The economic impact of international travel measures used during the COVID-19 pandemic: a scoping review

Abstract

Introduction

Assessment of the use of travel measures during COVID-19 has focused on their effectiveness in achieving public health objectives. However, the prolonged use of highly varied and frequently changing measures by governments, and their unintended consequences caused, has been controversial. This has led to a call for coordinated decision-making focused on risk-based approaches, which requires better understanding of the broader impacts of international travel measures (ITMs) on individuals and societies.

Methods

Our scoping review investigates the literature on the economic impact of COVID-19 ITMs. We searched health, social science and COVID-19-specific databases for empirical studies preprinted or published between 1 January 2020 and 31 October 2023. Evidence was charted using a narrative approach and included jurisdiction of study, ITMs studied, study design, outcome categories, and main findings.

Results

Twenty-six studies met the inclusion criteria and were included for data extraction. Twelve of them focused on the international travel restrictions implemented in early 2020. Limited attention was given to measures such as entry/exit screening and vaccination requirements. Eight studies focused on high-income countries, 6 on low-income and middle-income countries and 10 studies were comparative although did not select countries by income. Economic outcomes assessed included financial markets (n=13), economic growth (n=4), economic activities (n=1), performance of industries central to international travel (n=9), household-level economic status (n=3) and consumer behaviour (n=1). Empirical methods employed included linear regression (n=17), mathematical modelling (n=3) and mixed strategies (n=6).

Conclusion

Existing studies have begun to provide evidence of the wide-ranging economic impacts resulting from ITMs. However, the small body of research combined with difficulties in isolating the effects of such measures and limitations in available data mean that it is challenging to draw general and robust conclusions. Future research using rigorous empirical methods and high-quality data is needed on this topic.

WHAT IS ALREADY KNOWN ON THIS TOPIC.

• Previous reviews of the impact of international travel measures (ITMs) during COVID-19 have mainly focused on their effectiveness in achieving public health objectives.

• There is emerging evidence on the broader socioeconomic impacts of ITMs.

WHAT THIS STUDY ADDS

• This study conducts a scoping review of the literature on the economic impacts of ITMs implemented during COVID-19.

• This study finds that existing studies have begun to provide evidence of wide-ranging economic impacts, including the effects on financial market, economic growth, economic activities, performance of industries central to international travel, household-level economic status and consumer behaviour.

• However, there remain several research gaps—notably, the effect of types of ITMs beyond major closures, and household-level economic outcomes.

• Furthermore, some challenges faced by these studies, such as inconsistent terminology, data limitations and the difficulty of causal inference, make it challenging to draw general and robust empirical conclusions.

HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY

• Findings highlight a need to evaluate the cost–benefit of ITMs using a risk-based assessment process.

• We recommend that future research on this topic use consistent terminology, adapt empirical methods to the availability of data while also striving for higher-quality data, and transparently discuss limitations of methodology and findings due to data issues and causal inference problems.

Introduction

During the COVID-19 pandemic, most countries implemented international travel measures (ITMs) to reduce introductions of SARS-CoV-2 and its onward transmission into domestic populations. As early as 31 December 2019, some jurisdictions began implementing health screening for travellers from Wuhan, China. By March 2020, virtually all State Parties to the WHO International Health Regulations (IHR) had implemented some form of ITMs, despite the 2005 IHR Emergency Committee’s recommendation not to implement ‘any travel or trade restriction’.¹ Though WHO shifted its recommendations over the course of the COVID-19 pandemic to support some travel measures under certain conditions, its initial recommendation reflected its guidance during most of the other seven declared public health emergency of international concerns (PHEICs).² WHO has long held the view that the limited public health benefits of such measures are outweighed by their social and economic costs (indeed, at the start of COVID-19, existing research on past outbreaks suggested that some travel measures may delay outbreak spread but alone have not stopped spread^3–6).

