Ulrich Gunter

Using annual data for the period 1995-2012 for seven Central American and Caribbean countries, six different open-economy growth models that allow for international (eco-) tourism are estimated using panel-data estimation techniques. The main result of the investigation is that not only international tourist arrivals per capita have a highly significant impact on real GDP per capita but also that five different sustainability indicators interacted with international tourism have a positive impact on economic development in addition to international tourism. Furthermore, quantile regression shows that lower and medium income deciles in particular benefit most from international (eco-) tourism. The results are complemented by very similar estimation results for a set of 12 Central American and Caribbean countries using two sustainability indicators only, thus corroborating the validity of the specification. In addition, control variables are also generally significant and feature the algebraic signs as expected from economic theory.

The purpose of this study is to investigate whether using Google Trends indices for web and image search improves tourism demand forecast accuracy relative to a purely autoregressive baseline model. To this end, Vienna—one of the top-10 European city destinations—is chosen as a case example for which the predictive power of Google Trends is evaluated at the total demand and at the source market levels. The effect of the search query language on predictability of arrivals is considered, and differences between seasonal and seasonally adjusted data are investigated. The results confirm that the forecast accuracy is improved when Google Trends data are included across source markets and forecast horizons for seasonal and seasonally adjusted data, leaning toward native language searches. This outperformance not only holds relative to purely autoregressive baseline specifications but also relative to time-series models such as Holt–Winters and naive benchmarks, in which the latter are significantly outperformed on a regular basis.

Discussion on the growth limits and carrying capacity of tourism destinations is not new. Already for decades, carrying capacity has been at the core of sustainable tourism development and aims at offering ‘time/space-specific answers’ for individual localities of various European regions. There are many definitions of this concept and the calculation of a single ‘magic number’ quantifying the carrying capacity is infeasible for reasons such as differences in the thresholds established by visitors and residents, ecological limits, various resources, etc. The discussion about carrying capacity in the context of regional sustainability is linked to human activities impacting a region. This impact has to be within the region's ecological limits and consistent with the region's social and economic constraints in order to ensure adequate supporting functions for the population living in the region. This means that regions should learn as much as possible about the impact of tourism on their destinations in order to develop solid and adequate policies for regional and tourism development. This paper therefore introduces a novel methodology for assessing carrying capacity in tourism destinations, which (a) is specific enough to cater to destination-specific needs, as verified by pilot-testing on various representative case studies, and (b) is general enough to be applicable to any tourism destination throughout European regions. The results emphasize the importance of such a hands-on actionable methodology, while at the same time underlining the importance of dialogue between different stakeholder groups. The value added of the developed methodology is that it simultaneously addresses regional sustainability and tourism development, while acknowledging the fact that there is no single metric or value for carrying capacity. Finally, it is applicable to various types of destinations.

Based on the standard tourism demand model for quarterly tourism exports of six different world regions and their total, the authors applied a panel econometric approach to measure potential differences in income elasticities due to the medium-term speed of growth of the world economy. The evidence demonstrated that, related to the identified different growth periods, income elasticities showed significant variations. For 1977–1992, it was possible to measure the highest income elasticities of all periods. For 1994–2003 and 2004–2013, the income elasticities decreased from period to period. For the last decade, the values of the income elasticities were lower than one. The reasons for the decline in the income elasticities from the first to the second period were the ongoing saturation process and the slowing down of economic growth resulting in a change in consumer behaviour. The decline in income elasticities from the second to the third period was mainly due to the dramatic deterioration in the economic environment contributing to higher uncertainty about the future, with the result that precautionary saving increased, liquidity constraints limited expenditures on luxuries in favour of necessities and tourists preferred domestic destinations instead of going abroad.

Online news media coverage regarding a destination, a form of big data, can affect destination image and influence the number of tourist arrivals. Sentiment analysis extracts the valence of an author’s perception about a topic by rating a segment of text as either positive or negative. The sentiment of online news media can be viewed as a leading indicator for actual tourism demand. The aim of this study is to examine if web sentiment of online news media coverage of four European cities (Berlin, Brussels, Paris, and Vienna) possesses information to predict actual tourist arrivals. This study is the first to use web sentiment for forecasting tourism demand. Automated semantic routines were conducted to analyze the sentiment of online news media coverage. Due to the differing data frequencies of tourist arrivals (monthly) and web sentiment indicators (daily), the MIxed-DAta Sampling (MIDAS) modeling approach was applied. Results indicate that MIDAS models including various web sentiment indicators outperform time-series and naïve benchmarks in terms of typical accuracy measures. This study shows that utilizing online news media coverage as an indication of destination image can improve tourism demand forecasting. Because destination image is dynamic, the results can vary depending on time period of the analysis and the destination. A managerial implication of the forecast evaluation exercise is that destination management organizations (DMOs) should add models incorporating web sentiment data to their forecast modeling toolkit to further improve the accuracy of their tourism demand forecasts.

