© 2024 The authors. This article is published by IIETA and is licensed under the CC BY 4.0 license (http://creativecommons.org/licenses/by/4.0/).
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The tourism industry is extremely susceptible to political and social unrest. Countries aiming to develop their tourism sector understand this and strive to maintain peace and economic stability. However, for developing nations such as the Georgia, with its strategically attractive location, ensuring socio-economic stability presents a significant challenge. This difficulty is being solved by creating an adaptive environment that fosters both tourism growth and regional economic well-being. The main aim of the article is to examine Georgia as a case study to explore how political issues can influence tourism industry sustainable development. While political instability often hinders tourism, Georgia's case deviates from the norm, potentially offering new approaches for tourism development in such circumstances. This study employs a Vector Autoregression (VAR) model to analyse the impact of various factors, such as - the correlation between the Index of Global Real Economic Activity and tourism revenues, GPRH (Geopolitical Risk Index), and geopolitical events on Georgia's tourism industry from 2006 to 2022. The analysis will focus on the period before and after the war to assess its influence. By analysing the relationships between these variables, the study aims to understand how global economic conditions, geopolitical instability, and the war specifically, have influenced the evolution and economic effects on Georgia's tourism industry. This study's key finding reveals a positive correlation between tourism revenues and geopolitical risks in Georgia, even considering the war. This finding suggests a more nuanced relationship between these factors than previously assumed.
tourism industry, tourism development, sustainable development, geopolitical risk, global real economic, Georgia
Tourism is a powerful economic engine, generating jobs, boosting infrastructure development, and driving foreign exchange earnings. Overall, the economic impact of tourism is largely positive, but careful planning and management are crucial to maximize its benefits and minimize its negative consequences [1]. Tourism industry thrives on stability. Political and social unrest can quickly disrupt travel plans, causing significant economic hardship for destinations that rely heavily on tourism. Recognizing this vulnerability, countries with aspirations for a flourishing tourism sector prioritize maintaining peace and economic security. However, developing nations like Georgia face a unique challenge. Despite its strategically advantageous location, fostering both tourism growth and regional economic well-being requires navigating a complex path towards socio-economic stability. To overcome these hurdles, Georgia is pioneering an adaptive approach that aims to cultivate a tourism industry that thrives alongside a strong regional economy. Tourism flourishes as a wealth-generating industry for many nations, particularly when governments establish a stable political environment. This stability fosters the development of leisure destinations – cities and attractions that entice both domestic and international visitors [2]. The very essence of tourism – leisure, and exploration – is fundamentally at odds with an era of volatility and instability. Tourism, a highly vulnerable industry, thrives on political stability, peace, and above all, safety. Tourists and tourism markets are prone to panic, and events like civil unrest or terrorism can cause travelers to scrap travel plans in a heartbeat. The immediate impact and long-term aftermath of such occurrences can be catastrophic, not just for the destination country but for the entire region [3].
We have a wealth of historical examples to evaluate the impact of political instability on tourism development, including the Arab Spring uprisings and the ongoing conflicts in Gaza and the broader Middle East. This paper contributes to analyzing the relationship between political instability caused by the short Russian intervention and its impact on tourism development in Georgia.
Many countries, including Georgia, have come to rely heavily on tourism as a source of economic prosperity and foreign exchange. As a result, tourism plays a vital role on the global stage, and countries strive to attract visitors to reap its benefits [4]. However, the tourism sector is susceptible to external factors, particularly political instability and concerns about safety and security [5]. Recent events in Ukraine and Israel highlight this vulnerability, where ongoing political unrest has led to travel warnings and a significant decline in tourist arrivals. The concept of democratic peace suggests democracies promote stability. However, maintaining peace becomes complex when safety is threatened by regional conflicts or expansionist policies, even for democracies [6]. The concept of democratic peace suggests that democracies tend to promote stability, ultimately leading to a safer environment. Therefore, it's crucial for tourist destinations to take proactive measures to ensure safety and security. By prioritizing safety and security, destinations can create a thriving tourist industry that benefits both visitors and the local economy [7]. Since 2003, Georgia has actively supported tourism development by way of - Investment in Infrastructure. The Georgian Tourism Development Fund focuses on infrastructural projects in regions with high tourism potential. This could involve improvements to roads, transportation, and communication networks, making it easier and safer for tourists to navigate the country. Financial Support for Tourism Services: Programs like the Co-Financing Mechanism by Enterprise Georgia provide financial aid to develop tourism services. This could potentially improve the quality and safety standards of hotels, transportation companies, and other tourist facilities. Strategic Marketing: Explore Georgia, the state's tourism division, and promote Georgia as a tourist destination. By emphasizing safety and security aspects in their marketing efforts, they can attract tourists who prioritize these factors [8, 9].
