The exchange rate is one of the most important factors affecting the travel costs of tourists. Therefore, the depreciation of the national currency makes tourist travel cheaper. Similarly, the appreciation of the national currency makes travel more expensive for tourists. From this point of view, this study aims to seek the effects of real effective USD/TRY exchange rate on tourism income and average tourism expenditure per capita for the period 2003Q12019Q4. In the empirical analysis, the Granger causality test was used to examine the relationship between the variables. According to the results of the study, a bilateral Granger causality relationship was determined between the real effective USD/TRY exchange rate and the average tourism expenditure per capita. However, Granger causality could not be determined between the real effective USD/TRY exchange rate and tourism income. Afterwards, variance decomposition and impulseresponse functions analyses were performed to support the results obtained from the Granger causality test. According to the results of the variance decomposition analysis, the ratio of the average tourism expenditure per capita in Turkey to be explained by tourism income and real effective exchange rate is quite high.
Keywords: Tourism income, Real effective USD/TRY exchange rate ADF unit root test, Granger causality test, Variance decomposition test, Impulseresponse functions.
JEL Classification: C58; F31; Z39.
Received: 17 May 2022 / Revised: 8 July 2022 / Accepted: 20 July 2022/ Published: 3 August 2022
The results of the analysis reveal that the level of exchange rates should be taken into account in the policy making process and policies that reduce exchange rate volatility should be implemented for the development of the tourism sector, thus providing information to policy practitioners.
In the international markets, the disappearance of borders between countries affects open economies and thus economic transaction volumes. In terms of international trade, the value of the national currency of the countries in the international markets emerges as an important issue. Therefore, policy practitioners in Turkey started to give more importance to the movements of the Turkish Lira (TL) against the US Dollar and Euro (Sevim & Oğan, 2020) and to make direct and indirect central bank interventions to protect the value of the TL against foreign currencies and reduce volatility. The level of exchange rates is an important indicator also for the tourism sector. The exchange rate levels of the countries they visit play an important role in calculating the travel costs of tourists. The relationship between exchange rates and tourism arises as a result of the depreciation of the national currency of the country to be visited, making the travels of foreign visitors cheaper.
On the other hand, the increase in employment and in foreign exchange reserves as a result of tourism activities creates a positive effect on the balance of payments. In addition, tourism is very important for the country's economy in terms of increasing production diversity and international relations. From this point of view, this study aims to ascertain the effects of real effective USD/TRY exchange rate on tourism income and average tourism expenditure per capita for the period 2003Q12019Q4. It is expected that the study will contribute to the literature in terms of the analysis with current data and the applied methods, and the findings obtained as a result of the study will contribute to the policymaking process for the development of the tourism sector.
The study is organized as follows: Evaluating the current data of the tourism income in Turkey in the second part; summarizing the studies in the related literature in the third part; giving information about the econometric model and data set in the fourth part; and evaluating the results of the analysis and giving policy recommendations in the last part.
Being a bridge between Asia and Europe, its cuisine diversity, its beaches on the Mediterranean and its historical richness all increase Turkey's tourism potential. Turkey is among the countries that attract the most tourists in recent years.
In Table 1, total tourism income and average tourism expenditure per capita in Turkey in quarterly periods between 20032021 are given. According to Table 1, it is observed that total tourism income, which increased from the beginning of 2003 until 2015, experienced a decline in 2015. The tension that emerged between Turkey and Russia after the crash of the Russian jet in November 2015 caused a great decrease in the number of Russian tourists, especially in the summer months of 2016. In addition, the geopolitical risks created by the Syrian civil war in the region and the increase in security concerns after the attacks in Turkey led to a decrease in the number of foreign tourists. Thereby, the decrease in Turkey’s total tourism income deepened even more in 2016. Afterwards, tourism income entered a recovery process in 2017 and started to rise again. However, this recovery process was interrupted by the Covid19 pandemic announced in March 2020, countries closed their borders and tourism income decreased by 65% compared to the year 2019.
Year 
Tourism Income 
Average tourism expenditure per capita 

Period 
Total 
Foreigner 
Citizen 
Total ($) 
Foreigner ($) 
Citizen 

(million $) 
(million $) 
(resident abroad) 
(resident abroad) ($) 

(million $) 

