Investigating the Indicators of Attracting Foreign Tourists to Iran Using Panel Analysis and the Gravity Model

Heidari, Ali Asghar; Pirahan Siah, Sobhan; Aghandeh, Mohammad Ali

doi:10.22054/joer.2025.83414.1244

Document Type : Research Paper

Authors

Ph.D. Student of Tourism, Allameh Tabataba'i University, Tehran, Iran

https://doi.org/10.22054/joer.2025.83414.1244

Abstract

International tourism plays a significant role in the economic development of countries, particularly in newly industrialized economies. The present study aimed to identify and assess the determinants influencing the demand for foreign tourism to Iran during the period 1990–2021. A quantitative and explanatory research design was employed using a gravity model combined with a panel data approach. The statistical population included countries with considerable tourist flows to Iran. Variables such as per capita income of the origin country, geographical distance, relative exchange rate, relative prices, household debt, and money supply were analyzed. The data were subjected to panel unit root tests, fixed-effects modeling, and Generalized Least Squares (GLS) estimation to handle heteroscedasticity and autocorrelation. The findings revealed that per capita income, relative exchange rate, and money supply had a positive effect on tourist arrivals, while geographical distance, relative prices, and household debt had negative impacts. It is concluded that a policy focus on regional markets, financial facilitation, competitive pricing, and enhanced infrastructure and promotion strategies can boost Iran’s tourism sector.
Introduction
Tourism has emerged as a dynamic driver of economic growth, income generation, and cultural exchange. In the global context, international tourism not only contributes significantly to GDP and employment but also plays a central role in enhancing cross-cultural communication and national image. In recent decades, the tourism industry has experienced a shift of flows towards newly industrialized countries, thereby necessitating precise modeling and forecasting of tourism demand for effective planning. Iran, with its cultural heritage, historical attractions, and diverse landscapes, holds strong potential for attracting international tourists. However, identifying and understanding the influencing factors behind tourism demand is crucial for capitalizing on this potential.
Research Questions

What are the most significant determinants influencing the demand for international tourism to Iran?
How do economic and geographical variables such as income, distance, exchange rate, and prices affect inbound tourist arrivals?
How can the gravity model and panel data analysis help in evaluating and forecasting tourism flows?

Methods and Materials
This research adopted an applied and explanatory approach using panel data methodology covering the years 1990 to 2021. The gravity model was employed as the core theoretical framework, which posits that tourist flows between countries are directly related to their economic size and inversely related to the geographical distance. The study analyzed variables such as per capita income of the origin country, geographical distance (in kilometers between capitals), relative exchange rate, relative prices, household debt, and money supply. Data were obtained from reliable international databases, and statistical analysis included unit root testing, F-Limer and Hausman tests for model selection, and final estimation using Feasible Generalized Least Squares (FGLS) to control for autocorrelation and heteroscedasticity.
Results and Discussion
The empirical results indicated that all selected variables were statistically significant. The analysis confirmed:

Positive effects:
Per capita income of the origin country increased tourism demand.
A favorable relative exchange rate enhanced tourists’ purchasing power.
An increase in the money supply supported outbound travel.
Negative effects:
Greater geographical distance reduced tourist arrivals.
Higher relative prices in Iran discouraged travel.
Higher household debt in origin countries limited travel capacity.

These findings are consistent with theoretical expectations from the gravity model and consumer behavior theories. The high explanatory power of the model underscores the reliability of the results for policy formulation.
Conclusion
The study emphasizes the crucial role of economic and geographical factors in shaping international tourist flows to Iran. It is recommended that policymakers:

Focus on attracting tourists from neighboring and geographically closer countries.
Facilitate travel conditions and financial processes for tourists from high-income countries.
Improve tourism infrastructure and promote competitive pricing.
Enhance Iran's international image through targeted advertising campaigns.

Implementing these strategies may strengthen Iran’s position in the global tourism market and support the goal of sustainable tourism development.

Keywords

References

امیرآبادی، محمد. (1401). بررسی چارچوب مدل جاذبه گردشگری در ایران با رویکرد پانل دیتا، فصلنامه علمی پژوهش‌های گردشگری و توسعه پایدار، 1(5)، 66-55.

ضیاء‌آبادی، مریم.، جلائی اسفندآبادی، سید عبدالمجید.، جلال‌آبادی، لیلا. (1403). بررسی عوامل مؤثر بر تقاضای گردشگری ایران با تأکید بر شکاف نرخ ارز (رهیافت مارکوف سوئیچینگ-تصحیح خطای برداری). مجلس و راهبرد، 31(120)، 365-335.

