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International Journal of Mechanical Engineering and Technology (IJMET)
Volume 10, Issue 03, March 2019, pp. 718-725. Article ID: IJMET_10_03_075
Available online at http://www.iaeme.com/ijmet/issues.asp?JType=IJMET&VType=10&IType=3
ISSN Print: 0976-6340 and ISSN Online: 0976-6359
© IAEME Publication Scopus Indexed
TOURISM PROMOTION, TOURISM REVENUES
AND SECTORAL OUTPUTS IN THAILAND
Bundit Chaivichayachat
Department of Economics, Faculty of Economics, Kasetsart University
Bangkok, Thailand
ABSTRACT
Since 2010, tourism promoting policies have been implemented to drive economic
growth and also economic development in Thailand. Government allocated a
significant budget to promote tourism sector. As a result, tourism revenues have also
been increased significantly. The increasing in the number of visitors induced the
domestic final demand and the output in tourism related sectors. However, the
different group of visitors will response to the tourism promoting policy in the
different ways. Following the Johansen system cointegration, the results indicate that
the tourism revenue in each group of visitors was response to the difference set of
macroeconomic factors. The estimated normalized cointegration vectors confirm the
positive relationship between government budget for promoting tourism and tourism
revenue for all groups of visitors. For the sectoral analysis, tourism revenue,
naturally, induces final demand and initiates output only in a few sectors. According
to the results, the policies are (1) continuously promote tourism sectors in term of
government budget, (2) set up a specific policy for each group of visitors and (3)
income re-distribution to the sector which are not related to tourism sector.
Key words: Thai Tourism, Input-Output Table, Bridge Matrix, Tourism Revenue
Cite this Article: Bundit Chaivichayachat, Tourism Promotion, Tourism Revenues
and Sectoral Outputs in Thailand, International Journal of Mechanical Engineering
and Technology, 10(3), 2019, pp. 718-725.
http://www.iaeme.com/IJMET/issues.asp?JType=IJMET&VType=10&IType=3
1. INTRODUCTION
Since 2010, tourism promoting policies have been implemented to drive economic growth
and economic development in Thailand. Government allocated a significant budget to
promote tourism sector. (Figure 1) The target is to induce the number of tourists and
excursionists to spend and to stay more in Thailand. Not only the foreign visitors but also for
the Thai’s visitors. As a result, the number of 4 groups of visitors have been increased
significantly both in term of number and in term of revenue. The increasing in visitors
induced the domestic final demand for the output in tourism related sectors. However, we
cannot find the research which aimed to explore the results of the tourism promoting policy in
Tourism Promotion, Tourism Revenues and Sectoral Outputs in Thailand
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Thailand especially in the sectoral level. Then, this paper will be focused to explore the results
of policy promoting tourism on tourism revenue and sectoral output to quantify the results of
the policy. Finally, the results can be used to set up the effective policy to promote tourism
sector in Thailand.
Million Baht
Source: Ministry of Finance
Figure 1: Government Budget for Tourism Promoting Purposes
2. MODEL, METHODOLOGY AND DATA
To explore the results of policy promoting tourism on sectoral output, the various technique
will be invited to the study. First, system cointegration estimation will be employed for
estimate the long-run relationship between number of visitors and macroeconomic variables.