2003 
Annual 
13 855 
10 141 
3 600 
850 
740 
1 384 

I 
1 262 
845 
409 
742 
620 
1 207 

II 
2 365 
1 945 
405 
742 
679 
1 266 

III 
7 367 
5 141 
2 162 
976 
839 
1 521 

IV 
2 861 
2 210 
624 
740 
661 
1 199 

2004 
Annual 
17 077 
13 061 
3 863 
843 
759 
1 262 

I 
1 829 
1 321 
494 
796 
708 
1 145 

II 
3 512 
3 010 
467 
740 
696 
1 105 

III 
8 204 
5 967 
2 164 
934 
830 
1 353 

IV 
3 532 
2 764 
738 
797 
722 
1 217 

2005 
Annual 
20 322 
15 726 
4 374 
842 
766 
1 214 

I 
2 195 
1 620 
552 
769 
682 
1 150 

II 
4 218 
3 631 
536 
717 
680 
982 

III 
9 811 
7 297 
2 409 
955 
863 
1 320 

IV 
4 098 
3 178 
877 
803 
730 
1 169 

2006 
Annual 
18 594 
13 919 
4 464 
803 
722 
1 153 

I 
2 192 
1 507 
663 
801 
705 
1 111 

II 
4 100 
3 388 
661 
740 
690 
1 050 

III 
8 839 
6 510 
2 237 
872 
784 
1 217 

IV 
3 463 
2 514 
903 
732 
641 
1 117 

2007 
Annual 
20 943 
15 936 
4 704 
770 
692 
1 121 

I 
2 425 
1 683 
697 
760 
659 
1 090 

II 
4 263 
3 535 
650 
657 
612 
915 

III 
9 845 
7 385 
2 333 
829 
743 
1 209 

IV 
4 410 
3 333 
1 024 
779 
702 
1 115 

2008 
Annual 
25 415 
19 612 
5 418 
820 
742 
1 191 

I 
3 162 
2 292 
821 
849 
764 
1 131 

II 
5 520 
4 635 
777 
724 
679 
974 

III 
11 506 
8 731 
2 608 
862 
774 
1 259 

IV 
5 227 
3 955 
1 212 
833 
743 
1 271 

2009 
Annual 
25 065 
19 064 
5 691 
783 
697 
1 222 

I 
2 851 
2 086 
725 
784 
709 
1 046 

II 
5 076 
4 209 
787 
656 
603 
1 033 

III 
11 103 
8 359 
2 614 
811 
722 
1 228 

IV 
6 034 
4 409 
1 565 
871 
753 
1 455 

2010 
Annual 
24 931 
19 110 
5 558 
755 
670 
1 231 

I 
2 865 
2 097 
729 
763 
675 
1 130 

II 
5 499 
4 495 
937 
657 
588 
1 273 

III 
10 174 
7 821 
2 259 
734 
666 
1 066 

IV 
6 393 
4 698 
1 633 
908 
780 
1 606 

2011 
Annual 
28 116 
22 222 
5 63 
778 
709 
1 168 

I 
3 737 
2 751 
945 
850 
764 
1 183 

II 
6 600 
5 579 
949 
707 
656 
1 137 

III 
11 314 
8 996 
2 224 
755 
702 
1 024 

IV 
6 465 
4 897 
1 521 
871 
765 
1 488 

2012 
Annual 
29 007 
22 410 
6 354 
795 
715 
1 241 

I 
3 524 
2 519 
970 
835 
746 
1 148 

II 
7 066 
5 758 
1 235 
758 
684 
1 355 

III 
11 055 
8 637 
2 333 
716 
656 
1 025 

IV 
7 361 
5 497 
1 817 
984 
860 
1 667 

2013 
Annual 
32 309 
25 322 
6 760 
824 
749 
1 252 

I 
4 649 
3 270 
1 344 
974 
851 
1 442 

II 
8 316 
6 929 
1 329 
810 
747 
1 335 

III 
11 579 
9 152 
2 344 
721 
667 
1 001 

IV 
7 765 
5 972 
1 744 
956 
854 
1 542 

2014 
Annual 
34 306 
27 778 
6 289 
828 
775 
1 130 

I 
4 808 
3 632 
1 138 
949 
877 
1 230 

II 
8 976 
7 534 
1 379 
818 
759 
1 325 

III 
12 854 
10 439 
2 329 
752 
712 
963 

IV 
7 668 
6 172 
1 444 
924 
867 
1 224 

2015 
Annual 
31 465 
25 439 
5 843 
756 
715 
970 

I 
4 869 
3 815 
1 024 
911 
884 
994 

II 
7 734 
6 663 
1 026 
719 
691 
921 

III 
12 294 
9 894 
2 334 
706 
670 
881 

IV 
6 568 
5 067 
1 459 
810 
737 
1 183 

2016 
Annual 
22 107 
15 991 
5 965 
705 
633 
978 

I 
4 066 
2 880 
1 158 
796 
717 
1 059 

II 
4 981 
3 809 
1 133 
665 
602 
973 

III 
8 277 
5 888 
2 340 
686 
622 
901 

IV 
4 783 
3 414 
1 335 
714 
626 
1 072 

2017 
Annual 
26 284 
20 223 
5 909 
681 
630 
903 

I 
3 370 
2 405 
944 
696 
637 
880 

II 
5 413 
4 376 
1 004 
611 
570 
845 

III 
11 392 
8 728 
2 605 
684 
634 
900 

IV 
6 109 
4 715 
1 356 
741 
687 
978 

2018 
Annual 
29 513 
24 028 
5 346 
647 
617 
801 

I 
4 425 
3 348 
1 054 
723 
682 
869 

II 
7 045 
5 936 
1 073 
636 
602 
885 

III 
11 503 
9 372 
2 086 
612 
589 
724 

IV 
6 540 
5 372 
1 133 
678 
649 
828 

2019 
Annual 
34 520 
28 705 
5 688 
666 
642 
796 

I 
4 630 
3 704 
906 
697 
678 
765 

II 
7 974 
6 975 
967 
625 
607 
766 

III 
14 031 
11 485 
2 505 
649 
623 
789 

IV 
7 885 
6 542 
1 311 
727 
702 
859 

2020 
Annual 
12 059 
9 097 
2 887 
762 
716 
926 

I 
4 101 
3 292 
791 
727 
710 
788 

II 
 
 
 
 
 
 

