the tourism led growth

The Validation of the Tourism-Led Growth Hypothesis in the Next Leading Economies: Accounting for the Relevant Role of Education on Carbon Emissions Reduction?

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• Festus Victor Bekun 4 , 5 ,
• Festus Fatai Adedoyin 6 ,
• Daniel Balsalobre-Lorente 7 &
• Oana M. Driha 8  

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Over the last few decades, a significant volume of research has been documented on the tourism-led growth hypothesis (TLGH). However, the role of education over environmental degradation is yet to be given the desired attention. This study explores the impact of air transport over economic growth between 1994 and 2014 in China, India and the US, the three economies predicted to be the largest in forthcoming years. This way, TLGH is tested while also introducing the connection between education and pollutant emissions (CO 2 ) for these economies. Thus, suggesting how development in air transport contributes positively to enhance economic growth in the long run. In contrast, ascending CO 2 emissions are negatively connected to economic growth contributing to its reduction in selected countries. Further empirical results also confirm the positive effects of energy use and education on economic growth. Based on these results, education is seen to mitigate the pernicious effects of environmental degradation over economic growth's dampening effects.

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For brevity, here fixed and random effect Model are panel estimation techniques where fixed effect approach constant across individuals, but random effects vary. For easy of understanding 2SLS means two-stage least squares also a panel estimation technique that ameliorate for endogeneity issues with the use of instrumental variable (IV) approach. In summary, all highlighted test are panel estimators.

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Bekun, F.V., Adedoyin, F.F., Balsalobre-Lorente, D., Driha, O.M. (2021). The Validation of the Tourism-Led Growth Hypothesis in the Next Leading Economies: Accounting for the Relevant Role of Education on Carbon Emissions Reduction?. In: Balsalobre-Lorente, D., Driha, O.M., Shahbaz, M. (eds) Strategies in Sustainable Tourism, Economic Growth and Clean Energy. Springer, Cham. https://doi.org/10.1007/978-3-030-59675-0_14

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A literature review on the tourism-led-growth hypothesis

The aim of this paper is to provide a comprehensive literature review on the temporal relationship between tourism and economic growth. Specifically, the role of a such economic activity, as a promoter of short and long run economic growth, is investigated by assessing the so-called Tourism Led Growth Hypothesis (TLGH). To this aim, various methodological approaches have been used, such

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Research Article

Publication bias and the tourism-led growth hypothesis

Contributed equally to this work with: Nikeel Nishkar Kumar, Arvind Patel

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Software, Validation, Writing – original draft, Writing – review & editing

* E-mail: [email protected] , [email protected] , [email protected] , [email protected]

Affiliations School of Accounting, Finance, and Economics, The University of the South Pacific, Suva, Fiji Islands, School of Economics, Faculty of Business, Economics and Law, Auckland University of Technology, Auckland, New Zealand

Roles Conceptualization, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Writing – review & editing

Affiliation School of Accounting, Finance, and Economics, The University of the South Pacific, Suva, Fiji Islands

Roles Data curation, Project administration, Resources, Writing – original draft, Writing – review & editing

¶ ‡ These authors also contributed equally to this work.

Affiliation Department of Management, School of Business and Economics, The University of Fiji, Lautoka, Fiji

Roles Data curation, Resources, Software, Writing – original draft, Writing – review & editing

Affiliation School of Business and Management, The University of the South Pacific, Suva, Fiji Islands

• Nikeel Nishkar Kumar, 
• Arvind Patel, 
• Ravinay Amit Chandra, 
• Navneet Nimesh Kumar

• Published: October 14, 2021
• https://doi.org/10.1371/journal.pone.0258730
• Peer Review
• Reader Comments

This study attempts to solve the publication bias suggested by recent review articles in the tourism-growth literature. Publication bias is the tendency to report favourable and significant results. Method and data triangulation, and the Solow-Swan model are applied. A sample from 1995 to 2018 is considered with Tonga as a case study. The approach consists of multiple methods, data frequencies, exchange rates, structural breaks, and an overall tourism index developed using principal component analysis (PCA). Consistent results across these dimensions are obtained with the PCA models. Tourism has small, positive, and statistically significant economic growth effects. Theoretically consistent values of the capital share and exchange rates are obtained. The results indicate the importance of multiple methods and the overall tourism index in assessing the tourism-growth relationship and minimising publication biases. The practical implication is the provision of robust elasticity estimates and better economic policies.

