Nowadays, there is competition in attracting film producers to screen their productions in specific locations, but at the same time, there is also a lack of data‐driven academic research that measures the effects of film industry on local tourism. This study evaluates the effects of film industry on tourism outcomes in Dubrovnik, Croatia. Using the synthetic control approach, we estimate the causal effect of a highly broadcasted TV series, Game of Thrones, on tourist arrivals. The use of such a data‐driven procedure is an important step towards identification‐based empirical work in tourism research as it enables us to build a credible synthetic counterfactual and to answer the “what if” type of questions. We find a robust and positive effect of filming the TV series in Dubrovnik on the number of tourist arrivals. Additionally, we show that there are positive spillover effects on other counties and the whole country. Placebo tests show that the estimated effects are relatively large when compared to other counties implying our results are not driven simply by chance.
The authors use panel data models on a dataset covering EU new member states and candidate countries (Montenegro and Turkey) to investigate the relationship between tourism activity and price level. Along with modelling the overall price level, the authors also separately model the price level of consumer goods, of consumer services and of goods and services associated with tourism consumption (hotels and restaurants, recreation and culture, transportation and food and beverages). Thereby, they control for other factors that commonly influence the price level of an economy, such as income, productivity, trade openness and fiscal dominance. The results suggest that tourism activity increases the overall price level in the economy. This effect is, however, much stronger for prices of consumer services; in particular, for prices of recreation and culture, and hotels and restaurants.
The aim of this article is to construct a monthly coincident indicator of real economic activity in Croatia. For that purpose, we use a database containing altogether 278 time series, ranging from January 1998 to December 2010. In step one we use correlation analysis, logit and Markov switching (MS) model in order to select time series that closely follow the overall business cycle and its turning points. The following four series have been detected as having the best coincident properties: industrial production, volume of retail sales, VAT revenues and total credit to households. In step two we apply dynamic factor model methodology to the aforementioned coincident series in order to estimate their common component, which is then used to construct a monthly coincident indicator of real economic activity.
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