key: cord-0067412-ug8uon7m
authors: Ghosh, Sudeshna
title: Modelling inbound international tourism demand in Australia: Lessons from the pandemics
date: 2021-08-07
journal: nan
DOI: 10.1002/jtr.2483
sha: 41dea624e1ec55bdcc10975d3527262bd4c636a3
doc_id: 67412
cord_uid: ug8uon7m

The study explores how international tourism demand in Australia is impacted not only by traditional economic factors but also by uncertainty and risk emanating from pandemics. Using an augmented demand model the paper examines the main determinants of tourism in Australia during the favourable period and what lessons can be drawn to reboot international tourism in the post‐pandemic situation. To the best of our knowledge, this study is the first of its kind, which employs a robust second‐generation panel model to examine the impact of pandemic augmenting economic uncertainty upon the international tourism demand in Australia.

The tourism industry has gained immense importance globally during the last decade because of its direct and indirect growth augmenting and employment generating benefits. According to the World Travel and Tourism Council (2019) globally, the travel and tourism industry contributed US$8.9 trillion to the global share of GDP. It has a share of 10.3% of the world's GDP during 2019 and generated 1 in 10 jobs worldwide.

Before the occurrence of the pandemics in the early 2020, international tourist arrivals in Australia increased to the level of 3.0% during 2019 compared to 2018. According to the Tourism Australia Research (2020) tourism sector was a major driver of economic growth for Australia during 2018-2019. As far as tourism expenditure is concerned Australia was among the top 10 countries when ranked globally. Australia is ranked seventh globally by the Travel and Tourism Competitiveness Index (2019). Globally, Australia is promoted as the desired travel destination for high-value tourists. However, owing to the pandemic situation the arrival of international travellers to Australia started to decline at the beginning of 2020, and during April and May 2020 the number further declined to an ebb low. Although COVID-19 is having a substantial adversative effect on the tourism industry, in not only Australia but also globally, considerations on how to re-open borders and invigorate the tourism industry have been into a discussion of late (UNCTAD, 2020. Against this backdrop, this paper attempts to explore the major determinants of international tourist arrivals to Australia during periods of tourist boom and what lessons can be drawn to revitalise tourism in the recovery phase aftermath of This paper contributes to the existing seam of the literature in five major ways. First, we use comprehensive and extensive data sets covering eight origin countries of inbound international tourist that explain about 55% of total international tourist arrivals to Australia, the time range is 2007M1 to 2020M8. Second, the study explores a wide range of possible determinants of international tourism demand for Australia. Specifically, the paper discusses how (i) prices, (ii) the substitute price, (iii) income level in the country of origin, (iv) past pandemics, and (v) economic policy uncertainty impact international inbound tourism. Third, the study attempts to explore how economic uncertainty augmented with pandemics impact tourism. The rising significance of the effect of uncertainty in today's globalised world has motivated many researchers to include it as an additional explicatory variable in studying tourism demand along with the standard explanatory variables (Işık et al., 2020 . Following the useful and distinct index on economic policy uncertainty (EPU) postulated by Baker et al. (2016) the paper explores how during uncertainty travel plans get significantly affected. Kumar et al. (2020) observe that tourism demand is affected not only by prices and income but factors like economic instability, disasters and diseases that also affect tourism.

So, the paper adds to the on going debate on how both economic and non-economic factors are crucial drivers of tourism demand. Fourth, employing the panel econometric model the study examines the longrun association between Australia's inbound international tourism and its major determining factors. The selection of the panel model in the current study is driven by the superiority of estimation techniques compared to the cross-sectional and time-series analysis (Baltagi, 2005; Hsiao, 2007 . Fifth, given the different levels of development, the panel set of countries may be heterogeneous. The impact of economic policy uncertainty is thus different across the countries. Furthermore, the shocks emanating from one country due to disasters and uncertainty may have transmissions in other countries owing to globalisation. Therefore, there may be a cross-sectional dependence among the panels. To overcome the problems associated with heterogeneity in the panel behaviour and cross-sectional dependence, the paper adopts novel estimation techniques that are robust to panel heterogeneity and cross-sectional dependence. The study uses Common Correlated Effects Estimation (Pesaran, 2006) and Augmented Mean Group (AMG) Estimation (Eberhardt & Teal, 2010) . Furthermore, the study analyses the impact of pandemics along with the traditional variables in affecting tourism in Australia. Therefore, the study contributes to the extant seam of literature on the empirical context. The remainder of the paper is designed as follows: Section 1 discusses the main findings of the existing literature related to EPU and pandemics, impacting tourism; Section 2 describes the datasets and methodology used in the study; Section 3 presents the major empirical results; Section 4 makes the possible policy suggestion; and Section 5 concludes.

