Last week I attended the International Association of Tourism Economics’ biennial conference in Ljubjana Slovenia. I enjoyed it a lot and thought that it should be possible to share the love. Everyone knows what economists typically talk about at Macro, or Development, or Finance conferences, but what about Tourism Economics? What are the key questions that researchers are interested in? What are the methods that they are using? What follows is a quick and dirty typology of tourism economics research, drawing on the papers presented at the IATE conference 2013.
To me it seems that there are two very broad stands:
- The links between tourism and […]
- The links between […] and tourism
When I say links you can replace that with relationship, sometimes impact of/on and there are even some studies that try to claim causation.
On the links between tourism and […], you can fill in the blank with economic growth, economic development, poverty or inequality. The levels at which the relationships between tourism activities or the tourism sector and these other outcomes are studied, stretch from economy-wide, to regional, to local attractions and events. The methods by which these relationships are studied can be split into simulation models and econometric models.
The simulation models are typically SAM and CGE models that estimate the impact of increases or decreases in tourism arrivals on national, regional or local economies, as well as the welfare effects. The researchers link tourism satellite accounts to national-level SAMs and you find GAMS and GEMPACK users. The recommendations are aimed at industry and policymakers and what they can do to limit leakages of tourism expenditure and deal with negative welfare effects. There were a few of these papers at the conference with some interesting results. They showed that an increase in tourism activity, or growth of the tourism sector, can have adverse effects. In some cases it can be a cause of Dutch disease. In others it ends up benefiting mainly capital and skilled labour.
The econometric models used to examine the relationships between tourism and other outcomes are typically applied at the national level. Tourism arrivals or tourism expenditure is one of the independent variables explaining the variation in some or other macro variable. You find time series, cross-country and panel data analysis. The time series approaches employ Granger causality, VARs and VECMs and the cross-sections and panels sometimes use a gravity specification with bilateral tourist flows.
On the links between […] and tourism you can fill in the blank with different factors that may influence tourism demand = arrivals / expenditure. The motivation for this line of work is often market segmentation and marketing. I think that here it is possible to distinguish between macro and micro approaches. At the macro level researchers look, for example, at the importance of exchange rates. Some focus on forecasting tourism demand and estimating income and price elasticities. This where you find the use of panel VARs and sophisticated time-series forecasting techniques. At the micro level researchers tend to use primary data collected with surveys, asking tourists about their preferences, motivation or experience and linking that to their travel behaviour or spending. The methods include factor or cluster analysis of survey data and then regression models linking the type of tourist to certain spending patterns. Since the data is often not normally distributed, you find people using quite sophisticated estimators. Here the return visitor is the Holy Grail for industry and policymakers.
Finally, there is a subset of this second strand of work that focusses on competitiveness. At a country-level, or regional destination-level this about issues such as exchange rates, taxes or national carriers and how these influence the competitiveness of the tourism industry vis a vis other countries or destinations. At the level of attractions, hotels (and even individual ski-lifts), this is about firm-level studies of efficiency and innovation, often using data envelopment and stochastic frontier analysis.
The one thing that I did not hear at the conference (compared to what you run into at regular economics conferences) is the great obsession with causation and the consequent use of panels, natural experiments or RCTs to try show causation.
But then I would love to hear from a few more researchers and whether they agree with this typology. Are there other ways of sorting the major questions? Prof Andrea Saayman recommends a demand and supply side view. Are there methods that I have missed? Do tourism economists care more about relationships and less about causation? But then the point is, you can keep writing boring papers about inflation targeting or you can come over to the fun side and study tourism economics.