DETERMINING FACTORS OF THE NUMBER OF TOURISTS IN 30 COUNTRIESUSING GEOGRAPHYCALLY WEIGHTED PANEL REGRESSION - Sellyra, Eirene Christina; Anggara, Dimas; Ferezagia, Debrina Vita (UI Scholars Hub, 2022)
This study aims to determine the factors that influence the number of tourists in 30 countries. The research method used is quantitative.The research data is secondary data. The research unit is in the form of 30 countries of observation. The research variables were measured in three years of observation, namely 2018, 2019 and 2020. The variables used were the number of tourists (people), the currency exchange rate of tourist countries against the rupiah, GDP per capita, population density, visa-free visit, consumer price index, life expectancy, economic growth and imports. The results obtained are that the factors that influence the number of foreign tourists visiting the observation countries in 2018 to 2020 vary depending on the area of the observation country. Countries with the number of foreign tourist visits influenced by population density and imports are Malaysia and Singapore. Countries with the number of foreign tourist visits influenced by economic growth and imports are China and South Korea. Countries with the number of foreign tourist visits that are influenced by import factors are countriesBangladesh, Brunei Darussalam, Burma, Hong Kong, India, Pakistan, Thailand and Vietnam. While the rest are not influenced by any factor in the model with a 90% confidence level.The Geographically Weighted Panel Regression (GWPR) model that has been formed is appropriate and has a significant difference compared to the panel regression model due to the location effect which also significantly influences the number of foreign tourist visits to the observed countries. The model has an adjusted r-square value of50.84874% which means the model is able to explain the variance of the number of foreign tourists visiting50.84874% only by variablepopulation density, economic growth, and imports. Meanwhile, the rest is influenced by other variables outside the model.