On August 8, 2008, Russian forces began the invasion of Georgia, marking the start of Europe’s first twenty-first-century war. The conflict itself was over within a matter of days, but the repercussions of the Russo-Georgian War continued to reverberate fifteen years on, shaping the wider geopolitical environment [10].
Georgia is geographically complex. It sits at the crossroads of Eastern Europe and Western Asia, with the Greater Caucasus mountains forming a partial boundary. Georgia is considered a developing country according to the World Bank. Country has a very high Human Development Index, but its economy is still growing [11, 12].
From a statistical viewpoint, Georgia's tourism industry in 2019 defied expectations, experiencing a period of further remarkable growth. The number of non-resident trips surged by an impressive 678,420, shattering the nine million visitor mark for the first time. Domestic and international tourism are important factors in the Georgian economy [13].
Tourism is a significant revenue stream for the Georgian economy, generating a significant portion of its foreign currency earnings. International visitors' spending directly contributes to Georgia's balance of payments, with a staggering 71% of service export revenue stemming from tourism [11, 14].
Georgia's tourism boom translates into significant economic benefits. International tourism revenue soared to a staggering 3.3 billion USD in 2019, showcasing its growing importance. Tourism's significant contribution was undeniable, accounting for a substantial 8.1% of Georgia's total GDP. This impressive figure reflected a 511 million GEL increase in value-added from tourism-related activities. The hospitality sector was well-equipped, with 2,575 registered accommodation units offering 94,438 beds in 2019 [13].
Tourist behavior reveals distinct seasonal patterns. Occupancy rates peak in summer months (August: 77.1%, September: 74.5%) and shoulder seasons (July & October: 66.8%), while dropping in winter (January: 34.6%, February: 36.4%, December: 38.5%) [12, 13].
Popular destinations: Tbilisi reigns supreme as the most popular city for tourists (59.7% occupancy rate), followed closely by Batumi (58.9%). Other regions of Georgia also experience a notable tourist presence (46.7% occupancy rate) [13].
Georgia has been successful in international relations, as well. In 2019 several memoranda and agreements were signed with a number of countries, including Portugal, Argentina, and the Republic of Maldives.
This study aims to investigate the specific impact of a military special operation in 2008 on Georgia's booming tourism industry, considering its established growth trajectory before the pandemic. The analysis will explore how the sector functioned during the operation, the resulting consequences, and the pace of recovery [15, 16].
This article delves into Georgia's tourism industry as a case study to explore how political factors, beyond the typical negative influence, can shape tourism development. Georgia's experience, defying the usual correlation with political instability, offers valuable insights and potentially novel approaches for fostering tourism growth even in complex geopolitical settings.
This study utilizes a Vector Autoregression (VAR) model to delve into the multifaceted impact of global and regional factors on Georgia's tourism industry from 2006 to 2022. Specifically, the analysis will explore:
Before diving into the VAR model, it's crucial to clarify the specific aspects of tourism development we need to analyze. Key variables such as Tourism Revenue: Total revenue generated from tourism activities, based on time series it is possible to build VAR model (see Table 1).
Now we introduce the vector auto-regression (VAR) model for time series when it is needed, which is used to capture the linear interdependencies among multiple time series. VAR models generalize the univariate auto-regression (AR) models by allowing for more than one evolving variable. All variables in a VAR underwent pre-processing. All variables were log-transformed to address potential skewness and ensure stationarity; therefore, seasonal components were removed.
Seasonality is one of the key components that make up a time series. Seasonality refers to systematic movements that repeat over a given period with a similar intensity.
Seasonality means the average value in some periods will be different from the average value at other times. This issue causes the series to be non-stationary. This is why it is important to analyze seasonality when building a model.
Seasonality in tourism refers to the fluctuation of tourist arrivals and revenue throughout the year. This phenomenon is influenced by various factors, including climate, cultural events, and school holidays.