Airbnb has a major role to play in the competitiveness of the overall accommodation sector of individual destinations and it is rather unlikely that this role will diminish in the post-COVID-19 recovery of the tourism industry. Therefore, the present study motivates the Airbnb sector to look back at its past performance for insights that can be used in setting post-pandemic targets. In particular, this research assesses competitiveness of the Airbnb listings of 28 European cities by including hotel-related data as uncontrollable input variables within interactive data envelopment analysis modeling. The contribution lies in joining Airbnb listings and hotels into the benchmarking discussion and efficiency analysis, along with looking beyond the cumulative number of listings by dissecting the overall sector into commercial and private listings—something that has not been attempted as of yet, in spite of the ever-growing body of literature on the sharing economy.

This study employs a GVAR model on the passenger numbers of the top 20 busiest airports of the world and the Asia-Pacific and Latin America-Caribbean regions. With air passenger numbers representing a demand measure, country-level proxies for economic drivers are included as domestic and foreign variables. In terms of ex-ante forecast accuracy, the GVAR model performs best for several airports – yet not for the entirety of airports – compared to four benchmarks for horizons one and three quarters ahead. It also achieves several second and third ranks for these and two other horizons and when all horizons are evaluated jointly. Considering the connectivity of airports is worthwhile to achieve accurate and economically interpretable air passenger demand forecasts.

This study identifies key determinants of Airbnb demand and quantifies their marginal contributions in terms of demand elasticities. A comprehensive cross-sectional data set of all Viennese Airbnb listings that were active between July 2015 and June 2016 is examined. Estimation results, which are obtained by cluster-robust ordinary least squares, show that Airbnb demand in Vienna is price-inelastic. Significant positive drivers include listing size, number of photos, and responsiveness of the host. Significant negative drivers include listing price, distance from the city center, and response time of the host. Implications for the traditional accommodation industry are that, on the one hand, it should better communicate its sought-after advantages (e.g. lower average minimum duration of stay). On the other hand, it should increase its offer of bigger and better equipped hotel rooms since hosting more than two guests at a time is one of the major benefits of Airbnb.

We explore the benefits of forecast combinations based on forecast-encompassing tests compared to simple averages and to Bates–Granger combinations. We also consider a new combination algorithm that fuses test-based and Bates–Granger weighting. For a realistic simulation design, we generate multivariate time series samples from a macroeconomic DSGE-VAR (dynamic stochastic general equilibrium–vector autoregressive) model. Results generally support Bates–Granger over uniform weighting, whereas benefits of test-based weights depend on the sample size and on the prediction horizon. In a corresponding application to real-world data, simple averaging performs best. Uniform averages may be the weighting scheme that is most robust to empirically observed irregularities.

Following a sharp recession in 2009, the Ukrainian economy recovered in 2010 and 2011. In particular in 2011, domestic demand-led growth was accompanied by widening external imbalances. The economy’s external vulnerabilities – related to the current account deficit (2011: 5.6% of GDP) and the elevated foreign debt stock (77% of GDP) – entail risks for the banking sector, as exchange rate pressures against the hryvnia’s U.S. dollar peg have been recurrent and foreign exchange reserves declined in the second half of 2011. While the share of foreign currency loans in total loans has been steadily declining (thanks to a ban on extending new foreign currency loans to unhedged borrowers imposed by the National Bank of Ukraine in the fall of 2008), it remains sizeable (end-2011: 41%). Many of these loans are unhedged. The stabilization of nonperforming loans at a high level could be interrupted by a further deterioration of the economic situation or by a new bout of hryvnia depreciation. Moreover, the population’s confidence in the Ukrainian currency is prone to volatile swings. As deposit inflows have picked up and loan growth has remained subdued, the loan-to-deposit ratio has receded, but is still relatively high (end-2011: 163%). With the funding structure shifting to domestic deposits, the banking sector’s external position has improved (net external liabilities have fallen to 8% of total liabilities). In 2011, loan growth became positive in real terms again. Recapitalization efforts contributed to upholding capital adequacy. The banking sector’s profitability improved, but nevertheless stayed in negative territory.

