journal of tourism and economic

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• Published: 24 February 2024

Modeling the link between tourism and economic development: evidence from homogeneous panels of countries

• Pablo Juan Cárdenas-García   ORCID: orcid.org/0000-0002-1779-392X 1 ,
• Juan Gabriel Brida 2 &
• Verónica Segarra 2  

Humanities and Social Sciences Communications volume  11 , Article number:  308 ( 2024 ) Cite this article

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• Development studies

Having previously analyzed the relationship between tourism and economic growth from distinct perspectives, this paper attempts to fill the void existing in scientific research on the relationship between tourism and economic development, by analyzing the relationship between these variables using a sample of 123 countries between 1995 and 2019. The Dumistrescu and Hurlin adaptation of the Granger causality test was used. This study takes a critical look at causal analysis with heterogeneous panels, given the substantial differences found between the results of the causal analysis with the complete panel as compared to the analysis of homogeneous country groups, in terms of their dynamics of tourism specialization and economic development. On the one hand, a one-way causal relationship exists from tourism to development in countries having low levels of tourism specialization and development. On the other hand, a one-way causal relationship exists by which development contributes to tourism in countries with high levels of development and tourism specialization.

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Introduction.

Across the world, tourism is one of the most important sectors. It has undergone exponential growth since the mid-1900s and is currently experiencing growth rates that exceed those of other economic sectors (Yazdi, 2019 ).

Today, tourism is a major source of income for countries that specialize in this sector, generating 5.8% of the global GDP (5.8 billion US$) in 2021 (UNWTO, 2022 ) and providing 5.4% of all jobs (289 million) worldwide. Although its relevance is clear, tourism data have declined dramatically due to the recent impact of the Covid-19 health crisis. In 2019, prior to the pandemic (UNWTO, 2020 ), tourism represented 10.3% of the worldwide GDP (9.6 billion US$), with the number of tourism-related jobs reaching 10.2% of the global total (333 million). With the evolution of the pandemic and the regained trust of tourists across the globe, it is estimated that by 2022, approximately 80% of the pre-pandemic figures will be attained, with a full recovery being expected by 2024 (UNWTO, 2022 ).

Given the importance of this economic activity, many countries consider tourism to be a tool enabling economic growth (Corbet et al., 2019 ; Ohlan, 2017 ; Xia et al., 2021 ). Numerous works have analyzed the relationship between increased tourism and economic growth; and some systematic reviews have been carried out on this relationship (Brida et al., 2016 ; Ahmad et al., 2020 ), examining the main contributions over the first two decades of this century. These reviews have revealed evidence in this area: in some cases, it has been found that tourism contributes to economic growth while, in other cases, the economic cycle influences tourism expansion. Moreover, other works offer evidence of a bi-directional relationship between these variables.

Distinct international organizations (OECD, 2010 ; UNCTAD, 2011 ) have suggested that not only does tourism promote economic growth, it also contributes to socio-economic advances in the host regions. This may be the real importance of tourism, since the ultimate objective of any government is to improve a country’s socio-economic development (UNDP, 1990 ).

The development of economic and other policies related to the economic scope of tourism, in addition to promoting economic growth, are also intended to improve other non-economic factors such as education, safety, and health. Improvements in these factors lead to a better life for the host population (Lee, 2017 ; Todaro and Smith, 2020 ).

Given tourism’s capacity as an instrument of economic development (Cárdenas-García et al., 2015 ), distinct institutions such as the United Nations Conference on Trade and Development, the United Nations Economic Commission for Africa, the United Nations World Tourism Organization and the World Bank, have begun funding projects that consider tourism to be a tool for improved socio-economic development, especially in less advanced countries (Carrillo and Pulido, 2019 ).

This new trend within the scientific literature establishes, firstly, that tourism drives economic growth and, secondly, that thanks to this economic growth, the population’s economic conditions may be improved (Croes et al., 2021 ; Kubickova et al., 2017 ). However, to take advantage of the economic growth generated by tourism activity to boost economic development, specific policies should be developed. These policies should determine the initial conditions to be met by host countries committed to tourism as an instrument of economic development. These conditions include regulation, tax system, and infrastructure provision (Cárdenas-García and Pulido-Fernández, 2019 ; Lejárraga and Walkenhorst, 2013 ; Meyer and Meyer, 2016 ).

Therefore, it is necessary to differentiate between the analysis of the relationship between tourism and economic growth, whereby tourism boosts the economy of countries committed to tourism, traditionally measured through an increase in the Gross Domestic Product (Alcalá-Ordóñez et al., 2023 ; Brida et al., 2016 ), and the analysis of the relationship between tourism and economic development, which measures the effect of tourism on other factors (not only economic content but also inequality, education, and health) which, together with economic criteria, serve as the foundation to measure a population’s development (Todaro and Smith, 2020 ).

However, unlike the analysis of the relationship between tourism and economic growth, few empirical studies have examined tourism’s capacity as a tool for development (Bojanic and Lo, 2016 ; Cárdenas-García and Pulido-Fernández, 2019 ; Croes, 2012 ).

To help fill this gap in the literature analyzing the relationship between tourism and economic development, this work examines the contribution of tourism to economic development, given that the relationship between tourism and economic growth has been widely analyzed by the scientific literature. Moreover, given that the literature has demonstrated that tourism contributes to economic growth, this work aims to analyze whether it also contributes to economic development, considering development in the broadest possible sense by including economic and socioeconomic variables in the multi-dimensional concept (Wahyuningsih et al., 2020 ).

Therefore, based on the results of this work, it is possible to determine whether the commitment made by many international organizations and institutions in financing tourism projects designed to improve the host population’s socioeconomic conditions, especially in countries with lower development levels, has, in fact, resulted in improved development levels.

It also presents a critical view of causal analyses that rely on heterogeneous panels, examining whether the conclusions reached for a complete panel differ from those obtained when analyzing homogeneous groups within the panel. As seen in the literature review analyzing the relationship between tourism and economic development, empirical works using panel data from several countries tend to generalize the results obtained to the entire panel, without verifying whether, in fact, they are relevant for all of the analyzed countries or only some of the same. Therefore, this study takes an innovative approach by examining the panel countries separately, analyzing the homogeneous groups distinctly.

Therefore, this article presents an empirical analysis examining whether a causal relationship exists between tourism and economic development, with development being considered to be a multi-dimensional variable including a variety of factors, distinct from economic ones. Panel data from 123 countries during the 1995–2019 period was considered to examine the causal relationship between tourism and economic development. For this, the Granger causality test was performed, applying the adaptation of this test made by Dumistrescu and Hurlin. First, a causal analysis was performed collectively for all of the countries of the panel. Then, a specific analysis was performed for each of the homogeneous groups of countries identified within the panel, formed according to levels of tourism specialization and development.

This article provides information on tourism’s capacity to serve as an instrument of development, helping to fill the gap in scientific research in this area. It critically examines the use of causal analyses based on heterogeneous samples of countries. This work offers the following main novelties as compared to prior works on the same topic: firstly, it examines the relationship between tourism and economic development, while the majority of the existing works only analyze the relationship between tourism and economic growth; secondly, it analyzes a large sample of countries, representing all of the global geographic areas, whereas the literature has only considered works from specific countries or a limited number of nations linked to a specific country in a specific geographical area, and; thirdly, it analyzes the panel both individually and collectively, for each of the homogenous groups of countries identified, permitting the adoption of specific policies for each group of countries according to the identified relationship, as compared to the majority of works that only analyze the complete panel, generalizing these results for all countries in the sample.

Overall, the results suggest that a relationship exists between tourism and development in all of the analyzed countries from the sample. A specific analysis was performed for homogeneous country groups, only finding a causal relationship between tourism and development in certain country groups. This suggests that the use of heterogeneous country samples in causal analyses may give rise to inappropriate conclusions. This may be the case, for example, when finding causality for a broad panel of countries, although, in fact, only a limited number of panel units actually explain this causal relationship.

The remainder of the document is organized as follows: the next section offers a review of the few existing scientific works on the relationship between tourism and economic development; section three describes the data used and briefly explains the methodology carried out; section four details the results obtained from the empirical analysis; and finally, the conclusions section discusses the main implications of the work, also providing some recommendations for economic policy.

Tourism and economic development

Numerous organizations currently recognize the importance of tourism as an instrument of economic development. It was not until the late 20th century, however, when the United Nations World Tourism Organization (UNWTO), in its Manila Declaration, established that the development of international tourism may “help to eliminate the widening economic gap between developed and developing countries and ensure the steady acceleration of economic and social development and progress, in particular of the developing countries” (UNWTO, 1980 ).

From a theoretical point of view, tourism may be considered an effective activity for economic development. In fact, the theoretical foundations of many works are based on the relationship between tourism and development (Ashley et al., 2007 ; Bolwell and Weinz, 2011 ; Dieke, 2000 ; Sharpley and Telfer, 2015 ; Sindiga, 1999 ).

The link between tourism and economic development may arise from the increase in tourist activity, which promotes economic growth. As a result of this economic growth, policies may be developed to improve the resident population’s level of development (Alcalá-Ordóñez and Segarra, 2023 ).

Therefore, it is essential to identify the key variables permitting the measurement of the level of economic development and, therefore, those variables that serve as a basis for analyzing whether tourism results in improved the socioeconomic conditions of the host population (Croes et al., 2021 ). Since economic development refers not only to economic-based variables, but also to others such as inequality, education, or health (Todaro and Smith, 2020 ), when analyzing the economic development concept, it has been frequently linked to human development (Pulido-Fernández and Cárdenas-García, 2021 ). Thus, we wish to highlight the major advances resulting from the publication of the Human Development Index (HDI) when measuring economic development, since it defines development as a multidimensional variable that combines three dimensions: health, education, and income level (UNDP, 2023 ).

However, despite the importance that many organizations have given to tourism as an instrument of economic development, basing their work on the relationship between these variables, a wide gap continues to exist in the scientific literature for empirical studies that examine the existence of a relationship between tourism and economic development, with very few empirical analyses analyzing this relationship.

