Kochakorn Sutaruksanon
Bachelor of Economics, International program |
Thammasat University
The impact of the real
effective exchange rate on
the tourism industry
1
Table of content
TABLE OF CONTENT .......................................................................................................1
DEDICATION .....................................................................................................................3
LIST OF FIGURES .............................................................................................................4
LIST OF TABLES ...............................................................................................................5
ABSTRACT .........................................................................................................................6
CHAPTER ONE; INTRODUCTION .................................................................................7
1.1 Background .....................................................................................................................7
1.2 International arrivals and receipts in the tourism industry and future trend .......................9
1.3 The importance of REER to the tourism industry ........................................................... 10
1.4 Study question ............................................................................................................... 12
1.5 Study objectives ............................................................................................................. 12
CHAPTER TWO; LITERATURE REVIEW .................................................................. 13
2.1 Literature that related to the REER and tourism industry ................................................ 13
2.2 Literature that related to the tourism sector .................................................................... 14
2.3 Research gap .................................................................................................................. 14
CHAPTER THREE; METHODOLOGY ......................................................................... 15
3.1 Model specification ........................................................................................................ 15
3.2 Estimation technique ...................................................................................................... 15
3.3 Selected 8 countries in ASEAN...................................................................................... 16
3.4 Statistical test ................................................................................................................. 17
3.5 Data source .................................................................................................................... 17
CHAPTER FOUR; EMPIRICAL RESULTS & ANALYSIS .......................................... 19
2
4.1 Descriptive statistics ...................................................................................................... 19
4.2 Results and interpretatioofon the number of international arrivals .................................. 20
4.3 Results and interpretatiof on the international receipts.................................................... 21
4.4 Discussion and interpretation of the results on the number of international arrivals ........ 23
4.5 Discussion and interpretation of the results on the international receipts ......................... 24
CHAPTER FIVE; CONCLUSION & POLICY IMPLICATIONS ................................. 26
5.1 Conclusion ..................................................................................................................... 26
5.2 Policy implication .......................................................................................................... 26
5.3 Limitations of policy implications .................................................................................. 28
5.4 Further research ............................................................................................................. 28
BIBLIOGRAPHY .............................................................................................................. 29
3
Dedication
All my dedication goes to my professor Dr. Euamporn Phijaisanit, Ph.D., Tatre
Jantarakkolica, my family and my friend for all supports in the research paper and running the
STATA including the source of data types. I appreciate them dearly.
4
List of figures
Figure 1; Role of the tourism industry to the country’s GDP and employment rate
Figure 2; International arrivals trend of 8 countries in ASEAN from 2008 to 2017
Figure 3; International receipts trend of 8 countries in ASEAN from 2008 to 2017
Figure 4; Relationship between REER, number of international arrivals and receipts
Figure 5; The model specification
Figure 6; Show the empirical model of international arrivals
Figure 7; Show the empirical model of international receipts
Figure 8; Show the travel trend of generation Z
5
List of tables
Table 1; Show the variable name, description and data source
Table 2; Show the descriptive statistics
Table 3; Show the Panel results on international arrivals model
Table 4; Show the Panel results on international receipts model
6
Abstract
The main objective of this paper is to see whether the real effective exchange rate
(REER) impact to the number of international arrivals and international tourism’ receipts or
not. By using 10 years annual data between 2007 and 2017 from 8 countries in ASEAN;
Vietnam, Indonesia, Thailand, Malaysia, Lao People's Democratic Republic, Philippines,
Cambodia and Myanmar and use Panel as an estimation technique, this paper aims to find the
relationship between REER and the number of international arrivals, and the relationship
between REER and international tourism’s receipts. All variables that are possible to impact
the models are included as independent variables such as world gross national product (GDP),
world gross national income (GNI), government spending on tourism industry or corruption
perception index (CPI). The study found that REER is statistically significant to both 2
dependent variables; the number of international arrivals and international tourism’ receipts.
Plus, the study results are conforming to Ruane, M. C. (2014), Yap, G. C. (2012), Turner, R.
(n.d.), Ogbeba Ehigocho Peace, O.-O. I. (2016) and Ali Rıza Aktaş, B. O. (2014) who found
that REER significant affect the international tourism industry.
