economic tourism effects

10 Economic impacts of tourism + explanations + examples

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There are many economic impacts of tourism, and it is important that we understand what they are and how we can maximise the positive economic impacts of tourism and minimise the negative economic impacts of tourism.

Many argue that the tourism industry is the largest industry in the world. While its actual value is difficult to accurately determine, the economic potential of the tourism industry is indisputable. In fact, it is because of the positive economic impacts that most destinations embark on their tourism journey.

There is, however, more than meets the eye in most cases. The positive economic impacts of tourism are often not as significant as anticipated. Furthermore, tourism activity tends to bring with it unwanted and often unexpected negative economic impacts of tourism.

In this article I will discuss the importance of understanding the economic impacts of tourism and what the economic impacts of tourism might be. A range of positive and negative impacts are discussed and case studies are provided.

At the end of the post I have provided some additional reading on the economic impacts of tourism for tourism stakeholders , students and those who are interested in learning more.

 Foreign exchange earnings

Contribution to government revenues, employment generation, contribution to local economies, development of the private sector, infrastructure cost, increase in prices, economic dependence of the local community on tourism, foreign ownership and management, economic impacts of tourism: conclusion, further reading on the economic impacts of tourism, the economic impacts of tourism: why governments invest.

Tourism brings with it huge economic potential for a destination that wishes to develop their tourism industry. Employment, currency exchange, imports and taxes are just a few of the ways that tourism can bring money into a destination.

In recent years, tourism numbers have increased globally at exponential rates, as shown in the World Tourism Organisation data below.

There are a number of reasons for this growth including improvements in technology, increases in disposable income, the growth of budget airlines and consumer desires to travel further, to new destinations and more often.

Here are a few facts about the economic importance of the tourism industry globally:

• The tourism economy represents 5 percent of world GDP
• Tourism contributes to 6-7 percent of total employment
• International tourism ranks fourth (after fuels, chemicals and automotive products) in global exports
• The tourism industry is valued at US$1trillion a year
• Tourism accounts for 30 percent of the world’s exports of commercial services
• Tourism accounts for 6 percent of total exports
• 1.4billion international tourists were recorded in 2018 (UNWTO)
• In over 150 countries, tourism is one of five top export earners
• Tourism is the main source of foreign exchange for one-third of developing countries and one-half of less economically developed countries (LEDCs)

There is a wealth of data about the economic value of tourism worldwide, with lots of handy graphs and charts in the United Nations Economic Impact Report .

In short, tourism is an example of an economic policy pursued by governments because:

•      it brings in foreign exchange
•      it generates employment
•      it creates economic activity

Building and developing a tourism industry, however, involves a lot of initial and ongoing expenditure. The airport may need expanding. The beaches need to be regularly cleaned. New roads may need to be built. All of this takes money, which is usually a financial outlay required by the Government.

For governments, decisions have to be made regarding their expenditure. They must ask questions such as:

How much money should be spent on the provision of social services such as health, education, housing?

How much should be spent on building new tourism facilities or maintaining existing ones?

If financial investment and resources are provided for tourism, the issue of opportunity costs arises.

By opportunity costs, I mean that by spending money on tourism, money will not be spent somewhere else. Think of it like this- we all have a specified amount of money and when it runs out, it runs out. If we decide to buy the new shoes instead of going out for dinner than we might look great, but have nowhere to go…!

In tourism, this means that the money and resources that are used for one purpose may not then be available to be used for other purposes. Some destinations have been known to spend more money on tourism than on providing education or healthcare for the people who live there, for example.

This can be said for other stakeholders of the tourism industry too.

There are a number of independent, franchised or multinational investors who play an important role in the industry. They may own hotels, roads or land amongst other aspects that are important players in the overall success of the tourism industry. Many businesses and individuals will take out loans to help fund their initial ventures.

So investing in tourism is big business, that much is clear. What what are the positive and negative impacts of this?

Positive economic impacts of tourism

So what are the positive economic impacts of tourism? As I explained, most destinations choose to invest their time and money into tourism because of the positive economic impacts that they hope to achieve. There are a range of possible positive economic impacts. I will explain the most common economic benefits of tourism below.

One of the biggest benefits of tourism is the ability to make money through foreign exchange earnings.

Tourism expenditures generate income to the host economy. The money that the country makes from tourism can then be reinvested in the economy. How a destination manages their finances differs around the world; some destinations may spend this money on growing their tourism industry further, some may spend this money on public services such as education or healthcare and some destinations suffer extreme corruption so nobody really knows where the money ends up!

Some currencies are worth more than others and so some countries will target tourists from particular areas. I remember when I visited Goa and somebody helped to carry my luggage at the airport. I wanted to give them a small tip and handed them some Rupees only to be told that the young man would prefer a British Pound!

Currencies that are strong are generally the most desirable currencies. This typically includes the British Pound, American, Australian and Singapore Dollar and the Euro .

Tourism is one of the top five export categories for as many as 83% of countries and is a main source of foreign exchange earnings for at least 38% of countries.

Tourism can help to raise money that it then invested elsewhere by the Government. There are two main ways that this money is accumulated.

Direct contributions are generated by taxes on incomes from tourism employment and tourism businesses and things such as departure taxes.

Taxes differ considerably between destinations. I will never forget the first time that I was asked to pay a departure tax (I had never heard of it before then), because I was on my way home from a six month backpacking trip and I was almost out of money!

Japan is known for its high departure taxes. Here is a video by a travel blogger explaining how it works.

According to the World Tourism Organisation, the direct contribution of Travel & Tourism to GDP in 2018 was $2,750.7billion (3.2% of GDP). This is forecast to rise by 3.6% to $2,849.2billion in 2019.

Indirect contributions come from goods and services supplied to tourists which are not directly related to the tourism industry.

Take food, for example. A tourist may buy food at a local supermarket. The supermarket is not directly associated with tourism, but if it wasn’t for tourism its revenues wouldn’t be as high because the tourists would not shop there.

There is also the income that is generated through induced contributions . This accounts for money spent by the people who are employed in the tourism industry. This might include costs for housing, food, clothing and leisure Activities amongst others. This will all contribute to an increase in economic activity in the area where tourism is being developed.

The rapid expansion of international tourism has led to significant employment creation. From hotel managers to theme park operatives to cleaners, tourism creates many employment opportunities. Tourism supports some 7% of the world’s workers.

There are two types of employment in the tourism industry: direct and indirect.

Direct employment includes jobs that are immediately associated with the tourism industry. This might include hotel staff, restaurant staff or taxi drivers, to name a few.

Indirect employment includes jobs which are not technically based in the tourism industry, but are related to the tourism industry. Take a fisherman, for example. He does not have any contact of dealings with tourists. BUT he does sell his fish to the hotel which serves tourists. So he is indirectly employed by the tourism industry, because without the tourists he would not be supplying the fish to the hotel.

It is because of these indirect relationships, that it is very difficult to accurately measure the economic value of tourism.

It is also difficult to say how many people are employed, directly and indirectly, within the tourism industry.

Furthermore, many informal employments may not be officially accounted for. Think tut tut driver in Cambodia or street seller in The Gambia – these people are not likely to be registered by the state and therefore their earnings are not declared.

It is for this reason that some suggest that the actual economic benefits of tourism may be as high as double that of the recorded figures!

All of the money raised, whether through formal or informal means, has the potential to contribute to the local economy.

If sustainable tourism is demonstrated, money will be directed to areas that will benefit the local community most.

There may be pro-poor tourism initiatives (tourism which is intended to help the poor) or volunteer tourism projects.

The government may reinvest money towards public services and money earned by tourism employees will be spent in the local community. This is known as the multiplier effect.

The multiplier effect relates to spending in one place creating economic benefits elsewhere. Tourism can do wonders for a destination in areas that may seem to be completely unrelated to tourism, but which are actually connected somewhere in the economic system.

Let me give you an example.

A tourist buys an omelet and a glass of orange juice for their breakfast in the restaurant of their hotel. This simple transaction actually has a significant multiplier effect. Below I have listed just a few of the effects of the tourist buying this breakfast.

The waiter is paid a salary- he spends his salary on schooling for his kids- the school has more money to spend on equipment- the standard of education at the school increases- the kids graduate with better qualifications- as adults, they secure better paying jobs- they can then spend more money in the local community…

The restaurant purchases eggs from a local farmer- the farmer uses that money to buy some more chickens- the chicken breeder uses that money to improve the standards of their cages, meaning that the chickens are healthier, live longer and lay more eggs- they can now sell the chickens for a higher price- the increased money made means that they can hire an extra employee- the employee spends his income in the local community…

The restaurant purchase the oranges from a local supplier- the supplier uses this money to pay the lorry driver who transports the oranges- the lorry driver pays road tax- the Government uses said road tax income to fix pot holes in the road- the improved roads make journeys quicker for the local community…

So as you can see, that breakfast that the tourist probably gave not another thought to after taking his last mouthful of egg, actually had the potential to have a significant economic impact on the local community!

The private sector has continuously developed within the tourism industry and owning a business within the private sector can be extremely profitable; making this a positive economic impact of tourism.

Whilst many businesses that you will come across are multinational, internationally-owned organisations (which contribute towards economic leakage ).

Many are also owned by the local community. This is the case even more so in recent years due to the rise in the popularity of the sharing economy and the likes of Airbnb and Uber, which encourage the growth of businesses within the local community.

Every destination is different with regards to how they manage the development of the private sector in tourism.

Some destinations do not allow multinational organisations for fear that they will steal business and thus profits away from local people. I have seen this myself in Italy when I was in search of a Starbucks mug for my collection , only to find that Italy has not allowed the company to open up any shops in their country because they are very proud of their individually-owned coffee shops.

Negative economic impacts of tourism

Unfortunately, the tourism industry doesn’t always smell of roses and there are also several negative economic impacts of tourism.

There are many hidden costs to tourism, which can have unfavourable economic effects on the host community.

Whilst such negative impacts are well documented in the tourism literature, many tourists are unaware of the negative effects that their actions may cause. Likewise, many destinations who are inexperienced or uneducated in tourism and economics may not be aware of the problems that can occur if tourism is not management properly.

Below, I will outline the most prominent negative economic impacts of tourism.

Economic leakage in tourism is one of the major negative economic impacts of tourism. This is when money spent does not remain in the country but ends up elsewhere; therefore limiting the economic benefits of tourism to the host destination.

The biggest culprits of economic leakage are multinational and internationally-owned corporations, all-inclusive holidays and enclave tourism.

I have written a detailed post on the concept of economic leakage in tourism, you can take a look here- Economic leakage in tourism explained .

Another one of the negative economic impacts of tourism is the cost of infrastructure. Tourism development can cost the local government and local taxpayers a great deal of money.

Tourism may require the government to improve the airport, roads and other infrastructure, which are costly. The development of the third runway at London Heathrow, for example, is estimated to cost £18.6billion!

Money spent in these areas may reduce government money needed in other critical areas such as education and health, as I outlined previously in my discussion on opportunity costs.

One of the most obvious economic impacts of tourism is that the very presence of tourism increases prices in the local area.

Have you ever tried to buy a can of Coke in the supermarket in your hotel? Or the bar on the beachfront? Walk five minutes down the road and try buying that same can in a local shop- I promise you, in the majority of cases you will see a BIG difference In cost! (For more travel hacks like this subscribe to my newsletter – I send out lots of tips, tricks and coupons!)

Increasing demand for basic services and goods from tourists will often cause price hikes that negatively impact local residents whose income does not increase proportionately.

Tourism development and the related rise in real estate demand may dramatically increase building costs and land values. This often means that local people will be forced to move away from the area that tourism is located, known as gentrification.

Taking measures to ensure that tourism is managed sustainably can help to mitigate this negative economic impact of tourism. Techniques such as employing only local people, limiting the number of all-inclusive hotels and encouraging the purchasing of local products and services can all help.

Another one of the major economic impacts of tourism is dependency. Many countries run the risk of becoming too dependant on tourism. The country sees $ signs and places all of its efforts in tourism. Whilst this can work out well, it is also risky business!

If for some reason tourism begins to lack in a destination, then it is important that the destination has alternative methods of making money. If they don’t, then they run the risk of being in severe financial difficulty if there is a decline in their tourism industry.

In The Gambia, for instance, 30% of the workforce depends directly or indirectly on tourism. In small island developing states, percentages can range from 83% in the Maldives to 21% in the Seychelles and 34% in Jamaica.

There are a number of reasons that tourism could decline in a destination.

The Gambia has experienced this just recently when they had a double hit on their tourism industry. The first hit was due to political instability in the country, which has put many tourists off visiting, and the second was when airline Monarch went bust, as they had a large market share in flights to The Gambia.

Other issues that could result in a decline in tourism includes economic recession, natural disasters and changing tourism patterns. Over-reliance on tourism carries risks to tourism-dependent economies, which can have devastating consequences.

The last of the negative economic impacts of tourism that I will discuss is that of foreign ownership and management.

As enterprise in the developed world becomes increasingly expensive, many businesses choose to go abroad. Whilst this may save the business money, it is usually not so beneficial for the economy of the host destination.

Foreign companies often bring with them their own staff, thus limiting the economic impact of increased employment. They will usually also export a large proportion of their income to the country where they are based. You can read more on this in my post on economic leakage in tourism .

As I have demonstrated in this post, tourism is a significant economic driver the world over. However, not all economic impacts of tourism are positive. In order to ensure that the economic impacts of tourism are maximised, careful management of the tourism industry is required.

