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Italy’s Travel & Tourism Could Reach Pre-pandemic Levels Next Year, Reveals WTTC Report

Almost half a million jobs could be created in Italy’s Travel & Tourism sector over the next decade

The growth in Travel & Tourism to outstrip Italy’s GDP

London, UK: The World Travel & Tourism Council ( WTTC ) has revealed Italy’s Travel & Tourism sector will provide a significant boost to the country’s economic recovery and could almost reach pre-pandemic levels next year, just 0.3% below 2019 levels.

The latest forecast from WTTC’s Economic Impact Report (EIR) shows the sector’s contribution to GDP could reach more than €194 billion next year, while employment in the sector could also hit pre-pandemic levels.

The report from the global tourism body also reveals that the Travel & Tourism sector will grow at an annual average rate of 2.5% for the next 10 years, five times the 0.5% growth rate of  the overall Italian economy. It will be worth over €226 billion by 2032.

The forecast also reveals the Travel & Tourism sector in Italy is expected to create more half a million (533,000) jobs in the next 10 years, averaging more than 53,000 new jobs every year.

In 2022, the sector’s contribution to GDP is expected to grow 8.7% to more than €176 billion, representing 9.6% of the total economic GDP, while employment in the sector is set to grow by 2% to reach almost 2.7 million jobs.

Julia Simpson, WTTC President & CEO, said: “The pandemic was catastrophic for Italy’s Travel & Tourism sector, wiping billions from the economy as businesses collapsed, and thousands of people lost their jobs.

“After two very difficult years, the outlook is now much brighter. Travel & Tourism’s projections provide a massive boost, not only to Italy’s overall economy, but to the creation of new jobs.”

Before the pandemic, when Travel and Tourism was at its peak, the total contribution to GDP was 10.6% (€194.8 billion) in 2019, falling to just 6.1% (€102.6 billion) in 2020, representing a painful 47.3% loss.

The sector also supported nearly 2.9 million jobs, before an almost complete halt to international travel resulted in a loss of more than 400,000 (15.4%), to reach just over 2.4 million in 2020.

WTTC’s latest EIR report also reveals that 2021 saw the beginning of the recovery for the country’s Travel & Tourism sector.

Last year, its contribution to GDP climbed a positive 58.5% year on year to reach €162.6 billion, while employment in the sector grew 9.4%, to reach more than 2.6 million.

The sector’s contribution to the economy and employment could have been higher if it weren’t for the impact of the Omicron variant, which led to the recovery faltering around the world, with many countries reinstating severe travel restrictions.

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China’s Travel & Tourism Sector Set to Recover by More Than 60% This Year, Reveals WTTC

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UK Travel & Tourism Sector Sees a Massive Job Shortfall of More Than 200,000 This Year, Says WTTC Report

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How Much Of Italy’s Economy Is Dependent On Tourism

Published: December 12, 2023

Modified: December 28, 2023

by Kalina Sauceda

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Introduction

Italy, known for its rich history, stunning landscapes, and vibrant culture, has long been a top tourist destination. From the ancient ruins of Rome to the picturesque canals of Venice, this country offers a diverse range of attractions that draw millions of visitors each year. But have you ever wondered how much of Italy’s economy is dependent on tourism?

In this article, we will delve into the fascinating world of Italy’s tourism industry and explore its significance to the country’s economy. We will examine the factors that contribute to Italy’s heavy reliance on tourism, the impact it has on different sectors, and the challenges associated with this dependence. Finally, we will discuss potential measures to diversify Italy’s economy and reduce its reliance on tourism.

Italy’s tourism sector plays a crucial role in the country’s overall economic landscape. With its world-renowned monuments, UNESCO World Heritage sites, and rich cultural heritage, Italy has consistently been one of the most-visited countries in the world. In 2019 alone, Italy attracted more than 94 million international tourists, generating an estimated €41 billion in revenue.

The significance of tourism to Italy’s economy cannot be overstated. It accounts for a substantial portion of the country’s GDP and employment. According to the World Travel and Tourism Council, travel and tourism directly contributed 13% to Italy’s GDP in 2019. Furthermore, the sector employs approximately 4.4 million people, representing around 16% of the total employment in the country.

Italy’s natural and cultural attractions serve as a magnet for international tourists, driving the growth of the tourism industry. The historical cities, such as Rome, Florence, and Venice, attract history enthusiasts and art lovers, while the beautiful coastal regions like the Amalfi Coast and Cinque Terre entice sun-seeking vacationers. Additionally, Italy’s reputation for excellent cuisine, fashion, and luxury goods adds to its allure as a premier tourist destination.

However, while Italy’s tourism industry has undoubtedly brought significant economic benefits, it also presents potential challenges and risks. The overreliance on tourism leaves the country vulnerable to external shocks, such as global economic downturns, political instability, natural disasters, or pandemics, as demonstrated by the impact of the COVID-19 pandemic in 2020.

In the following sections, we will delve deeper into Italy’s tourism industry, examining the key factors contributing to its dependence on tourism and the far-reaching consequences it has on the Italian economy. Join us as we explore the multifaceted relationship between Italy and its booming tourism sector.

Overview of Italy’s tourism industry

Italy’s tourism industry is one of the largest and most dynamic in the world. The country offers a wealth of attractions, including historical landmarks, UNESCO World Heritage sites, stunning coastlines, and picturesque countryside. From the iconic Colosseum in Rome to the romantic canals of Venice, there is something for every type of traveler.

Italy’s tourism infrastructure is extensive, catering to the needs of millions of visitors each year. The country boasts a wide range of accommodation options, from luxurious hotels and resorts to budget-friendly hostels and bed and breakfasts. Additionally, transportation within Italy is well-developed, with an extensive network of trains, buses, and domestic flights, making it convenient for tourists to explore different regions.

The tourism industry in Italy is supported by a strong cultural heritage and a rich history, which dates back to ancient times. The country is home to countless archaeological sites, including Pompeii, Herculaneum, and the Roman Forum, offering visitors a glimpse into the past. Furthermore, Italy is renowned for its world-class museums, such as the Uffizi Gallery in Florence and the Vatican Museums in Rome, housing priceless works of art.

The natural beauty of Italy is also a major draw for tourists. The country is famous for its stunning coastlines, such as the Amalfi Coast and the Italian Riviera, which offer breathtaking views and idyllic beach towns. Inland, visitors can explore the picturesque countryside of Tuscany, known for its rolling hills, vineyards, and charming villages.

Italy’s cuisine is revered worldwide, and food tourism is another significant aspect of the country’s tourism industry. Italian cuisine is diverse and regionally distinct, with each area offering its own specialties. From Neapolitan pizza to Tuscan pasta and Sicilian cannoli, the culinary delights of Italy are a highlight for many visitors.

In recent years, Italy has also seen a rise in niche tourism segments, such as wine tourism, fashion tourism, and eco-tourism. Wine enthusiasts flock to regions like Tuscany and Piedmont to sample renowned Italian wines, while fashion lovers flock to Milan, the fashion capital of Italy. Eco-tourism is also on the rise, with visitors seeking eco-friendly accommodations and exploring Italy’s national parks and protected areas.

Overall, Italy’s tourism industry is thriving, attracting millions of visitors from around the globe. The combination of historical sites, cultural heritage, natural beauty, and culinary excellence make Italy a top choice for travelers seeking a unique and enriching experience.

Importance of tourism to Italy’s economy

Tourism is a vital component of Italy’s economy, playing a significant role in driving economic growth, creating jobs, and generating revenue. The sector’s impact is felt across various industries and regions, making it a critical pillar of the Italian economy.

The contribution of tourism to Italy’s gross domestic product (GDP) is substantial. In 2019, the direct contribution of travel and tourism accounted for approximately 5.2% of Italy’s GDP, according to the World Travel and Tourism Council. When considering both direct and indirect impacts, the total contribution rises to around 13%.

Tourism also plays a crucial role in employment generation. The sector provides millions of jobs directly and indirectly, supporting livelihoods in various sectors such as hospitality, transportation, retail, and entertainment. In 2019, the travel and tourism industry employed around 4.4 million people, representing approximately 16% of the total employment in Italy.

The revenue generated from tourism activities contributes significantly to Italy’s balance of payments, as international visitors spend money on accommodation, transportation, food, shopping, and entertainment. In 2019, total international visitor spending reached €41 billion, making tourism one of the leading sources of foreign exchange earnings for the country.

Furthermore, tourism encourages regional development and stimulates economic growth in less economically developed areas of Italy. The presence of popular tourist destinations in regions like Tuscany, Veneto, and Campania attracts investments in infrastructure, accommodation, and services, creating employment opportunities and boosting local businesses.

Italy’s cultural heritage and historical sites are a major draw for tourists, contributing significantly to the country’s economy. The maintenance and preservation of these sites require ongoing investment, and tourism revenue plays a crucial role in financing these efforts. Additionally, revenue generated from entrance fees to museums, archaeological sites, and cultural events directly contribute to the conservation and restoration of Italy’s cultural treasures.

Moreover, the tourism industry generates a multiplier effect, impacting various sectors and supporting related businesses. Accommodation providers, restaurants, souvenir shops, transportation services, tour operators, and other tourism-related businesses all benefit from the influx of tourists. The interdependence between these sectors creates a comprehensive tourism ecosystem that drives economic activity and creates a ripple effect through the supply chain.

Overall, tourism is of utmost importance to Italy’s economy. It not only drives economic growth, but also promotes cultural preservation, regional development, and job creation. However, the overreliance on tourism also presents certain challenges and risks, as we will explore in the following sections.

Factors contributing to Italy’s dependence on tourism

Several factors contribute to Italy’s heavy reliance on tourism as a significant driver of its economy. These factors have shaped the country’s economic landscape and made it highly dependent on the tourism industry.

Historical and Cultural Significance: Italy’s rich history and cultural heritage are major attractions for tourists. The country is home to numerous UNESCO World Heritage sites, ancient ruins, and iconic landmarks that draw visitors from around the world. The historical cities of Rome, Florence, and Venice, with their architectural marvels and artistic treasures, remain perennially popular. The preservation and promotion of these historical and cultural sites have fueled the growth of Italy’s tourism industry.

Geographical Diversity: Italy’s diverse geography is another contributing factor to its dependence on tourism. From the breathtaking coastlines of the Amalfi Coast and the Italian Riviera to the picturesque countryside of Tuscany and the stunning lakes in the north, Italy offers a variety of landscapes that appeal to different types of travelers. The natural beauty of these regions, along with their outdoor activities, such as hiking, sailing, and wine tours, attracts tourists looking for unique experiences.

World-Famous Cuisine: Italian cuisine is celebrated globally, and the country’s gastronomic offerings are a major attraction for tourists. From pizza and pasta to gelato and espresso, Italian culinary traditions are deeply ingrained in the country’s culture. Italy’s vibrant food scene, with its regional specialties and world-class wines, lures food enthusiasts and gourmet travelers, contributing to the growth of food tourism in the country.

Art and Fashion: Italy’s reputation as a hub of art, fashion, and design has also played a significant role in its dependence on tourism. The country is renowned for its centuries-old art masterpieces, with museums like the Uffizi Gallery and the Vatican Museums housing invaluable works of art. Milan, as the fashion capital of Italy, attracts fashion-conscious travelers who visit to explore its boutiques, attend fashion shows, and immerse themselves in Italian style.

Proximity and Connectivity: Italy’s geographical location in the heart of Europe has made it easily accessible to travelers from all over the world. The country benefits from its well-connected transportation infrastructure, with international airports in major cities, extensive rail networks, and a comprehensive highway system. This connectivity has made it convenient for tourists to reach Italy and explore multiple destinations within the country, boosting visitor numbers and tourism revenue.