The policy suggestions are through 2 channels which are fiscal policy and monetary policy. On
the fiscal policy, recommending the government intervene only in the tourism industry by
supporting the extra activities due to the increasing trend of sport tourism, for example.
Whereas on the monetary policy will focus on REER by controlling the foreign reserve and
central bank rate, for example.
7
Chapter one; Introduction
1.1 Background
Asia is one of the most population destinations for leisure time, business trip and
medical tourism. In 2018, 27% of global travelers went to the Asia Pacific and earned 30% of
global tourism receipt. From figure1, it represents the travel and tourism contribution to
employment (% share of total employment) and travel and tourism contribution to GDP (%
share of total GDP) from 2008 to 2017. First and foremost, all countries, except Vietnam,
illustrate that there exist of an increasing trend in travel and tourism contribution to
employment. The Philippines was the highest change of contribution of the tourism sector to
employment rate from 8.57% in 2007 to 19.20% in 2018 or compound annual growth rate was
at 8.40%. It creates job opportunities; the owners of hotel or hiring drivers in the transportation
system, for example. Secondly, Myanmar, Cambodia, Thailand, the Philippine, and Vietnam
have an increasing trend in travel and tourism contribution to GDP. Myanmar was the highest
change of contribution of the tourism sector to gross domestic product (GDP) from 1.45% in
2007 to 2.72% in 2017 or compound annual growth rate was at 6.51%. However, the highest
significance of the tourism industry to the country’s GDP was Cambodia at 14.09% in 2017,
following by Thailand at 9.40% in 2017. It shows the significance of travel and tourism
industry to the country’s economy. Furthermore, the tourism industry creates both forward and
backward linkages such as the transportation industry, hotel industry, food industry, and
construction industry. To sum up, the tourism industry is becoming more and more important
to country growth and sustainability.
8
Contribution to GDP
Contribution to employment
Figure 1; Role of the tourism industry to the country’s GDP and employment rate
9
1.2 International arrivals and receipts in the tourism industry and future
trend
In some country, international visitors play more role in generating income for the
nation. For example, in Thailand 2016, 64% of revenue came from international tourists
whereas the remaining 36% of revenue came from domestic. Aggregated number of
international tourists in 8 countries in ASEAN had dramatically increased from 35,483,000 in
2008 to 88,098,000 in 2017 accounted for about 148% growth over 10 years which is shown
in figure 2. Inadditionl to this, aggregated international receipts which are the expenditure by
foreign travelers had jumped from USD42,816 million in 2008 to USD108,933 million in 2017
accounted for about 154% growth over the period shown in figure 3. In the future, the number
of both international arrivals and receipts tend to increase due to infrastructure improvement,
the evolution of technology and transportation facilities.
Figure 3; international receipts trend of 8 countries in ASEAN from 2008 to 2017
Figure 2; international arrivals trend of 8 countries in ASEAN from 2008 to 2017
10
1.3 The importance of REER to the tourism industry
Since the exchange rate is seen as the price of domestic currency and use to measure
the competitive advantage in that country. For instance, the appreciation of domestic currency
causes a decrease in foreigners' purchasing power resulting in tourist choice of destination and
their spending decision. Unfortunately, Bath (Thailand), Rupiah (Indonesia), Ringgit
(Malaysia) and Peso (Philippines) were in the top of strongest currencies in the world on Jan
2019 at 4.2%, 3%, 1.1, and 0.9%, respectively. Therefore, this paper will be discussed how
does the change, either appreciation or depreciation, of the exchange rate, can deter the tourism
industry in term of the number of visitors coming to domestic country and their spending
behavior. As shown in the figure 4, there exist unclear the relationship between the REER and
tourism industry, thus this is the reason of conducting this study; to find the relationship
between REER and tourism industry in term of international arrivals and international receipts.
