An empirical analysis of the causal relationship between tourism growth and the service industry in Thailand
Main Article Content
Abstract
The relationship between economic growth and tourism development has been a central issue for researchers validating the tourism-led growth hypothesis. Much of this literature revolves around using aggregate measures of economic growth and tourism arrivals to test the tourism-led growth hypothesis. However, this paper aims to investigate the causality between growth in tourism and the expansion of services in Thailand. It is posited that evaluating the causal relationship between tourism and the development of the service sector is relevant since the service sector is a primary driver for economic growth and recovery. For this purpose, a Granger causality test was performed based on Toda-Yamamoto and a vector error correction model (VECM) using aggregated and disaggregated service production index (SPI) and international tourist arrivals. The findings show that causality runs from the development of the production of services as a whole to tourism growth. In turn, the hotel industry benefits from expanding international tourist arrivals. Evidence was also found of bidirectional causality between tourism growth and indices reflecting the production of real estate, transportation, and telecommunications. The findings suggest that the contribution of tourism growth to the production of services may depend on how integrated tourism is with the service sector. Therefore, it is necessary that strategic partnerships between tourism authorities and private stakeholders (i.e., telecommunications firms) be created and strengthened.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
All rights reserved. Apart from citations for the purposes of research, private study, or criticism and review,no part of this publication may be reproduced, stored or transmitted in any other form without prior written permission by the publisher.
References
Akaraphanth, L. (2016, November 23). Airbnb’s impact on the Thai hospitality sector. Siam Commercial Bank Public Company Limited Economic Intelligence Center. https://www.scbeic.com/en/detail/product/2960
Amornvivat, S., Charoenphon, V., Pruedsaradch, P., Laosopapirom, T., Sophonkeereerat, P., & Akaraphanth, L. (2017). Three megatrends to change the face of the Thai tourism industry. Siam Commercial Bank Public Company Limited Economic Intelligence Center. Bangkok Thailand. https://www.scbeic.com/en/detail/file/product/3368/eol77tgr4j/EIC_Insight_tourism_2017_EN.pdf
Antonakakis, N., Dragouni, M., & Filis, G. (2015). How strong is the linkage between tourism and economic growth in Europe? Economic Modelling, 44, 142–155. https://doi.org/10.1016/j.econmod.2014.10.018
Aratuo, D. N., & Etienne, X. L. (2019). Industry level analysis of tourism-economic growth in the United States. Tourism Management, 70, 333–340. https://doi.org/10.1016/j.tourman.2018.09.004
Barman, H., & Nath, H. K. (2019). What determines international tourist arrivals in India? Asia Pacific Journal of Tourism Research, 24(2), 180–190. https://doi.org/10.1080/10941665.2018.1556712
Box, S., & Lopez-Gonzalez, J. (2017). Chapter 2: The future of technology: Opportunities for ASEAN in the digital economy. In S. Tay & J. P. Tijaja (Eds.), Global megatrends implications for the ASEAN economic community (pp. 37–60). ASEAN Secreteriat.