The large-scale and prolonged use of travel measures during COVID-19 has prompted study of their effectiveness in achieving public health objectives. A systematic review of 62 studies assessed the effects of travel measures across international borders, and found some benefit of a time-limited nature for travel restrictions, screening and quarantine.⁷ A rapid systematic review of 29 studies found that travel measures, especially those implemented in Wuhan, changed the transmission dynamics of COVID-19, yet their effectiveness had limited duration.¹ However, evidence on the broader socioeconomic impacts of ITMs during the COVID-19 pandemic remains limited. Recent concerns have been raised on the political use of travel measures to control international migration under the pretext of public health, including the risk of human rights violations.^8–11 Additional social consequences such as family separation,^12–14 social stigmatisation and exposure to violence for immigrant workers^{15 16} and limited access to food¹⁷ have also been examined. The authors have written a separate review on these social impacts.

This paper conducts a scoping review of the existing evidence on the economic impacts of ITMs. A recent review of the economic impacts of COVID-19, and policy responses to the pandemic, identified three broad themes: (1) evidence of the negative impacts of COVID-19 on socioeconomic outcomes such as labour markets, production supply chains, financial markets and gross domestic product (GDP) levels; (2) mechanisms behind the negative impacts on socioeconomic outcomes; and (3) forecasts of economic recovery.¹⁸ The economic impact of ITMs was not covered in this review.

Anecdotal evidence points to potentially large economic costs of ITMs for selected industries, workforces and international trade and investment more broadly. For instance, the average global cost of transporting a container rose sevenfold from US$1362 in November 2019, to US$9628 in February 2022, and furloughs and layoffs shook the airline industry due to the cancellation of international flights and international travel restrictions adopted by many countries.¹⁹ A scoping review by Klinger et al summarised the unintended consequences of COVID-19 ITMs.²⁰ Outcomes included economic consequences, quality of life, individual well-being and mental health, environmental and social consequences and health system consequences. Our scoping review extends this study by focusing on economic outcomes, empirical methods and terminology usage, as well as including studies published in 2021–2022, when many countries made significant changes to their ITMs.

The purpose of this review is to map the scope, analytical approaches and findings of the current evidence on the economic impacts of ITMs during the COVID-19 pandemic. Our review identifies economic outcomes studied, countries and populations considered, the volume of available evidence and how existing studies have been conducted including their methodologies and data sources. The limitations of the current literature and existing knowledge gaps will also be identified. In this way, this scoping review provides a starting point for strengthening the evidentiary base on the economic impacts of ITMs, and its use in the future development of risk-based approaches to their use.

Methods

Our scoping review is guided by the framework by Arksey and O’Malley.²¹ Reporting followed the Preferred Reporting Items for Systematic and Meta-Analysis extension for scoping reviews (PRISMA-ScR) as presented in online supplemental appendix figure 1. Ethics approval was not required for this review.

We define a travel measure as a policy or intervention applied for the purpose of managing human mobility. Given the diverse and inconsistent terminology to describe the use of such measures, our team previously developed a taxonomy of ITMs adopted during the COVID-19 pandemic to standardise the coding of the WHO Public Health and Social Measures dataset.²² These measures may be applied to human mobility occurring locally, nationally or internationally. We define an ITM as a government-implemented policy or intervention to manage human mobility between two or more countries.

Identifying the research question

An exploratory review of the literature was conducted to inform our research questions. Our overarching objective was to determine the scope and volume of the literature on the economic impacts of travel measures used during COVID-19. Our specific research questions include:

1. Which countries, communities and sectors have been studied?

2. What ITMs have been studied?

3. What types of economic outcomes have been studied?

4. What methodologies and data sources are used to assess economic impacts?

Identifying relevant studies

Given the emergent nature of the topic, our scoping review covered studies published in both peer-reviewed journals and the grey literature such as reports and working papers. We searched the following six electronic databases: Medline (Ovid), EconLit, the Social Science Research Network, Web of Science the National Institute for Health Research Economic Evaluation Database, and WHO COVID-19 Global literature on COVID-19.

Our search covers three concepts: COVID-19, ITMs and economic outcomes. To search for COVID-19-related studies, we used the search filters developed by Campbell (2023).²³ These are preconstructed filters designed to search difficult concepts or concepts for which there are many search terms. We used the complete search filters in Medline Ovid, and adjusted them for other databases. For example, when using the Social Science Research Network database, we only used COVID-19 or SARS-CoV-2 or coronavirus for our search.

