ANALYSIS OF TOURIST FLOWSFROM RUSSIAN FEDERATION
TO THE COUNTRIES OF THE EUROPEAN UNION
Kirill Furmanov
Olga Balaeva
Marina Predvoditeleva
National Research University
Higher School of Economics
Higher School of Economics, Moscow, 2011www.hse.ru
Higher School of Economics, Moscow, 2011
FACTORS, AFFECTING THE NUMBER AND FREQUENCY OF TOURIST TRIPS MADE BY
RUSSIAN CITIZENS INTERNATIONALLY
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• Visa-free regime/negotiations on visa system simplification;• Increasing amount of tour operators and agencies;• Development of Russian relative and supporting services;• Development of ICT;• Increase of the income of Russian householders;• Health lifestyle trend; • Long holidays;• Climate; • Interest in getting acquainted with new, different cultures.
Higher School of Economics, Moscow, 2011
NUMBER OF TOURIST TRIPS MADE BY RUSSIAN CITIZENS TO THE NON-CIS AND EU COUNTRIES
(IN THOUSANDS)
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SHARE OF TOURIST TRIPS MADE BY RUSSIAN CITIZENS TO THE NON-CIS COUNTRIES
2000 2010
Higher School of Economics, Moscow, 2011
GROUPS OF THE EU COUNTRIES ACCORDING TO THE SHARE IN THE TOTAL TOURIST FLOW
FROM RUSSIA TO THE EU IN 2010
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The 1st group(≥ 10%)
The 2nd group(4 - 10%)
The 3rd group(< 4%)
• Finland • Germany• Italy • Spain
•Bulgaria•Cyprus•France•Greece •Czech Republic
•Austria•United Kingdom•Netherlands•Latvia•Lithuania•Poland•Other EU countries
with the share < 1%
Higher School of Economics, Moscow, 2011
FORECASTING: DATA & METHODOLOGY (1)
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Data available:annual data on number of tourist trips from Russia to the
countries of EU, 2000-2010 (11 observations) -> small sample!
Parsimony is crucial!
Models used:- Holt model (exponential smoothing with trend),- Box-Jenkins ARIMA
FORECASTING: DATA & METHODOLOGY (2)
Our choice: Holt model
Reason: non-stability of time series for many countries of destination
Empirical evidence: MSE for Holt model is lower for most destinations. In cases when ARIMA performs better, the forecasts obtained by ARIMA and Holt models are essentially similar
Higher School of Economics, Moscow, 2011
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Including explanatory variables into ARIMA:• consumer price indices for the destination country and for EU area,• real money income index in Russia,• exchange rates.
-> no significant improvement
Why?
• Inappropriateness of aggregate data due to heterogeneity of Russian consumers,• CPI is a poor proxy for tourism prices.
FORECASTING: DATA & METHODOLOGY (3)
Higher School of Economics, Moscow, 2011
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Number of tourist trips from Russia to European Union
FORECAST FOR ALL EU COUNTRIES
increase by 16% expected (2013 to 2010)
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1998 2000 2002 2004 2006 2008 2010 2012 2014
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FORECASTS FOR THE MOST POPULAR DESTINATIONS (1)
Finland Germany
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FORECASTS FOR THE MOST POPULAR DESTINATIONS (2)
Italy Spain
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Tour
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Higher School of Economics, Moscow, 2011
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2010
Group 1(Leaders)
Group 2
Group 3
2013
Group 1(Leaders)
Group 2
Group 3
2010 VS. 2013 CHANGES BETWEEN THE GROUPS
Spain Spain
GreeceGreeceGreeceGreece
Some minor changes within groups 2 & 3
Higher School of Economics, Moscow, 2011
LIMITATIONS AND FUTURE RESEARCH (1)
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Limitation Future research direction
Statistical data of Russian Federation at the national level are used, no regional specifics
Analysis of tourist flows from different regions of Russia to particular EU countries
Factors subject to statistical measurement only are considered
Qualitative analysis
No data on seasonality Analysis, forecasting and modeling of seasonal flows from Russia to the EU particular countries/countries’ regions
Higher School of Economics, Moscow, 2011
LIMITATIONS AND FUTURE RESEARCH (2)
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Limitation Future research
27 EU countries in focus of the study
Analysis of tourist flows of Russian citizens to the EU countries/groups of countries clustered on a certain basis
Tourist flows from Russia to the EU countries are considered, no distinguishing features of the EU countries’ particular regions considered
Analysis of the tourist flows from Russia to the particular regions of the particular EU countries
K. Furmanov: [email protected]
O. Balaeva: [email protected]
M. Predvoditeleva: [email protected]
Thank you
Grazie