11
106.45
106.39
105.48
106.86
109.85
111.62
114.94
123.90
128.20
133.20
2125
2162
2508
2882
3584
4210
4503
4775
5012
5602
1,280
1,463
1,671
2,258
2,663
2,895
3,220
3,418
3,523
4,023
-
20.00
40.00
60.00
80.00
100.00
120.00
140.00
0
1000
2000
3000
4000
5000
6000
2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
Cambodia
125.32 125.37
138.23 138.23
137.28
134.30
135.44
143.57
160.94
166.60
8,150
6,053
7,618
9,038
9,463
10,302
11,567
12,054
12,566
14,117
6234
6324
7003
7650
8044
8802
9435
10407
11519
14040
-
20.00
40.00
60.00
80.00
100.00
120.00
140.00
160.00
180.00
0
2000
4000
6000
8000
10000
12000
14000
16000
2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
Indonesia
82.04
79.72
84.10
84.40
84.19
84.61
83.99
77.32
73.97
75.50
18,553
17,231
18,152
19,649
20,251
21,500
22,600
17,666
18,085
18,352
22052
23646
24577
24714
25033
25715
27437
25721
26757
25948
68.00
70.00
72.00
74.00
76.00
78.00
80.00
82.00
84.00
86.00
0
5000
10000
15000
20000
25000
30000
2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
Malaysia
168.2
192.9
215.9
250.5
242.4
234.6
233.7
235
238.4
242.5
731
763
792
816
1059
2044
3081
4681
2907
3443
80
75
91
334
550
964
1,687
2,199
2,289
2,279
0
50
100
150
200
250
300
0
500
1000
1500
2000
2500
3000
3500
4000
4500
5000
2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
Myanmar
85.92
84.43
88.27
88.86
93.17
96.90
96.58
102.89
99.67
111.20
3,293
2,916
3,441
4,053
4,963
5,599
6,059
6,415
6,289
8,349
3139
3017
3520
3917
4273
4681
4833
5361
5967
6621
-
20.00
40.00
60.00
80.00
100.00
120.00
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
Philippines
102.04
99.16
105.66
105.59
105.67
111.53
107.27
107.68
103.30
104.70
22,510
19,811
23,796
30,924
37,766
45,738
42,047
48,527
52,465
62,158
14584
14150
15936
19230
22354
26547
24810
29923
32530
35592
92.00
94.00
96.00
98.00
100.00
102.00
104.00
106.00
108.00
110.00
112.00
114.00
0
10000
20000
30000
40000
50000
60000
70000
2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
Thailand
106.58
107.36
104.05
105.34
113.42
121.08
124.91
132.70 132.60
136.60
4236
3747
5050
6014
6848
7572
7874
7944
10013
12922
3,930
3,050
4,450
5,710
6,850
7,250
7,410
7,350
8,500
8,890
-
20.00
40.00
60.00
80.00
100.00
120.00
140.00
160.00
0
2000
4000
6000
8000
10000
12000
14000
2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
Vietnam
1295
1239
1670
1894
2291
2700
3164
3543
3315
3257
280
271
385
413
461
613
642
725 717
768
110.60
117.30
122.00
124.90
130.10
138.40
141.40
154.00
158.10
163.10
-
20.00
40.00
60.00
80.00
100.00
120.00
140.00
160.00
180.00
0
500
1000
1500
2000
2500
3000
3500
4000
2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
Lao PDR
REER
Number of international arrivals
International receipts
Figure 4; Relationship between REER, number of international arrivals and receipts
12
1.4 Study question
According to World tourism organization 2018 (UNWTO) and Bloomberg, the
highest percentage change of international inbounds in 2017 was Turkey at 24.10% due to the
fact that the dramatical decrease in REER resulting from unstable political issue. Therefore,
the question is raised ‘which factor, the real effective exchange rate or unstable political, impact
the tourism sector in term of international visitors and spending amount when other factors are
the same’. Particularly in ASEAN, emerging countries’ currencies continuously outperformed
on Jan 2019.
1.5 Study objectives
The general objective is to see what factors affecting the number of international
arrivals and international tourism receipt. Specific objectives are to see whether REER affect
the tourist's destination and their spending behavior or not and suggesting the policy
implications that giveae positive impactono population and nation.
13
Chapter two; Literature review
2.1 Literature that related to the REER and tourism industry
Maria Claret M. Ruane (2014) use Ordinary Least Squares regression (OLS) with
monthly data over 115 months to analyze the effect of appreciated USD against JPY or
depreciated JPY against USD on tourism in the island of Guam located at the USA. The
empirical model consists of a number of Japanese arrivals as the dependent variable and
exchange rate, Japanese growth, Tohoku disaster, trend, monthly seasonality and
autoregressive pattern of the dependent variable as independent variables. The result
manifested that a decrease of Japanese arrivals results from exchange rate being significantly
at 5 % and the remaining variables are significant at 1% confidence level. The implication of
this statistic outcome is that the appreciation of USD against JPY or depreciation of JPY against
USD is the reason for a reduction in Japanese travelers.