Citation: Kumar NN, Patel A, Chandra RA, Kumar NN (2021) Publication bias and the tourism-led growth hypothesis. PLoS ONE 16(10): e0258730. https://doi.org/10.1371/journal.pone.0258730

Editor: Hiranya K. Nath, Sam Houston State University, UNITED STATES

Received: February 18, 2021; Accepted: October 5, 2021; Published: October 14, 2021

Copyright: © 2021 Kumar 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 paper and its 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

International tourism is generally considered a key sector for growth and development in developing countries. It attracts foreign exchange and allows developing countries to import new capital goods [ 1 ]. Imported capital goods are infused with new technology which raises the technology level in the country. It supports human capital accumulation as workers acquire new skills and knowledge by using the new capital goods [ 1 ]. Apart from productivity gains, there are spill-over benefits because the new skills and knowledge circulates freely between industries. In contrast, tourism is associated with negative externalities such as environmental degradation and the overutilization of natural resources [ 2 ]. On balance however, research suggests that developing the tourism industry is supportive of economic growth [ 3 ].

Yet, recent meta-analyses by Nunkoo et al. [ 3 ] and Fonseca and Sánchez-Rivero [ 4 , 5 ] raise concerns over the issue of publication bias in empirical research on tourism and economic growth. Publication biases occur due to the preferential reporting of positive and statistically significant results [ 6 ]. Two types of publication bias are reported, types I and II [ 3 ]. Under type I bias, researchers tend to report strongly positive/negative estimates [ 7 , 8 ]. Under type II bias, researchers tend to report significant, yet economically meaningless results [ 3 ]. The non-reporting of inconclusive results, coupled with the publication of statistically significant but economically meaningless results leads to biased estimates, distorted inference, and skewed knowledge, which undermine the free exchange of information [ 3 ]. Publication biases result in overoptimistic inferences about an economic phenomenon such as the contribution of tourism to growth [ 3 – 5 ]. As a result, the belief in the efficacy of tourism policies may be unfounded or that policies may have a smaller than expected effect on economic growth [ 3 ].

The usual method to detect publication bias begins with a funnel asymmetry plot where the empirical effects are plotted against the inverse of the standard error of the estimates [ 4 ]. Without publication bias, the funnel graph tends to be symmetric and resemble an inverted funnel. Asymmetry in the funnel graph provides preliminary evidence on publication biases which is formally tested through meta-regression analysis (MRA) [ 9 ]. Type I bias is tested for by regressing the t -statistic against the inverse of the standard error of the empirical effect under analysis [ 4 , 5 ]. Type II bias is similarly tested except the absolute value of the t -statistics, instead of the t -statistic, is considered [ 4 , 5 ]. Types I and II publication biases are confirmed if the MRA intercept term is significantly greater than zero. Tourism genuinely affects growth if the slope of the standard error term in the MRA is significantly different from zero. These are termed the funnel asymmetry and precision effects tests, respectively [ 4 , 5 ].

By surveying 545 estimates across 113 studies published from 1994 to 2017, Nunkoo et al. [ 3 ] conclude that positive and significant effects are preferentially reported in the tourism-growth literature. Fonseca and Sánchez-Rivero [ 4 ] also confirm that the results reported in the tourism-growth association are non-genuine, but the variability of the empirical effects depends on the degree of tourism specialization, level of economic development, and size of the countries analysed. Fonseca and Sánchez-Rivero [ 5 ] further note that statistical significance may occur in small samples if econometric specifications are manipulated to find larger effects. This indicates type II bias because small samples are associated with larger standard errors and smaller effect estimates [ 5 ]. Overall, the effects are sensitive to specification and estimation characteristics, data frequency, and period considered [ 3 – 5 ].

