MODELLING SATURATION INTENSITY IN MODELLING SATURATION INTENSITY IN THE DESTINATION OF CROATIA: A THE DESTINATION OF CROATIA: A

PANEL DATA APPROACHPANEL DATA APPROACHZDRAVKO ŠERGO, Ph.D., Scientific Adviser

Institute of Agriculture and TourismDepartment of Tourism

K.Huguesa 8, 52440 Poreč, Croatia+385-52-408320

zdravko.sergo@iptpo.hrJASMINA GRŽINIĆ, Ph.D., Associate Professor

Juraj Dobrila University of Pula Faculty of Economics and Tourism “Dr. Mijo Mirković”

Preradovićeva 1, 52100 Pula, Croatia+385-52-377029

jasmine.grzinic@unipu.hrDARKO SAFTIĆ, Ph.D., Postdoctoral Researcher

Institute of Agriculture and TourismDepartment of Tourism

K.Huguesa 8, 52440 Poreč, Croatia+385-52-408326

darko.saftic@iptpo.hr

ToSEE - Tourism in South East Europe 3rd International Scientific Conference Sustainable Tourism, Economic Development and Quality of LifeUniversity of Rijeka, Faculty of Tourism and Hospitality Management Opatija

MODELLING SATURATION INTENSITY IN THE DESTINATION OF CROATIA: A PANEL DATA APPROACH

INTRODUCTION ¶

…..PROBLEMS:

•According to tourist saturation capacity indices of up to 15 nights per capita, Croatia is one of the worst destinations among its competitors. The average is eight in Spain and six in Italy, and in Turkey, it is one night per capita (Preveden, 2015). •Croatia is 31st, very highly ranked with its index of tourist saturation (next to the highly ranked Austria and a number of exotic countries) in a population of 200 countries in the world (http://berclo.net/page01/01en-toursat.html). According to this, tourism in Croatia remains on an unhealthy and unsustainable long-term development track. •The greater the intensity of tourist use and the level of saturation of the tourist’s assets, the more limited is the appeal of the tourist attraction, which also causes a probable decline of the destination (Maggi, Fredella, 2010).

ToSEE - Tourism in South East Europe 3rd International Scientific Conference Sustainable Tourism, Economic Development and Quality of LifeUniversity of Rijeka, Faculty of Tourism and Hospitality Management Opatija

•Basically, the scientific tourism world identifies cause and effect relationships between tourism demand (tourist spending, tourist arrivals, tourist overnights) and variables that affect the flow of tourists. •Much less scientific effort is spent on the study of the determinants of tourist saturation on the country level. •The saturation index that measures the degree of saturation or congestion of beaches lies on the opposite spectrum of consideration and belongs to the micro type of research that is not the focus of this paper. •TCC indicates the maximum number of people who may visit a tourism destination at the same time without deteriorating the physical, economic and sociocultural environment and inducing unacceptable changes in the quality of the visitors’ satisfaction (Buckley R., 1999).

•

Tourism demand (as a driving-force of modelled using a Tourism demand (as a driving-force of modelled using a variety of approaches, including structural equations and variety of approaches, including structural equations and time series techniques. The tourism literature comprises a time series techniques. The tourism literature comprises a

large number of papers regarding tourism demand and large number of papers regarding tourism demand and uses various techniques, starting with simple or uses various techniques, starting with simple or

multivariate regressions (Garín-Muñoz & Amaral, 2000; multivariate regressions (Garín-Muñoz & Amaral, 2000; Luzzi & Fluckiger, 2003; Allen & Yap, 2009), panel or pool Luzzi & Fluckiger, 2003; Allen & Yap, 2009), panel or pool

data analysis and co-integration procedures (Lim & data analysis and co-integration procedures (Lim & McAleer, 2001; Durbarry, 2002; Mervar & Payne; 2007).McAleer, 2001; Durbarry, 2002; Mervar & Payne; 2007). •we analyse the determinants of tourist inflow that

cause saturation in Croatia, taking into consideration a series of socio-demographical variables. •the modelling of tourist saturation with a macro-economic background.

• ResearcResearch questions are:– What are the characteristics that explain and

predict the probability of saturation intensity in regard to socio-demographic trends in macro-environment?