The sharing economy has allowed people from all over the world to more effectively utilize their assets. Owners or controllers of assets in the sharing economy are free to set any price they want subject to prevailing market demand as long as they operate in an imperfectly competitive market environment. This paper examines how various characteristics of an Airbnb listing (size, number of photos, ratings, host responsiveness, superhost status, distance from city centre, etc.) affect the prices of accommodation and determines which factors strongly affect price using weighted least squares (WLS) and quantile regression. A hedonic pricing model was developed and applied to data from the cities of Barcelona, Madrid, and Seville to determine how the different characteristics of an Airbnb listing affect the price of accommodation in these major three Spanish tourist cities. The estimation results, which are resilient to various robustness checks, indicate that overall rating as well as characteristics indicative of the size of the accommodation have the strongest positive influence on price, while the number of reviews and distance from the city centre have the strongest negative influence on price.

Agricultural expansion remains the most important proximate cause of tropical deforestation, while interactions between socio-economic, technological and institutional factors represent the fundamental drivers. Projected population increases could further raise the pressure on the remaining forests, unless agricultural intensification allows raising agricultural output without expanding agricultural areas. The purpose of this article is to understand the role of institutional factors in governing the intensification process towards the goal of preserving forests from agricultural pressures, with a focus on Indigenous Peoples’ and local communities’ rights to forests (as embedded in the various tenure regimes). In this paper we adopt an international dimension and analyse the process of agricultural expansion across eleven Latin American countries over the period 1990–2010 to assess whether, in a context of agricultural intensification, different land tenure regimes impact differently on the realization of land-sparing or Jevons paradox. The results, based on a number of multivariate statistical models that controls for socio-economic factors, strongly suggest that the formal recognition of Indigenous Peoples and local communities’ forest rights has played an important role in promoting land sparing or attenuating Jevons paradox.

This study uses geotagged photos from Instagram to identify differences between the popular places in Vienna for residents and visitors. Moreover, we explore whether geotagged data can be useful in determining tourism demand in Vienna. The spatial analysis of 627,632 geotagged photos reveals the top-50 locations in Vienna for all-, local-, and visiting-Instagram users based on three popularity indicators (numbers of likes, comments, and photos). The results show that the top locations unique to local users are closely linked to activities residents usually pursue or location types they usually visit at their place of dwelling. In using geotagged photos to predict actual tourist arrivals to Vienna, we conclude that only the popularity indicators number of likes and number of comments based on the location ID “Vienna, Austria” for visitors to Vienna should be used and not the number of photos, since this indicator does not automatically generate engagement.

The objective of this article is to identify key determinants of the net interest margin (NIM) in the Austrian banking sector. In Austria, the NIM is one of the most important income drivers of banks given the importance of relationship banking, where interest income dominates other sources of revenue. However, the NIM differs substantially among Austrian banks. Drawing on a unique supervisory dataset for the Austrian banking sector of around 42,000 observations between the first quarter of 1996 and the second quarter of 2012, we analyze under which circumstances a bank has a relatively high or low NIM. We contribute to the empirical literature on the NIM by factoring in a bank’s business model in terms of its balance sheet structure and by accounting for the financial crisis from the third quarter of 2007 onward. Our estimation results suggest that not only the determinants identified in the existing empirical literature (different types of non-interest income and expenses, various risk measures, competition, macroeconomic environment) have a significant influence on the NIM, but also our two innovations (balance sheet structure, financial crisis).

Facebook is a popular social media platform used by both the demand and the supply sides of the tourism industry. Since there is a huge amount of information on the Internet, which can lead to information overload, individuals tend to apply the principle of least effort in attempting to obtain useful information as quickly and easily as possible. One of the easiest ways to retrieve travel information is by visiting the Facebook pages of destinations. This study investigates the foundations of the usefulness of Facebook Statistics: in particular of likes on DMO Facebook pages as a potential predictor of tourism demand, in addition to previous arrival numbers. In- and out-of-sample results show that the DMOs of Graz, Innsbruck, Salzburg, and Vienna can already utilize likes as an expedient leading indicator for demand, albeit not the only one. These findings are recommended to be incorporated into the DMOs’ marketing efforts.

Drawing on the positive experience from Costa Rica, the study examines whether international ecotourism makes a significant contribution to comprehensive economic development for the Central American and Caribbean region and contributes to comprehensive economic convergence. Following a standard empirical growth model, a dynamic panel regression model is estimated using time-series data from 1995 until 2012 for a cross section of seven countries. The interaction of international tourism and various established sustainability indicators is employed allowing ecotourism to be consistently quantified across countries, while numerous country-specific structural characteristics are controlled for. The estimation results show that international ecotourism has a statistically significant positive effect on both traditional economic development (real GDP per capita) and comprehensive economic development (adjusted net savings; ANS per capita), which is a measure of a society’s potential future well-being, thus providing evidence in support of the tourism-led growth hypothesis and pointing towards an important role for ecotourism in driving comprehensive economic convergence.