To solve these problems, Nunkoo et al. [ 3 ] recommends re-visiting the tourism-growth association across various methodological characteristics, specifications, and estimation choices. Song and Wu [ 10 ] emphasize that research designs that ignore theoretical foundations that underpin the tourism-growth nexus lead to unreliable and misleading conclusions [ 10 ]. Song and Wu [ 10 ] underscore the Solow-Swan growth model and accentuate how tourism improves technological progress and productivity of capital and labor. To measure tourism, Shahzad et al. [ 11 ] suggest that combining multiple indicators such as arrivals and receipts into a composite index captures information on the traditional variables and is less affected by multicollinearity [ 11 ]. Solarin [ 12 ] notes that ignoring exchange rates in the specification may bias the growth effect of tourism. Controlling for structural breaks is also important because tourism is impacted by exogenous events [ 3 ]. On estimation, dynamic methods such as ARDL derive more accurate conclusions on the validity of the tourism-growth association [ 3 ].

Consequently, the objective of this study is to remedy publication biases in the tourism-growth literature. Method and data triangulation are adopted, and various methodological characteristics, specifications, and estimation choices are considered [ 13 ]. The earliest application of triangulation in the social sciences was by Eugene et al. [ 14 ] and its primary rationale is the recognition of data-set or methodological biases with a single method or dataset [ 15 ]. Triangulation is the use of multiple approaches to a research question enabling the researcher to “zero in” on the answers sought [ 13 ]. Method triangulation is the use of more than one research method in measuring the object of interest with a data set [ 13 ]. Data triangulation refers to using the same approach/method with different sets of data to verify/falsify the generalizable trends observed in one dataset [ 13 ].

For method triangulation, the autoregressive distributed lag (ARDL) [ 16 ], dynamic least squares (DOLS) [ 17 ], fully modified least squares (FMOLS) [ 18 ], and canonical cointegrating regression (CCR) [ 19 , 20 ] are used. These methods are consistent with cointegration theory, and provide robust estimates in the presence of endogeneity and small samples. Endogeneity and small samples can bias the effect estimates. Small samples can also inflate standard errors which may lead to type II publication bias [ 5 ]. However, using multiple methods with a single dataset makes the results more reliable by lowering the likelihood that econometric techniques are manipulated. The primary long-run results are supported with the ridge regression technique which controls for multicollinearity [ 21 – 23 ].

For data triangulation, three measures of tourism are considered. These are tourist arrivals, tourism receipts, and an overall tourism index which is developed using the Principal Component Analysis (PCA) technique. Because the results are affected by the frequency of the data, annual and quarterly data series are also considered. Shahzad et al. [ 11 ] and Shahbaz et al. [ 24 ] demonstrate the usefulness of frequency transformation techniques from low to high-frequency data in addressing the small sample bias. Shahzad et al. [ 11 ] and Shahbaz et al. [ 24 ] recommend the quadratic match-sum approach for frequency conversion. This approach is ideal in minimizing the seasonality problem by reducing point-to-point data variations and has previously been successfully applied in the tourism-growth literature [ 11 , 24 ].

Tonga is chosen as a case study with an annual sample from 1995 to 2018 which amounts to 24 years of data. Like other Pacific Island Countries (PICs), international tourism is for Tonga, a key driver of growth. In Tonga, tourism contributed 12.1 percent of Gross Domestic Product (USD 52.6 million) and supported 12 percent of total employment or about 5100 jobs in 2019 [ 25 ]. International arrivals and receipts to Tonga have been consistently rising ( Fig 1 ). There is also conflicting evidence, and dearth of country-specific studies in Tonga [ 26 – 28 ]. Current research shows a positive impact but differs in terms of elasticity estimates, explanatory variables, and specification. To reconcile the conflicting evidence, an overall assessment using theoretically founded models and triangulation is required.

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Data obtained from World Bank.

https://doi.org/10.1371/journal.pone.0258730.g001

The key methodological contribution of this paper is the development of a cohesive framework based on triangulation to remedy publication biases in the tourism-growth literature. The methodology developed draws from the recommendation of Nunkoo et al. [ 3 ], theoretical foundations from Song and Wu [ 10 ], measurement of tourism from Shahzad et al. [ 11 ], and inclusion of moderator variables from Solarin [ 12 ]. With this framework, the study provides new evidence on how tourism interacts with growth in small PICs, namely Tonga. Notably, the size and sign of the growth effect of tourism depends on the research method and the measure of tourism. Consistent results across methods and data frequencies are obtained using the overall PCA index models. The findings indicate that tourism has small but positive effects on growth, whilst structural breaks and exchange rates have negative effects on growth. Theoretically consistent values of the capital share are found. The practical implication is on better policies to promote economic growth by developing Tonga’s tourism industry.