If you enjoyed this article on the economic impacts of tourism I am sure that you will love these too-

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Economic Impact Research

• In 2023, the Travel & Tourism sector contributed 9.1% to the global GDP; an increase of 23.2% from 2022 and only 4.1% below the 2019 level.
• In 2023, there were 27 million new jobs, representing a 9.1% increase compared to 2022, and only 1.4% below the 2019 level.
• Domestic visitor spending rose by 18.1% in 2023, surpassing the 2019 level.
• International visitor spending registered a 33.1% jump in 2023 but remained 14.4% below the 2019 total.

Click here for links to the different economy/country and regional reports

Why conduct research?

From the outset, our Members realised that hard economic facts were needed to help governments and policymakers truly understand the potential of Travel & Tourism. Measuring the size and growth of Travel & Tourism and its contribution to society, therefore, plays a vital part in underpinning WTTC’s work.

What research does WTTC carry out?

Each year, WTTC and Oxford Economics produce reports covering the economic contribution of our sector in 185 countries, for 26 economic and geographic regions, and for more than 70 cities. We also benchmark Travel & Tourism against other economic sectors and analyse the impact of government policies affecting the sector such as jobs and visa facilitation.

Visit our Research Hub via the button below to find all our Economic Impact Reports, as well as other reports on Travel and Tourism. 

The COVID-19 travel shock hit tourism-dependent economies hard

• Download the paper here

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Gian maria milesi-ferretti gian maria milesi-ferretti senior fellow - economic studies , the hutchins center on fiscal and monetary policy.

August 12, 2021

The COVID crisis has led to a collapse in international travel. According to the World Tourism Organization , international tourist arrivals declined globally by 73 percent in 2020, with 1 billion fewer travelers compared to 2019, putting in jeopardy between 100 and 120 million direct tourism jobs. This has led to massive losses in international revenues for tourism-dependent economies: specifically, a collapse in exports of travel services (money spent by nonresident visitors in a country) and a decline in exports of transport services (such as airline revenues from tickets sold to nonresidents).

This “travel shock” is continuing in 2021, as restrictions to international travel persist—tourist arrivals for January-May 2021 are down a further 65 percent from the same period in 2020, and there is substantial uncertainty on the nature and timing of a tourism recovery.

We study the economic impact of the international travel shock during 2020, particularly the severity of the hit to countries very dependent on tourism. Our main result is that on a cross-country basis, the share of tourism activities in GDP is the single most important predictor of the growth shortfall in 2020 triggered by the COVID-19 crisis (relative to pre-pandemic IMF forecasts), even when compared to measures of the severity of the pandemic. For instance, Grenada and Macao had very few recorded COVID cases in relation to their population size and no COVID-related deaths in 2020—yet their GDP contracted by 13 percent and 56 percent, respectively.

International tourism destinations and tourism sources

Countries that rely heavily on tourism, and in particular international travelers, tend to be small, have GDP per capita in the middle-income and high-income range, and are preponderately net debtors. Many are small island economies—Jamaica and St. Lucia in the Caribbean, Cyprus and Malta in the Mediterranean, the Maldives and Seychelles in the Indian Ocean, or Fiji and Samoa in the Pacific. Prior to the COVID pandemic, median annual net revenues from international tourism (spending by foreign tourists in the country minus tourism spending by domestic residents overseas) in these island economies were about one quarter of GDP, with peaks around 50 percent of GDP, such as Aruba and the Maldives.

But there are larger economies heavily reliant on international tourism. For instance, in Croatia average net international tourism revenues from 2015-2019 exceeded 15 percent of GDP, 8 percent in the Dominican Republic and Thailand, 7 percent in Greece, and 5 percent in Portugal. The most extreme example is Macao, where net revenues from international travel and tourism were around 68 percent of GDP during 2015-19. Even in dollar terms, Macao’s net revenues from tourism were the fourth highest in the world, after the U.S., Spain, and Thailand.

In contrast, for countries that are net importers of travel and tourism services—that is, countries whose residents travel widely abroad relative to foreign travelers visiting the country—the importance of such spending is generally much smaller as a share of GDP. In absolute terms, the largest importer of travel services is China (over $200 billion, or 1.7 percent of GDP on average during 2015-19), followed by Germany and Russia. The GDP impact for these economies of a sharp reduction in tourism outlays overseas is hence relatively contained, but it can have very large implications on the smaller economies their tourists travel to—a prime example being Macao for Chinese travelers.

How did tourism-dependent economies cope with the disappearance of a large share of their international revenues in 2020? They were forced to borrow more from abroad (technically, their current account deficit widened, or their surplus shrank), but also reduced net international spending in other categories. Imports of goods declined (reflecting both a contraction in domestic demand and a decline in tourism inputs such as imported food and energy) and payments to foreign creditors were lower, reflecting the decline in returns for foreign-owned hotel infrastructure.

The growth shock

We then examine whether countries more dependent on tourism suffered a bigger shock to economic activity in 2020 than other countries, measuring this shock as the difference between growth outcomes in 2020 and IMF growth forecasts as of January 2020, just prior to the pandemic. Our measure of the overall importance of tourism is the share of GDP accounted for by tourism-related activity over the 5 years preceding the pandemic, assembled by the World Travel and Tourism Council and disseminated by the World Bank . This measure takes into account the importance of domestic tourism as well as  international tourism.

Among the 40 countries with the largest share of tourism in GDP, the median size of growth shortfall compared to pre-COVID projections was around 11 percent, as against 6 percent for countries less dependent on tourism. For instance, in the tourism-dependent group, Greece, which was expected to grow by 2.3 percent in 2020, shrunk by over 8 percent, while in the other group,  Germany, which was expected to grow by around 1 percent, shrunk by 4.8 percent. The scatter plot of Figure 2 provides more striking visual evidence of a negative correlation (-0.72) between tourism dependence and the growth shock in 2020.

Of course, many other factors may have affected differences in performance across economies—for instance, the intensity of the pandemic as well as the stringency of the associated lockdowns. We therefore build a simple statistical model that relates the “growth shock” in 2020 to these factors alongside our tourism variable, and also takes into account other potentially relevant country characteristics, such as the level of development, the composition of output, and country size. The message: the dependence on tourism is a key explanatory variable of the growth shock in 2020. For instance, the analysis suggests that going from the share of tourism in GDP of Canada (around 6 percent) to the one of Mexico (around 16 percent) would reduce growth in 2020 by around 2.5 percentage points. If we instead go from the tourism share of Canada to the one of Jamaica (where the share of tourism in GDP approaches one third), growth would be lower by over 6 percentage points.

Measures of the severity of the pandemic, the intensity of lockdowns, the level of development, and the sectoral composition of GDP (value added accounted for by manufacturing and agriculture) also matter, but quantitatively less so than tourism. And results are not driven by very small economies; tourism is still a key explanatory variable of the 2020 growth shock even if we restrict our sample to large economies. Among tourism-dependent economies, we also find evidence that those relying more heavily on international tourism experienced a more severe hit to economic activity when compared to those relying more on domestic tourism.

Given data availability at the time of writing, the evidence we provided is limited to 2020. The outlook for international tourism in 2021, if anything, is worse, though with increasing vaccine coverage the tide could turn next year. The crisis poses particularly daunting challenges to smaller tourist destinations, given limited possibilities for diversification. In many cases, particularly among emerging and developing economies, these challenges are compounded by high starting levels of domestic and external indebtedness, which can limit the space for an aggressive fiscal response. Helping these countries cope with the challenges posed by the pandemic and restoring viable public and external finances will require support from the international community.

Read the full paper here.

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The author thanks Manuel Alcala Kovalski and Jimena Ruiz Castro for their excellent research assistance.

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What is overtourism and how can we overcome it? 

The issue of overtourism has become a major concern due to the surge in travel following the pandemic. Image:  Reuters/Manuel Silvestri (ITALY - Tags: ENTERTAINMENT)

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• Overtourism has once again become a concern, particularly after the rebound of international travel post-pandemic.
• Communities in popular destinations worldwide have expressed concerns over excess tourism on their doorstep.
• Here we outline the complexities of overtourism and the possible measures that can be taken to address the problem.

The term ‘overtourism’ has re-emerged as tourism recovery has surged around the globe. But already in 2019, angst over excessive tourism growth was so high that the UN World Tourism Organization called for “such growth to be managed responsibly so as to best seize the opportunities tourism can generate for communities around the world”.

This was especially evident in cities like Barcelona, where anti-tourism sentiment built up in response to pent-up frustration about rapid and unyielding tourism growth. Similar local frustration emerged in other famous cities, including Amsterdam , Venice , London , Kyoto and Dubrovnik .

While the pandemic was expected to usher in a new normal where responsible and sustainable travel would emerge, this shift was evidently short-lived, as demand surged in 2022 and 2023 after travel restrictions eased.

Have you read?

Ten principles for sustainable destinations: charting a new path forward for travel and tourism.

This has been witnessed over the recent Northern Hemisphere summer season, during which popular destinations heaved under the pressure of pent-up post-pandemic demand , with grassroots communities articulating over-tourism concerns.

Concerns over excess tourism have not only been seen in popular cities but also on the islands of Hawaii and Greece , beaches in Spain , national parks in the United States and Africa , and places off the beaten track like Japan ’s less explored regions.

What is overtourism?

The term overtourism was employed by Freya Petersen in 2001, who lamented the excesses of tourism development and governance deficits in the city of Pompei. Her sentiments are increasingly familiar among tourists in other top tourism destinations more than 20 years later.

Overtourism is more than a journalistic device to arouse host community anxiety or demonize tourists through anti-tourism activism. It is also more than simply being a question of management – although poor or lax governance most definitely accentuates the problem.

Governments at all levels must be decisive and firm about policy responses that control the nature of tourist demand and not merely give in to profits that flow from tourist expenditure and investment.

Overtourism is often oversimplified as being a problem of too many tourists. While that may well be an underlying symptom of excess, it fails to acknowledge the myriad factors at play.

In its simplest iteration, overtourism results from tourist demand exceeding the carrying capacity of host communities in a destination. Too often, the tourism supply chain stimulates demand, giving little thought to the capacity of destinations and the ripple effects on the well-being of local communities.

Overtourism is arguably a social phenomenon too. In China and India, two of the most populated countries where space is a premium, crowded places are socially accepted and overtourism concerns are rarely articulated, if at all. This suggests that cultural expectations of personal space and expectations of exclusivity differ.

We also tend not to associate ‘overtourism’ with Africa . But uncontrolled growth in tourist numbers is unsustainable anywhere, whether in an ancient European city or the savannah of a sub-Saharan context.

Overtourism must also have cultural drivers that are intensified when tourists' culture is at odds with that of host communities – this might manifest as breaching of public norms, irritating habits, unacceptable behaviours , place-based displacement and inconsiderate occupation of space.

The issue also comes about when the economic drivers of tourism mean that those who stand to benefit from growth are instead those who pay the price of it, particularly where gentrification and capital accumulation driven from outside results in local resident displacement and marginalization.

Overcoming overtourism excesses

Radical policy measures that break the overtourism cycle are becoming more common. For example, Amsterdam has moved to ban cruise ships by closing the city’s cruise terminal.

Tourism degrowth has long been posited as a remedy to overtourism. While simply cutting back on tourist numbers seems like a logical response, whether the economic trade-offs of fewer tourists will be tolerated is another thing altogether.

The Spanish island of Lanzarote moved to desaturate the island by calling the industry to focus on quality tourism rather than quantity. This shift to quality, or higher yielding, tourists has been mirrored in many other destinations, like Bali , for example.

Dispersing tourists outside hotspots is commonly seen as a means of dealing with too much tourism. However, whether sufficient interest to go off the beaten track can be stimulated might be an immoveable constraint, or simply result in problem shifting .

Demarketing destinations has been applied with varying degrees of success. However, whether it can address the underlying factors in the long run is questioned, particularly as social media influencers and travel writers continue to give attention to touristic hotspots. In France, asking visitors to avoid Mont Saint-Michelle and instead recommending they go elsewhere is evidence of this.

Introducing entry fees and gates to over-tourist places like Venice is another deterrent. This assumes visitors won’t object to paying and that revenues generated are spent on finding solutions rather than getting lost in authorities’ consolidated revenue.

Advocacy and awareness campaigns against overtourism have also been prominent, but whether appeals to tourists asking them to curb irresponsible behaviours have had any impact remains questionable as incidents continue —for example, the Palau Pledge and New Zealand’s Tiaki Promise appeal for more responsible behaviours.

Curtailing the use of the word overtourism is also posited – in the interest of avoiding the rise of moral panics and the swell of anti-tourism social movements, but pretending the phenomenon does not exist, or dwelling on semantics won’t solve the problem .

Solutions to address overtourism

The solutions to dealing adequately with the effects of overtourism are likely to be many and varied and must be tailored to the unique, relevant destination .

The tourism supply chain must also bear its fair share of responsibility. While popular destinations are understandably an easier sell, redirecting tourism beyond popular honeypots like urban heritage sites or overcrowded beaches needs greater impetus to avoid shifting the problem elsewhere.

Local authorities must exercise policy measures that establish capacity limits, then ensure they are upheld, and if not, be held responsible for their inaction .

Meanwhile, tourists themselves should take responsibility for their behaviour and decisions while travelling, as this can make a big difference to the impact on local residents .

Those investing in tourism should support initiatives that elevate local priorities and needs, and not simply exercise a model of maximum extraction for shareholders in the supply chain.

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National tourist offices and destination management organizations must support development that is nuanced and in tune with the local backdrop rather than simply mimicking mass-produced products and experiences.