Ali Rıza Aktaş (2014) The objective of this paper is to investigate the volatility and
value othe f exchange rate onhe t tourism industry in Turkey by using monthly data from Jan
2003 to Dec 2011. The result shew thathe t high volatility othe f exchange rate will increase
tourism revenue whereas the increase ithe n real exchange rate will decrease the tourism
revenue.
Erick Akama (2016) This paper analyzed the significance ofhe t tourism industry
the to country's economy through international' receipts in Kenya from 1980 to 2013. The
estimation method is Engel-Granger for short run and cointegration test foa long-runun
relationship. The empirical result illustrated that tourism sector will less significant in 2030
implying that it requires government intervention to boost tourism activities, especially in
human and capital investment, expenditure in education whereas liberalization of exchange
rate will drive the tourist demand only in short-term.
14
2.2 Literature that related to the tourism sector
Ghialy Choy Lee Yap (2012) aim to measure the effect of exchange rate on
international tourist's volatility by using VARMA-GARCH and VARMA-AGARCH
regression with monthly data from 1991 to 2011. The article showed that the rise in the value
of the Australian dollar deters international tourism growth in only short-term.
Ogbeba Ehigocho Peace, Oji-Okoro Izuchukwu and Abba Abubakar Shehu (2016)
examined the exchange rate fluctuation and tourism sector output in Nigeria by using vector
error correlation model (VECM) with the share of the tourism sector to GDP between 1995
and 2015. The result showed that there exists a long-run relationship between real exchange
rate and the tourism sector to GDP.
2.3 Research gap
There are 3 considerable points which are the concentration of international
travelers, other variables that may not be included into the models and the purpose of traveling.
Firstly, the concentration of international arrivals in each country is not the same proportion.
For example, the most popular international traveler in Thailand is Chinese while in Philippines
is from South Korea. Secondly, some variables that contribute to the change in a number of
international arrivals and their receipts may not be included in the models such as capital
investment on the tourism industry, technological change and infrastructure improvement.
Thirdly, the purpose of traveling can be split into 3 groups which are leisure, hospital and
business trip. The differentnt purpose will cause the different structure of the estimated model.
For example, corruption will have an strong impact on a business trip rather than leisure and
hospital trip, hence corruption will be a significant impact on a business trip only but
insignificant impact on leisure and hospital trips.
15
Chapter three; Methodology
3.1 Model specification
Conducting two models which are number of international arrivals and
international receipts on tourism industry as the dependent variables. For the first model,
use international arrivals as dependent variable which depends on real effective exchange
rate (REER), corruption perception index (CPI), world GDP and government spending on
tourism industry as independent variables. For the second model, use international receipts
on tourism industry or the expenditure amount by international arrivals as dependent
variable which depends on real effective exchange rate (REER) and world GNI as
independent variables. The model will be shown in the figure 5.
3.2 Estimation technique
The estimation technique is Panel method with selected ASEAN8 which consist of
Vietnam, Indonesia, Thailand, Malaysia, Lao People's Democratic Republic, Philippines,
Cambod, a and Myanmar. The currently regional collaboration in tourism policy, planning,
infrastructure development, investment marketing, research and product development is the
core reason of choosing those 8 countries including the future collaboration in social
environment and culture.
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Figure 5; the model specification
16
3.3 Selected 8 countries in ASEAN
One of the popular tourist destinations is in ASEAN countries, resulting an increase
in the significance of the tourism industry to the country. Since ASEAN countries share several
common characteristics both in qualitative and quantitative terms such as, the closeness of
geography, ASEAN agreement, the intra-regional travel, and the level of currency appreciation
on Dec 2018 and Jan 2019. The most important one is the closeness of geography. Since the
cross-border of ASEAN countries are connected to each other causing the easier to travel. Most
Europeans and Americans plan to visit 4 countries in 3 months rather than one specific
destination, Thailand, Vietnam, Laos and Myanmar, so the benefits spillover among ASEAN.
Mae-Sai is the cross border between Thailand and Myanmar, for example. Secondly, ASEAN
agreement had indicated the development plan in 2017 - 2020 of the tourism industry.