There are four groups of visitors: foreign tourists, foreign excursionists, Thai tourists, and
Thai excursionists. The tourism revenue for each group of visitors, in broad idea, explained by
the optimized behavior of the consumer and the previous empirical works, including Kara et
al. (2005), Alvarez (2007), Allen and Yop (2009), Onder et al. (2009), HanafioHarun and
Jamaluddin (2011), Antindag (2013), Betonio (2013), Bentum-Ennin (2014) and Deluna and
Jeon (2014). Moreover, the different set of macroeconomic factors will be defined to explain
the different behaviors of each group of visitors. The tourism revenue for 4 group of visitors
can be defined as following:
FTR = f (YM, NE, PT, RT, TB, CR, PS)
FER = f (YO, PO, PT, TB, PS)
TTR = f (YT, UT, PT, TB, CR, PS)
TER = f (YT, UT, PT, PP, PS, TB)
Where FTR, FER, TTR and TER are tourism revenue for foreign tourists, foreign
excursionists, Thai tourists, and Thai excursionists, respectively (million baht). YM is per
capita income of foreign tourists (US dollar), NE is nominal effective exchange rate of Thai
baht (2012 = 100), PT is inflation in Thailand, RT is size of retail trade sector (percent of
GDP) which is presented by GDP in retail trade to total GDP, TB is government budget
allocated for tourism promoting purpose (million baht), CR is crime rate (times), PS is
dummy variable for economic and political instability situation (equals 1 when economic and
political instability occurred), YO is per capita for Thailand’s neighbors, including Myanmar,
Laos, Cambodia, Vietnam and Malaysia (US dollar) because visitor from these countries can
travel as excursionists, YT is per capita in Thailand (US dollar) which represent the budget of
this visitors, UT is unemployment rate in Thailand (percent) which represent the economic
Bundit Chaivichayachat
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condition and PP is share of population between 20 to 44 year old to total Thai’s population
(percent) which is the age that high propensity to travel.
Each function will be estimated by the system cointegration approach in order to find the
cointegrating vector for the long-run relationship. For the second, the tourism input-output
table will be organized for the calculation of bridge matrix. This matrix will be used for
disaggregate the aggregate tourism revenue and calculate the final demand generated by
tourism sector. The revenues which were induced by foreign visitors will be set in special
export column and the revenues received from Thai’s visitor will be set in consumption
column. Then, the matrix can be constructed as following:
1 2 89
B b b b

Where
89
i i i
i1
b E / b
and
i
E
is expenditure on sector i.
After defining the bridge matrix, the inverse Leontief’s matrix will be arranged to
calculate the output as following
1
XT (I A) (B TR)
Where XT is vector of output level initiate by tourism revenue, A is technology matrix
and TR is tourism revenue.
The quarterly data during 2010-2016 collecting from ministry of tourism and sports
(MOTS), bank of Thailand (BOT) and IMF, will used to estimate the tourism revenue
functions. For sectoral analysis, tourism input-output table including 89 sectors for 2010 will
be prepared to calculate bridge matrix, sectoral final demand and sectoral output. The
conceptual idea can be displayed as Figure 2.
Figure 2: Conceptual Idea
3. RESULTS AND DICUSSION
To complete the objective, there are three steps for this paper. The first step is to estimate the
cointegrating equation for the revenue functions. Then, in the second step, the structure of
visitor expenditures will be employed to set up the bridge matrices and used to calculate
sectoral final demand and sectoral output. The last step is used for simulating the results of
increasing in government budget to promote tourism. First, KPSS test were applied for all
variables listed above. Table 1 shows that all variable in tourism revenue function are I (1).
Then, the cointegrated behavior can be found for each function. For foreign tourists, there will
be 8 cointegrating vectors with statistical significant. The fifth cointegrating vector was
selected to determine the level of FTR. Following the trace statistic, there are 6 cointegrating
vectors for FEN. For Thai visitors, there are 7 cointegrating vectors and 5 cointegrating
Tourism Promotion, Tourism Revenues and Sectoral Outputs in Thailand
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vectors were found for Thai tourists and Thai excursionist’s functions, respectively (Table 2).
Then, the best cointegrating vectors employed to explain the behavior of each group visitors.
Table 3 represents the selected cointegrating equations for each tourism revenue function. The
relationships between tourism revenues and macroeconomics factors can be summarized in
Table 4. The results suggest that the crime rate, economic and political instability and
inflation in Thailand generate the negative effect on tourism revenues. The unemployment
rate shows the negative impacts only on Thai visitors. For the positive relationship, the
estimated normalized cointegration vectors confirm the positive relationship between
government budget for promoting tourism and tourism revenue for all groups of visitors.
The bridge matrix will be defined following the structure of the visitor’s expenditures in
TSA. Then, tourism input-output table for 89 sectors is used for set up the bridge matrix.