Government Support: The Italian government has recognized the economic significance of the tourism industry and has implemented policies to support its growth. Investments in infrastructure, promotion of cultural heritage, and development of tourist-friendly initiatives have contributed to Italy’s popularity as a tourist destination. The government’s commitment to preserving historical sites, improving tourism infrastructure, and facilitating visa procedures has further strengthened Italy’s position as a top choice for travelers.

While these factors have undoubtedly contributed to Italy’s dependence on tourism, it is essential to address the challenges associated with this heavy reliance. In the next section, we will explore the impact of tourism on different sectors of the Italian economy and the risks it poses.

Impact of tourism on different sectors of the Italian economy

The tourism industry in Italy has far-reaching effects on various sectors of the country’s economy, creating a significant impact on both direct and indirect beneficiaries. Let’s explore how tourism influences different sectors and contributes to their growth and development.

Hospitality and Accommodation: The hospitality sector is one of the primary beneficiaries of Italy’s booming tourism industry. Hotels, resorts, bed and breakfasts, and vacation rentals cater to the influx of tourists, providing them with a place to stay during their visit. The demand for accommodation drives investment in the construction and maintenance of hotels and accommodations, thereby creating job opportunities and supporting the local economy.

Food and Beverage: Italy’s renowned culinary culture plays a crucial role in attracting tourists. The food and beverage sector benefits from the increased visitor numbers, with tourists eager to indulge in authentic Italian cuisine. Restaurants, cafes, and street food vendors experience higher demand, leading to increased business opportunities and employment in the sector.

Retail and Shopping: Tourism contributes significantly to the retail sector in Italy. Tourists often seek out local crafts, souvenirs, and designer goods, boosting sales in shops and boutiques. Major shopping destinations such as Milan, renowned for its fashion scene, benefit from the influx of tourists who come to explore and purchase Italian-made products. This, in turn, stimulates economic activity and supports the retail sector.

Transportation: Italy’s well-developed transportation infrastructure caters to the needs of millions of tourists. The travel and tourism industry drive demand for domestic and international flights, train travel, rental cars, and public transportation. This sustained demand for transportation services leads to job creation and revenue generation in the transportation sector, benefiting airlines, railway companies, taxi operators, and other transportation service providers.

Heritage and Culture: Italy’s rich cultural heritage is a major draw for tourists, and the preservation and promotion of historical sites and cultural events contribute to the economy. Revenue generated from entrance fees to museums, archaeological sites, and cultural festivals directly support the conservation and maintenance of Italy’s cultural treasures. Investments in the restoration and preservation of historical sites create employment opportunities in the heritage sector.

Tour Operators and Travel Agencies: The tour operator and travel agency sector play a vital role in facilitating tourism in Italy. These businesses provide travel packages, organize tours, and offer guidance and assistance to tourists. Through partnerships with hotels, transportation companies, and local tour guides, these entities generate employment and stimulate economic activity in the tourism ecosystem.

Entertainment and Events: Italy’s vibrant entertainment scene, including music concerts, theater performances, film festivals, and sporting events, also benefits from tourism. Visitors attend these events, leading to increased ticket sales, hotel bookings, and restaurant patronage. The cultural and entertainment sectors thrive due to the support and spending of tourists.

Small Businesses and Local Communities: Tourism is often a lifeline for small businesses and local communities. Family-owned restaurants, artisan workshops, wineries, and local producers all benefit from the influx of tourists who seek authentic experiences and products. These small businesses contribute to the unique and authentic character of Italy’s tourism offerings.

Overall, tourism’s impact extends beyond the direct benefits to various sectors of Italy’s economy. The growth and sustenance of these sectors contribute to job creation, economic development, and the preservation of Italy’s cultural heritage.

Challenges and risks associated with Italy’s reliance on tourism

While Italy’s tourism industry brings significant economic benefits, it also exposes the country to certain challenges and risks due to its heavy reliance on this sector. Understanding these challenges is crucial for managing and diversifying the Italian economy effectively. Let’s explore some of the key challenges and risks associated with Italy’s dependence on tourism.

Seasonality and Overcrowding: Italy’s tourism is highly seasonal, with peak periods occurring during the summer months. This seasonality creates challenges for businesses that rely heavily on tourist spending, as they experience fluctuations in revenue throughout the year. Additionally, popular destinations often face issues of overcrowding, leading to strain on infrastructure, long queues at attractions, and adverse impacts on the local environment and residents’ quality of life.

Economic Vulnerability: Italy’s overreliance on tourism makes its economy vulnerable to external shocks. Economic crises, political instability, natural disasters, or pandemics, as evidenced by the COVID-19 pandemic, can severely impact the tourism industry and hamper the country’s overall economic stability. When a significant portion of the economy relies on tourism, any disruption can have a cascading effect across various sectors and lead to widespread consequences.

Environmental Sustainability: The environmental impact of tourism, particularly in popular destinations, poses significant challenges. Uncontrolled mass tourism can put a strain on natural resources, cause pollution, and contribute to the deterioration of fragile ecosystems. Italy’s iconic natural landscapes and protected areas need to be managed sustainably to ensure their preservation for future generations.

Rising Costs and Inflation: The influx of tourists often leads to a rise in prices, particularly in popular destinations. The increased demand for accommodation, transportation, and services can drive up prices, making it more expensive for both tourists and local residents. This can contribute to inflationary pressures and impact the affordability of tourism, potentially deterring travelers or redirecting them to alternative destinations.

Loss of Cultural Identity: Excessive tourism can gradually erode the unique cultural identity of local communities. As businesses cater to the demands of tourists, there is a risk of diminishing traditional practices, local crafts, and authentic experiences. Preserving the essence of local culture while simultaneously catering to tourist expectations is a delicate balance that should be carefully managed.

Dependency and Diversification: Reliance on a single industry for a significant portion of the economy limits diversification opportunities. Overdependence on tourism may hinder the development of other potential sectors and hinder efforts to create a more balanced and resilient economy. Diversification would reduce vulnerability to external shocks and create a more sustainable economic landscape.

Promotion of Sustainable Tourism: Implementing sustainable tourism practices is crucial for mitigating the risks associated with Italy’s dependence on tourism. This includes managing tourist flows, preserving cultural and environmental resources, promoting responsible travel behavior, and supporting local communities. By emphasizing sustainability, Italy can ensure a more resilient and inclusive tourism industry for the future.

Addressing these challenges and risks requires a comprehensive and strategic approach. Italy should focus on diversifying its economic base, investing in infrastructure, promoting off-peak and alternative destinations, and ensuring sustainability in tourism development. By doing so, Italy can reduce its vulnerability and create a more balanced and sustainable economy for the long term.

Measures to diversify Italy’s economy and reduce dependence on tourism

To reduce the country’s heavy reliance on tourism and create a more diversified and resilient economy, Italy can implement several measures. These measures aim to promote the development of other sectors and encourage economic growth that is less dependent on tourism. Let’s explore some strategies that can help diversify Italy’s economy:

Investing in Innovation and Technology: Italy can focus on fostering innovation and technological advancements in key sectors, such as manufacturing, information technology, and biotechnology. Encouraging research and development, providing support to startups and small businesses, and promoting collaboration between academia and industry can drive economic diversification and attract investment in high-tech industries.

Promoting Small and Medium-sized Enterprises (SMEs): Supporting the growth of SMEs is crucial for diversifying Italy’s economy. SMEs are the backbone of many sectors and can help stimulate regional development and job creation. By providing access to funding, improving business regulations, and offering mentorship and support programs, Italy can nurture entrepreneurship and foster a vibrant ecosystem of small businesses.

Strengthening the Manufacturing Sector: Italy has a long-standing tradition of excellence in manufacturing, particularly in sectors such as automotive, fashion, furniture, and machinery. Investing in advanced manufacturing technologies, promoting research and development, and fostering collaborations between manufacturers and academia can enhance Italy’s competitiveness in the global market and reduce its reliance on tourism-generated revenue.

Developing Knowledge-Based Industries: Italy can prioritize the development of knowledge-based industries, such as education, research, and creative sectors like design, architecture, and media. By investing in education and research institutions, attracting international talent, and providing support for creative industries, Italy can become a hub for knowledge-intensive activities, promoting economic diversification and long-term growth.

Expanding Export Opportunities: Italy can focus on expanding its export markets and diversifying its export portfolio. Encouraging Italian businesses to explore new markets and sectors, providing support in trade negotiations and export promotion, and fostering international business partnerships can help reduce dependency on domestic consumption and amplify the benefits of global trade.

Developing Rural and Agri-food Sectors: Italy’s agricultural heritage and quality food products offer opportunities for economic diversification. Prioritizing sustainable and high-value agricultural practices, investing in rural infrastructure, promoting organic and local food production, and supporting rural entrepreneurship can revitalize rural areas and create new avenues for economic growth beyond tourism.

Encouraging Cultural and Creative Tourism: Italy can leverage its rich cultural heritage and creative industries to attract a different type of tourist. By promoting cultural and creative tourism, which focuses on authentic experiences, local arts, crafts, and design, Italy can diversify its visitor base and attract tourists interested in cultural immersion and unique experiences that go beyond the traditional tourist hotspots.

Investing in Infrastructure and Connectivity: Enhancing Italy’s infrastructure, particularly in less-developed areas, can unlock new economic potentials. This includes improving transportation networks, expanding broadband internet access, and investing in renewable energy sources. It will create opportunities for businesses, stimulate investment, and reduce regional disparities, ultimately contributing to economic diversification.

Addressing Bureaucratic Barriers: Italy can streamline administrative processes and reduce bureaucratic obstacles to business growth. Simplifying regulations, improving transparency, and enhancing the ease of doing business can encourage investment and entrepreneurship, making Italy a more favorable destination for domestic and foreign businesses.

These measures should be implemented in a well-coordinated and long-term strategy to ensure sustainable economic diversification. By reducing dependence on tourism and fostering a more diverse economic landscape, Italy can build resilience, stimulate job creation, and navigate challenges more effectively, ultimately securing a prosperous future for the country.

Italy’s tourism industry is undeniably a vital pillar of its economy, contributing significantly to GDP, job creation, and foreign exchange earnings. The country’s rich cultural heritage, stunning landscapes, gastronomy, and renowned fashion industry have made it a top global tourist destination. However, while tourism has brought numerous benefits, it also exposes Italy to certain challenges and risks.

Italy’s heavy dependence on tourism makes its economy vulnerable to external shocks, such as economic crises, political instability, and pandemics. Additionally, the seasonality of tourism, overcrowding in popular destinations, rising costs, and potential loss of cultural identity all require careful management to ensure sustainable and responsible tourism practices.

To address these challenges, Italy must explore strategies to diversify its economy and reduce its reliance on tourism. Investing in innovation and technology, promoting SMEs, strengthening the manufacturing sector, and developing knowledge-based industries can spur economic growth and create new employment opportunities. Expanding export markets, supporting rural and agri-food sectors, encouraging cultural and creative tourism, and improving infrastructure and connectivity are additional avenues for economic diversification.

Italy should also emphasize sustainability and responsible tourism practices to preserve its cultural heritage, manage environmental impacts, and mitigate overcrowding in popular destinations. Promoting off-peak and alternative destinations, diversifying the tourism offering, and engaging local communities are key to creating a more inclusive and sustainable tourism industry.

In conclusion, while tourism remains a critical driver of Italy’s economy, the country must proactively pursue diversification strategies to reduce its vulnerability and build a more resilient economic foundation. By embracing innovation, fostering entrepreneurship, promoting sustainable practices, and investing in sectors beyond tourism, Italy can forge a path towards a prosperous and balanced future.

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The impact of COVID ‐19 on international tourism flows to Italy: Evidence from mobile phone data

Valerio della corte.