We consulted with librarians at Simon Fraser University to develop the search filters for these terms. We also referred to the search strategies from two existing systematic reviews of the effect of ITMs during COVID-19 to further develop our search filters.^{1 20} Examples of keywords/medical subject headings (MeSH) terms and their combinations include: border? adj3 (clos* or restrict* or control* or measure?)) and (travel* adj3 (suspen* or advice or warning or advisory).

We define the term ‘economic impact’ to mean the effect of an action or event on an economy, which may range from microeconomic impacts on individual agents (eg, income, expenditures or labour supply) to macroeconomic impacts on sectors, industries or whole economies (eg, trade, financial markets or gross domestic product). Some outcome variables are not themselves economic outcomes—for example education attainment and fertility decisions—but might have an economic impact. We covered these outcomes in a separate review paper on the social impacts of ITMs, and therefore do not include them in this study. We referred to the search filters developed by Klinger et al (2021)²⁰ and consulted with librarians at Simon Fraser University to develop the search filters for economic-related studies. More details of the search filters can be found in the online supplemental appendix, table 1.

Study selection

We use Covidence, a systematic review software, to manage references and remove duplicates. Two reviewers (YLB and SZ) independently conducted the title and abstract screening. The same reviewers then independently screened the full texts, and any disagreement was discussed with a third reviewer (EW) for the final decision.

Our inclusion criteria were original studies reporting ITMs adopted in response to COVID-19 as well as at least one economic outcome; published in English; published between 1 January 2020 and 31 October 2023; and reporting empirical evidence.

We excluded the following types of studies:

1. Studies that do not report economic outcomes.

2. Studies that report the impact of COVID-19 on economic outcomes, but not the impact of ITMs on economic outcomes.

3. Studies that do not attempt to disentangle the economic impact of ITMs from other concurrent policies, or from the effect of COVID-19 itself. We do not exclude any studies in which the authors acknowledge and seek to address this problem.

Extracting and charting of data

One reviewer (YB) extracted and charted study characteristics in tabular form based on the following headings: author(s), title, jurisdiction(s) of study, outcome variable(s), study design, scenario(s) of intervention and main findings. We synthesised the data qualitatively using textual descriptions. The extraction form (table 1) was pilot-tested by the review team and revised as needed. Five members of the review team (YLB, KL, BS, EW, CW) reviewed all extracted data.

Table 1.

