international publications (Daunfeldt and Halvarsson 2015) and many different methods and approaches have been applied. Mansfield (1962) applied Gibrat’s Law in different ways. First, he tested if smaller firms were more likely to leave the market than larger firms. Then, based on economic theory, he investigated whether the companies had to pass a certain scale level, at which production exceeds the minimum efficient scale (MES) level, before Gibrat’s Law holds. That is, it is possible that there is a threshold size at which a firm’s growth pattern changes. Several other researchers have reported similar results, with the general conclusion being that larger firms grow independently of their size, as for the largest U.S. companies (Hymer and Pashigian 1962;Simon and Bonini 1958). The literature yields a mixed picture for industrial firms, as others show that this does not hold for small- and medium-sized enterprises (SMEs) (Becchetti and Trovato 2002;Hart and Oulton 1999;Fotopoulos and Louri 2004), whereas others show that the LPE holds for entire sectors (Buckley et al. 1984;Hymer and Pashigian 1962;Lensink et al. 2005;Simon and Bonini 1958). Most studies that reject Gibrat’s Law show that the sector has a mean reverting tendency, meaning that smaller firms grows faster than larger firms (Almus 2000;Bartoloni et al. 2020;Daunfeldt and Elert 2013;Yadav et al. 2020). According to Jurado et al. (2021), Gibrat’s Law applies to large capital-intensive companies that use advanced technology, which is taken to the extreme in other studies who suggest that large firms grow faster than their smaller competitors (Mukhopadhyay and AmirKhalkhali 2010). Finally, there are articles that did not find any hold for Gibrat’s Law (Lotti et al. 2001). Previous research has shown that Gibrat’s Law applies to campsites based on data from the Netherlands (Audretsch et al. 2004) and Italy (Piergiovanni et al. 2003), meaning that it cannot be rejected that growth is independent of size. Furthermore, several authors have argued that Gibrat’s Law applies to the service sector to a far greater extent than to manufacturing (Audretsch et al. 2004). The articles that failed in rejecting Gibrat’s Law for campsites had rather short time dimensions of five years; however, this was in contrast to our ten years studied: from 2010 to 2019. This longer time dimension allows us to use the GMM framework to estimate the parameters of interest, whereas previous analyses of camping sites have used the less advanced OLS framework suggested by Chesher (1979). The choice of estimator is crucial when working with dynamic models using short panel 74 Economies 2021,9, 175 data, which is why we apply three different estimators that each have their strengths and weaknesses. Through this, we hope that we have attained results that are reliable, so that we can link them to economic theory with certainty. If there is a minimum size a firm must obtain for survival (the MES), negative growth for small firms may result in deficits, which could, in the long term, lead to the closure of these firms (Audretsch et al. 2004). Mansfield (1962) reported that small firms have relatively higher death rates, but those that survive seem to have higher variation and grow faster than the big firms. He also noticed that firms with successful innovators grow twice as fast as others. There is a substantial difference between the manufacturing and service industry (Audretsch et al. 2004). In the manufacturing industry, which depends heavily on capital as input, being small is obviously a drawback due to the economies of scale. If production exceeds the MES, the possibilities for profitable operation are far better. This might not be the case for the service industry, since the production is far less capital intensive. Consequently, there are less sunk costs, and economies of scale do not play as much of a key role as for manufacturing. This may explain why there are many small businesses in the service sector. There is a high proportion of family owned units in the Netherlands, and they often do not have ambitions to expand further. The same trend is also found in Italy, where a high proportion of companies in the hospitality sector have fewer than five employees (Piergiovanni et al. 2003). In Norway, the campsites are even smaller. In a study of Italian hospitality industry (cafeterias, restaurants, cafes, campsites, and hotels), only cafeterias and campsites did not reject Gibrat’s Law (Piergiovanni et al. 2003). An analysis based on Dutch data gave the same result for campsites (Audretsch et al. 2004). They state that growth in this sector is independent of firm size. For the four other sub-sectors within the field of hospitality, Gibrat’s Law failed to hold. Park and Kim (2010) rejected Gibrat’s Law for the restaurant industry, whereas Host et al. (2018) reported that the average growth of firms in the Croatian tourism sector was independent of their size. However, Ivandi´c (2015) did not confirm Gibrat’s Law in a study of the hotel sector in Croatia, instead showing that the hotels tend to revert to a certain mean: smaller firms grew faster than the bigger ones. The growth was also shown to depend on ownership, where publicly owned companies had lower growth than privately owned ones. 3. Applying Gibrat’s Law The literature has discussed the various reasons for why Gibrat’s Law may be valid, as well as the factors that contribute to rejecting it. Economy-wide and firm-specific effects can aid in both rejecting and accepting the random walk Gibrat describes, depending on if the effects explain the variance or level of firm size. Later in this paper, we include the exchange rate (economy-wide) and the debt level (firm-specific) as variables that explain the size of campsites. There are statistical and econometric challenges to testing Gibrat’s Law (Novoa 2011). When using dynamic panel data analysis, the first choice is that of the dependent variable. There are essentially two alternatives: firm growth and firm size. By choosing growth, one takes the first difference of size, while using size as the explanatory variable (Oliveira and Fortunato 2008). In this case, Gibrat’s Law holds if the parameter for size is insignificant. Alternatively, using size as the dependent and explanatory variable, the following model is applied: yit =α+βyi,t−1+εit, (1) where y it is the logarithmic value of size for the actual company in a specific sector at time t. The lagged dependent variable is the only explanatory variable, α is a constant and εit is random disturbance term. In this model, Gibrat’s Law holds if it is shown that firms follow a random walk; that is, if β = 1. Deviations from this random walk give insight into the distributional trend of the sector’s firms. 75 Economies 2021,9, 175 3.1. The Hypotheses Connected to Gibrat’s LPE If β > 1, the sector has an explosive trend, where larger firms grow proportionally faster than smaller firms. If this explosive trend persists over time, there will be few companies left, and the sector will convergence to oligopoly or monopoly. In young industries, the explosive trend could be relevant as an initial edge can exacerbate itself in succeeding years. This cannot last forever, however, which is one reason why older sectors tend to not reject the LPE. If β < 1, there is a mean reverting trend in the sector, where the mean growth is stronger among small companies as they are in a state of ‘catch-up’. Thus, we can assume there exists a ‘natural’ or ‘perfect’ firm size at which firms will eventually return if they diverge from it, their long-run growth being equal. The steady state size of each firm need not be the same, but the firms will revert to some mean. In the extreme case, where β = 0, every deviation from this mean will be cancelled out in the next period, meaning that firms do not deviate for more than one period. In this case, current size is no predictor of future size. Many papers (Novoa 2011;Oliveira and Fortunato 2008;Shehzad et al. 2009) have tested Gibrat’s Law by following the procedure of Tschoegl (1983), which is a stronger version of Gibrat’s Law that suggests three propositions (P1–P3). From Equation (1), we can write the growth for company ias: yit =α+βyi,t−1+εit, where εit =ρεit−1+ uit, and uit ~ N (0, σ2) (2) The sum of the error term’s (u it ) deviation ( σ ) is by construction equal to zero. In addition, the variance of the error term is written as: σ2it =δyit +ηit (3) which gives the three propositions (the null hypothesis) as: P1: β= 1 P2: ρ= 0 P3: δ= 0 First (P1), the relative growth of each firm is independent of the firm’s initial size, and firms follow a random walk. This is the firm size’s autoregressive process. Second (P2), if a firm deviates from its growth path in one year, this deviation does not carry over to the next year; that is, extraordinary success/failure in one year does not translate into extraordinary success/failure the next year. The second proposition differs from the first in the sense that the first proposition concerns the firm’s trend, whereas the second concerns deviations from this trend. This (P2) is equivalent to the firm size’s moving average process. The third proposition (P3) states that the relative variance in firm size is independent of the firm’s initial size. That is, small firms do not vary relatively more or less in their size than large firms. This is equivalent to the firm size’s heteroscedastic property. As stated, P1 holds if the firms collectively follow a random walk, meaning if the best prediction of future size is the current size and all deviations from this size follow a random process. If P2 holds, all outside effects on the firm size for a given year will be completely reflected in the firm size for this particular year, and those effects will have no impact for the firm’s growth in the coming years. A success one year will give an increase in the size of the firm, but this increase does not necessarily lead to a further increase in the next year. That is, the deviation in growth in one year will not carry over into the next year. This does not mean that the success/failure of one year disappears the next year, only that its effect is absorbed that year. If ρ = 0, there is no spill-over effect, and growth will normalize to the prior regular growth after an initial shock. If ρ < 0, firms with extraordinary success in one year will have considerably worse results the next year, with a growth below the average. A lucky period is followed by an unlucky following period, and vice versa: a failure one year results in good performance the next year, with growth stronger than the 76 Economies 2021,9, 175 others. If ρ > 0, extraordinary growth one year will persist into the following year. That is, growth over the average level for a given firm will persist into the following period. If a company has extraordinarily strong growth one year, it will also manage to maintain this above-average growth in the following period. If one year turns out badly for a company with a low growth, this will also yield negative consequences the following year. If firm growth is characterized by ρ > 0, we can view this a persistence in firm success/failure, or ‘slowness’ in firm growth. On the other hand, ρ < 0 can be viewed as success/failure being ‘cancelled out’. Particularly for campsites, P2 can go both ways, depending on whether the visitors are (dis)pleased with more/fewer other visitors at the same campsite. If P3 holds, the proportional variance of revenue for the companies is independent of their size—that is, firm size is not related to growth volatility. A negative δ value means there is a negative relationship between a firm’s growth volatility and its size: smaller firms have relatively more volatility in their revenue stream. One interpretation is that smaller companies experience greater uncertainty than large enterprises, perhaps because smaller firms are more sensitive to consumer tastes and market conditions, whereas larger firms have a more stable revenue stream. A study by Calvino et al. (2018) concluded that the value is negative, and remarkably stable across 21 selected countries. Goddard et al. (2004) investigated whether the previous year’s growth has an impact on the actual growth, and found a positive relationship, but with no significant impact. Many researchers have included independent and control variables to the estimators to see how this affects growth. By extending the model with other variables, one can test and explain how different factors contribute to growth and analyse why Gibrat’s Law is rejected (Oliveira and Fortunato 2008). For instance, Donati (2016) showed how liquidity constraints limited the growth of small firms. Debt leverage as a control variable yields a mixed result (Jang and Park 2011;Phillips 1995). Some report a negative relationship (Billett et al. 2007), because higher debts increased the number of poor projects. On the other hand, a higher debt level can increase firm performance through successful ventures—that is, the level of debt can be seen as risk-taking. 