1 Directorate General for Economics, Statistics, and Research, Bank of Italy, Roma Italy

Claudio Doria

Giacomo oddo, associated data.

The data that support the findings of this study are available upon request from the corresponding author, with the only exception of mobile phone data, as they were purchased from a private mobile phone company and cannot be made publicly available due to ownership restrictions. Data from the survey on international tourism in Italy (see Section Data annex in Appendix  1 for details) can be freely downloaded from the official website of the Bank of Italy in the following section: Home/Statistics/External transactions and positions/International tourism/Distribution of microdata.

This paper analyses the response to the COVID‐19 pandemic of inbound tourism to Italy looking at variation across countries and provinces. To this end, it uses weekly data on the number of foreign visitors in Italy from January 2019 until February 2021, as provided by a primary mobile telephony operator. We document a very robust negative relation at the province level between the local epidemic situation and the inflow of foreign travellers. Moreover, provinces with a historically higher share in art‐tourism, and those that used to be ‘hotel intensive’ were hit the most during the pandemic, while provinces with a more prevalent orientation to business tourism proved to be more resilient. Entry restrictions with varying degrees of strictness played a key role in explaining cross‐country patterns. After controlling for these restrictions, we observed that the number of travellers that could arrive by private means of transportation decreased proportionally less. Overall, this evidence emphasises that contagion risk considerations played a significant role in shaping international tourism patterns during the pandemic.

1. INTRODUCTION

The outbreak of the COVID‐19 pandemic in the early months of 2020 caused unprecedented disruption to tourism flows. 1 According to the World Tourism Organization (UNWTO), in 2020 international arrivals worldwide dropped by 74% (1 billion arrivals less than the previous year). Italy, a country for which the tourism industry is very important, 2 was among the first EU countries to be hit by the pandemic: between February and April 2020, positive cases rapidly rose from a few hundreds to over a hundred thousand, with a surge in the number of patients needing intensive care and in the number of deaths. 3

Fear of contagion and containment measures (including travel bans) resulted in tourism flows dropping to near‐zero levels since the end of March 2020. During the second quarter of 2020 conditions improved, allowing for the lifting of travel restrictions at the EU level in the summer. Italy, among other southern European countries (Spain, Portugal and Greece), benefited from the recovery of cross‐border tourism, although flows remained at around a half of pre‐pandemic levels. The second wave of the pandemic that hit Italy after the end of the summer halted again tourism flows. Overall, in 2020 foreign travellers' expenditure in Italy fell by about three fifths compared with previous year (from €44 to 17 billion), and the travel surplus of the balance of payments was halved to 0.5 per cent of GDP (from 1.0 per cent in 2019).

In this context, the adequate design and evaluation of policy responses clearly requires a thorough understanding of how inbound tourism is affected by contagion risk and to containment measures of different intensity. In particular, two main questions deserve closer investigation to inform policy decisions. The first is to what extent the fall in foreign arrivals reflects not only regulatory restrictions and containment measures (travel bans, quarantines, etc.) but also fears of contagion that spontaneously lead travellers to stay away from destinations with a locally higher epidemiological risk. Answering this question is highly relevant from a policy perspective: lifting restrictions while the epidemic is still not under control might not be sufficient to revamp tourism flows if travellers' behaviour actively responds to the risk of contagion. In fact, it may affect tourists decision in the future period.

The second related question is how travel preferences changed in reaction to the pandemic, looking at characteristics that were indirectly related to contagion risk, such as transport means, type of accommodation and amenities at the destination. A proper understanding of these factors is needed to formulate reasonable predictions about which destinations are going to record a larger drop in tourism inflows, so that adequate policy responses can be prepared. Understanding how tourists react to travel restrictions of varying intensity (from quarantine requirements to screening tests) would also be useful for the same purpose.

This paper uses a unique combination of weekly mobile phone data and survey data for Italy to provide answers to the above questions, through an overarching analysis of international tourism flows during the pandemic. The high frequency of mobile phone data on the number of foreign visitors by nationality and province allows us to identify precisely the impact of changing patterns in the epidemics and of the adopted policy measures. We estimate reduced‐form models (consistent with a gravity framework) where the number of foreign travellers in a given location is related to the risk of contagion in the province of stay as well as in the source country, controlling for an extensive set of fixed effects. We also look at how structural characteristics of destinations shaped the dynamics of tourism flows in interaction with the contagion dynamics. We provide compelling evidence that travellers paid a lot of attention to contagion risk during the second wave of contagion—when travel restrictions were looser—avoiding local Italian destinations with a higher number of COVID‐19 cases. Furthermore, destinations that were perceived as ‘less risky’ by tourists (for instance because they were reachable by private means of transport or had a larger share of private accommodations), were hit less, all other things being equal.

This paper is at the intersection of two strands of literature. The first and larger strand is about the adverse effects that infectious diseases cast on the economy, and on tourism in particular. It received an important boost in the 2000s, after the outbreak of the SARS and the ‘aviary flu’ in Asia (Chou et al.,  2004 ; Hanna & Huang,  2004 ; McKercher & Chon,  2004 ), followed by studies on MERS (Joo et al.,  2019 ) and the H1N1 influenza (Rassy & Smith  2013 ). All of these studies show that the tourism sector was hit the hardest, finding a negative relationship between contagion dynamics and foreign arrivals. In particular, Hanna and Huang ( 2004 ) find that the impact was higher in regions characterised by higher population density, higher mobility of people, and where public health infrastructure was less developed. Chou et al. ( 2004 ) conclude that a failure in disclosing the actual number of SARS cases can deliver additional GDP loss in the longer run, pointing to the fact that not only international travellers but also foreign investors need accurate information on the dynamic of the epidemic. More recently, Cevik ( 2020 ) compares the impact of different kind of diseases on bilateral tourism flows, showing that the impact on tourism is due more to the contagiousness of the disease than to its severity, and that negative effects are stronger for developing countries.

With the outbreak of COVID‐19, the first truly global pandemic after the 1918–1919 influenza (so‐called ‘Spanish flu’), a large and growing bulk of papers was added to this workstream. Given the pervasiveness of the shock and the strictness of countermeasures that were adopted worldwide, studies have analysed the impact not only on tourism but also on trade of goods (Bas et al.,  2022 ; Berthou & Stumpner,  2022 ; Liu et al.,  2021 ) and services in general (Ando & Hayakawa,  2022 ; Minondo,  2021 ). 4 The present crisis is in fact characterised by quick and wider developments, impacting all countries across the globe. As regards impact of COVID‐19 on tourism, existing studies are largely descriptive (MacDonald et al.,  2020 ; Metaxas & Folinas,  2020 ; Uğur & Akbıyık,  2020 ; see Sigala,  2020 for a preliminary survey) or focus on specific segments of the tourism industry, such as short‐term rental: Hu and Lee ( 2020 ) quantify the impact of lockdown on global AirBnB bookings. Focusing on the European short‐term rental market, Guglielminetti et al. ( 2021 ) find that the epidemic reduced markedly both the supply of apartments available for rents and the consumers' demand. Our paper contributes to this literature with a rich econometric analysis of the effects of COVID‐19 on foreign arrivals in Italy. We believe that Italy is an ideal setting for this analysis, for three reasons. First, it is one of the largest exporters of tourism services (Italian tourism exports rank sixth in the world, according to UNWTO), so it is a very relevant case study. Second, it is endowed with a well‐diversified range of destinations associated with different travel purposes (business trips, art visits, beach or mountain holidays, etc.), and it attracts visitors from a very diverse set of departure countries, which allows to study the interaction between characteristics of both local destinations and countries of departure. Third, the significant heterogeneity in the spread of contagion across the country allows a quite neat identification of the response of tourism to the differential level of the epidemic among local destinations, while controlling for developments at the country level. This allows us to draw several conclusions on the response of international tourism to the pandemic which are potentially useful for policymaking purposes.

The second strand of literature this study is related to is the growing number of research papers using location data derived from mobile phone networks for the analysis of mobility and consumer behaviour (Hu et al.,  2009 ; Tucker & Yu,  2020 ). Mobile phone data have been used in behavioural studies for almost two decades (Spinney,  2003 ) and the use of this data for tourism analysis is not entirely new. 5 The availability of such data accelerated when smartphones massively replaced first‐generation mobile phones. As this paper confirms, this type of data has become a very valuable complement to more conventional data sources (e.g. survey data), especially for tourism analysis.

The paper is structured as follows: Section  2 provides descriptive evidence on the changes that occurred in incoming tourism flows after the pandemic along various dimensions, paving the way for the subsequent econometric analysis. Section  3 presents the database and the empirical model adopted to measure the impact of the pandemic on the incoming tourism flows and its interaction with variables at the province and the country of departure level. In Section  4 , we present and discuss estimation results, robustness evaluations and economic interpretation of regression coefficients. Finally, Section  5 summarises our findings and draws concluding remarks.

2. AGGREGATE PATTERNS OF FOREIGN TOURISM FLOWS IN ITALY

This section of the paper presents the main aggregate patterns in foreign tourism to Italy in 2020, highlighting the heterogeneous impact of the pandemic. This evidence guides us in the selection of relevant variables for the empirical model presented in Section  3 .

The COVID‐19 disease started to spread in Italy in the second half of February 2020. The lockdown was applied initially in selected Northern provinces and, since March 9, in the entire country. It included a stay‐at‐home order, the shutdown of all non‐essential economic activities and restrictions to both internal and international mobility. In this phase, the outbreak remained concentrated in Northern Italy. These restrictions were lifted during the month of May 2020. The strong containment measures proved to be effective in halting the spread of the disease, and Italy benefited of near‐zero rate of new COVID‐19 cases throughout the summer. In early June travel restrictions between EU member countries, Schengen Area countries and United Kingdom were lifted, and inbound tourism gradually resumed. New case rates started picking up again at the end of August, and in the fall a second wave of contagion hit Italy throughout the country, with virtually no province spared from a rise in infections.

According to official statistics, in 2020 foreign visitors in Italy (i.e. including those who did not stay in Italy overnight) were 39 million overall, about 60% less than the previous year. 6 The drop in inbound tourism was sharp from all countries of origin, but particularly severe from farther countries (Table  1 and Figure  1 ): the number of arrivals from Europe (both EU and non‐EU) decreased by 56.2% with respect to 2019; those from the Americas and Asia fell by 87% and 81% respectively.

Changes in the number of foreign travellers in Italy.

Source : BISIT data. Changes refer to 2020 with respect to 2019.

Changes in the number of foreign arrivals by area of origin and new COVID‐19 cases. Lines represent monthly foreign arrivals in 2020 vis‐à‐vis the corresponding months in 2019 in percentage terms (scale on the left‐hand axis). Histograms (scale on the right‐hand side axis) represent the number of new COVID‐19 cases occurred in Italy in each month.

These patterns were likely affected by the travel bans adopted in many countries throughout the world (including Italy), but they may also reflect a preference by foreign tourists for destinations closer to home that can be reached by private means of transport. Indeed, the drop of arrivals in regions closer to Italian borders (such as Veneto and Lombardy) was relatively smaller than in the other regions.

The pandemic also induced changes along the dimension of the travel's motive, as suggested by the correlation between the ex ante shares of various travel purposes in each Italian province (which capture their ‘touristic specialisation’) and the change in arrivals between 2019 and 2020 (Figure  2 ). 7 Arrivals dropped systematically more in provinces specialised in cultural tourism purposes, while this correlation is weaker for ‘sea and nature’ holidays. The correlation is instead positive in the case of business tourism, meaning that the provinces that used to have a relatively higher share of tourism related to business reasons suffered much less in terms of decline in foreign arrivals.

Correlation between change in arrivals and travel purpose shares at province level. Each dot represents an Italian province. In all graphs, vertical axis reports the drop in arrivals between 2019 and 2020 in % terms, while horizontal axis reports the share of travellers that used to visit the province before 2020 for the specified travel purpose.