Table 5 represents the bridge matrix for disaggregate tourism revenue into sectoral final
demand. There are 32 sectors can be called as tourism related sector. Hotel (51), Restaurant
(54) and Health Services (85) are the major tourism related sectors. Then, tourism revenue
during 2010-2014 can be disaggregated into sectoral final demand in Table 6. The highest
demanded sector by visitors is hotel and resort (51) followed by health care services (85), and
food and beverage serving activities (54). Tourism revenue initiates final demand increasing
rapidly. In 2014, the final demand which demanded by tourism sector equals 1,881,303.3
million baht. For the simulation, the results of the increasing in tourism promotion will be
introduced to explore the impacts on sectoral final demand and output in 2015 and 2016. Ten
percent increasing in government budget is assumed. The results in Table 7, show that the
increasing of government budget to promote tourism will be followed by the increasing in
final demand for 2.21 and 2.47 percent increasing from the baseline in 2015 and 2016.
Finally, the output will be increased for 2.38 percent and 2.56 percent respectively.
Table 1: KPSS Test of Stationarity
Table 2: Trace Statistics and Unnormalized Cointegrating Vectors
LM Stat. Results LM Stat. Results
FTR 0.8521 Non-stationary 0.3847 Stationary
FER 0.7348 Non-stationary 0.3413 Stationary
TTR 0.8050 Non-stationary 0.2521 Stationary
TER 0.8986 Non-stationary 0.3285 Stationary
CR 0.7909 Non-stationary 0.1844 Stationary
NE 0.7967 Non-stationary 0.0316 Stationary
PO 0.7390 Non-stationary 0.1109 Stationary
PT 0.8164 Non-stationary 0.1466 Stationary
RT 0.8334 Non-stationary 0.3036 Stationary
TB 0.7230 Non-stationary 0.3444 Stationary
UT 0.8205 Non-stationary 0.2092 Stationary
YM 0.8913 Non-stationary 0.0964 Stationary
YO 0.9034 Non-stationary 0.1843 Stationary
YT 0.8914 Non-stationary 0.0935 Stationary
Critial Value for (0.10) = 0.739, (0.05) = 0.463 and (0.01) = 0.374
First Difference
Level
Unrestricted Cointegration Rank Test (Trace)
Hypothesized Trace 0.05 Prob. Number of CE
No. of CE(s) Statistic Critical Value at the 0.05 level
FTR At most 7 * 10.54935 9.164546 0.0271 8
FER At most 5 * 14.65366 9.164546 0.0042 6
TTR At most 4 * 42.38785 35.19275 0.0071 5
TER At most 4 * 37.08124 35.19275 0.0309 5
Normalized Cointegrating Equation: FTR
FTR YM NE PT RT TB CR PS C
1.0 197.7 487124.2 -877044.4 97603257.3 650.2 202.8 -5669526.0 -112289835.2
1.0 670.9 -433936.4 -844048.2 8266294.5 455.5 -91.8 5927979.1 4255608.2
1.0 -4313.1 4218261.7 -5678135.5 -82186261.7 23560.7 -537.4 6897233.6 -116677289.7
1.0 188.3 -328697.7 -238761.7 -71077382.8 -12631.3 -65.5 -3553143.0 62167843.8
1.0 -818.2 -1338187.2 775328.0 -73847379.7 -2803.9 91.8 3294222.8 -116439162.2
1.0 458.8 -1003045.3 -637948.6 -44671954.7 -11738.7 111.1 3526927.0 83046553.5
1.0 -486.6 -60576.3 603574.4 419662.2 -242.4 184.0 4505471.4 11165543.9
Normalized Cointegrating Equation: FER
FER YO PO PT TB PS C
1.00 -531.97 -42440.25 50883.48 -989.39 385260.40 -4019119.19