III 
4 044 
2 875 
1 138 
722 
649 
969 

IV 
3 914 
2 930 
958 
854 
804 
1 019 

2021 
Annual 
24 482 
18 790 
5 577 
834 
785 
1 029 

I 
2 452 
1 677 
762 
943 
918 
983 

II 
3 004 
2 183 
802 
739 
694 
871 

III 
11 395 
8 851 
2 501 
835 
773 
1 146 

IV 
7 631 
6 079 
1 512 
843 
809 
982 
The relationship between real exchange rate and tourism income is explained by the fact that tourism contributes to growth through foreign exchange returns and employment opportunities. In terms of countries, the exchange rate level plays an active role in increasing tourism income. The exchange rate level affects the sector stakeholders according to the foreign exchange inputoutput structure of each company. If an enterprise uses imported inputs or if its income is derived from the local currency while its prices are determined according to the exchange rate, a decrease in the exchange rate will be in favor of that enterprise. Because the depreciation of the foreign currency against the local currency will reduce the costs on the basis of the local currency. Whereas businesses with costs in local currency and income in foreign currency will be adversely affected by the decrease in the exchange rate. Since the sales revenues of these businesses are derived in foreign currency, the depreciation of the foreign currency will also reduce the revenues in local currency terms and cause the profit margins of the businesses to decrease. Finally, there will be changes in travel trends as the depreciation of the tourists' own national currency against the national currency of the country they will travel to will reduce the purchasing power of the tourists (Demir, 2021).
The literature on the relationship between real exchange rate and tourism income includes studies also on the tourism income of Turkey. One of them is the study by Kaya and Cömlekçi (2013). They found a negative relationship between tourism income and exchange rate volatility in their study, in which the data between 2002 and 2011 were used and the multiple linear regression method was applied.
Samırkaş and Samırkaş (2014) conducted a Granger causality analysis with the data belong to the 20032013 period, and determined a bidirectional relationship between tourism income and economic growth in Turkey.
Şen and Sit (2015) applied the TodaYamamoto causality analysis in their studies by using the data of the 20002012 period. According to the results of the analysis, it was observed that the real exchange rate and tourism income mutually affect each other.
Selim, Güven, and Eryiğit (2015) used VAR and the block Granger causality analysis method in their studies for the data between 1980 and 2012. According to the results, a unidirectional causality from economic growth to tourism income and real effective exchange rate was observed.
Öncel, İnal, and Torusdağ (2016) conducted a TodaYamamoto causality analysis with the data from the 20032015 period, and determined a unidirectional causality relationship from tourism income to real exchange rate in Turkey.
Dilber and Kılıç (2018) conducted a VAR analysis in their study using the data between 19952016. According to the results, it has been determined that there is a longterm relationship between tourism income and economic growth in Turkey.
The study by Dereli and Akiş (2019) that used the data between 1970 and 2016 and conducted (Toda & Yamamoto, 1995) causality analysis, found a unidirectional causality from tourism income to economic growth in Turkey.
Pekmezci (2020) determined that there is a oneway relationship between the number of foreign tourists visiting Turkey and economic growth in his study, using the data between 1998 and 2019 and applying TodaYamamoto causality analysis.
In the study by Arslan and Cetiner (2020) the relationship between exchange rates and tourism income was examined using 20082019 period data for Turkey. It has been concluded that there is a relationship between exchange rates and tourism income. However, they can explain each other at low percentages, that is, they are also affected by other variables. A rise in the exchange rate increases tourism income initially, and then loses its effect as a result of cyclical fluctuations. Likewise, an increase in tourism income decreases the exchange rate initially and then loses its effect.
Sevim and Oğan (2020) conducted a Granger causality analysis with the data from the 20122018 period, and determined that there is no causality relationship between the real exchange rate and tourism income in Turkey.
Demir (2021) investigated the relationships between exchange rate, tourism income and economic growth using quarterly data between 2003Q12020Q1. Zivot and Andrews structural break unit root test, Johansen cointegration analysis, FMOLS and DOLS methods, TodaYamamoto test and causality analysis were performed in the study. According to the results of the analysis, a longterm relationship was determined between the variables. Furthermore, the effect of real exchange rate on national income was found to be higher than the effect of real exchange rate on tourism income.
