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Analysis of Macroeconomic Variables Affecting International Tourism Consumption in Sweden

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Analysis of Macroeconomic Variables Affecting International Tourism Consumption in Sweden EXAMENSARBETE INOM TEKNIK, GRUNDNIVÅ, 15 HP STOCKHOLM, SVERIGE 2019 Analysis of Macroeconomic Variables Affecting International Tourism Consumption in Sweden JUN HO LEE NOEL MATTAR KTH SKOLAN FÖR TEKNIKVETENSKAP Analysis of Macroeconomic Variables Affecting International Tourism Consumption in Sweden JUN HO LEE NOEL MATTAR L Degree Projects in Applied Mathematics and Industrial Economics (15 hp) Degree Programme in Industrial Engineering and Management (300 hp) KTH Royal Institute of Technology year 2019 Supervisors at KTH: Jörgen Säve-Söderbergh, Julia Liljegren Examiner at KTH: Jörgen Säve-Söderbergh TRITA-SCI-GRU 2019:159 MAT-K 2019:15 Royal Institute of Technology School of Engineering Sciences KTH SCI SE-100 44 Stockholm, Sweden URL: www.kth.se/sci Abstract There is an evident trend of growing tourism in the world. Tourism in Sweden is gaining more economic and social attention. The main purpose of this thesis is to discover what macroeconomic variables contribute to the growth of annual international tourism income in Sweden. A multiple linear regression approach over a time period of 1978-2017 is used for the analysis. The final results show that GDP is the major macroeconomic factor that drives the annual international tourism income in Sweden across all time periods. NOK-SEK exchange rate seem to another relevant variable in the long term from 1978-2017, but not in shorter periods of time. USD-SEK exchange rate and unemployment rate hold no significant relevance to the international tourism consumption in Sweden for all time. The devaluation of Swedish krona in 1992 did not change the relationship between these variables and the response variable. However, these results can be unstable due to the limited number of observations used in the analysis, and therefore, we recommend other regression approaches, such as panel data regression, for this subject. i Abstrakt Det finns en märkbar trend av växande turism världen över. Turismen in i Sverige får allt mer ekonomisk och social uppmärksamhet. Syftet med detta arbete är att finna vilka makroekonomiska variabler som bidrar till de årliga intäkterna av internationell turism i Sverige. För analysen används multipel linjär regression över tidsperioden 1978-2017. Det slutgiltiga resultatet visar att BNP är den dominanta makroekonomiska faktorn som är drivande i de årliga intäkterna av internationell turism i Sverige, detta oavsett tidsperiod. Valutakursen NOK/SEK verkar vara signifikant i det långa loppet, från 1978-2017, men inte under de kortare tidsperioderna. Valutakursen USD/SEK och arbetslösheten är båda icke signifikanta variabler för internationell turism konsumtion i Sverige över alla tidsperioder. Devalveringen av den svenska kronan år 1992, förändrade inte relationen mellan de sistnämnda variablerna och y -variabeln. Dock kan dessa resultat vara ostabila på grund av de begränsade antalet observationer som använts i analysen och därför rekomenderar vi andra regressions modeller till detta ämne, såsom ”panel data regression”. ii Contents 1 Introduction 1 1.1 Background ................................. 1 1.2 Purpose ................................... 1 1.3 Research Question .............................. 2 2 Statistical Framework 3 2.1 Multiple Linear Regression Analysis .................... 3 2.1.1 Ordinary Least Squares ...................... 4 2.1.2 Assumptions ............................ 4 2.2 Error Validation ............................... 5 2.2.1 Multicollinearity .......................... 5 2.2.2 Micronumerosity .......................... 5 2.2.3 Heteroscedasticity ......................... 5 2.2.4 Endogeneity ............................. 6 2.2.5 R² and Adjusted R² ......................... 6 2.2.6 Akaike Information Criterion ................... 7 2.2.7 Breusch-Pagan Test ......................... 8 2.2.8 Shapiro - Wilk Normality Test ................... 8 3 Economic Framework 9 3.1 Macroeconomics and Tourism ....................... 9 3.2 Overview of Tourism in Sweden ...................... 12 4 Methodology 16 4.1 Previous Studies and Software ....................... 16 4.2 Selection of Variables ............................ 16 4.2.1 Response Variable ......................... 16 4.2.2 Explanatory Variable ........................ 16 4.3 Data Collection ............................... 18 4.4 Limitations ................................. 19 5 Results 20 5.1 Initial Models ................................ 20 iii 5.2 Model Reduction .............................. 26 5.3 Final Models ................................ 27 5.3.1 Testing the Final Models ...................... 27 5.3.2 Presentation of Final Models .................... 