3.2. Econometric Methods In early empirical testing of Gibrat’s Law using econometrics, the ordinary least squares (OLS) method of estimation was used. Due to the presence of the lagged dependent variable, this induces endogeneity issues through the feedback, or looping, mechanism, as shown by Chesher (1979). Consequently, as Chesher (1979) and Jang and Park (2011) have pointed out, this means that OLS will be inconsistent unless the number of variables representing firm size is equal to the number of time periods. When there are more than a few time periods, this becomes, at a minimum, inefficient, and infeasible at most. Even so, many researchers have used OLS to test Gibrat’s Law (Daunfeldt and Halvarsson 2015). This is true for the previous studies that have analysed the validity of Gibrat’s Law in camping sites (Italian and Dutch). An alternative approach is to use the generalized method of moments (GMM) and, specifically, those methods that are specifically created for dynamic panel data scenarios. Arellano and Bond (1991) proposed such a method for panel data to ensure a consistent evaluation of the parameters. They exploited the moment conditions of the first differenced error terms, which allowed for the use of the lagged level of two periods prior as instruments for the first differenced equation. The estimator has been called the AB or FD GMM method. Some researchers have used it to test Gibrat’s Law (Ivandi´c 2015), but when the autoregressive parameter ( β ) approaches unity, the instruments used become weaker. In the case that Gibrat’s Law holds, β = 1, the instruments are entirely invalid, as they are not correlated with the first differenced equation. This leads to inconsistent and downwardly biased estimators of β , as has been shown in several studies using Monte Carlo simulations (Blundell and Bond 1998;Jang and Park 2011;Moral-Benito et al. 2019). As a result, using the Arellano–Bond estimator will tend to lead to a rejection of Gibrat’s 77 Economies 2021,9, 175 8. Conclusions and Contribution The crux of Gibrat’s Law is that the best prediction one can make about future firm size is current firm size. This is the starting point of Gibrat’s Law, whereas the stricter version adds to more hypotheses. Firstly, all deviations from this initial size come from a white noise error term, and secondly, the variance of this error term is independent of size. If all three hypotheses hold, we can say that the firms follow ‘pure’ random walks. This is not the case for Norwegian campsites, in contrast to Italian and Dutch campsites, but in line with most other sectors and industries. We find that the size of Norwegian campsites is mean reverting, and its volatility decreases with size. Hypotheses 1 and 3 are thus rejected, the firms converge towards a ‘natural’ steady state, and they become more stable as they grow. Gibrat’s Law does not hold for Norwegian campsites. Furthermore, we find evidence of a threshold size for the Norwegian campsites, at which point their growth processes switches. At the point of about 25 employees, the distinction between the medium and large sub-samples, the processes change. When the threshold size is reached, there is no longer any gain of increased size in the stability of growth, and the current success/fiasco becomes a predictor of future success/fiasco. In addition, we can no longer reject a random walk after this threshold size. Consequently, hypothesis 2 is rejected for the large Norwegian campsites, whereas hypotheses 1 and 3 are not. This is the opposite result of the general result we obtained for all Norwegian campsites, and more in accordance with previous studies of campsites in Italy and the Netherlands. As for hypothesis 4, we can see that a depreciation of the Norwegian Krone translates into higher revenue for the sector, as more foreign tourists choose to visit the country, whereas fewer domestic tourists choose to leave the country. The differing degree to which the exchange rate affects the three sub-samples is grounds for further research. Hypothesis 5 shows that higher leverage leads to higher revenue streams, but not for small campsites. This can be due to an unwillingness or inability to invest or gain the means to do so. Whether the level of debt is positively related to profitability is another issue, investigated by Opstad et al. (2021b). We used three estimators to obtain the autoregressive and moving average components of Norwegian campsites, the FD-GMM, SYS-GMM, and ML-SEM estimators. Although they have differing strengths and weaknesses, the results were similar. The steady state assumption of the SYS-GMM seems to not cause too many problems, comparing it to the other two. The Monte Carlo evidence against the FD-GMM estimator when the autoregressive component approaches unity would seem to make it inappropriate for testing Gibrat’s Law, although our study does not show it conclusively. The ML-SEM estimator, combining the weak assumptions of the FD-GMM estimator with better precision than the SYS-GMM estimator, seems to be the best choice for testing the dynamic properties of firms, according to Monte Carlo evidence. We are not aware of any studies using it to investigate Gibrat’s Law in the literature yet, this being an introduction of the estimator to the literature. To conclude, another novel contribution of our paper to the general Gibrat’s Law literature, is the evidence of a threshold size, for the tourism industry at least. We show evidence of this threshold size (non) rejection of hypotheses 2 and 3, and to a lesser degree hypothesis 1. At the point of 25 employees, in the case of Norwegian campsites, size no longer translates into more stability for the firm, but rather into a spill-over dynamic where current success/fiasco is carried over into the next year. 9. Limitations and Further Research The data analysed were limited to 10 years and from only one country, and were also based on public statistics (from the Brønnøysund Register Center), thus some information from individual campsites that would have been of interest (e.g., prices) is lacking. There is limited research that applies such analysis to campsites, which limits the ability to compare 84 Economies 2021,9, 175 the present results with other findings, but we hope this article can be an important contribution in helping to explain the growth in campsites. Furthermore, there is no data available to differentiate the different types of campsites in the sample. Analysis on how different types of campsites grow, and in which specific market they operate (sightseeing, exploring, pitstop) and which customers they specialize in (domestic, foreign, one time-or yearly visitors) are fields in which further research can be carried out. Additionally, access to data from campsites in other countries can help investigate whether our differing results are due to differences in country-specific factors, or due to methodological differences. A comparison of campsites across countries, or a comparison of different sectors in the Norwegian tourism industry are obvious paths to take for further research. Inclusion of additional macroeconomic variables, such as the exchange rate, can give more answers that are relevant for the tourism industry of all countries, as tourists can only go one place at a time. Our analysis is limited in that the data at hand only contain firms that have been active for the entire sample period. Including entry/exit into the analysis could shed light on the high variance of the growth paths of the small firms. For instance: what decides which newly created campsites converge towards the steady state, and which fail? Accordingly, why do some campsites follow (potentially) explosive paths, whereas other campsites almost follow white noise paths? Another choice could be to include age as an explanatory variable. These points will help us to learn more about the life cycle of firms, where some become successful businesses, but whereas most die out. Author Contributions: These authors contribute equal to this work. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Public statistics. Conflicts of Interest: The authors declare no conflict of interest. References Allison, Paul D., Richard Williams, and Enrique Moral-Benito. 2017. Maximum likelihood for cross-lagged panel models with fixed effects. Socius 3: 1–17. [CrossRef] Almus, Matthias. 2000. Testing “Gibrat’s Law” for young firms-empirical results for West Germany. Small Business Economics 15: 1–12. [CrossRef] Arellano, Manuel, and Stephen Bond. 1991. 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[CrossRef] 86 economies Article Customer-Based Brand Equity for a Tourism Destination: The Case of Croatia Lenka Cervova * and Jitka Vavrova Citation: Cervova, Lenka, and Jitka Vavrova. 2021. Customer-Based Brand Equity for a Tourism Destination: The Case of Croatia. Economies 9: 178. https://doi.org/ 10.3390/economies9040178 Academic Editor: Aleksander Panasiuk Received: 22 October 2021 Accepted: 11 November 2021 Published: 15 November 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). Faculty of Economics, Technical University of Liberec, 460 01 Liberec, Czech Republic; [email protected] *Correspondence: [email protected] Abstract: Tourism has been negatively impacted by the global COVID-19 pandemic, making it even more important for tourist destinations to focus on their brand equity from the perspective of their customers—visitors. The aim of this paper is therefore to verify and modify the model of customerbased brand equity for a tourism destination (CBBETD) and its attributes for the destination of Croatia from the perspective of Czech tourists, among whom primary research was conducted using the CAWI method (n = 451). The main CBBE dimensions were extracted using factor analysis and a model with four dimensions (awareness, image, quality and loyalty) was created. The identified attributes explain between 55% and 82% of the variability of a given dimension. Although the study’s results follow the published models of CBBETD, the attributes in each dimension and the subdimension in the image dimension reflect the specificities of the destination of Croatia. Thus, the results of this paper extend the economic theory with another model and are also applicable in the field of destination management. Keywords: brand equity; customer-based brand equity; Croatia; destination; destination awareness; destination brand; destination image; destination loyalty; destination management; destination quality; tourism; visitors’ loyalty 1. Introduction In the current era, which is heavily influenced by the global pandemic caused by COVID-19, with international tourist arrivals falling by 74% in 2020 (UNWTO 2021), it is crucial for tourist destinations to work on their brand and bear themselves in the eyes of tourists as a safe and secure place to spend their holidays. In the past decades, an increasing number of tourist destinations—cities, countries and regions—have applied marketing and branding practices to attract visitors and investors (Gertner 2011). Destination branding is one of the main topics in tourism marketing in terms of enhancing differentiation and competitiveness. This urgent need for destination branding has led to an increase in the number of investigations done on different destinations’ brand equity (Oliveira and Panyik 2015). Destination brands are very different from product brands. Destinations provide another quality than a material or financial one that can be refunded. Gartner (2014) stated, “Destinations are places of life and change”. Change is the measure of brand stability, one of the main elements of branded consumer products. Destinations are multidimensional and provide different experiences to different tourists (Ruzzier 2010). Destination brands lack the brand stability that most product brands have. Several market segments consume it simultaneously; each consumer is compiling their unique product from the services on offer. Thus, destination marketers have less control over the brand experience than marketers of concrete material products or services (Hankinson 2009). This article presents the results of a research focused on the evaluation of the CBBE destination of Croatia from the perspective of the citizens of the Czech Republic. This destination was chosen because it has been very popular in the last years in the Czech Republic. The aim was to find out what dimensions of CBBE are important in the case of Economies 2021,9, 178. https://doi.org/10.3390/economies9040178 https://www.mdpi.com/journal/economies 87 Economies 2021,9, 178 holidays in Croatia and what attributes constitute them. Croatia is the 18th most popular tourist destination in the world. Most of the tourists come from Germany, Slovenia, Austria and the Czech Republic. Tourism is one of the main sources of state revenue; it accounts for 20% of the GDP. Thanks to its location in the Mediterranean and the rugged Adriatic coast with many islands, Croatia is one of the typical summer destinations with a predominant seaside tourism. Tourists also visit many historic cities such as Dubrovnik, Split, Zadar, Sibenik or Rijeka. There are ten monuments on the UNESCO list in the country (e.g., Plitvice Lakes National Park, the historic town of Trogir or the old town of Dubrovnik) (Croatian National Tourist Board 2021). For several years, Croatia has been one of the top destinations visited by Czech residents in terms of the number of arrivals. Before the pandemic, approximately 800,000 Czech tourists visited Croatia annually (ˇ CSÚ2021). A sharp decline occurred in 2020, when tourism worldwide was affected by the global COVID-19 epidemic and only 481,000 Czech tourists visited Croatia, despite the fact that Croatia was one of the first countries to open its borders to Czech tourists (Ministry of Tourism 2021). It is reasonable to assume that the total number of Czech tourists in Croatia will be lower in 2021, although the destination has set favourable conditions for tourist arrivals even before the summer season. It should be mentioned that the results presented in this article were obtained by research done in 2019, when the occurrence of coronavirus infections was not anticipated. 