Current curricula and learning environments provided by HEIs generally do not implement a transformative approach toward education. Learning for sustainable tourism is not only a matter of introducing the concept of sustainable tourism into the curriculum, but also of using teaching approaches which enable social learning towards a sustainable tourism future beyond the mere creation of a body of knowledge. The engagement in practical sustainability activities in a multi-stakeholder environment as suggested by the TRIANGLE knowledge alliance contributes to changing the students’ behavior and changing their mindsets.

This study models European Airbnb occupancy from January 2017 to December 2020 for 43 European countries within an econometric panel-data framework. Besides typical economic covariates, the COVID-19 Stringency Index is employed to allow for the recent pandemic. The counterfactual observations created by two versions of the econometric model (one with and one without European aggregates of macroeconomic controls) are benchmarked against traditional univariate time-series models and a seasonal Markov-switching autoregression. For pandemic times, the latter performs very well in terms of several forecast accuracy measures due to its ability of (a) detecting the pandemic-induced structural break and (b) accounting for seasonal patterns in the data. The econometric models also predict well as they outperform the time-series models in the majority of cases. Hence, the panel-data approach is suitable when looking for economically interpretable Airbnb occupancy forecasts, even during the time of the pandemic.

Using data for the period 1995Q1–2012Q4, single-equation error correction models for real tourism import demand to Brazil and real tourism export demand from Brazil are derived. According to breakpoint tests, two periods (pre-2003 and post-2003) have to be distinguished. While international travel to and from Brazil can be seen as a luxury good for the whole period, low economic growth rates at the global level and the real appreciation of the Brazilian currency ‘Real’ from 2003 onward have led to stagnating real tourism exports and decreases in long-term income and price elasticities. However, high economic growth rates in Brazil and the real appreciation from 2003 onward have led to strongly growing real tourism imports and increases in long-term income and price elasticities. In line with international travel being regarded as a status symbol in Brazil, a Veblen effect of conspicuous consumption can be confirmed for the post-2003 period.

Using data on Airbnb listings from San Francisco and the Bay Area, the present study investigates the relative importance of the four criteria that need to be fulfilled to obtain the Airbnb superhost status. In order to quantify the marginal contributions of the four criteria, different index models of binary response (logit, probit, and IV probit, which allows for the endogeneity of Airbnb demand) are applied. The results, which are consistent across models, show that in San Francisco and the Bay Area obtaining (and maintaining) excellent ratings is, by far, the most important criterion, followed by reliable cancellation behavior of the host, host responsiveness, and sufficient Airbnb demand. Moreover, commercial Airbnb providers are more likely to obtain the superhost status.

The objective of this paper was to evaluate the scientific value of econometric tourism demand studies. Based on a questionnaire answered by ourselves we analyzed articles published in Annals of Tourism Research, Journal of Travel Research, Tourism Management, and Tourism Economics during the period 2007 to 2017. The evaluation showed that current scientific practice generally failed to differentiate between substantive (economic) significance and statistical significance, and used these terms interchangeably in many cases. In line with these flaws, most authors avoided discussing the estimation results in terms of their size and their reliability, as well as failing to adequately address the limitations of their studies and to justify the chosen methods.

Agricultural expansion remains an important cause of deforestation in Latin America. There is an on-going debate about whether increasing agricultural productivity leads to land-sparing or Jevons paradox. At the same time, recognizing the customary rights of indigenous peoples and local communities can be particularly effective at slowing down deforestation. We consider ten Latin American countries over the period 1990 to 2010 and use dynamic panel data models to assess whether: a) there is a difference between short-run and long-run effects of improvements in agricultural productivity and b) different land tenure systems are capable of directing the process of agricultural intensification towards land-sparing. Our results allow us to draw a number of stylised conclusions. In general, we observe that higher agricultural productivity per-se is land-sparing, albeit the long-run effects appear smaller than the short-run effects. Most importantly, the overall effect of increased productivity crucially depends on the institutional context. In this respect, increasing the forest area owned or managed by indigenous peoples promotes land-sparing, while increasing the forest area administered by governments and/or owned by private individuals and companies promotes Jevons paradox. In the long-run, the agricultural expansion effects of increasing the forest areas owned by private individuals and companies are stronger than those associated with the expansion of forest areas administered by the government. We also note that the formal recognition of land ownership to indigenous peoples and local communities manifests its beneficial effects in the long-run.