The way tourist experiences are developed and shaped must be transformed to move away from outright consumerist fantasies to responsible consumption .

The overtourism problem will be solved through a clear-headed, collaborative and case-specific assessment of the many drivers in action. Finally, ignoring historical precedents that have led to the current predicament of overtourism and pinning this on oversimplified prescriptions abandons any chance of more sustainable and equitable tourism futures .

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Economic effects of tourism and its influencing factors

An overview focusing on the spending determinants of visitors, zusammenfassung.

Zahlreiche Studien belegen die ökonomische Bedeutung des Tourismus, die mit Hilfe verschiedener theoretischer Konzepte und Methodenansätze analysiert werden kann. Dieser Einführungsbeitrag in das Themenheft gibt einen Überblick über die unterschiedlichen Konzepte zu den wirtschaftlichen Wirkungen des Tourismus und arbeitet deren wichtigste Einflussfaktoren heraus. Häufig werden der räumliche Maßstab sowie die Kostenseite des Tourismus übersehen. Besonderes Augenmerk richtet der vorliegende Beitrag auf einen weiteren, entscheidenden Einflussfaktor ökonomischer Effekte, die Besucherausgaben. Die Rolle des Ausgabeverhaltens der Besucher wird unter Rückgriff auf einen umfassenden Literaturüberblick vorgestellt. Auf diese Weise ist es möglich, auf verallgemeinerbare und systematische Weise die wichtigsten Treiber des Ausgabeverhaltens von Besuchern zu identifizieren.

The economic relevance of tourism has been proven by numerous studies using various theoretical constructs and methodological approaches. This introduction to the special issue provides an overview of the different concepts of the economic effects of tourism and distinguishes their most relevant influencing factors. Often overlooked influences are the geographical scale and the cost side of tourism. A special focus of this paper lies on a further determinant of economic impact of utmost importance: visitor spending. The role of visitors’ expenditure behavior is comprehensively reviewed using an extensive literature base. Thus, we are able to identify the most important driving factors of visitor expenditure in tourism in a generalizable and systematic way.

1 Introduction

Tourism is often regarded (and used by regional developers and funding institutions) as an economic development path for structurally weak, peripheral areas, as a cure-all providing jobs and income, capital inflow and finally stopping outmigration by creating a positive socio-economic perspective for the future. However, more often than not these high hopes fall short and either the number of visitors or the resulting economic contribution or even both do not meet earlier expectations ( Vogt 2008 ; Blake et al. 2008 , p. 115; Lehmeier 2015 ; Mayer, Job 2016 ). In order to put these expectations on more realistic grounds, respectively to choose more suitable development strategies, a deeper understanding of the mechanisms is required: What influences the economic outcomes of tourism and can these determinants in turn be optimized by decision makers? However, before dealing with this question, it appears necessary to first clarify what the economic consequences of tourism activities actually are – as stakeholders tend to become confused by different concepts like economic contribution, impact or benefits, gross turnover, value added, or economic value (see section 2 )–, how they occur, how different measures vary and what costs have to be taken into account. In practice, these considerations should lead to a more realistic picture of tourism as a means of regional development and to better-reasoned strategies.

There are also additional reasons why the topic of the economic effects of tourism is very relevant for both academic research and practitioner-oriented consultancy: First, and in contrast to other sectors whose economic relevance is not contested, respectively broadly recognized (like car manufacturing in Germany for instance), tourism stakeholders need to underline the economic relevance of tourism in order to emphasize lobby efforts regarding financial resources, laws, planning, regulation, taxation and subsidies ( Hall, Page 2006 , p. 155; Stabler et al. 2010 , p. 199). There is a danger as Crompton ( 2006 , p. 67) puts it: “Most economic impact studies are commissioned to legitimize a political position rather than to search for economic truth”. Second, due to the complex structure of the different branches forming the tourism sector huge empirical efforts are required to measure the economic relevance of tourism for these sub-sectors and branches as well as for the national/regional economy in total. This complexity opened up the path for an own field of research dealing with economic analysis in tourism which has achieved considerable progress over the years. Third, studies evaluating the economic effects of tourism provide the only quantifiable results of tourism impact in monetary terms compared to image, infrastructure or competence effects of tourism where several other variables intervene ( Bieger 2001 ).

For these reasons, this special issue presents recent progress in the field of economic effects of tourism and its influencing factors by researchers from the German-speaking community. As an introduction to this special issue, this article provides an overview of the different measures of the economic importance of tourism and summarizes the influencing factors on the economic contribution of tourism using a self-developed framework ( Section 2 ). One of the most important drivers is the spending behavior of visitors. Thus, this paper offers a comprehensive review of studies dealing with the different determinants of visitor spending by systematizing the influences and drawing generalizable conclusions ( Section 3 ). Finally, this paper provides an outline of the special issue ( Section 4 ).

2 Economic effects of tourism and its influencing factors – an overview

2.1 definitions and differentiations.

The economic effects of tourism are often divided into tangible (quantitative or directly quantifiable in monetary values) and intangible (qualitative or not directly quantifiable) effects ( Woltering 2012 , p. 68; Metzler 2007 , p. 33). The positive tangible and intangible effects correspond to the benefits of tourism for societies and economies. Dwyer et al. ( 2010 , p. 222) point out that economic benefits of tourism equal neither the economic impact of tourism nor the economic contribution of tourism (see Figure 1 ). The notion of economic benefits of tourism require that a territorial entity or citizen has to be better off with tourism than without tourism. Thus, it is the net benefits that have to be analyzed, which encompass both the consideration of the costs of tourism development as well as the opportunity costs of tourism activities ( Dwyer et al. 2010 , p. 222) – defined as forgone income from alternative investment possibilities ( Job, Mayer 2012 ). This notion is related to the often neglected difference between real and distributive effects ( Hanusch 1994 , p. 8 f.; Schönbäck et al. 1997 , p. 4 f.): real effects lead to an overall improvement in the private households’ supply of goods and thus to a positive influence on the overall welfare level. In contrast, distributive effects sum up all monetary changes in the aftermath of a measure where the gains in one sector of the economy mirror the corresponding losses in another – the general welfare level remains constant ( Mayer 2013 , p. 93). Other negative economic effects of tourism for destinations are rising prices due to imported inflation and increasing demand ( Bull 1991 , p. 135) as well as potentially rising taxes because governments need to finance costly tourism infrastructure ( Stynes 1997 , p. 15).

Economic contribution, impact and benefit of tourism and influencing factors. Source: own draft, based on Ennew 2003 and Dwyer et al. 2010 , p. 213 ff.

“The economic contribution of tourism refers to tourism’s economic significance – to the contribution that tourism-related spending makes to … Gross Domestic (Regional) Product, household income, value added, foreign exchange earnings, employment” ( Dwyer et al. 2010 , p. 11, p. 213 f.). One common approach to quantify this economic contribution of tourism is Tourism Satellite Accounts (TSA) ( Spurr 2006 , Frechtling 2010 ). However, TSA as an accounting approach only measure direct effects ( Fig. 1 ), while indirect and induced effects have to be assessed using modeling approaches like input-output-(IO) models ( Ahlert 2003 , p. 19) or more recent advancements (see 2.3). This means that results of TSA and IO approaches are not directly comparable.

“While the economic contribution of tourism measures the size and overall significance of the industry within an economy, economic impact refers to the changes in the economic contribution resulting from specific events or activities that comprise ‘shocks‘ to the tourism system. This should not be confused with the contribution itself” ( Dwyer et al. 2010 , p. 216). These changes are brought about by new/non-regular tourism expenditure injected into a destination. Watson et al. (2007) provide two related definitions of economic impact underlining this understanding: “Economic impacts are the net changes in new economic activity associated with an industry, event, or policy in an existing regional economy” (p. 142). “Economic impact is the best estimation at what economic activity would likely be lost from the local economy if the event, industry, or policy were removed” (p. 143). In our case, this refers to tourism activities, such as a special event, a specific attraction or the shut-down of a previously popular accommodation. Thus, technically, the difference between the analysis of the economic contribution and the impact of tourism lies in the scope of the analysis (overall significance vs. the effect of “shocks”/”changes”) and not in the methods.

Central to both the evaluation of the economic contribution and the impact of tourism is the concept of leakages , occurring in the form of imported intermediate input from outside the country/region but also in the form of profit transfer to external headquarters or tax payments to a government. This means that not the complete share of tourism expenditure leads to income at the destination ( Hjerpe, Kim 2007 , p. 144 f.). On the national level, only the leakages to foreign countries are of interest while on a regional/ local level the share of income remaining in the survey area is crucial. This share is termed capture rate in English ( Stynes 1997 ) or “Wertschöpfungsquote“ in German ( Küpfer 2000 , p. 107 ff.; Job et al. 2009 , p. 33) and can be defined as “tourist expenditures minus leakage“ ( Hjerpe, Kim 2007 , p. 145). A similar concept is the Regional Purchase and Absorption Coefficient (RPC), defined as “the percentage of demand for a sector’s output from within the study area that is supplied by production within the study area” ( Watson et al. 2008 , p. 575). The higher the RPC, respectively the higher the capture rate, the higher the share of tourism income occurring in the survey area ( Hjerpe, Kim 2007 , p. 145; Watson et al. 2008 , p. 575). That means, from an economic geography perspective only the money actually remaining in the survey region is relevant.

As explained above, the economic contribution/impact of tourism refers to the actual expenditures of visitors. In economic valuation terminology, these expenditures represent the visitors’ revealed willingness to pay (WTP) and thus, a (quasi-)market price for recreation ( Moisey 2002 , p. 235 f.; Küpfer 2000 , p. 36). Based on this notion, one aspect is often overlooked, the value of the recreational experience , referring to the consumer surplus of visitors measured in their maximum WTP for visiting a destination/attraction minus their actual expenditure. In other words, the consumer surplus of visitation equals the difference between the visitors’ WTP and the actual expenditure. This is because visitors’ expenditures do not completely reveal their maximum WTP, which differs individually. Consequently, the economic impact only constitutes a subset of the tourism benefits and it does not equal the economic value of recreational use. This aspect is especially important where tourism attractions share characteristics of public goods, such as protected areas which do not charge entrance fees, etc. The recreational value can be estimated, for example, with the help of the travel cost method ( Carlsen 1997 ; Moisey 2002 ; Mayer, Job 2014 , p. 77).

2.2 Spatial aspects of the economic effects of tourism

The economic effects of tourism occur and are measurable on different spatial scales, from the global, continental, national to the regional and local level. Also the relevance of the effects vary along these scales. For instance, on the national level, the effects on the foreign exchange earnings are of great importance. Zooming in to the regional/ local level job creation and leakages become increasingly relevant ( Metzler 2007 , p. 33).

Not only the spatial scale should be taken into account but also the location where expenditures occur and which actors actually profit. Referring to Freyer ( 2011 , p. 41 ff.) we can distinguish between (a) the source area of tourists, (b) the travel area and (c) the destination. Each area has a differing mix of expenditure categories and empirical problems. Ad (a): in the source area, travelers seek information, book their trip and buy equipment. However, it is often problematic to assign expenditures for equipment to a specific trip or holiday. Ad (b): while on the trip, travelers spend money for gas, food, road toll, accommodation for stopovers, etc. Two problems occur: First, most transport expenditures are booked and paid for in advance in the source area and the area crossed by airplanes, trains or ships does not gain any benefits. Second, if one wants to assign the travel expenses to a specific attraction the multiple-trip bias has to be considered (round trips) ( Freeman 2003 , p. 421 f.). Ad (c): at the destination, tourists pay for accommodation, gastronomy, groceries, activities, souvenirs, services, etc. Thus, for a regional economic analysis (for instance of events or specific attractions), the spatial limitation, respectively the size of the destination are crucial influencing factors (see below) “because a good proportion of total spending by spectators might not [have] been incurred within the community” ( Gelan 2003 , p. 411). Furthermore, for these evaluations of attractions or events mostly only the expenditure at the destination is considered while expenditure on the trip or in the source area is disregarded. In general, the economic impact of an event/ attraction is likely inversely related to the distance from its location in space ( Gelan 2003 ). Additional insights into the spatial aspects of the economic effects of tourism can be found in several, mostly case study contributions (e.g. Connell, Page 2005 ; Daniels 2007 ; He et al. 2008 ) which cannot be presented here in detail due to space constraints. However, there is to date no comprehensive review of these works and it would be worth compiling.

2.3 Factors influencing economic effects of tourism

This sub-section presents the factors influencing the economic effects of tourism and discusses the input variables for its analysis. Loomis, Caughlan ( 2006 , p. 33 ff.) sum up the basic requirements for any analysis of the economic contribution/impact of tourism: (a) number of visitor days; (b) spending amounts per visitor; (c) types of visitors and trip purposes; and (d) an economic model to calculate multiplier effects. In addition, there is a moderating effect of the spatial limitation of the survey area.

Ad (a): The number of visitor days is often confused with the number of visitors which could be identical in some cases but most often both measures differ. For overnight tourism also the length of stay, the number of visits to specific attractions or the frequency of an activity have to be taken into account. In this issue, Arnberger et al. (2016) discuss the methods of visitor counting in detail.

Considering visitation it is debatable whether economic impacts of tourism should be used on a national scale, because those of domestic tourism represent distributive effects only ( Küpfer 2000 , p. 68 f.). These visitors would have spent their vacation in their home country anyway or would have visited another destination there instead. Only incoming tourists provide additional input for the national economy ( Schönbäck et al. 1997 , p. 191; Baaske et al. 1998 , p. 159 f.). However, one might argue that a domestic trip can avoid a trip abroad which would lead to leakage from the national economy ( Mayer, Job 2014 , p. 79).