2. Literature Review 2.1. Branding Branding is one of the most critical tasks in the development of a marketing strategy. Kotler (1991) defined a brand as “ . . . a name, term, sign, symbol or design . . . intended to identify the goods or services of one seller or group of sellers and to differentiate them from those of competitors”. Brands are important markers of international resources and communicators of the marketing intent of an organization (Hunt 2019). 2.2. Brand Equity A brand receives its value from customers by providing an image of stability, performance and other traits in reaction to a company marketing strategy. Therefore, customers know what to expect in the way of product performance. Keller (1993) named this response “customer-based brand equity”. The definition of brand equity has evolved over time and academic understanding varies. Brand equity has been perceived as the added value of a product when consumers have a good impression about a brand, as the source of brand loyalty and even as the increased cash flow on branded products. Brand equity ensures higher margins compared to non-branded products. It can give a sustainable and differentiated competitive advantage (Kim and Lee 2018). 2.3. Customer-Based Brand Equity for a Tourism Destination Brand equity is measured from two different perspectives. First, there is the financial value of the brand to the firm and then there is the measure of the value to the customer (Keller 2003;Pappu and Christodoulides 2017). The financial value of the brand to the firm is measured by the result of customer-based brand equity. There are several studies that developed and tested accounting methods for the appraisal of the asset value of a brand name (Lassar et al. 1995). However, our paper focuses on brand equity from the perspective of the value to the customer. Customer-based brand equity (CBBE) is at present more than 20 years old and a welldeveloped construct, the roots of which lead us to the 1980s (Fayrene and Lee 2011). During these years, this concept received much attention (Ruzzier 2010). The CBBE concept was defined “as the differential effect that brand knowledge has on consumer response to the marketing of that brand” (Keller 1998). There have been numerous attempts to summarize measures of brand equity, approaching the construct from different perspectives. The Table 1below demonstrates those dimensions (Almeyda and George 2020). 88 Economies 2021,9, 178 Table 1. Customer-based brand equity dimensions. Aaker (1991)Keller (1993,1998, 2003)Lassar et al. (1995)Konecnik and Gartner (2007) San Martín et al. (2019) Brand awareness Brand salience Performance Destination awareness Destination awareness Brand perceived quality Brand performance Social image Destination perceived quality Destination quality Brand imagery Brand association Brand judgements Price/value Destination image Destination image Brand feelings Trustworthiness Destination satisfaction Brand loyalty Brand resonance Identification/attachment Destination loyalty Destination loyalty Source: (Almeyda and George 2020). The basic concept of CBBE is that the measure of the brand strength depends on how consumers feel, think and act with respect to the brand. To achieve consumer resonance a brand first needs to elicit emotional reactions from consumers. To achieve that, a brand must have an appropriate identity and the right meaning. At best, customers therefore consider the product as relevant and “their kind” (Koththagoda 2017). The model of customer-based brand equity for a tourism destination was proposed and verified by Konecnik and Gartner (2007). It was confirmed that the level of CBBETD is positively related to an extent to destination brand equity dimensions, which are presented further. 2.4. Dimensions of the Customer-Based Brand Equity Based on the CBBE model, Konecnik and Gartner (2007) have investigated the different dimensions of customer-based brand equity for a tourism destination (CBBETD). Our paper continues their work, which listed awareness, image, quality and loyalty as the dimensions of a destination as antecedents to CBBETD. Tourists from different backgrounds sense various dimensions of a destination distinctly. 2.4.1. Destination Awareness The term destination awareness was introduced in behavioural studies of consumer and was described in the tourism decision process by Goodall and Ashworth (1993). Aaker (1991) defined destination awareness as “the ability of a potential buyer to recognize or recall that a brand is a member of a certain product category”. Brand awareness increases a destination’s potential of being preferred more often than other unknown destinations (Kladou and Kehagias 2014). It also brings a better chance of being chosen by potential customers among all rival destination brands (Hoyer and Brown 1990). Staying focused on destination brand awareness is important because it provides optimistic information and creates positive emotions that are likely to increase the possibility of making a purchase (Baldauf et al. 2003). Destination brand awareness also plays a critical role in tourists’ destination quality perception (Buil et al. 2013;Nikabadi et al. 2015). Awareness is only the first and necessary step in the decision process, and may lead to visit a destination; on the other hand, it is insufficient, because the very awareness provides only a set of choice (Goodall and Ashworth 1993). For getting more tourist visits, destination brand must first achieve awareness and then a positive destination image. 2.4.2. Destination Image Destination image is formed by the interaction of people and places (Pearce and Stringer 1991). Based on subjective interpretations, a tourist’s thoughts and feelings toward the destination are generated and affect their image formation (Tasci et al. 2007;Veasna et al. 2013). Destination image is described as “the sum of beliefs and impressions that a person has of a destination” (Chiu et al. 2014). Despite the significant effect of destination image on CBBETD, only a limited amount of research has focused on the moderating effect of destination image. Line and Hanks (2016) identified the moderating effect of destination image in relation to guests’ perceptions and 89 Economies 2021,9, 178 behavioural intentions in the green hotel industry. Other researchers have considered destination image as an antecedent of the intention to revisit a destination (Stylos et al. 2016) or as an outcome of destination marketing (Wong et al. 2016). For the purpose of this paper, destination image represents “an interactive system of thoughts, opinions, feelings, visualizations, and intentions toward a destination” (Tasci et al. 2007) . It has been proven that destination image has a large impact on customer loyalty. The image of a destination is the most important and significant dimension of CBBETD model. A leading destination image brings more customers to make an effort to visit or revisit a destination and also to recommend it (behavioural and attitudinal loyalty). Destination image creates an impact on loyalty through satisfaction (Marine-Roig 2021). 2.4.3. Destination Quality Another key aspect of CBBETD is the quality of a destination. Destination quality is defined as a visitor’s evaluation of the standard of tourism products at the destination (infrastructure of attractions, tourist facilities and services). Tourists judge if the destination products meet their requirements or expectations according to their real perceptions (Le Chi 2016). Nevertheless, quality measurement is a very difficult and complex process. In order to find out the quality, it is necessary to research the tourists’ evaluation of products and services and the tourists’ experience in the destination. All these elements affect consumer behaviour and preference. The aspect of destination quality is the most important component of CBBETD. When researching destination quality, attention should be paid to a distinction between perceived quality and tourists’ satisfaction (Ruzzier 2010). 2.4.4. Destination Loyalty From a marketing perspective, loyalty is defined as customers’ behaviour or intentions to re-buy or re-patronize certain product or service, causing repetitive purchasing of the same brand products (Hawkins et al. 1995). Loyalty measures a consumer’s strength of affection towards a brand. It is based on a consumer brand preference or their intention to buy a product of a certain brand. Customer satisfaction, customer experience, value, service quality, performance, price and brand name all contribute to loyalty (Backman and Crompton 1991). In destination brand research loyalty plays a big role, but it should be examined in a long-term range. It can serve as a useful tool for prediction of future destination choice (Oppermann 2000). 2.5. Executed Research on Customer-Based Brand Equity The concept of CBBETD started to be tested for many destinations by various researchers and from many perspectives more than 10 years ago. For example, Boo et al. (2009) measured the CBBE for Las Vegas and Atlantic City. However, in contrast to our paper, besides awareness, image quality and loyalty, they added another dimension of destination brand value to their model. Yousaf and Amin (2017) measured the CBBE for a tourist destination named the Kashmir valley in India. Their study suggests particular steps to ensure a strong brand equity of the Kashmir valley. Almeyda and George (2020) compared the CBBEs of Puerto Rico and the US Virgin Islands while using different dimensions of the CBBETD model (value, social image, performance, trustworthiness and identification). Their study claims that the core dimension that explains more than ninety percent of the customer-based brand equity is brand performance, which is a substitute of the destination quality in our CBBETD model. The study executed by Suta et al. (2019) investigated empirical information for testing the concept of cultural differences on the integration of variables in the CBBETD. The subject of their research was the tourist destination of Bali. Furthermore, their research applied the CBBETD model to investigate cultural differences as a mediating indicator of the correlation among brand loyalty and other indicators in the CBBETD. 90 Economies 2021,9, 178 Another study that needs to be listed is an empirical CBBETD study of the Liberec region in the Czech Republic executed by the authors of this paper ( ˇ Cervováand Pavl˚u 2018). The previous study used the same dimensions of CBBETD and also tested the concept very well. Based on the literature review, the following research questions are addressed in this study: – RQ 1: Is the model of CBBETD proposed by Ruzzier (2010) applicable also to Croatia from the perspective of Czech visitors? – RQ 2: Are the dimensions of the proposed model identical? – RQ 3: Are there any subdimensions that can be identified? 3. Methodology The purpose of this paper is to verify and modify the CBBETD model in the context of the destination of Croatia from the perspective of Czech visitors. The research methodology is based on the CBBETD concept introduced and modified by Ruzzier (2010). This concept of brand equity consists of four subdimensions, namely, awareness, image, quality and loyalty. Since the attributes within the subdimensions of awareness (three attributes) and loyalty (three attributes) are generally applicable regardless of the destination, they were adopted without change from the original model by Ruzzier (2010). However, the attributes included in the image and quality subdimensions had to be adapted to fit the characteristics of the destination. To this end, focus group research was conducted in the first phase of the research, involving 25 potential respondents. The aim of the focus group interviews was to identify suitable attributes specific to Croatia from the perspective of Czech visitors that would have an impact on image and quality. The focus groups were conducted with an emphasis on subjective perceptions, expectations and experiences; therefore, no attribute options were presented to the participants in order not to influence their opinion. The output of the focus groups was 21 attributes falling into the image subdimension and 9 attributes falling into the quality subdimension. Thus, the brand equity of the destination of Croatia was measured using a total of 36 attributes. In the second phase of our research, the data were collected through a structured questionnaire (see the Appendix A) using the method of online interviewing (CAWI). The first part of the questionnaire consisted of questions characterizing the respondents’ travels to Croatia (such as frequency of visits, length of stay, sources of information, way of organizing holidays, etc.). The second part of the questionnaire focused on the attributes of CBBETD, which were transformed into statements and rated on a scale of 1 to 5, with 1 indicating total disagreement and 5 total agreement with the statement. The respondents were selected by a quota selection method according to gender and age so that the sample would correspond to the profile of a Czech visitor to Croatia (Czechtourism 2019). However, only people over 18 years of age could participate in the survey. The data were analysed using IBM SPSS Statistics software. Factor analysis using principal components and the varimax rotation method was performed to identify significant attributes determining the four subdimensions of CBBETD. The appropriateness of using exploratory factor analysis was verified using Barlett’s test of sphericity, which showed significant correlations in the correlation matrix (value of 0.000 for all analyses performed). The validity and reliability for each of the subdimensions were verified using Kaiser–Meyer–Olkin (KMO) values and Cronbach’s alpha coefficients. All variables could be considered valid as KMO values ranged from 0.701 to 0.940. The values of Cronbach’s alpha coefficients were 0.803 to 0.934, indicating acceptable to excellent results. The identified factors within the four subdimensions explained 55.11 to 81.03% of the total variability. Three factors were identified in the image subdimension and one factor each in the other subdimensions (see Table 2). 