First, a group of studies has examined the causal relationship between tourism and economic development, using heterogeneous samples, and without previously grouping the subjects based on homogeneous characteristics. Croes ( 2012 ) analyzed the relationship between tourism and economic development, measured through the HDI, finding that a bidirectional relationship exists for the cases of Nicaragua and Costa Rica. Using annual data from 2001 to 2014, Meyer and Meyer ( 2016 ) performed a collective analysis of South African regions, determining that tourism contributes to economic development. For a panel of 63 countries worldwide, and once again relying on the HDI to define economic development, it was determined that tourism contributes to economic development. Kubickova et al. ( 2017 ), using annual data for the 1995–2007 period, analyzed Central America and Caribbean nations, determining the existence of this relationship by which tourism influences the level of economic development and that the level of development conditions the expansion of tourism. Another work examined nine micro-states of America, Europe, and Africa (Fahimi et al., 2018 ); and 21 European countries in which human capital was measured, as well as population density and tourism income, analyzing panel data and determining that tourism results in improved economic development. Finally, within this first group of works, Chattopadhyay et al. ( 2022 ), using a broad panel of destinations, (133 countries from all geographic areas of the globe) determined that there is no relationship between tourism and economic development.

Studies performed with large country samples that attempt to determine the causal relationship between tourism and economic development by analyzing countries that do not necessarily share homogeneous characteristics, may lead to erroneous conclusions, establishing causality (or not) for panel sets even when this situation is actually explained by a small number of panel units.

Second, another group of studies have analyzed the causal relationship between tourism and economic development, considering the previous limitation, and has grouped the subjects based on their homogeneous characteristics. Cárdenas-García et al. ( 2015 ) used annual data from 1990–2010, in a collective analysis of 144 countries, making a joint panel analysis and then examining two homogeneous groups of countries based on their level of economic development. They determined that tourism contributes to economic development, but only in the most developed group of countries. They determined that tourism contributes to economic development, both for the total sample and for the homogeneous groups analyzed. Pulido-Fernández and Cárdenas-García ( 2021 ), using annual data for the 1993–2017 period, performed a joint analysis of 143 countries, followed by a specific analysis for three groups of countries sharing homogeneous characteristics in terms of tourism growth and development level. They determined that tourism contributes to economic development and that development level conditions tourism growth in the most developed countries.

Finally, another group of studies has analyzed the causal relationship between tourism and economic development in specific cases examined on an individual basis. In a specific analysis by Aruba et al. ( 2016 ), it was determined that tourism contributes to human development. Analyzing Malaysia, Tan et al. ( 2019 ) determined that tourism contributes to development, but only over the short term, and that level of development does not influence tourism growth. Similar results were obtained by Boonyasana and Chinnakum ( 2020 ) in an analysis carried out in Thailand. In this case of Thailand (Boonyasana and Chinnakum, 2020 ), which relied on the HDI, the relationship with economic growth was also analyzed, finding that an increase in tourism resulted in improved economic development. Finally, Croes et al. ( 2021 ), in a specific analysis of Poland, determined that tourism does not contribute to development.

As seen from the analysis of the most relevant publications detailed in Table 1 , few empirical works have considered the relationship between tourism and economic development, in contrast to the numerous works from the scientific literature that have examined the relationship between tourism and economic growth. Most of the works that have empirically analyzed the relationship between tourism and economic development have determined that tourism positively influences the improved economic development in host destinations. To a lesser extent, some studies have found a bidirectional relationship between these variables (Croes, 2012 ; Kubickova et al., 2017 ; Pulido-Fernández and Cárdenas-García, 2021 ) while others have found no relationship between tourism and economic development (Chattopadhyay et al., 2022 ; Croes et al., 2021 ).

Furthermore, in empirical works relying on panel data, the results have tended to be generalized to the entire panel, suggesting that tourism improves economic development in all countries that are part of the panel. This has been the case in all of the examined works, with the exception of two studies that analyzed the panel separately (Cárdenas-García et al., 2015 ; Pulido-Fernández and Cárdenas-García, 2021 ).

Thus, it may be suggested that the use of very large country panels and, therefore, including very heterogeneous destinations, as was the case in the works of Biagi et al. ( 2017 ) using a panel of 63 countries, as well as that of Chattopadhyay et al. ( 2022 ) working with a panel of 133 countries, may lead to error, given that this relationship may only arise in certain destinations of the panel, although it is generalized to the entire panel.

This work serves to fill this gap in the literature by analyzing the panel both collectively and separately, for each of the homogenous groups of countries that have been previously identified.

The lack of relevant works on the relationship between tourism and development, and of studies using causal analyses to examine these variables based on heterogeneous panels, may lead to the creation of rash generalizations regarding the entirety of the analyzed countries. Thus, conclusions may be reached that are actually based on only specific panel units. Therefore, we believe that this study is justified.

Methodological approach

Given the objective of this study, to determine whether a causal relationship exists between tourism and socio-economic development, it is first necessary to identify the variables necessary to measure tourism activity and development level. Thus, the indicators are highly relevant, given that the choice of indicator may result in distinct results (Rosselló-Nadal and He, 2020 ; Song and Wu, 2021 ).

Table 2 details the measurement variables used in this work. Specifically, the following indicators have been used in this paper to measure tourism and economic development:

Measurement of tourist activity. In this work, we decided to consider tourism specialization, examining the number of international tourists received by a country with regard to its population size as the measurement variable.

This information on international tourists at a national level has been provided annually by the United Nations World Tourism Organization since 1995 (UNWTO, 2023 ). This variable has been relativized based on the country’s population, according to information provided by the World Bank on the residents of each country (WB, 2023 ).

Tourism specialization is considered to be the level of tourism activity, specifically, the arrival of tourists, relativized based on the resident population, which allows for comparisons to be made between countries. It accurately measures whether or not a country is specialized in this economic activity. If the variable is used in absolute values, for example, the United States receives more tourists than Malta, so based on this variable it may be that the first country is more touristic than the second. However, in reality, just the opposite happens, Malta is a country in which tourist activity is more important for its economy than it is in the United States, so the use of tourist specialization as a measurement variable classifies, correctly, both Malta as a country with high tourism specialization and to the United States as a country with low tourism specialization.

Therefore, most of the scientific literature establishes the need to use the total number of tourists relativized per capita, given that this allows for the determination of the level of tourism specialization of a tourism destination (Dritsakis, 2012 ; Tang and Abosedra, 2016 ); furthermore, this indicator has been used in works analyzing the relationship between tourism and economic development (for example, Biagi et al., 2017 ; Boonyasana and Chinnakum; 2020 ; Croes et al., 2021 ; Fahimi et al., 2018 ).

Although some works have used other variables to measure tourism, such as tourism income, exports, or tourist spending, these variables are not available for all of the countries making up the panel, so the sample would have been significantly reduced. Furthermore, the data available for these alternative variables do not come from homogeneous databases, and therefore cannot be compared.

Measurement of economic development. In this work, the Human Development Index has been used to measure development.

This information is provided by the United Nations Development Program, which has been publishing it annually at the country level since 1990 (UNDP, 2023 ).

The selection of this indicator to measure economic development is in line with other works that have defended its use to measure the impact on development level (for example, Jalil and Kamaruddin, 2018 ; Sajith and Malathi, 2020 ); this indicator has also been used in works analyzing the relationship between tourism and economic development (for example, Meyer and Meyer, 2016 ; Kubickova et al., 2017 ; Pulido-Fernández and Cárdenas-García, 2021 ).

Although some works have used other variables, such as poverty or inequality, to measure development, these variables are not available for all of the countries forming the panel. Therefore the sample would have been considerably reduced and the data available for these alternative variables do not come from homogenous databases, and therefore comparisons cannot be made.

These indicators are available for a total of 123 countries, across the globe. Thus, these countries form part of the sample analyzed in this study.

As for the time frame considered in this work, two main issues were relevant when determining this period: on the one hand, there is an initial time restriction for the analyzed series, given that information on the arrival of international tourists is only available as of 1995, the first year when this information was provided by the UNWTO. On the other hand, it was necessary to consider the effect of the Covid-19 pandemic and the resulting tourism sector crisis, which also affected the global economy as a whole. Therefore, our time series ended as of 2019, with the overall time frame including data from 1995 to 2019, a 25-year period.

Previous considerations

Caution should be taken when considering causality tests to determine the relationships between two variables, especially in cases in which large heterogeneous samples are used. This is due to the fact that generalized conclusions may be reached when, in fact, the causality is only produced by some of the subjects of the analyzed sample. This study is based on this premise. While heterogeneity in a sample is clearly a very relevant aspect, in some cases, it may lead to conclusions that are less than appropriate.

In this work, a collective causal analysis has been performed on all of the countries of the panel, which consists of 123 countries. However, given that it is a broad sample including countries having major differences in terms of size, region, development level, or tourism performance, the conclusions obtained from this analysis may lead to the generalization of certain conclusions for the entire sample set, when in fact, these relationships may only be the case for a very small portion of the sample. This has been the case in other works that have made generalized conclusions from relatively large samples in which the sample’s homogeneity regarding certain patterns was not previously verified (Badulescu et al., 2021 ; Ömer et al., 2018 ; Gedikli et al., 2022 ; Meyer and Meyer, 2016 ; Xia et al., 2021 ).

Therefore, after performing a collective analysis of the entire panel, the causal relationship between tourism and development was then determined for homogeneous groups of countries that share common patterns of tourism performance and economic development level, to analyze whether the generalized conclusions obtained in the previous section differ from those made for the individual groups. This was in line with strategies that have been used in other works that have grouped countries based on tourism performance (Min et al., 2016 ) or economic development level (Cárdenas-García et al., 2015 ), prior to engaging in causal analyses. To classify the countries into homogeneous groups based on tourism performance and development level, a previous work was used (Brida et al., 2023 ) which considered the same sample of 123 countries, relying on the same data to measure tourism and development level and the same time frame. This guarantees the coherence of the results obtained in this work.

From the entire panel of 123 countries, a total of six country groups were identified as having a similar dynamic of tourism and development, based on qualitative dynamic behavior. In addition, an “outlier” group of countries was found. These outlier countries do not fit into any of the groups (Brida et al., 2023 ). The three main groups of countries were considered, discarding three other groups due to their small size. Table 3 presents the group of countries sharing similar dynamics in terms of tourism performance and economic development level.