Similarly, it is contested whether local residents in the survey areas should be included in the regional economic assessments. Some maintain that locals should be excluded as their expenditures are considered a re-circulation of preexisting income in the region ( Dwyer et al. 2004 , p. 313 f.; Loomis, Caughlan 2006 ; Crompton et al. 2001 , p. 81). Conversely others argue that ignoring locals’ expenditures will lead to an underestimate of total impacts ( Johnson, Moore 1993 , p. 287). Locals could also spend their money outside their home region again leading to leakages ( Ryan 1998 , p. 345).

Ad (b) Expenditure : Stynes, White (2006) sum up the most important dos and don’ts when it comes to analyzing visitors’ spending behavior, while Frechtling (2006) reviews several methods and models used to estimate visitor expenditures. The third section of this article deals with the influences on expenditure patterns in detail. In addition, Butzmann (2016) analyzes the expenditures of nature tourists in his contribution to this issue.

Ad (c) Trip purpose : In order not to overestimate the economic contribution of specific attractions/ activities the trip purpose has to be analyzed. It is decisive that only those expenditures are considered which are spent in addition to the money spent anyway at the destination as the spenders would have traveled there even if the attraction in question did not exist ( Dixon, Sherman 1990 , p. 155 ff.; Küpfer 2000 ; Job et al. 2009 ; Loomis, Caughlan 2006 , p. 33 ff.).

Ad (d) Multipliers : Economic models are inevitably necessary to estimate the indirect and induced economic effects of tourism and are often regarded as the most complex part of the evaluation process. The evolution of methodologies started with comparatively simple multipliers ( Archer 1977 ) and continued with superior input-output models (IO) ( Fletcher 1989 ). The latter, however, exhibit methodological shortcomings owing to restrictive assumptions like the “free, unrestricted flow of resources to [...] the economy. [...] As a result, it [the IO model] does not capture the feedback effects, which typically work in opposite directions to the initial change“ ( Dwyer et al. 2004 , p. 307; Armstrong, Taylor 2000 , p. 56 ff.). As important improvements to the IO social accounting matrices (SAM) ( Wagner 1997 ) and computable general equilibrium models (CGE) [1] ( Dwyer et al. 2004 ) were proposed, which are able to incorporate resource restrictions and feedback effects ( Zhang et al. 2007 ). The CGE are most likely the most advanced group of multiplier models overcoming many of the overestimation effects of IO-models ( Blake 2005 ; Song et al. 2012 ), even though they still have their drawbacks. These include some restrictive assumptions like constant returns to scale in production functions and perfect markets ( Croes, Severt 2007 ), high input data quality requirements and related costs or the not very vivid presentation of results ( Pfähler 2001 ). Thus, when comparing CGE models to conventional IO Klijs et al. (2012) conclude that CGE models are inferior in terms of transparency (the predictability of results), efficiency (data, time and cost) and comparability (standardization of model structure, complexity and assumptions). In addition, the analysis of past data is beyond the scope of CGE models because they “simulate what will happen in the economy as a consequence of external shocks, but do not state what has already happened” ( Ivanov, Webster 2007 , p. 380). Further (dis)advantages of the modeling approaches are discussed in the academic literature ( West 1995 , Dwyer et al. 2010 , Chap. 7-9, Pratt 2015 , p. 151).

The magnitude of multiplier effects is decisively influenced by three factors ( Archer 1977 : 29 ff., Archer, Fletcher 1996 : p. 58 ff., Wall 1997 , p. 447; Hall, Page 2006 , p. 155): (1) The size of the survey area to which the multiplier refers because the possibilities for economic autarky largely depend on this size. The number of potential spending rounds is also influenced. The larger the survey area, the larger the multipliers and the lower the leakages. (2) The level of economic development of a region: “The more that the inputs of enterprises can be acquired locally, the smaller will be the leakage and the larger will be the multiplier“ ( Wall 1997 , p. 447). However, there is no automatism for higher multipliers due to complex interregional value chains nowadays. (3) The expenditure structure: the higher the locally produced share of goods/ services, the higher also the resulting direct and indirect effects.

The sensitivity of the economic contribution of tourism to changes in these influencing factors is seldom analyzed, one important exception being Woltering ( 2012 , p. 249 ff.). Finally, all estimation approaches necessarily rely on reliable empirical data input about the number of visitors and their expenditures. Without those appropriate measures, even the most detailed, theoretically sound economic model would provide misleading results ( Tyrrell et al. 2001 , p. 94). Besides, Crompton et al. ( 2001 , p. 80 ff.) stress that “economic impact analysis is an inexact process, and output numbers should be regarded as a ‘best guess’ rather than as being inviolably accurate”. This quotation refers to the inherent problems of all economic valuation approaches, as does the lack of comparability of TSA and IO results: estimations of the economic effects of tourism should not be regarded as incontestable, because they are open to interpretation and misuse ( Crompton 2006 ). Consequently, a critical assessment of different economic valuation studies should take into account who is estimating which values using which approaches and models based on which assumptions and data input funded by whom. As issues of power and attempts to influence results can never be completely excluded it would be a task for a critical (economic) geography of tourism in the sense of Britton (1991) to deal with these related questions.

The following section focuses on one of the four basic requirements for any economic evaluation of tourism, variable expenditure. Along with visitor days spending behavior is the most influential driver of the economic effects of tourism and, thus, warrants special attention. Section 4 makes clear how the other influencing factors on the economic importance of tourism are addressed in this special issue.

3 Tourist expenditure: an overview of spending drivers

3.1 general issues.

The research history of tourists’ spending patterns is comparatively short. Wang and Davidson (2010) highlight that apart from a study undertaken in the 1970s ( Mak et al. 1977a , b ) the research community started focusing on the issue only in the 1990s. Most of these studies have been case studies ( Xiao, Smith 2006 ), so conclusions referring to a larger population cannot be drawn ( Gerring 2007 ). For a validation of such findings they can be triangulated by comparing results with those from case studies at different sites ( Decrop 1999 ). Brida, Scuderi (2012) , however, point out the problems of generalizing such empirical findings, as different models, dependent variables and regressors using inter alia different scales of measure are employed. Mak et al. (1977a) showed, furthermore, that different spending measurement methods (spending diaries vs. recall after their return home) lead to different results. Considering the caveats mentioned this chapter aims at outlining the most significant findings on expenditure patterns using a narrative review approach. Sampling of studies was based on a systematic research in the web of knowledge® provided by Thompson Reuters; search terms were “tourism* expenditure* determinants”, “tourism* expenditure behavior”, “tourism* expenditure”, combined with tourism forms (nature tourism, mountain tourism) in addition. We have included only destination-based studies in the analysis and omitted studies comprising expenditure in the areas of origin. Database entries up to December 2015 have been taken into account. 50 papers fulfilled the criteria and form the basis for the following evaluation.

To obtain a quick overview, it helps to systematize the predictor variables analyzed. Following Pouta et al. (2006) and Woltering (2007) , we systematize drivers of expenditure of the empirical studies into tourist-, travel- and destination-based variables analyzed. Omitted are macroeconomic variables such as the GDP or the price level in the countries of origin, destination and competing destinations (analyzed e.g. by Saayman, Saayman 2015 ); they are mainly relevant for explaining spending behavior of international tourists in different countries.

Tables 1 - 3 summarize the findings from previous studies regarding the statistical significance and the signs of the independent variables. The statistical methods used range from variance analyses to regression methods (OLS or quantile regression) or more advanced econometric techniques (double-hurdle, Heckit and similar methods). Moreover, the expenditure variable varies and takes the level or the log form ( Thrane 2014 ). Some studies apply several statistical models in the same paper to compare results (being usually but not in all cases quite similar); only the first mentioned model is included here. Studies differ, furthermore, regarding how they define spending (average per person or group, respectively per day or journey). Moreover, few studies not only measure spending at the destination itself but also in the country or region of origin (e.g. Alegre et al. 2011 ). In these cases, only expenditure at the destinations or total spending has been considered. Further studies do not use a single expenditure variable but differentiate spending in categories such as accommodation or food and beverages (e.g. Marcussen 2011 , Brida et al. 2013 ). If these studies include a total expenditure indicator only this variable is analyzed, if not, the variables considered are indicated.

Tourist-based drivers of visitor expenditure

Significant results (p < 0.05):+ = positive  - = negative  o = neutral

s. = significant categorical variable; n.s. = tested, but results not significant; p. (= partly)/m. (= mostly) s.: categorical variable with some but not all significant feature characteristics / … with significant feature characteristics except for one.

Significant results (p < 0.05): + = positive  - = negative  o = neutral; n.s. = tested, but results not significant; s. = significant categorical variable; p. (= partly)/m. (= mostly) s.: categorical variable with some but not all (not) significant feature characteristics / … with significant feature characteristics except for one.

d.v.: dependent variable; (1) group spending per stay; (1a) group spending per day; (2) individual spending per stay; (2a) individual spending per day; (3) not specified, probably group spending per day; (4) total travel spending per stay; (5) spending per day; (6) overnight; (7) dayvisitor

Destination-based drivers of visitor expenditure

Significant results (p < 0.05): + = positive - = negative o = neutral

s. = significant, categorical variable; n.s. = tested, but results not significant; p. (= partly) s. = categorical variable with some but not all significant feature

3.2 Tourist-based variables

Tourist-based variables relate to the travelers themselves and are based upon variables identified as decisive for consumption decisions in general ( Meffert 2000 ). They include socio-demographic variables such as age, gender, marital status, income, education and profession, and geographical variables reflecting the spatial and economic structure in the visitors’ region of origin ( Table 1 ). In many studies, age has been tested as a predictor variable with ambiguous results: in 11 out of 31 studies age was not found to influence spending in a statistically significant way, in seven studies spending depends positively on the age of visitors and four times a negative relation was found. The findings of Aguilo Perez, Juaneda Sampol (2000) , Pouta et al. (2006) , Thrane, Farstad (2011) or García-Sánchez et al. (2013) suggest that age might not be related to expenditure in a linear but curvilinear way. That means low spending is found in the youngest and the oldest age segments whereas high spenders are middle aged. Gender and marital status do not seem to predict spending in general ( Lawson 1994 ; Wang et al. 2006 ); this is reflected in the large share of non-significant results in the studies reviewed even though, for example, Mak et al. (1977b) found the latter variable to be significant. In contrast, income can generally be regarded as a reliable predictor ( Fish, Waggle 1996 ): consistent with economic theory the relationship between income level and tourism expenditure is positive in 21 out of 29 studies with Agarwal and Yochum (1999) , Downward, Lumsdon (2003) , Fredman (2008) as well as Thrane, Farstad (2011) reporting inelastic relations. This means with growing income, tourism expenditure increases as well but at a lower rate. Profession and level of education are only significant occasionally (possibly due to multicollinearities with the income variable), whereas the country of origin tends to be a good indicator of spending levels. The type of residential location does not seem to influence travel expenditure.

3.3 Travel-based variables

Table 2 summarizes the results of visitor expenditure studies regarding observable characteristics of the journey. The sign of group size seems to vary ambiguously: 10 out of 29 studies report positive signs, 14 studies negative signs, and two different signs according to the dependent variable ( Kozak et al. 2008 ; Marcussen 2011 ). The most straightforward explanation for the varying sign is the dependent variable. With group spending, expenditure tends to rise the larger the group, whereas with individual spending expenditure tends to fall due to cost-sharing.

The effect of travel length depends on the exact specification of the dependent variable as well. It is usually positive when total travel expenditure is analyzed. The influence of length of stay tends to be negative with per day expenditure as a dependent variable. Non-linear effects can be observed: for longer trips the generally positive relationship between length of stay and total expenditure becomes weaker, a diminishing positive effect was observed, theoretically explained by economies of scale ( Thrane, Farstad 2011 ; Aguilo Perez, Juaneda Sampol 2000 ; Roehl, Fesenmaier 1995 ).

The variable visitor type (vacationists vs. day trippers) has a significant influence as day trippers spend significantly less than overnight visitors due to lack of accommodation expenditure. The type of accommodation is usually a significant variable as well. As expected, commercial accommodation (i.e. hotels) is generally economically most relevant, followed by rented apartments, with campgrounds and friends/ relatives generating the lowest expenditures (e.g. Agarwal, Yochum 1999 ; Fredman 2008 ; García-Sánchez et al. 2013 ). Interestingly, Kastenholz (2007) found camping tourists to be the heaviest spenders in nature tourism destinations in Portugal, and Kozak et al. (2008) reported a negative relationship between hotel accommodation and spending in Turkey, which might be explained by the sun-and-sand character of the destination. Individually organized travelers tend to spend more than package tourists in the destination region. In visitor surveys, however, it remains unclear which part of the package tour expenses paid in the region of origin flows to the destination. Only eight studies measured the influence of the means of transportation on visitor spending. In most cases the influence on expenditure levels is significant. Following Downward, Lumsdon (2004) and Svensson et al. (2011) visitors traveling by car spent more than those using public transport, whereas Fredman (2008) , Marcussen (2011) , Thrane, Farstad (2011) , Abbruzzo et al. (2014) report higher expenditure by visitors using planes and trains. The number of visits to a destination usually reflects loyalty to a destination as well as familiarity with the place and insider knowledge, with more visits possibly associated with less spending ( Alegre, Juaneda Sampol 2006 ). However, eight of the 13 studies with significant results for this predictor found that repeaters spent more. The existence of potential nonlinear effects has not been controlled for, yet. The variable travel motives often produces significant results as motives tend to be very heterogeneous and can be both push and pull motives.

