Effects of Tourism Seasonality at Local Level

Abstract

The paper underlines the importance of identifying seasonality effects over tourism development. The study applies a simple test for examining the presence of seasonality in tourism demand in the line of exploring its concentration and strength at local level. The investigation is covered by calculating some commonly applied indicators for measuring tourism seasonality, like Gini coefficient, Seasonality Indicator and Coefficient of Variation. The data set addresses the total tourist arrivals between 2000-2013 and elaborates the case of Ohrid, as the most famous tourist destination in Macedonia. The research results point to high level of tourism seasonality with significant flow distribution to tourism development. Finally, the study may serve as a base for identifying measures and activities necessary for creating comprehensive local and regional tourism policy.

Sustainable Tourism and Unesco Status Benefits: Perceptions of Residents of Ohrid

“…Part-time employed are the only ones who strongly agree that "Myself, or member from my family directly benefits from tourism or tourists who visit us". This confirms the strong and robust seasonality of tourism in Ohrid (Petrevska, 2015). Although overall all residents agree that tourism brings economic benefits only during the tourist season, it is interesting to note a moderate percentage (18%) of full-time employed locals who disagree with that statement.…”

Coping With Seasonality: Exploratory Study of Tourism in Ohrid

“…Ohrid (North Macedonia) is a historic city with a population of about 52,000 people and is the most well-known tourist attraction in the country. It is one of Europe's oldest human settlements, and it has been dubbed "Jerusalem of the Balkans" because of its 365 churches (Vankovska and Wiberg, 2003;Petrovski and Talevski, 2004). The Lake Ohrid region has been designated as a transboundary mixed World heritage site due to its natural exceptional value as well as its cultural and historical area (UNESCO, 1979(UNESCO, , 1980.…”

Forecasting Dynamic Tourism Demand Using Artificial Neural Networks

“…The peak points for the international tourism demand are visible in the third quarter (summer months July-September) (Figure 2). So Ohrid is characterized by an unequal seasonal distribution of tourist arrivals and the presence of strong and powerful seasonality [20], [21].…”

“…LITERATURE REVIEW Forecasting tourism demand is vastly explored in academia. The forecasting methodology varies as scholars employ both the time series and econometric approaches in predicting tourism demand [21]. Often, a combined forecast is advocated for obtaining more accurate models [10], [11], [12].…”