Timur and Mert (2021) used nonlinear ARDL analysis method in their study, which includes the data between 20032020. As a result of the study, an asymmetrical relationship in the long run and a symmetrical one in the short run were determined between the exchange rate and tourism income in Turkey.
In the study by Akar and Özcan (2021) the relationship between the real exchange rate and tourism income in Turkey was examined. The data set of the study includes monthly data belong to the period of 20122019. The structural VAR model and the Generalized Least Squares estimation method were used in the study. According to the findings obtained, it was concluded that the reactions that the variables gave to each other were negligable for the specified period.
Demir and Bahar (2021) examined the effect of tourism income on economic growth in their study by using the EngleGranger cointegration method, and found that tourism income had positive effects on Turkey's economic growth parameters for the 2003Q12018Q4 period.
The main question of the study is: “does the real effective USD/TRY exchange rate have an impact on tourism income?”. In addition to this question, the secondary question is: “does the real effective USD/TRY exchange rate have an impact on average tourism expenditure per capita?”. To answer these two questions, quarterly data consisting of 68 observations for the period 2003Q12019Q4 were used in the econometric model in which tourism income and per capita tourism expenditure variables were dependent variables and USD/TRY exchange rate was the independent variable.
With the announcement of the Covid19 pandemic in March 2020, countries closed their borders and tourism income decreased by 65% compared to the year 2019, regardless of the exchange rate movements. Therefore, the years 2020 and 2021 were not included in the econometric analysis of the study.
The data set was obtained from the websites of the Turkish Statistical Institute and the Central Bank of the Republic of Turkey. The abbreviations and variable names of the data used in the analyzes are presented in Table 2.
Variables in the Model  
Income  Tourism Income 
Percapita  Average Tourism Expenditure Per Capita 
USD/TRY  CPI Based Real Effective USD/TRY Exchange Rate (2003=100) 
A correlation between two variables, even if it is a high correlation, does not provide sufficient information about the causeeffect relationship between the variables. The Granger causality test investigates how effective the lagged values of two different variables (x and y) are in explaining the other variable. The Granger causality test reveals whether either variable x or y leads to the other (Granger, 1969) and is one of the most frequently used methods in empirical analysis. Granger causality originated from the idea that the cause of the past cannot be the future or the present, and that if an event occurs before another event, the event that occurred first could be the cause of the event that occurred later. Although the Granger causality test is quite applicable, it has some shortcomings. First of all, the variables to be tested for Granger causality must be stationary (Granger, 1988). In other words, to apply the Granger (1988) method, the nonstationary series must be integrated of the same order and there must be a cointegration relationship between the series. Hence, a unit root test should be applied to determine the stationary propeties of the variables (Öner & Satıcı, 2020).
Therefore, as the first step of econometric analysis, it will be investigated whether the series are stationary or not by applying the Augmented DickeyFuller (ADF) unit root test. If the variance and mean of a time series do not change over time and the common variance between two periods does not depend on this common period, but only on the distance between the two periods, this time series has a stationary structure (Gujarati, 1999). Series that are not stationary are called “series with unit roots”. If it is determined as a result of the ADF unit root test that the series is not stationary at the level value, the difference of the series will need to be taken (Içellioğlu & Oztürk, 2018).
ADF unit root testing is performed using these three models:
The t statistical values obtained as a result of Equation 1, Equation 2 and Equation 3 can be compared with the 1%, 5% or 10% Mackinnon critical values. In this study, the 5% Mackinnon critical value, which is the most widely used critical value by the researchers, were used.