Activities are not a straightforward predictor of visitor spending because, as with motives, results depend on the heterogeneity of activities sampled. In general, there seems to be a tendency for more infrastructure-related activities to influence higher expenditure. Kozak et al. (2008) find, for example, that those tourists who rate the standard of nightlife and entertainment as very important are heavy spenders.

3.4 Destination-based variables

Table 3 sums up the impact of destination-based factors on spending. According to Mak et al. (1977a) , Leones et al. (1998) , Lee (2001) and Pouta et al. (2006) distance to the destination and visitors’ expenditure are positively related. The perception of prices at the destination affects spending as well. Aguilo Perez, Juaneda Sampol (2000) found that visitors, who regard the destination as expensive tend to spend more. Abbruzzo et al. (2014) focused on satisfaction with the price level and figured out that those tourists spent more who had a positive opinion of the price level but the opposite was also true. Satisfaction with the holiday had a positive effect on visitor spending ( Aguilo Perez, Juaneda Sampol 2000 ; Craggs, Schofield 2009 ; Serra et al. 2015 ). The characteristics of a destination or site are only testable if the sample covers different destinations/sites at a destination. Lee (2001) and Díaz-Pérez et al. (2005) analyzed the influence of various types of boating trips and accommodation on different Canary Islands and obtained some significant results. Abbruzzo et al. (2014) show that visitors at famous destinations in Uruguay spent significantly more than tourists who stayed in other places. Brida et al. (2013) demonstrated that different spending levels of visitors for food and beverages were related to different Christmas markets in Northern Italy. Territorial effects of tourist spending were also found by Svensson et al. (2011) for Andalusia: visitors in cities caused larger turnover than those in rural areas and both types spent more than tourists at coastal destinations. Likewise Thrane, Farstad (2011) found that depending on the urbanity, respectively location size and remoteness the expenditures of Norwegian domestic tourists vary significantly with visitors in rural and mountain/wilderness settings spending the least. Pouta et al. (2006) tested the influence of the supply of outdoor services and recreation opportunities on expenditure at a destination level. Downhill skiing possibilities are associated with a probability for higher expenditure, while berry and mushroom picking opportunities are related to a low spending level. These findings show, that, as expected the roles of outdoor activities and their supply are strongly connected.

Finally, the seasonal effect is unclear. In two studies low-season tourists spent more than high-season visitors, but in other studies the opposite was observed. Three studies did not find any statistically significant results.

3.5 Discussion and implications

To sum up, the most significant tourist-based determinant of tourism spending is income no matter how operationalized. This finding fits well in general microeconomic theory that postulates the importance of income for demand. Profession and education did not achieve significant results in many cases. A better proxy for income seems to be the country of origin. Gender, marital status, and the type of residential location are not drivers of tourism expenditure according to most of the studies. This shows that these variables influence neither spending capacities nor preferences. Age, in contrast, determines spending in some studies, but not, if included as a linear metric variable. This might be due to a link between age and income with middle aged groups earning and spending the most, but this might also be caused by a correlation of age groups with specific interests and activities, which differ in price. Future analyses could therefore limit tourist-based variables to income and age. However, although economic factors are decisive as they permit people to travel, they cannot fully explain tourist expenditure. Several travel-based variables increase the explanatory power of spending models. Group size obviously influences the amount of spending in a significant way. Apparently, scale effects occur, leading to cost savings. Economies of scale also come into effect with length of stay of tourists. Nevertheless, for destination organizations it might be more efficient, from a cost-benefit perspective, to convince tourists to stay longer (as difficult as that may be) than to make great efforts to attract more visitors. Comparing vacationists and day-trippers, the dominant role of accommodation as a spending variable becomes evident. Overnight tourists spend significantly more. Higher sales are generated with specific types of accommodation, in detail dependent on destinations. Concerning the impact of the trip organization it might be interesting from a regional economic perspective to take a closer look at the share of all-inclusive prices paid by package tourists that actually becomes effective at destinations. Due to inconsistent results concerning the transportation mode, conclusions cannot be drawn. It could be assumed that specific means of transportation are related to the place of origin of tourists and the distance to destination. So, transportation mode might be a proxy for the country of origin, the GDP and the income. The influence of means of transportation on spending levels would thus depend on the destination.

Further investigation is needed for the relation between the number of visits and spending levels as results are quite contradictory. Moreover, the travel motives and activities examined are very heterogeneous so that clear results are missing, although infrastructure related activities seem to have a positive influence. Therefore, it might be interesting to compare the impact of specific travel activities on expenditure at specific types of destinations (such as monumental cities, mountains, sun and sea).

We suggest that the incorporation of variables related to supply and characteristics of destinations would help to further understand spending behavior as demand usually also depends on attributes of supply and satisfaction with it. Especially regarding the characteristics of destinations the findings show a significant impact on the spending level. Further research is needed to verify these results. Particular attention should be paid to differentiate destination characteristics if implications for destination management are to be provided. The same holds true for the influence of season on expenditure as existing results do not provide a clear picture. According to microeconomic theory, high expenditure levels would be expected for high seasons (when demand is high for scarce resources) but some findings contradict this assumption. Future studies should thus analyze the impact of season together with travel motives, activities and destination characteristics.

In conclusion, this review showed that several travel- and destination-based variables are under-researched and future studies should devote special attention to these factors. Further practical implications are very difficult to make at this aggregate level without referring to specific destinations (cp. Mayer, Vogt 2016 ).

4 Outline of the special issue

The remaining articles of this special issue either center on the factors influencing the economic effects of tourism or deal with the actual economic outcome of tourism activities.

Arnberger et al. (2016) focus on visitor monitoring methods and exemplify their best practice approaches for the case of the frequentation of three shortdistance recreation areas in and near Vienna, Austria (among others, the Donau-Auen National Park). These detailed analyses of visitation intensities and patterns showcase the essential base for reliable economic contribution/ impact assessments.

The next influencing factor in the logical sense of an economic impact analysis is the expenditure behavior of visitors. While the introductory article concentrates on the general determinants of visitor spending (see Chap. 3) Butzmann (2016) conducts an in-depth analysis of the visitor expenditures in Berchtesgaden National Park situated in the German Alps. He uses two different visitor samples of park visitors to work out an expenditure-, attitude- and behavior-based visitor segmentation employing statistical latent class procedures. This segmentation aims at closing the often bemoaned sustainability-profitability gap in nature tourism by trying to identify economically as well as ecologically favorable visitor groups.

The next contribution by Stettler et al. (2016) demonstrates how economic impact analyses could be practically applied to assess the profitability of several sport events of varying scope, prominence and impact (among others the European Football Championships 2008, jointly hosted by Switzerland and Austria) from a societal perspective: Is it worth investing public funds in these events? Their article also clearly shows the limitations of this approach as the economic impact of event-motivated tourism only constitutes parts of the benefits of these events while the cost side is mostly disregarded.

Finally, Küblböck and Standar (2016) deal with one of the major economic benefits of tourism, the tourism labor market. They analyze the effects and reasons for shortages of a skilled labor force in the hospitality sector in Germany, exemplified with detailed empirical fieldwork in the region Braunschweig-Wolfsburg. In addition, they discuss potential strategies for coping with this apparent problem which could reduce the economic benefits of tourism in the near future.

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The relationship between tourism and economic growth among BRICS countries: a panel cointegration analysis

• Haroon Rasool   ORCID: orcid.org/0000-0002-0083-4553 1 ,
• Shafat Maqbool 2 &
• Md. Tarique 1  

Future Business Journal volume  7 , Article number:  1 ( 2021 ) Cite this article

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Tourism has become the world’s third-largest export industry after fuels and chemicals, and ahead of food and automotive products. From last few years, there has been a great surge in international tourism, culminates to 7% share of World’s total exports in 2016. To this end, the study attempts to examine the relationship between inbound tourism, financial development and economic growth by using the panel data over the period 1995–2015 for five BRICS (Brazil, Russia, India, China and South Africa) countries. The results of panel ARDL cointegration test indicate that tourism, financial development and economic growth are cointegrated in the long run. Further, the Granger causality analysis demonstrates that the causality between inbound tourism and economic growth is bi-directional, thus validates the ‘feedback-hypothesis’ in BRICS countries. The study suggests that BRICS countries should promote favorable tourism policies to push up the economic growth and in turn economic growth will positively contribute to international tourism.

Introduction

World Tourism Day 2015 was celebrated around the theme ‘One Billion Tourists; One Billion Opportunities’ highlighting the transformative potential of one billion tourists. With more than one billion tourists traveling to an international destination every year, tourism has become a leading economic sector, contributing 9.8% of global GDP and represents 7% of the world’s total exports [ 59 ]. According to the World Tourism Organization, the year 2013 saw more than 1.087 billion Foreign Tourist Arrivals and US $1075 billion foreign tourism receipts. The contribution of travel and tourism to gross domestic product (GDP) is expected to reach 10.8% at the end of 2026 [ 61 ]. Representing more than just economic strength, these figures exemplify the vast potential of tourism, to address some of the world´s most pressing challenges, including socio-economic growth and inclusive development.

Developing countries are emerging as the important players, and increasingly aware of their economic potential. Once essentially excluded from the tourism industry, the developing world has now become its major growth area. These countries majorly rely on tourism for their foreign exchange reserves. For the world’s forty poorest countries, tourism is the second-most important source of foreign exchange after oil [ 37 ].

The BRICS (Brazil, Russia, India, China and South Africa) countries have emerged as a potential bloc in the developing countries which caters the major tourists from developed countries. Tourism becomes major focus at BRICS Xiamen Summit 2017 held in China. These countries have robust growth rate, and are focal destinations for global tourists. During 1990 to 2014, these countries stride from 11% of the world’s GDP to almost 30% [ 17 ]. Among BRICS countries, China is ranked as an important destination followed by Brazil, Russia, India and South Africa [ 60 ].

The importance of inbound tourism has grown exponentially, because of its growing contribution to the economic growth in the long run. It enhances economic growth by augmenting the foreign exchange reserves [ 38 ], stimulating investments in new infrastructure, human capital and increases competition [ 9 ], promoting industrial development [ 34 ], creates jobs and hence to increase income [ 34 ], inbound tourism also generates positive externalities [ 1 , 14 ] and finally, as economy grows, one can argue that growth in GDP could lead to further increase in international tourism [ 11 ].

The tourism-led growth hypothesis (TLGH) proposed by Balaguer and Cantavella-Jorda [ 3 ], states that expansion of international tourism activities exerts economic growth, hence offering a theoretical and empirical link between inbound tourism and economic growth. Theoretically, the TLGH was directly derived from the export-led growth hypothesis (ELGH) that postulates that economic growth can be generated not only by increasing the amount of labor and capital within the economy, but also by expanding exports.

The ‘new growth theory,’ developed by Balassa [ 4 ], suggests that export expansion can trigger economic growth, because it promotes specialization and raises factors productivity by increasing competition, creating positive externalities by advancing the dispersal of specialized information and abilities. Exports also enhance economic growth by increasing the level of investment. International tourism is considered as a non-standard type of export, as it indicates a source of receipts and consumption in situ. Given the difficulties in measuring tourism activity, the economic literature tends to focus on primary and manufactured product exports, hence neglecting this economic sector. Analogous to the ELGH, the TLGH analyses the possible temporal relationship between tourism and economic growth, both in the short and long run. The question is whether tourism activity leads to economic growth or, alternatively, economic expansion drives tourism growth, or indeed a bi-directional relationship exists between the two variables.

To further substantiate the nexus, the study will investigate the plausible linkages between economic growth and international tourism while considering the relative importance of financial development in the context of BRICS nations. Financial markets are considered a key factor in producing strong economic growth, because they contribute to economic efficiency by diverting financial funds from unproductive to productive uses. The origin of this role of financial development may is traced back to the seminal work of Schumpeter [ 50 ]. In his study, Schumpeter points out that the banking system is the crucial factor for economic growth due to its role in the allocation of savings, the encouragement of innovation, and the funding of productive investments. Early works, such as Goldsmith [ 18 ], McKinnon [ 39 ] and Shaw [ 51 ] put forward considerable evidence that financial development enhances growth performance of countries. The importance of financial development in BRICS economies is reflected by the establishment of the ‘New Development Bank’ aimed at financing infrastructure and sustainable development projects in these and other developing countries. To the best of the authors’ knowledge, no attempt has been made so far to investigate the long-run relationship Footnote 1 between tourism, financial development and economic growth in case of BRICS countries. Hence, the present study is an attempt to fill the gap in the existing literature.