Finally, another relevant change was observed along a third dimension of interest: the type of accommodation chosen by visitors during their sojourn in Italy. As shown in Table  2 , comparing 2020 data with the pre‐COVID‐19 three‐year period (2017–2019), shares of ‘traditional’ accommodations (hotel, B&B, tourist resort) decreased significantly (for over 14 percentage points), mainly to the advantage of independent non‐shared accommodations (rented houses or own properties) or other less common accommodations (campers, tents, caravans, etc.). The share of visitors who stayed at home with relatives or friends during their sojourn also grew significantly.

Accommodation choices pre‐ and post‐COVID‐19.

Note : ‘Other accommodations’ includes also camping, caravans and farmhouses.

Source : BISIT data. All values are shares. Values for 2017–2019 are averages.

3. THE HETEROGENEOUS IMPACT OF COVID ‐19 ON TOURISM: DATA AND EMPIRICAL MODEL

3.1. data sources and variables definition.

We combine various sources of information about tourism, epidemiological patterns and policy measures, to build a comprehensive and detailed dataset for our empirical exercise. The dataset covers the period from January 2019 to February 2021.

Two main sources are used for tourism data, to quantify the number of foreign tourists and to gather information on tourism characteristics. The first source of data comes from a primary Italian mobile phone operator. It provides the total number of foreign phone SIM cards on the Italian territory, by province and by issuer country. We use the former as information about the province of destination and the latter as a proxy for the country of origin of the traveller. Mobile phone data are available at a daily frequency (we aggregate them into weekly data). This source provides several important advantages. First, the data cover also the months in which the Bank of Italy Survey on International Travel was discontinued because of the restrictions against the spread of the pandemic. Second, the higher (weekly) data frequency allows to assess the impact that the contagion dynamics and the policy responses had on tourism patterns in a much more precise way than what could be done with monthly data: for instance, we can match the increase in cases occurred in a given week with the tourism flows observed in subsequent weeks, while controlling for the travel restriction in place in that specific week of the year. Finally, the extensive coverage provided by mobile phone data allows to look at combinations of ‘country of origin – province – time’ that in BISIT data may be subject to significant measurement error (e.g. for smaller countries and provinces). One limitation however is that the number of foreign tourists derived from mobile phone data may be distorted by the presence of communities of foreign residents in Italy. To avoid this potential bias, in our analysis we considered the first 40 countries, in terms of the number of tourists in 2017–2019, excluding those having large communities of residents in Italy. The selected countries account for about 94 per cent of the total inbound tourism flows to Italy (over the period 2017–2019); half of them belong to the European Union. 8

The second source of tourism data is the Bank of Italy Survey on International Tourism (BISIT). The survey questionnaire asks the interviewed traveller to provide information about the kind of transportation used to reach the destination, the purpose of the trip and the type of accommodation used during the trip (if any). We use data for the period 2017–2019 to construct indicators before the pandemic outbreak: for each province and origin, we quantify the shares of travellers by travel purpose, accommodation type and means of transport.

The epidemiological data regarding the spread of the contagion in Italy are sourced from the Italian Civil Protection Department. 9 At province level, the only available information is the cumulative number of positive COVID‐19 cases, at a daily frequency. From this, we compute the number of new cases of COVID‐19 (gross of recovered patients) over a period of 14 days, per 1000 inhabitants. The resident population in the province at the end of 2019 is retrieved from ISTAT, the Italian national statistical institute.

The corresponding information on the evolution of COVID‐19 in the foreign countries of origin was obtained from the European Center for Disease Prevention and Control (ECDC), which provides harmonised and comparable data on the rate of contagion in all European countries and in all other non‐European countries considered in our analysis.

As for the containment measures adopted by foreign countries, we used the Oxford Stringency Index (Hale et al.,  2021 ), which reflects restrictions to different aspects of economic and social life, such as mandatory closure of schools and offices to remote functioning, shops and restaurants closures, restrictions on public transportation and international travel bans. To control for the different intensity of the restrictions by Italian regions enforced since November 2020, we relied on the index developed for Italy by Conteduca ( 2021 ). 10

We also constructed a set of dummies related to the intensity of bilateral travel restrictions enforced by the Italian Government. This information was collected from the legislation acts adopted throughout the period, also relying on the website ‘ reopen.europa.eu ’, and on the website of Italy's Foreign affairs Ministry ‘ www.viaggiaresicuri.it ’.

Finally, variables on bilateral distance were retrieved from the CEPII data warehouse (Mayer & Zignago,  2011 ).

3.2. The empirical strategy

Our empirical exercise aims at explaining the heterogeneous impact of COVID‐19 on international tourism to Italy disentangling the contribution of various factors at the province and the country‐of‐origin level. In practice, the empirical strategy relies on two mirror‐like reduced‐form models for inbound tourism to Italy that are in line with a gravity framework. We estimate those models using the Poisson pseudo maximum likelihood estimator on weekly data from January 2019 to February 2021. 11

Our first model estimates the effects of contagion at the province level and of province's characteristics, while controlling for time‐varying characteristics of tourists' countries of origin with fixed effects (Equation  1 ).

The dependent variable, Tourists opt , is the total number of days spent by tourists from country o in province p at time t , where temporal unit t denotes a combination of year‐week. Our identification strategy exploits the granularity of the data set, and it includes an extensive set of fixed effects to control for unobservable factors. Country–province–week factors ( α opw ) control for the preference of travellers from a specific country for a specific province p in a week w . 12 Such preferences may be motivated by the availability of convenient flight connections, by business links and of course by the characteristics of the touristic offer of the destination compared to the domestic market (for instance, German tourists may favour beach destinations in Italy in summer weeks relatively more than French tourists, because France also offers attractive seaside destinations to domestic tourists). We also include time‐varying factors related to the country of departure α ot , which control for all developments that occurred at time t in the country of departure, in Italy, or third countries, that could affect the number of arrivals (for instance in terms of the epidemic or in containment measures). 13

Our main explanatory variable in Equation ( 1 ) is cases pt  − 1 , which is the number of new COVID‐19 cases on 1000 inhabitants that were recorded in the province during the previous 2 weeks, a commonly used metric to measure epidemic developments. This variable allows us to verify whether tourists were concerned about the level of contagion risk not only at the country level (which is captured by the fixed effects) but also at the local level. Indeed, information on local developments of the COVID‐19 epidemic is widely and easily available on the web. Therefore such information may be consulted by travellers before travelling to a given country, in order to avoid destinations where the epidemic is spreading faster.

To elicit the effect of the pandemic outbreak on tourists' choices, we interact variables Purpose op , Accommodation op and Transport op with a dichotomic variable that marks the COVID‐19 period, taking value one from the last week of February 2020 onward. These variables are vectors of shares extracted from BISIT data for the years 2017–2019, as explained in Section  3.1 . Purpose op reports the shares of various purposes of the trips, as declared by foreign travellers from country o when they visited province p before the pandemic: ‘art and culture holiday’, ‘sea and nature holiday’, ‘other purposes trip’ and ‘business reasons’ (the latter being the base category). These shares are computed for each season to take into account possible seasonality in the purpose of travel for some destination.

In the same fashion, Accommodation op reports the shares of various accommodation choices made by travellers: ‘hotels and hostels’, ‘camping, farmhouses, and caravans’, ‘day‐trip (and others)’, which is associated with no accommodation at all or with alternative types of accommodation, and the base category ‘own house, or hosted by relatives/friends, or at a rented house/flat’. Finally, Transport op indicates the shares of transports typologies chosen by travellers from country o to reach their destination p before the pandemic. We classified them into two categories: (i) collective and/or mass transports (planes, ships and trains) and (ii) individual/private transports (cars, caravans, bikes and motorcycles), our base category.

As mentioned, we estimate the model by Pseudo Poisson Maximum Likelihood regression—PPML, in line with the literature on gravity models of trade (Santos Silva & Tenreyro,  2006 ). 14 An advantage of PPML is that it allows the inclusion of null observations, namely provinces that tourists from country o visited in week w in 2019 but they did not visit in 2020. In our case, these are potentially meaningful observations as they refer to flows that were hit the hardest by the pandemic. Moreover, PPML is a consistent estimator in presence of heteroskedasticity (even if the dependent variable does not follow a Poisson distribution) and lends itself well to model count variables, as it is our dependent variable. In our inference, we assume double‐clustering by country of departure–time and by province–time.

In a second step, we drop the country–time fixed effects α ot from the model and introduce variables related to the evolution of the epidemic, the containment measures, the bilateral entry restrictions imposed by Italy, and distance, to explain the cross‐country variation in international tourism inflows (Equation  2 ):

Here, we include fixed effects α pt to control for any factor at play at time t in province p (including COVID‐19) that can have an impact on inbound tourism in that province from any destination. This specification is thus designed to estimate the effects of variables indexed by ot (country‐of‐origin and time), exploiting variation across countries at time t , while controlling for time‐varying province‐specific pt factors.

We consider the following additional explanatory variables: cases ot − 1 is the number of new COVID‐19 cases over 1000 inhabitants over a period of 14 days ending in week t  − 1 in the country of departure o (Section  3.1 ). Entry restrictions ot is a set of dummies indicating the bilateral travel restrictions (if any) imposed by Italy vis‐à‐vis other countries. We distinguished between (i) the travel restrictions that allow entry from a country only for urgent and/or essential reasons, like health motives or repatriations ( Necessity only ot , IT ), (ii) restrictions that allow entry only for work reasons and/or upon a quarantine period ( Quarantine ot , IT ), (iii) restrictions that allow entry upon a negative result of swab test (either at arrival or before departure) ( Swab ot , IT ). Stringency ot is the Oxford Stringency index (which takes values in the 0–100 interval, depending on the intensity of containment measures adopted by the country o at time t ). 15

We further interact the indicator variable for the COVID‐19 period with two variables measuring distance, to check whether foreign tourists from closer countries reduced their presence in Italy relatively less than tourists from more distant countries, in addition to what is already captured by the variable Transport op , which varies by the province of destination p and country of departure o . These two variables are the logarithm of the bilateral population‐weighted distance between Italy and country o , and an indicator variable which is equal to one if the country has a common border with Italy. 16

4. RESULTS AND DISCUSSION

4.1. analysis by local destination.

Table  3 reports results from the estimation of the model in Equation ( 1 ). Column (1) includes only the ‘local contagion’ variable (new positive cases in the province) and the full set of fixed effects: the coefficient of the contagion variable is negative and statistically significant. Given our specification of fixed effects, it means that if a province records 100 new positive cases per 100,000 inhabitants more than other provinces over 2 weeks, that province will experience on average a reduction in the number of foreign tourists about 6 percentage points larger than other provinces in the subsequent week, ceteris paribus. The contagion variable remains highly significant, with a slightly larger coefficient (column 2), when we add controls for the interaction between province–country structural characteristics and a dichotomous variable signalling the start of the pandemic. 17 The remaining columns of Table  3 report the results for three different phases of the epidemic in Italy. The first phase goes from February 25th to June 2nd 2020, and it covers the lockdown period (column 3). The second phase includes the summer period until September 15th, a period characterised by a gradual recovery of inbound tourism and by negligible rates of new COVID‐19 cases (column 4). The third phase covers the second wave of contagion, and it extends from mid‐September 2020 to February 2021 (column 5). Estimation results show that the negative relation between the number of foreign arrivals and new COVID‐19 cases materialised only during the latter phase, which includes the second wave of COVID‐19. This period is in our view the most appropriate setting to study the impact of new cases on inbound tourism because it was characterised by milder travel restrictions, by a larger degree of awareness about the health situation and by more information accessible to tourists about the local evolution of the epidemic. 18 On the contrary, we do not include the cases variable for contagion when estimating the model for the summer period (column 4), given the extremely low number of new cases in most provinces during summertime, as otherwise the estimate would be driven by a few observations only. For similar reasons, when we include the cases variable and estimate the model for the first wave, we are aware that the subsequent results should be taken with caution, since the travel restrictions in place during that period effectively blocked all tourists, except those travelling for reasons of need or work. Indeed, the sign of the estimated coefficient over this period is found to be, counter‐intuitively, positive. This result could be however rationalised considering that during the first wave, contagion occurred overwhelmingly in northern regions of Italy, which were also the regions more frequented by foreigners travelling for business reasons. For instance, cross‐border workers, which typically work in northern Italy, could enter and exit the country even during the first wave. When we exclude from ‘the first wave’ estimation the countries bordering Italy (Table  A3 in Appendix  1 ), the significance of the coefficient on the ‘new cases’ vanishes for this period. 19

Analysis by province.