91 Economies 2021,9, 178 Table 2. Validity and reliability check. Dimension Number of Attributes Assigned to Subdimension KMO Cronb. Alpha Total Variance Explained (%) Number of Extracted Factors (Attributes) Awareness 3 0.701 0.803 72.18 1 (3) Image 21 0.940 0.934 60.32 3 (20) Quality 9 0.888 0.873 55.11 1 (8) Loyalty 3 0.706 0.879 81.03 1 (3) Source: own processing. There were 465 completed questionnaires. Nevertheless, the elimination of problematic questionnaires reduced the sample size to 451. In terms of gender of the visitors, 47% were male and 53% female. Out of the total number of respondents, 30% of tourists were 18–30-year-olds, 22% were 31–40-year-olds, 25% of respondents were 41–50-year-olds, 12% were 51–60-year-olds and 11% were older than 61. As per the monthly net income of the household it was found that 31% earned less than CZK 25,000, 22% earned in the range of CZK 25,001–35,000, 19% in the range of CZK 35,001–45,000, 11% in the range of CZK 45,001–55,000 and 17% earned more than CZK 55,000 a month (Table 3). Table 3. Sample characteristics. Number of Respondents N 451 Sex male 46.80 female 53.20 Age 18–30 29.70 31–40 22.40 41–50 51–60 61 and older 25.10 11.80 11.10 Income (CZK) * less than 25,000 31.10 25,001–35,000 21.80 35,001–45,000 19.00 45,001–55,000 11.30 55,001 and more 16.80 * Exchange rate (3 November 2021): 25.50 CZK/1 EUR. Source: own processing. 4. Results A factor analysis was conducted to test and eliminate attributes within the four CBBETD subdimensions. The first subdimension examined was awareness. As can be inferred from Table 4, respondents rated awareness very well (means ranging from 4.34 to 4.40). All three attributes examined reached a factor loading of more than 0.500, thus constituting a single factor (“awareness”), explaining 72.18% of the total variability. Table 4. Awareness. Variables Mean Factor Loading Popular TD 4.40 0.869 Attractive and known TD 4.34 0.867 Imagining of TD 4.35 0.810 % Variance extracted 72.18 Note: TD = tourist destination. Source: own processing. The second subdimension analysed was image. In this case, the factor analysis was conducted a total of three times with the successive elimination of variables that were not 92 Economies 2021,9, 178 part of either factor. The aim of this procedure was to eliminate variables with low factor loading (less than 0.500) and to explain as much of the variability as possible. The third factor analysis identified three factors explaining 60.32% of the variability (see Table 5). The first factor, named attractions, includes variables such as towns and villages, nature, cultural attractions, beaches, mountains and historical attractions. The second factor can be named amenities and includes opportunities for water recreation, opportunities for recreational activities, wide range of gastronomy and accommodation facilities, pleasant weather, summer destination, friendly and hospitable people and easy accessibility. Within the image subdimension, a third factor was also identified and named ambiance. It contains variables such as modern wellness resorts, shopping facilities, exciting atmosphere, good nightlife and entertainment. Looking closely at the averages of all variables within the image subdimension, it is clear that the variables that respondents rated the highest were summer destination, opportunities for recreational activities including water recreation, pleasant weather, relaxing atmosphere (means from 4.18 to 4.63). On the other hand, the lowest rated variables were wellness resorts (2.98), shopping facilities (3.30) and exciting atmosphere (3.45). Similar results emerged from the qualitative study (focus groups) in which participants most frequently mentioned Croatia as a summer, relaxing destination with many opportunities for recreation at the seaside, including a variety of beaches. Table 5. Image. Variables Mean Factor Loading Attractions Amenities Ambiance Lovely towns and villages 4.04 0.705 0.339 0.239 Beautiful nature 4.16 0.692 0.392 0.157 Interesting cultural attractions 3.75 0.687 0.197 0.437 Beautiful beaches 4.00 0.684 0.339 0.113 Beautiful mountains 3.91 0.678 0.180 0.163 Interesting historical attractions 3.78 0.658 0.144 0.462 Good opportunities for water recreation 4.27 0.152 0.742 0.254 Good opportunities for recreation activities 4.34 0.278 0.738 0.155 Pleasant weather 4.22 0.317 0.717 0.076 Wide range of gastronomy facilities, local food 4.00 0.171 0.702 0.391 Summer destination 4.63 0.122 0.672 −0.133 Wide range of accommodation facilities 4.11 0.207 0.670 0.316 Friendly and hospitable people 4.07 0.267 0.660 0.206 Transportation accessibility 4.10 0.235 0.646 0.125 Relaxing atmosphere 4.18 0.438 0.614 0.117 Good opportunities for adventure 3.92 0.197 0.545 0.534 Modern wellness resorts 2.98 0.189 −0.017 0.788 Good shopping facilities 3.30 0.139 0.143 0.758 Exciting atmosphere 3.45 0.290 0.222 0.682 Good nightlife and entertainment 3.84 0.297 0.348 0.526 % Variance extracted 60.32 Source: own processing. Within the third subdimension “quality”, one factor explaining 55.11% of the variability (see Table 6) was identified. A factor analysis was conducted twice in total, with the successive elimination of variables that did not reach a factor loading of 0.500. The quality subdimension included variables such as quality of gastronomy, services, accommodation, infrastructure, unpolluted environment, good value for money and personal safety. The latter two variables were also rated the highest by respondents—a mean of 3.85 for personal 93 Economies 2022,10, 47 Researchers involved in the development of sustainable technologies have begun to propose strategies (Calderón-Vargas et al. 2019;Calderón-Vargas et al. 2021) that can eliminate barriers to sustainability and to direct their interest toward the use of renewable energy sources such as solar and wind. Alongside the development of sustainable infrastructure with an emphasis on energy demand issues (Nguyen and Su 2021), other researchers emphasize the need for tourism to be sustainable. An example of this is the use of renewable energy in tourist destinations (Nguyen and Su 2021;Gössling 2010;Le and Nguyen 2020). Accordingly, the World Tourism Organization (UNWTO) describes sustainable tourism as a model of economic development conceived to improve the quality of life of the host community and to provide visitors with a high quality experience while maintaining the quality of the environment (Cardoso Jiménez 2006). Moreno Freites et al. (2019) argue that sustainable tourism means satisfying the needs of tourists and local development, minimizing poverty and exclusion, and ensuring the sustainable use of biodiversity without neglecting the protection of local values, customs, and historical context. Relevant strategies must thus be created to help reduce poverty. Sustainable tourism is a comprehensive scheme that must not only contribute to sustainability and present a sustainable tourism product but also generate local development. Previous work emphasizes the importance of sustainable tourism in local development. Varisco, in his study of tourism and local development, highlighted the importance of the degree of endogeneity in tourism development processes, analyzing its impact on local development. He concluded that tourist activity contributes to local development but cannot be generated purely as an isolated activity (Varisco 2008). Likewise, Mora, in his study of local development and community tourism under globalization, examined the case of San Gerardo de Dota and concluded that the community is endowed with various types of endogenous resources that have the capacity to contribute economic value based on community capital (Mora Sánchez 2012). Consequently, Álvarez and Gil proposed tourism as an engine of economic growth in Colombia, since departmental public investment in tourism has contributed positively to GDP growth in each department (Álvarez Cáceres and Galvis 2019). This study aims to measure the influence of sustainable tourism, in terms of the use of renewable energy resources, in motivating local development in the community of La Florida, Huaral, Peru and ensuring that it becomes a sustainable destination. It is important to note that tourism has already generated basic development in the community under study, producing both direct and indirect employment and forging an appreciation of the local customs and environment. 2. Literature Review Despite the great economic benefits that tourism generates in various countries, the sector presents an environmental concern, as it gives rise to massive CO 2 emissions (Li et al. 2019). A study carried out by the UNWTO and the United Nations (UN) reveals that tourism contributes 5% of all anthropogenic CO 2 , while between 50 and 60% of carbon emissions are indirectly related to the industry (Dwyer et al. 2010;Calderón-Vargas et al. 2019;OMT-a 2019). The need thus arises to direct tourism activity using sustainability guidelines and to think about sustainable tourism. The latter must fully take into account current and future economic, social, and environmental repercussions while satisfying the needs of visitors, the industry, the environment, and host communities (UNWTON 2021). Some authors firmly believe that the sustainability of tourism development is based on the creation of a tourism product with particular characteristics that suit the present and future needs of tourists (Michalena et al. 2009). The concept of “sustainable tourism development” thus refers to economic, social, and environmental development that continually aims to improve the experiences of tourists. For others, this type of development is an additional opportunity for local communities to benefit from the products of their particular local identity and natural resources (Burns and Sancho 2003;Michalena et al. 2009). Sustainable 100 Economies 2022,10, 47 tourism is positively linked to economic development and has been an important source of income (Comerio and Strozzi 2019). The optimal management of sustainable tourism must take into account the principles of sustainability, encompassing the environmental, economic, and sociocultural aspects of tourism development. An adequate balance must be struck between these three dimensions to guarantee long-term sustainability. In this sense, well-articulated sustainable tourism contributes effectively to local development. This in turn allows a society to offer alternatives for collective well-being, using the potential of local residents to generate innovative ideas that are economically beneficial to their home community (Mendoza-Moheno et al. 2021). Vásquez Barquero classifies this as a strategy that seeks social progress and local sustainable development based on the continuous improvement of available resources, particularly historical and cultural heritage, and thus contributes to improving the well-being of the population (Vázquez Barquero 2009). Conversely, Sergio Boisier maintains that local development is an endogenous process that occurs in small territorial units and human settlements capable of promoting economic dynamism and improving the population’s quality of life (Boisier 2005). Local development involves three fundamental aspects: the local economy, the process of reactivating and revitalizing the local economy, and the efficient use of an area’s existing endogenous resources to stimulate economic growth, create jobs, and improve quality of life. This implies a participatory and equitable process that promotes the sustainable use of local and external resources and in which key local actors are encouraged to generate employment and income to improve the population’s quality of life (Silva and Sandoval 2012). Dinis maintains that, if the environmental component is integrated into local development, one can speak of sustainable local development as socially equitable, economically viable, and environmentally friendly (Dinis et al. 2019). It is thus necessary to consider the importance of fostering sustainable tourism that generates local development through the care and preservation of the environment. Accordingly, since several authors affirm a positive correlation between the consumption of renewable energy and economic growth (Chen et al. 2021;Apergis and Payne 2010;Omri 2013;Ozturk and Bilgili 2015), we propose a study of renewable energy and its influence on local development and sustainable tourism. Apergis’s study of OECD countries reveals a long term equilibrium relationship between real GDP, renewable energy consumption, real gross fixed capital formation, and the labor force. This long term relationship indicates that a 1% increase in renewable energy consumption increases real GDP by 0.76%; a 1% increase in gross real fixed capital formation increases real GDP by 0.7%; and a 1% increase in the labor force increases real GDP by 0.24% (Apergis and Payne 2010). Tourism is a driving force for both economic growth and environmental sustainability, so the interaction between pollution and renewable energy consumption requires more attention (Sarpong et al. 2020). Tourism-related CO 2 emissions can be mitigated through the use of renewable energy in the tourism industry (Ali et al. 2021). Moreover, it is reported that tourists are willing to pay for activities that are likely to promote environmental quality (Sarpong et al. 2020). The regions of Central and South America have the potential to generate 100% of their electricity from renewable sources (Ben Jebli et al. 2019). Ideally, Peru should move gradually toward “cleaner” growth that generates fewer emissions and does not compromise economic and social development, thus improving its competitiveness and productivity. This must be done, however, through the gradual implementation of clean technologies, beginning with those that offer the lowest costs (Gamio Aita 2021). It is also necessary to take advantage of the country’s exceptional wind resources, great potential for solar energy, and products of its geographical and climatological characteristics (Ministry of Energy and Mines (MINEM) 2001). The Wind Atlas of Peru estimates the country to possess 20,493 MW of usable wind resources out of a total wind resource of 28,395 MW, which is of interest for the installation of wind power generation systems (MOCICC—Movimiento Ciudadano frente al Cambio Climático 2020b). Conversely, the most important technical and economic determining factor for the 101 Economies 2022,10, 47 installation of thermoelectric solar systems is to have an annual direct solar radiation not less than 2000 kWh/m2, while the total potential of Peru is 2860 MW (MOCICC 2020a). 