In this paper we estimate and forecast with a small-scale DSGE model of the Euro area and the USA characterized by diverging interest-rate rules using quarterly data from 1996Q2 to 2011Q2. These diverging rules reflect the differing mandates of the ECB and the Fed, respectively. Due to its primary objective of price stability, the ECB is supposed to conduct monetary policy by considering producer-price inflation only (single mandate), whereas the Fed is assumed to conduct its policy by taking into account the output gap in addition to producer-price inflation (dual mandate). In terms of the RMSE and the MAE, the DSGE model with diverging interest-rate rules outperforms a DSGE model with identical interest-rate rules in almost 70% of all cases for almost all variables across forecast horizons out of sample. It also compares well with BVAR benchmarks. For shorter horizons, we find some statistically significant differences in forecast accuracy between rival models. For forecast horizons three and four quarters ahead, the null hypothesis of equal forecast accuracy can seldom be rejected.

We examine the determinants of the net interest margin (NIM) and the net fee and commission income ratio (NFCIR) of Austrian banks as well as their interrelationship and whether portfolio separation between loan and deposit categories holds. We describe a conceptual framework for the profit optimization problem faced by banks as a Bertrand game with differentiated products and intrafirm product interactions. We contribute to the literature by factoring in banks’ business models in terms of their balance sheet structure. We empirically assess the implications of our conceptual framework using a unique supervisory data set of around 48,000 observations between 1998 and 2014. We estimate two panel vector autoregression models with a novel panel vector autoregression code. Apart from quantifying the contributions of the determinants (e.g., risk weighted assets, leverage ratio, loan loss provision ratio) to NIM and NFCIR, the empirical results show that interest income and fee and commission income should be regarded as strategic complements within a bank. We further conclude that portfolio separation between different loan and deposit categories does not hold.

In this paper we estimate and forecast with a small-scale DSGE model of the Euro area and the United States characterized by diverging interest-rate rules using quarterly data from 1996Q2 to 2011Q2. These diverging rules reflect the differing mandates of the ECB and the Fed, respectively. Due to its primary objective of price stability, the ECB is supposed to conduct monetary policy by considering producer-price inflation only, whereas the Fed is assumed to conduct its policy by taking into account the output gap in addition to producer-price inflation (dual mandate). In terms of the RMSE and the MAE, the DSGE model with diverging interest-rate rules outperforms a DSGE model with identical interest-rate rules in almost 70% of all cases for almost all variables across forecast horizons out of sample. It also compares well with BVAR benchmarks. For shorter horizons we find some statistically significant differences in forecasting accuracy between rival models. For forecast horizons three and four, the null hypothesis of equal forecasting accuracy can seldom be rejected.

Using spatial panel data comprising a cross section of 1,461 continuously active Airbnb listings obtained from AirDNA, as well as time series data from NYC and Company and the OECD covering the time period September 2014 to June 2016, the present study quantifies own price, cross price, and income elasticities of Airbnb demand to New York City within an empirical tourism demand framework. The particular goal of the study is to establish whether the relationship between Airbnb and the traditional accommodation industry is of a substitutional or of a complementary nature. Employing a one-way fixed-effects spatial Durbin model, it can be concluded that demand is price-inelastic for Airbnb accommodation in New York City, which is a luxury good, and that the city's traditional accommodation industry as well as neighboring Airbnb listings are substitutes for the investigated Airbnb listings. The estimation results are robust against several alternative specifications of the regression equation.

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases.

The ability of 10 Google Analytics website traffic indicators from the Viennese DMO website to predict actual tourist arrivals to Vienna is investigated within the VAR model class. To prevent overparameterization, big data shrinkage methods are applied: Bayesian estimation of the VAR, reduction to a factor-augmented VAR, and application of Bayesian estimation to the FAVAR, the novel Bayesian FAVAR. Forecast accuracy results show that for shorter horizons (h = 1, 2 months ahead) a univariate benchmark performs best, while for longer horizons (h = 3, 6, 12) forecast combination methods that include the predictive information of Google Analytics perform best, notably combined forecasts based on Bates–Granger weights, on forecast encompassing tests, and on a novel fusion of these two.

Employing the metadata from 627,632 Instagram posts for the Austrian capital city of Vienna over the period of October 30th, 2011 to February 7th, 2018, the present study extracts sentiment, as well as single basic emotions according to Plutchik’s Wheel of Emotions, from the photo captions including hashtag terms. In doing so, an algorithm falling into the category of dictionary-based approaches to study emotions contained in written text was developed and applied. Not only are the overall polarity and the single emotions contained in Instagram posts within Vienna investigated, but also the top 54 Viennese Instagram locations. A particular novelty of this study is the measurement of longitudinal developments from emotive text and the fine-grained analysis of single emotions in addition to the overall polarity. One crucial empirical result of the study is that more experience and self-confidence in Instagram posting, as well as increasing expectations, seem to result in becoming a more critical poster over time. Companies interested in the use of influencer marketing to promote their products and services via Instagram should take this finding into consideration in order to be successful.