32 6 Discussions 33 6.1 Interpretation of the Final Result ...................... 33 6.1.1 GDP ................................. 33 6.1.2 Exchange Rates ........................... 33 6.1.3 Removed Variables ......................... 34 6.2 Review of the Research Approach ..................... 35 6.2.1 Model ................................ 35 6.2.2 Data ................................. 35 6.2.3 Methodology ............................ 35 7 Conclusion 37 8 References 39 iv 1 Introduction 1.1 Background Tourism has been continuously growing over last decades and become one of the fastest growing economic sectors in the world according to the UNWTO, Tourism Highlights, 2018 Edition. Modern tourism is closely linked to a wide range of service industries which include, but not limited to, accommodation, transportation, national parks, shopping facilities, entertainment facilities, food and beverage establishments. Tourism in industrialized and developed countries has produced economic and employment benefits in related business sectors (UNWTO, 2018). The business volume of tourism in the world equals or even surpasses that of oil exports, food products or automobiles and has generated 1.6 trillion USD in 2017 in its export earnings (UNWTO, 2018). Sweden has also experienced a growth in tourism during the last decade (Tillväxtverket, 2017). The international tourism consumption in Sweden has shown positive growths since the global financial crisis in 2008 (Tillväxtverket, 2017). The total consumption by foreign tourism in Sweden has increased by 11.4 percent during 2017, and 229 percent in between 2000 and 2017 as published by Tillväxtverket, Fakta om svensk turism, 2017. However, there is no clear answer to why there is such a significant growth in tourism in Sweden. There are countlessly many socio-economic factors on the individual level that could affect tourists’ decision- making processes in their destination choice for travel. In this paper, we therefore limit our scope to a macroeconomics to explain the growth of international tourism consumption in Sweden by foreign visitors using a multiple linear regression approach. 1.2 Purpose The main purpose of this paper is to combine mathematical statistics with industrial economics to identify which macroeconomic variables are affecting the growth of international tourism in Sweden. The paper uses exchange rates, GDP, trade openness, and unemployment rate as explanatory variables, and the net international tourism 1 consumption (SEK) as the response variable. This paper aims to identify macroeconomic factors that are affecting the tourism consumption in Sweden during a time period of from 1978 to 2017. 1.3 Research Question The main research question of this paper is: • Which macroeconomic variables play a statistically significant role in the growth of international tourism consumption Sweden? 2 2 Statistical Framework 2.1 Multiple Linear Regression Analysis A model that is constructed from analyzing the relation between variables in a data set is called a regression model. There are multiple regression model wich includes several covariates (more than one). The following formula is a multiple regression model including n observations: Xn yi = xijβj + "i i = 1; ::::; n (1) j=0 yi corresponds to the response variable and xj are called covariates. The coefficients of the covariates are called βj and the error term is denoted by "i. The different variables can be expressed in matrix notations: Y = Xβj + " (2) (Lang, 2016) Where the matrices and vectors can be written as: 2 3 2 3 2 3 y β ϵ 6 17 6 17 6 17 6 7 6 7 6 7 6y 7 6β 7 6ϵ 7 6 27 6 27 6 27 Y =6 . 7 β = 6 . 7 ϵ = 6 . 7 4 . 5 4 . 5 4 . 5 yn βk ϵn 2 3 1 x x : : : x 6 11 12 1k 7 6 7 61 x x : : : x 7 6 21 22 2k 7 X = 6. 7 (3) 4. 5 1 xn1 xn2 : : : xnk 3 2.1.1 Ordinary Least Squares In multiple linear regression models, Ordinary Least Squares (OLS) are applied as an estimation of the relationship between a dependent variable and a set of regressors. More specifically, the OLS method is used to estimate the βj coefficients in a regression model by minimizing the following least-square function: n ( ) X ( )0 ( ) 2 − − S β = ϵi = y Xβ y Xβ (4) i=1 ( ) Taking the derivative of S β with a respect to β and putting it equal to zero will turn the least-square normal equation into the following: X0Xβ^ = X0y (5) ( 0 ) 0 β^ = X X X y (6) Solving this normal equation will provide the least-square estimators β^. (Lang, 2016) 2.1.2 Assumptions In order for the linear regression model to be stable, five assumptions have to be met. • A linear relationship between the predictor variables and the response variable. • Error terms have a mean of zero. • All the error terms are normally distributed. • The variance of the error term is constant. • There exists an uncorrelated relationship between the errors. These assumptions are required for the regression
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