3. Materials and Methods A quantitative approach was used, as numerical data were collected and subjected to statistical analysis to verify the correlation of two variables, as well as the generalization and objectification of sample results. The design was non-experimental since there was no manipulation of the variables; rather, they will be examined and compared as they occur in the natural environment. The design is transverse, as data was collected only for the year 2021 (Hernández Sampieri 2010). This research focuses on a case study of the rural community of La Florida, located in the Atavillos Bajo District, Huaral Province, Lima Department, Peru. The community is considered the base tourist center of the “Rúpac-Marca Kullpi” archaeological complex, also called “El Machu Picchu Limeño”, which was designated as national cultural heritage through National Directorial Resolution 283/INC on 25 June 1999. This archaeological site dates to 1200 CE and belongs to the pre-Inca culture of Los Atavillos (Congreso de la República 2017a). During the research process, direct contact was made with residents of La Florida to obtain information and to learn about the residents’ perspective on the relationship between sustainable tourism and local development in their area. The statistical population was delimited by a selection criterion for those over the age of majority. All individuals over 18 years of age who live in this population center were considered, yielding a total of 843 persons of undifferentiated sex. Using a simple random probability sampling under the finite population formula, given a confidence level of 95% and a margin of error of 5%, a sample number (n) of 265 inhabitants was selected. These individuals participated in a structured survey with closed questions based on the Likert scale, addressing relevant social, economic, and environmental dimensions To certify the quality of the survey’s content, it was subjected to an expert judgment process. Three specialists, in community development, sustainable tourism, and methodology, respectively, evaluated the consistency, clarity, and concordance of the questions. Regarding the statistical reliability of the survey questionnaire, Cronbach’s Alpha test ( α ) was applied. This test establishes a coefficient that theoretically varies from 0 to 1, distributed as follows: values from 0 to 0.2 are considered to indicate very low reliability, 0.2 to 0.4 low reliability, 0.4 to 0.6 moderate reliability, 0.6 to 0.8 good reliability, and 0.8 to 1 high reliability. If α is close to 0, then the quantized responses are not reliable at all, and if close to 1 the responses are very reliable. As a general rule, if α≥ 0.8, the answers are considered reliable (Leontitsis and Pagge 2007). After all the surveys had been administered, the results were processed using the statistical software SPSS version 27. To obtain test results, the following procedure was used: first select the “Analyze” option, then the “Scale” option, and third “Reliability Analysis.” Then, select the items to evaluate, and finally choose the option “Alpha Model.” Following these steps, an α value of 0.8 was obtained, thus indicating high reliability according to the Alpha scale. To carry out relevant documentary analysis, an extensive search was undertaken for scientific articles indexed in prestigious databases such as Scopus and Web of Science with the keywords: sustainable tourism, sustainable tourism and local development, benefits of local development, tourism and renewable energy. This search extended to official national and supranational organizations: World Tourism Organization (UNWTO), MINEM, Instituto Nacional de Estadística e Informática (INEI), and Peruvian Institute of Economy (INEI). Figures from accommodation associations, travel agencies and the like (AHORA), and the Ministry of Foreign Trade and Tourism (MINCETUR) have also been used to obtain tourism data and identify new trends in the national and international tourism market. The analysis of renewable energy potential was specifically linked to the use of solar and wind energy, involving the use of photovoltaic panels and wind turbines, respectively. For this purpose, computer simulations were used to determine solar radiation intensity through SOLARGIS, a simulator belonging to the World Bank, and EnAir, a simulator that 102 Economies 2022,10, 47 generates energy demand and/or generation calculations (in kWh) for a given geographic location. On this basis, we performed calculations to estimate projected energy demand and contributions by the aforementioned systems, all with a high degree of precision (98.5%). Various studies have considered the use of geographic information tools to evaluate tourism resources and renewable energy potential (Valjarevi´c et al. 2018;Rahayuningsih et al. 2016). 4. Results and Discussion In September 2015, the UN General Assembly adopted the 2030 Agenda with the aim of promoting sustainable development through an action plan that seeks to end poverty, safeguard our planet, and ensure peace and prosperity (UNWTO 2015). The SDGs that take up and expand on the Millennium Development Goals include 17 goals and 169 targets and will be the framework for the new world development agenda for the next 15 years (ONWTO Organización Mundial del Turismo 2015). It is acknowledged that each country faces specific challenges in its search for sustainable development. Accordingly, UN member states recognize that the world’s greatest challenges are the elimination of poverty and the preservation of the environment (UNWTO 2015). Within the 2030 agenda’s framework, the World Charter for Sustainable Tourism +20 is recapitulated, recognizing that SDGs present an opportunity to direct tourism activity along inclusive and sustainable pathways (Naciones Unidas 2015a). The document thus stipulates that tourism must contribute effectively to reducing inequality, promoting peaceful and inclusive societies, achieving gender equality, and creating permanent opportunities for all. It also highlights that the ecological footprint of tourism can be significantly reduced, and that this process should drive innovation by developing green, inclusive, low carbon economies. Finally, it emphasizes that indigenous cultures, traditions, and local knowledge, in all their forms, must be respected and valued, underlining the importance of promoting the full participation of local communities and indigenous peoples in tourism development decisions that affect them (Urkullo 2015). Regarding the Peruvian legislative framework, tourism activity is governed by Law 29408, the general tourism law, which aims to promote, encourage, and regulate the sustainable development of tourism activity and is mandatory at all three levels of government: national, regional, and local. This legal framework applies to the development and regulation of tourist activity, and MINCETUR is the national governing body for matters related to tourism. Article 3 of this law sets out the principles of tourism activity, which are: sustainable development, inclusion, non-discrimination, promotion of private investment, decentralization, quality, competitiveness, fair trade in tourism, tourism culture, identity, and conservation (Congreso de la República 2017a). It is necessary to ascertain a community’s conditions prior to designing an implementation of sustainable tourism that can contribute to its local development. La Florida, together with the Pampas community, is strategically located as a base location for the reception of tourists intending to visit the archaeological center of Rúpac, the traditional local festivals, the anniversary of Rúpac, the festival of San Salvador de Pampas, etc. All of these result in an increasing number of visits each year, but with very short stays. The surveys undertaken in our study reveal that 52.5% of residents believe tourists stay in the area less than a day, which is a very short time to provide opportunities for active economic revitalization. The “Rúpac Marca Kullpi” archaeological complex belongs to the Atahuallos culture that flourished from 900 until the mid-1400s CE (IPerú2016). It is presently called the “Lima Machu Picchu” since it is located at the top of the mountain (3580 MSL) and, despite its age, is well-conserved. The archaeological complex is a citadel with fortified vaulted ceilings and stone structures up to 10 m high. (IPerún.d.). In 2016, Bill 1012/2016-CR was presented and passed, which made the recovery, conservation, protection, and promotion of the Rúpac Marca Cullpi archaeological site a public necessity and preferential national interest (Congreso de la República 2017a). Nonetheless, much remains to be done to ensure that the mountain range of the city of Huaral is a tourist focus for Lima. Rúpac 103 Economies 2022,10, 47 is not yet prepared to receive a large influx of tourists, while neighboring population centers still lack optimal infrastructure and facilities to accommodate additional tourism. The president of the Association of Hotels, Restaurants and Related—Huaral (AHORA— Huaral) has stated that approximately 10,000 tourists visit the area annually, of whom 10% are foreign (Andina 2019) . Conversely, he affirms that the place has become highly attractive to national and foreign tourists because of its inclusion in the Short Routes of Lima. To reach Rúpac, one must first take a bus from Lima to Huaral, then take local transport from Huaral to the town of La Florida and Pampas, and finally undertake a walk of approximately three and a half hours to the complex. La Florida’s role as a base center is the reason for this study’s focus on that rural community. The town had 843 inhabitants as of the most recent census (carried out in 2017), of whom 53% were female (INEI 2017). Our surveys indicate that 58.9% of residents have completed secondary school but only 7.9% have higher education, while the remainder of the population has an educational level between primary and initial (Naciones Unidas 2015b). 4.1. Economic Aspect The main economic activities revolve around tourism and the agriculture sector. The predominant crops include peaches, avocados, apricots, potatoes, and corn, which are cultivated and harvested by the local community. Nonetheless, a visit to the town center revealed that the population’s limitations have been improving in response to the development of tourist activity. According to community members, as recently as five years ago they lacked basic services, i.e., in the populated center there was no water, sewage, electricity, or gas service. Much was therefore needed to improve their quality of life. For example, preparation of food required the use of wood stoves, while access to water involved the local government occasionally sending cisterns to fill containers that had to last the inhabitants for a certain period. Thanks to development spurred by tourist activity, precariousness has diminished, and now the community has access to all basic services and even internet. This is a direct consequence of increased tourist activity, which has boosted the economy and attracted the interest of local and regional governments. Economically, sustainable tourism development must take the necessary steps to maximize economic benefits to the host community while creating strong links with the local economy of the destination and with other economic activities in the environment. Thus, the UNWTO proposes that sustainable tourism should promote the creation of viable economic activities in the long term. These should provide all agents with well-distributed socio-economic benefits, including opportunities for stable employment, to obtain income and social services for host communities and to help reduce poverty (UNWTON 2021). In the community under study, the survey indicated that 32.1% of the population has tourism as its main economic activity, with restaurant and accommodation services being predominant, as 7.5% of residents are employed in each area. The second most important economic activity in the community is agriculture, which is the main occupation for 26.4% of the population (see Table 1). Table 1. Main economic activity, according to La Florida residents. Activity Frequency Percentage Tourism 85 32.1 Agricultural 70 26.4 Commerce 52 19.6 Forest 32 12.1 Construction 26 9.8 Total 265 100.0 Source: Prepared by the research team on the basis of data from the survey of La Florida’s residents. While 71.3% of residents are aware that tourist activity always generates work and continuous income, which contributes to revitalizing the economy of local households, 104 Economies 2022,10, 47 94.8% of residents