Note : The table presents results of the model 1 estimated over different periods. Columns (1) and (2) look at the whole sample (January 2019–February 2021). The 1st wave period (column 3) includes the weeks from 25 February to 2 June 2020. The Summer period (column 4) goes from 3 June to 15 September 2020. The 2nd wave (columns 5–7) goes from 16 September 2020 onward. The reference categories for the variable interacted with a dummy for the COVID‐19 period are, respectively, ‘business‐reasons’ for the purpose of travel, and ‘rented house or private house’ for the accommodation. The reference category for ‘airplane’ (short for public means of transport) is private means of transport (like own car). Standard errors, in parenthesis, are clustered by province–time and country of departure–time. Stars (***, ** and *) indicate statistical significance at 1, 5 and 10 per cent, respectively. Fixed effects by country of departure–province–week ( α opw ) and country of departure–time ( α ot ) are always included.

Since early November 2020, new restrictive measures were introduced in Italy, based on an assessment of epidemiological risk at the regional level. After this policy change, epidemiological risk per se cannot be considered anymore the main explanatory variable for the decrease in inbound tourism, as internal mobility restrictions may also contribute to it, reducing the attractiveness of a province. We thus include in column (6) a one‐week lag of the regional restriction index (RR‐Index) constructed by Conteduca ( 2021 ). 20 As expected, we find the coefficient of the RR‐Index to be negative and statistically significant, meaning that tourists avoided provinces where more stringent restrictions were in place. Nevertheless, the coefficient of our contagion variable remains significant and almost unaffected in size, meaning that even after controlling for internal mobility restrictions, foreign tourists decreased more in provinces where contagion risk was higher, all other things being equal.

As a further robustness check, we replace the restriction index with region–time fixed effects (column 7). This structure of fixed effects is able to control for the new system of region‐based restrictions while also capturing the correlation of the epidemic within provinces of the same region. Yet, the contagion variable remains negative and significant, and only marginally lower, further corroborating the robustness of our results about the adverse effect of contagion on foreign arrivals. 21 Overall, this result suggests that tourists paid attention not only to the national dynamics of the epidemic (which in our case is captured by the country–time fixed effects α ot ) but also to local developments of the epidemic, with noteworthy policy implications: even at the local level, there is a trade‐off implied by loosening restrictions: on the one hand, it may attract more tourists in the short term; on the other hand, if more arrivals are associated with an increase in the number of cases, it may discourage inbound tourism later in time.

Results from the interaction between province–country structural characteristics and the COVID‐19 period also indicate that travellers took contagion risk into account in their decisions. The coefficients of these variables can be interpreted as the average differential impact of the outbreak of the pandemic across our observational units (country–province–week). Column (2) shows that provinces that were more ‘specialised’ in art and culture tourism were hit the hardest: a coefficient of −1.1 for art tourism means that an increase in 10 % in the proportion of tourists that used to visit the province for that purpose is associated to a 7 % larger drop in inbound tourism. The drop would be only about 4 % for provinces visited for beach or nature holidays, with tourism for personal reasons purposes (like leisure tourism) hit generally harder than business tourism (our base level). A possible driving factor underlying this result is related to the fact that trips motivated by work reasons were generally exempted by travel restrictions, hence visitors travelling for work reasons could come to Italy even when tourists that would visit for holiday reasons could not (for instance, this was the case during the first lockdown for visitors arriving from countries outside Europe). This may have favoured provinces receiving historically higher shares of business travellers, even in a period when conferences and big events were moved on virtual platforms or cancelled.

Results also show that provinces in which tourists used to stay in ‘hotel‐like’ accommodations were hit harder than provinces characterised by a larger share of private housing and/or rental houses (our base level). The latter type of accommodation may indeed be perceived as relatively safer by tourists, as it implies less social interaction with other people. Provinces with a higher share of tourists staying in ‘green’ accommodations, like camping and farmhouses, also appear to have been more resilient on average.

Finally, the third feature of interest under consideration is the means of transport used; in line with our expectations, provinces that used to have a larger share of visitors arriving by plane (or other shared means of transport, like train or ship) were hit harder, reflecting the perception of a higher risk of infection compared to private non‐mass transport means, like cars or caravans. Using an extreme case as an illustrative example, the number of visitors in a province from a country in which all tourists come by collective means of transport recorded a 30 % larger drop than a province in which tourists from the same country arrive by car.

The behaviour of these variables in the different sub‐samples is overall consistent to what described for the whole sample. In the summer, interestingly, the relative loss by hotel‐intensive provinces appears to be only a half than what estimated for the overall period, suggesting that during this period tourists may have been less concerned with contagion risk, consistently with the near‐zero cases in most provinces.

In Table  4 , we report several robustness tests of the result on the variable measuring contagion at the province level using different metrics and specifications, finding robust and statistically significant coefficients with a comparable size. First, we consider a longer temporal lag (4 weeks, rather than 1 week) to compute the number of new cases, to account for the fact that tourists may make their travel plans sufficiently in advance. We obtain a coefficient almost identical (column 2). 22 We further check against the effects of few big outliers by winsorising the variable cases p , t − 1 at the 1st and 99th percentiles. Doing so delivers an even higher coefficient (column 3). In column (4) we include a quadratic term, which we find to be significant, suggesting a non‐linearity in the impact of this variable on arrivals: in other words, tourists seem to refrain more from travelling to Italian locations when the notification rate of new positive cases becomes high. We then include the cumulative number of positive cases at the province level (column 5). This metric takes into account the hypothesis that tourists may be sensitive to the past dynamics of positive cases in the destination province, rather than only to the current situation (although the two variables are to some extent correlated). We find that the notification rate of new positive cases remains highly significant and of similar magnitude. 23 As a robustness check, we estimate the baseline model 1 in log‐linear formulation by OLS (column 6). The coefficient on our contagion variable again remains negative and statistically significant, and only marginally lower. Finally, we consider two sub‐samples: first, we limit the analysis to the first 40 provinces in terms of inbound tourism in previous years (column 7), obtaining similar results. Second, we exclude the first 2 months of 2021 from our sample, to rule out the possibility that our results are distorted by a change occurred in the way new positive cases were recorded before and after 15 January 2021 (before the date, new positive cases were counted based only on the results of PCR molecular tests, while after that date, positive cases detected through rapid antigenic tests were included in the counter for the number of cases). Our results remain substantially unchanged. 24

Analysis by province: Different measures of COVID‐19 spread.

Note : The table reports estimates of the model 1 over the period January 2019–February 2021, for different specifications of the variable measuring contagion at local level (columns 1–5). Column (6) report estimates of the model rewritten in log form and estimated with OLS. Column (7) restricts the sample to the first 40 provinces. Column (8) excludes the first months of 2021. The reference categories for the variable interacted with a dummy for the COVID‐19 period are, respectively, ‘business‐reasons’ for the purpose of travel, and ‘rented house or private house’ for the accommodation. The reference category for ‘airplane’ (short for public means of transport) is private means of transport (like own car). Standard errors, in parenthesis, are clustered by province–time and country of departure–time. Stars (***, ** and *) indicate statistical significance at 1, 5 and 10 per cent level, respectively. Fixed effects by country of departure–province–week ( α o p w ) and country of departure–time ( α o t ) are always included.

Table  A2 shows the estimates of our model in Equation ( 1 ) in which only EU countries, Schengen members and the United Kingdom are included in the analysis. Travellers from these countries were allowed to enter Italy for tourism after June 3rd without quarantine requirements (unlike other countries), and they accounted for most of inbound tourism to Italy in our sample period. We obtain almost identical results. Finally, we estimate our model excluding travellers from countries sharing a common border with Italy to remove the impact of cross‐border workers. Again, we obtain similar results overall and an even larger coefficient on the contagion variable (Table  A3 ).

4.2. Analysis by country of departure

In this section we shift our focus to the variation of incoming tourism flows by country of departure of the tourists. To do so, as explained in Section  3.2 , we drop our Country–Time fixed effects and we augment our model with the variables described in Section  3 (Equation  2 ). We estimate the model over two sets of countries: the entire sample of 40 countries (Table  5 ) and the sub‐sample of ‘passport‐free’ countries (EU and Schengen Area member countries, and the United Kingdom, whose citizens from 3 June onward were allowed to enter Italy for touristic reasons without almost any quarantine requirements). This sub‐sample includes the countries that account for most of the inbound tourism in our period of analysis and that faced very similar restrictions, which makes them more comparable. 25

Analysis by country of departure: All countries.

Note : The table presents estimates of the model 2 over different periods for the first 40 countries in terms of tourism receipts to Italy. The 1 st wave period (column 1) includes the weeks from 25 February to 2 June 2020. The Summer period (column 2) goes from 3 June to 15 September 2020. The 2 nd wave, column (5), goes from 16 September 2020 onward. Variable cases ot − 1 was winsorised at the 1%–99% per cent level to mitigate possible measurement errors and outliers (there are cases in the original dataset where the number of new cases is negative). The model includes the variables X op (coefficients not shown), namely d COVID 19 * β 1 ′ Purpose op + β 2 ′ Accommodation op + β 3 ′ Transport op . Standard errors, in parenthesis, are clustered by province–time and country of departure–time. ***, ** and * indicate statistical significance at 1, 5 and 10 per cent, respectively. Fixed effects by country of departure–province–week ( α opw ) and province–time ( α ot ) are always included.

Column (1) in Table  5 indicates that, unsurprisingly, the most important variables in explaining cross‐country variation in the presence of foreign tourists are related to the strictness of the bilateral travel restrictions imposed by Italy. The coefficient of the dummy variable Quarantine ot , IT (which takes value 1 if there is either a mandatory quarantine period for tourists coming from that country, or if entry for leisure tourism is forbidden), implies a reduction in tourist presence by about 60 per cent larger than what are recorded by countries not subject to this requirement. The relative drop in international tourism is even more dramatic when entry was allowed only for urgent/essential reasons. On the contrary, screening measures at entry (e.g. swab tests) cause a substantially milder reduction in entry flows: the coefficient of the dummy for swab test requirement indicates a 20 per cent decrease in tourism flows. In fact, the coefficient of the swab test requirement is not statistically different from zero when we limit the analysis to EU and Schengen countries (and United Kingdom; Table  6 ), suggesting that this type of screening could limit the international spread of contagion without significantly hampering inbound tourism flows.

Analysis by country of departure: EU, Schengen members and UK.