Review of past studies

From last few decades there has been a surge in the research related to tourism-growth nexus. The importance of growth and development and its determinants has been studied extensively both in developed and developing countries. Extant literature has recognized tourism as an important determinant of economic growth. The importance of tourism has grown exponentially, courtesy to its manifold advantages in form of employment, foreign exchange production household income and government revenues through multiplier effects, improvements in the balance of payments and growth in the number of tourism-promoted government policies [ 21 , 41 , 53 ]. Empirical findings on tourism and economic development have produced mixed finding and sometimes conflicting results despite the common choice of time series techniques as a research methodology. On empirical grounds, four hypotheses have been explored to determine the link between tourism and economic growth [ 12 ]. The first two hypotheses present an account on the unidirectional causality between the two variables, either from tourism to economic growth (Tourism-led economic growth hypothesis-TLGH) or its reserve (economic-driven tourism growth hypothesis-EDTH). The other two hypotheses support the existence of bi-directional hypothesis, (bi-directional causality hypothesis-BC) or that there is no relationship at all (no causality hypothesis-NC), respectively. According to TLEG hypothesis, tourism creates an array of benefits which spillover though multiple routes to promote the economic growth [ 55 ]. In particular, it is believed that tourism (1) increases foreign exchange earnings, which in turn can be used to finance imports [ 38 ], (2) it encourages investment and drives local firms toward greater efficiency due to the increased competition [ 3 , 31 ], (3) it alleviates unemployment, since tourism activities are heavily based on human capital [ 10 ] and (4) it leads to positive economies of scale thus, decreasing production costs for local businesses [ 1 , 14 ]. Other recent studies which find evidence in favor of the TLGH hypothesis include [ 44 , 52 ]. Even though literature is dominated by TLGH, few studies produce a result in support of EDTH [ 40 , 41 , 45 ]. Payne and Mervar [ 45 ] posit that tourism growth of a country is mobilized by the stability of well-designed economic policies, governance structures and investments in both physical and human capital. This positive and vibrant environment creates a series of development activities which proliferate and flourish the tourism. Pertaining to the readily available information, bi-directional causality could also exist between tourism income and economic growth [ 34 , 49 ]. From a policy view, a reciprocal tourism–economic growth relationship implies that government agendas should cater for promoting both areas simultaneously. Finally, there are some studies that do not offer support to any of the aforementioned hypotheses, suggesting that the impact between tourism and economic growth is insignificant [ 25 , 47 , 57 ]. There is a vast literature examining the relationship between tourism and growth as a result, only a selective literature review will be presented here.

Banday and Ismail [ 5 ] used ARDL cointegration model to test the relationship between tourism revenue and economic growth in BRICS countries from the time period of (1995–2013). The study validates the tourism-led growth hypothesis for BRICS countries, which evinces that tourism has positive influence on economic growth.

Savaş et al. [ 54 ] evaluated the tourism-led growth hypothesis in the context of Turkey. The study employed gross domestic product, real exchange rate, real total expenditure and international tourism arrivals to sketch out the causality among variables. The result reveals a unidirectional relationship between tourism and real exchange rate. The findings suggest that tourism is the driving force for economic growth, which in turn helps turkey to culminate its current account deficit.

Dhungel [ 15 ] made an effort to investigate causality between tourism and economic growth, In Nepal for the period of (1974–2012), by using Johansen’s cointegration and Error correction model. The result states that unidirectional causality exists in the long run, while in short run no causality exists between two constructs. The study emphasized that strategies should be devised to attain causality running from tourism to economic growth.

Mallick et al. [ 36 ] analyzed the nexus between economic growth and tourism in 23 Indian states over a period of 14 years (1997–2011). Using panel autoregressive distributed lag model based on three alternative estimators such as mean group estimator, pooled mean group and dynamic fixed effects, Research found that tourism exerts positive influence on economic growth in the long run.

Belloumi [ 8 ] examines the causal relationship between international tourism receipts and economic growth in Tunisia by using annual time series data for the period 1970–2007. The study uses the Johansen’s cointegration methodology to analyze the long-run relationship among the concerned variables. Granger causality based Vector error correction mechanism approach indicates that the revenues generated from tourism have a positive impact on economic growth of Tunisia. Thus, the study supports the hypothesis of tourism-driven economic growth, which is specific to developing countries that base their foreign exchange earnings on the existence of a comparative advantage in certain sectors of the economy.

Tang et al. [ 58 ] explored the dynamic Inter-relationships among tourism, economic growth and energy consumption in India for the period 1971–2012. The study employed Bounds testing approach to cointegration and generalized variance decomposition methods to analyze the relationship. The bounds testing and the Gregory-Hansen test for cointegration with structural breaks consistently reveals that energy consumption, tourism and economic growth in India are cointegrated. The study demonstrated that tourism and economic growth have positive impact on energy consumption, while tourism and economic growth are interrelated; with tourism exert significant influence on economic growth. Consequently, this study validates the tourism-led growth hypothesis in the Indian context.

Kadir and Karim [ 24 ]) examined the causal nexus between tourism and economic growth in Malaysia by applying panel time series approach for the period 1998–2005. By applying Padroni’s panel cointegration test and panel Granger causality test, the result indicated both short and long-run relationship. Further, the panel causality shows unidirectional causality directing from tourism receipts to economic growth. The result provides evidence of the significant contribution of tourism industry to Malaysia’s economic growth, thereby justifying the necessity of public intervention in providing tourism infrastructure and facilities.

Antonakakis et al. [ 2 ] test the linkage between tourism and economic growth in Europe by using a newly introduced spillover index approach. Based on monthly data for 10 European countries over the period 1995–2012, the findings suggested that the tourism–economic growth relationship is not stable over time in terms of both magnitude and direction, indicating that the tourism-led economic growth (TLEG) and the economic-driven tourism growth (EDTG) hypotheses are time-dependent. Thus, the findings of the study suggest that the same country can experience tourism-led economic growth or economic-driven tourism growth at different economic events.

Oh [ 41 ] verifies the contribution of tourism development to economic growth in the Korean economy by applying Engle and Granger two-stage approach and a bivariate Vector Autoregression model. He claimed that economic expansion lures tourists in the short run only, while there is no such long-run stable relationship between international tourism and economic development in Korea.

Empirical studies have pronouncedly focused on the literature that tourism promotes economic growth. To further substantiate the nexus, the study will investigate the plausible linkages between economic growth and international tourism while considering the relative importance of financial development in the context of BRICS nations. The inclusion of financial development in the examination of tourism-growth nexus is a unique feature of this study, which have an influencing role in economic growth as financial development has been theoretically and empirically recognized as source of comparative advantage [ 22 ].

This study employs panel ARDL cointegration approach to verify the existence of long-run association among the variables. Further, study estimated the long-run and short-run coefficients of the ARDL model. Subsequently, Dumitrescu and Hurlin [ 16 ] panel Granger causality test has been employed to check the direction of causality between tourism, financial development and economic growth among BRICS countries.

Database and methodology

Data and variables.

The study is analytical and empirical in nature, which intends to establish the relationship between economic growth and inbound tourism in BRICS countries. For the BRICS countries, limited studies have been conducted depicting the present scenario. Therefore, present study tries to verify the relevance of tourism in economic growth to further enhance the understanding of economic dynamics in BRICS countries. The data used in the study are annual figures for the period stretching from 1995 to 2015, consisting of one endogenous variable (GDP per capita, a proxy for economic growth) and two exogenous variables (international tourism receipts per capita and financial development). The variables employed in the study are based on the economic growth theory, proposed by Balassa [ 4 ], which states that export expansion has a relevant contribution in economic growth. Further, this study incorporates financial development in the model to reduce model misspecification as it is considered to have an influencing role in economic growth both theoretically and empirically [ 22 , 33 ].

The annual data for all the variables have been collected from the World Development Indicators (WDI, 2016) database. The variables used in the study includes gross domestic product per capita (GDP) in constant ($US2010) used as a proxy for economic growth (EG), international tourism receipts per capita (TR) in current US$ as it is widely accepted that the most adequate proxy of inbound tourism in a country is tourism expenditure normally expressed in terms of tourism receipts [ 32 ] and financial development (FD). In line with a recent study on the relationship between financial development and economic growth by Hassan et al. [ 19 ], financial development is surrogated by the ratio of the broad money (M3) to real GDP for all BRICS countries. Here we use the broadest definition of money (M3) as a proportion of GDP– to measure the liquid liabilities of the banking system in the economy. We use M3 as a financial depth indicator, because monetary aggregates, such as M2 or M1, may be a poor proxy in economies with underdeveloped financial systems, because they ‘are more related to the ability of the financial system to provide transaction services than to the ability to channel funds from savers to borrowers’ [ 26 ]. A higher liquidity ratio means higher intensity in the banking system. The assumption here is that the size of the financial sector is positively associated with financial services [ 29 ]. All the variables have been taken into log form.

Unit root test

To verify the long-run relationship between tourism and economic growth through Bounds testing approach, it is necessary to test for stationarity of the variables. The stationarity of all the variables can be assessed by different unit root tests. The study utilizes panel unit root test proposed by Levin et al. [ 35 ] henceforth LLC and Im et al. [ 23 ] henceforth IPS based on traditional augmented Dickey–Fuller (ADF) test. The LLC allows for heterogeneity of the intercepts across members of the panel under the null hypothesis of presence of unit root, while IPS allows for heterogeneity in intercepts as well as in the slope coefficients [ 48 ].

Panel ARDL approach to Cointegration

After checking the stationarity of the variables the study employs panel ARDL technique for Cointegration developed by Pesaran et al. [ 23 ]. Pesaran et al. [ 23 ] have introduced the pooled mean group (PMG) approach in the panel ARDL framework. According to Pesaran et al. [ 23 ], the homogeneity in the long-run relationship can be attributed to several factors such as arbitration condition, common technologies, or the institutional development which was covered by all groups. The panel ARDL bounds test [ 46 ] is more appropriate by comparing other cointegration techniques, because it is flexible regarding unit root properties of variables. This technique is more suitable when variables are integrated at different orders but not I (2). Haug [ 20 ] has argued that panel ARDL approach to cointegration provides better results for small sample data set such as in our case. The ARDL approach to cointegration estimates both long and short-run parameters and can be applied independently of variable order integration (independent of whether repressors are purely I (0), purely I(1) or combination of both. The ARDL bounds test approach used in this study is specified as follows:

where Δ is the first-difference operator, \(\alpha_{0}\) stands for constant, t is time element, \(\omega_{1} , \omega_{2} \;\;{\text{and}}\;\; \omega_{3}\) represent the short-run parameters of the model, \(\emptyset_{1} , \emptyset_{2} ,and \emptyset_{3}\) are long-run coefficients, while \(V_{it}\) is white noise error term and lastly, it represents country at a particular time period. In the ARDL model, the bounds test is applied to determine whether the variables are cointegrated or not.

This test is based on the joint significance of F -statistic and the χ 2 statistic of the Wald test. The null hypothesis of no cointegration among the variables under study is examined by testing the joint significance of the F -statistic of \(\omega_{1} , \omega_{2} ,\omega_{3}\) .

In case series variables are cointegrated, an error correction mechanism (ECM) can be developed as Eq. ( 2 ), to assess the short-run influence of international tourism and financial development on economic growth.

where ECT is the error correction term, and \(\varPhi\) is its coefficient which shows how fast the variables attain long-term equilibrium if there is any deviation in the short run. The error correction term further confirms the existence of a stable long-run relationship among the variables.

Panel granger causality test

To examine the direction of causality Dumitrescu and Hurlin [ 16 ] test is employed. Instead of pooled causality, Dumitrescu and Hurlin [ 16 ] proposed a causality based on the individual Wald statistic of Granger non-causality averaged across the cross section units. Dumitrescu and Hurlin [ 16 ] assert that traditional test allows for homogeneous analysis across all panel sets, thereby neglecting the specific causality across different units.

This approach allows heterogeneity in coefficients across cross section panels. The two statistics Wbar-statistics and Zbar-statistics provides standardized version of the statistics and is easier to compute. Wbar-statistic, takes an average of the test statistics, while the Zbar-statistic shows a standard (asymptotic) normal distribution.

They proposed an average Wald statistic that tests the null hypothesis of no causality in a panel subgroup against an alternative hypothesis of causality in at least one panel. Following equations will be used to check the direction of causality between the variables.

Estimation, results and Discussion

Descriptive statistics.

Table  1 presents descriptive statistics of variables selected for the period 1995–2015. The variable set includes GDP, FD and TR for all BRICS countries. Brazil tops the list with GDP per capita of 4.18, while India lagging behind all BRICS nations. In the recent economic survey by International Monetary Fund (IMF report 2016), India was ranked 126 for its per capita GDP. India’s GDP per capita went up to $7170 against all other BRICS countries which were placed in the above $10,000 bracket. China has the highest tourism receipts in comparison to other BRICS countries. China is a very popular country for foreign tourists, which ranks third after France and USA. In 2014, China invested $136.8 billion into its tourist infrastructure, a figure second only to the United States ($144.3 billion). Tourism, based on direct, indirect, and induced impact, accounted for near 10% in the GDP of China (WTTC report 2017).

Stationarity results

Primarily, we employed LLC and IPS unit root test to assess the integrated properties of the series. The results of IPS and PP tests are presented in Table  2 . Panel unit root test result evinces that FD and TR are stationary at level, while GDP per capita is integrated variable of order 1. The result exemplifies that GDP per capita, Tourism receipts and Financial Development are integrated at 1(0) and 1(1). Consequently, the panel ARDL approach to cointegration can be applied.

Cointegration test results

In view of the above results with a mixture of order integration, the panel ARDL approach to cointegration is the most appropriate technique to investigate whether there exists a long-run relationship among the variables [ 42 ]. Table  3 illustrates that the estimated value of F-statistics, which is higher than the lower and upper limit of the bound value, when InEG is used as a dependent variable. Hence, we reject the null hypothesis of no cointegration \(H_{0 } : \emptyset_{1} = \emptyset_{2} = \emptyset_{3} = 0\) of Eq. ( 1 ). Therefore, the result asserts that international tourism, financial development and economic growth are significantly cointegrated over the period (1995–2015).

