PurposeThe purpose of this paper is to examine the relationship between tourism sector development and poverty reduction in India using annual data from 1970 to 2018. The paper attempts to answer the critical question: Is tourism pro-poor in India?Design/methodology/approachStationarity properties of the series are checked by using the ADF unit root test. The paper uses the Auto Regressive Distributed Lag (ARDL) bound testing approach to cointegration to examine the existence of long-run relationships; error-correction mechanism for the short-run dynamics, and Granger non-causality test to test the direction of causality.FindingsThe cointegration test confirms a long-run relationship between tourism development and poverty reduction for India. The ARDL test results suggest that tourism development and economic growth reduces poverty in both the long run and the short run. Furthermore, inflation had a negative and significant short-run impact on the poverty reduction variable. The causality test confirms that there is a positive and unidirectional causality running from tourism development to poverty reduction confirming that tourism development is pro-poor in India.Research limitations/implicationsThis study implies that poverty in India can be reduced by tourism sector growth and price stability. For a fast-growing economy with respect to economic growth and tourism sector growth, this may have far-reaching implications toward inclusive growth in India.Originality/valueThis paper is the first of its kind to empirically examine the causal relationship between tourism sector development and poverty reduction in India using modern econometric techniques.

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.

The present study aims to investigate the impact of tourism growth on human development in Indian economy. For this purpose, the study uses annual data from 1980 to 2018 and utilizes two proxies for tourism growth-tourism receipt and tourist arrivals-and uses human development index calculated by UNDP. The study uses control variables such as government expenditure and trade openness. The study employs auto regressive distributed lag (ARDL) approach to investigate the cointegrating relationship among the variables in the model. Further, the study also explores the causal nexus between tourism sector and human development by using the Toda-Yamamoto Granger non-causality test. The result of ARDL bounds test reveals the existence of cointegrating relationship between human development indicators, government expenditure, trade openness, and tourism sector growth. The cointegating coefficient confirms a positive and significant relationship between tourism sector growth and human development in India. The causality result suggests that economic growth and tourism have a positive impact while trade openness has a negative impact on human development in India. The major findings of this study suggest that tourism plays an important role in the socioeconomic development of Indian economy in recent years and the country must develop this sector to achieve sustainable development.

PurposeThe main purpose of the present research is to explore the possible effectiveness of information and communication technology (ICT), infrastructure development, exchange rate and governance on inbound tourism demand using time series data in India.Design/methodology/approachThe stationarity of the variables is checked by using the ADF, PP and KPSS unit root tests. The paper uses the Bayer-Hanck and auto-regressive distributed lag (ARDL) bounds testing approach to cointegration to examine the existence of long-run relationships; the error-correction mechanism for the short-run dynamics and the vector error correction method (VECM) to test the direction of causality.FindingsThe findings of the research indicate the presence of cointegration among the variables. Further, long-run results indicate infrastructure development, word-of-mouth and ICT have a positive and significant linkage with international tourist arrivals in India. However, ICT has a positive and significant effect on tourist arrivals in the short run as well. The VECM results indicate long-run unidirectional causality from infrastructure, ICT, governance and exchange rate to tourist arrivals.Research limitations/implicationsThis study implies that inbound tourism demand in India can be augmented by improving infrastructure, governance quality and ICT penetration. For an emerging country like India, this may have far-reaching implications for sustaining and improving tourism sector growth.Originality/valueThis paper is the first of its kind to empirically examine the impact of ICT, infrastructure and governance quality in India using modern econometric techniques. Inbound tourism demand research aids government and policymakers in developing effective public policies that would reposition India to gain from a highly competitive global tourism industry.

The present study analyzes the asymmetric association of exchange rate and world income with inbound tourism demand in India using a recently developed nonlinear autoregressive distributed lag model. For this purpose, the study uses monthly data from January 2003 to December 2020 for inbound tourism demand, real effective exchange rate, and world income as the variables of the model. The study used an asymmetric causality test on the lines of Hatemi-J. The findings confirm the existence of a nonlinear association between exchange rate and tourism demand in the long run. Furthermore, the increases in the world income have a positive and significant effect on tourist arrivals in India. In addition, the findings indicate that exchange rate shocks play a vital role in the long run. The cointegration test is supplemented with nonlinear causality analysis. The causal result depicted positive shocks in the exchange rate and world income sharing a unidirectional causal relationship with tourist arrivals. The result of this research can significantly facilitate the policymakers for devising short-run as well as long-run policies to consolidate the macroeconomic fundamentals such that tourism demand can be enhanced in India.