Tourism promotion, tourism revenues and sectoral outputs in Thailand

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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.

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Nội dung Text: Tourism promotion, tourism revenues and sectoral outputs in Thailand

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 http://www.iaeme.com/IJMET/index.asp 718 editor@iaeme.com
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 http://www.iaeme.com/IJMET/index.asp 719 editor@iaeme.com
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 http://www.iaeme.com/IJMET/index.asp 720 editor@iaeme.com
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 Prob. Number of CE Equation 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 1.0 197.7 487124.2 -877044.4 97603257.3 650.2 202.8 -5669526.0 -1 1.0 670.9 -433936.4 -844048.2 8266294.5 455.5 -91.8 5927979.1 http://www.iaeme.com/IJMET/index.asp 1.0 -4313.1 4218261.7721 -5678135.5 -82186261.7 editor@iaeme.com 23560.7 -537.4 6897233.6 -1 1.0 188.3 -328697.7 -238761.7 -71077382.8 -12631.3 -65.5 -3553143.0 1.0 -818.2 -1338187.2 775328.0 -73847379.7 -2803.9 91.8 3294222.8 -1 1.0 458.8 -1003045.3 -637948.6 -44671954.7 -11738.7 111.1 3526927.0
Bundit Chaivichayachat Table 3: Normalized Cointegrating Equation 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 Table 4: Relationship between Tourism Revenue and Macroeconomic Variables 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 Table 5: Bridge Matrix Sector Coefficient Sector Coefficient Sector Coefficient Sector Coefficient Sector Coefficient 1 0.0000 21 0.0000 41 0.0000 61 0.0152 81 0.0028 2 0.0000 22 0.0000 42 0.0000 62 0.0035 82 0.0062 3 0.0000 23 0.0000 43 0.0000 63 0.0106 83 0.0077 4 0.0000 24 0.0000 44 0.0000 64 0.0267 84 0.0013 5 0.0000 25 0.0000 45 0.0000 65 0.0120 85 0.1548 6 0.0000 26 0.0000 46 0.0000 66 0.0382 86 0.0444 7 0.0000 27 0.0000 47 0.0000 67 0.0000 87 0.0604 8 0.0000 28 0.0000 48 0.0000 68 0.0000 88 0.0000 9 0.0000 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 http://www.iaeme.com/IJMET/index.asp 722 editor@iaeme.com
Tourism Promotion, Tourism Revenues and Sectoral Outputs in Thailand Table 6: Sectoral Final Demand based on the Tourism Revenues Million Baht Sector 2010 2011 2012 2013 2014 Sector 2010 2011 2012 2013 2014 049 41,569.4 52,952.7 65,550.3 78,348.5 78,827.9 066 37,867.4 48,236.9 59,712.6 71,371.1 71,807.8 051 248,849.6 316,994.1 392,407.7 469,022.6 471,892.5 073 479.6 611.0 756.3 904.0 909.5 052 4,352.3 5,544.1 6,863.0 8,203.0 8,253.2 074 171.4 218.4 270.3 323.1 325.1 053 874.5 1,114.0 1,379.0 1,648.3 1,658.3 075 372.8 474.9 587.9 702.7 707.0 054 149,951.2 191,013.5 236,456.1 282,622.5 284,351.9 076 1,194.3 1,521.4 1,883.3 2,251.0 2,264.8 055 49,656.4 63,254.2 78,302.5 93,590.5 94,163.2 077 2,511.8 3,199.7 3,960.9 4,734.2 4,763.2 056 51,328.6 65,384.3 80,939.3 96,742.2 97,334.1 078 7,940.1 10,114.3 12,520.6 14,965.1 15,056.7 057 14,542.3 18,524.6 22,931.6 27,408.9 27,576.6 079 1,694.2 2,158.1 2,671.5 3,193.1 3,212.6 058 13,220.1 16,840.2 20,846.5 24,916.7 25,069.1 080 2,541.2 3,237.1 4,007.3 4,789.6 4,818.9 059 12,644.4 16,106.9 19,938.8 23,831.7 23,977.5 081 2,755.1 3,509.5 4,344.4 5,192.6 5,224.4 060 7,412.2 9,441.9 11,688.1 13,970.2 14,055.6 082 6,131.6 7,810.6 9,668.8 11,556.6 11,627.3 061 15,103.7 19,239.7 23,816.8 28,466.9 28,641.1 083 7,623.7 9,711.4 12,021.7 14,368.9 14,456.8 062 3,471.6 4,422.3 5,474.3 6,543.2 6,583.2 084 1,300.3 1,656.4 2,050.4 2,450.8 2,465.8 063 10,552.6 13,442.3 16,640.3 19,889.1 20,010.8 085 153,587.2 195,645.2 242,189.7 