2 | REVIEW OF LITERATURE 2.1 | Tourism demand and its major determinants Dogru et al. (2017) contrary to the law of demand find positive elasticities and negative income elasticities affecting tourism demand. Such confounding results could be due to the choice of the indicator to measure tourism prices. According to Nguyen (2020) data on tourism-related product prices are scant; so the literature proxy's tourism prices with consumer price index weighted by bilateral exchange rates. The literature on tourism economics also suggests that substitute prices of tourism of the destination country could also affect tourism demand in the concerned country. Baker et al. (2016) formulated the economic policy uncertainty index (EPU) which is widely used in the tourism literature to study its implications upon tourism demand. The studies by Ongan and Gozgor (2018) in the context of international inbound tourists from Japan to the United States; Gozgor and Ongan (2017) for international inbound tourists to the United States and Işık et al. (2020) for international inbound tourists in the United States from Mexico and Canada find that EPU has a significant adverse impact on tourism demand. Khan et al. (2021) explain that there is a need for timevarying estimates to forecast tourism demand with accuracy. The general agreement that emerges from the literature is that EPU has adverse implications on tourism in the end.

Non-economic factors have been explored in the literature to include how external shockwaves may affect tourism demand. These include seasonality behaviour, political unrest, wars, terrorism, diseases and tourism-related policies (Sio-Chong and So (2020), Ridderstaat et al. (2014) ; Kumar et al., 2020) .

The occurrence of diseases and pandemics also unfavourably affect tourism demand (Wilder-Smith, 2006; Hu and Lee 2020; Foo et al., 2020) . study on the impact of hotel bookings in Hong Kong during the pandemic situation showed that the 4-star hotels were severely impacted rather than the 5-star hotels. Sharma and Nicolau (2020) study related to the impact of pandemics on airlines, hotel and cruise industries found that among all the cruise industry is the worst affected. The paper recommended that there is an increasing need for prioritisation of resources so that the cruise industries receive financial assistance to uplift the situation in the postcrisis period. Using the long and short-term memory method Polysoz et al. (2020) found that international inbound tourism to the United States from China will face a substantial decline and it may take more than 6 months for the current situation to resume normalcy. Gossling et al. (2021) reviewing how the earlier pandemics impacted the tourism sector, explains that COVID-19 would bring unprecedented damage to the tourism industry. The paper concludes that even when business resumes tourism sector unlike other industries cannot sell its unsold accommodation and this will have major implications on tourism revenue. The discussion on the impact of pandemics on tourism particularly COVID-19 reviews how the occurrence of pandemics has modified society, the economy and the tourism sector. There is a need for further research to understand how the changes owing to the pandemics will impact the growth of the tourism demand.

Despite the rapid expansion of Australia's international inbound tourism, empirical studies based on the panel model on tourism demand in the context of Australia continue to be scarce. The existing studies mostly examined bilateral international tourist flows to Australia from the major markets (Kulendran, 1996; Lim & McAleer (2001); Chan et al., 2005) . Seetaram and Dwyer (2009) The findings from the previous literature confirm that apart from the economic and non-economic factors significantly impact international tourism in Australia. As international tourists respond adversely to disaster situation, there is a need for further research to examine the international tourism demand against the backdrop of the pandemics and what countermeasures can be adopted to mitigate the impact of uncertainty arising due to the pandemics.