Summary characteristics of the included studies*

Study	Jurisdictions	Study design	Intervention(s)	Outcome(s)	Main findings
Briones et al 37	3 countries†	Regression	ITMs for each country in early 2020	Stock market index, GDP	Negatively related with stock index in all three countries
Bourit et al 27	New Zealand	Regression	New Zealand’s ITMs on March 2020	Stock market index	No effect on NZ50 market returns, and positive effect on most industries
Narayan et al 30	G7 countries‡	Regression	ITMs for each country	Abnormal stock returns	Positive effect on stock returns in Canada, France, Japan, UK and the USA; no effect in Italy and Germany
Bannigidadmath et al 38	25 countries§	Regression	ITMs for each country	Abnormal stock returns	Positive effect in Germany, UK, Turkey, South Korea and Poland; negative effect in China, Canada and Russia
Bilal et al 44	8 countries¶	Regression	ITMs for each country	Abnormal stock returns	Negatively affect Chinese and Indian entertainment sectors’ returns; negative impact on US stock returns
Aharon et al 24	USA	Regression	US ITMs on February 2020	Abnormal stock returns	Negative impact on food industry; no effect on hospitality industry
Chiu31	Cross countries**	Regression	US ITMs in early 2020	Abnormal stock returns	Negative effect on South Korea, Europe and North America stock markets
Farooq et al 47	8 countries††	Regression	ITMs for each country	Abnormal stock returns	Negative effect on stock returns in the USA; no effect on other countries
Zaremba et al 43	49 countries	Regression	ITMs stringency index	Stock liquidity	Impact is limited in scale and scope
Zaremba et al 45	67 countries	Regression	ITMs stringency index	Stock volatility	No effect on the stock volatility
Liu25	Canada	Modelling	Simulated border closure	GDP, tourism income, employment	Border closure result in a 1.2%–1.7% reduction in GDP, and impact 30 k–50k jobs
Deb et al 39	4 cities‡‡	Regression	ITMs stringency index for each country	Economic activity indicators	ITM is among the least costly measures in economic terms compared with other NPIs
Klinsrisuk and Pechdin32	Thailand	Modelling	Simulated border closure/reopen	GDP, tourism income	Border closure largely reduces Thailand’s GDP and household income
Munawar et al 28	Australia	Mixed	Border closure and quarantine	GDP, employment	Negative impact on income in tourism and education, and the associated employment
Baggs et al 26	Canada	Regression	Closure of the Canada–US border	Retail industry	Generated an offsetting gain in revenues for Canadian retailers located close to the border
Kumari et al 40	21 countries	Regression	ITMs towards India	Abnormal stock returns	Negative effect on stock returns
Mataba et al 36	ACFTA§§	Mixed	Land borders closure	Trade cost	ITMs are particularly costly for live-stock producers practicing transhumance
Keane and Neal48	54 countries	Regression	Travel ban and quarantine	Consumer panic index	ITMs do not appear to generate consumer panic
McDermid et al 41	Cross country	Mixed	ITMs for each country	Financial stress	Over 64% participants report financial distress while stranded abroad
Marzantowicz et al 34	Poland	Mixed	Border closure of Poland	Global supply chain	Closed borders obstacled trainings or receive deliveries of construction equipment
Della Corte et al 29	Italy	Regression	Italian ITMs	Tourism	Screening requirements for inbound visitors did not significantly discourage arrivals
Cai et al 46	Singapore, Thailand	Modelling	ITMs on Singapore–Thailand border	Tourism	The best ITM for Thailand is no quarantine but with testing; for Singapore is no quarantine and no testing
Kpodar et al 42	52 countries	Regression	ITMs for each country	Remittance	Stricter ITMs dampened remittances
Chia and Liew33	Malaysia	Regression	Movement control order	Abnormal stock return	Travel restrictions positive affected stock returns
Olani et al 16	Ethiopia	Mixed	Ethiopian ITMs	Food system	Travel restrictions disrupted the food system
Zaremba et al 45	Czech	Qualitative	Germany–Czech border closure	Life experience of cross-border workers	Czech cross-border workers reported difficulties in commuting, inequality and xenophobia in the workplaces

*This table is created and owned by Ying Liu Bazak.

†Kenya, Singapore and Thailand.

‡Canada, France, Germany, Italy, Japan, the UK and the USA.

§The USA, Spain, Italy, France, Germany, the UK, China, Turkey, Belgium, Netherlands, Brazil, Canada, Russia, Switzerland, Portugal, Austria, India, Israel, Ireland, Sweden, Peru, South Korea, Japan, Chile and Poland.

¶USA, China, UK, India, Thailand, Turkey, Mexico and France.

**North American, Asian and European countries.

††USA, UK, France, China, India, Mexico, Turkey and Thailand.

‡‡Wuhan, Rome, New York city and Stockholm.

§§African Continental Free Trade Area countries, including Ghana, Kenya, Rwanda, Niger, Chad, Eswatini, Guinea, Côte d’Ivoire, Mali, Namibia, South Africa, Congo, Republic, Djibouti, Mauritania, Uganda, Senegal, Togo, Egypt, Ethiopia, Gambia, Sahrawi Arab Democratic Republic, Sierra Leone, Zimbabwe, Burkina Faso, São Tomé & Príncipe, Equatorial Guinea, Gabon, Mauritius, Central African Republic, Angola, Lesotho, Tunisia, Cameroon, Nigeria, Malawi, Zambia, Algeria, Burundi, Seychelles, Tanzania, Cabo Verde, Democratic Republic of the Congo, Morocco, Guinea-Bissau, Botswana and Comoros.

ACFTA, African Continental Free Trade Area; GDP, gross domestic product; ITMs, international travel measures; NPIs, non-pharmaceutical interventions.

Patient and public involvement

No patient and public involvement for this study.

Results

Search results

We identified 2549 records in the search. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses flow chart in figure 1 illustrates the screening and selection process. After full-text screening, 26 studies were selected for inclusion in this review.

Figure 1.

Preferred Reporting Items for Systematic Reviews and Meta-Analyses flow-chart of the study selection process (this figure is created and owned by Ying Liu Bazak).

Which countries, communities or sectors have been studied?