Analysis results were tested against null and alternative hypothesis in terms of the stationarity test. According to the t statistical values obtained as a result of the ADF unit root test, either the null hypothesis (H0) or the alternative hypothesis (H1) is accepted. The definitions of the H0 and H1 hypothesis are as follows:
H0: If δ = 0, Yt is nonstationary, it has a unit root. (4)
H1: If δ < 0, Yt is stationary and has no unit root. (5)
Equation 4 presents that Yt is nonstationary, while Equation 5 presents that Yt is stationary. If the series are not stationary, the lag length of the variables need to be determined. For this purpose, lag lengths that minimize Akaike, HannanQuinn and Schwartz information criteria were determined. After the appropriate lag length was found, the Granger causality test was applied. Granger causality test, which is one of the most widely used tests by researchers because it is easy to use and interpret, is analyzed through these two equations (Öner., 2018):
According to the results of Granger causality analysis, if the coefficients in Equation 6 are different from zero at a certain significance level, it is concluded that y1 is the cause of y2; while if the coefficients in Equation 7 are found to be different from zero at a certain significance level, it is concluded that y2 is the cause of y1 (Granger, 1969). These causality results are expressed as Granger causality from y1 to y2 and from y2 to y1 (Brooks, 2002).
The following two hypotheses were established for the probability values obtained from the Granger causality test analysis results:
H0: Changes in y1 are not the cause of changes in y2 (8)
H1: Changes in y1 are the cause of changes in y2 (9)
Equation 8 presents that changes in y1 are not the cause of changes in y2; while Equation 9 presents that Changes in y1 are the cause of changes in y2. As a result of the Granger causality test, if the probability value is below 0.05, the H1 hypothesis is accepted so that the H0 hypothesis is rejected. Acceptance of the alternative hypothesis is called the Granger cause of the variable y2 of y1 (Öner, 2018). After evaluating the results of the Granger causality test, variance decomposition analysis was carried out and impulseresponse functions. figures were created to determine the shocks between the variables.
The variance decomposition method is a method that investigates what percentage of the variation in the variance of each of the examined variables is explained by their own lags and what percentage is explained by other variables, in a certain time period. It can also be used as a side evaluation of whether the variables are internal or external. On the other hand, impulseresponse analysis investigates the effect of a random shock in one of the variables on other variables in the system and in this respect plays an important role in shaping economic policies. To determine how the shocks will occur, the movements of the variables within 10 periods were examined first. The reactions of the other series against the 1unit shock change in the series are illustrated with the help of graphics (Akyüz, 2018; Tarı, 2010).
As stated before, quarterly data consisting of 68 observations for the period 2003Q12019Q4 were used in the econometric model in which tourism income and per capita tourism expenditure variables of Turkey were dependent variables and USD/TRY exchange rate is the independent variable. It will be useful to examine the statistical results of the variables before moving on to the econometric analysis part of the study.
Statistics 
USD/TRY 
INCOME (000 $) 
PERCAPITA 
Minimum 
62.740 
1,261,787 
610.719 
Maximum 
127.710 
14,031,122 
983.605 
Mean 
103.578 
6,379,813 
776.357 
Median 
105.135 
5,776,933 
756.292 
Std. Dev. 
13.812 
3,108,422 
95.888 
Skewness 
0.810 
0.544 
0.473 
Kurtosis 
3.379 
2.394 
2.487 
JarqueBera 
7.850 
4.401 
3.285 
Probability 
0.019 
0.110 
0.193 
Observations 
68 
68 
68 
According to the 68 observations in Table 3, the mean value of real USD/TRY is 103.57, while the smallest value is 62.74 and the largest value is 127.710. Apart from that, the mean value of tourism income is 6,379,813,000 US Dollars, and mean values of average tourism expenditure per capita is 776.357 US Dollars.
Figure 1 consists of the figures of the time series of 68 quarterly observations for the period 2003Q12019Q4, and shows that the series can have a certain constant and trend. In the analyses, including nonstationary time series, the problem of spurious regression may be encountered and this may lead to misleading results. Therefore, before the causality analysis, the stationarities of the variables were analyzed with the ADF unit root test. The ADF unit root test results of USD/TRY, INCOME and PER CAPITA variables are given in Table 4.
Variables 
Intercept 
Trend & Intercept 

