Introduction
There are barely any studies that examine the presence of seasonality in Argentina tourism demand, and in them, only time series methods are used to evaluate the seasonality tourism causes in the country. Also, in the tourism planning instruments of Argentina, the calculation of seasonal factors for overnight stays in hotel establishments is applied with data obtained from the Hotel Occupancy Survey by using a simple disaggregation which distinguishes between domestic and international travelers in the tourist regions of the country.
The most common quantitative tools for analyzing seasonality, such as the indices of seasonal factors or the Gini index (without disaggregating) focus on representative models on seasonality, which reach general conclusions about the level of concentration of tourism demand. However, these techniques do not always allow us to know exactly what type of tourist is truly favorable for reducing seasonality in a given destination. Therefore, this paper aims to complement the quantitative tools mentioned above with the additive decomposition of the Gini index, and by obtaining the Relative Marginal Effect (RME) on the index to identify the most favorable travelers for the reduction of seasonality. By applying this decomposition in the tourist regions of Argentina (with a sufficient level of disaggregation to reveal nuances that with aggregated information can be diluted) the objective is to provide additional information to tourism managers in terms of which travelers they should direct their catchment policies, as long as their objective is to reduce seasonality.
Methodology
The incidence of seasonality of the hotel demand in Argentina and its tourist regions is analyzed in this work using the monthly variable "Travelers staying in hotel establishments" from the Hotel Occupancy Survey elaborated by the Ministry of Tourism and the National Institute of Statistics and Census of Argentina. The investigation period covers the years 2005-2013.
In a first descriptive stage, the estimated seasonal indices using the multiplicative method to delimit seasonal patterns have been used. To facilitate the analysis, peak season has been considered as the one which is comprised of the months with values higher than 1, and the remaining months are categorized as low season. In addition, the months have been grouped by seasons: summer (January, February and March), autumn (April, May and June), winter (July, August and September), and spring (October, November and December). It should be noted that Argentina is a country located in the southern hemisphere, and therefore, the seasons are temporarily reversed with respect to Spain.
As a measure of the annual seasonal concentration, we used the Gini index, which quantifies the degree of the concentration of travel demand between the months of a given year. The Gini index varies between 0 and 1, and the extent to which it is closer to 1 indicates a higher seasonal concentration in the observed year (higher annual seasonal concentration), whereas a value closer to 0 determines a more equal distribution in the time (lower annual seasonal concentration). Furthermore, the annual Gini index has been decomposed. This decomposition provides the estimated RME which produces a variation in some of the components on the overall Gini index. Thus, this RME represents variation in the overall Gini index when the number of travelers from a given origin is increased by 1%, keeping constant the monthly distribution of them and the number and monthly distribution of the remaining travelers. Also, it should be noted that the sum of the ERM must be equal to 0.
Main results
Considering the total number of travelers in Argentina, according to the estimated seasonal indices, the peak season takes place between the months of October and March, coinciding with spring and summer; the low season is during April and September as well as in the month of July. Domestic and international travelers differ mainly in July (low season for international travelers), and in November and December (low season for domestic ones). The rebound of domestic travelers in July is mainly due to the school holiday period. When the total of travelers in Argentina are disaggregated into domestic and international travelers, the former are disaggregated according to their region of origin and the latter according to their countries of residence, very similar seasons are observed. Thus, it was considered more appropriate to apply this disaggregation in the tourist regions of Argentina since they had features of greater interest not discernible in an analysis of the whole country.
In Argentina, the seasonal concentration is usually not high when compared to destinations more specialized in a single tourism product; nevertheless, this does not mean that it is a factor that should not be analyzed in order to reduce it as much as possible. Throughout the analyzed period, the total number of travelers exhibited a low level of concentration, with an average of 0.08 in the Gini index, and with a few variations. This level of concentration is almost identical to that shown by domestic travelers, and this is because the hotel demand in the regions of Argentina is dominated by them. However, the international travelers present a slightly higher level of concentration, with an average of the period of 0.11, i.e., a medium level of concentration.
Among the tourist regions of Argentina, however, major differences occur in concentration levels. To analyze the results the average of the Gini indices calculated in the period analyzed was considered more appropriate as the annual variation observed in all the years was scarce. The region of Buenos Aires is the only one that has a high concentration average concentration (0.24). The regions with a medium concentration are Patagonia (0.17), Centro (0.13) and Norte (0.11); and with a low concentration, the regions of Cuyo (0.08), Litoral (0.05) and CABA (0.05).
Obtaining the RME can be a very useful tool for establishing a classification in terms of preference to attract a type of traveler whose seasonal pattern is prone to reducing the seasonal concentration. With the analysis of the evolution of the RME throughout the period observed, one can foresee each year how the increase of a certain type of traveler can contribute to the reduction of the overall Gini index, which has previously been used to measure the level of seasonal concentration. Regarding to this, for destinations analyzed in this work, it has been specified as favorable those travelers whose RME are negative, and on the other hand, unfavorable those with positive RME.
In Argentina, domestic travelers have been more favorable for reducing seasonality throughout most of the period analyzed except for the years 2008 and 2013 in which the reverse situation occurred. However, it is more convenient to have a detailed disaggregation due to the significant differences between seasonal patterns, especially in concentration levels by origins. Thus, the most favorable travelers for reducing the seasonal concentration according to the results of 2013 are the travelers from neighboring countries (with a RME value of -0.07), the rest of America (-0.02), and to a lesser extent, those from the Centro and Litoral regions (-0.02), Cuyo and Patagonia (-0.01). The remaining travelers would not be favorable for reducing the seasonal concentration, e.g., those from Buenos Aires region (0.09) that throughout the period analyzed had an RME above 0.6. Travelers from Europe and the rest of the world also show positive RME throughout the period. When distinguishing by region, the international travelers, on the whole, are the most favorable for reducing seasonality in all tourist regions in 2013 (as well as in the whole country) except in the region of Patagonia. This effect is more pronounced in CABA (-0.13), Litoral (-0.11), and Norte (-0.08). However, only in Centro and Norte it stayed steadily throughout the period analyzed.
Conclusions
This paper aims to provide a methodology, the additive decomposition of the Gini index and obtaining the RME, which serves as a useful tool for reducing the seasonal concentration of the hotel demand in Argentina since it helps identify less seasonal travelers. By applying this methodology, the effectiveness of policies aimed at combating seasonality can be improved since with the information obtained these policies may be directed towards demand segments less prone to seasonality.
The methodology applied in this paper is proposed as a controlling and monitoring measure which can be used by tourism managers and hoteliers of destinations with high seasonal concentration; they can analyze the evolution of RME throughout the period for which there are sufficient disaggregated data available, above all, the last year observed, and the tourism policies aimed at reducing the seasonal concentration can be adjusted. Finally, it was found that it is much more effective to establish the classification of the travelers specified in this paper, given the heterogeneity of all the tourists regions in Argentina as well as in the group of domestic and international travelers. Furthermore, it was proven that the seasonal patterns and the consequent level of seasonal concentration observed in the travelers analyzed differ significantly when they were studied on a disaggregated basis, so it is essential to use an adequate level of disaggregation in the planning policies against seasonality Classification-JEL: R1 Keywords: Argentina, Indice de Gini, Metodología, Efecto Relativo Marginal, Estacionalidad, Argentina, Gini Index, Methodology, Relative Marginal Effect, Seasonality Pages: 197-221 Volume: 2 Year: 2016 File-URL: http://www.revistaestudiosregionales.com/documentos/articulos/pdf-articulo-2499.pdf File-Format: Application/pdf Handle: RePEc:rer:articu:v:2:y:2016:p:197-221