The Determinants of Inbound Tourism Demand in India: New Evidence from ARDL Co-Integration Approach

Abstract

The research used the Autoregressive Distributive Lag (ARDL) model to examine the long- and short-term impact of changes in currency rates and global income on tourist demand in India employing monthly data from 2003 (1) to 2020 (12). We find that exchange rate volatility, global income, and tourism demand are all significantly interrelated. A 15% convergence to the long-run equilibrium path of tourism demand occurs in line with the pace of adjustment through the channel of global income and currency rate. Positive and substantial effects of rising global income are shown over the short and long terms. There is, nevertheless, a positive short-term relationship between currency depreciation and visitor numbers. Additionally, the Toda–Yamamoto method is used for Granger non-causality. The results point to a one-way causal relationship between the currency exchange rate and the number of visitors. It has also been shown that there is a causal relationship in both directions between the demand for tourism and global GDP. The nation is in a special position due to its location, physical characteristics, cultural heritage, and other comparative advantages. According to the findings, a stable currency rate and global income are the two most important factors in increasing tourist interest in India.