USD/TRY 
Level 1.st Difference 
1.525 9.419 
0.515 0.000 
3.254 9.508 
0.083 0.000 
Income 
Level 1.st Difference 
1.762 3.474 
0.395 0.011 
2.760 3.427 
0.217 0.046 
Per Capita 
Level 1.st Difference 
1.357 4.523 
0.597 0.001 
1.731 4.464 
0.725 0.003 
As seen in Table 4, the level values of USD/TRY, INCOME and PER CAPITA variables have unit root and the first difference values of all three variables are stationary.
The results of VAR lag order selection criteria are given in Table 5. As seen, according to the most widely used criterions such as AIC, SC and HQ, the lag length was specified as 5.
Lag 
LogL 
LR 
FPE 
AIC 
SC 
HQ 
0 
1575.137 
NA 
1.41e+19 
52.604 
52.709 
52.645 
1 
1505.697 
129.621 
1.88e+18 
50.589 
51.008 
50.753 
2 
1474.468 
55.169 
8.98e+17 
49.848 
50.581 
50.135 
3 
1462.048 
20.699 
8.06e+17 
49.734 
50.782 
50.144 
4 
1394.589 
105.685 
1.16e+17 
47.786 
49.147 
48.318 
5 
1373.133 
31.469 
7.81e+16 
47.371 
49.046 
48.026 
6 
1366.855 
8.580 
8.80e+16 
47.461 
49.451 
48.240 
Finally, it is essential to determine whether the predicted model satisfies the stationarity condition. The stationarity of the VAR model depends on the eigen values of the coefficient matrix. The system is considered stable if the eigen values of the coefficient matrix are inside the unit circle, and unstable if at least one of the eigen values is above or outside the unit circle.
According to Figure 2, the position of the inverse roots of the AR characteristic polynomial of the predicted model within the unit circle illustrates that the model does not have any problems in terms of stationarity.
Dependent variable: USD/TRY 

