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
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Tourism Promotion, Tourism Revenues and Sectoral Outputs in Thailand
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
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Bundit Chaivichayachat
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:
B  b1 b2
b89 
89
Where bi  Ei /  bi and E i is expenditure on sector i.
i 1
After defining the bridge matrix, the inverse Leontief’s matrix will be arranged to
calculate the output as following
XT  (I  A)1(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
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Tourism Promotion, Tourism Revenues and Sectoral Outputs in Thailand
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
Level
First Difference
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
Table 2: Trace Statistics and Unnormalized Cointegrating Vectors
Unrestricted Cointegration Rank Test (Trace)
Hypothesized
Trace
0.05
Equation
No. of CE(s)
Statistic
Critical Value
FTR
At most 7 *
10.54935
9.164546
FER
At most 5 *
14.65366
9.164546
TTR
At most 4 *
42.38785
35.19275
TER
At most 4 *
37.08124
35.19275
Normalized Cointegrating Equation: FTR
FTR
YM
NE
1.0
197.7
487124.2
1.0
670.9
-433936.4
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721
1.0
-4313.1
4218261.7
1.0
188.3
-328697.7
1.0
-818.2
-1338187.2
1.0
458.8
-1003045.3
PT
-877044.4
-844048.2
-5678135.5
-238761.7
775328.0
-637948.6
Prob.
0.0271
0.0042
0.0071
0.0309
RT
97603257.3
8266294.5
-82186261.7
-71077382.8
-73847379.7
-44671954.7
Number of CE
at the 0.05 level
8
6
5
5
TB
CR
650.2
202.8
455.5
-91.8
editor@iaeme.com
23560.7
-537.4
-12631.3
-65.5
-2803.9
91.8
-11738.7
111.1
PS
-5669526.0 -1
5927979.1
6897233.6 -1
-3553143.0
3294222.8 -1
3526927.0
Bundit Chaivichayachat
Table 3: Normalized Cointegrating Equation
Normalized Cointegrating Equation: FTR
FTR
YM
NE
1.0
-818.2
-1338187.2
PT
775328.0
RT
-73847379.7
TB
-2803.9
Normalized Cointegrating Equation: FER
FER
YO
PO
1.00
-531.97
-42440.25
PT
50883.48
TB
-989.39
PS
385260.40
Normalized Cointegrating Equation: TTR
TTR
YT
UT
1.00
-12412.40
17669.77
PT
1960122.48
TB
-6531.78
CR
331.78
Normalized Cointegrating Equation: TER
TER
YT
UT
1.00
9382.80
37134.39
PT
908982.80
TB
-4802.55
CR
PS
C
3294222.8 -116439162.2
91.8
C
-4019119.19
PS
C
1297260.47 -16119875.97
PS
C
1316363.69 -61847445.86
Table 4: Relationship between Tourism Revenue and Macroeconomic Variables
CR
NE
PO
PS
PT
RT
TB
UT
YM
YO
YT
FTR
-91.8
1338187.2
-3294222.8
-775328.0
73847379.7
2803.9
FER
TTR
TER
-1.3
42440.3
-385260.4
-50883.5
989.4
-29805.7
-26270.9
-1316363.7
-908982.8
34.7
-310.3
4802.6
-37134.4
196.1
9382.8
818.2
532.0
Table 5: Bridge Matrix
Sector Coefficient
1
0.0000
2
0.0000
3
0.0000
4
0.0000
5
0.0000
6
0.0000
7
0.0000
8
0.0000
9
0.0000
10
0.0000
11
0.0000
12
0.0000
13
0.0000
14
0.0000
15
0.0000
16
0.0000
17
0.0000
18
0.0000
19
0.0000
20
0.0000
Sector Coefficient
21
0.0000
22
0.0000
23
0.0000
24
0.0000
25
0.0000
26
0.0000
27
0.0000
28
0.0000
29
0.0000
30
0.0000
31
0.0000
32
0.0000
33
0.0000
34
0.0000
35
0.0000
36
0.0000
37
0.0000
38
0.0000
39
0.0000
40
0.0000
Sector Coefficient
41
0.0000
42
0.0000
43
0.0000
44
0.0000
45
0.0000
46
0.0000
47
0.0000
48
0.0000
49
0.0419
50
0.0000
51
0.2508
52
0.0044
53
0.0009
54
0.1511
55
0.0501
56
0.0517
57
0.0147
58
0.0133
59
0.0127
60
0.0075
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722