Marketing the Southeast Asian by positioning and investment will attrace travelers. The
objectives of the plans are increasing the inspiration to travel Southeast Asian region,
developing the integration in term of digital marketing and innovating the visitors' experiences.
Thirdly, the increasing trend of intra-visitorinon 2010 accounteforto 42% of international
inbound due to visa policy. According to the ASEAN tourism marketing strategies 2017 –
2020, the indicator illustrated that free visa increased the international visitors up to 10 million
to the ASEAN region. Lastly, 4 out of 8 of the strongest currencies in the world on Jan 2019
were in ASEAN countries, Thailand, Indonesia, Malays, and Philippines, therefore those
countries are facing the same situation.
In conclusion, those are the reasons why this paper chooses Vietnam, Indonesia,
Thailand, Malaysia, Lao People's Democratic Republic, Philippines, Cambod, a and Myanmar
to conduct the analysis.
17
3.4 Statistical test
a. Generalized least squares
Using both cross-section and time-series data set in Panel method will create the
heteroskedasticity, autocorrelation and cross-sectional correlation. Thus, to see the problems,
the study will test on Generalized least squares approach. However, the result will be biased,
so use the following approach to solve the problem.
b. Fixed effect estimation
Controlling unobserved variables across the countries to identify the intercept for
each individual country.
c. Random effect estimation
This method will be applied when the relationship othe f coefficient is not high
enough.
d. Hausman test
To see whether use the Fixed effect estimation or Random effect estimation
G
.
H 1)A:9I$@JJ@D>
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3.5 Data source
The frequency of data will be based on 10 years annually from 2008 to 2017 with
2 dependent variables; Arrivals and Receipt and 6 independent variables of macroeconomics
variables; REER, CPIWorldGDPDP, WorldG, I and government spending. The sources of
observations are from TransparencInternationalal, The World Bank, Islamic Development
Bank k and Knoema. Note that the range of CPI is between -2.5 and 2.5, the lower number
indicate the weak political stability whereas the higher numbeindicateste the strong political
stability.
18
Variable proxy
Variable name
Description
Expected sign
Source
%&&'()*+
Number of
international
arrivals to
domestic country
International inbound
tourists who arthe e
main purposofin
visiting in the
country.
dependent
variable
Knoema
1@D@'?>
International
tourism receipts
expenditures by
international inbound
visitors in current
USD in million.
dependent
variable
Knoema
1221
the real exchange
rate adjusted to
the price
the weighted
average of a
country's currency in
relation to an index
based on 1997 = 100.
(-)
Islamic
Development
Bank
456
Political stability
index
The underlying
indexes reflecthe a
disorderly transfer of
government power,
armed conflict,
violent
demonstrations,
social unrest,
international
tensions, terrorism,
ethnic, and regional
conflicts.
(+)
The global
econom
89&*:;<5<5
World real gross
domestic
producatin
constant prices of
2010
To see the rest of the
world GDP, total
world GDP minus
the country’s GDP.
Dollar figures for
GDP are converted
from domestic
currencies using
2010 official
exchange rates in
million.
(+)
Knoema
89&*:;F6
World real gross
national income
based on
purchasing-
power-parity in
constant prices of
2011
Reflecting people's
living standards. An
international dollar in
million has the same
purchasing power
over GNI as a USD
has in the United
States.
(+)
Knoema
;9(>+?@A:'AB
Government
spending on travel
and Tourism
service
USD in billion (Real
prices)
(+)
The World
Bank
Table 1; Show the variable name, description and data source
19
Chapter four; Empirical results & analysis
4.1 Descriptive statistics
Use time-series annual data between 2008 and 2017 of 8 countries in ASEAN
which consist of Vietnam, Indonesia, Thailand, Malaysia, Lao People's Democratic Republic,
Philippines, Cambod, a and Myanmar, so the total number of observations in each variable are
80 resulting in balanced data set. All figures are given in USD million, except Arrivals is given
ia n million people and, REER and CPI are given ithe n index. The average of Arrivals is 7.12
million people with a standard deviation of 7.27 million people. The minimum of foreign
visitors is 0.731 million while the maximum is 35 million. The average of Receipts is USD
8,688 million with a standard deviation of USD 12,745 million. The minimum of tourism's
receipts is USD 75 million while the maximum is USD 62,158 million. The average of REER
is 128, meaning that the domestic currency keeps appreciating since 1997, with a standard
deviation of 42. The minimum of REER is 84 while the maximum is 250.The average of CPI
is 0.48, meaning thae political stability is quite strong, witahe standard deviation of 0.64. The
minimum of CPI is -1.78 (very weak political stability) while the maximum is 0.54. The
average of WorldGDP is USD70,767,510 million or USD70 billion with a standard deviation
of 5,454,493 million. The minimum of WorldGDP is USD62,525,416 million while the
maximum is USD80,082,990 million. The average of WorldGNI is USD 102,110,922 million
or USD102 billion with a standard deviation of USD14,938,969 million. The minimum of
WorldGNI is USD80,547,370 million while the maximum is USD127,233,663 million. The
average of Govtspending is USD158 million witahe standard deviation of USD290 million.