Table 1. Revenues from international travel (thousands of USD)
2006 QI |
58597.2 |
2006 QII |
82760.9 |
2006 QIII |
101743 |
2006 QIV |
69469.8 |
2007 QI |
63889.6 |
2007 QII |
90174.2 |
2007 QIII |
129086.9 |
2007 QIV |
100595.1 |
2008 QI |
85434.3 |
2008 QII |
132430.9 |
2008 QIII |
125883.1 |
2008 QIV |
102897.6 |
2009 QI |
77676.3 |
2009 QII |
114353.3 |
2009 QIII |
158388.4 |
2009 QIV |
125470.9 |
2010 QI |
100685.8 |
2010 QII |
136565.1 |
2010 QIII |
231913.4 |
2010 Q IV |
190081 |
2011 QI |
163989.8 |
2011 QII |
222249.6 |
2011 QIII |
326568.5 |
2011 QIV |
242100.6 |
2012 QI |
235930.4 |
2012 QII |
330826.5 |
2012 QIII |
487721.4 |
2012 QIV |
356423.4 |
2013 QI |
302817.7 |
2013 QII |
426524.5 |
2013 QIII |
604201.8 |
2013 QIV |
386155.8 |
2014 QI |
315.461 |
2014 QII |
429.69 |
2014 QIII |
649.775 |
2014 QIV |
392.215 |
2015 QI |
306.365 |
2015 QII |
460.056 |
2015 QIII |
741.807 |
2015 QIV |
427.688 |
2016 QI |
342.59 |
2016 QII |
497.441 |
2016 QIII |
813.161 |
2016 QIV |
457.517 |
2017 QI |
425.673 |
2017 QII |
629.854 |
2017 QIII |
1,078,274 |
2017 QIV |
570.538 |
2018 QI |
550.73 |
2018 QII |
809.969 |
2018 QIII |
1,210,561 |
2018 QIV |
650.814 |
2019 QI |
578.442 |
2019 QII |
877.626 |
2019 QIII |
1,126,771 |
2019 QIV |
685.814 |
2020 QI |
427.699 |
2020 QII |
28.842 |
2020 QIII |
42.534 |
2020 QIV |
42.611 |
2021 QI |
53,551 |
2021 QII |
246,123 |
2021 QIII |
565,981 |
2021 QIV |
379,333 |
2022 QI |
393,708 |
2022 QII |
748,822 |
Source: Georgian Tourism Administration, https://gnta.ge/ [11, 15, 17]
This is a common approach in time series analysis, especially when the primary focus is on understanding the political instability influence as a particular phenomenon, rather than the seasonal fluctuations themselves.
1) All variables are stationary, corrected for seasonal components, and expressed in their logarithm forms (i.e., each variable has been rescaled in order to obtain strict positive values).
2) In a Vector Autoregression (VAR) model, all the variables are considered endogenous. This means that the model explains each variable by its past values (lags) and the past values of all the other variables in the model. Also in a VAR model a process can be affected by other observable variables that are determined outside the system of interest. Such variables are called exogenous (independent) variables. Exogenous variables can be stochastic or non-stochastic. The process can also be affected by the lags of exogenous variables. A model used to describe this process is called a VARX(p,s) model. In our VAR model, the endogenous factors are, Revenues from Tourism Industry in Georgia and Global Real Economic Activity and exogenous factor is, Geopolitical Risk Index. Lag order is the number of previous observations of each variable that are included in the VAR model as explanatory variables. For example, a VAR model uses two lags of each variable to predict the current values of the variables. The lag order of a VAR model reflects how far back the historical data can influence the future outcomes of the variables.
This revised specification captures the interplay between economic and geopolitical factors influencing tourism industry in Georgia.
Endogenous Variables:
L-rev (log of revenue): This remains a core endogenous variable, reflecting the tourism revenue. It's influenced by both global economic conditions and geopolitical risks.
L-gre (log of economic risk): This is now an endogenous variable, suggesting that economic risk may effect on other factors within the model, such as tourism revenue. This could be due to feedback effects, where economic downturns can lead to reduced tourism, which in turn can impact economic sentiment.
Exogenous Variable (Control):
L-gpr (log of geopolitical risk): This is an independent variable that's not influenced by other variables within the model. It's included as a control variable to account for the potential impact of geopolitical risk on revenue, even if it's not the primary focus of the analysis.
By considering L-gre as an endogenous variable, the model acknowledges that economic risk might be influenced by other factors and is not solely determined by external forces. This can lead to more nuanced and accurate analysis of the relationship between economic risk, geopolitical risk, and revenue.