Subsequently, the study investigates the long-run and short-run impact of international tourism and financial development on economic growth. Lag length is selected on the principle of minimum Bayesian information criterion (SBC) value, which is 2 in our case. The long-run coefficients of financial development and tourism receipts with respect to economic growth in Table  4 indicate that tourism growth and financial development exerts positive influence on economic growth in the long run. In other words, an increase in volume of tourism receipts per capita and financial depth spurs economic growth and both the coefficients are statistically significant in case of BRICS nations in the long run. The results are interpreted in detail as below:

The elasticity coefficient of economic growth with respect to tourism shows that 1% rise in international tourism receipts per capita would imply an estimated increase of almost 0.31% domestic real income in the long run, all else remaining the same. Thus, the earnings in the form of foreign exchange from international tourism affect growth performance of BRICS nations positively. This finding of our study is in consonance with the empirical results of Kreishan for Jordan [ 30 ], Balaguer and Cantavella-Jordá [ 3 ] for Spain and Ohlan [ 43 ] for India.

Further our finding lend support to the wide applicability of the new growth theory proposed by Balassa which states that export expansion promote growth performance of nations. Thus, validates TLGH coined by Balaguer and Cantavell-Jorda [ 3 ] which states that inbound tourism acts a long-run economic growth factor. The so called tourism-led growth hypothesis suggests that the development of a country’s tourism industry will eventually lead to higher economic growth and, by extension, further economic development via spillovers and other multiplier effects.

Likewise, financial development as expected is found to be positively associated with economic growth. The coefficient of financial development states that 1% improvement in financial development will push up economic growth by 0.22% in the long run, keeping all other variables constant. The empirical results are consistent with the finding of Hassan et al. [ 19 ] for a panel of South Asian countries. Well-regulated and properly functioning financial development enhances domestic production through savings, borrowings & investment activities and boosts economic growth. Further, it promotes economic growth by increasing efficiency [ 7 ]. Levine [ 33 ] believes that financial intermediaries enhance economic efficiency, and ultimately growth, by helping allocation of capital to its best use. Modern growth theory identifies two specific channels through which the financial sector might affect long-run growth; through its impact on capital accumulation and through its impact on the rate of technological progress. The sub-prime crisis which depressed the economic growth worldwide in 2007 further substantiates the growth-financial development nexus.

In the third and final step of the bounds testing procedure, we estimate short-run dynamics of variables by estimating an error correction model associated with long-run estimates. The empirical finding indicates that the coefficient of error correction term (ECT) with one period lag is negative as well as statistically significant. This finding further substantiates the earlier cointegration results between tourism, financial development and economic growth, and indicates the speed of adjustment from the short-run toward long-run equilibrium path. The coefficient of ECT reveals that the short-run divergences in economic growth from long-run equilibrium are adjusted by 43% every year following a short-run shock.

The short-run parameters in Table  5 demonstrates that tourism and financial development acts as an engine of economic growth in the short run as well. The coefficient of both tourism receipts per capita and financial development with one period lag is also found to be progressive and significant in the short run. These results highlight the role of earnings from international tourism and financial stability as an important driving force of economic growth in BRICS nations in the short run as well.

Further, a comparison between short-run and long-run elasticity coefficients evince that long-run responsiveness of economic growth with respect to tourism and financial development is higher than that of short run. It exemplifies that over time higher international tourism receipts and well-regulated financial system in BRICS nations give more boost to economic growth.

Analysis of causality

At this stage, we investigate the causality between tourism, financial development and economic growth presented in Table  6 . The result shows bi-directional causal relationship between tourism and economic growth, thereby validates ‘feedback hypothesis’ and consequently supported both the tourism-led growth hypothesis (TLGH) and its reciprocal, the economic-driven tourism growth hypothesis (EDTH). The bi-directional causality between inbound tourism and GDP, which directs the level of economic activity and tourism growth, mutually influences each other in that a high volume of tourism growth leads to a high level of economic development and reverse also holds true. These results replicate the findings of Banday and Ismail [ 5 ] in the context of BRICS countries, Yazdi et al. [ 27 ] for Iran and Kim et al. [ 28 ] for Taiwan. One of the channels through which tourism spurs economic growth is through the use of receipts earned in the form of foreign currency. Thus, growth in foreign earnings may allow the import of technologically advances goods that will favor economic growth and vice versa. Thus, results demonstrate that international tourism promotes growth and in turn economic expansion is necessary for tourism development in case of BRICS countries. With respect to policy context, this finding suggests that the BRICS nations should focus on economic policies to promote tourism as a potential source of economic growth which in turn will further promote tourism growth.

Similarly, in case of economic growth and financial development, the findings demonstrate the presence of bi-directional causality between two constructs. The findings validate thus both ‘demand following’ and supply leading’ hypothesis. The findings suggests that indeed financial development plays a crucial role in promoting economic activity and thus generating economic growth for these countries and reverse also holds. Our findings are in line with Pradhan [ 48 ] in case of BRICS countries and Hassan et al. [ 19 ] for low and middle-income countries. This suggests that finance development can be used as a policy variable to foster economic growth in the five BRICS countries and vice versa. The study emphasizes that the current economic policies should recognize the finance-growth nexus in BRICS in order to maintain sustainable economic development in the economy. The empirical results in this paper are in line with expectations, confirming that the emerging economies of the BRICS are benefiting from their finance sectors.

Finally, two-sided causal relationship is found between tourism receipts and financial development. That is, tourism might contribute to financial development and, in return, financial development may positively contribute to tourism. This means that financial depth and tourism in BRICS have a reinforcing interaction. The positive impact of tourism on financial development can be attributed to the fact that inflows of foreign exchange via international tourism not only increases income levels but also leads to rise in official reserves of central banks. This in turn enables central banks to adapt expansionary monetary policy. The positive contribution of financial sector to tourism is further characterized by supply leading hypothesis. Further, better financial and market conditions will attract tourism entrepreneurship, because firms will be able to use more capital instead of being forced to use leveraging [ 13 ]. Hence, any shocks in money supply could adversely affect tourism industry in these countries. Song and Lin [ 56 ] found that global financial crisis had a negative impact on both inbound and outbound tourism in Asia. This result is in consistent with Başarir and Çakir [ 6 ] for Turkey and four European countries.

Stability tests

In addition, to test the stability of parameters estimated and any structural break in the model CUSUM and CUSUMSQ tests are employed. Figs.  1 and 2 show blue line does not transcend red lines in both the tests, thus provides strong evidence that our estimated model is fit and valid policy implications can be drawn from the results.

Plot of CUSUM

Plot of CUSUMQ

Summary and concluding remarks

A rigorous study of the relationship between tourism and economic growth, through the tourism-led growth hypothesis (TLGH) perspective has remained a debatable issue in the economic growth literature. This study aims to empirically investigate the relationship between inbound tourism, financial development and economic growth in BRICS countries by utilizing the panel data over the period 1995–2015. The study employs the panel ARDL approach to cointegration and Dumitrescu-Hurlin panel Granger causality test to detect the direction of causation.

To the best of authors’ knowledge, this is the first study which explored the relationship between economic growth and tourism while considering the relative importance of financial development in the context of BRICS nations. The empirical results of ARDL model posits that in BRICS countries inbound tourism, financial development and economic growth are significantly cointegrated, i.e., variables have stable long-run relationship. This methodology has allowed obtaining elasticities of economic growth with respect to tourism and financial development both in the long run and short run. The result reveals that international tourism growth and financial development positively affects economic growth both in the long run and short run. The coefficient of tourism indicates that with a 1% rise in tourism receipts per capita, GDP per capita of BRICS economies will go up by 0.31% in the long run. This finding lends support to TLGH coined by Balaguer and Cantavell-Jorda [ 3 ] which states that inbound tourism acts a long-run economic growth factor. The so called tourism-led growth hypothesis suggests that the development of a country’s tourism industry will eventually lead to higher economic growth and, by extension, further economic development via spillovers and other multiplier effects.

Likewise, 1% improvement in financial development, on average, will increase economic growth in BRICS countries by 0.22% in the long run. The result seems logical as modern growth theory identifies two channels through which the financial sector might affect long-run growth: first, through its impact on capital accumulation and secondly, through its impact on the rate of technological progress. The sub-prime crisis which hit the economic growth Worldwide in 2007 further substantiates the growth-financial development nexus.

The negative and statistically significant coefficient of lagged error correction term (ECT) further substantiates the long-run equilibrium relationship among variables. The negative coefficient of ECT also shows the speed of adjustment toward long-run equilibrium is 43% per annum if there is any short-run deviation. The estimates of parameters are found to be stable by applying CUSUM and CUSUMQ for the time period under consideration. Therefore, inbound tourism earnings and financial institutions can be used as a channel to increase economic growth in BRICS economies.

Further, Granger causality test result indicates the bi-directional causation in all cases. Hence, the causal relationship between international tourism and economic growth is bi-directional. And, consequently this empirical finding lends support to both the tourism-led growth hypothesis (TLGH) and its reciprocal, the economic-driven tourism growth hypothesis (EDTH). This means that tourism is not only an engine for economic growth, but the economic outcome on itself can play an important role in providing growth potential to tourism sector.

The Granger causality findings provide useful information to governments to examine their economic policy, to adjust priorities regarding economic investment, and boost their economic growth with the given limited resources. Thus, it is suggested that more resources should be allocated to tourism industry and tourism-related industries if the tourism-led growth hypothesis holds true. On the other side, if economic-driven tourism growth is supported then more resources should be diverted to leading industries rather than the travel and tourism sector, and the tourism industry will in turn benefit from the resulting overall economic growth. And, when bi-directional causality is detected, a balanced allocation of economic resources for the travel and tourism sector and other industries is important and necessary. The policy implication is that resource allocation supporting both the tourism and tourism-related industries could benefit both tourism development and economic growth.

To sum up, the major finding of this study lends support to wide applicability of the tourism-led growth hypothesis in case of BRICS countries. Thus, in the Policy context, significant impact of tourism on BRICS economy rationalizes the need of encouraging tourism. Tourism can spur economic prosperity in these countries and for this reason; policymakers should give serious consideration toward encouraging tourism industry or inbound tourism. BRICS countries should focus more on tourism infrastructure, such as, convenient transportation, alluring destinations, suitable tax incentives, viable hostels and proper security arrangements to attract the potential tourists. Most of these countries are devoid of rich facilities and popular tourist incentives, to get promoted as important destination and in the long-run promotes economic growth. Further, they need a staunch support from all sections of authorities, non-government organizations (NGOs), and private and allied industries, in the endeavor to attain sustainable growth in tourism. Both state and non-state actors must recognize this growing industry and its positive implication on economy.

For future research, we suggest that researchers should consider the nonlinear factor in the dynamic relationship of tourism and economic growth in case of BRICS countries. Further one can go for comparative study to examine the TLGH in BRICS countries.

Availability of data and materials

Data used in the study can be provided by the corresponding author on request.

There are no fixed definitions of short, medium and long run and generally in macroeconomics, short run can be viewed as 1 to 2 or 3 years, medium up to 5 years and long run from 5 years to 20 or 25 years.

Abbreviations

autoregressive distributed lag model

Brazil, Russia, India, China and South-Africa

United Nations World Tourism Organization

World Travel & Tourism Council

gross domestic product

world development indicators

tourism-led growth hypothesis

export-led growth hypothesis

economic-driven tourism hypothesis

augmented Dickey–Fuller test

error correction model

error correction term

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Rasool, H., Maqbool, S. & Tarique, M. The relationship between tourism and economic growth among BRICS countries: a panel cointegration analysis. Futur Bus J 7 , 1 (2021). https://doi.org/10.1186/s43093-020-00048-3

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• Economic growth
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Estimating the economic impact of tourism is an important research direction. As an industry involving multiple sectors in the economy, tourism not only creates jobs and brings in income to the industry itself (Campos Soria et al. 2019 ), but it also facilitates growth in the primary and secondary circles. This is known as the multiplier effect. In simple terms, the tourism multiplier effect refers to how many times money spent by a tourist circulates through a country’s economy (Archer 1982 ).

There are three types of multiplier effects: direct, indirect, and induced impacts. Direct effects refer to the first-round effect of spending by tourists (Vanhove 2005 ), including the initial injection of money providing revenues and jobs for director stakeholders such as hotels, airlines, travel agencies, restaurants, and attractions (Turgarini et al. 2018 ). Tourism can further cause indirect and induced effects (Vanhove 2005 ). Indirect effects include the ripple effect of recirculating the...

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Archer, B.H. 1982. The value of multipliers and their policy implications. Tourism Management 3 (4): 236–241.

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Report details tourism patterns, economic benefits.

Garrett Neese/Daily Mining Gazette University of Michigan’s Economic Growth Institute director of research Sarah Crane and research project manager Eli McClain present their findings on a study of the western U.P. tourism economy at the Wake Up Keweenaw breakfast Wednesday.

HOUGHTON — Tourism contributes about $360 million annually to the local economy in the Western Upper Peninsula, a recent study found.

Researchers from the University of Michigan’s Economic Growth Institute presented the findings of a multi-year study of the patterns and economic impact of tourism in the western U.P.’s at Wednesday’s Wake Up Keweenaw breakfast.

Visit Keweenaw and the Western Upper Peninsula Planning and Development Region had pursued the study, which was funded through the Economic Development Administration. The study is aimed at providing information to aid in decision-making and future strategies.

“We really believe that this project will help all of our community businesses, not only our tourist business, but for everyone to understand exactly what the tourism industry does for our community,” said Lisa McKenzie, regional planner with WUPPDR.