1.00 505.68 8361.44 -91698.56 -624.15 36358.90 -3115063.56
1.00 398.15 -53142.61 -42063.67 -642.73 1705062.81 -4204423.65
1.00 469.77 -58940.76 56811.45 -600.15 287063.13 -3285102.29
1.00 714.16 12974.42 103243.72 -808.76 -364127.17 -4922415.04
1.00 -222.15 65627.68 -201839.79 1340.48 1882449.13 -1822187.20
Normalized Cointegrating Equation: TTR
TTR YT UT PT TB CR PS C
1.00 5303.15 29266.54 575865.35 -9111.42 56.69 -62673.62 -50351377.95
1.00 -12412.40 17669.77 1960122.48 -6531.78 331.78 1297260.47 -16119875.97
1.00 -5897.52 -762.81 227602.89 -7356.20 66.53 -3243909.50 928695.87
1.00 -8985.21 -13447.18 -725473.24 3137.32 -110.56 1692061.27 24492795.77
1.00 -23410.81 -36025.23 -938668.47 -1480.18 43.78 3053027.03 68784522.52
Normalized Cointegrating Equation: TER
TER YT UT PT TB PS C
1.00 9382.80 37134.39 908982.80 -4802.55 1316363.69 -61847445.86
1.00 60400.00 101905.88 -4465023.53 -33196.08 -382576.47 -209191215.69
1.00 -4660.00 26906.67 -1647688.89 5388.89 13390662.22 -24931977.78
1.00 -11662.45 -17408.70 -47545.85 -1256.92 -195124.11 32153952.57
1.00 -3932.08 4979.58 279017.50 -1187.08 -948484.17 -4336012.50
Bundit Chaivichayachat
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Table 3: Normalized Cointegrating Equation
Table 4: Relationship between Tourism Revenue and Macroeconomic Variables
Table 5: Bridge Matrix
Normalized Cointegrating Equation: FTR
FTR YM NE PT RT TB CR PS C
1.0 -818.2 -1338187.2 775328.0 -73847379.7 -2803.9 91.8 3294222.8 -116439162.2
Normalized Cointegrating Equation: FER
FER YO PO PT TB PS C
1.00 -531.97 -42440.25 50883.48 -989.39 385260.40 -4019119.19
Normalized Cointegrating Equation: TTR
TTR YT UT PT TB CR PS C
1.00 -12412.40 17669.77 1960122.48 -6531.78 331.78 1297260.47 -16119875.97
Normalized Cointegrating Equation: TER
TER YT UT PT TB PS C
1.00 9382.80 37134.39 908982.80 -4802.55 1316363.69 -61847445.86
FTR FER TTR TER
CR -91.8 -1.3
NE 1338187.2
PO 42440.3
PS -3294222.8 -385260.4 -29805.7 -1316363.7
PT -775328.0 -50883.5 -26270.9 -908982.8
RT 73847379.7
TB 2803.9 989.4 34.7 4802.6
UT -310.3 -37134.4
YM 818.2
YO 532.0
YT 196.1 9382.8
Sector Coefficient Sector Coefficient Sector Coefficient Sector Coefficient Sector Coefficient
10.0000 21 0.0000 41 0.0000 61 0.0152 81 0.0028
20.0000 22 0.0000 42 0.0000 62 0.0035 82 0.0062
30.0000 23 0.0000 43 0.0000 63 0.0106 83 0.0077
40.0000 24 0.0000 44 0.0000 64 0.0267 84 0.0013
50.0000 25 0.0000 45 0.0000 65 0.0120 85 0.1548
60.0000 26 0.0000 46 0.0000 66 0.0382 86 0.0444
70.0000 27 0.0000 47 0.0000 67 0.0000 87 0.0604
80.0000 28 0.0000 48 0.0000 68 0.0000 88 0.0000
90.0000 29 0.0000 49 0.0419 69 0.0000 89 0.0000
10 0.0000 30 0.0000 50 0.0000 70 0.0000
11 0.0000 31 0.0000 51 0.2508 71 0.0000
12 0.0000 32 0.0000 52 0.0044 72 0.0000
13 0.0000 33 0.0000 53 0.0009 73 0.0005
14 0.0000 34 0.0000 54 0.1511 74 0.0002
15 0.0000 35 0.0000 55 0.0501 75 0.0004
16 0.0000 36 0.0000 56 0.0517 76 0.0012
17 0.0000 37 0.0000 57 0.0147 77 0.0025
18 0.0000 38 0.0000 58 0.0133 78 0.0080
19 0.0000 39 0.0000 59 0.0127 79 0.0017
20 0.0000 40 0.0000 60 0.0075 80 0.0026