- selected lag is 5 suggested by VAR Lag Order Selection Criteria (see Figure 1)
Figure 1. Eliminated seasonal component for each variable
Source: Vector Autoregressive models performed by author
- endogenous variables:
L_rev=log(rev)
L_gre=log(gre)
- exogenous variable (control):
L_gpr=log(gpr)
Vector Autoregression (VAR) estimates refer to the statistical results obtained by fitting a VAR model to our data (see Table 2). These estimates provide insights into the relationships between the endogenous variables we included in the model (see Table 3).
Here's a breakdown of the key aspects of VAR estimate components:
Coefficients: These are the numerical values associated with each lag of each variable in the model. They show the magnitude and direction of the impact that one variable's past values have on the current value of another variable.
Significance Levels: These values indicate the statistical significance of the estimated coefficients. A statistically significant coefficient suggests that the relationship between the variables is unlikely to be due to chance.
Error Correction Terms: These terms account for any unexplained variations in the model. Diagnostic Tests: These tests assess the overall performance of the VAR model and ensure it meets the underlying assumptions (e.g., stationarity of variables).
This polynomial is a mathematical equation derived from the VAR model's structure. It captures the dynamic properties of the system represented by the model (see Table 4).
Our analysis of the Vector Autoregression (VAR) model's roots confirms that it satisfies the stability condition. This ensures that the estimated relationships between tourism revenue, global economic activity and Geopolitical Risk Index are reliable for further analysis.
Table 2. Vector autoregression estimates
Vector Autoregression Estimates |
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Date: 10/07/22 Time: 21:35 |
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Sample (adjusted): 2006M06 2022M06 |
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Included observations: 193 after adjustments |
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Standard errors in ( ) & t-statistics in [ ] |
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|
L_REV |
L_GRE |
L_REV(-1) |
0.638354 |
0.017043 |
|
(0.06892) |
(0.02429) |
|
[9.26189] |
[0.70153] |
L_REV(-2) |
-0.222242 |
-0.012617 |
|
(0.07881) |
(0.02778) |
|
[-2.82006] |
[-0.45421] |
L_REV(-3) |
0.022581 |
0.014014 |
|
(0.07678) |
(0.02706) |
|
[0.29411] |
[0.51785] |
L_REV(-4) |
-0.023071 |
0.002117 |
|
(0.07634) |
(0.02691) |
|
[-0.30220] |
[0.07868] |
L_REV(-5) |
0.013274 |
0.019027 |
|
(0.06678) |
(0.02354) |
|
[0.19877] |
[0.80830] |
L_GRE(-1) |
0.167641 |
0.994295 |
|
(0.21012) |
(0.07406) |
|
[0.79784] |
[13.4250] |
L_GRE(-2) |
-0.775997 |
-0.382553 |
|
(0.29466) |
(0.10386) |
|
[-2.63356] |
[-3.68329] |
L_GRE(-3) |
1.477228 |
0.342384 |
|
(0.29943) |
(0.10554) |
|
[4.93350] |
[3.24401] |
L_GRE(-4) |
-1.685160 |
-0.190893 |
|
(0.30504) |
(0.10752) |
|
[-5.52433] |
[-1.77538] |
L_GRE(-5) |
1.160526 |
0.107345 |
|
(0.21615) |
(0.07619) |
|
[5.36901] |
[1.40891] |
C |
3.788240 |
0.675257 |
|
(2.02932) |
(0.71530) |
|
[1.86675] |
[0.94402] |
L_GPR |
0.398938 |
-0.125777 |
|
(0.35727) |
(0.12593) |
|
[1.11664] |
[-0.99879] |
R-squared |
0.425021 |
0.763401 |
Adj. R-squared |
0.390078 |
0.749022 |
Sum sq. resids |
219.4548 |
27.26602 |
S.E. equation |
1.101117 |
0.388125 |
F-statistic |
12.16312 |
53.09147 |
Log likelihood |
-286.2512 |
-84.99992 |
Akaike AIC |
3.090686 |
1.005181 |
Schwarz SC |
3.293547 |
1.208042 |
Mean dependent |
12.61171 |
4.680621 |
S.D. dependent |
1.409925 |
0.774736 |
Determinant resid covariance (dof adj.) |
0.181556 |
|
Determinant resid covariance |
0.159681 |
|
Log likelihood |
-370.6736 |
|
Akaike information criterion |
4.089882 |
|
Schwarz criterion |
4.495605 |
Source: Vector Autoregressive models performed by author
Table 3. VAR model