The minimum of Govtspending is USD10 million while the maximum is USD1,092 million.
The skewness indicates the symmetry of the graph. The cutting point is 0 which
mean perfectly symmetry whereas the positive number means long right tail and negative
20
number means long left tail. Therefore, all variables have long right tail except CPI has long
left tail. However, CPI, WorldG, P and WorldGNI are fairly symmetry because the values of
skewness in absolute term are less than 0.5 but the remaining variables are highly skewed
because the absolute values are greater than 1.
The kurtosis indicates the degree of peakedness of a distribution. The cutting point
is 0 which means normal distribution whereas the positive number means heavy tail ana d
negative number means light tail. Therefore, all variables havahe heavy tail.
Variables
Arrivals
Receipts
REER
CPI
WorldGDP
WorldGNI
Govtspending
Obs
80
80
80
80
80
80
80
Unit
Million
people
USD
million
Index
(1997 = 100)
index
USD
million
USD
million
USD
million
Mean
7.1255
8688.142
128.739
0.483625
7.08e+07
1.02e+08
158.2357
SD
7.271941
12745.64
42.29738
0.6408247
5454493
1.49e+07
289.756
Min
.731
75
84.429
-1.78
6.25e+07
8.05e+07
10
Max
35.592
62158
250.5
0.54
8.01e+07
1.27e+08
1092.37
Skewness
2.244043
2.565462
1.617858
-0.195961
0.1514512
0.0929745
2.199675
Kurtosis
7.764018
9.062845
4.793127
1.722089
1.78232
1.737768
6.481939
4.2 Results and interpretation of the number of international arrivals
After testing the Hausman method, the prob > chi2 = 0.9299 which is greater than
0.05, thus the most fitted model ia s random effect. Hence, the following numbers are from the
random model.
Variables
Observations
Sign
coefficient
Standard
error
Prob > Z
Significance
Arrivals
80
-
Dependent
variable
-
-
-
Constant (-
.
)
80
(-)
-23.24888
5.253391
0.000**
Yes
REER
80
(-)
-0.0890739
0.0240931
0.000**
Yes
CPI
80
(-)
-1.2109
1.310117
0.355
No
WorldGDP
80
(+)
5.68e-07
7.57e-08
0.000**
Yes
Govtspending
80
(+)
0.0069181
0.0040379
0.087*
Yes
R-sq (within)
0.6139
Table 2; Show the descriptive statistics
Table 3; Show the Panel results on international arrivals model
21
According to the table 3, an R-square is 0.6139 indicating that 61.39% oa f number
of international inbounds are explained by the independent variables in the model. If the
probability of t-test (p-value) is less than 0.05, there exist of the statistically strong significance
of the estimated results at 95 confidence level. There are three variables; constant, REER and
WorldGDP that fall into the p-value at 95 confidence level, so those three variables have a
strong significant impact to explain the model. Furthermore, if the probability of t-test is less
than 0.1, there exist of statistical significance of estimated results as well at 90 confidence level.
Thus, there is only one variable which is the amount of government spending on tourism
industry (Govtspending).
While another variable; Corruption perception index (CPI)does not fallll into p-
value either at 90% or 95 confidence level, so they are not significant to explain the model.
There are 2 reasons from María Santana-Gallego, J. R.-N. (2016) with the topic of The effects
of terrorism, crime and corruption on tourism. The most important one is thhe weak political
stability affects only business tourists. Due to the fact that the types of international visitors
can be categorized into 3 core groups which are leisure, hospital and business purpose.