Note : The table presents estimates of the model 2 over different periods for EU countries, Schengen countries plus United Kingdom. These were the only countries for which after the first wave visits for holiday tourism were allowed without the need to quarantine and accounted for about two‐thirds of total tourism receipts in 2020. The 1 st wave period (column 1) includes the weeks from 25 February to 2 June 2020. The Summer period (column 2) goes from 3 June to 15 September 2020. The 2 nd wave, column (5), goes from 16 September 2020 onward. Variable cases ot − 1 was winsorised at the 1%–99% per cent level to mitigate possible measurement errors (there are cases in the original database where the number of new cases is negative). The model includes the variables X op (coefficients not shown), namely d COVID 19 * β 1 ′ Purpose op + β 2 ′ Accommodation op + β 3 ′ Transport op . Standard errors, in parenthesis, are clustered by province–time and country of departure–time. ***, **, and *Statistical significance at 1, 5 and 10 per cent, respectively. Fixed effects by country of departure–province–week ( α opw ) and province–time ( α ot ) are always included.

A second result is related to the impact of distance. Our model includes the interaction between a dummy for the COVID‐19 period and the share of visitors that used to arrive at the local destination by plane or other public means of transport, which displayed a negative and statistically significant coefficient, pointing to the renewed importance of distance during the pandemic. We further add a variable measuring bilateral distance between Italy and the country of departure and a dummy for bordering countries. We find that distance also had an additional negative effect on the number of foreign travellers when we consider European countries, in particular during the summer months, suggesting that tourists preferred closer destinations, ceteris paribus. This remains true, with the only exception of the first wave, if we exclude countries bordering with Italy (Table  A4 ).

While we had clear priors about the coefficients of the above‐mentioned variables, we had ambiguous expectations about the effect of contagion and stringency measures in the country of departure. On one hand, an increase of COVID‐19 cases in the home country of the tourists may induce them to raise caution and curb their plans to travel abroad, given the uncertainty of the health situation at home. By the same token, a tightening in containment measures in the home country may produce a similar effect, also in consideration that future stronger containment policies may hinder the travel on the way back home or make it more costly (e.g. because of reduced number of flights). On the other hand, a surge of positive cases at home may push the tourist to travel abroad (if the destination is perceived as ‘safer’) to minimise contagion risk during holidays and/or avoid domestic restrictions (substitution effect).

As regards the new COVID‐19 cases variable, our results are inconclusive: the coefficient is not consistently different from zero in the whole sample (columns 1 and 2) in Table  5 , while it turns out to be negative on the sub‐sample of European countries (Table  6 , columns 2 and 5). Moreover, the coefficient is positive during the summer months while negative or not statistically different from zero afterward. 26 The coefficient of the stringency index is instead more stable, as we find consistent positive estimates over the whole sample (column 1 in Tables  5 and ​ and6). 6 ). The sign of the stringency index coefficient remains positive even if we separately introduce dummies that control for mobility restrictions at home (column 2). 27

A possible relevant source of cross‐country variation that we are not controlling for in column (1) stems from travel restrictions to outbound tourism in the countries of origin. Unfortunately, we do not have information on these restrictions. As a proxy remedy to this concern, we include a categorical variable from the Oxford database (Hale et al.,  2021 ) that measures the strictness of travel restrictions to inbound tourism in the tourist's home, as we assume that the restrictions to outbound tourism are generally symmetric with restrictions on inbound tourism, as suggested by anecdotal evidence observed for the Italian case. Our assumption seems validated, as we find that the introduction of these measures is negatively associated with a reduction in the number of arrivals, but their inclusion does not alter our results (column 2).

5. CONCLUDING REMARKS

In this paper we analysed inbound tourism to Italy during the COVID‐19 pandemic, looking at variation across Italian provinces of destination as well as across travellers' countries of origin. To this end, we relied on unique mobile phone data about the weekly number of foreign visitors in Italy, broken down by Italian province of stay and by visitors' nationality, for a period going from January 2019 to February 2021.

Our first result is that there is a negative and statistically significant relationship between the flow of foreign travellers in a given province and the local epidemiological situation, even after controlling for restrictive measures at the national and regional level. In other words, tourists appear to have paid a lot of attention to the risk of contagion not only at the national level (as somewhat expected), but also at the local destination level, and they make their travel plans accordingly. The resulting policy implication is that revamping international tourism flows during an epidemic is not simply a matter of lifting restrictions, but it also requires a substantial reduction of contagion risk, at least until the immunisation of the population reduces the health risks associated with getting ill with COVID‐19. With this regard, we can expect the negative elasticity of tourism flows to contagion to be sensibly reduced by progress in the vaccination campaigns.

Our second related result is that, since the start of the pandemic, provinces specialised in art tourism were hit the most, while provinces with a more prevalent orientation to business tourism proved to be significantly more resilient. Furthermore, provinces that used to be more ‘hotel intensive’ in terms of accommodation choices made by visitors were hit harder than provinces characterised by a larger use of private housing and/or rental houses. Finally, we also found that arrivals to local destinations more easily reachable by private means of transport (such as cars) decreased significantly less. This evidence is overall consistent with the hypothesis that contagion risk significantly affects not only tourists' decisions to travel but also how to travel and where to stay, thus implying heterogeneous effects across local destinations. Some local destinations appear to have suffered a larger fall in international arrivals, because they were perceived as ‘riskier’, given their local characteristics. Therefore, well‐diversified accommodation facilities and travel infrastructures enhance the resilience of a touristic destination to this type of adverse shocks.

Thirdly, we found that the different degrees in intensity and extension of entry restrictions across countries were key factors in explaining cross‐country patterns in international arrivals. However, screening requirements for incoming visitors (such as swab tests) do not seem to significantly discourage arrivals. Screening upon entry may thus be considered by policymakers an effective tool to reconcile the need to contain the expansion of the epidemic with the need to mitigate its impact on tourism flows. We also observed that arrivals from more remote European countries decreased comparatively more, even after controlling for entry restrictions and excluding neighbouring countries, pointing to the increased importance of distance in affecting tourists' choices during a pandemic.

ACKNOWLEDGEMENTS

The authors wish to thank Silvia Fabiani, Stefano Federico, Fadi Hassan, Alfonso Rosolia, Simonetta Zappa, Alessandro Borin, the editor and two anonymous referees for useful comments and suggestions on a previous version of this paper, while retaining full responsibility for all remaining errors and omissions. The views expressed in this study are those of the authors and do not involve the responsibility of the Bank of Italy.

APPENDIX 1. 

In this section, we provide further details on the data we used.

Mobile phone data

The total number of foreign SIM cards in Italy was calculated by the mobile operator based on roaming data. The cell network coverage of our provider is very large, so the number of foreign SIM cards detected is also large. In practice, however, not all foreign SIM cards are captured by this network, because there are also other Italian mobile phone operators offering roaming services to foreign SIM cards. In order to overcome this issue and estimate the total population of foreign SIM cards in Italy, our provider added also an estimate of the number of SIM cards roaming on other competing Italian networks, based on proprietary commercial data and market shares calibration. While we do not have access to their methodology, we could verify that their final data are consistent with BISIT data for the period common to the two data sources, and the two time series show very similar dynamics (Figure  A1 ). This suggests that mobile phone data provides a good tracker for inbound tourism flows, supporting the use of this source for the analysis.

Number of inbound travellers (indices: August 2019 = 100).

A SIM card (Subscriber Identity Module) contains an integrated circuit that encodes the subscriber's identity and the nationality of the operating company that has issued the card. We take this information as a proxy for the residency of the card owner (i.e. country of departure). This approximation is good as far as phone users resort to resident mobile companies. This may not be the case for migrants, as mentioned in Section  3.1 , since they may prefer SIM cards issued in their home country instead of cards issued in their host country, to call their relatives at home at cheaper prices. For this reason, we excluded foreign SIM cards issued by countries associated with large immigrant communities in Italy.

As for the location, foreign SIM cards were attributed to Italian provinces based on the ‘cells’ (i.e. mobile phone antenna towers) they were connected with. If a SIM is detected in more than one province on the same day, it is assigned to the province where it was detected for a longer time. The Italian data protection legislation does not allow the diffusion of information derived from mobile phone data referring to less than 15 individual users. Therefore, if the three dimensions day , country of origin and province of destination are populated by 15 or less observations, the phone operator set the province of destination equal to ‘non specified’. The impact of this censoring on the data used in the paper is however quite low: the share of SIMs in the ‘undisclosed’ provinces was about 1.5 per cent in 2019 (2.5 in 2020). Moreover, those SIMs are prevalently associated with relatively ‘small’ countries, that were already excluded from the analysis for the reasons specified in the sub‐section List of countries included.

The Bank of Italy Survey on International Tourism (BISIT) is based on two pillars: (i) counting the number of travellers that enter/leave the country at a selected number of border crossing points, and (ii) conducting interviews with a sample of international travellers, both residents and not residents, crossing the Italian borders. The counting process aims at estimating the reference universe (i.e. the total number of inbound and outbound travellers), broken down by country of residence or destination, while the survey collects information about tourists' expenditure and their personal characteristics.

The BISIT survey asks the surveyed traveller to specify the reason for her trip to Italy choosing one among the possible answers: (A) personal reasons (it includes: A1 holidays and leisure; A2 Studying; A3 Pilgrimage or other religious reasons; A4 health or thermal tourism; A5 honeymoon; A6 visiting relatives and/or friends; A7 shopping; A8 other personal reasons). (B) Business reasons. (C) Transit only. If the respondent chooses A1, she is invited to further specify if it was holidays A1.1 at the beach; A1.2 on the mountains; A1.3 at the lake; A1.4 in a città d'arte (city of art); A1.5 green holidays; A1.6 sport and fitness holidays; A1.7 wine & food holidays. The complete questionnaire form can be downloaded from the Bank of Italy website section on international tourism statistics.

List of countries included

For readers' information, we list here (according to the alphabetical order of their ISO code) the 40 countries of origin included in our sample: Argentina, Austria, Australia, Bosnia and Herzegovina, Belgium, Brazil, Belarus, Canada, Switzerland, Chile, Czech Republic, Germany, Denmark, Spain, Finland, France, Great Britain, Greece, Croatia, Hungary, Ireland, Israel, Japan, Lithuania, Luxembourg, Latvia, Malta, Mexico, Netherlands, Norway, New Zealand, Poland, Portugal, Russia, Saudi Arabia, Sweden, Slovenia, Slovakia, Turkey and the United States. From this selection we excluded the Principality of Monaco (as it was not identifiable using mobile phone data) and the countries with large foreign resident communities, namely: Roumania, Bulgaria, Colombia, China, Serbia, Ukraine, Albania, India, Macedonia, and Moldova.

In guiding our choice, we adopted a simple quantitative criterion based on the ratio between the number of foreign residents living in Italy by country of origin (from the National Statistical Agency; ISTAT) and the number of travellers from the same country in 2019 (from the BISIT). We excluded the countries for which this ratio exceeds 10 per cent, because for such countries data may be distorted by the travels and the foreign SIMS owned by foreign residents living in Italy. Since the choice of the 10 per cent threshold is somewhat discretionary, we checked that lowering the threshold to 5 per cent leaves the results of the analysis substantially unchanged.

Table  A1 reports some statistics on their weight on total inbound tourism, both in terms of night spent and in terms of total travellers, and a comparison between BISIT data and mobile phone data.

Weight of included countries in terms of inbound tourism to Italy.

Source : BISIT and mobile phone data.

ROBUSTNESS ANALYSIS

Analysis by province: EU, Schengen members and UK.

Note : The table reports estimates of the model 1 over different periods. Column (1) and (2) look at the whole sample (January 2019–February 2021). The 1st wave period (column 3) includes the weeks from 25 February to 2 June 2020. The Summer period (column 4) goes from 3 June to 15 September 2020. The 2nd wave (columns 5–7) goes from 16 September 2020 onward. The reference categories for the variable interacted with a dummy for the COVID‐19 period are, respectively, ‘business‐reasons’ for the purpose of travel, and ‘rented house or private house’ for the accommodation. The reference category for ‘airplane’ (short for public means of transport) is private means of transport (like own car). Standard errors, in parenthesis, are clustered by province–time and country of departure–time. ***, ** and *Statistical significance at 1, 5, and 10 per cent, respectively. Fixed effects by country of departure – province – week ( α opw ) and country of departure – time ( α ot ) are always included.