The COVID-19 pandemic has been a major shock to the global tourism industry. Given its peculiarity, this paper analyzes one of the most intriguing questions in the Airbnb literature – the pricing of Airbnb listings – by taking advantage of a difference-in-differences methodology that largely draws on variations in country-level policy responses to the pandemic. Relying on a dataset containing weekly information from 130,999 continuously active listings across 27 European countries from 2019 to 2020, this study first investigates the exogenous impact of response policies (proxied by the COVID-19 Stringency Index) on demand. Secondly, accounting for the endogeneity of both demand and prices, this research analyzes pricing responses to demand variations. Results show that: i) increases in the COVID-19 Stringency Index cause significant declines in Airbnb demand; ii) increases in demand cause, on average, increases in Airbnb prices; and iii) pricing strategies between commercial and private hosts differ substantially.

The present study investigates the role of ecotourism as a potential catalyst for land sparing in Latin America, with a particular focus on indigenous and community-based ecotourism. The research question is investigated within a comprehensive empirical land sparing–agricultural expansion framework, which uses the Jevons paradox as its theoretical foundation. It also allows for environmental governance and includes several socioeconomic control variables. In doing so, a panel data set based on secondary data from institutional sources and comprising 10 Latin American countries for the period from 1995 to 2015 is employed, which resulted in 209 observations in total. Panel estimation results show that there is only moderate evidence of land sparing associated with ecotourism, when it occurs on indigenous peoples’ and local communities’ land. To achieve land sparing through ecotourism, titling land to indigenous peoples and local communities as stakeholders is therefore crucial, but this beneficial effect should not be overestimated.

Purpose

The purpose of this paper is to analyze the ex ante projected future trajectories of real tourism exports and relative tourism export prices of the EU-15, conditional on expert real gross domestic product growth forecasts for the global economy provided by the Organisation for Economic Co-operation and Development for the years 2013-2017.

Design/methodology/approach

To this end, the global vector autoregression (GVAR) framework is applied to a comprehensive panel data set ranging from 1994Q1 to 2013Q3 for a cross-section of 45 countries. This approach allows for interdependencies between countries that are assumed to be equally affected by common global developments.

Findings

In line with economic theory, growing global tourist income combined with decreasing relative destination price ensures, in general, increasing tourism demand for the politically and macroeconomically distressed EU-15. However, the conditional forecast increases in tourism demand are under-proportional for some EU-15 member countries.

Practical implications

Rather than simply relying on increases in tourist income, the low price competitiveness of the EU-15 member countries should also be addressed by tourism planners and developers in order to counter the rising competition for global market shares and ensure future tourism export earnings.

Originality/value

One major contribution of this research is that it applies the novel GVAR framework to a research question in tourism demand analysis and forecasting. Furthermore, the analysis of the ex ante conditionally projected future trajectories of real tourism exports and relative tourism export prices of the EU-15 is a novel aspect in the tourism literature since conditional forecasting has rarely been performed in this discipline to date, in particular, in combination with ex ante forecasting.

Using data for the period 2010M06–2017M02, this study investigates the possibility of predicting total tourist arrivals to four Austrian cities (Graz, Innsbruck, Salzburg, and Vienna) from LIKES of posts on the Facebook pages of the destination management organizations of these cities. Google Trends data are also incorporated in investigating whether forecast models with LIKES and/or with Google Trends deliver more accurate forecasts. To capture the dynamics in the data, the autoregressive distributed lag (ADL) model class is employed. Taking into account the daily frequency of the original LIKES data, the mixed data sampling (MIDAS) model class is employed as well. While time-series benchmarks from the naive, error–trend–seasonal, and autoregressive moving average model classes perform best for Graz and Innsbruck across forecast horizons and forecast accuracy measures, ADL models incorporating only LIKES or both LIKES and Google Trends generally outperform their competitors for Salzburg. For Vienna, the MIDAS model including both LIKES and Google Trends produces the smallest forecast accuracy measure values for most forecast horizons.

The prices of cash crops impact the livelihoods of millions of households in developing countries. While the influence of speculators on global commodity prices determined through derivatives exchanges is extensively discussed, the contribution of hedgers to short-term changes in futures prices has largely been disregarded in the financialization of commodities discourse over the past two decades. This results in a failure to account for the interconnected activities of increasingly consolidated lead firms within physical global value chains (GVCs) and derivatives markets. This article examines the pricing and hedging strategies of lead firms in the coffee, cocoa and cotton GVCs in relation to their activities on commodity derivatives markets. Based on Open Interest data as an indicator of derivatives markets activity, a measure of buying and selling pressure by trader categories is applied in a Generalized ARCH (GARCH) model. The findings of this article show that hedgers’ activities allow speculators to drive global benchmark prices so that they can benefit through combinations of financial hedging and physical trading strategies. As these practices of lead firms contribute to the transmission of futures prices along GVCs, smallholders and other actors in cash crops in producer countries are exposed to heightened price changes.