According to the study of María Santana-Gallego, J. R.-N. (2016) show that terrorism and
crime have a negative impact on personal reason but corruption has a negative impact on
business reason because the most consideration for running business in oversee is the
environment of that country especially strong economic and political issue.
4.3 Results and interpretation of the international receipts
After testing the Hausman method, the prob > chi2 = 0.3987 which is greater than
0.05, thus the most fitted model a is random effect. Hence, the following numbers are from the
random model.
22
According to the table 4, an R-square is 0.438 indicating that 43.8% of international
tourists' receipt is explained by the independent variables in the model. If the probability of t-
test (p-value) is less than 0.05, there exist of statistical significance of the estimated results at
9 confidence level. All two variables; REER and WorldGNI that fall into the p-value at 9
confidence level, so those two variables have a significant impact to explain the model.
The coefficient of constant is -108.6421 The negative estimated result confirms that
as no REER and WorldGNI, the international tourists’ receipt will decrease USD108 million a
year.
The coefficient of REER is -180.8892. The negative estimated result confirms that,
holding other variables constant, the as REER index increase by 1 unit meaning the domestic
currency appreciated, the international tourists' receipt will decrease by USD180 million per
year.
The coefficient of WorldGNI is 0.0003142. The positive estimated result confirms
that, holding other variables constant, as WorldGNI increase by 1 million meaning people are
wealthier, the international tourists’ receipt will increase USD314.2 per year.
Variables
Observations
coefficient
Standard error
Prob > Z (95%
CI)
Significance
Receipts
80
Dependent
variable
-
-
-
Constant (-
.
)
80
-108.6421
6192.738
0.986
No
REER
80
-180.8892
47.07379
0.000
Yes
WorldGNI
80
0.0003142
0.0000442
0.000
Yes
R-sq (within)
0.438
Table 4; Show the Panel results on international receipts model
23
4.4 Discussion and interpretation of the results on the number of
international arrivals
The model of international arrivals depend on three main factors; REER, World,
DP and Govtspending. It can be written as the figure 6.
The coefficient of constant is -23.2489. The negative estimated result confirms that
as no REER and WorldGDP, the number of international tourists decrease 23 million a year.
The coefficient of REER is -0.0890739. The negative estimated result confirms that
holding other variables constant, the as REER index increase by 1 meaning the domestic
currency appreciated, the number of international tourists will decrease by 0.089 million per
year. The reason behind that since REER is seen as the price competitivenerelativeely to world
market, thus higher REER means appreciated of domestic currency or higher price of traveling,
so foreign visitors tend to travel less, thus there is a negative relationship between REER and
number of international arrivals.
The coefficient of WorldGDP is 0.0000000568. The positive estimated result
confirms that holding other variables constant, as WorldGDP increase by USD1 million, the
number of international tourists will increase by 0.0000000568 million per year. Since higher
world GDP means the global economy expansion resulting visitors are wealthier and consume
more leisure time, so they tend to travel more, thus there is a positive relationship between the
world GDP aa nd number of international arrivals.
%&&'()*+ , MCNOCPQQQ M RORQSRTNS
!
1221
#
/ RORRRRRRRUVQ
!
89&*:;<5
#
/ RORRVS"Q"!WXYZ[\]^_`^a#
Figure 6; show the empirical model of international arrivals
24
The coefficient of Govtspending is 0.006918. The positive estimated result
confirms that holding other variables constant, as Govtspending increase by USD1 million, the
number of international tourists will increase by 0.006918 million per year. Since the
government spending ohe tn tourism industry will create the incentive to foreign visitors to
travel in the country, thus there is a positive relationship between government spending on
tourism industry and the number of international arrivals.
4.5 Discussion and interpretation of the results on the international receipts
The model of international arrivals depend on three main factors; REER, World,
GDP and Govtspending. It can be written as the figure 7.
The coefficient of constant is -108.6421 The negative estimated result confirms that
as no REER and WorldGNI, the international tourists’ receipt will decrease USD108 million a
year.
The coefficient of REER is -180.8892. The negative estimated result confirms that
holding other variables constant, the as REER index increase by 1 unit meaning the domestic
currency appreciated, the international tourists' receipt will decrease by USD180 million per
year. Since REER seen as the price competitiveness relatively to the world market, thus higher
REER meathe ns higher price of traveling, so visitors tend to spend less, thus there is a negative
relationship between REER and international receipts.