Sector Coefficient
61
0.0152
62
0.0035
63
0.0106
64
0.0267
65
0.0120
66
0.0382
67
0.0000
68
0.0000
69
0.0000
70
0.0000
71
0.0000
72
0.0000
73
0.0005
74
0.0002
75
0.0004
76
0.0012
77
0.0025
78
0.0080
79
0.0017
80
0.0026
Sector Coefficient
81
0.0028
82
0.0062
83
0.0077
84
0.0013
85
0.1548
86
0.0444
87
0.0604
88
0.0000
89
0.0000
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Tourism Promotion, Tourism Revenues and Sectoral Outputs in Thailand
Table 6: Sectoral Final Demand based on the Tourism Revenues
Million Baht
Sector
049
051
052
053
054
055
056
057
058
059
060
061
062
063
064
065
2010
2011
41,569.4
248,849.6
4,352.3
874.5
149,951.2
49,656.4
51,328.6
14,542.3
13,220.1
12,644.4
7,412.2
15,103.7
3,471.6
10,552.6
26,455.0
11,945.6
52,952.7
316,994.1
5,544.1
1,114.0
191,013.5
63,254.2
65,384.3
18,524.6
16,840.2
16,106.9
9,441.9
19,239.7
4,422.3
13,442.3
33,699.4
15,216.7
2012
65,550.3
392,407.7
6,863.0
1,379.0
236,456.1
78,302.5
80,939.3
22,931.6
20,846.5
19,938.8
11,688.1
23,816.8
5,474.3
16,640.3
41,716.6
18,836.8
2013
2014
78,348.5
469,022.6
8,203.0
1,648.3
282,622.5
93,590.5
96,742.2
27,408.9
24,916.7
23,831.7
13,970.2
28,466.9
6,543.2
19,889.1
49,861.5
22,514.6
Sector
78,827.9
471,892.5
8,253.2
1,658.3
284,351.9
94,163.2
97,334.1
27,576.6
25,069.1
23,977.5
14,055.6
28,641.1
6,583.2
20,010.8
50,166.6
22,652.4
066
073
074
075
076
077
078
079
080
081
082
083
084
085
086
087
Total
2010
2011
2012
2013
2014
37,867.4
48,236.9
59,712.6
71,371.1
71,807.8
479.6
611.0
756.3
904.0
909.5
171.4
218.4
270.3
323.1
325.1
372.8
474.9
587.9
702.7
707.0
1,194.3
1,521.4
1,883.3
2,251.0
2,264.8
2,511.8
3,199.7
3,960.9
4,734.2
4,763.2
7,940.1
10,114.3
12,520.6
14,965.1
15,056.7
1,694.2
2,158.1
2,671.5
3,193.1
3,212.6
2,541.2
3,237.1
4,007.3
4,789.6
4,818.9
2,755.1
3,509.5
4,344.4
5,192.6
5,224.4
6,131.6
7,810.6
9,668.8
11,556.6
11,627.3
7,623.7
9,711.4
12,021.7
14,368.9
14,456.8
1,300.3
1,656.4
2,050.4
2,450.8
2,465.8
153,587.2 195,645.2 242,189.7 289,475.6 291,246.8
44,082.8
56,154.4
69,513.6
83,085.7
83,594.1
59,910.8
76,316.6
94,472.5 112,917.6 113,608.6
992,093.6 1,263,766.6 1,564,419.8 1,869,861.8 1,881,303.3
Table 7: Impacts of Increasing in Government Budget on Final Demand and Output
Final Demand
Output
Baseline
(Million Baht)
2015Q1
2015Q2
2015Q3
2015Q4
2016Q1
2016Q2
2016Q3
2016Q4
2015
2016
5,190
4,920
4,972
5,375
5,661
5,192
5,202
5,567
20,457
21,621
10 percent increasing in
government budget
Million Baht
5,316
5,042
5,083
5,467
5,823
5,342
5,328
5,661
20,909
22,155
Baseline
(Million Baht)
%
2.42
2.50
2.23
1.72
2.86
2.90
2.42
1.70
2.21
2.47
2015Q1
2015Q2
2015Q3
2015Q4
2016Q1
2016Q2
2016Q3
2016Q4
2015
2016
55,033
44,749
47,794
50,748
62,099
49,822
52,036
53,640
198,324
217,597
10 percent increasing in
government budget
Million Baht
56,376
46,005
48,955
51,708
63,831
51,364
53,357
54,625
203,044
223,177
%
2.44
2.81
2.43
1.89
2.79
3.10
2.54
1.84
2.38
2.56
4. CONCLUSION
This paper explores the impacts of tourism promoting policy on tourism revenue, sectoral
final demand and sectoral output. The results were set up by the estimation of cointegrating
equation, bridge matrix and tourism input-output table. For the estimation of cointegrating
equations, the behaviors of tourism are difference. 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. For the sectoral analysis, tourism revenue, naturally, induces final demand and
initiates output only in a few sectors. Thus, the tourism promoting campaign do not contribute