After spending several months in fall 2022 developing a visitor survey, the group launched it in January 2023, collecting data for a full year. Flyers directing visitors to the online survey were distributed at more than 175 locations, from gas stations to trailheads, while tablets for real-time surveys were available at visitors centers. In that time, they got 3,334 responses, representing more than 10,000 people coming to the Keweenaw.

Additionally, researchers used visitation data from state parks, local campgrounds, short-term rentals and other sites.

The median trip to the western U.P. lasted four days, with a median of three days spent in Houghton County. The most visitors came from Michigan, followed by Wisconsin and Minnesota. About 35% of those trips included visiting or staying in multiple counties during the trip.

“What we saw across the entire sample is that travel in the Western U.P., visitation into the Western U.P. is highly regional,” said EGI research project manager Eli McClain. “And so folks see this area as being somewhere to explore and drive around and visit many different places.”

Visitors were also asked to name what activities they’d done in the Keweenaw by season. Downhill skiing and snowboarding topped the list in winter, with 37% of respondents. Art, culture and history was the biggest driver in spring (35%), while hiking led the way in summer and fall (68% and 61%, respectively). Waterfall viewing — 60% in summer, 55% in fall — was the other activity to crack 50% in any season.

Northern Lights/Dark Sky viewing stood out as the only category with a top-five finish in all four seasons.

The high numbers for cross country skiing (27%) registered as one of the biggest surprises in the data for Brad Barnett, executive director of Visit Keweenaw. He thought part of the reason may be the number of spots like chalets where skiers can congregate (and spot survey flyers), where snowmobiling has fewer activity-specific bottlenecks.

The survey asked people to list the activities they had participated in while in the area but did not make a distinction between ones that were the primary reason for the trip or ones that people did because they happened to be in the area.

“Let’s just say you come up here in the summer, and you’re ORVing, but you might also go say, ‘I’m going to check out a waterfall here, I’m going to check out the beach, I’m going to check out the dark skies,'” Barnett said.

McClain said the limitations of the survey were the result of a tradeoff, keeping the survey short enough to ensure responses.

The tourism visits have a large economic impact for the area. Overnight visitor spending averages $356.61 in the western U.P., and $407.17 in Houghton County, the largest portion going to lodging.

Daytrippers spend $101.61 in the western U.P., more than 40% going toward food and beverage.

The money injected by tourism created an estimated 3,060 jobs, resulting in $99.2 million in income.

Of those jobs, 2,401 were directly tied to tourism. Nearly half (1,163) were tied to lodging, closely followed by recreation and entertainment (839).

Of the $357.8 million in economic output, tourism directly accounted for $249.5 million, with almost $110 million coming in indirect and induced spending.

“The main impacts for tourism are in those direct jobs,” said Sarah Crane, director of research for EGI. “The ripple effects are not as large when I start doing things for manufacturing or different industries like that. There’s much higher indirect purchases.”

The tax revenue from the visits added up to $35.5 million between municipalities, county, state and special districts such as police, fire and schools.

Visitors were also asked for an open-ended list of services they want to see added in the Keweenaw. The largest chunk, accounting for 32% of the responses, dealt with infrastructure issues such as road quality, cell service, transportation and electric vehicle chargers. The next largest grouping was “nothing,” from people who want to see the Keweenaw kept as it is.

The sort of comprehensive data for the western U.P. is unique, Barnett said.

“The data that we usually get from studies like for Michigan, the sample size is spread out across the entire state,” he said. “Typically their sample of the entire state is around 2,000, so it’s hard to drill down at the county level to start figuring that stuff out.”

The Keweenaw scored extremely well in the Net Promoter Score, which measures whether visitors would recommend the trip to their friends. A 50 is considered very good, McClain said; the Keweenaw got an 81.7.

More than 75% of visitors said they would be likely to return, McClain said.

“I don’t think it surprised me, but I was like, ‘This is wonderful, this is a great thing to see,'” he said.

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Solar Eclipse Boosted U.S. Hotels With Record Revenue Gains

Dawit Habtemariam , Skift

April 19th, 2024 at 4:04 PM EDT

The eclipse brought historic tourism revenue to hotels. But not all local businesses felt the same impact.

Dawit Habtemariam

Hotels in cities in the path of the total solar eclipse on April 8 saw record revenue increases, according to a new analysis published by CoStar’s STR .

The night before the eclipse, U.S. hotel markets in the path of totality saw a 288% year-over-year increase in revenue per available room, a key industry metric.

That was much better than America’s overall hotel performance for the week, which was up 42%.

Some destinations received especially heavy visitation. “At least five of the major hotels downtown were all sold out,” said Bill Solleder, director of marketing for Arkansas’ Visit Hot Springs .

The solar eclipse also boosted hotel revenue in Canada and Mexico. Canada’s hotels in the path of totality got a 224% boost, while the entire country saw a 28% rise.

Mexico’s four hotel markets caught in the eclipse’s path enjoyed a 438% increase.

Totality, Partiality, and Economic Disparity

While tourists filled up hotels, they didn’t spread their dollars everywhere equally, Skift found.

“Some of our locations, especially our outdoor concessions, got crushed while others were the exact same as you would expect for a Monday,” said Jason Dady , who owns several restaurants in the San Antonio area.

Businesses located outside tourist areas like downtowns didn’t see the same boost in foot traffic. Repeated warnings that visitors would cause congested roads led some locals to stay home during the eclipse days, said Solleder.

“When you moved out of the downtown area, especially restaurants, didn’t do as well as normal because they weren’t getting that local income,” he said. “People weren’t going out to dinner because everybody stayed home.”

Over 30 million live in the path of totality, according to the Great American Eclipse , an eclipse tracking group.

“Overall, it was busier but not the craziness as projected,” said San Antonio’s Dady.

Here Comes the Sun. The Eclipse Is This Year’s Top Travel Phenomenon

On April 8, a total solar eclipse will pass diagonally across the U.S., from south to east, promising a tourism surge.

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Tags: hotels , solar eclipse , tourism

• Solar Eclipse 2024

The Eclipse Could Bring $1.5 Billion Into States on the Path of Totality

T he total solar eclipse passing through parts of the U.S. on April 8 stands to have a major economic impact on cities across the country as stargazers flock to the path of totality. 

Factors including the date of the eclipse and the number of states in the path of totality means that millions of people will have the opportunity to view the event— and that the cities hosting them could see a combined $1.5 billion injected into their states’ economies.

“That number will include lodging costs for visitors coming from out of state or far away parts of their own state, as well as gas costs and food costs,” says Bulent Temel, assistant professor of practice in economics at the University of Texas at San Antonio, San Antonio, who performed the calculations to arrive at the $1.5 billion figure.

One to four million people are expected to travel for the eclipse, according to Great American Eclipse , an informational site that tracks solar eclipses around the world. The Federal Aviation Administration (FAA) estimates the days leading up to the eclipse will be some of the busiest travel days of the season, with 50,670 flights on Thursday, April 4 and 48,904 flights on Friday, April 5. That means the spending will be spread out: “[The eclipse] is on a Monday, so you might have folks coming Friday, Saturday, Sunday, spending a few days somewhere ahead of the event,” says John Downen, Director of Impact Analysis at Camoin Associates.

Read More : How Cities Around the U.S. Are Celebrating the Eclipse

Many regions along the path of totality have spent months—if not years—preparing for the upcoming surge of visitors and money. Rochester, NY, is expecting 300,000 to 500,000 visitors across the nine - county Greater Rochester region. Local businesses have a slate of specials and planned events the weekend leading up to the event—including eclipse themed beers from local breweries and a three-day pass from the Rochester Museum and Science Center for visitors to attend a range of talks and performances. 

The area’s tourism board says that some hotels have reported demand skyrocketing an average of 1200% for the four-day span leading up to April 8— unusual demand for a Monday in the region’s off-peak season. 

It’s an economic boost that no amount of planning— or marketing—can replicate. “It’s a really great tourism opportunity,” says Shannon Ealy, Director of Communications and Marketing for the Greater Rochester Chamber of Commerce. “You can spend millions of dollars on media buys to get our regional brand out there, but you can't exactly buy the sun and the moon crossing over us.” 

Read More: See the 2024 Solar Eclipse’s Path of Totality

But unfortunate weather could still put a damper on things, especially for businesses that might be stocking up for an influx of visitors, since many eclipse chasers decide where to view the eclipse based on weather that can’t be predicted until the event draws closer. “Even a simple factor like a cloudy day could just compromise all these expectations quite a bit,” Temel says. 

The real task for local business and tourism boards lies in converting one-time visitors into ones that return—without the promise of a solar eclipse. “Every single one of those visitors is a potential future visitor to the same area as well,” says Temel. “In the long run, the economic impact would be magnified quite significantly. 

Adds Downen: “It definitely presents an opportunity, especially in smaller communities, to showcase themselves and hopefully capture some future repeat visitors.”  

Read More : Where to Find Solar Eclipse Glasses—And Spot Fake Ones

Lebanon, Indiana, for example, is expecting its population to triple during the weekend before the eclipse. Joe Lepage, the city’s communication and community development director, says he hopes that the eclipse will change the way both locals and out-of-towners talk about Lebanon. 

“We have a large business park, great hospitals, establishments where people can work, but actually staying and living in Lebanon has been difficult to sell.” he says. "It'll give people that are going back home a chance to visit and realize, ‘Hey, that little town is nice.’ But then our locals can see all the things they have in their backyard and realize, ‘Hey, my community is pretty special too.’”

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Overtourism effects: Amsterdam bans construction of new hotels and slashes river cruise stays

Times of India TIMESOFINDIA.COM / Created : Apr 19, 2024, 13:00 IST

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Amsterdam combats over-tourism by restricting new hotel construction, capping tourist stays, and reducing river cruises. The city aims to preserve livability while addressing the economic impact of these measures.

Amsterdam combats over-tourism by restricting new hotel construction, capping tourist stays, and reducing river cruises. The city aims to preserve livability while addressing the economic impact of these measures. Read less

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Amsterdam combats over-tourism by restricting new hotel construction, capping tourist stays, and reducing river cruises. The city aims to preserve livability while addressing the economic impact of th...

UN Tourism | Bringing the world closer

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European Committee of the Regions and UN Tourism break new ground with study on Rural Tourism and Development in Europe

• 15 Apr 2024

UN Tourism has partnered with the European Committee of the Regions for a comprehensive study of the significant impact and potential of tourism in fostering socio-economic development in rural areas.

The report emphasizes the critical role of cooperation between these two organizations in bridging local and global efforts towards shared objectives. It showcases the potential of rural tourism to contribute to the resilience of regional and local communities, addressing challenges such as depopulation, inequality and limited access to basic services.

Zurab Pololikashvili , Secretary-General of UN Tourism says: "Tourism has the potential to transform societies, stimulate local economic development, and empower local communities. This joint study with the European Committee of the Regions underscores the importance of rural tourism in contributing to sustainable development in Europe."

Alves Vasco Cordeiro , President of the European Committee of the Regions says: "Free movement has always been at the heart of the European project. Tourism, as part of this mobility, has helped shape our European identity and is a powerful driver for growth and jobs, breathing new life into communities across Europe."

Key Findings

Tourism has the potential to transform societies, stimulate local economic development, and empower local communities

The study offers a comprehensive understanding of rural tourism and its impact on European regions, with a focus on:

• Resilience of Rural Tourism post-COVID : The report sheds light on the remarkable resilience displayed by rural tourism during and post-COVID-19. Despite global challenges, rural tourism witnessed a surge in popularity, particularly among local travelers. This resilience underscores its potential as a robust economic driver and showcases its ability to adapt to changing circumstances.
• Economic Diversification and Cultural Preservation : A key insight is the potential of rural tourism to drive economic diversification and job creation in rural areas. The study also underscores tourism's contribution to preserving cultural heritage. Rural tourism is seen as a bridge between tradition and modernity, fostering community participation and sustaining local services while preserving the unique identity of rural regions.
• Challenges and Opportunities for Sustainable Development : The study identifies challenges faced by rural areas, such as inadequate infrastructure, limited financial resources, and declining local populations. Simultaneously, it recognizes these challenges as opportunities for growth. By providing a nuanced understanding of the complexities involved, the report sets the stage for sustainable rural tourism development, urging stakeholders to navigate challenges thoughtfully.

Recommendations

The report offers a roadmap for policymakers, local governments, and stakeholders to harness the potential of rural tourism while addressing its challenges.

• Integrated Rural Value Chains and Collaboration : An overarching recommendation is the integration of rural value chains in tourism. The report emphasizes the need for collaboration between businesses and stakeholders to create synergies within the local economy. By maximizing economic benefits, this approach ensures a holistic and sustainable development trajectory for rural tourism.
• Digital Connectivity and Skills : Recognizing the digital divide in rural areas, the report recommends a focused effort on improving digital connectivity. It underscores the importance of digital skills development to empower rural stakeholders in leveraging technology effectively. By addressing these aspects, the report aims to enhance the digital readiness of rural communities, unlocking new opportunities in the digital era.
• Alignment with Consumer Trends and Sustainable Practices : Acknowledging evolving consumer trends and the need to place sustainability at the heart of tourism development, the report encourages responsible travel initiatives, that not only meet changing consumer expectations but also contribute to broader sustainability goals. This recommendation positions rural tourism as a catalyst for positive environmental and social impact.

Related links

• Download News Release on PDF
• UN Tourism: Tourism and Rural Development
• “Tourism and Rural Development: A Policy Perspective”
• “Tourism and Rural Development: Understanding Challenges on the Ground – Lessons learned from the Best Tourism Villages by UNWTO Initiative”
• Best Tourism Villages by UN Tourism
• Studies (europa.eu)

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