Applied methodology

As indicated above, this work uses the Tourist Specialization Rate (TIR) and the Human Development Index (HDI) to measure tourism and economic development, respectively. In both cases, we work with the natural logarithm (l.TIR and l.HDI) as well as the first differences between the variables (d.l.TIR and d.l.HDI), which measure the growth of these variables.

A complete panel of countries is used, consisting of 123 countries. The three main groups indicated in the previous section are also considered (the first of the groups contains 36 countries, the second contains 29 and the last group contains 43).

The Granger causality test ( 1969 ) is used to analyze the relationships between tourism specialization and development level; this test shows if one variable predicts the other, but this should not be confused with a cause-effect relationship.

In the context of panel data, different tests may be used to analyze causality. Most of these tests differ with regard to the assumptions of homogeneity of the panel unit coefficients. While the standard form of the Granger causality test for panels assumes that all of the coefficients are equal between the countries forming part of the panel, the Dumitrescu and Hurlin test (2012) considers that the coefficients are different between the countries forming part of the panel. Therefore, in this work, Granger’s causality is analyzed using the Dumitrescu and Hurlin test (2012). In this test, the null hypothesis is of no homogeneous causality; in other words, according to the null hypothesis, causality does not exist for any of the countries of the analyzed sample whereas, according to the alternative hypothesis, in which the regression model may be different in the distinct countries, causality is verified for at least some countries. The approach used by Dumitrescu and Hurlin ( 2012 ) is more flexible in its assumptions since although the coefficients of the regressions proposed in the tests are constant over time, the possibility that they may differ for each of the panel elements is accepted. This approach has more realistic assumptions, given that countries exhibit different behaviors. One relevant aspect of this type of tests is that they offer no information on which countries lead to the rejection of the lack of causality.

Given the specific characteristics of this type of tests, the presence of very heterogeneous samples may lead to inappropriate conclusions. For example, causality may be assumed for a panel of countries, when only a few of the panel’s units actually explain this relationship. Therefore, this analysis attempts to offer novel information on this issue, revealing that the conclusions obtained for the complete set of 123 countries are not necessarily the same as those obtained for each homogeneous group of countries when analyzed individually.

Given the nature of the variables considered in this work, specifically, regarding tourism, it is expected that a shock taking place in one country may be transmitted to other countries. Therefore, we first analyze the dependency between countries, since this may lead to biases (Pesaran, 2006 ). The Pesaran cross-sectional dependence test (2004) is used for the total sample and for each of the three groups individually.

First, a dependence analysis is performed for the countries of the sample, verifying the existence of dependence between the panel subjects. A cross-sectional dependence test (Pesaran, 2004 ) is used, first for the overall set of countries in the sample and second, for each of the groups of countries sharing homogeneous characteristics.

The results are presented in Table 4 , indicating that the test is statistically significant for the two variables, both for all of the countries in the sample and for each of the homogeneous country clusters, for the variables taken in logarithms as well as their first differences.

Upon rejecting the null hypothesis of non-cross-sectional dependence, it is assumed that a shock occurs in a country that may be transmitted to other countries in the sample. In fact, the lack of dependence between the variables, both tourism and development, is natural in this type of variables, given the economic cycle through the globalization of the economic activity, common regions visited by tourists, the spillover effect, etc.

Second, the stationary nature of the series is tested, given that cross-sectional dependence has been detected between the variables. First-generation tests may present certain biases in the rejection of the null hypothesis since first-generation unit root tests do not permit the inclusion of dependence between countries (Pesaran, 2007 ). On the other hand, second-generation tests permit the inclusion of dependence and heterogeneity. Therefore, for this analysis, the augmented IPS test (CIPS) proposed by Pesaran ( 2007 ) is used. This second-generation unit root test is the most appropriate for this case, given the cross-sectional dependence.

The results are presented in Table 5 , showing the statistics of the CIPS test for both the overall set of countries in the sample and in each of the homogeneous clusters of countries. The results are presented for models with 1, 2, and 3 delays, considering both the variables in the logarithm and their first differences.

As observed, the null hypothesis of unit root is not rejected for the variables in levels, but it is rejected for the first differences. This result is found in all of the cases, for both the total sample and for each of the homogeneous groups, with a significance of 1%. Therefore, the variables are stationary in their first differences, that is, the variables are integrated at order 1. Given that the causality test requires stationary variables, in this work it is used with the variation or growth rate of the variables, that is, the variable at t minus the variable at t−1.

Finally, to analyze Granger’s causality, the test by Dumitrescu and Hurlin ( 2012 ) is used. This test is used to analyze the causal relationship in both directions; that is, whether tourism contributes to economic development and whether the economic development level conditions tourism specialization. Statistics are calculated considering models with 1, 2, and 3 delays. Considering that cross-sectional dependence exists, the p-values are corrected using bootstrap techniques (making 500 replications). Given that the test requires stationary variables, primary differences of both variables were considered.

Table 6 presents the result of the Granger causality analysis using the Dumitrescu and Hurlin test (2012), considering the null hypothesis that tourism does not condition development level, either for all of the countries or for each homogeneous country cluster.

For the entire sample of countries, the results suggest that the null hypothesis of no causality from tourism to development was rejected when considering 3 delays (in other works analyzing the relationship between tourism and development, the null hypothesis was rejected with a similar level of delay: Rivera ( 2017 ) when considering 3–4 delays or Ulrich et al. ( 2018 ) when considering 3 delays). This suggests that for the entire panel, one-way causality exists whereby tourism influences economic development, demonstrating that tourism specialization contributes positively to improving the economic development of countries opting for tourism development. This is in line with the results of Meyer and Meyer ( 2016 ), Ridderstaat et al. ( 2016 ); Biagi et al. ( 2017 ); Fahimi et al. ( 2018 ); Tan et al. ( 2019 ), or Boonyasana and Chinnakum ( 2020 ).

However, the previous conclusion is very general, given that it is based on a very large sample of countries. Therefore, it may be erroneous to generalize that tourism is a tool for development. In fact, the results indicate that, when analyzing causality by homogeneous groups of countries, sharing similar dynamics in both tourism and development, the null hypothesis of no causality from tourism to development is only rejected for the group C countries, when considering three delays. Therefore, the development of generalized policies to expand tourism in order to improve the socioeconomic conditions of any destination type should consider that this relationship between tourism and economic development does not occur in all cases. Thus, it should first be determined if the countries opting for this activity have certain characteristics that will permit a positive relationship between said variables.

In other words, it may be a mistake to generalize that tourism contributes to economic development for all countries, even though a causal relationship exists for the entire panel. Instead, it should be understood that tourism permits an improvement in the level of development only in certain countries, in line with the results of Cárdenas-García et al. ( 2015 ) or Pulido-Fernández and Cárdenas-García ( 2021 ). In this specific work, this positive relationship between tourism and development only occurs in countries from group C, which are characterized by a low level of tourism specialization and a low level of development. Some works have found similar results for countries from group C. For example, Sharma et al. ( 2020 ) found the same relationship for India, while Nonthapot ( 2014 ) had similar findings for certain countries in Asia and the Pacific, which also made up group C. Some recent works have analyzed the relationship between tourism specialization and economic growth, finding similar results. This has been the case with Albaladejo et al. ( 2023 ), who found a relationship from tourism to economic growth only for countries where income is low, and the tourism sector is not yet developed.

These countries have certain limitations since even when tourism contributes to improved economic development, their low levels of tourism specialization do not allow them to reach adequate host population socioeconomic conditions. Therefore, investments in tourism are necessary there in order to increase tourism specialization levels. This increase in tourism may allow these countries to achieve development levels that are similar to other countries having better population conditions.

Therefore, in this group, consisting of 43 countries, a causal relationship exists, given that these countries are characterized by a low level of tourism specialization. However, the weakness of this activity, due to its low relevance in the country, prevents it from increasing the level of economic development. In these countries (details of these countries can be found in Table 3 , specifically, the countries included in Group C), policymakers have to develop policies to improve tourism infrastructure as a prior step to improving their levels of development.

On the other hand, in Table 7 , the results of Granger’s causal analysis based on the Dumitrescu and Hurlin test (2012) are presented, considering the null hypothesis that development level does not condition an increase in tourism, both in the overall sample set and in each of the homogeneous country clusters.

The results indicate that, for the entire country sample, the null hypothesis of no causality from development to tourism is not rejected, for any type of delay. This suggests that, for the entire panel, one-way causality does not exist, with level of development influencing the level of tourism specialization. This is in line with the results of Croes et al. ( 2021 ) in a specific analysis in Poland.

Once again, this conclusion is quite general, given that it has been based on a very broad sample of countries. Therefore, it may be erroneous to generalize that the development level does not condition tourism specialization. Past studies using a large panel of countries, such as the work of Chattopadhyay et al. ( 2022 ) analyzing panel data from 133 countries, have been generalized to all of the analyzed countries, suggesting that economic development level does not condition the arrival of tourists to the destination, although, in fact, this relationship may only exist in specific countries within the analyzed panel.

In fact, the results indicate that, when analyzing causality by homogeneous country groups sharing a similar dynamic, for both tourism and development, the null hypothesis of no causality from development to tourism is only rejected for country group A when considering 2–3 delays. Although the statistics of the test differ, when the sample’s time frame is small, as in this case, the Z-bar tilde statistic is more appropriate.

Thus, development level influences tourism growth in Group A countries, which are characterized by a high level of development and tourism specialization, in accordance with the prior results of Pulido-Fernández and Cárdenas-García ( 2021 ).

These results, suggesting that tourism is affected by economic development level, but only in the most developed countries, imply that the existence of better socioeconomic conditions in these countries, which tend to have better healthcare systems, infrastructures, levels of human resource training, and security, results in an increase in tourist arrivals to these countries. In fact, when traveling to a specific tourist destination, if this destination offers attractive factors and a higher level of economic development, an increase in tourist flows was fully expected.