The remainder of the study is set as follows. Section 2 discusses the methodology, Section 3 presents the results, and Section 4 concludes with policy implications.

Materials and methods

Theoretical model.

A total of 24 years of annual data over the periods from 1995 to 2018 is used for analysis. The data for real GDP in constant 2010 US dollars was available from 1981 to 2018, gross capital formation to proxy for investment was available from 1975 to 2018, tourist arrivals and tourism receipts in constant 2010 US dollars was available from 1995 to 2018, population data was available from 1960 to 2018, and the labor force participation rate was available from 1990 to 2019. The nominal exchange rate, Tongan Pa’anga against the US dollar was available from 1961 to 2020. GDP deflator for the USA and Tonga was available from 1960 to 2019, and 1981 to 2019, respectively. The average labor force participation rate was multiplied by the population to compute the labor series. The capital stock series was computed using the perpetual inventory method. The initial capital stock was set as 1.5 times the 1981 real GDP, and the depreciation rate is set at 5 percent. The real exchange rate is derived by multiplying the nominal exchange rate with the ratio of GDP deflator in the USA against Tonga. The data are sourced from the World Development Indicators and Global Development Finance database [ 30 ]. Data for gross capital formation from 2013 to 2018 is handpicked from the Asian Development Bank’s Key Indicators for Asia and the Pacific Series database [ 31 ]. Data for the official exchange rate is sourced from the IMF’s International Financial Statistics database [ 32 ]. Exchange rate data over the period 2014 to 2019 is sourced from the exchange rates UK website [ 33 ].

PCA and frequency conversion.

Principal component analysis is used to construct the overall tourism activity indicator. A key benefit of the PCA is that the resulting indicator is not subject to multicollinearity and is less sensitive to missing data in the underlying components [ 11 ]. The relevance of the underlying components is determined by the estimated eigenvalues. Following the Kaiser criterion, components with eigenvalues greater than 1 are important in the index.

Frequency conversion techniques are used to mitigate problems associated with small samples and improve degrees of freedoms [ 11 , 24 ]. The quadratic match sum technique is used to convert from low to high frequencies [ 11 , 24 ]. The approach fits a quadratic polynomial for each observation of the low-frequency series. The polynomial is formed via three adjacent points from the lower frequency series, and is then used to fill in observations of the higher frequency series for that period. The fitted quadratic is either the average or sum of the higher frequency points that match the lower frequency data. Points earlier and after the current period are used in the interpolation process. For endpoints, two periods earlier/after the endpoint are considered [ 11 , 24 ]. This approach avoids issues related to seasonality, and the converted data are comparable to seasonally adjusted series [ 11 , 24 ].

Unit root and structural breaks.

The null hypothesis is that the underlying series has a unit root, H 0 : β 1 = β 2 = 0. Rejection of the null hypothesis employing an F-test implies stationarity [ 36 ]. The critical values of this test are obtained from Becker, Enders, and Lee [ 38 ]. Lags of the differenced dependent variable in Eq (6) are included up to where autocorrelation is corrected [ 36 , 37 ].

Testing for structural breaks is important because tourism development is affected by exogenous events [ 3 ]. To examine the presence of structural breaks, the Bai and Perron sequential break test is used [ 39 , 40 ]. The procedure is useful in that one can examine the presence of multiple structural breaks. The method produces a consistent estimation of the location and number of breaks and corrects for serial correlation across the break segments by robust standard errors.