Data envelopment analysis (DEA) is a non-parametric, deterministic method for evaluating performance. DEA estimates best practice production frontiers and measures the relative efficiency of peer entities. These entities are called decision-making units (DMUs) and are assumed to be homogeneous. This assumption implies that all DMUs (e.g., hotels, Airbnb listings, destinations) pursue the same or at least similar goals (e.g., increasing occupancy rate, increasing guest satisfaction). Then, their performances are evaluated. DEA has been successfully used in various contexts, with continuous applications across disciplines and sectors as diverse as agriculture, Airbnb listings, banking, education, hospitals, hotels, transportation and travel agencies. The literature also highlights the method’s superior benchmarking abilities over other operations research techniques. This characteristic alone makes DEA interesting to decision makers. It gives them an opportunity to improve the performance of their DMUs. Benchmarking is all about improvement.

The purpose of this study is to compare the predictive accuracy of various uni- and multivariate models in forecasting international city tourism demand for Paris from its five most important foreign source markets (Germany, Italy, Japan, UK and US). In order to achieve this, seven different forecast models are applied: EC-ADLM, classical and Bayesian VAR, TVP, ARMA, and ETS, as well as the naïve-1 model serving as a benchmark. The accuracy of the forecast models is evaluated in terms of the RMSE and the MAE. The results indicate that for the US and UK source markets, univariate models of ARMA(1,1) and ETS are more accurate, but that multivariate models are better predictors for the German and Italian source markets, in particular (Bayesian) VAR. For the Japanese source market, the results vary according to the forecast horizon. Overall, the naïve-1 benchmark is significantly outperformed across nearly all source markets and forecast horizons.

Recently, blockchain and cryptocurrencies have become topics of discussion in both research and industry. Iansiti and Lakhani perceive blockchain as a foundational technology rather than a disruptive one, since potentially new economic and social systems can be based on blockchain. Therefore, understanding blockchain and contemplating its impact on the tourism and hospitality industry is essential. The tourism and hospitality industry has to focus not on the technology itself but on how it can be used for the benefit of consumers and suppliers, while at the same time creating new tourism products or systems. The purpose of this study is to explore and identify use cases for blockchain for the tourism and hospitality industry. In addition, an outlook on potential future blockchain applications given the current COVID-19 pandemic is provided.

The present study develops a methodology to assess transportation-related CO2 emissions of European city tourism. In doing so, not only is travel distance considered, but also the chosen transportation modes and the particularities of the different cities’ source markets. The major contribution of this study is the implementation of this learning methodology into a decision support system for destination management organizations of cities. Based on a sample from 2018 of 48 European cities with at least 40 source markets, the range of aggregate transportation-related CO2 emissions of European city tourism is estimated. Moreover, a longitudinal analysis of the exemplary city of Vienna covering the period of 1990 to 2018 is carried out. Finally, some policy recommendations of how destination management organizations can contribute to make the estimated transportation-related CO2 emissions even more precise and on how to make European city tourism more environmentally sustainable are drawn.

The present study employs daily data made available by the STR SHARE Center covering the period from 1 January 2010 to 31 January 2020 for six Viennese hotel classes and their total. The forecast variable of interest is hotel room demand. As forecast models, (1) Seasonal Naïve, (2) Error Trend Seasonal (ETS), (3) Seasonal Autoregressive Integrated Moving Average (SARIMA), (4) Trigonometric Seasonality, Box–Cox Transformation, ARMA Errors, Trend and Seasonal Components (TBATS), (5) Seasonal Neural Network Autoregression (Seasonal NNAR), and (6) Seasonal NNAR with an external regressor (seasonal naïve forecast of the inflation-adjusted ADR) are employed. Forecast evaluation is carried out for forecast horizons h = 1, 7, 30, and 90 days ahead based on rolling windows. After conducting forecast encompassing tests, (a) mean, (b) median, (c) regression-based weights, (d) Bates–Granger weights, and (e) Bates–Granger ranks are used as forecast combination techniques. In the relative majority of cases (i.e., in 13 of 28), combined forecasts based on Bates–Granger weights and on Bates–Granger ranks provide the highest level of forecast accuracy in terms of typical measures. Finally, the employed methodology represents a fully replicable toolkit for practitioners in terms of both forecast models and forecast combination techniques.