The studies reviewed cover a broad range of countries. Eight studies report on high-income countries, including the USA,²⁴ Canada,^{25 26} New Zealand,²⁷ Australia,²⁸ Italy,²⁹ and cross-country studies on North America and European countries.^{30 31} Six studies report on low-income and middle-income countries (LMICs), specifically Thailand,³² Malaysia,³³ Ethiopia,¹⁶ Poland,³⁴ Czech Republic³⁵ and the African Continental Free Trade Area (ACFTA) countries.³⁶ Ten cross-country studies did not select a specific group of countries based on income.^37–46 For example, one study looked at the stock volatility of 67 countries whose stock market data are reported by Datastream Global Equity Indices.⁴⁵

What ITMs have been studied?

Most studies identified in our review focus on the effects of international travel restrictions implemented during early 2020 (n=12).16 24 26 27 30 32 34–36 41 44 47 The second most studied is the intensity of ITMs in general, using the stringency index of travel measures from the Oxford Coronavirus Government Response Tracker (OxCGRT).33 39 42 43 45 This stringency index reflects the effect of a combination of ITMs, including health screening on arrival, quarantine of arrivals from some or all regions, entry restrictions in terms of banning arrivals from some regions and border closure or ban on all regions. Besides these, one study looked at international travel restrictions targeting India in 2021.⁴⁰ Two studies simulated full border closures or the lifting of international travel restrictions to estimate the economic impacts.^{25 32} One study simulated the relaxation of different ITMs and combinations of ITMs between Singapore and Thailand.⁴⁶ And finally, two studies looked at the combination of 14-day mandatory quarantine and international travel restrictions.^{28 48}

What types of economic outcomes have been studied?

The outcomes reported in the included studies can be grouped into five broad categories: (1) financial market performance (n=13)24 27 30 31 33 37–40 43–45 47; (2) economic growth (n=4)25 28 32 37; (3) economic activity, measured by high-frequency data including nitrogen dioxide emissions³⁹; (4) performance of industries central to international travel (n=9), namely the airline industry,⁴⁰ tourism industry,28 29 32 46 retail industry,²⁶ hospitality industry,²⁴ global supply chain,³⁴ agriculture¹⁶ and international trade³⁶; (5) household-level economic status (n=3), including individuals’ financial stress,⁴¹ remittance⁴² and work life experience for cross-border workers³⁵; and (6) consumer behaviour (n=1), measured by a consumer panic index.⁴⁸ Some studies examined multiple outcomes, for example both stock market performance and the subsequent impact on GDP.³⁷ Additionally, outcomes may fall under multiple categories, for example, the impact on airline industry’s performance being reflected in its stock returns.⁴⁰

What methodologies and data sources are used to assess economic impacts?

Several empirical methods were used in the included studies: (1) multiple linear regressions (n=17)24 26 27 29–31 33 37–40 42–45 47 48; (2) mathematical modelling (n=3)^{25 32 46}; and (3) mixed methods, including surveys, summary statistics, in-depth interviews and case studies (n=6).16 28 34–36 41 The most frequently used data source for ITMs is OxCGRT, which reports the stringency index for ITMs for each country over time. Economic outcome data are sourced from publicly accessible data such as stock market trends, GDP, and unemployment figures, or are collected through surveys and interviews.

Narrative summary of findings

Generally, these papers found that ITMs negatively affect ‘real’ economic outcomes—the production and exchange of goods and services. Specifically, the research indicated that industries related to tourism and international trades experienced significant setbacks, resulting in reductions in income and employment.^{28 32} However, the studies found little to no effect, or even a positive impact, of ITMs on financial market indicators, such as the stock market index,²⁷ abnormal returns,^{24 28 30} liquidity⁴³ and volatility.³⁶ This could be because financial market data immediately take into account expectations of future profits. Strict ITMs create expectations for ending the pandemic sooner, and this positive expectation was revealed by financial market performance.³³