289,475.6 291,246.8 064 26,455.0 33,699.4 41,716.6 49,861.5 50,166.6 086 44,082.8 56,154.4 69,513.6 83,085.7 83,594.1 065 11,945.6 15,216.7 18,836.8 22,514.6 22,652.4 087 59,910.8 76,316.6 94,472.5 112,917.6 113,608.6 Total 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 10 percent increasing in 10 percent increasing in Baseline Baseline government budget government budget (Million Baht) (Million Baht) Million Baht % Million Baht % 2015Q1 5,190 5,316 2.42 2015Q1 55,033 56,376 2.44 2015Q2 4,920 5,042 2.50 2015Q2 44,749 46,005 2.81 2015Q3 4,972 5,083 2.23 2015Q3 47,794 48,955 2.43 2015Q4 5,375 5,467 1.72 2015Q4 50,748 51,708 1.89 2016Q1 5,661 5,823 2.86 2016Q1 62,099 63,831 2.79 2016Q2 5,192 5,342 2.90 2016Q2 49,822 51,364 3.10 2016Q3 5,202 5,328 2.42 2016Q3 52,036 53,357 2.54 2016Q4 5,567 5,661 1.70 2016Q4 53,640 54,625 1.84 2015 20,457 20,909 2.21 2015 198,324 203,044 2.38 2016 21,621 22,155 2.47 2016 217,597 223,177 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. http://www.iaeme.com/IJMET/index.asp 723 editor@iaeme.com
Bundit Chaivichayachat REFERENCES [1] Allen, David, Yap, Ghialy, and Shareef Riaz. 2009. Modeling interstate tourism demand in Australia: A cointegration approach. Mathematics and Computers in Simulation 79(9): 2733-2740, http://dx.doi.org/ 10.1016/j.matcom.2008.10.006 [2] Azevedo. 2011. “Forecasting Tourism Demand with Artificial Neural Networks,” Book of Proceedings Vol.II, International Conference on Tourism & Management Studies. [3] Bentum-Ennin. 2014. “Modelling international tourism demand in Ghana,” Global Business and Economics Research Journal. Vol. 3 (12): 1 – 22. [4] Betonio, M. 2013. Tourism in Asia: Determinants of Tourist arrivals in Asia Countries. De La Salle University. [5] Bloom, Jonathan Z. 2004. “Market Segmentation, A Neutral network Application,” Annals of Tourism Research, 32(1): 93-111. [6] Chaivichayachat, Bundit, 2013. “Forecasting Demand for Labor in Services based on MACRO-IO Model, presented in “2013 The International Symposium on Society, Technology, Education and Politics Conference, Singapore. [7] Chaivichayachat, Bundit, 2014. “The impacts of expiration of EU’s GSP in food industry on aggregate output in Thailand,” presented in “International Research Conference On Business, Economics and Social Sciences, IRC- 2014”, 25-26 September, Singapore. [8] Chaivichayachat, Bundit, 2014. “Impacts of Thai Baht on Tourism, Sectorial Final Demand and Sectoral Output in Thailand,” presented in “International Conference on Advances in Economics, Management and Social Study (EMS14),” 2-3 August, Kuala Lumpur, Malaysia. [9] Deluna, R. Jr. and Jeon, N. 2014. Determinants of International Tourism Demand for the Philippines: An Augmented Gravity Model Approach. MPRA Paper No. 55294 [10] Hanafiah, M.H., M.F. Harun and Jamaluddin M.R., 2010. Bilateral Trade and Tourism Demand. World Applied Sciences Journal, 10 (Special Issue of Tourism & Hospitality), 110-114. [11] Kara, A., Lonial, S., Tarim, M., Zaim, S. 2005. A paradox of service quality in Turkey: the seemingly contradictory relative importance of tangible and intangible determinants of service quality. European Business Review, 17(1), 5-20. [12] Önder, A., Candemir, A., & Kumral, N. 2009. An empirical analysis of the determinants of international tourism demand: The case of Izmir. European Planning Studies, 17(10), 1525. 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 http://www.iaeme.com/IJMET/index.asp 724 editor@iaeme.com
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 http://www.iaeme.com/IJMET/index.asp 725 editor@iaeme.com