Cointegration, endogeneity, and small sample consistent estimates.

where y and x are time series variables, μ 1 is the deterministic component which includes intercept, trend, and structural breaks, and -1 < θ 1 < 0 is the adjustment coefficient. The bounds test of cointegration tests whether the lagged level variables in Eq (7) estimated by OLS is significantly different from zero. Cointegration exists if the resulting F-statistic exceeds the upper critical bound, does not exist if the F-statistic is below the lower critical bound, and is inconclusive if the F-statistic falls within the upper and lower bounds [ 16 ].

Long-run estimates are also provided by the dynamic least squares (DOLS) [ 17 ], fully modified least squares (FMOLS) [ 18 ], and the canonical cointegration regression (CCR) [ 19 , 20 ]. FMOLS is an optimal single-equation method-based least squares estimator with semi-parametric corrections for serial correlation and endogeneity of the explanatory variables [ 41 ]. DOLS is useful in small samples, can be applied with mixed and higher orders of integration, and can accommodate for possible simultaneity between regressors [ 42 ]. CCR approach has similar benefits as FMOLS which is achieved by incorporating stationary components in the cointegrating models [ 19 , 20 ]. The resulting CCR estimates are asymptotically efficient [ 19 , 20 ].

Multicollinearity consistent estimates.

Multicollinearity is a phenomenon in which two or more predictors in multiple regression are highly correlated [ 23 ]. Strong multicollinearity creates difficulties in testing individual least squares regression coefficients due to inflated standard errors [ 23 ]. The Farrar-Glauber (FG) test is used to detect the presence of severe multicollinearity. The FG test defines multicollinearity in terms of departures from orthogonality and helps detect the presence and pattern of multicollinearity. The null hypothesis of no multicollinearity is rejected if the p-value from the test statistic is less than 5 percent.

The ridge penalty factor is set to ensure that the coefficients of the estimated ridge regression stabilize to avoid overfitting. The closer lambda is to zero, the smaller the biasing constant, and the closer the ridge estimates will be to OLS. Standard errors are generally not available with penalized estimation methods because it is difficult to obtain a precise estimate of the bias which forms a major component of the mean square error. Reliable estimates of the bias are only available if unbiased estimates are available. Bootstrapped standard errors are however available in standard software packages such as Stata.

Causality test.

Results and discussion

Basic statistics and index development.

As noted from Tables 1 and 2 , there is a strong positive and significant correlation between tourist arrivals, tourism receipts, official exchange rates, and real GDP per worker. Although correlation does not imply cointegration, the strength of the correlation and level of significance can influence the statistical significance of the long-run association.

https://doi.org/10.1371/journal.pone.0258730.t001

https://doi.org/10.1371/journal.pone.0258730.t002

Table 3 below summarizes the PCA results. Noting the vast differences in the mean value of arrivals and receipts, the PCA is run on the log of both indicators. The eigenvalue for arrivals exceeds 1 which indicates its relevance in the index [ 11 ]. The factor loading of the first component reveals that arrivals and receipts enter the first component with a similar weight [ 11 ]. Around 88 percent of the variation in the tourism index is explained by tourist arrivals.

https://doi.org/10.1371/journal.pone.0258730.t003

Unit root, structural breaks, and cointegration

Table 4 presents the results of the unit root tests. The results indicate that the variables are stationary in their first differences and suitable for the subsequent estimations. The optimal value of k* is determined by minimizing the sum of squared residuals obtained from Eq (5) .

https://doi.org/10.1371/journal.pone.0258730.t004

Table 5 presents the results of the multiple break test. Both the identified breaks were negative and significant in the subsequent estimations. The year 2007 could reflect the Nuku’alofa riots and the subsequent declaration of a state of emergency. The second break, 2010, could reflect the lagged effect of the Tonga Tsunami and earthquake in late 2009 [ 44 ].

https://doi.org/10.1371/journal.pone.0258730.t005

Table 6 presents the results of the Bounds test for cointegration. Cointegration is strongly suggested at the 1 percent level in the estimated models.

https://doi.org/10.1371/journal.pone.0258730.t006

Long-run results

Tables 7 – 9 present the results of the growth model discussed earlier. A total of 6 models are considered. The lag length for each variable in the ARDL model is determined by the Schwarz information criteria. The arrivals models ( Table 7 ) generally indicate that tourism promotes growth. The exchange rates, both real and nominal, are appropriately signed which suggests that an increase of the Tongan Pa’anga against the USD reduces growth. Structural breaks have negative effects on growth, although only the break for 2007 is significant across the estimates irrespective of the tourism or exchange rate assumption.