L_REV = C(1,1)*L_REV(-1) + C(1,2)*L_REV(-2) + C(1,3)*L_REV(-3) + C(1,4)*L_REV(-4) + C(1,5)*L_REV(-5) + C(1,6)*L_GRE(-1) + C(1,7)*L_GRE(-2) + C(1,8)*L_GRE(-3) + C(1,9)*L_GRE(-4) + C(1,10)*L_GRE(-5) + C(1,11) + C(1,12)*L_GPR L_GRE = C(2,1)*L_REV(-1) + C(2,2)*L_REV(-2) + C(2,3)*L_REV(-3) + C(2,4)*L_REV(-4) + C(2,5)*L_REV(-5) + C(2,6)*L_GRE(-1) + C(2,7)*L_GRE(-2) + C(2,8)*L_GRE(-3) + C(2,9)*L_GRE(-4) + C(2,10)*L_GRE(-5) + C(2,11) + C(2,12)*L_GPR |
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VAR Lag Order Selection Criteria |
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|
|
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Endogenous variables: L_REV L_GRE |
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|
|
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Exogenous variables: C L_GPR |
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|
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Date: 10/07/22 Time: 21:35 |
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Sample: 2006M01 2022M06 |
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|
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Included observations: 190 |
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|
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Lag |
LogL |
LR |
FPE |
AIC |
SC |
HQ |
0 |
-553.1123 |
NA |
1.207543 |
5.864340 |
5.932699 |
5.892031 |
1 |
-397.9532 |
303.7852 |
0.245969 |
4.273192 |
4.409909* |
4.328574 |
2 |
-394.7605 |
6.183799 |
0.248075 |
4.281689 |
4.486765 |
4.364762 |
3 |
-386.1447 |
16.50597 |
0.236324 |
4.233102 |
4.506536 |
4.343866 |
4 |
-382.8191 |
6.301117 |
0.238030 |
4.240202 |
4.581994 |
4.378656 |
5 |
-366.6384 |
30.31758 |
0.209416* |
4.111983* |
4.522134 |
4.278129* |
6 |
-365.1517 |
2.754363 |
0.215072 |
4.138439 |
4.616948 |
4.332276 |
7 |
-358.8109 |
11.61371* |
0.209893 |
4.113799 |
4.660666 |
4.335326 |
8 |
-357.4401 |
2.481733 |
0.215857 |
4.141475 |
4.756701 |
4.390694 |
* indicates lag order selected by the criterion |
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LR: sequential modified LR test statistic (each test at 5% level) |
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FPE: Final prediction error |
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AIC: Akaike information criterion |
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SC: Schwarz information criterion |
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HQ: Hannan-Quinn information criterion |
Source: Vector Autoregressive models performed by author
Table 4. Roots of characteristic polynomial
Roots of Characteristic Polynomial |
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Endogenous variables: L_REV L_GRE |
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Exogenous variables: C L_GPR |
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Lag specification: 1 5 |
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Date: 10/07/22 Time: 21:35 |
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Root |
Modulus |
0.930632 |
0.930632 |
0.388345 - 0.601509i |
0.715979 |
0.388345 + 0.601509i |
0.715979 |
0.633551 - 0.330562i |
0.714604 |
0.633551 + 0.330562i |
0.714604 |
-0.364188 - 0.557640i |
0.666029 |
-0.364188 + 0.557640i |
0.666029 |
-0.051834 - 0.610166i |
0.612363 |
-0.051834 + 0.610166i |
0.612363 |
-0.509732 |
0.509732 |
No root lies outside the unit circle. |
Source: Vector Autoregressive models performed by author
Table 5. VAR Residual Serial Correlation LM Test
Null Hypothesis: no serial correlation at lag order h |
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Date: 10/07/22 Time: 21:35 |
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Sample: 2006M01 2022M06 |
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Included observations: 193 |
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Lags |
LM-Stat |
Prob |
1 |
2.961007 |
0.5644 |
2 |
3.303434 |
0.5084 |
3 |
2.571090 |
0.6320 |
4 |
6.755420 |
0.1494 |
5 |
9.403843 |
0.0518 |
6 |
15.43892 |
0.0039 |
Probs from chi-square with 4 df. |
Source: Vector Autoregressive models performed by author
In our model, we analyze the relationships between multiple time series variables. A crucial assumption for VAR models is that the errors (residuals) are not serially correlated. This means the errors at any given period are not dependent on the errors at previous periods (see Table 5).