indicate that the development of tourism activity has improved basic family income (see Figure 1). Thus, 63.8% of residents claim that before tourism development they had an average income of between 100 and 150 USD per month, whereas with the development and promotion of tourism, 87.5% of residents claim that they have now considerably exceeded this income (see Table 2). Economies 2022, 10, x FOR PEER REVIEW 7 of 18 Table 1. Main economic activity, according to La Florida residents. Activity Frequency Percentage Tourism 85 32.1 Agricultural 70 26.4 Commerce 52 19.6 Forest 32 12.1 Construction 26 9.8 Total 265 100.0 Source: Prepared by the research team on the basis of data from the survey of La Florida’s residents. While 71.3% of residents are aware that tourist activity always generates work and continuous income, which contributes to revitalizing the economy of local households, 94.8% of residents indicate that the development of tourism activity has improved basic family income (see Figure 1). Thus, 63.8% of residents claim that before tourism development they had an average income of between 100 and 150 USD per month, whereas with the development and promotion of tourism, 87.5% of residents claim that they have now considerably exceeded this income (see Table 2). Figure 1. Contribution to the improvement of basic family income. Source: Prepared by the research team on the basis of data from the survey of La Florida’s residents. Table 2. Monthly income after tourism development. Monthly Income Frequency Percentage Valid percentage 100 USD–150 USD 33 12.5 12.5 More than 150 USD 232 87.5 87.5 Total 265 100.0 100.0 Source: Prepared by the research team on the basis of data from the survey of La Florida’s residents. Given the above, tourist activity has clearly helped to generate income for the community’s residents, encouraging local development based on production and employment opportunities that energize and diversify the local economy. Nonetheless, much work remains, since the poverty index is still above average. Moreover, it has been noted that many informal services exist, particularly in the areas of catering and accommodation services. During contact with the population, it was observed that lodgings are provided within people’s homes (rustic and improvised). When tourists visit, they stay in said houses sharing a small room with several people and paying for each bed that is used, rather than per room. A similar pattern holds true for restaurants, which are informal and scarce establishments (there are only three in the entire community) located in inhabitants’ homes. Because they are rustic, these establishments lack quality and safety control for Figure 1. Contribution to the improvement of basic family income. Source: Prepared by the research team on the basis of data from the survey of La Florida’s residents. Table 2. Monthly income after tourism development. Monthly Income Frequency Percentage Valid Percentage 100 USD–150 USD 33 12.5 12.5 More than 150 USD 232 87.5 87.5 Total 265 100.0 100.0 Source: Prepared by the research team on the basis of data from the survey of La Florida’s residents. Given the above, tourist activity has clearly helped to generate income for the community’s residents, encouraging local development based on production and employment opportunities that energize and diversify the local economy. Nonetheless, much work remains, since the poverty index is still above average. Moreover, it has been noted that many informal services exist, particularly in the areas of catering and accommodation services. During contact with the population, it was observed that lodgings are provided within people’s homes (rustic and improvised). When tourists visit, they stay in said houses sharing a small room with several people and paying for each bed that is used, rather than per room. A similar pattern holds true for restaurants, which are informal and scarce establishments (there are only three in the entire community) located in inhabitants’ homes. Because they are rustic, these establishments lack quality and safety control for the handling and preparation of food. It is recognized that tourism-related income from informal activities can benefit a community significantly (Ketchen et al. 2014). If, however, steps are not taken to regularize this informality, challenges may arise, e.g., government regulations that limit access to resources such as capital and commercial space. Moreover, there is a latent risk that those involved may encounter problems such as low salaries, long working hours, high work intensity, poor work environment, and lack of social welfare (Tian and Guo 2021;Damayanti et al. 2017;Briassoulis 2001). It should be noted that SDGs 1, 2, and 10 stipulate that tourism must be promoted to promote economic growth and development at all levels. Moreover, by providing income through job creation, tourism must contribute to reducing poverty and reducing inequality. Tourism is among the sectors with the most rapid economic growth and is capable of generating development at all levels and of providing income through job creation. It also contributes to rural development by giving community members the opportunity to prosper in their place of origin (ONWTO Organización Mundial del Turismo 2015). The development of sustainable tourism, and its impact on communities, can be 105 Economies 2022,10, 47 linked to national poverty reduction objectives. This is particularly true of objectives related to the promotion of entrepreneurship and small businesses and to the empowerment of less favored groups, particularly women and youth (ONWTO Organización Mundial del Turismo 2015;Urkullo 2015). The UNWTO affirms that tourism is an effective means for developing countries to participate in the world economy. In 2014, the least-developed countries received 16.4 billion USD in exports from international tourism, up from 2.6 billion USD in 2000. This considerable increase has made tourism an important pillar of developing economies, constituting 7% of total exports and helping some to ameliorate their condition (ONWTO Organización Mundial del Turismo 2015). 4.2. Sociocultural Aspect Socioculturally, tourism activity should be directed to empower local communities and endogenous peoples and to facilitate their participation in tourism planning and development (Urkullo 2015). The UNWTO argues that this ensures respect for the sociocultural authenticity of host communities, helping to conserve cultural and architectural assets and traditional values while contributing to intercultural understanding and tolerance (UNWTON 2021). Thus, to achieve local development, the preservation and revaluation of customs must also be taken into account. In this vein, 55.1% of La Florida’s residents indicated having had positive interactions with tourists in their community, which enhances their awareness of the value of their endogenous customs. Moreover, 92.8% of residents indicated that said tourist activity in their community significantly promotes and influences the valuation of their culture and customs (Table 3). Finally, 84.2% of residents indicated that tourism in their community encourages respect and tolerance for interculturality. To this end, awareness workshops are planned to help spread their culture and traditions. Table 3. Promotion of interculturality, valuation of culture, and interaction with tourists. Valuation Interculturality Culture Valuation Interaction with Tourists Never 0.4% 0.0% 0.4% Almost never 0.8% 0.0% 5.3% Sometimes 3.0% 0.0% 20.8% Usually 11.7% 7.2% 18.5% Always 84.2% 92.8% 55.1% Total 100.0% 100.0% 100.0% Source: Prepared by the research team on the basis of data from the survey of La Florida’s residents. It is well appreciated that local populations take initiative to continuously undertake activities that elevate their culture, customs, and cultural manifestations, thus helping to strengthen their identity and endogenous customs and encouraging the revaluation of their traditions. The World Tourism Charter indicates that tourism activity must be directed to empowering local communities and indigenous peoples and to facilitating their participation in the planning and development of tourism (Urkullo 2015). Thus, in destination management, it is necessary to ensure the revaluation of culture. This applies in places where tangible and intangible cultural heritage coexist, which is the most important cultural tourist resource (Lin et al. 2021) and where the cultural aspect is the main inspiration of the visitor to learn, discover, experience, and consume the cultural heritage of their destination (Liu 2020). The development of a sustainable cultural tourism policy may thus be a practical way to foster a new business model that increases employment and promotes the conservation of heritage landscapes (Aquino et al. 2018). Notably, and pertinently to the alliance between tourism and culture in Peru, the UNWTO states that society, culture, and tourism maintain a symbolic relationship. Artistic and craft activities, dance, rituals, and legends that run the risk of falling into oblivion among new generations can be reactivated if tourists show great interest in them (OMT 2016). 106 Economies 2022,10, 47 4.3. Environmental Aspect Particular emphasis is placed on the optimal use of environmental resources, which are fundamental elements of tourism development, while maintaining essential ecological processes and conserving natural resources and biological diversity (UNWTON 2021). A wide range of economic sectors have joined strategies to reduce climate change, and tourism is no stranger. Thus, strategies can be promoted that contribute to lowering the carbon footprint through the management of sustainable destinations and the construction of ecological tourist infrastructure (Urkullo 2015). In this regard, 73.2% of La Florida’s residents affirm that they always promote the social responsibility of tourists to protect natural attractions, while 9.4% do so regularly. Nonetheless, this leaves 17.4% with whom local governments must to work to achieve greater awareness (Table 4). Meanwhile, 78.5% claim to actively collaborate in programs, workshops, and training for the care and preservation of green areas, while 15.5% do so regularly. Similarly, 66.8% confirm that they always take into account the conservation of local resources. They also note a commitment from the local government, in which the municipality promotes action and awareness to maintain green areas in good condition. Table 4. Promotion of social responsibility to safeguard natural attractions, active collaboration in workshops, and conservation of biodiversity. Valuation Social Responsibility to Safeguard Natural Attractions Active Collaboration in Workshops Conservation of Biodiversity Never 0.0% 0.0% 0.0% Hardly ever 0.0% 0.4% 1.1% Sometimes 17.4% 5.7% 11.7% Usually 9.4% 15.5% 20.4% Always 73.2% 78.5% 66.8% Total 100.0% 100.0% 100% Source: Prepared by the research team on the basis of data from the survey of La Florida’s residents. Excessively high tourist influxes are known to entail a series of negative aspects, e.g., environmental pollution, degradation of ecosystems, soil erosion, and even desertification (Drius et al. 2019). Challenges introduced by overtourism have also been reported in Barcelona, Amsterdam, and Rio de Janeiro (Brtnickýet al. 2020). Our results indicate that, while the community is positively predisposed toward the preservation and care of the environment, it needs a more concrete understanding of what environmental sustainability encompasses. The entire community must be involved in developing plans and strategies, not only in terms of local knowledge but also in taking action and implementing sustainable tourism infrastructure, since the greatest threat to the planet is the construction of new infrastructure (Davenport and Davenport 2006). The seriousness of global environmental problems now requires rapid action at the highest level to avoid catastrophic degradation (Thommandru et al. 2021). Such actions are not only the responsibility of government, but also of each individual, each district, and each community, all of whom must help in any way they can to achieve this objective (Thommandru et al. 2021). SDGs 7 and 9 assert that tourism activity can incentivize national governments to renew infrastructure and modernize industry. When based on the use of renewable energy sources, this can contribute to reducing greenhouse gas emissions, mitigating climate change, and implementing new and innovative energy solutions (ONU 2022). 