فرزین، محمدرضا.، پور جهان، ساره.، قنبری، رقیه.، مهاجر، بشری. (1401). بررسی عوامل تعیین‌کننده تقاضای گردشگری ورودی به ایران: تجزیه‌وتحلیل داده‌های پانل. گردشگری و اوقات فراغت، 7(14)، 100-85.

کمالی، مهدی.، آسایش، حمید. (1398). تعیین عوامل مؤثر بر تقاضای گردشگری سلامت خارجی در مناطق مختلف ایران. فصلنامه جغرافیا (برنامه‌ریزی منطقه‌ای)، 9(37)، 678-659.

محمدی، حسین.، محمودی، مهدی. (1396). بررسی اثرات متقابل نرخ بهره با متغیرهای منتخب اقتصاد کلان در کشورهای اسلامی و غیر اسلامی. تحقیقات مدل‌سازی اقتصادی، 8(28)، 128-103.

مرکز آمار ایران. (1401). ریاست جمهوری، سازمان برنامه‌وبودجه کشور، سالنامه آماری کشور، 1401.

مروت، حبیب.، سالم، علی‌اصغر.، خادم نعمت‌الهی، محبوبه. (1397). شناسایی عوامل مؤثر بر تقاضای گردشگری خارجی. پژوهشنامه اقتصادی، 18(69)، 275-306.

مودودی ارخودی، مهدی.، فردوسی، سجاد. (1399). سنجش الگوی رفتاری عرضه و تقاضای گردشگری مبتنی بر تحولات نظام سرمایه‌داری مطالعه موردی: شهر کرج و روستاهای پیرامونی. مطالعات شهری، 9(36)، 100-85.

Amirabadi, Mohammad. (2022). An examination of the framework of the tourism gravity model in Iran using a panel‐data approach. Tourism Research and Sustainable Development, 1(5), 55–66. [in Persian]

Anderson, J. E. (2011). The gravity model. Annu. Rev. Econ., 3(1), 133-160.

Athanasopoulos, G., & Hyndman, R. J. (2008). Modelling and forecasting Australian domestic tourism. Tourism Management, 29(1), 19–31.

Bayram, G. E., Abbasi, A. R., Raza, A., & Soomro, S. A. (2024). The U-shaped relationship between corruption and international tourism demand: A gravity model approach. Turyzm/Tourism, 34(1), 97–108.

Botti, L., Peypoch, N., & Solonandrasana, B. (2008). Time and tourism attraction. Tourism Management, 29(3), 594-596.

Chang, C.-L., & Mcaleer, M. (2012). Aggregation, heterogeneous Autoregression, and volatility of daily international tourist arrivals and exchange rates. The Japanese Economic Review, 3(3), 397–419.

Dergiades, T., Mavragani, E., & Pan, B. (2018). Google Trends and tourists' arrivals: Emerging biases and proposed corrections. Tourism Management, 66, 108–120.

Elgin, C., & Elveren, A. Y. (2024). Unpacking the economic impact of tourism: A multidimensional approach to sustainable development. Journal of Cleaner Production, 478, 143947.

Farzin, Mohammadreza; Pourjahan, Sareh; Ghanbari, Roghieh; Mohajer, Boshra. (2022). Investigating the Determinants of Iran's Inbound Tourism Demand: Panel Data Analysis. Tourism and Leisure time journal, 7(14), 85–100. [in Persian]

Garı´n-Mun˜oz, T. (2006). Inbound international tourism to Canary Islands: a dynamic panel data model. Tourism Management, 27, 281–291.

Goh, C., & Law, R. (2002). Modeling and forecasting tourism demand for arrivals with stochastic nonstationary seasonality and intervention. Tourism Management, 23 (5), 499–510.

Goswami, G. G., Chowdhury, M. H., Rahman, M., & Atique, M. A. (2024). Australian Tourist Flow: A Gravity Model Approach. Sustainability, 16(13), 5740. DOI:10.20944/preprints202403.1791.v1

Jiao, E. X., & Chen, J. L. (2019). Tourism forecasting: A review of methodological developments over the last decade. Tourism Economics, 25(3), 469–492.

Jong, , Hong, , & Arip, (2020). Modelling Tourism Demand: An Augmented Gravity Model. Jurnal Ekonomi Malaysia, 54(2), 105–112.

Kamali, Mehdi; Asayesh, Hamid. (2019). Determining Factors Influencing Foreign Health Tourism Demand in Different Regions of Iran. Geography & regional planning, 9(37), 659–678. [in Persian]

Kaplan, F., & Aktas, A. R. (2016). The Turkey tourism demand: A gravity model. The Empirical Economics Letters, 15(3), 265–272.