VAR Model - Substituted Coefficients:
L_REV = 0.638354310455*L_REV(-1) - 0.222242402434*L_REV(-2) + 0.0225814561865*L_REV(-3) - 0.0230707596134*L_REV(-4) + 0.0132744660411*L_REV(-5) + 0.167641117497*L_GRE(-1) - 0.775996941178*L_GRE(-2) + 1.47722792278*L_GRE(-3) - 1.68515952577*L_GRE(-4) + 1.1605257563*L_GRE(-5) + 3.78823957643 + 0.398938381063*L_GPR
L_GRE = 0.0170431098973*L_REV(-1) - 0.0126172772917*L_REV(-2) + 0.0140144130223*L_REV(-3) + 0.00211715387649*L_REV(-4) + 0.0190270759934*L_REV(-5) + 0.99429507057*L_GRE(-1) - 0.38255300261*L_GRE(-2) + 0.342384230103*L_GRE(-3) - 0.190892903852*L_GRE(-4) + 0.107345144613*L_GRE(-5) + 0.675257171467 - 0.125777404604*L_GPR
VAR model highlights the strong economic influences on Georgia's tourism revenue. However, external factors like geopolitical tensions can also significantly impact tourism. For instance, Russian intervention in Georgia in 2008 would likely contribute to the decline in tourism flows, revenue, and therefore other important economic indicators. Geopolitical risk can significantly impact the stability of a country in multiple ways, affecting not just the political landscape but also the social and economic well-being. Through the statistical data observed by us using the VAR analysis, we have quite staggering results (see Figure 2).
The 2008 Russian intervention in Georgia presents a fascinating case study. While such events often have negative consequences for tourism, Georgia's tourism industry appears to have exhibited a positive trend in the aftermath. Understanding the specific dynamics at play in this instance can provide valuable insights for destinations facing similar challenges. Georgia's tourism industry offers a compelling case study. Despite the 2008 Russian intervention, which is typically associated with a decline in tourism, Georgia's tourism sector appears to have flourished. This unique situation calls for a closer examination of the factors that may have contributed to this positive development (see Table 6) [11, 12, 18].
Figure 2. Response
Source: Vector Autoregressive models performed by author
Table 6. Tourist flows in Georgia (million)
Country |
2005 |
2006 |
2007 |
2008 |
2009 |
2010 |
2011 |
2012 |
2013 |
2014 |
2015 |
Sum |
559753 |
763231 |
1051749 |
1290108 |
1500049 |
203171 |
2822363 |
4428221 |
5392303 |
5515559 |
5901094 |
Source: Georgian National Tourism Administration, Statistical Report, http://www.gnta.ge [11, 12, 18]
Percentage difference
between V1 (2005) = 559753 and V2 (2008) = 1290108
Increase: 78.9632% difference
between V1 (2008) = 1290108 and V2 (2009) = 1500049
Increase: 15.0487% difference
A positive correlation between political instability and tourism flows in Georgia, as suggested by a VAR analysis, may seem counterintuitive. Typically, political instability tends to deter tourists. However, several factors could contribute to this unexpected relationship:
It's important to note that this positive correlation might be a temporary phenomenon. As political stability returns, it's likely that tourism flows will normalize or even decline if the factors driving the initial increase are no longer present.
To further explore this relationship, it's crucial to consider other factors such as:
By conducting a more in-depth analysis, it may be possible to identify the primary drivers of this correlation and to predict future trends in tourism flows [6, 9, 19].
Potential Reasons Behind Georgia's Unexpected Tourism Growth The positive correlation between political instability and tourism development in Georgia is indeed intriguing. Here are the factors, which might contribute to this phenomenon:
The Russian invasion of Georgia in 2008 had a significant impact on the country's tourism industry. However, the Georgian government has implemented several initiatives to rebuild and revitalize the sector:
The case of Georgia following the 2008 war offers a fascinating example. While war typically deters tourism, Georgia's tourism industry appears to have exhibited a surprising growth trajectory in the aftermath [8, 9, 20].
Our study surprisingly revealed a positive correlation between political instability, specifically the 2008 Russian intervention, and tourism industry development in Georgia [21, 22]. While political instability often deters tourism, it's crucial to consider the specific context. Further research is needed to explore the mechanisms behind this unexpected finding and to see if similar patterns hold true in other cases.
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