https://doi.org/10.1371/journal.pone.0258730.t007

https://doi.org/10.1371/journal.pone.0258730.t008

https://doi.org/10.1371/journal.pone.0258730.t009

The receipts model returns mixed effects on tourism and growth. Considering the real exchange rate, tourism receipts promote growth when estimated using the ARDL or DOLS methods ( Table 8 ). Considering the nominal exchange rate, the FMOLS estimates also suggest this outcome. However, the effect is negative according to the CCR estimator irrespective of whether the model is estimated using annual or quarterly data ( Table 8 ).

The most consistent results are obtained with the PCA tourism development index models ( Table 9 ). The growth effect of tourism is between 0.02 to 0.04, respectively. The results with the tourism indicator ( Table 9 ) are qualitatively similar to those with tourism arrivals ( Table 7 ). The predominance of tourism arrivals in the index which explains 88 percent of the variations in the index could explain this similarity. Unlike earlier results (Tables 7 and 8 ), the effects are consistent across methods, data frequencies, and exchange rate assumptions. Therefore, developing an overall tourism index is better suited to assess the tourism-growth association. The capital share is between 0.27 to 0.37 which is smaller than those from the earlier estimates (Tables 7 and 8 ). Consistency is also evident in the exchange rates despite the unit of measurement and is in between 0.08 to 0.12. Overall, the growth effect of tourism is small, positive, and statistically significant in Tonga.

The results indicate that tourism is an important driver of long-run growth in Tonga like other Pacific islands [ 45 , 46 ]. However, the effect of tourism is noticeably smaller than in earlier studies. This suggests that although the tourism sector does affect growth, it requires further development. For this reason, factors promoting tourism demand are necessary, and building resilience is critical. Policymakers in Tonga could capitalize on factors such as a favorable word of mouth by establishing good reviews on sites such as TripAdvisor and distinguishing the Tongan tourism experience from the other PICs. Promoting a safe and secure environment for tourists by strengthening law enforcement, and knowledge of overall tourism demand elasticities may prove beneficial.

The estimated models (Tables 7 – 9 ) are free from auto-correlated residuals and heteroscedasticity, and the residuals are normally distributed. The estimates do not exhibit the endogeneity bias, have correct functional forms, and are stable. These results are not presented to conserve space but are available upon request.

Multicollinearity test and ridge estimates

Noting the consistency of the results from the PCA models ( Table 9 ), the PCA model is re-visited using the ridge regression technique. Strong multicollinearity between the explanatory variables is found evidenced by the strong correlation between the variables ( Table 2 ) and according to the Farrar-Glauber test ( Table 10 ). Minor differences are found between the ridge and cointegrating models (Tables 7 – 10 ). The ridge coefficient evolution plot indicates that the estimated coefficients stabilize at the chosen ridge lambda penalty factor ( Fig 2 ).

Estimated in EViews 10.

https://doi.org/10.1371/journal.pone.0258730.g002

https://doi.org/10.1371/journal.pone.0258730.t010

Causality test

To undertake causality analysis, we rely on the PCA tourism index-real exchange rate model. We set a lag of 1 in the test VAR model which is within the sum of the order of integration and maximum lag of the ARDL model, both which are 1, respectively [43; 45]. The significant causal relations are reported in Table 11 below. Notably, the causality results in Table 11 indicate that tourism, real exchange rates, and capital granger cause growth. Table 11 further indicates that the real exchange rate granger causes tourism and that capital investments granger cause the real exchange rate and tourism, respectively which reaffirms the findings in Table 9 . In this regard, predicting Tonga’s economic growth requires a careful analysis of the effect of tourism, real exchange rate, and capital noting the potential inter-relationships. Fig 3 suggests that the test VAR model is stable and hence causality outcomes are reliable.