In this group, consisting of 36 countries, the high development level, that is, the proper provision of socio-economic factors in their economic foundations (training, infrastructures, safety, health, etc.) has led to the attraction of a large number of tourists to their region, making their countries having high tourism specialization.

Although international organizations have recognized the importance of tourism as an instrument of economic development, based on the theoretical relationship between these two variables, few empirical studies have considered the consequences of the relationship between tourism and development.

Furthermore, some hasty generalizations have been made regarding the analysis of this relationship and the analysis of the relationship of tourism with other economic variables. Oftentimes, conclusions have been based on heterogeneous panels containing large numbers of subjects. This may lead to erroneous results interpretation, basing these results on the entire panel when, in fact, they only result from specific panel units.

Given this gap in the scientific literature, this work attempts to analyze the relationship between tourism and economic development, considering the panel data in a complete and separate manner for each of the previously identified country groups.

The results highlight the need to adopt economic policies that consider the uniqueness of each of the countries that use tourism as an instrument to improve their socioeconomic conditions, given that the results differ according to the specific characteristics of the analyzed country groups.

This work provides precise results regarding the need for policymakers to develop public policies to ensure that tourism contributes to the improvement of economic development, based on the category of the country using this economic activity to achieve greater levels of economic development.

Specifically, this work has determined that tourism contributes to economic development, but only in countries that previously had a lower level of tourism specialization and were less developed. This highlights the need to invest in tourism to attract more tourists to these countries to increase their economic development levels. Countries having major natural attraction resources or factors, such as the Dominican Republic, Egypt, India, Morocco, and Vietnam, need to improve their positioning in the international markets in order to attain a higher level of tourism specialization, which will lead to improved development levels.

Furthermore, the results of this study suggest that a greater past economic development level of a country will help attract more tourists to these countries, highlighting the need to invest in security, infrastructures, and health in order for these destinations to be considered attractive and increase tourist arrival. In fact, given their increased levels of development, countries such as Spain, Greece, Italy, Qatar, and Uruguay have become attractive to tourists, with soaring numbers of visitors and high levels of tourism specialization.

Therefore, the analysis of the relationship between tourism and economic development should focus on the differentiated treatment of countries in terms of their specific characteristics, since working with panel data with large samples and heterogenous characteristics may lead to incorrect results generalizations to all of the analyzed destinations, even though the obtained relationship in fact only takes place in certain countries of the sample.

Conclusions and policy implications

Within this context, the objective of this study is twofold: on the one hand, it aims to contribute to the lack of empirical works analyzing the causal relationship between tourism and economic development using Granger’s causality analysis for a broad sample of countries from across the globe. On the other hand, it critically examines the use of causality analysis in heterogeneous samples, by verifying that the results for the panel set differ from the results obtained when analyzing homogeneous groups in terms of tourism specialization and development level.

In fact, upon analyzing the causal relationship from tourism to development, and the causal relationship from development to tourism, the results from the entire panel, consisting of 123 countries, differ from those obtained when analyzing causality by homogeneous country groups, in terms of tourism specialization and economic development dynamics of these countries.

On the one hand, a one-way causality relationship is found to exist, whereby tourism influences economic development for the entire sample of countries, although this conclusion cannot be generalized, since this relationship is only explained by countries belonging to Group C (countries with low levels of tourism specialization and low development levels). This indicates that, although a causal relationship exists by which tourism contributes to economic development in these countries, the low level of tourism specialization does not permit growth to appropriate development levels.

The existence of a causal relationship whereby the increase in tourism precedes the improvement of economic development in this group of countries having a low level of tourism specialization and economic development, suggests the appropriateness of the focus by distinct international organizations, such as the United Nations Conference on Trade and Development or the United Nations Economic Commission for Africa, on funding tourism projects (through the provision of tourism infrastructure, the stimulation of tourism supply, or positioning in international markets) in countries with low economic development levels. This work has demonstrated that investment in tourism results in the attracting of a greater flow of tourists, which will contribute to improved economic development levels.

Therefore, both international organizations financing projects and public administrations in these countries should increase the funding of projects linked to tourism development, in order to increase the flow of tourism to these destinations. This, given that an increase in tourism specialization suggests an increased level of development due to the demonstrated existence of a one-way causal relationship from tourism to development in these countries, many of which form part of the group of so-called “least developed” countries. However, according to the results obtained in this work, this relationship is not instantaneous, but rather, a certain delay exists in order for economic development to improve as a result of the increase in tourism. Therefore, public managers must adopt a medium and long-term vision of tourism activity as an instrument of development, moving away from short-term policies seeking immediate results, since this link only occurs over a broad time horizon.

On the other hand, this study reveals that a one-way causal relationship does not exist, by which the level of development influences tourism specialization level for the entire sample of countries. However, this conclusion, once again, cannot be generalized given that in countries belonging to Group A (countries with a high development level and a high tourism specialization level), a high level of economic development determines a higher level of tourism specialization. This is because the socio-economic structure of these countries (infrastructures, training or education, health, safety, etc.) permits their shaping as attractive tourist destinations, thereby increasing the number of tourists visiting them.

Therefore, investments made by public administrations to improve these factors in other countries that currently do not display this causal relationship implies the creation of the necessary foundations to increase their tourism specialization and, therefore, as shown in other works, tourism growth will permit economic growth, with all of the associated benefits for these countries.

Therefore, to attract tourist flows, it is not only important for a country to have attractive factors or resources, but also to have an adequate level of prior development. In other words, the tourists should perceive an adequate level of security in the destination; they should be able to use different infrastructures such as roads, airports, or the Internet; and they should receive suitable services at the destination from personnel having an appropriate level of training. The most developed countries, which are the destinations having the greatest endowment of these resources, are the ones that currently receive the most tourist flows thanks to the existence of these factors.

Therefore, less developed countries that are committed to tourism as an instrument to improve economic development should first commit to the provision of these resources if they hope to increase tourist flows. If this increase in tourism takes place in these countries, their economic development levels have been demonstrated to improve. However, since these countries are characterized by low levels of resources, cooperation by organizations financing the necessary investments is key to providing them with these resources.

Thus, a critical perspective is necessary when considering the relationship between tourism and economic development based on global causal analysis using heterogeneous samples with numerous subjects. As in this case, carrying out analyses on homogeneous groups may offer interesting results for policymakers attempting to suitably manage population development improvements due to tourism growth and tourism increases resulting from higher development levels.

One limitation of this work is its national scope since evidence suggests that tourism is a regional and local activity. Therefore, it may be interesting to apply this same approach on a regional level, using previously identified homogeneous groups.

And given that the existence of a causal relationship (in either direction) between tourism and development has only been determined for a specific set of countries, future works could consider other country-specific factors that may determine this causal relationship, in addition to the dynamics of tourism specialization and development level.

Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.

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Cárdenas-García, P.J., Brida, J.G. & Segarra, V. Modeling the link between tourism and economic development: evidence from homogeneous panels of countries. Humanit Soc Sci Commun 11 , 308 (2024). https://doi.org/10.1057/s41599-024-02826-8

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Tourism and economic growth: A global study on Granger causality and wavelet coherence

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft

Affiliation SLIIT Business School, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka

Roles Conceptualization, Data curation, Formal analysis, Methodology, Software, Validation, Visualization, Writing – original draft

Roles Conceptualization, Data curation, Formal analysis, Investigation, Visualization, Writing – original draft

Roles Conceptualization, Formal analysis, Methodology, Project administration, Resources, Supervision, Validation, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation Department of Information Management, SLIIT Business School, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka

Roles Supervision, Validation, Writing – original draft

• Chathuni Wijesekara, 
• Chamath Tittagalla, 
• Ashinsana Jayathilaka, 
• Uvinya Ilukpotha, 
• Ruwan Jayathilaka, 
• Punmadara Jayasinghe

• Published: September 12, 2022
• https://doi.org/10.1371/journal.pone.0274386
• Reader Comments

This paper empirically investigates the relationship between tourism and economic growth by using a panel data cointegration test, Granger causality test and Wavelet coherence analysis at the global level. This analysis examines 105 nations utilising panel data from 2003 to 2020. The findings indicates that in most regions, tourism contributes significantly to economic growth and vice versa. Developing trade across most of the regions appears to be a major influencer in the study, as a bidirectional association exists between trade openness and economic growth. Additionally, all regions other than the American region showed a one-way association between gross capital formation and economic growth. Therefore, it is crucial to highlight that using initiatives to increase demand would advance tourism while also boosting the economy.

Citation: Wijesekara C, Tittagalla C, Jayathilaka A, Ilukpotha U, Jayathilaka R, Jayasinghe P (2022) Tourism and economic growth: A global study on Granger causality and wavelet coherence. PLoS ONE 17(9): e0274386. https://doi.org/10.1371/journal.pone.0274386

Editor: Vu Quang Trinh, Newcastle University Business School, UNITED KINGDOM

Received: July 18, 2022; Accepted: August 26, 2022; Published: September 12, 2022

Copyright: © 2022 Wijesekara et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its with Supporting information files.

Funding: The authors received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Tourism is one of the world’s major industries, and people have been travelling for pleasure since the dawn of time. It has become one of the fastest expanding sectors of the global economy in recent years. Tourism arose as a result of modernisation and contributed significantly to shaping the experience of modernity. Economic growth and tourism development are intertwined, according to previous literature, therefore, an increase in the general economy will support tourism development [ 1 ]. As a result, it’s critical to investigate how tourism and other factors (including macroeconomic) are linked to economic growth. Economic growth can be defined as an increase in the real gross domestic product (GDP) or GDP per capita. Global tourism, as a key contributory business, has contributed to approximately 10% of global GDP through possible employment opportunities, extending client markets, encouraging export trades, and gains from foreign exchanges [ 2 , 3 ]. Another study that looked at the relationship between tourism and economic growth using variables like tourist receipts and tourism spending added to the literature by suggesting that tourism receipts impacted economic growth [ 4 ]. Additionally, according to Marin [ 5 ], tourism receipts have an upward link to the country’s economy and can thus aid in economic growth. Globally developed tourism business fosters economic growth over time, supporting the economy more than anticipated.