The effects of ITMs varied widely across countries. For example, a study that investigated abnormal stock returns in 25 countries found a negative effect of international travel restrictions in China, Canada and Russia, a positive effect in Germany, the UK, Turkey, South Korea and Poland, and no effect in the remaining countries.³⁸ The included studies suggest several potential mechanisms behind the observed heterogeneity. First, the significance of the tourism industry in a country: for instance, the international tourism sectors of Australia and Thailand experienced significant losses,^{28 32} while the US’s hospitality industry witnessed a smaller reduction.²⁴ Second, a country’s level of industrialisation and technology stock: one study across Thailand, Singapore and Kenya reported that Thailand’s stock index decreased the most after the ITMs, while Singapore was affected the least. The authors argue that businesses in Singapore quickly pivoted to digitalisation, offering products and services suited to pandemic times, while Kenya and Thailand heavily depend on agriculture and manufacturing, which are more challenging to digitalise.³⁷

Discussion

Knowledge gaps in existing literature

Our scoping review demonstrates that evidence on the economic impacts of ITMs adopted during the COVID-19 pandemic has begun to emerge. However, we identify several knowledge gaps. First, despite the anecdotally massive economic consequences of ITMs, there is a paucity of evidence in general on their economic impact during the COVID-19 pandemic.

Second, the existing literature is particularly short of studies examining the economic impact at the micro level such as on individuals, households and firms. Micro-level studies are valuable for various reasons. First, ITMs can affect different populations differently, such as households closer to borders being affected more than those further away, and individuals working in the tourism industry being affected more than others. Recent studies also show the gendered impact of the travel measures: for example, the financial challenges and uncertainty faced by mostly female foreign domestic workers.⁴⁹ Additionally, inequality may deepen or arise as a result of these measures, for example, immigrant workers may bear a larger economic cost. Furthermore, individuals may alter their behaviour or decisions in response to ITMs. For example, in Singapore, some international business shifted online after the implementation of ITMs.³⁷

Third, the studies included in this review primarily focus on the effects of international travel restrictions implemented during early 2020. The impact of other measures, such as testing, health screenings and vaccination requirements at the border, is understudied. As the pandemic progressed and countries eased ITMs, these milder travel measures may have become increasingly relevant.

Fourth, current research is limited to short-term economic impacts, such as immediate financial market fluctuations and industry incomes. However, travel measures could also be associated with longer-term effects such as educational disruptions, immigration declines, and international trade reallocations, which remain unstudied.

Last but not the least, the inconsistent terminology in ITMs poses a threat to the reliability, validity, comparability and generalisability of research in this field. For example, studies investigating ITMs in March 2020 referred to them as ‘border closures’ or ‘travel bans’. However, there was significant variation in these measures across countries, with some allowing exemptions for certain populations. This makes it difficult to compare and generalise findings across studies. As a result, the inconsistent terminology in ITMs highlights the need for standardisation and greater clarity in defining and measuring key concepts in this field.

Inconsistent findings across existing studies

Substantively, existing studies indicate predominantly negative effects of international travel restrictions on ‘real’ economic outcomes such as tourism, airline and trade, aligning with anecdotal reporting. However, several studies show no effect, or even some positive effect, on financial market outcomes. This could be because ‘efficient’ financial markets immediately take into account expectations of future profits. The introduction of strict ITMs may create expectations for a less negative impact of the pandemic on profitability, resulting in positive short-run financial market performance. In addition, the findings indicate that the economic impact of ITMs varies across countries and industries. Altogether, existing findings collectively highlight the importance of specifying the relevant structural aspects of economies when macroeconomic outcomes are under scrutiny, and the relationship between real economic outcomes and financial or monetary metrics.

Challenges in data and empirical methods

The rigour of the methods and data sources used in the studies varied depending on the economic outcomes being analysed. The studies examining financial market outcomes benefit from easy access and daily reporting of stock market data, making it ideal for studying the impact of a policy within a short time frame. Additionally, the causal inference methodology used in these studies is well-developed, with most relying on the standard event-study design to analyse external shocks.

However, the empirical context outside financial markets is more challenging. Many studies fail to distinguish the effects of ITMs from the pandemic itself and other non-pharmaceutical interventions (NPIs). This is important because existing studies have shown that voluntary changes in travelling behaviour in response to the pandemic itself contribute a significant ratio in the observed changes in travel volume.⁵⁰ Other NPIs, such as social distancing, business closure and face masking requirement, could also reduce people’s willingness to travel.²⁹ Not appropriately distinguishing the impact of ITMs from these factors can result in divergent outcomes and policy inferences. For example, a surge in COVID-19 cases might prompt governments to impose stricter travel restrictions, coinciding with a drop in international travel. However, this decline could be driven by travellers voluntarily cancelling their plans due to rising infection rates, rather than the imposition of new restrictions. Failing to separate these variables may overestimate the impact of ITMs.