Analysis by province: Exclusion of countries with a border in common with Italy.

Note : The table reports estimates of the model 1 over different periods but excluding countries of origin that share a common border with Italy, namely Austria, France, Switzerland and Slovenia. Columns (1) and (2) look at the whole sample (January 2019–February 2021). The 1st wave period (column 3) includes the weeks from 25 February to 2 June 2020. The Summer period (column 4) goes from 3 June to 15 September 2020. The 2nd wave (columns 5–7) goes from 16 September 2020 onward. Standard errors, in parenthesis, are clustered by province–time and country of departure–time. The reference categories for the variable interacted with a dummy for the COVID‐19 period are, respectively, ‘business‐reasons’ for the purpose of travel, and ‘rented house or private house’ for the accommodation. The reference category for ‘airplane’ (short for public means of transport) is private means of transport (like own car). ***, ** and *Statistical significance at 1, 5 and 10 per cent, respectively. Fixed effects by country of departure – province – week ( α opw ) and country of departure – time ( α ot ) are always included.

Analysis by countries of origin: Exclusion of countries with a border in common with Italy.

Note : The table reports estimates of the model 2 over different periods but excluding countries of origin that share a common border with Italy, namely Austria, France, Switzerland and Slovenia. Columns (1) and (2) look at the whole sample (January 2019–February 2021). The 1st wave period (column 3) includes the weeks from 25 February to 2 June 2020. The Summer period (column 4) goes from 3 June to 15 September 2020. The 2nd wave (columns 5–7) goes from 16 September 2020 onward. Variable cases ot − 1 was winsorised at the 1%–99% per cent level to mitigate possible measurement errors (there are cases in the original database where the number of new cases is negative). The model includes the variables X op (coefficients not shown), namely d COVID 19 * β 1 ′ Purpose op + β 2 ′ Accommodation op + β 3 ′ Transport op . Standard errors, in parenthesis, are clustered by province–time and country of departure–time. ***, ** and *Statistical significance at 1, 5, and 10 per cent, respectively. Fixed effects by country of departure – province – week ( α opw ) and province – time ( α pt ) are always included.

VARIANCE DECOMPOSITION

As discussed in Section  3.2 , our empirical approach relies on fixed effects to achieve a clean identification of the variation explained by our set of independent variables. In this respect, this section shows a comparison exercise on the amount of variance that our models can explain, with a view of assessing the relative importance of variables by country of origin and by province. We do this exercise by incrementally adding variables and fixed effects to a model and looking at the square of the correlation between our dependent variable and its fitted values. This is conceptually equivalent to looking at the R2 in the case of a linear model. Results are reported in Table  A5 .

A variance decomposition.

Note : The table shows the variance explained by several models with different sets of fixed effects and variables. The explained variance is the square of the correlation between fitted values and observed values. The residual variance is computed as the share of variance in addition to model (1), taken as a reference term. X pt and d COVID 19 # X op are the variables included in Equation ( 1 ); X ot are the variables added in Equation ( 2 ).

As a first comparison term, we compute this statistic for a model in which we only include the fixed effects α opw (column 1). These fixed effects, as discussed in Section  3.2 , control for all factors that render a province more attractive for tourists from a specific country, as well as for possible seasonal patterns in these relationships. This simple model alone can explain about 84% of the total variation of the data, leaving 16% residual variance, exhaustively capturing the gravity structure in our tourism data. We then add to the model the time fixed effects α t (column 2). They capture the effect of time‐varying shocks that affect all Italian destinations and flows from all countries of origin in the same way. As expected, this model explains a large share of the residual variance (about 70%), clearly reflecting the nature of COVID‐19 as a common shock that hit international tourism flows. The residual 30% is the variation during the pandemic that was country or destination specific and which is the focus of this paper. In column (3) we thus report the same statistic as we add to the model all our explanatory variables. Overall, augmenting the model with our variables lead to a significant improvement in terms of explained variance (by about 12%).

We then look at the explanatory power of our variables along a specific dimension (province versus country of origin), controlling for the other with fixed effects. In particular, we first include country‐of‐origin fixed effects, leaving the province‐time variation explained by our variables (column 4). This model explains 94% of residual variance. Adding province–time fixed effects to the model in column (3) leads to a similar accounting, as it raises the explained variance to 92% (column 5). To sum up, this evidence suggests that province characteristics and country factors played a comparable role in explaining heterogeneous patterns at the country–province level during the pandemic.

Della Corte, V. , Doria, C. , & Oddo, G. (2023). The impact of COVID‐19 on international tourism flows to Italy: Evidence from mobile phone data . The World Economy , 00 , 1–24. 10.1111/twec.13380 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]

1 The views expressed in this study are those of the authors and do not involve the responsibility of the Bank of Italy. While retaining full responsibility for all remaining errors and omissions, the authors wish to thank Silvia Fabiani, Stefano Federico, Fadi Hassan, Alfonso Rosolia, Simonetta Zappa, Alessandro Borin, the editor and two anonymous referees for useful comments and suggestions on a previous version of this paper.

2 The World Travel and Tourism Council (WTTC) estimated that in 2017 5.5 per cent of Italian GDP was generated by domestic and international tourism. Taking into account the indirect and the induced impacts in relation to consumption by workers in the sector, the share would rise to 13.2 per cent of GDP. On the basis of the Tourism Satellite Account published by Istat, the Italian national statistical agency, over a third of these effects were attributable to international tourism alone.

3 See Borin et al. ( 2020 ).

4 Di Mauro ( 2020 ) offers a comprehensive overview of the many economic issues raised by the global pandemic.

5 For a historical survey on the use of mobile phone data for tourism analysis and an interesting country‐case application, see Ahas et al. ( 2008 ).

6 The information derives from the Bank of Italy Survey on International Tourism (BISIT, henceforth), which was established in the mid‐'90s to gather data for the compilation of the ‘travel’ item in the current account of the Italian balance of payments. More details on this survey are provided in Section Data annex of Appendix  1 .

7 Thanks to the granularity of BISIT data, we could distinguish not only business from leisure tourism, but also holidays aiming at ‘open air’ purposes, such as sojourning by the sea or at the mountains, from more ‘indoor’ purposes, like visiting cities of art and historical landmarks. Further details on the questionnaire are reported in Appendix  1 (Section Data annex).

8 Section Data annex of Appendix  1 provides further details on this data source. In particular, limited to the time interval for which the two data sources overlap, we could verify that the dynamics of foreign visitors in Italy as conveyed by mobile phone data tracks very well the dynamics of foreign arrivals as conveyed by BISIT data (see Figure  A1 in Appendix  1 ). In the same section of the Appendix we also report the list of countries included in the sample, and we explain the criterion adopted for their selection. We also provide additional statistics related to their weight in terms of total inbound tourism to Italy.

9 Dipartimento di Protezione Civile is the national body in Italy that deals with the prediction, prevention and management of emergency events. Data on COVID‐19 can be retrieved at https://github.com/pcm‐dpc/COVID‐19 .

10 We thank Paolo Conteduca for kindly sharing the data with us.

11 Morley et al. ( 2014 ) show that a gravity equation for tourism can be derived from individual utility theory, after modelling the destination choice problem faced by the tourist. Usage of gravity models for empirical applications in tourism literature is standard; see for instance Cevik ( 2020 ).

12 Notice that subscript w refers to the ordering of the week in a generic year in our sample, while the t subscript indicates a specific week in a specific year and thus uniquely identifies our observational unit (a pair country‐province).

13 Since we only have data on inbound tourism to Italy, we cannot identify the response of international tourism to developments in Italy separately from developments in Italy's competitors. Doing so would require a cross‐country comparison, that is tourism flows towards Italy and other foreign destinations.

14 In practice, we rely on the Stata routine developed by Correia et al. ( 2019 ).

15 We also consider separately two indicators related to internal mobility restrictions in the country of departure, which we derived from some categorical variables that constitute the Stringency index. These are: d stayathome , which is equal to one if citizens are given a general stay‐at‐home order and can move only for work‐related reasons and/or other essential activities (e.g. grocery shopping), and d noreg . movement which is equal to one if mobility across regions in the country of departure is restricted. We include these variables under the hypothesis that tourists may be more likely to choose to travel abroad (i.e. to Italy) if they face more stringent limitations at home, all things equal.

16 The population‐weighted distance measures the geographical distance between the largest cities of Italy and the country o , where inter‐city distances are weighted by the city's population share over the country's population. See Mayer and Zignago ( 2011 ) for further methodological details.

17 We run a number of robustness checks on our contagion variable, described later.

18 For instance, Wikipedia had included a clickable map of Italy displaying the number of cases by province since the end of July 2020 at the entry ‘COVID‐19 pandemic in Italy’.

19 A similar result is obtained also if we include only northern regions in the regression for the first wave sample (results available upon request).

20 We lag this variable to ensure that the level of regional restrictions was in the information set of the tourist before departure. However, the coefficient on our contagion remains unchanged if we use its value at time t .

21 As a further robustness check, we limited our sample to the weeks before the introduction of the zone‐system, obtaining an even larger negative coefficient (results available upon request).

22 Robustness checks with different lags produce similar results, also given the inertia in the spread of the contagion.

23 In an additional robustness check, we consider the possibility that tourists are also interested in the acceleration of contagion rather than only in the speed of contagion, which we measure as the difference between the notification rate of new positive cases in a week and the previous week. The coefficient on this additional variable is however not statistically significant (results not reported but available upon request).

24 A PCR (Polymerase Chain Reaction) molecular test for COVID‐19 is a test used to diagnosis people who are currently infected with SARS‐CoV‐2 and it is considered the most reliable test for diagnosing COVID‐19.

25 Indeed, notice that for this set of countries we can include only the ‘swab test’ dummy among the dummies measuring bilateral travel restrictions, as there is no cross‐country variation that allows identification of the other coefficients (given that the other restrictions were equal across these countries).

26 As an alternative approach, we considered the difference in the number of cases between Italy and the country of departure, distinguishing between positive and negative values. Results remain mixed. It must be noticed that the interpretation of this coefficient requires caution, in consideration of the wide differences in testing ability across countries (that may in turn affect the comparability of this variable across countries, if this testing ability or criteria change over time in a given country) and of the fact that our specification of fixed effects is already absorbing the strong commonality across countries over time in the spread of the contagion.

27 A note of caution on the interpretation of this coefficient is in order. Due to our fixed effects specification (which includes time‐province fixed effects), a positive sign on this coefficients is telling us that countries that had relatively stronger restrictions at a specific point in time were also countries associated with relatively higher outbound tourism to Italy. This is not the same as claiming that stronger restrictions over time (as measured by higher values of the stringency index) led to more outbound tourism. By omitting time‐province fixed effects the coefficient of the stringency index turns negative, as one would expect ex ante. However, time‐fixed effects are needed in our view to control for many unobserved factors at play (for instance developments in competing touristic markets).

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Travel and tourism in Italy

Statistics report on travel and tourism in Italy

This report presents a range of statistics and facts on travel and tourism in Italy. It provides key data on inbound, outbound, and domestic tourism, as well as figures on accommodation establishments. For further data on the travel and tourism markets in Italy, please also check the following reports: Tourism in Italian cities and Hotel industry in Italy .

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Table of contents.