In recent years, research studies analysing the direction of the relationship between economic growth and tourism have been a popular area of interest in literature. A study of 12 Mediterranean nations in 2015 demonstrated a bidirectional causality relationship between tourism development and economic growth [ 6 ]. In a study conducted in Romania [ 7 ], a bidirectional causal relationship exists between GDP and the number of international tourist arrivals, whereas in an African study, a unidirectional causal relationship exists between international tourism earnings and real GDP, both in the short and long run [ 8 ]. According to previous research, this link appears to be both unidirectional and bidirectional.

Some of the processes by which tourism contributes to socioeconomic development include creating jobs, decreasing unemployment rates, and introducing of new tax income streams. In research conducted to investigate the relationship between tourism spending and economic growth in 49 nations, it was discovered that the two are inextricably linked, with a bidirectional causal relationship [ 9 ]. Investigating this relationship could be a useful for prioritising resource allocation across industries to improve overall tourism and economic outcomes.

Furthermore, a study based on 11 Asian regions discovers a close link between real international tourist revenues, capital formation, and GDP, confirming the tourism industry’s contribution to GDP [ 10 ]. Another study that looked at the relationship between tourism and economic growth based on tourist arrivals found that tourism is a good driver of economic growth [ 11 ]. This study looked into data of 94 countries, although there was no geographical examination of this association. Similarly, as previously mentioned, many authors have focused their research on a few countries or a single region when exploring the link between tourism and economic growth. The present study will contribute to filling the above-said research gap whilst providing an overall picture of the relationship between tourism and economic growth at the global level.

Many research papers have been written to determine the relationship between tourism demand and economic growth in diverse regions of the world. Based on certain regions, this link has been demonstrated to be bidirectional as well as unidirectional in the literature. The investigation of the relationship between tourism demand and global economic growth would provide a broad view of the relationship between these two factors. However, limited research has been done to examine this connection, which spans 18 years and includes regional data worldwide. Furthermore, because tourism is not the only element that influences GDP, other factors that considerably influence economic growth too must be considered. In the past, there hasn’t been much research conducted on the moderate impact of tourism on GDP. To address this gap in the literature, this research will examine the relationship between tourism demand and economic growth, as well as the moderating impact of variables such as gross capital formation and trade openness on economic growth in nations around the world. As a result, the current study focuses on all five regions, as there hasn’t been much research done on this topic.

The goal of this research paper is to examine the empirical relationship between tourism and economic growth along with the moderate impact of trade openness and gross capital formation for the worldwide regions. In four ways, the goals of this study can help improve the existing literature. Firstly, this study will be the most recent addition to the literature, focusing on an eighteen-year timeframe using panel data from 2003 to 2020. Secondly, this study will collect and analyse valid data from 105 countries including 42 countries in Europe, 25 countries in Asia & the Pacific, 18 countries in the Americas, and 20 countries from Africa and the Middle East region. The study’s emphasis on an 18-year time period and data from 105 countries allow the conclusions to be generalised and applied to any country. As a result, the study addresses one of the most significant flaws in the literature. Thirdly, in addition to the direct relationship between tourism on economic growth, this study attempts to examine the relationship between tourist receipts modulated by trade openness and gross capital formationon a region’s per capita GDP. These moderating effects on a country’s and region’s economic growth have yet to be investigated. Moreover, to the author’s knowledge, the wavelet technique hasn’t been used in previous research to analyse the relationship between per capita GDP and international tourist receipts. Additionally, analysis of this would produce precise and reliable data for future research and decision-making.

The next sections of the article are organised as follows: the first part analyses the existing literature, followed by the data used and the technique used in this investigation, then the findings and discussion, and lastly, the general conclusion of the study.

Literature review

This section includes contributions to the literature by a variety of scholars from various nations and locations. The conclusions of the study done for a particular region were segregated into regions, whilst studies were divided according to the manner of causal relationship.

Bidirectional causality between tourism and economic growth

The majority of earlier studies investigated the impact of tourism on economic growth in the European region. By adopting the Granger causality test Bilen, Yilanci [ 6 ] analysed the bidirectional causal connection between tourism development and economic growth, in the 12 Mediterranean countries with data from 1995-to 2012. Dritsakis [ 12 ] examined the impact of tourism on Greece’s economic development between 1960 and 2000, by using the Multivariate autoregressive and Granger causality tests. Here, the data revealed a ’Granger causal’ relationship between international tourism earnings and economic growth, a ’strong causal’ relationship between real exchange rate and economic growth, as well as simple ’causal’ relationships between economic growth and international tourism earnings, and real exchange rate and international tourism earnings. However, the above study conducted their research only for Greece. Further, the results of the above stated investigations based on 20 th century data, can vary with time. It is noteworthy that specially with the Eurozone crisis that started in 2009, Greece economy was among the severely affected in the region and hence, data do not reflect this situation. Surugiu and Surugiu [ 13 ] conducted a study using Romanian data, identified a long-term correlation between tourism development and economic growth.

According to the literature, several studies were conducted related to Tourism and economic growth. However, only a few studies have been conducted to analyse the causal relationship of both variables for countries worldwide. Most commonly utilised analytical tool is the Granger Causality test to identify the relationship between these two variables. A study conducted for 135 countries by Şak, Çağlayan [ 14 ] revealed that tourism revenue and GDP show bidirectional causality in Europe in contrast to unidirectional causality in America, Latin America, East Asia, South Asia, Oceania, Caribbean, and countries worldwide. However, the results of the above investigation were conducted based on data from 1995 to 2008, which can vary with time. Economic upheavals changes to economic policies in East Asia (including China, India) where geopolitical strategies are dominant, the impact of tourism revenue on GDP may not be significant. Moreover, Fahimi, Akadiri [ 15 ] tested the causality between tourism, economic growth, and investment in human capital in the microstates using data from 1995 to 2015. The results indicate that there is a bidirectional relationship between tourism and GDP. In the same period, Sokhanvar, Çiftçioğlu [ 16 ] performed a Granger causality analysis on 16 countries to investigate the causal relationship between tourism and economic development. The results proved bidirectional causality only in Chile. Further, this study found that seven countries do not show causality between variables. But as both studies were conducted only for selected countries, these results cannot be generalised about the global situation. Most recently, Pulido-Fernández and Cárdenas-García [ 17 ] explained the bidirectional link between tourism growth and economic development in 143 countries. According to them, tourism supports economic growth in the countries where tourism occurs. However, the study employed the level of economic development and tourism growth as a factor to cluster the countries for analysis; the results would most possibly change if another factor was used to cluster the countries.

Unidirectional causality between tourism and economic growth

In the European region, a long-run link was tested between economic growth and tourism based on international tourist receipts, real GDP, and the real effective exchange rate for Croatian nations using quarterly data from 2000-to 2008. Using the Granger causality test as the analysis tool, the results proved that a positive unidirectional causal relationship exists between economic growth and foreign tourism revenues [ 18 ]. Moreover, by adopting the Granger causality test for the annual GDP, the number of foreign visitors to South Tyrol and the relative prices (RP) between South Tyrol and Germany from 1980 to 2006, Brida and Risso [ 19 ] proved that the causation from tourism and RP to real GDP is unidirectional. A study published in 2013 asserted the link between tourist spending and economic growth. For Cyprus, Latvia, and Slovakia, the study discovered a growth hypothesis. whereas a negative relationship for Czech Republic and Poland [ 20 ]. Furthermore, Lee and Brahmasrene [ 21 ] found that tourism has a positive impact on economic growth and is inversely related to carbon dioxide emissions, using the panel cointegration technique and Fixed Effect (FE) model for the European region. Besides, the majority of previous investigators employed the Granger causality test to determine whether a bidirectional or unidirectional link exists between tourism and economic growth among European regions.

For the Asian Region, Oh [ 22 ] conducted on the Korean economy revealed that there is a one-way causal relationship between economy-driven tourism growth by using the Granger causality test for the period from the first quarter of 1975 to the first quarter of 2001. Furthermore, according to the Granger causality test and co-integration, no co-integration exists between tourism and economic growth in the long run and Tourism-Led Growth Hypothesis (TLGH) did not exist in the short term. However, the author noted that in order to generalise the study’s findings, it is necessary to investigate the TLGH under economic conditions of numerous nations. Examining the most recent study in further detail, Wu, Wu [ 10 ] used a multivariate panel Granger causality test to show a growth hypothesis between real GDP and real international tourism receipt in China, Cambodia, and Malaysia. However, an opposite growth hypothesis has been validated in the Philippines, Hong Kong, Indonesia, and South Korea. In Macau and Singapore, an inverse growth theory has been discovered.

Many researchers have studied the relationship between tourism and the African continent’s economic growth, with various kinds of dimensions and methodologies. In the early 20s, Akinboade and Braimoh [ 8 ] used the Granger causality test to assert the link between international tourism and economic expansion in Southern Africa, where the findings demonstrated a one-way causal relationship between international tourism earnings to real GDP with the use of data from 1980 to 2005. Providing more evidence in the same period utilising the same method, Belloumi [ 23 ] too disclosed that tourism has a beneficial influence unidirectionally on economic growth. Moreover, Ahiawodzi [ 24 ] employed the Augmented Dickey-Fuller (ADF) test for unit root, cointegration test, and Granger Causality to investigate the cointegration and causality of tourism revenues and economic growth. It found a unidirectional causality from economic growth to tourism in Ghana as well as a positive relationship and cointegration in the long run. Similarly, Bouzahzah and El Menyari [ 25 ] also discovered significant unidirectional causation from economic growth to international tourist receipts in the long term by analysing data of Morocco and Tunisia. However, since these studies are limited to one or two countries in the region, researchers were unable to view the bigger picture as a region. The most recent study by Kyara, Rahman [ 26 ] was conducted based on data from Tanzania from 1989 to 2018, considering the country’s international tourist receipts, real GDP, and the real effective exchange rate as variables. Here, findings of Granger Causality, and the Wald test supported the existence of one-way causation between tourism and economic expansion.