Excluded 
Chisq 
df 
Prob. 
INCOME 
6.807 
5 
0.235 
PER CAPITA 
13.038 
5 
0.023 
Dependent variable: INCOME 

Excluded 
Chisq 
df 
Prob. 
USD/TRY 
1.646 
5 
0.895 
PER CAPITA 
2.225 
5 
0.817 
Dependent variable: PERCAPITA 

Excluded 
Chisq 
df 
Prob. 
USD/TRY 
11.855 
5 
0.049 
INCOME 
8.865 
5 
0.114 
VAR Granger causality/block exogeneity Wald test results are given in Table 6. According to the results, Granger causality is determined from the real effective USD/TRY exchange rate to the average tourism expenditure per capita, and also from the average tourism expenditure per capita to the real effective USD/TRY exchange rate. In other words, a bilateral causality relationship was determined between the real effective USD/TRY exchange rate and the average tourism expenditure per capita. However, Granger causality could not be determined between the real effective USD/TRY exchange rate, which is the main subject of the study, and tourism income. Summarized results of Granger causality test are given in Table 7.
Independent variable 
Granger direction 
Dependent variable 
Results 
USD/TRY 
<> <> 
Per Capita Income 
Bilateral Causality No Causality 
Income 

Period 
S.E. 
USD/TRY 
Income 
Per Capita 

1 
899214 
0.071 
99.928 
0.000 

2 
1062772 
1.161 
98.554 
0.284 

3 
1094033 
1.182 
98.354 
0.463 

4 
1097488 
1.258 
98.263 
0.478 

5 
1374933 
0.802 
98.707 
0.489 

6 
1439002 
0.757 
98.754 
0.487 

7 
1443461 
1.166 
98.337 
0.496 

8 
1454821 
1.614 
97.379 
1.006 

9 
1587991 
1.662 
96.770 
1.567 

10 
1610871 
1.695 
96.360 
1.943 

Percapita 

Period 
S.E. 
USD/TRY 
Income 
Per Capita 

1 
43.770 
0.0127 
28.588 
71.399 

2 
52.391 
7.4759 
35.047 
57.476 

3 
56.834 
15.256 
35.641 
49.101 

4 
58.145 
18.550 
34.342 
47.107 

5 
65.561 
16.047 
32.657 
51.295 

6 
68.774 
16.856 
34.346 
48.796 

7 
70.452 
18.445 
34.830 
46.724 

8 
71.130 
19.824 
34.242 
45.933 

9 
74.085 
19.123 
32.312 
48.564 

10 
76.021 
20.239 
32.348 
47.411 
According to the results of the variance decomposition analysis, which reveal how much the dependent variable is affected by the shocks of the independent variables, the dependent variable INCOME is affected by its own shocks 99.92% on the first day, and over 96% on the following days. In addition, the INCOME variable is affected by the shocks of the USD/TRY variable by 1.16% and 1.18% on the second and third days, respectively, and by 1.69% on the tenth day.
As seen in Table 8, the dependent variable PER CAPITA is highly explained by other variables. It is affected by its own shocks 71.39% on the first day, and by 47.41% on the tenth day. In addition, the PER CAPITA variable is affected by the shocks of the USD/TRY variable by 7.47% and 15.25% on the second and third days, respectively, and by 20.23% on the tenth day. It is also affected by the shocks of the INCOME variable by 28.58% and 35.04% on the first and second days, respectively, and by 32.34% on the tenth day. According to these results, the ratio of the average tourism expenditure per capita in Turkey to be explained by tourism income and real exchange rate is quite high.
ImpulseResponse functions figures, which are the last stage of the analysis, are given in Figure 3. Accordingly, the effect of the real USD/TRY exchange rate on tourism income is observed to be positive between the 1st and 3rd quarters, negative after the 3rd quarter, zero in the 6th quarter and negative again after the 6th quarter. On the other hand, the effect of the real USD/TRY exchange rate on average tourism expenditure per capita is observed to be zero in the 1st quarter, and positive in all subsequent quarters.
The tourism sector is one of the fastest growing industries in the world today. The said growth rate has been beyond expectations due to the rapid change in information and transportation technologies. Tourism, which has become very important economically and socially since the second half of the Twentieth Century, constitutes a potential source of income for the economies of developing countries.
The rapid growth trend observed in the tourism sector in the world has also shown itself in Turkey. Tourism, which is a laborintensive sector, provides an important foreign currency inflow for Turkey, which has a young population. In addition, tourism is very important for the country's economy in terms of increasing production diversity and international relations. From this point of view, this study examined the effect of the real effective USD/TRY exchange rate on tourism income and average tourism expenditure per capita. For this purpose, first of all, the relationship between real effective USD/TRY exchange rate, tourism income and average tourism expenditure per capita was analyzed with the Granger causality test. According to the Granger causality test results, a bilateral causality relationship was determined between the real effective USD/TRY exchange rate and the average tourism expenditure per capita. However, a Granger causality relationship could not be determined between the real effective USD/TRY exchange rate and tourism income.