Estimated in EViews 10. Inverse roots within the unit circle indicate stable estimates.

https://doi.org/10.1371/journal.pone.0258730.g003

https://doi.org/10.1371/journal.pone.0258730.t011

Further tests

Two further tests are conducted with the PCA models. First, nonlinear effects of tourism are considered. On nonlinearity, we draw insights from Balsalobre-Lorente et al. [ 47 ] and invoke the partial sum decomposition technique to decompose positive and negative changes in tourism. Then, the threshold effects of tourism on growth is considered through threshold regressions [ 48 ], and by including the square of tourism in the specification [ 49 ]. Nonlinear effects are rejected in all specifications. Second, the model is re-estimated using Bayesian techniques [ 50 ]. The posterior mean of the Bayesian estimator resembles the estimates in Tables 7 – 10 . These results are not presented to conserve space but are available upon request.

Conclusions

In this study, data and method triangulation are proposed as potential solutions to the publication bias problem [ 3 – 5 ]. The specification incorporates the effects of exchange rates, capital-labor ratio, and structural breaks. On method triangulation, the autoregressive distributed lag, fully modified least squares, dynamic least squares, canonical cointegrating regression, and ridge regression are considered. On data triangulation, tourism is measured by arrivals, receipts, and an index of tourism performance developed using principal component analysis. A data frequency conversion method is used to derive the quarterly data series. Tonga is used as a case study over the sample period 1995 to 2018. Tonga is chosen given the importance of tourism and the dearth of country-specific research for this country.

The findings indicate that the size of the effect of tourism in growth regressions depends on the measure of tourism. Consistent results across all three dimensions are obtained using the PCA index models. Tourism has small, positive, and significant growth effects. A 1 percent increase in tourism would increase growth by about 0.02 to 0.04 percent, ceteris paribus. A 1 percent decline of the Tongan Pa’anga against the USD would increase growth by about 0.10 to 0.12 percent, ceteris paribus. The capital elasticity has an average value of about 0.32. The structural break which represents the 2006/07 Nuku’alofa riots has significant negative effects. Unidirectional causality from tourism to growth is found with the causality test.

Nonetheless, the study could have benefitted from a larger sample size but was restricted to a sample of 1995 to 2018 due to a lack of earlier tourism data. Yet, the key scientific implication/contribution is the development of a cohesive framework that attempts to solve publication biases in the tourism-growth literature. Synthesizing the literature, the framework developed draws from the Solow-Swan growth model, controls for exchange rates and structural breaks, and utilizes an overall indicator of tourism performance. Multiple methods which correct for small sample and endogeneity biases, and multicollinearity are used. Nonlinearity is also considered but found statistically insignificant. Future research can apply the framework advanced in this study to potentially circumvent the publication bias critique.

Based purely on the results, any policy promoting tourism in Tonga would contribute to economic growth of the country. The practical policy implications need to consider the positive and significant growth effect of tourism, and negative effects arising from political issues and other exogenous shocks. Based on the authors knowledge of Tonga and its tourism industry, policymakers need to make careful decisions in how capital projects are implemented, and how budget shares are allocated to an industry like tourism. This is because resources are limited and there are many other equally urgent competing social projects. To develop the tourism sector, enabling investments in basic infrastructures such as roads, airports and international and domestic air transportation, information and communication technology, public amenities, and easing of restrictions to access financial services is needed. Demand-side factors such as the sensitivity of tourism demand to price and income shocks, and a favourable word of mouth is also important. Tailor-made tourist packages catered for Australian and New Zealand tourists, and the establishment of direct travel routes may also prove beneficial.

However, policy decisions to invest in the tourism industry and related areas need to be cautioned based on the noticeably small growth effects found in this study. This implies that although the tourism sector influences growth, its magnitude is small relative to competing destinations, and requires further development. Additionally, the effect of COVID 19 on the tourism industry is unprecedented and requires a radical shift in the way countries depend on tourism [ 51 ]. Given the small positive impact of tourism on growth in Tonga, alternative growth strategies such as agriculture, back-office data processing, and call centers that work in tandem with tourism are needed. Further, Tonga needs to pay attention to political stability to avoid the negative effects of tourism on growth. Further research is thus needed to address how much tourism contributes multiplicatively to other industries, like agriculture, and the level of direct and induced employment generated through tourism activities.