Only a few researchers have studied the causation between tourism and economic growth in the Middle East region. Countries such as Bahrain, Saudi Arabia, and Jordan should implement strategies to boost tourist arrivals with receipts by uplifting their tourism to tourists from outside the Middle East region [ 27 ]. Also, the scholars conducted panel cointegration and causality test based on data from 1981 to 2008, which revealed that tourism has a long-term relationship with economic growth. However, this research might be improved to include additional countries in the region, allowing for a more realistic comparison. In the meantime, the impact of tourism on economic growth in oil-rich nations was stated by Alodadi and Benhin [ 28 ]. In Jordan, Kreishan [ 29 ] discovered a unidirectional causal relationship between tourism earnings and economic growth by investigating data from 39 years up to 2009 using the Granger causality test. The importance of tourism to economic growth was explained by Tang and Abosedra [ 30 ] using annual data for the period 1995–2010 in Lebanon. Their findings demonstrated that tourism and economic expansion in Lebanon have a long-term association as tourism and growth are cointegrated and the results supported that the Tourism led Growth hypothesis is valid in this country. However, this analysis was performed with a small sample without considering additional variables apart from tourist arrivals and the real GDP. Providing more evidence, the same conclusion was provided [ 31 , 32 ], who tested data for Iran and Saudi Arabia, respectively. In addition, Ozcan and Maryam [ 33 ] claimed that measures to boost economic growth and development in the tourism sector of Qatar should be continued since a positive link exists between the said two factors. Ozcan and Maryam [ 33 ]. It may be determined from previous literature that the Middle East region exhibits a link between tourism and economic growth. Moreover, previous studies found that the tourism sector makes a small contribution to economic growth in oil-rich countries.

Many studies focusing on the countries of the American continent have deliberated the link between tourism and economic growth. According to Risso, Brida [ 34 ] the expenditure of international tourists has a favourable impact on Chile’s economic growth. The elasticity of real GDP to tourism spending (0.81) demonstrates that a 100% increase in tourism expenditure results in a long-run growth increase of more than 80%. With an elasticity of 0.35, the actual exchange rate also has a beneficial influence. This was examined using the Granger causality test as a basis for analysis using data from 1988 to 2009. Another study which was conducted by Brida and Risso [ 35 ], discovers that the causality of tourism and the real exchange rate to real GDP is unidirectional. Analysis of this study used the Granger test and the cointegrated vector model over data during the period 1988–2008. However, the above study only looked into data up to 2008. Similarly, Brida, Lanzilotta [ 36 ] analysed the causal relationship between Uruguay by adopting a Granger causality test. This study used variables such as GDP, Argentinean tourism expenditure, and the real exchange rate from 1987-to 2006, where it showed a positive relationship among the variables. However, this study was limited to Uruguay and Argentina. Using panel data from nine Caribbean nations from 1995 to 2007, a long-run relationship between economic growth and tourism was investigated by Payne and Mervar [ 18 ]. Here, researchers used international tourist arrivals per capita, real GDP per capita, and the real effective exchange rate. It proved that tourism has a large impact on per capita real GDP. Research conducted in Jamaica from 1970 to 2005 unveiled that increasing visitor receipts positively impacted on GDP. As a result, it was suggested that strategies should be focused on attracting more tourists, as this scenario would enhance not only tourism receipts but also Jamaica’s total economic growth [ 37 ]. However, the study described above, solely considered tourism receipts and GDP, excluding the other factors that affect GDP. Sánchez López [ 38 ] confirmed that international tourism has a positive influence on the Mexican economy by considering quarterly data from 1993 to 2017 and utilising GDP and tourist arrivals as variables.

Focusing on the worldwide studies, the case of Mediterranean countries, Tugcu [ 39 ] found a substantial and favourable correlation between tourism and economic growth. As these scholars affirmed, the relationship between economic growth and tourism has been studied for several groups of countries or nations. According to, the relationship between travel and economic growth varies per country, although European nations can experience economic growth through travel to European, Asian, and African nations. The most recent research, Enilov and Wang [ 40 ] examined the causal relationship between foreign tourist arrivals and economic growth using 23 developing and developed countries, in 1981–2017. It used a bootstrap mixed-frequency Granger causality approach using a rolling window technique to evaluate the approach’s stability and persistency over time concerning economic growth. The findings demonstrated that, in contrast to wealthy nations, the tourism industry in developing nations continues to be a major contributor in future economic growth.

In conclusion, many scholars have examined the connection between tourism and economic growth. However, the moderating impact of gross capital formation and trade openness with tourism receipts is yet to be studied. Moreover, limited studies were conducted to analyse the causal relationship between tourism and economic growth by employing the Granger Causality test. To fill this gap, this research investigates the direction of the causality between economic growth and demand for tourism whilst analysing the effect of gross capital formation and trade openness for the world regions.

Conceptual framework

To address the gaps in this analysis, the conceptual framework was developed to investigate the relationship between tourism and economic growth, including the moderate effect of gross capital formation and trade openness, for worldwide regions as stated in the study’s objectives. Fig 1 depicts the conceptual framework for investigating the empirical relationship between tourism and economic growth, as well as the moderate influence of gross capital formation and trade openness, globally and for each region separately.

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Source: Authors’ illustrations.

https://doi.org/10.1371/journal.pone.0274386.g001

The endogenous growth theory, which often views economic growth as an endogenous product of an economic system rather than the result of factors that affect it from the outside, serves as the theoretical foundation [ 41 ]. In comparison to non-high-tech service industries like tourism, the endogenous growth theory tends to highlight the benefits of high-tech industries as possibly more favourable for high long-run growth. Yet, specialising in tourism can be strongly linked to higher returns, which in turn reinforces the benefits enjoyed by marketplaces, firms, and sectors.

Data and methodology

This section presents a detailed view of the data, the statistical models employed in this study, and descriptive statistics for the variables.

This study was reviewed and approved by the SLIIT Business School and the SLIIT ethical review board. The following Table 1 illustrates the secondary data sources from which the information was gathered. The data file used for the study is presented in S2 Appendix .

https://doi.org/10.1371/journal.pone.0274386.t001

To measure economic growth across all regions, the current study employs yearly GDP per capita data from 2003. The amount of a country’s entire volume of goods and services produced relative to its total population is per capita GDP. To measure tourism growth, we use tourist receipts from 2003 until 2020. Tourism receipts were chosen over tourist arrivals because they incorporate both visitor arrivals and expenditure levels, resulting in a more accurate reflection of information on crucial aspects. Furthermore, the moderate impact on GDP per capita will be measured using gross capital formation and trade openness. Gross capital formation is a measure of a country’s yearly net capital accumulation as a proportion of GDP. The sum of goods and services and imports and exports represented as a percentage of GDP is known as trade openness. All the variables were converted as natural logarithms.

Methodology

The causal link between PGDP and TOUR by analysing the moderating effect of GCF and TRADE is tested using the panel Granger causality test [ 42 ]. According to Wang, Zhang [ 43 ], to assess if the sequence of data is stationary the unit root test will be performed and the co-integration tests will be used to analyse the connection between the variables if they are non-stationary. Based on the co-integration test, the Panel Granger causality test will be adopted to determine the existence of the direction and the causal connection between tourism and economic growth by analysing the moderate effect of GCF and TRADE .

The CUSUM test was carried out to assess the stability of the parameters for countries in the regions separately. Brown, Durbin [ 45 ], Hawkins [ 46 ], Koshti [ 47 ] and Rasool, Maqbool [ 48 ] provided more explanation on how to identify and analyse the plot of CUSUM.

With the help of the above-mentioned equation and to prove the dynamics between the PGDP and TOUR from 2010 to 2020, the Wavelet Coherence approach is used in order to deeply analyse the existence of a correlation among the variables discussed. Goupillaud, Grossmann [ 49 ] developed the wavelet technique in its natural form, and the concept’s foundation is based on their expertise knowledge. A time series is decomposed into a frequency-time domain using the wavelet technique. Pal and Mitra [ 50 ], Adebayo and Beton Kalmaz [ 51 ], Kalmaz and Kirikkaleli [ 52 ] and Adebayo, Onyibor [ 53 ] explained how to analyse and the explanation of the wavelet coherence. The wavelet method is used in this study to further visually confirm the existence of a causal relationship among PGDP and TOUR .

The panel granger causality test was carried out using STATA whereas R Studio was used for the CUSUM test and Wavelet coherence.

Empirical results and discussions

Before analysing Granger causality, Table 2 shows descriptive statistics for the major variables concerning worldwide countries and each region separately. This includes 1,890 total observations, of which 360, 324, 450, and 756 observations are for Africa & Middle East, America, Europe, and Asia & Pacific, respectively.

https://doi.org/10.1371/journal.pone.0274386.t002

Fig 2 illustrates the mean PGDP and the mean TOUR for the world’s countries from 2003 to 2020, discovering the trend and patterns of key factors.

Note: The data points were converted as natural logarithms. Source: Authors’ illustration based on data from the world bank, UNWTO, and WorldData.info.

https://doi.org/10.1371/journal.pone.0274386.g002

According to Fig 2(A) , the African & the Middle East region has the lowest PGDP when compared to other regions, while the European regions have the highest PGDP . The PGDP of the Americas and Asia & Pacific areas fluctuated similarly until 2017, thereafter, the gap between these two countries narrowed. As indicated in Fig 2(B) , the disparity in tourist receipts between America and the Asia-Pacific area has been nearly identical throughout the years. The European region has recorded the highest tourist receipts when compared to other regions. The graph shows that tourist revenues have dropped sharply after 2019. This is because tourism has been one of the most affected industries due to the covid pandemic. A massive drop in demand due to increased worldwide travel restrictions, including the closure of several borders worldwide led to tourism sector collapse.

The unit root tests are used in this study to determine if the data set of PGDP , TOUR , TRADE , and GCF is stationary or non-stationary. The following Table 3 shows the test results for unit roots.

https://doi.org/10.1371/journal.pone.0274386.t003

The variables PGDP , TRADE , and GCF are stationary, according to the findings of the unit root tests. The Fisher-type unit-root test shows that some panels of the variable TOUR are stationary, but according to the Levin-Lin-Chu unit root test, the variable TOUR is nonstationary. As a result, the cointegration test is used to identify whether there is a long-term link between the variables PGDP and TOUR .