4.4. Renewable Energy Potential in La Florida as an Alternative for Sustainable Development Given new national and international demands, it is important for any projection of tourism development to include the involvement and empowerment of local communities to boost their economy. Likewise, it must help to address climate change by aiming to 107 Economies 2022,10, 47 progressively reduce greenhouse gases (GHG) emissions, thereby growing in a sustainable way (Urkullo 2015). This can be achieved by implementing eco-efficient technologies and processes in all areas of the tourism industry, including buildings, infrastructure, etc., and by reducing energy consumption and using renewable sources, especially in the transport sector and accommodation. All of this can be achieved if the implementation of renewable energy sources in tourist destinations is promoted to reduce the carbon footprint of the tourism sector (Urkullo 2015). Peru has significant potential for developing sustainable tourist destinations, since it has a diversity of geographical contexts accompanied by a variety of climates, providing the country with a range of options to take advantage of renewable energy sources. This context is addressed from a technical-professional perspective that undertakes an analysis of Peru’s energy potential. Sustainable tourism activity managed in an appropriate way can be a strategic ally to preserve the environment, generate economic growth, and safeguard endogenous customs and traditions (Calderón-Vargas et al. 2019). To this end, the Peruvian state has been supporting programs that encourage members of different local communities to establish their own businesses. As of 2017, this includes the “Turismo Emprende” program, an initiative of the Ministry of Foreign Trade and Tourism to promote the economic reactivation and reconversion of micro and small businesses (Mypes). The goal is for these businesses to promote the tourism sector by providing accommodation, food, tourist operations, travel agencies, and crafts, while improving and strengthening local businesses to enable them to adapt to current market needs. In 2020, a non-refundable 4,500,000 USD was allocated to rejuvenate the country’s tourism businesses (MINCETUR 2021). Another program is the Inter-American Institute for Cooperation on Agriculture (IICA), which supports small renewable energy ventures in rural areas of Peru. To date, 35,000 homes and 191 institutions have benefited from the IICA’s efforts to reduce rural poverty (El Peruano 2019) . The MINEM plans to continue supporting projects that promote sustainable development through renewable energy (MINEM 2021). Finally, ENGIE “Energía Perú”, one of the country’s largest electricity generation and infrastructure companies, seeks to strengthen the technical and infrastructure capacities of small local entrepreneurs. These entrepreneurs are encouraged to implement proper business management practices for insertion into commercialization chains or to start their own enterprises and thus improve the standard of living and income of families (ENGIE 2021). An evaluation of renewable energy potential was carried out, specifically of solar and wind, in the vicinity of La Florida, located in the province of Huaral, department of Lima ( − 11.308177, − 76.795476). Energy demand was calculated for a total of 10 lodging houses, each containing five basic bedrooms with a maximum capacity of two people (these calculations reflect the total annual proportion of visitors to the study location). The energy demand for each basic lodging house was evaluated first, followed by the average energy contributions in kWh/month for each type of renewable energy source and the engineering design necessary to respond to demand (photovoltaic panels and wind turbines). Finally, in light of the sustainable project profile, a calculation was made of equivalent savings in CO 2 emission, equivalence in trees planted per hectare, and economic savings (based on local electricity cost per kWh), both for solar (photovoltaic panels) and wind power (wind turbines). Table 5shows values for average daily solar photovoltaic electric potential (PSEP) based on the electric production of a solar photovoltaic (PV) plant of 1 kWp (generation capacity of a solar panel) as evaluated with two types of software (EnAir and Solargis). For this purpose, precise coordinate values were used for the study location. Averaging the figures provided by the two programs yielded a solar electric potential of around 4.40 kWh/day, which is within the desired range. 108 Economies 2022,10, 47 Table 5. Solar radiation intensity in La Florida. Source Photovoltaic Solar Electric Potential (kWh/Day) EnAir 3.8 Solargis 5.012 Average 4.406 Source: Data from EnAir and Solargis simulators. Because the town of La Florida is located within a rugged and mountainous geographical context, it has good conditions in terms of average hours of sunshine per day (9 h), from roughly 8:00 to 17:00, with the highest intensity being from May to September. Figure 2 presents the relevant values in a heat map, with red representing the maximum values reached and light blue the minimums. Economies 2022, 10, x FOR PEER REVIEW 12 of 18 Figure 2. (a) Average hourly profiles of direct normal solar irradiation (Wh/m2), (b) Solar resource map at the study site. The circumference shows the location of the La Florida community. Source: SOLARGIS. As for wind potential, the geomorphological characteristics of the area are a main factor supporting the use of this type of energy. Table 7 provides figures for the wind potential of La Florida, assuming the introduction of a basic wind generation system (wind turbine) with an energy production of 200 kWh/day. Such a design would meet the basic energy demands of, e.g., a lodging house, given an operating range based on wind speeds of 8–11 m/s. Table 7. Average wind potential in La Florida. Wind Potential Wind energy 4.2 kWh/day Average output potential 180 W Annual energy 1522 kWh Average monthly energy 127 kWh Average wind speed 1.2 m/s Source: EnAir. Figure 2. ( a ) Average hourly profiles of direct normal solar irradiation (Wh/m 2 ), ( b ) Solar resource map at the study site. The circumference shows the location of the La Florida community. Source: SOLARGIS. Table 6presents detailed values for solar irradiation characteristics, which are necessary for determining the engineering design of photovoltaic electrical systems. The units, kWh/m 2 , represent values of energy and time specifically related to electricity generation 109 Citation: Chen, Tzong-Shyuan, and Chaang-Iuan Ho. 2022. The Application of a Two-Stage Decision Model to Analyze Tourist Behavior in Accommodation. Economies 10: 71. https://doi.org/10.3390/ economies10040071 Academic Editor: Aleksander Panasiuk Received: 16 February 2022 Accepted: 21 March 2022 Published: 23 March 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). economies Article The Application of a Two-Stage Decision Model to Analyze Tourist Behavior in Accommodation Tzong-Shyuan Chen and Chaang-Iuan Ho * Department of Leisure Services Management, Chaoyang University of Technology, Taichung 413310, Taiwan; [email protected] *Correspondence: [email protected] Abstract: As tourism products are not necessities for people’s livelihood, zero consumption data are usually observed while conducting studies on topics that are relevant to tourism expenditure using cross-sectional research data, and a similar problem exists in tourist accommodation expenditure. This study adopts a two-stage process to examine the factors influencing tourist accommodation decisions in the domestic market, applying the dependent double-hurdle (DDH) model while using the dataset on Survey of Travel by R.O.C. (Taiwan) Citizens for the years 2014–2018. The findings reveal that, in the two decision-making equations, the social stratum, family life cycle, residential area, tourism behavior, vacation policy, and economic variables have different degrees and directions of influence on the intention to use and expenditure on tourist accommodation. Such information presents the processes involved in deciding to accommodate and how much to spend on accommodation, thereby indicating that it is inappropriate to use the single-equation analysis consisting of zero consumption expenditure data and to assume that the same variables influence the participation and consumption decisions. Keywords: two-stage decision model; zero expenditure; dependent double-hurdle model; demand for accommodation 1. Introduction Tourism is a major force in global trade that plays a vital role in the social, cultural, and economic development of most nations (Smith 1995). According to statistics compiled by the World Travel and Tourism Council, in 2019, the scale of the global tourism industry reached USD 8.9 trillion, with a contribution rate of 10.3% to the world’s gross domestic product. At the same time, the industry employed 330 million people worldwide, accounting for approximately 10% of global employment. A country’s tourism market generally consists of two markets with different customer sources, namely, inbound and domestic tourism. The domestic tourism market gradually expands with economic growth, increases in residents’ income, and adjustments to vacation arrangements. According to the World Tourism Organization, the scale of the domestic tourism market is 10 times that of the international market (Page et al. 2001). Therefore, domestic tourism contributes significantly to a country’s tourism revenue. If one considers the example of Taiwan, in 2019 the number of inbound tourists reached 11.86 million, of which 90% were from within Asia, and the tourism revenue amounted to USD 14.411 billion (Tourism Bureau, Ministry of Transportation and Communications 2020). There were 169 million domestic travelers, 14.24 times the number of inbound tourists, although the tourism revenue was USD 12.698 billion, or 88 percent of that for the inbound tourism market. The key reason for the substantial disparity in the number of tourists despite identical revenue levels was the differences in tourist behavior between the two tourism markets. The average length of stay of inbound tourists was 6.20 nights, whereas that of domestic tourism was mainly 1.51 days, with 66% choosing to return the same day without staying in accommodation facilities. The low level of demand for accommodation Economies 2022,10, 71. https://doi.org/10.3390/economies10040071 https://www.mdpi.com/journal/economies 117 Economies 2022,10, 71 was the main reason why the performance of domestic tourism failed to surpass that of inbound tourism. Therefore, understanding the factors influencing the demand for accommodation on the part of domestic tourists in order to increase the duration of stay is an important topic when it comes to expanding the domestic tourism market. When establishing an econometric model to discuss the factors influencing the demand of domestic tourists for accommodation, the first issue is to deal with a large influx of tourists who do not spend any money on accommodation. The traditional least squares method assumes that dependent variables have continuity and can be measured. If this approach is used to estimate model parameters when observed values are constrained by censored data, it may result in such parameters being biased and inconsistent (Maddala 1983;Judge et al. 1988). As tourism is not necessary for livelihood, the phenomenon of zero expenditure widely exists in research on tourism spending (Dardis et al. 1994;Hong et al. 1996;Cai 1999;Lee 2001;Zheng and Zhang 2013;Weagley and Huh 2004;Nicolau and Màs 2005;Jang and Ham 2009;Alegre et al. 2013;Bernini and Cracolici 2015;Sun et al. 2015). This fact makes the choice of appropriate econometric techniques crucial for the consistency of the empirical results (Maddala 1983;Amemiya 1984). With regards to zero expenditure in tourism, the models commonly used by scholars include the doublehurdle (DH) model (Cragg 1971) and the Heckit model (Heckman 1979). Unlike traditional economic models that consider the purchase and consumption decisions of consumers to occur simultaneously, these two models divide consumer behavior into two decisionmaking processes, i.e., whether to buy and how much to buy—also referred to as the two-stage decision model. According to the two-stage decision model that is in line with the theory of consumer behavior, consumers will collect information before purchasing products and will use that information as a reference to decide whether or not to buy, and then decide how much to spend once they have made their purchase decision. Past studies on tourism expenditure reveal that a few of the discussions focus on the demand for tourist accommodation, for example, Hong et al. (1996) and Cai (1999). However, while both studies have adopted the Tobit model that considers zero expenditure as no consumption (Su and Yen 1996), they neglect the fact that no consumption may be the result of a lack of willingness to participate. Thus, using the Tobit model to analyze tourist accommodation expenditure may have certain limitations, resulting in an inability to grasp different influencing factors between the intention to make use of and the decision to actually spend money on tourist accommodation. More recently, a few studies have discussed this issue by using a different approach. For example, Masiero et al. (2015) utilized a quantile regression model to analyze the relationship between key travel characteristics and the price paid to book the accommodation. Ismail et al. (2021) adopt a two-step Chi-square automatic interaction detection (CHAID) procedure to segment spending on accommodation for visitors according to demographic, trip-related, and psychographic factors. Accommodation is a major component of tourist expenditure (Laesser and Crouch 2006). However, in the case of domestic tourism, accommodation may not be made use of by everyone, i.e., not all individuals participate in this expenditure activity, thus reporting values of expenditure equal to zero. Therefore, the analytical tool should be adequate to account for a large proportion of observations with a value of accommodation expenditure equal to zero. This study considers a data-oriented approach, employs the nonnested test method and selects an appropriate two-stage decision model to discuss the factors influencing the consumer behavior of domestic tourists in regard to accommodation. By estimating the double-hurdle model, the effects of the associated determinants on the intention to use tourist accommodation and expenditure decisions can be identified. Furthermore, despite numerous empirical studies that examine the determinant factors of total tourism expenses, a particular determinant factor may have varying impacts on a specific expenditure type. The research results may help to improve the economic benefits of the domestic tourism market and serve as valuable reference for relevant businesses in developing marketing strategies. 