One difficulty in isolating the impact of ITMs is the lack of geographic variation in their implementation within a country. Typically, ITMs were imposed at the national level, resulting in similar policy treatment for all regions within the nation, making it challenging to compare the effects across regions. To overcome this, most studies use time-series data for analysis. However, this approach also has limitations. If the data on the outcome variable are not collected at high frequency, it becomes difficult to disentangle the effects of the travel measures from concurrent changes in domestic policies or the evolution of the pandemic itself. For instance, GDP data are collected at best quarterly. If the GDP of a region is compared before and after the implementation of ITMs, the difference in GDP will capture all the changes that occurred in those 3 months.

To overcome data limitations, some studies incorporate higher frequency variables which are closely correlated with economic activities. For example, one study used daily indicators of economic activity, including nitrogen dioxide emissions, flight counts, energy consumption, maritime trade and mobility indices as outcome variables.³⁹ However, such data may be challenging to access in LMICs. In these cases, more easily accessible high-frequency data such as satellite data, mobility data and price data may be useful for future research. Another potential solution is to use mixed or qualitative approaches. Indeed, a higher share of studies that focus on LMICs in our review applied qualitative or mixed methods such as in-depth interviews.^{16 34 36}

Data limitations also exist for the independent variables. Many studies used the stringency index from OxCGRT as a measure of the intensity of ITMs. These data are very useful when comparing the effect across countries. However, the metric does not distinguish between different types of measures, such as entry or exit restrictions, quarantine requirements or health screening measures. This limitation of data makes it hard to study the effect of specific types of ITMs on economic outcomes.

Finally, the modelling studies rely on estimation using historical tourism data, but this may not be appropriate during a pandemic. The health risk alone would cause a decrease in travel, regardless of ITMs, leading to biased estimation.

Implications for future research

Our scoping review identified potential future research directions. First, with the availability of high-quality data, rigorous causal inference methods can be employed to isolate the effect of ITMs from concurrent policies and the pandemic itself. However, when access to high-quality data is not available, the adaptation of empirical methods to data that are accessible, such as using qualitative approaches, offers a viable alternative. Second, more household-level and firm-level data should be analysed to examine the microeconomic outcomes. Third, studies on the longer-term economic outcomes are needed. Finally, to inform policy decision-making, evidence of the impact of less intense ITMs, such as testing, health screenings, and vaccination requirements at the border on economic outcomes is needed.

Limitations and strengths of this scoping review

Our scoping review has some limitations. First, there is a possibility that some relevant studies were omitted: (1) our search was limited to English-language publications; (2) our inclusion criteria excluded non-empirical studies, and there may be valuable anecdotal evidence in the excluded studies; and (3) many studies (n=188) were excluded because the authors did not try to disentangle the effects of ITMs from other factors. Second, inconsistent terminology makes it challenging to systematically search and synthesise a body of research; however, we have sought to mitigate against this by including a wide array of search terms.

Our review’s strengths lie in its ability to draw on multiple disciplines. In public health, scoping or systematic reviews often overlook studies in economics and other social sciences due to gaps in interdisciplinary knowledge. However, the pandemic’s far-reaching effects on all aspects of human life, including the relationship between the state and society, as well as household well-being, have underscored the need for a more interdisciplinary approach to understanding and addressing health issues and informing public health policy.

Conclusion

This scoping review has demonstrated that evidence of the wide-ranging economic impact resulting from the use of ITMs during COVID-19 is emerging. However, there remain several research gaps—notably, the effect of types of ITMs beyond major closures, and micro-level economic outcomes. Furthermore, some challenges faced by these studies, such as inconsistent terminology, data limitations and the difficulty of causal inference, make it challenging to draw general and robust empirical conclusions. Therefore, future research using robust empirical methods and high-quality data is needed to enable decision-makers to take account of such impacts when making choices about the use of ITMs.

Data availability statement

Data sharing not applicable as no datasets generated and/or analysed for this study.

Ethics statements

Patient consent for publication

Not applicable.