The coefficient of WorldGNI is 0.0003142. The positive estimated result confirms
that holding other variables constant, as WorldGNI increase by USD1 million, the international
tourists’ receipt will increase USD0.0003142 million per year. Since higher world GNI means
1@D'@?> , M"RQOVPC" M "QROQQSC
!
1221
#
/ RORRRN"PC!89&*:;F6#
Figure 7; show the empirical model of international receipts
25
higher wealth accumulation and purchasing power, so travelers tend to spend more, thus there
is a positive relationship between world GNI and international receipts.
26
Chapter five; conclusion & policy implications
5.1 Conclusion
This paper aimed to analyze the factors that impact the tourism industry in term of
number of international arrivals and international receipts. In addition to this, the main
objective to see whether REER has the strong impact on international tourists' destination and
their spending or not by use panel as the estimation technique with 10 years annual data
between 2007 and 2018 from 8 countries in ASEAN; Vietnam, Indonesia, Thailand, Malaysia,
Lao People’s Democratic Republic, Philippines, Cambodia and Myanmar. On the first model,
international arrivals depend on three main variables; real effective exchange rate (REER),
world GDP and government spending ohe tn tourism industry. REER has a negative
relationship with international arrivals whereas world GDP and government spending has the
positive relationship with international arrivals. On the second model, international receipts
depend on two variables; real effective exchange rate (REER) and world GNI. REER has a
negative relationship with international receipts whereas world GNI ha as positive relationship
with international receipts. From both models, the study indicates that REER is the most
influence on tourism industry.
5.2 Policy implication
Based on the study results, the policies can be imposed through two channels which
are fiscal policies by governors and monetary policies the by central bank. On the fiscal
policies, governors can contrthe ol domestic economy and government spending ohe tn tourism
industry. First of all, government expenditure is one factor that impact the domestic GDP (GDP
= Consumption + Investment + Government expenditure + Export – Import) which result in
domestic currency appreciation. However, it an is only partial and small impact, so the strength
the of domestic economy is uncontrollable. Secondly, government spending ohe tn tourism
27
industry is micro-prudential which a is more effective approach. For example, the case of
Thailand, according to gen Z travel trend by wyse travel confederation, the top 5 travel trend
in 2019 are food experience, festivals, performing of arts, sports and guide tours account to
37%, 27%, 18%, 6% and 2%, respectively which is shown in the figure 8. Hence, the policy
suggestion is promoting extreme activities especially hiking, running or cycling since Thailand
already hathe reputation in food experience and festivals particularly Songkran festival on
April.
On monetary policy, there are 2 tools which are foreign reserve (FX reserve) and
interest differential. First and the foremost, use FX reserve as a tool to intervene the foreign
exchange currency market. For example, an increase in FX reserve will cause an increase the
demand for foreign currency resulting in foreign currency appreciation or domestic currency
depreciation which will have a positive impact on the tourism industry. Next, interest rate
represents the return of holding that currency, thus the widening gap between foreign rate and
domestic rate will cause the foreign currency appreciation or domestic currency depreciation
which will have a positive impact on tourism industry. Hence, in order to boost the tourism
37%
27%
18%
16%
12%
Food experience Festivals Performing arts Sports Guided tours
Top travel trend
Source: Wyse travel confederation
Figure 8; show the travel trend of generation Z
28
sector, the goal is to depreciate domestic currency by increasing FX reserve and decrease the
domestic rate, assumed constant foreign rate, or widen the gap.
5.3 Limitations of policy implications
Since the change of exchange rate depends on both internal and external factors
such as the strength the of global and domestic economy, reserve ratio, interest differential and
the investors' expectation, so each factor has only partial impact to the value of the currency.
Moreover, the change of exchange rate has impact not only on tourism sector but also other
sectors as well as export or import sectors. For instance, the depreciation the of domestic
currency has a positive impact the on-export sector but it has a negative impact the on-import
sector.
5.4 Further research
Tourism sector create both forward and backward linkages, such as drivers for
transportation or waiters and waitress in restaurants. Plus, the increasing trend of travelling
oversee because of infrastructure improvement or technological evolution, for example will
increase the significance of tourism industry to the country. Thus, the next topic in further
research would be ‘the impact of tourism industry to the country growth’
29
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