– What are the characteristics that explain the frequency of saturation intensity in Croatia?

macro-environment=tourist-generating countries

•

DATA Source: DATA Source: Croatia’s Department of Statistics, and the Croatia’s Department of Statistics, and the Penn World Tables (PWT) version 6.3 (released in 2009).Penn World Tables (PWT) version 6.3 (released in 2009).

In our empirical analysis, the estimation of tourism saturation in Croatia from In our empirical analysis, the estimation of tourism saturation in Croatia from different countries, which represent almost all arrivals and overnight stays in different countries, which represent almost all arrivals and overnight stays in

Croatia between the years 1996 and 2010, was performed.Croatia between the years 1996 and 2010, was performed.

A panel was compiled of 21 countries: Austria, Bosnia and A panel was compiled of 21 countries: Austria, Bosnia and Herzegovina, Canada, Croatia, Czech Republic, France, Herzegovina, Canada, Croatia, Czech Republic, France, Germany, Hungary, Italy, Netherlands, Poland, Slovak Germany, Hungary, Italy, Netherlands, Poland, Slovak

Republic, Slovenia, Switzerland, United Kingdom, United Republic, Slovenia, Switzerland, United Kingdom, United

States, Belgium,States, Belgium, Denmark, Denmark, Norway, Russia and Sweden.Norway, Russia and Sweden.

• SATURit = f (POPit, AGE it, GEN it, URB it, OPENit,) • + + ? + +

SATUR = NIGHT / D * POP_CROSATUR = NIGHT / D * POP_CRO

• Table 1: Summary of Variables (1996-2010 year average) used

in the Regression

MIN MAX MEAN STDEV SATUR 0.001 (CAN 1996) 0.593 (GER 2002) 0.101 0.123

POP 2003 (SVN 2010) 310233 (USA 2010) 41203 65775.70 AGE 63.72 (SWE 1996) 72.79 (SVK 2010) 67.7 1.95 GEN 49.97 (NOR 2009) 53.74 (RUS 2010) 51.29 0.72 URB 41.41 (BIH 1996) 97.45 (BEL 2010) 71.58 12.44

OPENK 20.87 (USA 96) 186.14 (HUN 2010) 85.7 34.41

• Figure 1: Index of Saturation in Croatia (1996-2010 year average) ¶

AU

T

B

EL

B

IH C

AN

C

HE

C

ZE

D

NK

F

RA

G

BR

G

ER

H

RV

H

UN

IT

A

N

LD

N

OR

P

OL

R

US

S

VK

S

VN

S

WE

U

SA

0 50 100 150

Econometric model :

where the variables are expressed in logarithm form, alpha is the constant term, beta are the coefficients of each variable taken into consideration, and eta is the error term.

ititititititit OPENKURBGENAGEPOPSATUR loglogloglogloglog 54321

2. REGRESSION RESULTS2. REGRESSION RESULTS

• Table 2: Results of panel unit root

testing for the dependent

Panel unit root test

Maddala-Wu

Levin-Lin-Chu Im-Pesaran-Shin Hadri Test

log(SATUR) 315.19*** -17.80** -7.33** 34.27***

Source: Author’s calculations Note: reject the null of unit root at the level of significance *** 1%, ** 5%.

Table 3 FE vs. RE Estimator: Diagnostic Results

Dependent variable (model)

Breusch-Pagan LM Test

Hausman Specification

Test Tourist saturation 2(2) = 1173.28* 2(5) = 122.861

Source: Author’s calculations.

Note: * Null hypothesis rejected.