• Basic Statistic Travel and tourism's total contribution to GDP in Italy 2019-2034
• Basic Statistic GDP share generated by travel and tourism in Italy 2019-2023
• Premium Statistic Monthly tourism balance in Italy 2019-2024
• Basic Statistic Distribution of travel and tourism spending in Italy 2019-2023, by type
• Basic Statistic Distribution of travel and tourism spending in Italy 2019-2023, by tourist type
• Basic Statistic Travel and tourism's total contribution to employment in Italy 2019-2034

Inbound tourism

• Premium Statistic Total number of international tourist arrivals in Italy 2015-2023
• Premium Statistic International tourist arrivals in Italy 2006-2023
• Premium Statistic International tourist arrivals in Italy 2019-2023, by country
• Premium Statistic Inbound business travelers in Italy 2015-2023
• Premium Statistic Number of inbound tourist overnight stays in Italy 2014-2023, by travel reason
• Premium Statistic Average length of stay of international tourists in Italy 2009-2023
• Premium Statistic Inbound tourist expenditure in Italy 2007-2023
• Premium Statistic Inbound tourist expenditure in Italy 2019-2023, by country

Outbound tourism

• Premium Statistic Number of outbound travelers from Italy 2015-2023, by type
• Premium Statistic Number of outbound trips from Italy 2019-2023, by destination
• Basic Statistic Number of outbound tourist overnight stays from Italy 2015-2023
• Premium Statistic Overnight stays for outbound trips from Italy 2019-2023, by destination
• Premium Statistic Expenditure of Italian outbound travelers 2007-2023
• Premium Statistic Expenditure of Italian outbound travelers 2019-2023, by destination
• Premium Statistic Share of outbound holiday trips taken by Italians 2023, by purpose
• Premium Statistic Share of outbound holiday trips taken by Italians 2023, by destination type
• Premium Statistic Travel intentions of Italians in the next six months 2024, by destination
• Premium Statistic Italian travelers' preferred European countries for trips in the next six months 2024

Domestic tourism

• Premium Statistic Number of domestic trips in Italy 2014-2023
• Premium Statistic Number of domestic trips in Italy 2019-2023, by accommodation type
• Premium Statistic Overnight stays for domestic trips in Italy 2022-2023, by region of destination
• Premium Statistic Domestic business trips in Italy 2015-2023
• Premium Statistic Overnight stays during domestic business trips in Italy 2023, by destination
• Premium Statistic Number of same-day domestic trips in Italy 2019-2023, by purpose
• Basic Statistic Domestic tourism spending in Italy 2019-2034

Accommodation

• Premium Statistic Number of hotel and non-hotel accommodation in Italy 2019-2023
• Premium Statistic Number of hotels in Italy 2012-2023, by rating
• Premium Statistic Number of hotels in Italy 2023, by region
• Premium Statistic Revenue of the hotels industry in Italy 2020-2029
• Premium Statistic Leading international hotel chain brands in Italy 2023, by number of hotels
• Premium Statistic Leading domestic hotel chain brands in Italy 2023, by number of hotels
• Premium Statistic Number of bed and breakfasts in Italy 2010-2023
• Premium Statistic Number of agritourism establishments in Italy 2012-2023
• Premium Statistic Distribution of trips made by Italians 2023, by accommodation

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Italy’s digital economy has grown significantly in recent years, led by factors such as increased broadband connectivity and internet penetration, government initiatives, and technological advancements. The digital economy is driving growth, productivity, and innovation across industries, but there is still untapped potential in key areas. The government recognizes the importance of the digital sector and, as part of its digital transformation efforts, has been actively investing to foster innovation and drive overall digitalization. Italy’s Recovery and Resilience Plan (PNRR), supported by the European Union’s largest allocation of pandemic recovery funds, and its specific digital initiatives (e.g., Digital Italy, Transition 4.0, Digital School, Digital Health) includes billions of euros for investments to accelerate the adoption of emerging technologies and improve the country’s digital capabilities and its global competitiveness. Italy has earmarked about €47 billion euro (26 percent) of its total PNRR allocation and another €5.5 billion euro in EU Cohesion Policy funding for digital initiatives. As investments continue, the digital market will benefit from the effects of the PNRR for years to come. These investments present opportunities for businesses to collaborate with the government, access funding support, and contribute to Italy’s digital transformation. U.S. companies are leading providers, and they have a strong presence in Italy in all segments of the market. The big players not only sell their products and services, but also invest heavily in research and development and support educational and training programs in Italy.

While Italy has made great strides in investing in and adopting digital technologies, challenges remain. There is a noticeable disparity in digital infrastructure between northern and southern Italy and some rural areas still face connectivity issues, limiting their ability to fully leverage digital technologies. There is also a digital skills gap, particularly among older individuals and those in traditional industries, and a resistance to change within organizations and among individuals.  

The EU digital economy is highly regulated and compliance with EU regulations may be just the initial step for U.S. companies selling to the Italian market. Companies should also be prepared to navigate Italy’s complex regulatory landscape and bureaucracy, which can be time-consuming and costly. For information about recently enacted or proposed EU regulations, see the Digital Economy section of the EU Country Commercial Guide.

Italy, like many European countries, has specific requirements regarding the storage and processing of personal data. While the EU’s General Data Protection Regulation (GDPR) and the EU-U.S. Data Privacy Framework provide a comprehensive framework for data transfers, the Italian Data Protection Authority may issue additional or sector-specific guidelines and regulations. It has been particularly active, for example, in oversight of emerging artificial intelligence applications. Some requirements relate to periods for data retention, the process for notifying authorities of data breaches, and obtaining consent, and can be more stringent for data processing in the healthcare, financial, and telecommunications sectors. Compliance with these requirements is one reason many companies have set up data centers or cloud infrastructure inside Italy.

The EU Artificial Intelligence (AI) Act, enacted in March 2024, seeks to set a global standard for AI technologies and divides AI applications into different risk categories. It outlines the regulatory framework within which member states will have to issue implementing decrees. Italy’s government has approved a draft AI law, whose rules are more restrictive than the EU AI Act, although it may still undergo significant changes before it becomes official legislation. We recommend that U.S. companies monitor these developments as well as other potential requirements specific to Italy as more EU regulations are implemented and the EU AI Act’s provisions begin to take effect.  

In addition to regulatory challenges, U.S. companies should be aware that PRC companies are major players in the sector, including supplying telecommunications equipment and infrastructure for Italy’s 4G and 5G networks. Despite U.S. and EU security concerns, Italian operators continue to purchase digital technologies from untrusted PRC vendors. It has been difficult for U.S. companies to compete in tenders when lowest initial price is prioritized over value or technical capability factors. Trustworthiness is not commonly an evaluative factor in tenders.

Most laws are set at the EU level. The Digital Economy Chapter of our Country Commercial Guide for the European Union provides a more comprehensive overview.

Digital Trade Opportunities

Italy’s digital initiatives and strategies and its investments in digital infrastructure (e.g., fiber optic networks, data centers, cloud computing infrastructure) are driving demand for digital technologies from the private and public sectors. The government’s push for digital transformation across various sectors and a focus on Industry 4.0 is creating a need for digital solutions to modernize operations, improve efficiency and productivity, automate processes, and enhance competitiveness. There are opportunities in cloud computing, AI, IoT, Big Data, and cybersecurity and anything that enables digitalization. The expected growth rates are high because Italy needs to make up for gaps in the digital sector and the funds are available.

Cloud computing

Italy’s cloud strategy includes creation of a national cloud for the public administration, compliance requirements for cloud service providers, and incentives and support to encourage private businesses to adopt cloud computing. Overall, the strategy seeks to ensure that both the public and private sectors can benefit from cloud computing while maintaining data security, privacy, and national control. About 61 percent of Italian companies with 10+ employees had used cloud computing in 2023, allowing more flexibility and scalability while reducing technology costs. The greatest demand for cloud-based solutions is currently concentrated in the financial services, healthcare, public administration, manufacturing, retail, and telecommunication sectors. The increased popularity of cloud computing services, combined with concerns about data sovereignty and security, has led to a surge in demand for data center capacity in Italy, as cloud providers expand their infrastructure to meet customer needs.

Artificial Intelligence

Italy’s recently updated AI strategy focuses on making the country a global leader in AI research, development, and application, and leveraging AI technologies to improve productivity, public services, and economic growth. It is closely aligned with the EU’s broader AI strategy, sharing a common goal of promoting ethical, responsible, and human-centric AI development. The strategy supports the creation of AI applications and incentives for businesses (primarily SMEs) to invest in AI technologies. It also sets goals for the creation of Italian-language large language models. While only about five percent of Italian companies used AI in 2023, lower than the EU average of eight percent, Italy’s ongoing digital transformation and widespread adoption of cloud computing has made it easier for businesses to access AI solutions. AI is being applied by large companies in a wide range of industries in Italy, including manufacturing, healthcare, finance, and agriculture, but adoption by small and medium-sized enterprises (SMEs) still lags behind.

Because AI has the potential to automate many job processes, Italy is also looking to the adoption of AI to mitigate the impacts of a shrinking workforce. Policymakers hope new technologies may make possible the maintenance of economic output in the face of Italy’s falling birthrate and aging population.

Internet of Things (IoT)

Italy has started to embrace IoT technology in various sectors, including manufacturing, agriculture, healthcare, and transportation. Adoption rates are increasing but infrastructure gaps in rural areas and high costs still hinder nationwide 5G coverage and IoT access. Italian companies can take advantage of tax credits from the PNRR’s largest digital measure (€13.4 billion), Transition 4.0, dedicated to the digitalization of companies, by purchasing Industry 4.0 assets (tangible and intangible) such as IoT to improve production processes. Italy wants to digitize its otherwise strong SME sector and support investments in smart factories; however, many SMEs across all industries have either postponed these investments or only recently started the switch to smarter production processes. Italian cities are also expected to integrate more IoT devices and smart solutions to increase efficiency and improve sustainability and livability. The city of Milan, which uses IoT for smart parking, waste management, air quality monitoring, traffic management, smart lighting, smart buildings, and more, is leading the way.  

Italy’s digital transformation initiatives have created a demand for data-driven decision-making and insights in multiple sectors. The focus on Industry 4.0 and use of IoT devices has increased the demand for big data technologies to store, process, and analyze the large quantity of data produced. The retail, e-commerce, and financial services sectors are using Big Data to improve customer experiences and personalize services. The healthcare sector, primarily in regions with advanced healthcare infrastructure, also uses Big Data to manage patient data, personalize medicine, and improve clinical outcomes.

The significant growth of the cybersecurity market and demand for cybersecurity solutions in Italy can be attributed to several key factors. As the digital transformation advances, businesses and public institutions are relying more on digital platforms, cloud services, and connected devices, exposing themselves to a great risk of cyber-attacks. In 2023, Italy received 11 percent of all global attacks detected and was the fourth-most targeted country globally. Certain high-profile data breaches and cyberattacks have raised awareness of the importance of cybersecurity and prompted organizations to invest in stronger defenses. While large companies are investing the most, more SMEs are seeing it as a priority. In addition, organizations must invest in cybersecurity solutions to comply with data protection regulations like the EU GDPR.  

The Government of Italy has prioritized cybersecurity as a strategic objective and has a cybersecurity strategy, supported by funds, that outlines the country’s digital roadmap. It was developed by the National Cybersecurity Agency and is aligned with the EU’s cybersecurity strategy. It seeks to make Italy a safer place for data by protecting critical infrastructure, government systems, and citizens from cyber threats.  

Banks are the largest sector in terms of cybersecurity spending. Industry is second, although average spending is much lower despite the large number of companies that make up the sector. The public administration follows, which includes large administrations and local authorities. Spending in this sector is supported by investments related to the national cybersecurity strategy and by PNRR funding, with the aim of filling current vulnerabilities. Demand is expected to remain high in the coming years, with a particular focus on areas such as critical infrastructure protection, data privacy compliance, cloud security, endpoint security, network security, threat intelligence, and incident response.