Khadaroo, J., & Seetanah, B. (2008). The role of transport infrastructure in international tourism development: A gravity model approach. Tourism Management, 29, 831–840.

Koçak, E., Ulucak, R., & Ulucak, Z. Ş. (2020). The impact of tourism developments on CO2 emissions: An advanced panel data estimation. Tourism Management Perspectives, 33, 100611.

Kozak, M., Kim, S.(S.), & Chon, K. (2017). Competitiveness of overseas pleasure destinations: A comparison study based on choice sets. International Journal of Tourism Research, 19(5), 569–583.

Krasniqi, S., Dreshaj, K., & Shala Dreshaj, F. (2023). Determinants of Tourism Demand in Selected Countries of Meta: Empirical Panel Analysis. DETUROPE - The Central European Journal of Regional Development and Tourism, 15(1), 23-46.

Kucukaltan, D., & Terzioglu, M. K. (2013). Economic impact of tourism demand: Evidence from Turkey. Anatolia, 24(3), 484–488.

Kulendran, N. (1996). Modelling quarterly tourist flows to Australia using cointegration analysis. Tourism Economics, 2(3), 203–222.

Li, H., Chen, J. L., Li, G., & Goh, C. (2016). Tourism and regional income inequality: Evidence from China. Annals of Tourism Research, 58, 81–99.

Li, H., Hu, M., Li, Gang. (2020). Forecasting tourism demand with multisource big data. Annals of Tourism Research, 83, https://doi.org/10.1016/j.annals.2020.102912.

Li, H., Song, H., & Li, L. (2017). A dynamic panel data analysis of climate and tourism demand: Additional evidence. Journal of Travel Research, 56(2), 158–171.

Lim, C. (2006). A survey of tourism demand modelling practice: Issues and implications. In L. Dwyer, & P. Forsyth (Eds.). International handbook on the economics of tourism (pp. 45–72). Cheltenham: Edward Elgar Publishing.

Massidda, C., Etzo, I., & Piras, R. (2015). Migration and inbound tourism: An Italian perspective. Current Issues in Tourism, 18(12), 1152–1171.

Matijová, M., Šenková, A., Vargová, T. D., & Matušíková, D. (2023). Tourism indicators and their differentiated impact on sustainable tourism development. Journal of Tourism and Services, 14(27), 89-117.

Modudi Arkhudi, Mahdi; Ferdowsi, Sajad. (2020). Assessing the behavioral pattern of tourism supply and demand Based on the Changes of the Capitalist System (Case: Karaj City and Surrounding Villages). Motaleate Shahri, 9(36), 85–100. [in Persian]

Mohammadi, Hossein; Mahmoudi, Mehdi. (2017). Reviews of mutual interest rate effects to macroeconomic variables in Islamic and un-Islamic countries. Journal of Economic Modeling Research, 8(28), 103–128. [in Persian]

Morley, C., Rosselló, J., Santana-Gallego, M. (2014). Gravity models for tourism demand: theory and use, Ann. Tour. Res. 48, 1–10, http://dx.doi. org/10.1016/j.annals.2014.05.008.

Morovat, Habib; Salem, Ali-Asghar; Khadem, Mahboobeh. (2018). Identifying Effective Factors on Foreign Tourism Demand. Economic Research Journal, 18(69), 275–306. [in Persian]

Neumayer, E. (2010). Visa restrictions and bilateral travel. The Professional Geographer, 62(2), 171–181.

Oh, C.-O., & Ditton, R. B. (2006). An evaluation of price measures in tourism demand models. Tourism Analysis, 10, 257–268.

Okoroafor, I. B., & Nwabueze, J. C. (2013). Seasonal Analysis of Gateway Prices of Yam in Abia State, Nigeria: A Least Square Dummy Variable Regression Model. International Organization for Scientific Research Journal of Mathematics, 8(5), 24-28.

Park JY, Jang S (2014) An extended gravity model: applying destination competitiveness. Journal of Travel & Tourism Marketing, 31(7), 799–816.

Paulino, I., Lozano, S., Prats, L. (2021). Identifying tourism destinations from tourists’ travel patterns. Journal of Destination Marketing & Management, 19, https://doi.org/10.1016/j.jdmm.2020.100508.

Peng, B. Song, H., Crouch, G.I. (2014). A meta-analysis of international tourism demand forecasting and implications for practice. Tourism Management. 45, 181–193, http://dx.doi.org/10.1016/j.tourman.2014.04.005.

Permatasari, M. F., & Esquivias, M. A. (2020). Determinants of tourism demand in Indonesia: A panel data analysis. Tourism Analysis, 25(1), 77-89.