Çakmak, E., & Çenesiz, M. A. (2020). Measuring the size of the informal tourism economy in Thailand. International Journal of Tourism Research, 22(5), 637–652. https://doi.org/10.1002/jtr.2362
Çakmak, E., Lie, R., & Selwyn, T. (2019). Informal tourism entrepreneurs’ capital usage and conversion. Current Issues in Tourism, 22(18), 2250–2265. https://doi.org/10.1080/13683500.2018.1448763
De la Hoz-Correa, A., Muñoz-Leiva, F., & Bakucz, M. (2018). Past themes and future trends in medical tourism research: A co-word analysis. Tourism Management, 65, 200–211. https://doi.org/10.1016/j.tourman.2017.10.001
Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74(366), 427–431. https://doi.org/10.2307/2286348
Dickey, D. A., & Fuller, W. A. (1981). Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica, 49(4), 1057–1072. https://doi.org/10.2307/1912517
Dogru, T., & Bulut, U. (2018). Is tourism an engine for economic recovery? Theory and empirical evidence. Tourism Management, 67, 425–434. https://doi.org/10.1016/j.tourman.2017.06.014
Dogru, T., Sirakaya-Turk, E., & Crouch, G. I. (2017). Remodeling international tourism demand: Old theory and new evidence. Tourism Management, 60, 47–55. https://doi.org/10.1016/j.tourman.2016.11.010
Fareed, Z., Meo, M. S., Zulfiqar, B., Shahzad, F., & Wang, N. (2018). Nexus of tourism, terrorism, and economic growth in Thailand: New evidence from asymmetric ARDL cointegration approach. Asia Pacific Journal of Tourism Research, 23(12), 1129–1141. https://doi.org/10.1080/10941665.2018.1528289
Fereidouni, H. G., & Al-mulali, U. (2014). The interaction between tourism and FDI in real estate in OECD countries. Current Issues in Tourism, 17(2), 105–113. https://doi.org/10.1080/13683500.2012.733359
Gao, Y., Su, W., & Wang, K. (2019). Does high-speed rail boost tourism growth? New evidence from China. Tourism Management, 72, 220–231. https://doi.org/10.1016/j.tourman.2018.12.003
Gutiérrez, J., García-Palomares, J. C., Romanillos, G., & Salas-Olmedo, M. H. (2017). The eruption of Airbnb in tourist cities: Comparing spatial patterns of hotels and peer-to-peer accommodation in Barcelona. Tourism Management, 62, 278–291. https://doi.org/10.1016/j.tourman.2017.05.003
Johansen, S. (1988). Statistical analysis of cointegration vectors. Journal of Economic Dynamics and Control, 12(2–3), 231–254. https://doi.org/10.1016/0165-1889(88)90041-3
Johansen, S., & Juselius, K. (1990). Maximum likelihood estimation and inference on cointegration — With applications to the demand for money. Oxford Bulletin of Economics and Statistics, 52(2), 169–210. https://doi.org/10.1111/j.1468-0084.1990.mp52002003.x
Johnson, T. J., & Garman, A. N. (2010). Impact of medical travel on imports and exports of medical services. Health Policy, 98(2-3), 171–177. https://doi.org/10.1016/j.healthpol.2010.06.006
Koo, C., Ricci, F., Cobanoglu, C., & Okumus, F. (2017). Special issue on smart, connected hospitality and tourism. Information Systems Frontiers, 19, 699–703. https://doi.org/10.1007/s10796-017-9776-9
Li, K. X., Jin, M., & Shi, W. (2018). Tourism as an important impetus to promoting economic growth: A critical review. Tourism Management Perspectives, 26, 135–142. https://doi.org/10.1016/j.tmp.2017.10.002
Liu, Y., & Shi, J. (2019). How inter-city high-speed rail influences tourism arrivals: Evidence from social media check-in data. Current Issues in Tourism, 22(9), 1025–1042. https://doi.org/10.1080/13683500.2017.1349080
Lütkepohl, H. (2005). New introduction to multiple time series analysis. Springer Berlin, Heidelberg.
Park, J. Y., & Phillips, P. C. B. (1988). Statistical inference in regressions with integrated processes: Part 1. Econometric Theory, 4(3), 468–497. www.jstor.org/stable/3532336
Perles-Ribes, J. F., Ramón-Rodríguez, A. B., Rubia, A., & Moreno-Izquierdo, L. (2017). Is the tourism-led growth hypothesis valid after the global economic and financial crisis? The case of Spain 1957–2014. Tourism Management, 61, 96–109. https://doi.org/10.1016/j.tourman.2017.01.003
Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics, 16(3), 289–326. https://doi.org/10.1002/jae.616
Ridderstaat, J., Croes, R., & Nijkamp, P. (2014). Tourism and long-run economic growth in Aruba. International Journal of Tourism Research, 16(5), 472–487. https://doi.org/10.1002/jtr.1941
Sims, C. A., Stock, J. H., & Watson, M. W. (1990). Inference in linear time series models with some unit roots. Econometrica, 58(1), 113–144.