economic expansion equally among 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, (3) income re-distribution to the sector which are not related
to tourism sector, and (4) the policy to protect the negative impacts of tourism should be
implemented to sustain the tourism sector. Finally, for the future work, it is necessary to
evaluate the tourism promoting policies in the sub-region because the behaviors of visitors are
difference which will generate the various of results and needed a specific promoting policy.
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editor@iaeme.com
Bundit Chaivichayachat
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APPENDIX: IO CODE
001 Paddy, 002 Maize, 003 Cassava, 004 Beans and Nuts, 005 Vegetables and Fruits, 006
Sugarcane, 007 Rubber (Latex), 008 Other Crops, 009 Livestock, 010 Forestry, 011 Fishery,
012 Crude Oil and Coal, 013 Metal Ore, 014 Non-Metal Ore, 015 Slaughtering, 016
Processing and Preserving of Foods, 017 Rice and Other Grain Milling, 018 Sugar Refineries,
019 Other Foods, 020 Animal Food, 021 Beverages, 022 Tobacco Processing and Products,
023 Spinning, Weaving and Bleaching, 024 Textile Products, 025 Paper and Paper Products,
026 Printing and Publishing, 027 Basic Chemical Products, 028 Fertilizer and Pesticides, 029
Other Chemical Products, 030 Petroleum Refineries, 031 Rubber Products, 032 Plastic Wares,
033 Cement and Concrete Products, 034 Other Non-metallic Products, 035 Iron and Steel,
036 Non-ferrous Metal, 037 Fabricated Metal Products, 038 Industrial Machinery, 039
Electrical Machinery and Apparatus, 040 Motor Vehicles and Repairing, 041 Other
Transportation Equipment, 042 Leather Products, 043 Saw Mills and Wood Products, 044
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Tourism Promotion, Tourism Revenues and Sectoral Outputs in Thailand
Other Manufacturing Products, 045 Electricity and Gas, 046 Water Works and Supply, 047
Building Construction, 048 Public Works and Other Construction, 049 Retail trade of country
specific tourism characteristic goods, 050 Wholesale and Retail Trade, 051 Hotel and resort,
052 Guesthouse, 053 Home Stay / Community Based Tourism / Rural Tourism, 054 Food and
beverage serving activities, 055 Other food service, 056 Drinking Places, 057 Other beverage
service, 058 Interurban & Rural Bus Transportation, 059 Passenger bus and other local
transportation, 060 Nonscheduled Transit Passenger Transportation, 061 Road passenger
transport, 062 Railways passenger transports, 063 Water passenger transports, 064 Air
passenger transports, 065 Transport equipment rental, 066 Travel agencies and other
reservation services activities, 067 Other transports, 068 Communication, 069 Banking and
Insurance, 070 Real Estate, 071 Business Services, 072 Public Services, 073 Performing arts
and nature reserve services, 074 Museum and preservation services, 075 Botanical and
zoological garden services, 076 Independent Artists, 077 Spa and massage, 078 Golf course,
079 Adventure Travel and Extreme Sports, 080 Amusement Park and Theme Park, 081
Recreational activities and entertainment, 082 Participant sport, 083 Conference Centers and
exhibition, 084 Other vehicle rental, 085 Health Care services, 086 Service Training /Service
Training of culture/ Recovery Language School, 087 personal service for tourism
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