Rachmad, Y. E. (2024). The Evolution of Consumer Behavior: Theories of Engagement, Influence, and Digital Interaction. PT. Sonpedia Publishing Indonesia.

Rookayyah, I., Seetanah, B., Nunkoo, R., & Jaffur, Z. K. (2024). The impact of exchange rate and exchange rate volatility on tourism demand using disaggregated data. International Journal of Tourism Research, 26(2), e2640.

Santana-Gallego, M., Ledesma-Rodríguez, F., Pérez-Rodríguez, J. (2010a). Does a common currency promote countries' growth via trade and tourism. World Econ. 33 (12), 1639–1972.

Seetaram, N. (2012). Immigration and international inbound tourism: Empirical evidence from Australia. Tourism Management, 33(6), 1535–1543.

Seetaram, N., Forsyth, P., & Dwyer, L. (2016). Measuring price elasticities of demand for outbound tourism using competitiveness indices. Annals of Tourism Research, 56, 65–79.

Shi, L., Han, L., Yang, F., & Gao, L. (2019). The evolution of sustainable development theory: Types, goals, and research prospects. Sustainability, 11(24), 7158.

Sirakaya, E., & Woodside, A. G. (2005). Building and testing theories of decision making by travellers. Tourism management, 26(6), 815-832.

Song H, Wong, K.K, Chon, K.K. (2003). Modelling and forecasting the demand for Hong Kong tourism. International Journal of Hospitality Management ,22(4), 435–451.

Song, H., & Li, G. (2008). Tourism demand modelling and forecasting. A review of recent research. Tourism Management, 29(2), 203–220.

Song, H., Kim, J. H., & Yang, S. (2010). Confidence intervals for tourism demand elasticity. Annals of Tourism Research, 37(2), 377–396.

Song, H., Li, G., & Cao, Z. (2017). Tourism and economic globalization: An emerging research agenda. Journal of Travel Research. https://doi.org/10.1177/ 0047287517734943.

Song, H., Qiu, R. T., & Park, J. (2019). A review of research on tourism demand forecasting. Annals of Tourism Research, 75, 338–362.

Song, H., Qiu, R.T.R., Park, J. (2019). A review of research on tourism demand forecasting. Annals of Tourism Research, 75, 338–362.

Statistical Center of Iran. (2022). Statistical Yearbook of the Country 2022: The Presidency and the Plan and Budget Organization of Iran.[in Persian]

Su, Y. W., & Lin, H. L. (2014). Analysis of international tourist arrivals worldwide: The role of world heritage sites. Tourism management, 40, 46-58.

Ulucak R., Yücel A.G., & İlkay S.Ç. (2020). Dynamics of tourism demand in Turkey: Panel data analysis using gravity model. Tourism Economics, 26(8), 1394-1414.

Ulucak, R., Go¨khan Yu¨ cel, A., & C¸ ag˘rı _ Ilkay. (2020). Dynamics of tourism demand in Turkey: Panel data analysis using gravity model. Journal of Tourism Economics, DOI: 10.1177/1354816620901956.

Vatsa, P. (2020). Movement amongst the demand for New Zealand tourism. Annals of Tourism Research, 83, https://doi.org/10.1016/j.annals.2020.102965.

Weaver, A. (2015). Target markets as working archetypes: Classifying consumers at tourism New Zealand. Journal of Vacation Marketing, 21(2), 163–174.

Yang, C. H., Lin, H. L., & Han, C. C. (2010). Analysis of international tourist arrivals in China: The role of World Heritage Sites. Tourism management, 31(6), 827-837.

Yang, Y., & Fik, T. (2014). Spatial effects in regional tourism growth. Annals of Tourism Research, 46, 144–162.

Yang, Y., & Wong, K. K. (2012). A spatial econometric approach to model spillover effects in tourism flows. Journal of Travel Research, 51(6), 768–778.

Ziaabadi, Maryam; Jalaei Esfandabadi, Seyed Abdolmajid; Jalalabadi, Leila. (2024). Examining the Factors Influencing Tourism Demand in Iran with Emphasis on the Exchange Rate Gap (Markov Switching Approach - Vector Error Correction). Majlis & Rahbord, 31(120), 335–365. [in Persian]

Economics Research

Investigating the Indicators of Attracting Foreign Tourists to Iran Using Panel Analysis and the Gravity Model

References

References

Volume 24, Issue 93 - Serial Number 2
July 2024
Pages 60-95

Investigating the Indicators of Attracting Foreign Tourists to Iran Using Panel Analysis and the Gravity Model

References

References

Volume 24, Issue 93 - Serial Number 2July 2024Pages 60-95

Volume 24, Issue 93 - Serial Number 2
July 2024
Pages 60-95