To study the determinants of international tourism demand to Australia from the country of origin i, following Song et al. (2003) the Equation (1) explains the basic mathematical function of the model.

here T it denotes international tourism demand in Australia from the country of origin i during the time t; P t denotes the relative price level of tourism in Australia at the time t; IP it denotes the income level of the country i during the time t; SP t indicates the substitute price of tourism during time t in the competitive tourism destination of Australia; A is a positive constant and u it is the usual residual term.

The demand function for tourism as explained in Equation (1) analogous to the demand for any other goods and services is a function of its own prices, income, price of substitute products and other noneconomic factors which may be captured through u it .

We, rewrite the international tourism demand for Australia, Equation (1) see Equation (2) in its econometric form (taking logarithmic conversion) including the augmenting impact of economic policy uncertainty and shocks emanating from the pandemics: Breitung (2005) , the assumption of slope homogeneity will produce erroneous results if the underlying panel is heterogeneous. Swamy (1970) proposes that crosssectional heterogeneity has to be controlled in the process of empirical estimation. This study applies 

When the null hypothesis of cross-sectional independence, slope homogeneity, and stationery can be rejected at a level of significance then the long-run impacts can be estimated by adopting the AMG estimation method proposed by Eberhardt and Teal (2010) and

the common correlated effect mean group (CCEMG) estimation method postulated by Pesaran (2006) . This study applies the AMG method and CCEMG estimation method to assess how economic policy uncertainty and shocks emanating from pandemics impact tourism demand in the long-run, controlling for the price level, income and substitute prices.

The data on international tourist arrivals in Australia is obtained from eight major source markets, which explain about 55% of international The relative price for Australia is measured by the bilateral real exchange rate of the Australian dollar vis-à-vis of the local currency of the source market. Thus, the relative price abbreviated as P i is expressed as, To calculate the substitute prices of tourism for Australia denoted as SP we follow the methodology of Kumar et al. (2020) and calculate

tourism-based substitute prices for the tourism competing country of Australia namely New Zealand. SP for Australia is calculated as follows:

where TOU denotes the share of tourist arrivals in New Zealand from the country i. Following, Song et al. (2003) The pandemic dummy takes into account the specified pandemics that happened during the 2007-2020 period. It takes the value 1 for the months in which the pandemic happened, and 0 otherwise. Table 1 provides the detail of the data source of the dependent and independent variables. All the variables except the dummy are converted into their natural logarithmic transformation. According to Alam and Paramati (2016) and Bhattacharya et al. (2016) , the conversion into the natural logarithmic form reduces the problems associated with distributional properties. In its logarithmic transformation, the estimated coefficients are elasticities. 3.2 | Results of the unit root test and cointegration tests

As the results based on the first-generation unit root tests generate spurious outcomes when there exist cross-sectional dependence and slope heterogeneity in the panel set of the observations, the paper adopts the unit root test procedure that is robust to such concerns. (Table 6) confirm the existence of cointegration across the observations.

The results confirm the existence of a long-run association between tourism demand, relative prices, income level, substitute price and economic policy uncertainty.

This study adopts the AMG estimation process of Eberhardt and Teal (2010) and the CCEMG estimation process of Pesaran (2006) to study how pandemics, economic policy uncertainty along with tourism demand for the previous year, income and prices impact international tourism demand for Australia. Kuo et al. (2009) and Lee et al. (2021) . The impact of the interactive term of EPU and pandemics is also negative upon tourism demand in Australia. As reported in Table 7 Dummy 1 upon GEPU increases by 7% (AMG method) and 2.1%

(CCEMG method) respectively; the negative impact of Pandemics Dummy 2 upon GEPU increases by 3% (AMG method) and 4%

(CCEMG method), respectively; the negative impact of Pandemics Dummy 3 upon GEPU increases by 5% (AMG method) and 10% but the results are not significant (CCEMG method) respectively; the negative impact of Pandemics Dummy 4 upon GEPU increases by 2%

(AMG method) and 4% (CCEMG method), respectively; as far as the negative impact of the Pandemics Dummy 5 is concerned the AMG estimation shows an absence of impact however according to CEMG it rises by 1.2%.