Supporting information

https://doi.org/10.1371/journal.pone.0258730.s001

Acknowledgments

The authors thank the academic editor and anonymous reviewers for their useful comments which have considerably improved this paper. The authors also thank Associate Professor Saten Kumar, Dr Antony Andrews, and Mr. Sean Kimpton of AUT for insights on Ridge and Bayesian analysis, and for proof-reading the revised text. Nikeel would also like to thank Nandani for her constant encouragement and for proofreading the revised text.

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Tourism’s Importance for Growth Highlighted in World Economic Outlook Report

• All Regions
• 10 Nov 2023

Tourism has again been identified as a key driver of economic recovery and growth in a new report by the International Monetary Fund (IMF). With UNWTO data pointing to a return to 95% of pre-pandemic tourist numbers by the end of the year in the best case scenario, the IMF report outlines the positive impact the sector’s rapid recovery will have on certain economies worldwide.

According to the World Economic Outlook (WEO) Report , the global economy will grow an estimated 3.0% in 2023 and 2.9% in 2024. While this is higher than previous forecasts, it is nevertheless below the 3.5% rate of growth recorded in 2022, pointing to the continued impacts of the pandemic and Russia's invasion of Ukraine, and from the cost-of-living crisis.

Tourism key sector for growth

The WEO report analyses economic growth in every global region, connecting performance with key sectors, including tourism. Notably, those economies with "large travel and tourism sectors" show strong economic resilience and robust levels of economic activity. More specifically, countries where tourism represents a high percentage of GDP   have recorded faster recovery from the impacts of the pandemic in comparison to economies where tourism is not a significant sector.

As the report Foreword notes: "Strong demand for services has supported service-oriented economies—including important tourism destinations such as France and Spain".

Looking Ahead

The latest outlook from the IMF comes on the back of UNWTO's most recent analysis of the prospects for tourism, at the global and regional levels. Pending the release of the November 2023 World Tourism Barometer , international tourism is on track to reach 80% to 95% of pre-pandemic levels in 2023. Prospects for September-December 2023 point to continued recovery, driven by the still pent-up demand and increased air connectivity particularly in Asia and the Pacific where recovery is still subdued.

Related links

• Download the News Release on PDF
• UNWTO World Tourism Barometer
• IMF World Economic Outlook

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• Solid growth projected for tourism this year

The tourism sector will continue to grow in the first half of this year, after a prosperous first three months, experts said.

A report released recently by the China Tourism Academy said that in the first quarter of the year the tourism economy had recovered to roughly the level seen at the start of 2019 — before COVID-19 hit — following three quarters of growth. Tourism industry operators expressed growing confidence and people felt a stronger desire for travel in the first quarter.

The report said domestic tourism and related consumption rose 20 percent in the first quarter, with inbound and outbound visits averaging around 20 million a month.

"The tourism economy saw a good opening and stable operation in the first quarter, and the market has stepped into a period of new development," Ma Yiliang, the academy's chief statistician, said at a recent meeting in Beijing.

He said the tourism market has benefited from preferential policies on visas and payments as well as an increase in international flights.

"We've noticed that the increasing travel has brought increasing consumption," he said. "Also, some small cities or less-known destinations such as Harbin, in the northeastern province of Heilongjiang, and Tianshui, in northwest China's Gansu province, have gained popularity among young people because of their lower travel costs and good services. With more cities growing as trending destinations, they will invigorate the tourism industry."

Nanjing, capital of the eastern province of Jiangsu, received 65 million visits during the quarter, and the number of travelers on holidays such as Spring Festival was 3.5 times higher than usual, according to the city's culture and tourism bureau. More than 200 million people visited Nanjing last year.

"We estimate that the tourism economy will see a continuous and steady recovery in the first half of the year," Ma said. "Domestic tourist travel and tourism-related revenue will be roughly close to that of the same period of 2019, and inbound and outbound tourism will continuously increase."

In February, the academy estimated that domestic tourism visits will exceed 6 billion this year, with tourism-related revenue of over 6 trillion yuan ($830 billion). It said it expected inbound and outbound visits would surpass 260 million, bringing in international tourism revenue of $100 billion.

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