Table 4 presents the panel data cointegration test and results of the unit root tests proved that the variable TOUR is nonstationary. The findings of all the tests, except the Kao cointegration test, indicated that PGDP has a long-term connection with TOUR . It is possible to claim that there is at least a one-way Granger causality as the variables are co-integrated. According to the results of the stability test in Fig 3 , the blue line in in the plot of recursive CUSUM does not cross the red line, it provides strong support that the model fits the data and that the variables are stable for all regions.

Source: Authors’ illustration using R-Software.

https://doi.org/10.1371/journal.pone.0274386.g003

https://doi.org/10.1371/journal.pone.0274386.t004

According to Table 5 , a bidirectional causal relationship exists between PGDP and TOUR for all the regions. However, the existence of a bidirectional relationship between TRADE and PGDP was discovered for all the regions except for the European region. On the other hand, a one-way causal connection (unidirectional) between PGDP and GCF was discovered for the American region, whereas all other regions proved the existence of a two-way relationship (bidirectional) between PGDP and GCF .

https://doi.org/10.1371/journal.pone.0274386.t005

Based on the findings of all countries, it can be observed that all the estimated z values of the variables PGDP , TOUR , TRADE , and GCF are significant at 0.001. Therefore, with the current estimators, it can be stated that in most countries worldwide, tourism growth Granger causes economic growth and vice versa. Subsequently, it could be assumed that tourism can drive economic growth in a majority of countries and economic growth can boost tourism growth. Fahimi, Akadiri [ 15 ] asserted that tourism to real GDP has a bidirectional causality relationship, where GDP Granger causes tourism and vice versa. However, Enilov and Wang [ 40 ] provide evidence for the validity of the economic-driven tourist growth in developing economies, while providing less support for developed ones. Similarly, according to Tugcu [ 39 ], Mediterranean area shows a favourable correlation between tourism and economic growth. This is likely attributable to a change in sample size, since our data set includes 105 nations and data spanning 12 years. But the research described above used a sample fewer than 25 nations. Furthermore, at the 1% significant level, the empirical findings prove that the PGDP Granger causes TRADE , GCF , and vice versa. Implications of these are that in most nations, the variables TRADE and GCF in PGDP have predictive ability amongst each other.

Similar to the worldwide countries, the values of the African and the Middle East region along with the Asia and Pacific region showed a significant relationship. At the 1% significance level, a Granger causal link between PGDP and tourist receipts was discovered, i.e., This means that tourism leads to economic growth and vice versa in the African and Middle East regions, as well as the countries in the Asia and the Pacific region. This finding was reconfirmed in a previous study conducted in Lebanon where it concluded that a bidirectional Granger causality exists between tourism and economic growth in the short run [ 30 ] in the Middle East Region. Similarly, these results were validated in South Africa by Odhiambo and Nyasha [ 54 ]. Moreover, for the Asian and Pacific region, Wang, Zhang [ 43 ] confirmed that there is a bidirectional Granger connection between China’s domestic tourism and economic growth. Additionally, using the Granger causality test, Mohapatra [ 4 ] proved the same results for the Asian and Pacific regions. According to the findings of these studies, the governments of these regions should promote practices and policies that would benefit the tourism industry and the economy, as tourism growth stimulates general growth in the economy and vice versa. Tourist revenues have surged across the Asia-Pacific region along with PGDP , as the region has evolved into a popular tourism destination for all sorts of diverse tourists. The rich biodiversity of several countries in the Asia and Pacific region has sparked the development of numerous sectors that have increased GDP, which in turn has had a substantial influence on tourism. A few countries in the Asia and Pacific area offer as much natural beauty, which makes them popular tourist destinations. The hospitality, infrastructure, convenient accommodation, and variety of attractions in these countries offer a solid basis for the Asia and Pacific region’s tourism industry. The proportion of international tourist arrivals in the African region is relatively low due to the region’s political unrest, yet tourism is one of Africa’s most promising industries concerning economic growth. The Middle Eastern nations are situated in the middle of important geographical locations. This aspect made it easier to establish global economic connections, which helped the economic growth of the countries over time. The Middle East led urbanisation and other development strategies that gave the region the required infrastructure and setting for the tourist destinations to begin providing of travel and tourism services. As a result, the Middle Eastern countries are increasingly opening their doors to tourists. Moreover, according to the finding, the null hypothesis of the Granger Causality test for the variables PGDP to TRADE , TRADE to PGDP , PGDP to GCF and GCF to PGDP can be rejected at a 1% significant level.

In contrast to countries worldwide, the American region revealed that a significant connection exists between PGDP , TOUR , and TRADE . Findings of this study affirmed that a one-way causal connection exists only from GCF to PGDP in the Americas region. These results mainly indicate that an increase in tourism could increase economic growth in the American region and vice versa. Several American countries, such as the United States and Canada, have a well-established tourism industry that contributes significantly to their GDP and, in turn, their highly developed economic systems encourage the development of infrastructure and tourist destinations. Governments are actively implementing regulations that intend to improve the economic, biological, and social advantages that tourist industry may offer, whilst lessening the challenges that occur when this expansion is unprepared and uncontrolled. Overall, tourist growth patterns in the Americas area are favourable. For the nations of the Americas region, in order to guarantee that their measures to improve tourism are conducted within the larger framework of local, regional, and country’s economic targets. Furthermore, to assist the shift to a green and low-emissions, additional initiatives are also being made to incorporate sustainability in tourism policy and industry regulations.

Considering the European region, a significant connection exists only among the variables PGDP , TOUR , and GCF . As a result, these findings show that tourist revenue and PGDP are mutually influenced. Furthermore, a significant link between TRAD E and PGDP was identified only in European region nations, demonstrating that PGDP does not cause TRADE, but TRADE has the predictive potential over PGDP at a 1% significance level. Europe is regarded as the overall dominant participant in the tourism industry, which fosters economic growth, due to the increasing affordability of travel for bigger groups of people. As tourism directly affects economic growth, it is possible to obtain economic growth in the European region by safeguarding the environment, preserving natural resources, generating jobs, enhancing cultural variety, and respecting cultural traditions. Authorities should focus on developing the tourist industry to obtain high economic growth, and to improve tourism, essential efforts should be taken to enhance economic growth. This is because bidirectional causation exists between tourism development and economic growth of the 12 Mediterranean countries [ 6 ].

The summary of Granger-causality analysis results for PGDP – TOUR , PGDP – TRADE and PGDP – GCF were presented in Table 6 .

https://doi.org/10.1371/journal.pone.0274386.t006

All four regions show a bidirectional causal relationship between PGDP and TOUR . Furthermore, for the Africa & Middle East, America, Asia & Pacific areas, a two-way causal (bidirectional) link between PGDP and TRADE is demonstrated, whereas there is a one-way causal (unidirectional) link between PGDP and TRADE for the European area nations. When considering the causative relationship between PGDP and GCF , it is discovered that there is a bidirectional causal relationship in all regions except the Americas. In order to examine the relationship between variables among country’s separately, this study summarised the results of Granger Causality for the countries in each region separately in S1 Appendix .

Table 7 interprets the direction of the arrows and the frequency. The direction of the arrows will indicate whether the variables move in phase (rightward arrow indicating a positive correlation), or anti phase (leftward arrow indicating a negative correlation) and the cold (blue) regions of the figure indicates no correlation while the warm (red) regions depict the analysed variables are correlated. The wavelet coherence graph is identified according to the scale as the upper portion, middle portion, and lower portion which represents the short term, medium term and long term respectively.

https://doi.org/10.1371/journal.pone.0274386.t007

The correlation between PGDP and TOUR for each region individually from 2003 to 2020 is shown in Fig 4 . When considering the entire period, the arrows in Fig 4(A) are pointing right in the short and medium terms (high and medium frequencies), indicating a worldwide positive impact between PGDP and TOUR when assessing the entire period.

Source: Authors’ compilation using R-Software.

https://doi.org/10.1371/journal.pone.0274386.g004

In Africa & Middle east region Fig 4(B) between 2009 and 2020, there are rightward arrows indicating a positive connection in the short and medium term with a high and medium frequency. Additionally, the rightward and downward arrows between 2009 to 2011 and 2016 to 2020 show that PGDP led TOUR in the short term with high frequencies. However, there is a negative association between 2006 to 2008 because of the existence of leftward arrows in the short with high frequency.

Overall, in American Region, Fig 4(C) demonstrates a favourable relationship with a high and medium frequency in all terms from 2003 to 2020. Furthermore, the rightward and downward arrows between 2008 to 2012 PGDP is leading to TOUR , in the short and long term (high and low frequencies).

Fig 4(D) illustrates a positive impact between Asia & Pacific Regions PGDP and TOUR in the short term with high and medium frequency over the years from 2003 to 2019, expect 2005 to 2006, 2008 to 2009, 2012 to 2013 and 2017 to 2018. There is a negative association in mentioned years because of the existence of leftward arrows in the short term with high frequency.

Fig 4(E) indicates a positive impact in the short and medium term (high and low frequencies) from 2003 to 2020 for the European region. Moreover, between 2006 and 2010, the arrows pointing right and up show a positive influence from TOUR to PGDP in the long term with low frequency. Similarly, the arrows in the medium term (medium frequency) between 2008 to 2011 and 2016 to 2018 are pointing downward and right, indicating that PGDP leads to TOUR .

Table 8 summarises the results of our Granger-causality analysis and wavelet coherence for PGDP and TOUR .

https://doi.org/10.1371/journal.pone.0274386.t008

As the wavelet coherence technique captures the time dependence of the variables which is conjointly captured under the Granger causality approach, the findings revealed that overall finding of both techniques brings unanimous results, bringing justifications to the study. Both Granger Causality and Wavelet Coherence methods demonstrated that PGDP and TOUR had a bidirectional link in each region separately and globally. Where it demonstrates that tourism drove economic expansion and vice versa.