118 Economies 2022,10, 71 2. Literature Review 2.1. Studies on Tourism Expenditure Using the Tobit Model In past empirical studies, the Tobit model was the first model to be applied (Tobin 1958) to discuss the phenomenon of zero expenditure. Hong et al. (1996) used consumer expenditure survey data for the United States in 1990 and adopted the Tobit model to discuss the factors influencing accommodation expenditure in relation to family trips. Cai (1999) used consumer expenditure survey data for the United States in 1993 and investigated 3176 households while adopting the Tobit model to discuss the relationship between family characteristics and accommodation expenditure in leisure tourism. In the Tobit model, zero expenditure represents a true corner solution, whereas other possible factors causing zero expenditure are ignored. Other studies on tourism expenditure using the Tobit model include those by Dardis et al. (1994), Lee (2001), and Zheng and Zhang (2013). 2.2. Studies on Tourism Expenditure Using the DH Model and the Heckit Model A few researchers have also employed the DH model or the Heckit model in studies on tourism expenditure. Weagley and Huh (2004) used the DH model to discuss the factors influencing the leisure expenditures of retired and near-retired households in the United States. Nicolau and Màs(2005) decomposed the tourist choice process into two stages using the Heckit model, namely, taking a holiday and holiday expenditure. They found that the expenditure decision is correlated with that of taking a holiday. Jang and Ham (2009) used the Consumer Expenditure Survey (CES) and performed Heckman’s DH analysis to provide information on the two-step process for making travel consumption decisions. Alegre et al. (2013) examined Spanish household tourism participation and expenditure decisions by adopting a Heckit model. By means of the hurdle model, Bernini and Cracolici (2015) analyzed two stages of the tourist decision process: whether or not to participate in the domestic and overseas tourism markets in Italy and how much to spend. The DH model has also been applied in relation to expenditure on dining out (Jang et al. 2007). 2.3. Studies on Tourism Expenditure Using Other Models In recent years, in order to better understand tourists’ expenditure behavior, some researchers have employed new modeling frameworks to perform in-depth analyses. D’Urso et al. (2020) propose the fuzzy double-hurdle model, which combines the doublehurdle model with fuzzy set theory to take into account the effect of satisfaction on tourists’ expenditure behavior. The new model allows the researchers to handle the imprecision of both collected information (i.e., levels of satisfaction) and the kind of measurement used (i.e., a Likert-type scale). Pellegrini et al. (2021) investigated tourists’ expenditure behavior by implementing a framework that jointly adopts the stochastic frontier (SF) regression and multiple discrete–continuous extreme value (MDCEV) models. This framework allows the researchers to not only identify the maximum level of spending that the individual is willing to incur but also to assess two interrelated decisions: whether to allocate a budget for a specific expenditure category as well as the amount to be spent on that chosen category. Besides, a conditional quantile regression model has been applied in identifying leisure tourism expenditure patterns (e.g., Alfarhan et al. 2022). In addition, other explanatory factors that may influence tourists’ decision-making have been considered using various analytical techniques. Park et al. (2020) applies different estimation procedures, namely, ordinary least squares (OLS), two-stage least squares (2SLS), the Heckit model, and quantile regression (QR) to perform an analysis of the determinant factors in relation to total expenses. The role of information sources in predicting travel spending behaviors represents new possibilities for analyzing the determinants of expenditure by using QR. Chulaphan and Barahona (2021) investigated the determinants of tourist expenditure per capita in Thailand by utilizing an autoregressive distributed lag model (ARDL) and using panel-estimated generalized least square (EGLS). Such knowledge is essential for tourist authorities to develop profitable and sustainable 119 Economies 2022,10, 71 tourism projects in destinations whose natural resources have been affected by profitseeking tourism. 2.4. Proposed Research Framework According to the two-stage decision model, the decision on the intention to use tourist accommodation and that of accommodation expenditure constitute the consumer behavior of tourist accommodation. Based on a summary of the previous literature on tourism expenditure (e.g., Dardis et al. 1981,1994;Cai 1999;Nicolau and Màs 2005;Sun et al. 2015) and by considering the implementation of vacation policy, the variables influencing the intention to use and actual expenditure on tourist accommodation can be classified into six categories, namely, the economic factor, social stratum, geographical location, family life cycle, tourism behavior, and vacation policy. In this study, it is assumed that the economic factor influences the expenditure on tourist accommodation but does not influence the intention to use accommodation. This is mainly because if the same explanatory variable is included in the two sets of decision equations, it may be impossible to correctly identify the model’s parameters (Newman et al. 2001). Therefore, it is necessary to add certain exclusion restrictions (Jones 1992;Newman et al. 2001;Aristei et al. 2008) to facilitate the estimation of the parameters in the model equations. In terms of the empirical application, it is usually assumed that the participation equation is a function of noneconomic factors; thus, the economic factor can be excluded from this equation (Newman et al. 2001;Aristei et al. 2008). The research framework of this study is presented in Figure 1. The research hypotheses are presented as follows. Economies 2022, 10, x FOR PEER REVIEW 4 of 22 terminant factors in relation to total expenses. The role of information sources in predicting travel spending behaviors represents new possibilities for analyzing the determinants of expenditure by using QR. Chulaphan and Barahona (2021) investigated the determinants of tourist expenditure per capita in Thailand by utilizing an autoregressive distributed lag model (ARDL) and using panel-estimated generalized least square (EGLS). Such knowledge is essential for tourist authorities to develop profitable and sustainable tourism projects in destinations whose natural resources have been affected by profit-seeking tourism. 2.4. Proposed Research Framework According to the two-stage decision model, the decision on the intention to use tourist accommodation and that of accommodation expenditure constitute the consumer behavior of tourist accommodation. Based on a summary of the previous literature on tourism expenditure (e.g., Dardis et al. 1981; Dardis et al. 1994; Cai 1999; Nicolau and Màs 2005; Sun et al. 2015) and by considering the implementation of vacation policy, the variables influencing the intention to use and actual expenditure on tourist accommodation can be classified into six categories, namely, the economic factor, social stratum, geographical location, family life cycle, tourism behavior, and vacation policy. In this study, it is assumed that the economic factor influences the expenditure on tourist accommodation but does not influence the intention to use accommodation. This is mainly because if the same explanatory variable is included in the two sets of decision equations, it may be impossible to correctly identify the model’s parameters (Newman et al. 2001). Therefore, it is necessary to add certain exclusion restrictions (Jones 1992; Newman et al. 2001; Aristei et al. 2008) to facilitate the estimation of the parameters in the model equations. In terms of the empirical application, it is usually assumed that the participation equation is a function of noneconomic factors; thus, the economic factor can be excluded from this equation (Newman et al. 2001; Aristei et al. 2008). The research framework of this study is presented in Figure 1. The research hypotheses are presented as follows. q Social stratum q Family life cycle q Residential area q Tourism behavior q Vacation policy q Economic factor Intention to use tourist accommodation Expenditure on tourist accommodation Tourist accommodation behavior Figure 1. The research framework for the two-stage decision model of the intention to use and consumption expenditure on tourist accommodation. 2.4.1. Participation Decision Figure 1. The research framework for the two-stage decision model of the intention to use and consumption expenditure on tourist accommodation. 2.4.1. Participation Decision According to Nicolau and Màs(2005), Jang and Ham (2009), Alegre et al. (2013), and Bernini and Cracolici (2015), there is a positive link between the tourism participation decision and an individual’s education level. Indeed, higher educational levels may provide training and preparation for some types of recreational activities (Dardis et al. 1981) and also easier access to information and knowledge (Cai 1998). Such information and knowledge are likely to increase the desire to discover new destinations and enjoy new experiences (Bernini and Cracolici 2015). Furthermore, individuals with a high level of education are more likely to reach adequate job positions and a higher level of income, which could be spent on non-basic needs like tourism. Occupation status was found to be a significant 120 Economies 2022,10, 71 social discriminating factor in tourism participation (Bernini and Cracolici 2015). Thus, we propose the following hypothesis: Hypothesis H1a. The social stratum has a significant impact on the intention to use tourist accommodation. In Jang and Ham’s (2009) study, the variables of age and marital status were found to be significant for the travel decisions of elderly seniors. The research findings of Alegre et al. (2013) indicated that a positive effect was detected for tourism participation in the case of the presence of children in the household. Bernini and Cracolici (2015) found that the tourism participation decision was affected by cohort effects: the oldest cohorts were more inclined to participate in tourism than the youngest ones. The empirical results of Sun et al. (2015) indicated that the family travel intention varies at different stages of the household life cycle. Therefore, we hypothesize the following: Hypothesis H1b. The family life cycle has a significant impact on the intention to use tourist accommodation. By referring to Cai (1998), Nicolau and Màs(2005), Jang and Ham’s (2009), and Bernini and Cracolici (2015), the empirical analysis has emphasized the role of population location and consequently the attributes of the tourists’ region of residence. These studies have found that geographical variables are significant to the tourism participation decision. In a wider sense, the residential area takes in both territorial differences in tourism resources and socio-economic differences among residents’ living conditions. Therefore: Hypothesis H1c. The residential area has a significant impact on the intention to use tourist accommodation. Four variables have been selected to represent tourism behavior, including days of the trip, travel season, travel date, and favorite activity during the trip. Li et al. (2021) revealed that tourists’ behaviors in selecting travel seasons and the associated trip duration were influenced by a few factors and the correlation between these two tourism decisions was conditional upon the covariates. Dellaert et al. (1998) argued that tourists may be restricted by school holidays when choosing the period in which to travel. Indeed, time factors, including time convenience, were the most often cited reasons for not participating in recreational tourism (McGuire 1984). The finding of Wu et al. (2011) indicated that time constraints reduced the number of long trips, the number of short trips, and, to a greater extent, travel intention. Tourists expect to recover more completely during a vacation by removing themselves from daily settings and actively engaging in various restful activities. Laybourn (2004) stated that the decision-making of festival participants may be associated with personal factors, such as lifestyle. Nicolau and Màs(2005) concluded that a greater propensity to go on holiday was associated with a favorable opinion of going on holiday. Both lifestyle and tourists’ favorable opinions may reflect on their engagement in a certain activity which implies the benefits they seek (Moscardo et al. 1996). Those who seek more benefits from leisure and recreational activities may tend to lay emphasis on the high quality of travel and the use of accommodation. Thus, we hypothesize the following: Hypothesis H1d. Tourism behavior has a significant impact on the intention to use tourist accommodation. A vacation has been regarded as a basic human right which involves time off from work by the United Nations since 1948 and by the World Tourism Organization since 1980. In China, a vacation has been recognized as a form of human welfare (Chen et al. 2013). Vacation policies reflect the economic prosperity of a nation and have been classified into three categories: regulations regarding public holidays, regulations regarding weekly working hours, and regulations regarding paid holidays (Richards 1999). According to Chen et al. (2013), Chinese people legally have over 115 days off from work each year, 121 [Document text truncated for crawler view.]