Table 4: Fixed and random effects models: estimation results ¶ Unrestricted models Restricted Models Fixed

effects Random effects

Fixed effects

Random effects b)

Constant 22.54 (-0.456) [0.812]

-14.776 (-0.331) [0.732]

55.948 (1.632)

-67.029* (-6.412) [0.000] (-2.293) [0.022]

Log (POP) 0.561 (0.361) [0.732]

0.455 * (2.541) [0.011]

0.640 * (3.049 ) [0.002] (2.160) [0.031]

Log (AGE) 15.961*** (6.537) [0.000]

13.583*** (5.003) [0.000]

15.710*** (6.744 ) [0.000]

11.620 (4.574) [0.000] (1.628) [0.104]

Log (GEN) -48.951*** (-3.678) [0.000]

-15.161 (-1.412) [0.15 ]

-47.204*** (-3.856) [0.000]

Log (URB) 14.234*** (6.098) [0.000]

0.846 (0.773) (0.446)

13.635 *** (9.097) [0.000]

Log (OPENK) 1.181*** (5.365) [0.000]

1.812*** (7.656) [0.000]

1.201*** (5.676) [0.000]

1.985*** (9.020) [0.000] (3.544) [0.000]

Number of observations 315 (21

countries * 15 years)

315 (21

countries * 15 years)

310 (20

countries * 15 years)

315 (20

countries * 15 years)

F-stat. 61.807 [0.000]

31.312 [0.000]

77.466 [0.000]

Adj. R-squared 0.56 0.42 0.51 0.34 Breusch_Godfrey/

Wooldridge Test of serial correlation

12.485 [0.05]

12.556 [0.051]

18.685 [0.054]

16.146 [0.052]

Breusch-Pagan LM test of independence

1205.363 [0.000]

1385.708 [0.000]

1210.593 [0.000]

1409.668 [0.000]

Pasaran CD tests of independence

6.622 [0.000]

7.009 [0.000]

6.628 [0.000]

7.106 [0.000]

Breusch-Pagan LM test of

heteroskedasticity a)

16.739 [0.005]

16.739 [0.005]

15.141 [0.004]

6.342 [0.096]

• Source: Author’s calculations. • Notes:• - Numbers within parentheses () denote asymptotic t-values, and those

within [], p-values.• - Signif. codes: 0 ‘***’ 0.001 ‘**’, 0.01 ‘*’ • - Due to a balanced panel, Bosnia & Herzegovina was omitted from the

observations• - The bolded t-values are shown in brackets and were obtained from the

standard errors of the 's, which were White-adjusted:• a) Based on the OLS estimates, tested for heteroskedasticity. The null

hypothesis for the Breusch-Pagan test is homoskedasticity.• b) The bold t-values and p-values are shown in brackets and were

obtained from the standard errors of the 's, which were White-adjusted.

• the size of the population in the country of origin is significant. The larger the POP is, the higher the tourist multiplier, which corresponds to the results of Hanafiah and Harun (2010) and Leitão (2010). An increase of 1% in population in the origin country would on average generate a 0.55% increase in the tourist saturation index in terms of overnight stays in Croatia. At first glance, the law of increasing returns for Croatia’s marketing of tourism products abroad seems to be the modus operandi that justified this fact: the more people you reach with marketing activities, or the more populated the country targeted for the marketing, the more efficient the process becomes.

• Furthermore, the population density or the parts of the origin countries with the highest population density are related to urbanized areas or cities. The urbanization average elasticity value of 14% with respect to the fixed effect estimation confirms our hypothesis that higher urbanization trends can produce tourism demand and can cause the dangers resulting from excessive tourist saturation. This result is more consistent with theoretical considerations than that obtained from the OLS regression, with the negative log of URB.

• The average OPEN elasticity value is almost identical for un-restricted and restricted models in the amounts of 1.19 and 1.9 (FE and RE estimation, respectively), and as expected, the coefficient values are significant. This result corresponds to the findings of Eilat and Einav (2004), Muhammad and Andrews (2008), and Leitão (2010). Trade partners are an important vehicle to expand tourism (Leitão, 2010). The variable used is specifically a proxy for business-motivated travel to Croatia to determine how it might affect the level of tourist saturation. However, in a more indirect way, it can also be seen as a proxy for other shocks that exogenously impact the tourist industry in Croatia.

CONCLUSION ¶• We point specifically to three risk factors that can

generate excess saturation at Croatian destinations (increasing rate of population, urbanization and openness in tourism demand-generating countries).

• Decreasing the excessive concentration of tourism in the high season, which has an impact on the increase in human pressure in a specific period, could be a successful policy to lessen saturation. It would be highly desirable for the same number of international tourists to be spread out among the three seasons and among other regions besides the coastal areas.