This research was conducted to obtain evidence supporting the connection between tourism and global economic growth, using the panel Granger causality test with panel data from 2003 to 2020. The results of the link between TOUR and PGDP revealed a strong bidirectional connection. The results, firstly, indicated that tourism has the ability to boost economic growth in all regions, and vice versa. Secondly, a bidirectional relationship between TRADE and PGDP was observed in all regions except in the European region countries. Thirdly, the American area indicated a one-way causal association between PGDP and GCF , whereas the other regions revealed a two-way relationship between PGDP and GCF . Thus, based on these results, it is evident that tourism plays a substantial role in economic growth and vice versa across most regions. Therefore, it is important to emphasize that the use of demand-creation strategies to progress tourism would also boost economic growth.

Further to the bidirectional relationship between TRADE and PGDP , developing trade appears to be a powerful influencer in this study. Having said that, countries with increased tourism also have achieved developed trade and according to analysis, these two variables seem interrelated and mutually beneficial. It also suggests that in most countries, the variables TRADE and GCF in PGDP have the potential to forecast one another since the empirical findings show that the PGDP Granger causes TRADE , GCF , and vice versa. This paper differs from previous research in that it examines the relationship over 18 years, as well as the moderating impact of variables such as GCF and TRADE on economic growth in countries worldwide. Since the data set utilised in this study has a significant number of records, the analysis is more accurate, as the statistical soundness of results grows with the number of observations. As a result, the findings derived from this study could be generalised to the larger population including the entire world. In conclusion, it can be argued that tourism may be used as a catalyst for economic growth and vice versa. It is advised that nations in all regions proceed with caution when deploying more measures to attract visitors, as tourism has a strong influence PGDP . Moreover, the governments of these regions should support practices and policies that would benefit the tourism sector and eventually, the economy. The decision-makers should focus more effective tourism policies on addressing the demand generated by the rise in tourism-related businesses. Additionally, governments should promote investments in tourism-related industries to all types of investors as these ultimately boost the nation’s GDP. Global events such as the pandemic, economic downturns, and the war eruptions have triggered an unprecedented tourism economic crisis, due to the rapid and massive shock to the tourist industry. Due to this, tourism can be a vulnerable channel attracting refugees. This scenario can be risky as the increased pressure on the public finances exerts a higher burden on tax income and economic growth due to the migration of refuges in some countries. In this context, it is critical to overcome this predicament, as the negative repercussions could have a significant impact on the industry, and recovery will take time.

Here by examining the Wavelet Coherence graphs which had been drawn for the regions, American Region has the highest correlation between PGDP and TOUR from 2010 to 2017 compared to the other regions. Most of the graphs indicate a Bidirectional link, which is line with the findings of the panel granger causality. The visual representation of the bidirectional association between TOUR and PGDP in these results reflects the conclusions of the panel granger causality.

Limitations

For this study, data were collected from 105 countries over 18 years, from 2003 to 2020. Other potential variables that influence tourism demand and economic growth, such as the real effective exchange rate, destination attractiveness, seasons, people’s spending capacity, security, urbanization, weather patterns etc., were not included in this study, which is a significant limitation. Moreover, the negative externalities of tourism and economic growth were not taken into account in this study due to the availability of data. For study purposes, countries were divided into regions, and those that depend heavily on tourism were not considered specific. As a result, the limitations mentioned above will need to be addressed in future studies. Future research studies should target to analyse the impact of tourism on economic growth and vice versa by adopting methodologies like the panel regression or generalised method of moments (GMM) which would further clarify the behaviour of these two variables more richly. Additionally, future study might assess the connection between tourism and economic development for each country in the relevant region independently.

Supporting information

S1 appendix. granger causality test results for the countries in each region..

https://doi.org/10.1371/journal.pone.0274386.s001

S2 Appendix. Data file.

https://doi.org/10.1371/journal.pone.0274386.s002

Acknowledgments

The authors would like to thank Ms. Gayendri Karunarathne for proof-reading and editing this manuscript.

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On this occasion, topics related to publications regarding French tourist guides in the Ijen Crater, Indonesia were explored. Apart from that, the performance of workers in the hotel sector was also discussed. The behavior of generation z and generation y regarding culinary also received a portion of the study. And let's not overlook the extensive studies on the subject of environmental deterioration brought on by tourism-related activities. Aside from that, this publication highlights the role that coffee producers have in the operations of tourist villages.

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Tourism Economics is an international peer reviewed journal, covering the business aspects of tourism in the wider context. It takes account of constraints on development, such as social and community interests and the sustainable use of tourism and recreation resources, and inputs into the production process. The definition of tourism used includes tourist trips taken for all purposes, embracing both stay and day visitors. Articles address the components of the tourism product (accommodation; restaurants; merchandizing; attractions; transport; entertainment; tourist activities); and the economic organization of tourism at micro and macro levels (market structure; role of public/private sectors; community interests; strategic planning; marketing; finance; economic development). This journal is a member of the Committee on Publication Ethics (COPE). Core subject areas:

• forecasting
• public policy (strategies, fiscal and other intervention policies)
• economic development
• market structures and competition
• sources of capital provision
• labour economics (quality and productivity issues)
• business aspects of marketing
• private and public sector interaction
• economic appraisal at sector and project level
• mathematical modelling
• developments in the components of the product
• structure of the tourism industry (including such issues as ownership, corporate size, international operations, etc)
• regional economic effects of tourism developments
• analysis of international data on tourism, such as WTO statistics

Tourism Economics publishes regular special issues. Current calls for papers are listed here .  

Tourism Economics is an international peer reviewed journal, covering the economic and business aspects of tourism in the wider context. It takes account of constraints on development, such as social and community interests and the sustainable use of tourism and recreation resources, and inputs into the production process. The definition of tourism used includes tourist trips taken for all purposes, embracing both stay and day visitors.

Articles address the components of the tourism product (accommodation; restaurants; merchandizing; attractions; transport; entertainment; tourist activities); the economic organization of tourism at micro and macro levels (market structure; role of public/private sectors; community interests; strategic planning; marketing; finance; economic development; sustainability and the economic analysis of tourism demand).

Core subject areas: • public policy (strategies, fiscal and other intervention policies) • economic development • market structures and competition • sources of capital provision • labor economics (quality and productivity issues) • consumer economics • private and public sector interaction • economic appraisal at sector and project level • forecasting and economic analysis • economic modelling • methodologies for data analysis • developments in the components of the product • structure of the tourism industry (including such issues as ownership, corporate size, international operations, etc.) • regional economic effects of tourism developments • analysis of international data on tourism • impact analysis • tourism performance and productivity: modelling and analysis • tourism competitiveness • sharing economy • economic analysis from big data

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• Open access
• Published: 05 January 2021

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

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

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

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

Introduction

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

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

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

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

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

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

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

Review of past studies

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

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

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

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

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

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

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

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

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

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

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

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

Database and methodology

Data and variables.

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

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

Unit root test

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

Panel ARDL approach to Cointegration

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

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

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

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

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

Panel granger causality test

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

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

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

Estimation, results and Discussion

Descriptive statistics.

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

Stationarity results

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

Cointegration test results

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

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

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

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

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

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

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

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

Analysis of causality

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

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

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

Stability tests

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

Plot of CUSUM

Plot of CUSUMQ

Summary and concluding remarks

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

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

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

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

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

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

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

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

Availability of data and materials

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

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

Abbreviations

autoregressive distributed lag model

Brazil, Russia, India, China and South-Africa

United Nations World Tourism Organization

World Travel & Tourism Council

gross domestic product

world development indicators

tourism-led growth hypothesis

export-led growth hypothesis

economic-driven tourism hypothesis

augmented Dickey–Fuller test

error correction model

error correction term

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

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• Economic growth
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This work analyzes whether tourism development affects the economic growth. Using yearly data of 1995–2017 for the sample of 35 countries, the empirical findings reveal that tourism arrivals and tourism receipts stimulate the economic growth. Similarly, renewable and non-renewable energy consumption enhance economic growth in the selected countries. Further, human capital positively affects economic growth in case of developed countries, African, and European regions. However, the coefficients of human capital are negative on economic growth in case of aggregate panel as well as for developing countries, Asian and Latin American regions. The pairwise causality test performed mixed results in case of direction of causality. Notwithstanding, the empirical outcomes vary across various subpanels. Thus, policy should focus on enhancing cognitive skills, knowledge, high-quality educational infrastructure, and increasing school enrolment which are key to achieve higher economic growth in developing countries, Asian and Latin American regions. Moreover, for sustainable economic growth, use of more renewable energy with protective environmental quality is a win-win position in the long run.

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Data Availability

Data will be made available upon request.

See Table 1 for more details about tourism contribution to GDP and employment.

The readers should not get confused that there are 35 countries, we have first arranged these countries on the basis of income into subpanels namely developing and developed countries collected from the WDI ( 2018 ). Later we have arranged these 35 countries from same developing and developed nation into four other sub panels namely Latin American, European, African, and Asian based on their regional level (UNWTO, 2018 ). It is also to be noticed that while dividing them into regional blocks we have mentioned only 29 countries out of 35 countries. This is because other 4 countries (Australia and New Zealand comes under Oceania region; Canada and US are the part of North America) do not come under selected regions; therefore, we could not include them here.

Consequently, the prominent method is to consider the assumption and condition of each estimator. DFE technique is similar to PMG method in that it restricts the coefficient of the co-integrating vector to be equal across all panels across time. Furthermore, the DFE estimator requires that the short-term coefficient and the speed of the adjustment and be the same or equal, and panel-specific intercepts are permitted. MG technique was revealed by Pesaran and Smith ( 1995 ) including the distinct regressions for each nation, as well as the coefficients as the unweighted averages of the estimated coefficients for each country.

This is because the characteristic of all these three estimators is almost similar

The argument here is that the usage of non-renewable energy is very small that it may have no or very less impact on economic growth, for which developed countries should not be much worried about their environmental consequences. This is one case where environmental Kuznets curve can be valid for developed countries which depicts that better technology and renewable energy consumption could support green economy and at the same time reduction in carbon emission.

Except human capital which is found to be insignificant on economic growth in case of FMOLS model

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Ansari, M.A. Tourism and Economic Growth: Evidence from Cross-Country Data with Policy Insights. J Knowl Econ (2024). https://doi.org/10.1007/s13132-024-02031-7

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