Song, H., Witt, S. F., & Li, G. (2003). Modelling and forecasting the demand for Thai tourism. Tourism Economics, 9(4), 363–387. https://doi.org/10.5367/000000003322663186
Sritama, S. (2018, February 17). Thai Airbnb hosts serve 1.2M. Bangkok Post. https://www.bangkokpost.com/business/1413510/thai-airbnb-hosts-serve-1-2m
Tang, C. F. (2011). Is the tourism-led growth hypothesis valid for Malaysia? A view from disaggregated tourism markets. International Journal of Tourism Research, 13(1), 97–101. https://doi.org/10.1002/jtr.807
Tang, C. F., & Abosedra, S. (2016). Tourism and growth in Lebanon: New evidence from bootstrap simulation and rolling causality approaches. Empirical Economics, 50, 679–696. https://doi.org/10.1007/s00181-015-0944-9
Tang, C. F., & Tan, E. C. (2013). How stable is the tourism-led growth hypothesis in Malaysia? Evidence from disaggregated tourism markets. Tourism Management, 37, 52–57. https://doi.org/10.1016/j.tourman.2012.12.014
Tang, C. F., & Tan, E. C. (2015a). Tourism-led growth hypothesis in Malaysia: Evidence based upon regime shift cointegration and time-varying granger causality techniques. Asia Pacific Journal of Tourism Research, 20(Suppl.1), 1430–1450. https://doi.org/10.1080/10941665.2014.998247
Tang, C. F., & Tan, E. C. (2015b). Does tourism effectively stimulate malaysia’s economic growth? Tourism Management, 46, 158-163. https://doi.org/10.1016/j.tourman.2014.06.020
Tang, C. F., & Tan, E. C. (2016). The determinants of inbound tourism demand in Malaysia: Another visit with non-stationary panel data approach. Anatolia-International: Journal of Tourism and Hospitality Research, 27(2), 189–200. https://doi.org/10.1080/13032917.2015.1084345
Tang, C.-H., & Jang, S. (2009). The tourism–economy causality in the United States: A sub-industry level examination. Tourism Management, 30(4), 553–558. https://doi.org/10.1016/j.tourman.2008.09.009
Thailand Incentive and Convention Association. (2015). Annual report 2015. Thailand Incentive and Convention Association. Bangkok, Thailand. https://www.tica.or.th/assets/images/annual_report/2015/ebook/mobile/index.html#p=1
Toda, H. Y., & Yamamoto, T. (1995). Statistical inference in vector autoregressions with possibly integrated processes. Journal of Econometrics, 66(1–2), 225–250. https://doi.org/10.1016/0304-4076(94)01616-8
Tourism Council of Thailand. (2019). Thailand tourism confidence index 2019/3. Newsletter. Thailand Authority of Tourism. Bangkok, Thailand. https://www.thailandtourismcouncil.org/product/thailand-tourism-confidence-index-2019-3-2/
Tugcu, C. T. (2014). Tourism and economic growth nexus revisited: A panel causality analysis for the case of the mediterranean region. Tourism Management, 42, 207–212. https://doi.org/10.1016/j.tourman.2013.12.007
Tussyadiah, I. P., & Pesonen, J. (2016). Impacts of peer-to-peer accommodation use on travel patterns. Journal of Travel Research, 55(8), 1022–1040. https://doi.org/10.1177/0047287515608505
Verougstraete, M., & Enders, I. (2014). Traffic demand risk: The case of Bangkok’s skytrain (BTS). Public-Private Partnerships. Case Study. United Nations ESCAP. https://www.unescap.org/sites/default/files/Case%201%20_Traffic%20Demand_%20Bangkok%20BTS.pdf