To support the results obtained from the Granger causality test, variance decomposition analysis was carried out and impulseresponse functions figures were created to determine the shocks between the variables. According to the results of the variance decomposition analysis, the ratio of the average tourism expenditure per capita in Turkey to be explained by tourism income and real effective exchange rate is quite high. Therefore, policy practitioners in Turkey should consider the level of exchange rates in the policymaking process for the development of the tourism sector, and also implement policies that reduce exchange rate volatility.
Funding: This study received no specific financial support. 
Competing Interests: The author declares that there are no conflicts of interests regarding the publication of this paper. 
Akar, G., & Özcan, M. (2021). Relationship between real exchange rate and tourism income: VAR analysis with structural break. Journal of Ömer Halisdemir University, Faculty of Economics and Administrative Sciences, 14(2), 413431.Available at: https://doi.org/10.25287/ohuiibf.705341.
Akyüz, H. E. (2018). Statistical analysis of climatic variables with vector autoregression (VAR) model. International Journal of Engineering Research and Development, 10(2), 183192.
Arslan, E., & Cetiner, T. (2020). Tourism income exchange rate relationship: The case of Turkey (20082019). Ankara Hacı Bayram Veli University Journal of Tourism Faculty, 23(1), 117.
Brooks, C. (2002). Introductory econometrics for finance. Cambridge: Cambridge University Press.
Demir, Y. (2021). Empirical analysis of the relationship between real economic growth, real exchange rate and tourism revenues under structural breaks. Hitit University Journal of Social Sciences Institute, 14(1), 2640.
Demir, E., & Bahar, O. (2021). The effect of tourism revenues on economic growth: Empirical analysis on Turkey. International Journal of Social Sciences and Education Research, 7(2), 162172.Available at: https://doi.org/10.24289/ijsser.699497.
Dereli, D. D., & Akiş, E. (2019). Analysis of the relationship between tourism revenues and economic growth in Turkey (19702016). Ataturk University Journal of Economics and Administrative Sciences, 33(2), 467478.
Dilber, I., & Kılıç, J. (2018). The relationship between tourism revenues and economic growth in Turkey: Engle granger cointegration test and VAR model. TESAM Academy, 5(2), 95118.Available at: https://doi.org/10.30626/tesamakademi.456006.
Granger, C. W. J. (1969). Investigating causal relation by econometric and crosssectional method. Econometrica, 37(3), 424–438.Available at: https://doi.org/10.2307/1912791.
Granger, C. W. (1988). Some recent development in a concept of causality. Journal of Econometrics, 39(12), 199211.Available at: https://doi.org/10.1016/03044076(88)900450.
Gujarati, D. N. (1999). Econometrics (3rd ed.). New York: McGrawHill, Inc.
Içellioğlu, C. Ş., & Oztürk, M. B. E. (2018). Investigation of the relationship between Bitcoin and selected exchange rates: Johansen test and Granger causality test for the period 20132017. Finance and Finance Writings, 2018(109), 5170.
Kaya, V., & Cömlekçi, S. (2013). The effects of exchange rate volatility on the tourism sector: The case of Turkey (20022011). Journal of Travel and Hotel Management, 10(2), 8289.
Öncel, Y., İnal, A., & Torusdağ, A. (2016). The relationship between real exchange rate and tourism incomes in Turkey: An empirical application for the 20032015 period. Yüzüncü Yıl University Faculty of Economics and Administrative Sciences Journal Bahar, 2, 125142.
Öner, H., & Satıcı, H. K. (2020). How does gold and oil price volatility affect Turkish financial markets? International Journal of Research in Business and Social Science (21474478), 9(4), 262270.Available at: https://doi.org/10.20525/ijrbs.v9i4.733.
Öner, H. (2018). The effects of international financial indices on exchange rates: An empirical analysis. Selcuk University Journal of Social Sciences Vocational School, 21(2), 173185.
Pekmezci, A. (2020). The relationship between tourism and economic growth in Turkey: TodaYamamoto causality approach. Journal of Management and Economics Studies, 18(4), 317325.Available at: https://doi.org/10.11611/yead.814470.
Samırkaş, M., & Samırkaş, M. C. (2014). The impact of the tourism sector on economic growth: The case of Turkey. Journal of Dokuz Eylul University Faculty of Business Administration, 15(1), 6376.Available at: https://doi.org/10.24889/ifede.268176.
Selim, S., Güven, E. T. A., & Eryiğit, P. (2015). The place of tourism in the Turkish economy: Time series analysis. International Journal of Alanya Faculty of Business, 7(3), 1933.
Şen, A., & Sit, M. (2015). Empirical analysis of the effect of real exchange rate on tourism revenues of Turkey. Journal of Yaşar University, 10(40), 67526762.
Sevim, U., & Oğan, E. (2020). Evaluation of causality between real exchange rate and tourism sector: The case of Turkey. Gumushane University Journal of Social Sciences, 11(3), 858869.
Tarı, R. (2010). Econometrics (6th ed.). Kocaeli: Umuttepe Publications.
Timur, M., & Mert, N. (2021). Analysis of the asymmetric relationship between tourism revenues and real exchange rate. Fiscaoeconomia, 5(1), 219237.Available at: https://doi.org/10.25295/fsecon.848247.
Toda, H. Y., & Yamamoto, T. (1995). Statistical inference in vector autoregressions with possibly integrated processes. Journal of Econometrics, 66(12), 225250.Available at: https://doi.org/10.1016/03044076(94)016168.
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