Using annual data for the period 1995–2012 for seven Central American and Caribbean countries, six different open-economy growth models that allow for international (eco-) tourism are estimated using panel-data techniques. Two main results of the investigation are that international tourist arrivals per capita have a highly significant impact on real GDP per capita, and also that five different sustainability indicators interacted with international tourism have a positive impact on economic development. Furthermore, quantile regression shows that lower and medium income deciles of the population in particular benefit from international (eco-) tourism. The results are complemented by very similar findings for a set of 12 Central American and Caribbean countries using only two sustainability indicators, thus corroborating the validity of the specification. In addition, control variables are also generally significant and they feature the algebraic signs expected from economic theory.

This chapter has the twofold objectives of reassessing the European city tourism transportation-related CO2 emissions presented in Gunter and Wöber (2022) and highlighting the importance of management information and decision support systems for evidence-based decision making by DMOs. First, the impact of the COVID-19 pandemic on CO2 emissions is measured by comparing the pre (year 2019), during (year 2000), and post (year 2021) pandemic travel emissions, whereby ‘post pandemic’ should be understood as the year in which the most severe travel restrictions and other pandemic-related measures were gradually lifted. Second, as representatives of city DMOs have started entering their own data in the online management information and decision support system TourMIS (for example, modal split according to source markets, average CO2 emissions per transportation mode, average number of destinations visited) beyond the calibrated parameters and approximated modal split proposed by Gunter and Wöber (2022), a preliminary evaluation is provided on the progress of stakeholder involvement in developing the learning methodology.

Airbnb is arguably the world’s most popular accommodation sharing platform. Its impact on demand and supply within the tourism and hospitality industry is nowadays unquestionable. The present study delves into inspecting the efficiency of Airbnb listings of European cities, as, in spite of the success of Airbnb as a whole, it cannot be presupposed that all listings are equally successful. More specifically, data envelopment analysis (DEA) is employed in this first comprehensive benchmarking attempt within the domain of the sharing economy to date. This article also makes a contribution to robustness by introducing an interactivity note to the base model, thus, inspecting the results for corroboration/discrepancies and going beyond the static analyses that are common in DEA modeling. Ultimately, this is done with the goal of highlighting opportunities for inefficient Airbnb listings to properly utilize their inputs and therefore become more competitive.

The COVID-19 pandemic has restricted both business and social life over the last two years. Stop-and-go policies enacted as containment measures have further impacted the global economy, and tourism in particular. Tourism demand shows only weak signs of a sustainable recovery. The medium-term outlook remains highly uncertain, and yet few studies have addressed the development of the tourism and leisure industries in the years ahead. In this context, we forecast demand in selected EU countries in terms of total expenditure on outbound travel (tourism imports) using a panel pooled Fully Modified Ordinary Least Squares (FMOLS) approach. Baseline and downside scenarios are elaborated to project demand for foreign travel until 2025.

Accounting for the instability of tourism income elasticities in the European Union-15 since 2004, estimations show that income elasticities in the period 2004–2014 were greater in slow-growth periods (above 1) than in fast-growth periods (below 1). Due to the gradual deterioration of the economic environment since 2004, the small income improvements in the fast-growth periods were used relatively more for satisfying pent-up demand for necessary consumer goods or precautionary savings than for traveling abroad. The relatively high income elasticities in the slow-growth periods resulted from negative adjustments due to the effects of the economic downturn.

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Using a quarterly data sample of 28 EU countries ranging from 2004Q1 to 2016Q4, real tourism exports are modeled and estimated as a function of relative tourism export prices and aggregate real GDP. For almost all countries, HEGY tests applied to first-differenced variables cannot reject the null hypothesis of the presence of seasonal unit roots. Seasonal differencing would be necessary to remove the seasonal patterns from the data, yet employing seasonally differenced data results in information loss, distorted elasticity estimates, and mediocre forecast accuracy. Our suggestion is therefore to perform estimation and forecasting for each quarter separately. This approach avoids the use of seasonal differencing, results in plausible, seasonally varying estimates of income elasticities, and yields statistically significantly superior forecast accuracy, also in comparison to two data-driven benchmarks for seasonal data (i.e., SARIMA and ETS).

This study, which was contracted by the European Commission and is geared towards easy replicability by practitioners, compares the accuracy of individual and combined approaches to forecasting tourism demand for the total European Union. The evaluation of the forecasting accuracies was performed recursively (i.e., based on expanding estimation windows) for eight quarterly periods spanning two years in order to check the stability of the outcomes during a changing macroeconomic environment. The study sample includes Eurostat data from January 2005 until August 2017, and out of sample forecasts were calculated for the last two years for three and six months ahead. The analysis of the out-of-sample forecasts for arrivals and overnights showed that forecast combinations taking the historical forecasting performance of individual approaches such as Autoregressive Integrated Moving Average (ARIMA) models, REGARIMA models with different trend variables, and Error Trend Seasonal (ETS) models into account deliver the best results.