As regards the impact of the control variables, it can be seen from Table 7 that the lagged impact of the dependent variable is positive and significant suggesting that factors that generated tourism demand in Australia in the previous year will bring in more demand in the current year. As far as the income of the country of origin is concerned it positively impacts tourism demand, as found from the results of Table 7 when the income of the country of origin rises by 1% international inbound tourist inflows rise by 0.33% (AMG estimation) and 0.37% (CCEMG estimation). So rising income implies a rise in the spending power of the consumers and hence leading to a boost in tourism.

The rise in the relative price level negatively impacts tourist arrivals in the long-run. Results based on Table 7 indicate that when the relative price level rises by 1% international tourist inflows fall by 0.50% (AMG method) and 0.81 (CCEMG method) respectively. The 

The empirical results based on the econometric methods indicate that economic policy uncertainty has a long-run adverse impact upon international tourism demand in the destination country of Australia.

Both the local and global economic policy uncertainty are significant factors in impacting tourism demand and hence the economy of the destination country. Tourism in the most countries is an important driver for economic growth, Akadiri, Lasisi, et al. (2020) and Liu and Song (2018) , the boom in tourism demand is critically dependent upon economic policies and the thrust of emphasis should be to remove asymmetry in the information emanating from economic policy. This will affect the perceptions of tourists in a positive way. The rapid expansion of Australia's inbound international tourism demand from the major markets of Asia and Europe and the United States was to a large extent due to the government of Australia's efforts to make the tourism industry competitive in the international markets. Further, the familiarity of language and common culture also favoured tourism expansion in Australia (Ghosh, 2020 . However, the pandemics have in the past and the current situation adversely impacted tourism in Australia. Pandemics experienced by mankind has brought health devastations and adverse impact on the economy and the society.

This study makes a unique attempt to infer how the outbreak of past epidemics impacted the international tourism demand for Australia. The way a destination responds is crucially dependent upon the tourism business in the competing destinations.

The fundamental reasons for the rise in the pandemic threat in the 21st century are due to rise in international mobility, urbanisation, the rise in consumption of processed food including meat and the rise in the global transport networks (Labonté et al., 2011 . Gössling et al. (2020 discuss that systematic understanding and research on country studies relating to challenges of pandemics and vulnerability of the tourism industry is still lacking. The current study renews the scope of research in its attempt to understand the challenges of pandemics, uncertainty and its implications for the tourism industry in the context of Australia. To ensure gradual recovery in the international flow of inbound tourists the Australian government needs to seriously consider the interplay between tourism demand and public health interventions.

The tourism sector will be gradually revamping if concerted efforts are put on the conditions of hygiene in the airlines and the hospitality sector. Against the background of COVID-19 situation, investment in technology that is "touch-less" will be effective in expanding the tourism The study incorporated five dummy variables to explore the impact of past pandemics and the current one which devasted the economy in general and the tourism economy in particular namely H1N1 (2009) The number of inbound international tourists decline in the year 2020 when COVID-19 pandemic spread to about 3.8%, (AMG method) and 7.7% (CCEMG method), respectively. The impact of the pandemic weighted EPU is also negative upon tourism demand in Australia.

Tourists' decision to travel is not only impacted by income and prices but also by uncertainty emanating from economic policy and risks of pandemics. Economic stability, fiscal packages to boost tourism and investment in "touch-less technology" will help to reboot Australian tourism in the long run.

The study has two major limitations: first owing to a paucity of data it could not consider how different categories of tourism is impacted by economic and non-economic factors, the different categories of tourism include travel for business purpose, travel for leisure, travel for work and travel for education. Second, owing to lack of data it considered only one competitive destination in the Oceania region as a substitute destination of Australia. We, therefore, suggest that with the increasing availability of tourism data the scope for future research lies in exploring tourism in a more disaggregate context.

No funding is associated with this research. Further, there is no conflict of interest. This is an original research work not submitted elsewhere. 

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